2024-03-29T05:18:38Z
https://zenodo.org/oai2d
oai:zenodo.org:6421461
2022-08-26T08:50:44Z
user-a_wear
user-tau_wireless
software
Lucie Klus
Darwin Quezada Gaibor
Joaquín Torres-Sospedra
2022-06-06
<p>The file includes source code for the method described in "Towards Accelerated Localization Performance<br>
Across Indoor Positioning Datasets" (<a href="https://doi.org/10.1109/ICL-GNSS54081.2022.9797035">10.1109/ICL-GNSS54081.2022.9797035</a>), example dataset, and readme file with all necessary information.</p>
https://doi.org/10.5281/zenodo.6421461
oai:zenodo.org:6421461
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://doi.org/10.5281/zenodo.6421460
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Cascade , Fingerprinting , Indoor positioning , Localization , Machine learning , Prediction speed
Supplementary materials for "Towards Accelerated Localization Performance Across Indoor Positioning Datasets"
info:eu-repo/semantics/other
oai:zenodo.org:3528274
2020-01-20T16:49:52Z
user-a_wear
Klus, Lucie
Nurmi, Jari
Lohan, Elena Simona
Granell, Carlos
2019-10-18
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, <a href="http://www.a-wear.eu/">http://www.a-wear.eu/</a>).</p>
https://doi.org/10.5281/zenodo.3528274
oai:zenodo.org:3528274
eng
Zenodo
https://zenodo.org/communities/a_wear
https://doi.org/10.5281/zenodo.3528273
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
URSI, XXXV Finnish URSI Convention on Radio Science, Tampere, Finland, 18 October 2019
Crowdsourcing, Repository, Wearable
Crowdsourcing Solutions for Data Gathering from Wearables
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4947588
2021-06-15T01:48:16Z
user-a_wear
Justyna Skibinska
Radim Burget
2021-06-11
<p>Due to population aging, society is struggling with an increasing number of patients with neurodegenerative diseases. One of them is Parkinson’s disease. Early detection of Parkinson’s disease is very important since there is no cure and the treatment is more effective when administered early. Wearable devices can be of great help - they are cheap and reachable, they can last for many days without charging, can provide long time monitoring, and are minimally invasive to human life. In the paper, we briefly describe the sensors and actigraphs suitable for the analysis of sleep disturbance in Parkinson’s patients and nocturnal symptoms of Parkinson’s disease. Moreover, we pointed out how to collect the data and what could have an influence on the final performance of the automatic models. Additionally, as the main aim of this paper, we have analyzed and described the machine learning algorithms used in the area of analysis accelerometer signal for sleep / awake stages recognition or diseases which manifested in changes in sleep patterns. We thought that these algorithms, because of the nature of Parkinson’s patients’ sleep patterns, will be simultaneously appropriate for the detection of Parkinson’s disease.</p>
https://doi.org/10.5281/zenodo.4947588
oai:zenodo.org:4947588
eng
Zenodo
https://zenodo.org/communities/a_wear
https://doi.org/10.5281/zenodo.4947587
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
machine learning, Parkinson disease, actigraphy, wearable device, IoT healthcare device, eHealth, Health 4.0
The Transferable Methodologies of Detection Sleep Disorders Thanks to the Actigraphy Device for Parkinson's Disease Detection
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7858187
2023-05-13T02:27:51Z
user-a_wear
openaire_data
user-eu
Tomas Bravenec
Joaquín Torres-Sospedra
Michael Gould
Tomas Fryza
2023-04-24
<p>Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"</p>
<p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
https://doi.org/10.5281/zenodo.7858187
oai:zenodo.org:7858187
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7858186
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
probe requests
wi-fi
privacy
pcap
Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"
info:eu-repo/semantics/other
oai:zenodo.org:6779475
2022-08-26T08:50:41Z
user-a_wear
user-tau_wireless
user-eu
Lucie Klus
Quezada-Gaibor, Darwin
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Granell, Carlos
Nurmi, Jari
2022-06-21
<p>The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's.</p>
The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/). FCT – Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the PhD fellowship PD/BD/137401/2018. J. Torres-Sospedra acknowledges funding from MICIU (INSIGNIA, PTQ2018-009981).
https://doi.org/10.1109/ICL-GNSS54081.2022.9797035
oai:zenodo.org:6779475
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS, International Conference on Localization and GNSS, Tampere, Finland, 7. - 9. June 2022
Cascade , Fingerprinting , Indoor positioning , Localization , Machine learning , Prediction speed
Towards Accelerated Localization Performance Across Indoor Positioning Datasets
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4091706
2020-10-16T06:32:15Z
user-a_wear
user-tau_wireless
user-eu
Klus, Lucie
Quezada-Gaibor, Darwin
Torres-Sospedra, Joaquın
Lohan, Elena Simona
Granell, Carlos
Nurmi, Jari
2020-10-15
<p>Abstract—Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.</p>
Supplementary materials are available at https://doi.org/10.5281/zenodo.4026370
https://doi.org/10.1109/ICUMT51630.2020.9222458
oai:zenodo.org:4091706
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
clustering, compression ratio, data compression, fingerprinting, indoor positioning, k-means, k-nearest neighbors
RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3983634
2020-10-18T00:27:25Z
user-a_wear
user-tau-tltpos
software
user-eu
Quezada-Gaibor, Darwin
Klus, Lucie
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Nurmi, Jari
Huerta, Joaquı́n
2020-10-15
<p>This package contains all the functions used to run the experiments in the paper cited below. Please, if you want to use this software, don't forget to cite the source.</p>
<p>* Quezada-Gaibor, D., Klus, L., Torres-Sospedra, J., Lohan, E. S., Nurmi, J. and Huerta, J., 2020, October. Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices, In 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (pp. 208-213). IEEE. https://doi.org/10.1109/ICUMT51630.2020.9222411</p>
<p>* Quezada-Gaibor, D., Klus, L., Torres-Sospedra, J., Lohan, E. S., Nurmi, J. and Huerta, J., 2020, October. Supplementary Materials for "Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices", Zenodo, [Available Online] https://zenodo.org/record/3983634</p>
<p>If you would like to run the experiments, please follow the instructions in the README file.</p>
<p>Note: This package is based on the material provided by Torres-Sospedra et al. (https://zenodo.org/record/3751042)<br>
* Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. "New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting". In Proceedings of the Tenth International Conference on Localization and GNSS (ICL-GNSS), 2020.</p>
<p>If you would like to re-use the databases included in this paper, please cite the corresponding sources as indicated in the readme file in the folder 'databases'.</p>
<p>Don't hesitate to contact me if you have any questions (quezada@uji.es, darwinqg1@hotmail.com)</p>
https://doi.org/10.5281/zenodo.3983634
oai:zenodo.org:3983634
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3983633
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICUMT, 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Online (Brno, Czech Republic), 5-7 October 2020
Clustering; DBSCAN; PCA; RSS; Software; Wi-Fi fingerprinting
Supplementary Materials for "Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices"
info:eu-repo/semantics/other
oai:zenodo.org:6876987
2022-07-22T07:46:27Z
user-a_wear
user-eu
Salwa Saafi
Olga Vikhrova
Sergey Andreev
Jiri Hosek
2022-07-11
<p>The evolution of cellular fifth-generation (5G) tech-nologies shapes the future of the manufacturing industry by enabling sector automation and digitalization. Smart factories rely primarily on wireless connectivity provided by new radio (NR) systems to meet the stringent requirements of industrial applications. Among these, several industrial wearable and sensor-based services involve devices with relaxed communication capabilities as compared to Rel-15 NR user equipment. Hence, a new category of reduced-capability (RedCap) devices becomes essential in industrial private networks. As RedCap devices may experience degradation of uplink (UL) performance due to simplifications in radio frequency and baseband capabilities, this paper focuses on enhancing NR RedCap operations with existing 5G solutions for UL improvement, namely, dual connectivity, carrier aggregation, and supplementary UL. Specifically, we discuss these options for RedCap wearable devices and evaluate the performance gains of the selected technology using link-level simulations.</p>
https://doi.org/10.1109/ICCWorkshops53468.2022.9814497
oai:zenodo.org:6876987
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
2022 IEEE International Conference on Communications Workshops (ICC Workshops), Seoul, South Korea, 16-20 May 2022
Industrial 5G
NR RedCap
Dual Connectivity
Carrier Aggregation
Supplementary UL
Enhancing Uplink Performance of NR RedCap in Industrial 5G/B5G Systems
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7134882
2022-10-03T05:45:19Z
user-a_wear
user-eu
Nadezhda Chukhno
Olga Chukhno
Dmitri Moltchanov
Anna Gaydamaka
Andrey Samuylov
Antonella Molinaro
Yevgeni Koucheryavy
Antonio Iera
Giuseppe Araniti
2022-09-28
<p>Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate<br>
a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G)<br>
New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the<br>
exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem<br>
of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin<br>
packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for<br>
large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this<br>
issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast<br>
grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an<br>
increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast<br>
grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-<br>
range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast<br>
beams to serve multicast users.</p>
https://doi.org/10.1109/TBC.2022.3206595
oai:zenodo.org:7134882
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Transactions on Broadcasting, (2022-09-28)
5G
machine learning
millimeter Wave
multicast
multi-beam antennas
New Radio
optimization
The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems
info:eu-repo/semantics/article
oai:zenodo.org:3908174
2020-06-25T22:18:23Z
user-a_wear
user-tau_wireless
user-eu
Klus, Roman
Klus, Lucie
Lohan, Elena Simona
Granell, Carlos
Nurmi, Jari
2020-06-04
<p>With the increasing popularity, diversity, and utilization of wearable devices, the data transfer-and-storage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods.</p>
https://doi.org/10.5281/zenodo.3908174
oai:zenodo.org:3908174
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3908173
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS 2020, 2020 International Conference on Localization and GNSS, Tampere, Finland,, 2-4 June 2020
Compression, Discrete Cosine Transform (DCT), Lightweight Temporal Compression (LTC), Heart Rate, Symbolic Aggregation Approximation (SAX), Wearables
Lossy Compression Methods for Performance-Restricted Wearable Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4947193
2021-06-15T01:48:16Z
user-a_wear
user-eu
Ometov, Aleksandr
Shubina, Viktoriia
Klus, Lucie
Skibińska, Justyna
Saafi, Salwa
Pascacio, Pavel
Flueratoru, Laura
Quezada Gaibor, Darwin
Chukhno, Nadezhda
Chukhno, Olga
Ali, Asad
Channa, Asma
Svertoka, Ekaterina
Qaim, Waleed Bin
Casanova-Marqués, Raúl
Holcer, Sylvia
Torres-Sospedra, Joaquín
Casteleyn, Sven
Ruggeri, Giuseppe
Araniti, Giuseppe
Burget, Radim
Hosek, Jiri
Lohan, Elena Simona
2021-07-05
<p>Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.</p>
https://doi.org/10.1016/j.comnet.2021.108074
oai:zenodo.org:4947193
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Computer Networks, 193, 37, (2021-07-05)
Wearables
Communications
Standardization
Privacy
Security
Data processing
Interoperability
User adoption
Localization
Classification
Future perspective
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges
info:eu-repo/semantics/article
oai:zenodo.org:5303180
2021-08-30T06:25:27Z
user-a_wear
user-eu
Asma Channa
Nirvana Popescu
Justyna Skibinska
Radim Burget
2021-08-28
<p>The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.</p>
https://doi.org/10.3390/s21175787
oai:zenodo.org:5303180
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review †
info:eu-repo/semantics/article
oai:zenodo.org:6767482
2022-06-28T13:49:11Z
user-a_wear
user-tau-tltpos
user-eu
Quezada-Gaibor, Darwin
Torres-Sospedra, Joaquín
Nurmi, Jari
Koucheryavy, Yevgeni
Huerta, Joaquín
2022-01-04
<p>A preprint version of the paper entitled “Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm”, presented in the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN).</p>
<p>Nowadays, several indoor positioning solutions support Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance, similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting.</p>
The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/).; J. Torres-Sospedra gratefully acknowledge funding from Ministerio de Ciencia, Innovación y Universidades (INSIGNIA, PTQ2018-009981)
https://doi.org/10.1109/IPIN51156.2021.9662612
oai:zenodo.org:6767482
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IPIN, 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Barcelona, Spain, 29 November 2021 - 02 December 2021
Indoor Positioning
Wi-Fi fingerprinting
Clustering
Computing Efficiency
Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6387619
2022-03-29T07:55:20Z
user-a_wear
user-eu
Olga Chukhno
Nadezhda Chukhno
Olga Galinina
Sergey Andreev
Yuliya Gaidamaka
Konstantin Samouylov
Giuseppe Araniti
2022-03-21
<p>The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks – directional deafness – which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.</p>
https://doi.org/10.1109/TWC.2022.3159086
oai:zenodo.org:6387619
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Transactions on Wireless Communications, (2022-03-21)
3D
deafness
directional antenna
distributed networks
stochastic geometry
millimeter wave
A Holistic Assessment of Directional Deafness in mmWave-based Distributed 3D Networks
info:eu-repo/semantics/article
oai:zenodo.org:5507458
2021-12-07T13:36:37Z
user-a_wear
user-eu
Justyna Skibinska
Radim Burget
Asma Channa
Nirvana Popescu
Yevgeni Koucheryavy
2021-07-29
<p>Early detection of COVID-19 positive people are now extremely needed and considered to be one of the most effective ways how to limit spreading the infection. Commonly used screening methods are reverse transcription polymerase chain reaction (RT-PCR) or antigen tests, which need to be periodically repeated. This paper proposes a methodology for detecting the disease in non-invasive way using wearable devices and for the analysis of bio-markers using artificial intelligence. This paper have reused a publicly available dataset containing COVID-19, influenza, and Healthy control data. In total 27 COVID-19 positive and 27 healthy control were pre-selected for the experiment, and several feature extraction methods were applied to the data. This paper have experimented with several machine learning methods, such as XGBoost, k-nearest neighbour k-NN, support vector machine, logistic regression, decision tree, and random forest, and statistically evaluated their perfomance using various metrics, including accuracy, sensitivity and specificity. The proposed experiment reached 78 % accuracy using the k-NN algorithm which is significantly higher than reported for state-of-the-art methods. For the cohort containing influenza, the accuracy was 73 % for k-NN. Additionally, we identified the most relevant features that could indicate the changes between the healthy and infected state. The proposed methodology can complement the existing RT-PCR or antigen screening tests, and it can help to limit the spreading of the viral diseases, not only COVID-19, in the non-invasive way.</p>
https://doi.org/10.1109/ACCESS.2021.3106255
oai:zenodo.org:5507458
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Artificial intelligence, COVID, signal processing
COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques
info:eu-repo/semantics/article
oai:zenodo.org:5803086
2021-12-27T10:16:16Z
user-a_wear
user-eu
Nadezhda Chukhno
Olga Chukhno
Dmitri Moltchanov
Antonella Molinaro
Yuliya Gaidamaka
Konstantin Samouylov
Yevgeni Koucheryavy
Giuseppe Araniti
2021-12-17
<p>The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. However, the capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.</p>
https://doi.org/10.1109/TMC.2021.3136298
oai:zenodo.org:5803086
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Transactions on Mobile Computing, (2021-12-17)
5G
New Radio
Millimeter Wave
Multicast
Multi-beam antennas
Optimization
Heuristic Algorithms
Optimal Multicasting in Millimeter Wave 5G NR with Multi-beam Directional Antennas
info:eu-repo/semantics/article
oai:zenodo.org:7243148
2022-11-01T14:26:29Z
user-a_wear
user-eu
Flueratoru Laura
Lohan Elena Simona
Niculescu Dragoș
2022-10-26
<p>Open-source version of:</p>
<p>Laura Flueratoru, Elena Simona Lohan, and Dragos Niculescu. 2022. <strong>Challenges in platform-independent UWB ranging and localization systems</strong>. In Proceedings of the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (WiNTECH '22). Association for Computing Machinery, New York, NY, USA, 9–15. https://doi.org/10.1145/3556564.3558238</p>
https://doi.org/10.1145/3556564.3558238
oai:zenodo.org:7243148
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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ultra-wideband
indoor localization
indoor positioning
ranging
distance measurements
Challenges in Platform-Independent UWB Ranging and Localization Systems
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6623794
2022-06-14T11:40:01Z
user-a_wear
user-eu
Casanova-Marqués, Raúl
Dzurenda, Petr
2022-04-26
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.5281/zenodo.6623794
oai:zenodo.org:6623794
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.6623793
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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EEICT, 2022 28th Conference STUDENT EEICT 2022, Brno, Czech Republic, 26 April 2022
PEAS
Privacy-Enhancing
Authentication
Identity
Cryptography
Deployment
Constrained Devices
MULTOS
Java Card
Android
Wearables
Readiness of Anonymous Credentials for Real Environment Deployment
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4948278
2021-06-15T05:33:05Z
user-a_wear
user-tau-tltpos
user-eu
Potorti, Francesco
Torres-Sospedra, Joaquín
Quezada-Gaibor, Darwin
Ramón Jiménez, Antonio
Seco, Fernando
Pérez-Navarro, Antoni
Ortiz, Miguel
Zhu, Ni
et al.
2021-05-25
<p>A preprint version of the paper entitled "Off-line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences from IPIN 2020 Competition".</p>
<p>Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1m for the Smartphone Track and 0.5m for the Footmounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.</p>
https://doi.org/10.5281/zenodo.4948278
oai:zenodo.org:4948278
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4948277
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Sensor phenomena and characterization
Indoor navigation
Testing
Standards
Satellite broadcasting
Recurrent neural networks
Received signal strength indicator
Off-line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences from IPIN 2020 Competition
info:eu-repo/semantics/article
oai:zenodo.org:5090552
2021-07-19T06:37:58Z
user-a_wear
user-tau_wireless
user-eu
Laura Flueratoru
Viktoriia Shubina
Dragoș Niculescu
Elena Simona Lohan
2021-07-08
<p>This paper presents a measurement-based analysis of the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) signals, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios, performed in tandem at two universities in Tampere, Finland, and Bucharest, Romania. We adopted the same hardware and methodology for measurements in both places, and paid particular attention to the impact of RSS on various environmental factors, such as LOS and NLOS scenarios and interference in 2.4 GHz band. In addition, we considered the receiver orientation and the different frequencies of BLE advertising channels. We show that snapshot RSS measurements typically have high variability, not easily explainable by classical path-loss models. A snapshot recording is defined here as one continuous recording at fixed device locations in a static setup. Our observations also show that aggregated RSS data (i.e., considering several snapshot measurements together) is more informative from a statistical point of view and more in agreement with current theoretical path-loss models than snapshot measurements. However, in BLE applications such as contact tracing and proximity detection, the receivers typically have access only to snapshot measurements (e.g., taken over a short duration of 10–20 minutes or less), so the accuracy of contact-tracing and proximity detection can be highly affected by RSS instabilities. In addition to presenting the measurement-based BLE RSS analysis in a comprehensive and well-documented format, our paper also emphasizes open challenges when BLE RSS is used for contact tracing, ranging, and positioning applications.</p>
https://doi.org/10.1109/JSEN.2021.3095710
oai:zenodo.org:5090552
eng
Zenodo
https://doi.org/10.5281/zenodo.4643668
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Sensors Journal, (2021-07-08)
Indoor navigation
Indoor localization
Indoor radio communication
Received signal strength indicator (RSSI)
Received Signal Strength (RSS)
Bluetooth
Bluetooth Low Energy (BLE)
Measurements
Fluctuations
Contact tracing
Proximity detection
Ranging
On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection
info:eu-repo/semantics/article
oai:zenodo.org:6778367
2022-06-29T17:24:08Z
user-a_wear
user-eu
Hima Zafar
Asma Channa
Varun Jeoti
Goran M. Stojanović
2022-06-29
<p>The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.</p>
https://doi.org/10.3390/s22020638
oai:zenodo.org:6778367
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring
info:eu-repo/semantics/article
oai:zenodo.org:6393006
2022-03-29T13:49:19Z
user-a_wear
user-eu
Torres-Sospedra, Joaquín Torres-Sospedra
Lohan, Elena Simona
Molinaro, Antonella
Moreira, Adriano
Rusu-Casandra, Alexandru
Smékal, Zdenek
2022-03-29
<p>Multiple sensors are embedded in wearable devices. Sensors are mainly included for tracking information on the user’s physical activity and physiological parameters, but additional sensors are also included for radio-communications and other purposes. This advanced complex sensory system enables wearables to be a source of invaluable crowdsourced data, where sensor fusion may enable innovative applications in many fields (engineering, telecommunications, computer science, eHealth, the Internet of Things, Sensor Networks, etc.).</p>
<p>The Special Issue on “Applications and Innovations on Sensor-Enabled Wearable Devices” in Sensors (MDPI) welcomed submissions of technological innovations and novel applications for wearable devices, with special interest in indoor positioning, from both Academia and the Industry.</p>
<p>A total of nine papers were published, contributing to the research community with the description of applications and reviews about the Special Issue’s topics. The pub- lished papers summed up a total of 63 citations in the Web of Science (WOS) records and 120 citations in the Google Scholar (GS) records at the moment of writing this editorial.</p>
https://doi.org/10.3390/s22072599
oai:zenodo.org:6393006
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Sensors, 22, 2599, (2022-03-29)
Applications and Innovations on Sensor-Enabled Wearable Devices
info:eu-repo/semantics/other
oai:zenodo.org:7936409
2023-05-18T02:27:08Z
user-a_wear
user-eu
Najeeb ur Rehman Malik
Syed Abdul Rahman Abu-Bakar
Usman Ullah Sheikh
Asma Channa
Nirvana Popescu
2023-05-15
<p>Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed based on handcrafted methods for action recognition. However, the handcrafted techniques are inefficient in case of recognizing interaction level actions as they involve complex scenarios. Meanwhile, the traditional deep learning-based approaches take the entire image as an input and later extract volumes of features, which greatly increase the complexity of the systems; hence, resulting in significantly higher computational time and utilization of resources. Therefore, this research focuses on the development of an efficient multi-view interaction level action recognition system using 2D skeleton data with higher accuracy while reducing the computation complexity based on deep learning architecture. The proposed system extracts 2D skeleton data from the dataset using the OpenPose technique. Later, the extracted 2D skeleton features are given as an input directly to the Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) architecture for action recognition. To reduce the complexity, instead of passing the whole image, only extracted features are given to the CNN-LSTM architecture, thus eliminating the need for feature extraction. The proposed method was compared with other existing methods, and the outcomes confirm the potential of the proposed technique. The proposed OpenPose - CNNLSTM achieved an accuracy of 94.4% for MCAD (Multi-camera action dataset) and 91.67% for IXMAS (INRIA Xmas Motion Acquisition Sequences). Our proposed method also significantly decreases the computational complexity by reducing the number of inputs features to 50.</p>
https://doi.org/10.3390/signals4010002
oai:zenodo.org:7936409
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
info:eu-repo/semantics/article
oai:zenodo.org:3522085
2020-01-20T16:43:17Z
user-a_wear
Shubina, Viktoriia
Ometov, Aleksandr
Niculescu, Dragos
Lohan, Elena-Simona
2019-10-18
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.5281/zenodo.3522085
oai:zenodo.org:3522085
eng
Zenodo
https://zenodo.org/communities/a_wear
https://doi.org/10.5281/zenodo.3522084
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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URSI, XXXV Finnish URSI Convention on Radio Science, Tampere, Finland, 18 October 2019
Privacy
Challenges
Localization
Wearable devices
Challenges of Privacy-aware Localization on Wearable Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4603181
2021-03-14T12:08:23Z
user-a_wear
user-eu
Nadezhda Chukhno
Olga Chukhno
Sara Pizzi
Antonella Molinaro
Antonio Iera
Giuseppe Araniti
2021-03-11
<p>Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.</p>
https://doi.org/10.1109/TBC.2021.3061979
oai:zenodo.org:4603181
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Transactions on Broadcasting, 1 - 13, (2021-03-11)
multicast
millimeter wave communication
radio resource management
wearable devices
Efficient Management of Multicast Traffic in Directional mmWave Networks
info:eu-repo/semantics/article
oai:zenodo.org:4108686
2020-10-20T12:26:58Z
user-a_wear
user-eu
Salwa Saafi
Jiri Hosek
Aneta Kolackova
2020-10-14
<p>With the aim of offering services and products that ensure the safety of people and properties, public safety organizations are responsible for providing the first responders, i.e., police officers, firefighters, and emergency medical service workers, with devices and communication systems that help them exchange time-sensitive and critical information. To address the mission-critical requirements and to target new broadband public safety applications, these organizations started migrating from traditional land mobile radio towards cellular communication systems with the consideration of a new set of deployed devices, such as wearables. In this paper, we first provide a state of the art overview of the features that are introduced by the 3rd Generation Partnership Project (3GPP) and that can be used for public safety services. Second, we discuss the role of wearable devices, more precisely cellular-enabled wearables, in creating several new use cases as part of the concept of the Internet of Life Saving Things. Finally, we conduct a performance evaluation of a mission-critical service using cellular-enabled wearables, specifically a mission-critical push-to-talk (MCPTT) application using LTE Cat-M2-enabled smartwatches. In this evaluation, we examine the impact of different parameters related to the wearable device capabilities and the MCPTT call scenarios on the key performance indicator defined by 3GPP for this type of applications, which is the MCPTT access time.</p>
https://doi.org/10.1109/ICUMT51630.2020.9222459
oai:zenodo.org:4108686
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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ICUMT, 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Brno, Czech Republic, Czech Republic, 5-7 October 2020
Public safety
Cellular connectivity
Wearables
Internet of Life Saving Things
Cellular-enabled Wearables in Public Safety Networks: State of the Art and Performance Evaluation
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4067970
2020-10-08T00:26:57Z
user-a_wear
user-eu
Waleed Bin Qaim
Aleksandr Ometov
Antonella Molinaro
Ilaria Lener
Claudia Campolo
Elena Simona Lohan
Jari Nurmi
2020-10-06
<p>Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged – therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables’ energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.</p>
https://doi.org/10.1109/ACCESS.2020.3025270
oai:zenodo.org:4067970
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE ACCESS, 8, 175412-175435, (2020-10-06)
Wearables, Internet of Wearable Things, Energy Consumption, Wearable Applications, Energy Efficiency, Computing, Systematic Literature Review
Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review
info:eu-repo/semantics/article
oai:zenodo.org:8041971
2023-07-10T10:06:51Z
user-a_wear
user-tau_wireless
user-eu
Viktoriia Shubina
Aleksandr Ometov
Dragoș Niculescu
Elena Simona Lohan
2023-06-15
<p>Location privacy poses a critical challenge as the use of mobile devices and location-based services becomes more and more widespread. Proximity-detection data can reveal sensitive information about individuals, making it essential to preserve their location data. One way to achieve privacy protection is by adding noise to ground-truth data, which can introduce uncertainty while still allowing moderate utility for proximity-detection services and Received Signal Strength (RSS)-based localization. However, it is important to carefully adjust the amount of noise added in order to balance the privacy and accuracy concerns. This paper expands our previous work on evaluating location privacy bounds based on measurement error and intentionally added noise. Our model builds upon existing work in differential privacy and introduces other techniques to estimate privacy bounds specific to proximity data. By using real-world measurement data, we measure the privacy-accuracy trade-off and suggest cases where additional noise could be added. Our framework can be utilized to inform privacy-preserving location-based applications and guide the selection of appropriate noise levels in order to achieve the desired privacy-accuracy balance.</p>
https://doi.org/10.1109/ICL-GNSS57829.2023.10148925
oai:zenodo.org:8041971
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS, 2023 International Conference on Localization and GNSS, Castellón, Spain, 06-08 June 2023
Location privacy
RSS
BLE
localization
Proximity detection
Acceptable Margin of Error: Quantifying Location Privacy in BLE Localization
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7236698
2022-10-24T05:47:53Z
user-a_wear
openaire_data
user-eu
Ekaterina Svertoka
Alexandru Rusu-Casandra
Radim Burget
Ion Marghescu
Jiri Hosek
Aleksandr Ometov
2022-10-11
<p>These datasets are published as supplementary materials to the journal article "LoRaWAN: Lost for Localization?". Fingerprinting datasets contain data collected in Bucharest, Romania and Brno, Czech Republic using LoRaWAN technology for various types of environments: outdoor, indoor and underground.</p>
https://doi.org/10.1109/JSEN.2022.3212319
oai:zenodo.org:7236698
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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LoRaWAN, dataset, LoRa, fingerprinting, measurements, outdoors, localization,
Supplementary materials to the journal article "LoRaWAN: Lost for Localization?"
info:eu-repo/semantics/other
oai:zenodo.org:7405183
2022-12-06T17:02:21Z
user-a_wear
user-eu
Asma Channa
Nirvana Popescu
Muhammad Faisal
2022-06-30
<p>Gait evaluation is important for apprehension and management of different neurocognitive disorders (NCD). The gait events are changing with the age factor and this variability is being incorrectly linked with people with NCD. So, there is a high need to analyze gait events correctly. The gait analysis is mostly performed on temporal and spectral feature extraction in which there is a high rate of missing important features. Apart from this, monitoring and quantification of Parkinson’s disease patients raise many therapeutic challenges in terms of severity analysis of motor symptoms i.e. freezing of gait (FOG), bradykinesia and continuous remote monitoring of patients. The objective of this study is to use a smart insole dataset for the assessment of computational techniques focusing on gait evaluation. The objective of this research study is to use continuous wavelet transform to convert time series signals into an images instead of using more traditional techniques for dealing with time series based on e.g. recurrent architectures. The results evidence that the proposed system works efficiently with the accuracy of 96.5% in gait variability analyzing three cohorts i.e. adults, elderly, and patients with Parkinson’s disease (PwPD) and 91% for analyzing the gait symptoms in different severity stages of PD patients.</p>
https://doi.org/10.1109/CoDIT55151.2022.9804064
oai:zenodo.org:7405183
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Parkinson's Disease Gait Evaluation Leveraging Wearable Insoles and Deep Learning Approach
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7193602
2022-11-25T09:36:18Z
user-a_wear
openaire_data
user-eu
Bravenec, Tomáš
Gould, Michael
Frýza, Tomáš
Torres-Sospedra, Joaquín
2022-10-13
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p>
<p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
<p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p>
<p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p>
<p><strong>Data</strong></p>
<p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p>
<table>
<caption>Data Point Coordinates</caption>
<thead>
<tr>
<th scope="row"> </th>
<th scope="col">X</th>
<th scope="col">Y</th>
<th scope="col"> </th>
<th scope="col">X</th>
<th scope="col">Y</th>
<th scope="col"><strong>...</strong></th>
</tr>
</thead>
<tbody>
<tr>
<th scope="row">A1</th>
<td>0.85</td>
<td>0.1</td>
<td><strong>B1</strong></td>
<td>1.85</td>
<td>0.1</td>
<td><strong>...</strong></td>
</tr>
<tr>
<th scope="row">A2</th>
<td>0.85</td>
<td>1.1</td>
<td><strong>B2</strong></td>
<td>1.85</td>
<td>1.1</td>
<td><strong>...</strong></td>
</tr>
<tr>
<th scope="row">A3</th>
<td>0.85</td>
<td>2.1</td>
<td><strong>B3</strong></td>
<td>1.85</td>
<td>2.1</td>
<td><strong>...</strong></td>
</tr>
<tr>
<th scope="row">...</th>
<td><strong>...</strong></td>
<td><strong>...</strong></td>
<td><strong>...</strong></td>
<td><strong>...</strong></td>
<td><strong>...</strong></td>
<td><strong>...</strong></td>
</tr>
<tr>
<th scope="row">A11</th>
<td>0.85</td>
<td>10.1</td>
<td><strong>B11</strong></td>
<td>1.85</td>
<td>10.1</td>
<td><strong>...</strong></td>
</tr>
</tbody>
</table>
<p><strong>Data_Eval</strong></p>
<p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p>
<p><strong>Processed_Data</strong></p>
<p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p>
<p>The CSV files are in a format:</p>
<blockquote>
<p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p>
</blockquote>
<p><strong>Data_Scenarios</strong></p>
<p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p>
<table>
<caption>Scenario Descriptions</caption>
<thead>
<tr>
<th scope="col">
<p>Data Name</p>
</th>
<th scope="col">
<p>Scenario Description</p>
</th>
</tr>
</thead>
<tbody>
<tr>
<td>GPR00</td>
<td>Only measured data, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR01</td>
<td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR02</td>
<td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR03</td>
<td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR04</td>
<td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR05</td>
<td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR06</td>
<td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR07</td>
<td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td>
</tr>
<tr>
<td>GPR08</td>
<td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td>
</tr>
<tr>
<td>GPR09</td>
<td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td>
</tr>
<tr>
<td>GPR10</td>
<td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td>
</tr>
<tr>
<td>GPR11</td>
<td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td>
</tr>
<tr>
<td>GPR12</td>
<td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td>
</tr>
<tr>
<td>GPR13</td>
<td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td>
</tr>
</tbody>
</table>
<p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p>
<table>
<caption>File Suffix Descriptions</caption>
<tbody>
<tr>
<td>
<p><strong>Suffix</strong></p>
</td>
<td>
<p><strong>Suffix Description</strong></p>
</td>
</tr>
<tr>
<td>_trncrd</td>
<td>Training Labels</td>
</tr>
<tr>
<td>_trnrss</td>
<td>Training RSSI Values</td>
</tr>
<tr>
<td>_tstcrd</td>
<td>Evaluation Labels</td>
</tr>
<tr>
<td>_tstrss</td>
<td>Evaluation RSSI Values</td>
</tr>
</tbody>
</table>
<p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p>
<p>The RSSI data are in format:</p>
<blockquote>
<p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p>
</blockquote>
<p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p>
<blockquote>
<p>X, Y, 0, 0, 0</p>
</blockquote>
https://doi.org/10.5281/zenodo.7193602
oai:zenodo.org:7193602
eng
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https://doi.org/10.5281/zenodo.7193601
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probe requests
wi-fi
pcap
csv
radio map
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
info:eu-repo/semantics/other
oai:zenodo.org:6381384
2022-12-09T14:26:31Z
user-a_wear
user-tau-tltpos
software
user-eu
Quezada-Gaibor, Darwin
Klus, Lucie
Torres-Sospedra, Joaquín
Lohan, Elena-Simona
Nurmi, Jari
Granell, Carlos
Huerta, Joaquín
2022-03-24
<p>This package contains all the software used to run the experiments in the paper cited below. Please, if you want to use this software, don't forget to cite the source.</p>
<p>* Quezada-Gaibor, Darwin, Lucie Klus, Joaquín Torres-Sospedra, Elena Simona Lohan, Jari Nurmi, Carlos Granell, and Joaquín Huerta."Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets," 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022, pp. 349-354, doi: 10.1109/MDM55031.2022.00079.</p>
<p>If you would like to run the experiments, please follow the instructions in the README file.</p>
<p>If you would like to re-use the databases included in this paper, please cite the corresponding sources as indicated in the readme file in the folder 'dataset'.</p>
<p>Don't hesitate to contact me if you have any questions (quezada@uji.es)</p>
https://doi.org/10.5281/zenodo.6381384
oai:zenodo.org:6381384
eng
Zenodo
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https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.6381383
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Creative Commons Attribution 4.0 International
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Supplementary material "Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets"
info:eu-repo/semantics/other
oai:zenodo.org:4575153
2021-03-04T00:27:26Z
user-a_wear
openaire_data
user-eu
Ometov, Aleksandr
Shubina, Viktoriia
2021-03-03
<p>During the preparation of "A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges", A-WEAR representatives have analyzed the market and exiting research projects, which resulted in the dataset allowing for easy processing of the available wearable devices. The dataset provides the data about 224 wearable devices stored in JSON format. The dataset would be extended during the entire project duration.<br>
<br>
The entry example is provided in the following listing:<br>
[<br>
{<br>
"Device": "Focals by North",<br>
"Location type": "Head_mounted",<br>
"Application type": "Eyewear",<br>
"Device type": "AR",<br>
"Hi or low end": "high",<br>
"Prototype": "yes",<br>
"Energy-related information": "700 mAh/18 hours",<br>
"CPU": "Qualcomm APQ8009w",<br>
"GPU": "n/a",<br>
"Camera": "yes",<br>
"Cellular": "no",<br>
"WiFi": "no",<br>
"Bluetooth": "yes",<br>
"RFID": "no",<br>
"Other connectivity": "no",<br>
"GNSS": "no",<br>
"RAM": "n/a",<br>
"Storage": "n/a",<br>
"Display": "yes",<br>
"Audio output": "yes",<br>
"Mic": "yes",<br>
"Release": "n/a",<br>
"Price $": "n/a",<br>
"Lyxury": "yes",<br>
"Acitivity tracking": "yes",<br>
"Sensors": "9-axis IMU, Ambient Light Sensor, Proximity sensor",<br>
"Description": "Smart AR glasses. Could be used as handsfree and with assistants.",<br>
"Other comments": "IP55",<br>
"Link": "https://www.bynorth.com/tech"<br>
},<br>
<...><br>
]</p>
https://doi.org/10.5281/zenodo.4575153
oai:zenodo.org:4575153
eng
Zenodo
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https://doi.org/10.5281/zenodo.4575152
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Creative Commons Attribution 4.0 International
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wearable devices
market
characteristics
Supplementary Materials for "A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges"
info:eu-repo/semantics/other
oai:zenodo.org:4314992
2021-06-15T11:00:50Z
user-a_wear
openaire_data
user-ipin
user-eu
Joaquín Torres-Sospedra
Darwin Quezada Gaibor
Antonio R. Jiménez
Antoni Pérez-Navarro
Fernando Seco
2020-12-10
<p>This package contains the datasets and supplementary materials used in the IPIN 2020 Competition.</p>
<p><strong>Contents:</strong></p>
<ul>
<li>IPIN2020_Track03_TechnicalAnnex_V1-01.pdf: Technical annex describing the competition </li>
<li>01-Logfiles: This folder contains a subfolder with the 78 training logfiles, 72 of them single floor, 4 in bookshelves areas and 2 of them in floor-transition zones, a subfolder with the 13 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li>
<li>02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.</li>
<li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors. It contains a sample of reported estimations and the corresponding results. Additionally, we provide a second script to provide a more detailed report on the results file (requires export_fig folder to run).</li>
</ul>
<p><strong>Please, cite the following works when using the datasets included in this package:</strong></p>
<ul>
<li>Torres-Sospedra, J.; Quezada-Gaibor, D.; Jimenez, A.R.; Perez-Navarro, A.; Seco, F.; Datasets and Supporting Materials for the IPIN 2020 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.4314992</li>
<li>Potortì, F.; Torres-Sospedra, J.; Quezada-Gaibor, D.; Jiménez, A.R.; Seco, F.; Pérez-Navarro, A.; Ortiz, M.; Zhu, N.; Renaudin, V.; Ichikari, R.; Shimomura, R.; Ohta, N.; Nagae, S.; Kurata, T.; Wei, D.; Ji, X.; Zhang, W.; Kram, S.; Stahlke, M.; Mutschler, C.; Crivello, A.; Barsocchi, P.; Girolami, M.; Palumbo, F.; Chen, R.; Wu, Y.; Li, W.; Yu, Y.; Xu, S.; Huang, L.; Liu, T.; Kuang, J.; Niu, X.; Yoshida, T.; Nagata, Y.; Fukushima, Y.; Fukatani, N.; Hayashida, N.; Asai, Y.; Urano, K.; Ge, W.; Lee, N.T.; Fang, S.H.; Jie, Y.C.; Young, S.R.; Chien, Y.R.; Yu, C.C.; Ma, C.; Wu, B.; Zhang, W.; Wang, Y.; Fan, Y.; Poslad, S.; Selviah, D.R.; Wang, W.; Yuan, H.; Yonamoto, Y.; Yamaguchi, M.; Kaichi, T.; Zhou, B.; Liu, X.; Gu, Z.; Yang, C.; Wu, Z.; Xie, D.; Huang, C.; Zheng, L.; Peng, A.; Jin, G.; Wang, Q.; Luo, H.; Xiong, H.; Bao, L.; Zhang, P.; Zhao, F.; Yu, C.A.; Hung, C.H.; Antsfeld, L.; Chidlovskii, B.; Jiang, H.; Xia, M.; Yan, D.; Li, Y.; Dong, Y.; Silva, I.; Pendão, C.; Meneses, F.; Nicolau, M.J.; Costa, A.; Moreira, A.; Cock, C.D.; Plets, D.; Opiela, M.; Džama, J.; Zhang, L.; Li, H.; Chen, B.; Liu, Y.; Yean, S.; Lim, B.Z.; Teo, W.J.; Lee, B.S.; Oh, H.L. Off-line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences from IPIN 2020 Competition IEEE Sensors Journal, Early Access (in press), 2021. https://doi.org/10.1109/JSEN.2021.3083149</li>
</ul>
We would like to thank ISTI-CNR for managing the Virtual competition and find sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable help in organizing and promoting the IPIN competition and conference. Parts of this work were carried out with the financial support received from projects and grants:
- A-WEAR (H2020-MSCA-ITN-2018, Grant Agreement 813278)
- INSIGNIA (PTQ2018-009981)
- REPNIN+ network (TEC2017-90808-REDT)
- LORIS (TIN2012-38080-C04-04)
- SmartLoc(CSIC-PIE Ref.201450E011)
- TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)
- MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)
https://doi.org/10.5281/zenodo.4314992
oai:zenodo.org:4314992
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https://doi.org/10.5281/zenodo.4314991
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Creative Commons Attribution 4.0 International
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Indoor Positioning; IPIN Competition
Datasets and Supporting Materials for the IPIN 2020 Competition Track 3 (Smartphone-based, off-site)
info:eu-repo/semantics/other
oai:zenodo.org:7979821
2023-05-29T09:36:41Z
user-a_wear
user-eu
Casanova-Marqués, Raúl
Torres-Sospedra, Joaquín
Hajny, Jan
Gould, Michael
2023-05-06
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1016/j.iot.2023.100801
oai:zenodo.org:7979821
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Internet of Things, 22, (2023-05-06)
attribute-based credentials
decentralized authentication
privacy
anonymity
collaborative indoor positioning systems
bluetooth low energy
wearables
Maximizing Privacy and Security of Collaborative Indoor Positioning using Zero-knowledge Proofs
info:eu-repo/semantics/article
oai:zenodo.org:4329811
2021-01-29T08:34:15Z
user-a_wear
user-tau_wireless
user-eu
Roman Klus
Lucie Klus
Dmitrii Solomitckii
Jukka Talvitie
Mikko Valkama
2020-12-11
<p>Abstract:</p>
<p>The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.</p>
Additional funding:
Academy of Finland grants #323244 and #319994
https://doi.org/10.3390/s20247124
oai:zenodo.org:4329811
eng
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https://zenodo.org/communities/tau_wireless
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5G New Radio; artificial neural network; beam-level mobility; handover; mobility management; supervised learning
Deep Learning-Based Cell-Level and Beam-Level Mobility Management System
info:eu-repo/semantics/article
oai:zenodo.org:6779141
2022-06-30T01:48:32Z
user-a_wear
user-eu
Pascacio, Pavel
Torres-Sospedra, Joaquín
Jiménez, Antonio R.
Casteleyn, Sven
2022-06-08
<p>The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included.</p>
https://doi.org/10.1038/s41597-022-01406-2
oai:zenodo.org:6779141
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Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments
info:eu-repo/semantics/article
oai:zenodo.org:5504056
2021-09-14T01:48:24Z
user-a_wear
user-eu
Hajny, Jan
Dzurenda, Petr
Casanova-Marqués, Raúl
Malina, Lukas
2021-03-22
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1109/PerComWorkshops51409.2021.9431139
oai:zenodo.org:5504056
eng
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IEEE PerCom 2021, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Online, 22-26 March 2021
access control
anonymity
identity
privacy
smart cards
Privacy ABCs: Now Ready for Your Wallets!
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7540329
2023-01-16T14:26:33Z
user-a_wear
user-eu
Gianluca Brancati
Olga Chukhno
Nadezhda Chukhno
Giuseppe Araniti
2022-12-20
<p>The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m.</p>
https://doi.org/10.1109/PIMRC54779.2022.9978019
oai:zenodo.org:7540329
eng
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2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
5G NR/6G
cellular networks
network planning
mmWave
reconfigurable intelligent surfaces
deployment
Reconfigurable Intelligent Surface Placement in 5G NR/6G: Optimization and Performance Analysis
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4954748
2021-06-16T01:48:19Z
user-a_wear
user-eu
Pavel Masek
Martin Stusek
Ekaterina Svertoka
Jan Pospisil
Radim Burget
Elena Simona Lohan
Ion Marghescu
Jiri Hosek
Aleksandr Ometov
2021-06-10
<p>This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.</p>
https://doi.org/10.3390/data6060062
oai:zenodo.org:4954748
eng
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Creative Commons Attribution 4.0 International
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industrial IoT; LPWAN; LoRaWAN; urban measurements; dataset
Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor
info:eu-repo/semantics/article
oai:zenodo.org:4064135
2020-10-20T10:06:57Z
user-a_wear
user-eu
Aneta Kolackova
Salwa Saafi
Pavel Masek
Jiri Hosek
Jan Jerabek
2020-07-07
<p>Carrier Aggregation (CA) was introduced by the 3GPP, in its Release 10 i.e., Long Term Evolution - Advanced (LTE-A), to address the peak data rate requirement set by the IMT-Advanced standard. As it enables for quick adoption of the fragmented radio spectrum, it was recognized by the telecommunication operators as a game-changing technology for achieving significantly increased data rates. In this paper, we detail how the implementation of CA with up to five Components Carriers (CCs) impacts the achievable throughput of connected end-users. In the simulation tool Network Simulator 3 (NS-3), the intra-band contiguous CA was implemented for both downlink and uplink channels. In addition, a uniform 2D grid of values that represent the Signal-to-Noise Ratio (SINR) in the downlink with respect to the eNodeB (eNB) i.e., Radio Environment Map (REM) was implemented. As the previously published results for the CA contain mostly the data for the downlink channel, the implemented scenario provides new insights related to the uplink channel communication. Also, in the performance evaluation, we illustrate the expected data rates for the 5G New Radio (NR) systems and compare them with the achieved results in the case of 4G CA setup.</p>
https://doi.org/10.1109/TSP49548.2020.9163440
oai:zenodo.org:4064135
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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TSP, International Conference on Telecommunications and Signal Processing, Milan, Italy, Italy, 7-9 July 2020
Carrier Aggregation
Cellular Systems
LTE- Advanced
Performance Evaluation
Network Simulator 3
Performance Evaluation of Carrier Aggregation in LTE-A Pro Mobile Systems
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3824749
2020-05-13T20:20:39Z
user-a_wear
user-eu
Viktoriia Shubina
Aleksandr Ometov
Sergey Andreev
Dragos Niculescu
Elena Simona Lohan
2020-06-04
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/).</p>
https://doi.org/10.5281/zenodo.3824749
oai:zenodo.org:3824749
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3824748
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS 2020, International Conference on Localization and GNSS, Tampere, Finland, 2-4 June 2020
Location privacy
Location accuracy
Wearable
Opportunistic networks
Measurement errors
intentional errors
Obfuscation
Privacy versus Location Accuracy in Opportunistic Wearable Networks
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7239893
2022-10-24T05:47:50Z
user-a_wear
user-eu
Olga Chukhno
Olga Galinina
Sergey Andreev
Antonella Molinaro
Antonio Iera
2022-10-17
<p>Emerging extended reality (XR) services and applications that submerge users into a virtual universe pave the way towards ubiquitous contextualized experiences. Immersive interactions on-the-go not only bring new use cases but also distract users from the real world and modify their behavior and motion, which in turn may affect the operation of communication networks. This article explores the effects of XR user motion from the communication and computing perspectives. To this end, we offer a review of mobility patterns in XR and a detailed simulation study on the impact of interaction-dependent gait patterns on the delay and resource utilization. The results confirm the uniqueness of XR applications in terms of the user behavior patterns, which calls for novel application-centric algorithms, protocols, and mechanisms to facilitate high-performance connectivity under demanding XR requirements.</p>
https://doi.org/10.1109/MCOM.009.2200238
oai:zenodo.org:7239893
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Communications Magazine, (2022-10-17)
XR
6G
communication
computing
motion
Interplay of User Behavior, Communication, and Computing in Immersive Reality 6G Applications
info:eu-repo/semantics/article
oai:zenodo.org:5779109
2021-12-14T13:48:41Z
user-a_wear
user-eu
Laura Flueratoru
Silvan Wehrli
Michele Magno
Elena Simona Lohan
Dragoș Niculescu
2021-12-14
<p>Ultra-wideband (UWB) communications have gained popularity in recent years for being able to provide distance measurements and localization with high accuracy, which can enhance the capabilities of devices in the Internet of Things (IoT). Since energy efficiency is of utmost concern in such applications, in this work we evaluate the power and energy consumption, distance measurements, and localization performance of two types of UWB physical interfaces (PHYs), which use either a low- or high-rate pulse repetition (LRP and HRP, respectively). The evaluation is done through measurements acquired in identical conditions, which is crucial in order to have a fair comparison between the devices. We performed measurements in typical line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our results suggest that the LRP interface allows a lower power and energy consumption than the HRP one. Both types of devices achieved ranging and localization errors within the same order of magnitude and their performance depended on the type of NLOS obstruction. We propose theoretical models for the distance errors obtained with LRP devices in these situations, which can be used to simulate realistic building deployments and we illustrate such an example. This paper, therefore, provides a comprehensive overview of the energy demands, ranging characteristics, and localization performance of state-of-the-art UWB devices.</p>
Please find supporting dataset at the following link (DOI 10.5281/zenodo.4686379):
https://zenodo.org/record/4686379
https://doi.org/10.1109/JIOT.2021.3125256
oai:zenodo.org:5779109
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Internet of Things Journal, (2021-12-14)
Ultra-wideband
UWB
indoor localization
indoor positioning
ranging
energy efficiency
internet of things
distance measurements
High-Accuracy Ranging and Localization with Ultra-Wideband Communications for Energy-Constrained Devices
info:eu-repo/semantics/article
oai:zenodo.org:7954926
2023-05-22T06:29:24Z
user-a_wear
user-tau_wireless
software
Klus, Lucie
Klus, Roman
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Nurmi, Jari
2023-05-21
<p>The file includes supplementary data for "EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets". If you would like to re-use the software provided please cite the two following items.</p>
<p>- Klus, Lucie, Roman Klus, Joaquín Torres-Sospedra, Elena Simona Lohan, Carlos Granell, and Jari Nurmi. "EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets." IEEE Transactions on Mobile Computing (2023).</p>
<p><br>
- This Zenodo package: L. Klus et al. (2023). Supplementary materials for “EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets”. version v1, 21.05.2023, [Online]. Available: https://doi.org/10.5281/zenodo.7954926</p>
<p>If you have any questions about this package, please do not hesitate to contact Lucie Klus (lucie.klus@tuni.fi or llucie.kklus@gmail.com).</p>
https://doi.org/10.5281/zenodo.7954926
oai:zenodo.org:7954926
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://doi.org/10.5281/zenodo.7954925
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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clustering
compression
dimensionality reduction
fingerprinting
indoor positioning
k-means
k-nearest neighbors
on-device computing
Supplementary materials for "EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets"
info:eu-repo/semantics/other
oai:zenodo.org:3543782
2020-05-13T08:07:54Z
user-a_wear
Ivanescu, Mircea
Popescu, Nirvana
Popescu, Decebal
Channa, Asma
Poboroniuc, Marian
2019-10-23
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, http://www.a-wear.eu/).</p>
<p>This paper deals with the fractional order control for the complex systems,hand exoskeleton and sensors, that monitor and control the human behavior. The control laws based on physical significance variables, for fractional order models, with delays or without delays, are proposed and discussed. Lyapunov techniques and the methods that derive fromYakubovici-Kalman-Popovlemma are used and the frequency criterions that ensure asymptotic stability of the closed loop system are inferred. An observer control is proposed for the complex models, exoskeleton and sensors. The asymptotic stability of the system, exoskeleton hand-observer, is studied for sector control laws. Numerical simulations for an intelligent haptic robot-glove are presented. Several examples regarding these models, with delays or without delays, by using sector control laws or an observer control, are analyzed. The experimental platform is presented</p>
https://doi.org/10.3390/s19214608
oai:zenodo.org:3543782
eng
Zenodo
https://zenodo.org/communities/a_wear
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
exoskeleton hand
fractional order model
control
Exoskeleton Hand Control by Fractional Order Model
info:eu-repo/semantics/article
oai:zenodo.org:7417606
2022-12-09T14:26:30Z
user-a_wear
user-tau-tltpos
user-eu
Quezada-Gaibor, Darwin
Torres-Sospedra, Joaquín
Nurmi, Jari
Koucheryavy, Yevgeni
Huerta, Joaquín
2022-09-05
<p>A preprint version of the paper entitled "SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning".</p>
<p> </p>
<p>Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.</p>
https://doi.org/10.1109/IPIN54987.2022.9918146
oai:zenodo.org:7417606
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IPIN, 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Online, 05-08 September 2022
generative networks
indoor positioning
machine learning
Wi-Fi fingerprinting
SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7043620
2022-09-02T14:26:25Z
user-a_wear
user-eu
Casanova-Marqués, Raúl
Dzurenda, Petr
Hajny, Jan
2022-08-23
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1145/3538969.3543798
oai:zenodo.org:7043620
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ARES 2022, ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna, Austria, 23-26 August 2022
attribute-based credentials
anonymous credentials
revocation
identity
privacy
cryptography
elliptic curves
smart cards
java card
microcontrollers
Implementation of Revocable Keyed-Verification Anonymous Credentials on Java Card
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4289880
2020-11-25T12:27:14Z
user-a_wear
user-tau_wireless
user-eu
Ali, Asad
Galinina, Olga
Andreev, Sergey
2020-10-08
<p>To improve the quality of experience (QoE) and prolong the battery life, high-end wearable devices may offload their computations -- partially or fully -- to a paired computing device. One of the promising connectivity solutions, due to heavy load, is millimeter-wave (mmWave) technologies, which offer wide bandwidth and promise to provide extreme throughput and low latency. The features of the mmWave access and the use of sophisticated beamforming techniques have posed a whole new set of problem formulations related to directionality. Over the past decade, stochastic geometry has been extensively used to study directional mmWave connectivity in static deployments; however, there remains a research gap of employing directionality in highly dynamic scenarios. To bridge this gap, in this paper, we analyze the effects of mmWave directionality for non-static device-to-device (D2D) links, typical for high-end wearable applications. We propose a queueing-theoretical approach to capturing the dynamics of the representative mmWave D2D scenario and derive approximations for the key system-level metrics of interest. Our numerical results yield important insights on the role that the directivity has in changing the interference footprint in dynamic D2D systems.</p>
https://doi.org/10.1109/PIMRC48278.2020.9217119
oai:zenodo.org:4289880
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
PIMRC2020, 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, United Kingdom, 31 Aug.-3 Sept. 2020
mmWave
D2D communication
Advanced Wearabels
Directionality
System Dynamics
Modeling System-Level Dynamics of Direct XR Sessions over mmWave Links
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4320049
2020-12-14T07:54:54Z
user-a_wear
user-eu
ASMA CHANNA
Nirvana Popescu
Najeeb ur Rehman Malik
2020-12-13
<p>The novel corona virus (COVID-19) created a havoc all around the globe without any prediction of its eradication. All the previous methods seemed to fail and exceptional considerations are now required to be deployed in order to deal with this pandemic. The aim of this retrospective study is to highlight the new solutions to manage and deal with the pandemic. This study discusses different e-health wearable devices that help in early diagnosis of COVID-19 symptoms and also presents an overview of some artificial intelligence and machine learning techniques applied on CT-scan or Chest X-ray images to refine the correct diagnosis of patients. Finally, this work addresses the importance of smart chat-bots that provides assistance to the people suffering from stress and anxiety during quarantine. These chat-bots can offer psychological therapies in isolation and can be very useful.</p>
https://doi.org/10.1109/ICUMT51630.2020.9222428
oai:zenodo.org:4320049
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Managing COVID-19 Global Pandemic With High-Tech Consumer Wearables: A Comprehensive Review
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4836288
2021-05-31T10:22:29Z
user-a_wear
user-tau_wireless
user-eu
Ali, Asad
Galinina, Olga
Andreev, Sergey
2021-04-13
<p>While highly directional communications may offer considerable improvements in the link data rate and over-the-air latency of high-end wearable devices, the system-level capacity trade-offs call for separate studies with respect to the employed multiple access procedures and the network dynamics in general. This letter proposes a framework for estimating the system-level area throughput in dynamic distributed networks of highly-directional paired devices. We provide numerical expressions for the steady-state distribution of the number of actively communicating pairs and the probability of successful session initialization as well as derive the corresponding closed-form approximation for dense deployments.</p>
https://doi.org/10.1109/LWC.2021.3073054
oai:zenodo.org:4836288
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Wireless Communications Letters, (2021-04-13)
System-Level Dynamics of Highly Directional Distributed Networks
info:eu-repo/semantics/article
oai:zenodo.org:10045028
2023-10-27T04:44:48Z
user-a_wear
user-eu
Skibińska, Justyna
Hosek, Jiri
Skibińska, Justyna
Hosek, Jiri
2023-10-23
<p>Background and Objective: An aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. Methods: We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm. Results: The acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. Conclusions: The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life</p>
https://doi.org/10.1016/j.heliyon.2023.e21175
oai:zenodo.org:10045028
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Heliyon, 9(11), (2023-10-23)
Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease
info:eu-repo/semantics/other
oai:zenodo.org:4975540
2021-06-18T01:48:17Z
user-a_wear
user-tau-tltpos
user-eu
Torres-Sospedra, Joaquín
Aranda, Fernando J.
Álvarez, Fernando J.
Quezada-Gaibor, Darwin
Silva, Ivo
Pendão, Cristiano
Moreira, Adriano
2021-04-25
<p>A preprint version of the paper entitled “Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning", presented in the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring).</p>
<p>Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.</p>
https://doi.org/10.1109/VTC2021-Spring51267.2021.9448947
oai:zenodo.org:4975540
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 25-28 April 2021
Indoor Positioning
Fingerprinting
Radio Map
Noisy samples
Ensemble
Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5338505
2021-08-31T06:37:47Z
user-a_wear
user-eu
Salwa Saafi
Jiri Hosek
Aneta Kolackova
2021-08-28
<p>Public safety agencies have been working on the modernization of their communication networks and the enhancement of their mission-critical capabilities with novel technologies and applications. As part of these efforts, migrating from traditional land mobile radio (LMR) systems toward cellular-enabled, next-generation, mission-critical networks is at the top of these agencies’ agendas. In this paper, we provide an overview of cellular technologies ratified by the 3rd Generation Partnership Project (3GPP) to enable next-generation public safety networks. On top of using wireless communication technologies, emergency first responders need to be equipped with advanced devices to develop situational awareness. Therefore, we introduce the concept of the Internet of Life-Saving Things (IoLST) and focus on the role of wearable devices—more precisely, cellular-enabled wearables, in creating new solutions for enhanced public safety operations. Finally, we conduct a performance evaluation of wearable-based, mission-critical applications. So far, most of the mission-critical service evaluations target latency performance without taking into account reliability requirements. In our evaluation, we examine the impact of device- and application-related parameters on the latency and the reliability performance. We also identify major future considerations for better support of the studied requirements in next-generation public safety networks.</p>
https://doi.org/10.3390/s21175790
oai:zenodo.org:5338505
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sensors, 21(17), 5790, (2021-08-28)
Public safety
Cellular connectivity
Wearable technology
IoLST
Mission-critical services
Enabling Next-Generation Public Safety Operations with Mission-Critical Networks and Wearable Applications
info:eu-repo/semantics/article
oai:zenodo.org:5504087
2021-09-14T01:48:22Z
user-a_wear
user-eu
Casanova-Marqués, Raúl
Pascacio, Pavel
Hajny, Jan
Torres-Sospedra, Joaquín
2021-07-06
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.5220/0010582507910797
oai:zenodo.org:5504087
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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SECRYPT 2021, 18th International Conference on Security and Cryptography, Online, 6-8 July 2021
attribute-based credentials
collaborative indoor positioning systems
privacy
anonymity
bluetooth low energy
wearables
Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7900378
2023-05-06T14:26:41Z
user-a_wear
user-eu
Salwa Saafi
Olga Vikhrova
Gabor Fodor
Jiri Hosek
Sergey Andreev
2023-02-03
<p>Extending the existing near-shore terrestrial infrastructure with non-terrestrial network capabilities helps maritime operators alleviate the high costs of communication and meet the requirements imposed by time-sensitive applications. Recognizing that the deployment of terrestrial and non-terrestrial networks necessitates selecting from the available wireless backhaul solutions, which have dissimilar data transmission costs and communication link qualities, it is essential to propose an appropriate backhaul selection policy. Specifically, in this letter, we coin a backhaul selection policy that manages the inherent trade-off between data transmission expenses and timely throughput guarantees for maritime communications. We formulate the backhaul selection problem as a Markov decision process and show that the proposed solution is not only more cost-efficient, but also satisfies the timely throughput requirements in contrast to the currently used greedy strategies.</p>
https://doi.org/10.1109/LCOMM.2023.3242363
oai:zenodo.org:7900378
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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IEEE Communications Letters, 27(4), 1235 - 1239, (2023-02-03)
Backhaul selection
Cost efficiency
Near-shore communications
Non-terrestrial networks
Timely throughput
Cost- and Delay-Efficient Backhaul Selection for Time-Sensitive Maritime Communications
info:eu-repo/semantics/article
oai:zenodo.org:7417598
2022-12-09T14:26:30Z
user-a_wear
user-tau-tltpos
user-eu
Quezada-Gaibor, Darwin
Klus, Lucie
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Nurmi, Jari
Granell, Carlos
Huerta, Joaquín
2022-06-06
<p>A preprint version of the paper entitled "Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets".</p>
<p> </p>
<p>Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.</p>
https://doi.org/10.1109/MDM55031.2022.00079
oai:zenodo.org:7417598
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
MDM, 2022 23rd IEEE International Conference on Mobile Data Management (MDM), Online, 06-09 June 2022
Data cleansing
Data pre-processing
Indoor positioning
Localisation
Wi-Fi Fingerprinting
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7619764
2023-04-05T02:26:39Z
user-a_wear
openaire_data
user-eu
Pătru, George-Cristian
Flueratoru, Laura
Vasilescu, Iuliu
Niculescu, Dragoș
Rosner, Daniel
2023-02-08
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p>
<p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p>
<p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p>
<p>If you find the dataset useful, please consider citing our work:</p>
<blockquote>
<p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p>
</blockquote>
https://doi.org/10.5281/zenodo.7619764
oai:zenodo.org:7619764
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7619763
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
uwb
ultra-wideband
tdoa
time-difference of arrival
localization
positioning
indoor localization
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
info:eu-repo/semantics/other
oai:zenodo.org:5865843
2022-01-18T01:48:54Z
user-a_wear
user-tau_wireless
user-eu
Elena Simona Lohan
Viktoriia Shubina
Dragoș Niculescu
2022-01-17
<p>Future social networks will rely heavily on sensing data collected from users’ mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user’s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.</p>
https://doi.org/10.3390/s22020687
oai:zenodo.org:5865843
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
MDPI, Sensor Networks: Physical and Social Sensing in the IoT, (2022-01-17)
location privacy
perturbation mechanism
proximity detection
digital contact tracing
multi-floor areas
Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications
info:eu-repo/semantics/article
oai:zenodo.org:7405072
2022-12-06T17:02:22Z
user-a_wear
user-eu
Asma Channa
Oana Cramariuc
Madeha Memon
Nirvana Popescu
Nadia Mammone
Giuseppe Ruggeri
2022-12-06
<p>In resting tremor, the body part is in complete repose and often dampens or subsides entirely with action. The most frequent cause of resting tremors is known as idiopathic Parkinson's disease (PD). For examination, neurologists of patients with PD include tests such as finger-to-nose tests, walking back and forth in the corridor, and the pull test. This evaluation is focused on Unified Parkinson's disease rating scale (UPDRS), which is subjective as well as based on some daily life motor activities for a limited time frame. This study performs severity analysis on an imbalanced dataset of patients with PD. This is the reason why the classification of various data containing imbalanced class distribution has endured a notable drawback of the performance achievable by various standard classification learning algorithms. This work used resampling techniques including under-sampling, over-sampling, and a hybrid combination. Resampling techniques are incorporated with renowned classifiers, such as XGBoost, decision tree, and K-nearest neighbors. The results concluded that the Over-sampling method performed much better than under-sampling and hybrid sampling techniques. Among the over-sampling techniques, random sampling has obtained 99% accuracy using XGBoost classifier and 98% accuracy using the decision tree. Besides, it is observed that different resampling methods performed differently with various classifiers.</p>
https://doi.org/10.3389/fnins.2022.955464
oai:zenodo.org:7405072
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Parkinson's disease resting tremor severity classification using machine learning with resampling techniques
info:eu-repo/semantics/article
oai:zenodo.org:7956339
2023-05-22T14:26:53Z
user-a_wear
user-eu
Nadezhda Chukhno
Olga Chukhno
Sara Pizzi
Antonella Molinaro
Antonio Iera
Giuseppe Araniti
2023-05-05
<p>The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.</p>
https://doi.org/10.1109/MCOM.001.2200659
oai:zenodo.org:7956339
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Communications Magazine, 61(5), (2023-05-05)
6G mobile communication
Extended reality
Multicast communication
Approaching 6G Use Case Requirements with Multicasting
info:eu-repo/semantics/article
oai:zenodo.org:8420920
2023-10-09T14:26:55Z
user-a_wear
user-eu
Tomas Bravenec
Michael Gould
Tomas Fryza
Joaquín Torres-Sospedra
2023-07-24
<p>Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.</p>
https://doi.org/10.1109/JSEN.2023.3296752
oai:zenodo.org:8420920
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Sensors Journal, 23(17), 11, (2023-07-24)
Indoor localization
indoor positioning
interpolation
radio map (RM)
received signal strength indicator (RSSI)
Wi-Fi
wireless communication
Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms
info:eu-repo/semantics/article
oai:zenodo.org:4243022
2020-11-04T12:26:59Z
user-a_wear
user-eu
Olga Chukhno
Nadezhda Chukhno
Giuseppe Araniti
Claudia Campolo
Antonio Iera
Antonella Molinaro
2020-10-30
<p>In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.</p>
https://doi.org/10.3390/s20216181
oai:zenodo.org:4243022
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Internet of Things
Social Internet of Things
edge computing
digital twin
optimization problem
Optimal Placement of Social Digital Twins in Edge IoT Networks
info:eu-repo/semantics/article
oai:zenodo.org:3534266
2020-01-20T17:20:22Z
user-a_wear
Ali, Asad
2019-10-18
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, <a href="http://www.a-wear.eu/">http://www.a-wear.eu/</a>).</p>
https://doi.org/10.5281/zenodo.3534266
oai:zenodo.org:3534266
eng
Zenodo
https://zenodo.org/communities/a_wear
https://doi.org/10.5281/zenodo.3534265
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
URSI, XXXV Finnish URSI Convention on Radio Science, Tampere, Finland, 18 October 2019
mmWave
XR
Directional Deafness
Mobility-Aware Analysis of Directional Deafness in mmWave Communications
info:eu-repo/semantics/article
oai:zenodo.org:6780419
2022-07-04T13:54:07Z
user-a_wear
user-eu
Laura Flueratoru
Elena Simona Lohan
Dragoș Niculescu
2021-12-01
<p>Non-line-of-sight (NLOS) propagation is one of the main error sources in indoor localization, so a large body of work has been dedicated to identifying and mitigating NLOS errors. The most accurate NLOS detection methods often rely on large training data sets that are time-consuming to obtain and depend on the environment and hardware. We propose a method for detecting NLOS distance measurements without manually collected training data and knowledge of channel statistics. Instead, the algorithm generates LOS/NLOS labels for sets of distance measurements between fixed sensors and the mobile target based on distance residuals. The residual-based detection has 70--80% accuracy but has high complexity and cannot be used with high confidence on all measurements. Therefore, we use the predicted labels and the channel impulse responses of the measurements to train a classifier that achieves over 90% accuracy and can be used on all measurements, with low complexity. After we train the classifier during an initial phase that captures specifics of the devices and of the environment, we can skip the residual-based detection and use only the trained model to classify all measurements. We also propose an NLOS mitigation method that reduces, on average, the mean and standard deviation of the localization error by 2.2 and 5.8 times, respectively.</p>
https://doi.org/10.1109/IPIN51156.2021.9662532
oai:zenodo.org:6780419
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ultra-wideband
uwb
indoor localization
positioning
Self-Learning Detection and Mitigation of Non-Line-of-Sight Measurements in Ultra-Wideband Localization
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4182165
2020-11-02T12:27:25Z
user-a_wear
user-eu
Ekaterina Svertoka
Mihaela Bălănescu
George Suciu
Adrian Pasat
Alexandru Drosu
2020-10-20
<p>As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO<sub>2</sub>) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution. </p>
https://doi.org/10.3390/s20205931
oai:zenodo.org:4182165
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Internet of Things; sensors; environment; monitoring; health; Air Quality Index; pollutant
Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization
info:eu-repo/semantics/article
oai:zenodo.org:4113582
2020-10-21T12:26:56Z
user-a_wear
user-tau_wireless
user-eu
Viktoriia Shubina
Sylvia Holcer
Michael Gould
Elena Simona Lohan
2020-09-23
<p>Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.</p>
https://doi.org/10.3390/data5040087
oai:zenodo.org:4113582
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Data, 5, (2020-09-23)
Internet of Things (IoT) mobile devices
wearables
location estimation
user tracking
proximity detection
contact tracing
decentralized architectures
blockchain
Received Signal Strength (RSS)
Bluetooth Low Energy (BLE)
COVID-19
Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era
info:eu-repo/semantics/article
oai:zenodo.org:4892867
2021-06-02T13:53:33Z
user-a_wear
user-eu
Ekaterina Svertoka
Salwa Saafi
Alexandru Rusu-Casandra
Radim Burget
Ion Marghescu
Jiri Hosek
Aleksandr Ometov
2021-06-02
<p>Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety.</p>
https://doi.org/10.3390/s21113844
oai:zenodo.org:4892867
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
wearables; smart devices; occupational safety; IIoT; data collection; communications; localization
Wearables for Industrial Work Safety: A Survey
info:eu-repo/semantics/article
oai:zenodo.org:8130986
2023-07-10T14:26:46Z
user-a_wear
user-tau_wireless
user-eu
Elena Simona Lohan
Viktoriia Shubina
2023-07-07
<p>This paper proposes a geometric dilution-of-precision approach to quantize the privacy-aware location errors in a cooperative wearable network with opportunistic positioning. The main hypothesis is that, a wearable inside a multi-floor building could localize itself based on cooperative pseudoranges measurements from nearby wearables, as long as the nearby wearables are heard above the sensitivity limit and as long as nearby wearables choose to disclose their own positions. A certain percentage of wearables, denoted by 𝛾, is assumed to not want to disclose their positions in order to preserve their privacy. Our paper investigates the accuracy limits under the privacy constraints with variable 𝛾 and according to various building maps and received signal strength measurements extracted from real buildings. The data (wearable positions and corresponding power maps) are synthetically generated using a floor-and-wall path-loss model with statistical parameters extracted from real-field measurements. It is found that the network is tolerant to about 30% of the wearables not disclosing their position (i.e., opting for a full location-privacy mode).</p>
https://doi.org/10.5281/zenodo.8130986
oai:zenodo.org:8130986
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8130985
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS, 2023 International Conference on Localization and GNSS, Castellón, Spain, 06-08 June 2023
wearables
indoor localization
location privacy
Geometric Dilution of Precision (GDOP)
Privacy-Constrained Location Accuracy in Cooperative Wearable Networks in Multi-Floor Buildings
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4066541
2020-10-21T12:43:56Z
user-a_wear
user-eu
Ekaterina Svertoka
Alexandru Rusu-Casandra
Ion Marghescu
2020-07-16
<p>Wearable devices have been broadly studied and explored in the last ten years in many various directions such as sports, education, healthcare, transportation, military, and many others. Currently, one of the sectors widely using such devices is industries and factories interested in improving the level of labor safety as part of the broader Industry 4.0 paradigm. This paper aims to overview the current state of the wearable devices market related to this niche and highlight modern industrial wearables, new techniques, and approaches in this research field. The purpose of this article is not to criticize but to provide information for developers in this scientific field and identifying possible areas where improvements are required.</p>
https://doi.org/10.1109/COMM48946.2020.9141982
oai:zenodo.org:4066541
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Industry 4.0, wearable devices, industrial wearables, work safety, biosensor, survey
State-of-the-Art of Industrial Wearables: A Systematic Review
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7043622
2022-09-02T14:26:25Z
user-a_wear
user-eu
Dzurenda, Petr
Casanova-Marqués, Raúl
Malina, Lukas
Hajny, Jan
2022-08-23
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1145/3538969.3543803
oai:zenodo.org:7043622
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ARES 2022, ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna, Austria, 23-26 August 2022
peas
privacy
anonymity
authentication
identity
revocation
attribute-based credential
cryptography
security
deployment
wearables
smartwatch
smartphone
smart card
Real-world Deployment of Privacy-Enhancing Authentication System using Attribute-based Credentials
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6921264
2022-08-25T05:55:03Z
user-a_wear
user-eu
Ekaterina Svertoka
Ion Marghescu
Alexandru Rusu-Casandra
Radim Burget
Jiri Hosek
Pavel Masek
Aleksandr Ometov
2022-06-16
<p>Modern outdoor localization is commonly dependent on various Global Navigation Satellite Systems (GNSSs). On the other hand, they are known to be power-hungry and not suitable for resource-constrained devices currently flooding the Industrial Internet of Things (IIoT). Nonetheless, some of those devices may be equipped with Low-Power Wide-Area Network (LPWAN) communication chip that could be utilized for positioning. Current work examines two outdoor datasets collected using LoRa Wan in Brno, Czech Republic, to assess the possibility of applying technology for localization solutions for industrial outdoor scenarios. The main localization approach applied in this is work is k-NN fingerprinting. For the first dataset gathered over the whole city, the minimal mean localization error turned out to be not stable, while accuracy for the second one covering a small rectangular area 8.5×70 m is 6.42 m that sounds promising in terms of LoRaWAN-based localization. Moreover, by analyzing data collected in two independent measurement campaigns, this work provides some derivations related to the accuracy dependencies on parameters of the measurement campaign (gateways (G W s), coverage area, the average distance between measurement points). It makes a step towards comparing the results of published papers in this area obtained for different datasets.</p>
https://doi.org/10.1109/COMM54429.2022.9817302
oai:zenodo.org:6921264
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
positioning, measurements, outdoors, dataset, localization, LoRaWAN, LoRa
Evaluation of Real-Life LoRaWAN Localization: Accuracy Dependencies Analysis Based on Outdoor Measurement Datasets
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6855405
2022-07-19T01:49:15Z
user-a_wear
user-eu
Olga Chukhno
Nadezhda Chukhno
Giuseppe Araniti
Claudia Campolo
Antonio Iera
Antonella Molinaro
2022-07-13
<p>As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities (Social Digital Twins, SDTs) at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement, while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.</p>
https://doi.org/10.1109/JIOT.2022.3190737
oai:zenodo.org:6855405
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Internet of Things Journal, (2022-07-13)
Digital Twin
Social Internet of Things
Edge Computing
Orchestration
Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks
info:eu-repo/semantics/article
oai:zenodo.org:4095163
2020-10-17T00:26:57Z
user-a_wear
user-tau-tltpos
user-eu
Quezada-Gaibor, Darwin
Klus, Lucie
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Nurmi, Jari
Huerta, Joaquín
2020-10-15
<p>A preprint version of the paper entitled “Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices”, presented in the 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT).</p>
<p>IoT devices and wearables may rely on Wi-Fi fingerprinting to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dimensionality of the radio map by using an enhanced DBSCAN. Moreover, we provide an additional analysis of combining DBSCAN + PCA analysis for further dimensionality reduction. Thereby, we implement a post- processing method based on the correlation coefficient to join “noisy” samples to the formed clusters with Density-based Spatial Clustering of Applications with Noise (DBSCAN). As a result, the positioning error was reduced by 10% with respect to the plain DBSCAN, and the radio map dimensionality was reduced in both dimensions, samples and Access Points (APs).</p>
https://doi.org/10.1109/ICUMT51630.2020.9222411
oai:zenodo.org:4095163
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICUMT, 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Online (Brno, Czech Republic), 5-7 October 2020
Clustering; DBSCAN; PCA; RSS; Wi-Fi finger- printing
Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4882818
2021-06-01T07:12:01Z
user-a_wear
user-eu
Ometov, Aleksandr
Chukhno, Olga
Chukhno, Nadezhda
Nurmi, Jari
Lohan, Elena Simona
2021-05-09
<p>Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.</p>
https://doi.org/10.1145/3457388.3458614
oai:zenodo.org:4882818
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Wearables
Wireless Networks
Computing
Challenges
EU projects
When Wearable Technology Meets Computing in Future Networks: A Road Ahead
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6984698
2022-10-24T14:26:33Z
user-a_wear
openaire_data
Flueratoru Laura
Lohan Elena Simona
Niculescu Dragoș
2022-08-12
<p>This dataset contains distance measurements acquired with three different ultra-wideband (UWB) platforms developed by Qorvo (DW3000), TDSR (P452A), and 3db Access (3DB6380C) at the same locations.</p>
<p>The dataset accompanies the paper: "Challenges in Platform-Independent UWB Ranging and Localization Systems" by Laura Flueratoru, Elena Simona Lohan, Dragoș Niculescu, published in the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation and Characterization (WiNTECH) 2022. If you find this dataset useful, please consider citing our paper.</p>
<p>The dataset (<strong>uwb_multiple_platforms.zip</strong>) contains the following directories:</p>
<ul>
<li><strong>parallel_measurements</strong> -- The actual dataset, containing all the measurements acquired with the three UWB platforms at the same locations. This directory contains three subdirectories, one for each device. The structure of the subdirectory of each platform is the following:
<ul>
<li><strong>[location_name] </strong>
<ul>
<li><strong>[LOSi/NLOSi]</strong> -- where i is the index of the recording and LOS/NLOS indicates whether that recording was acquired in LOS or NLOS
<ul>
<li><strong>info.csv</strong> -- CSV file which contains information about the recording, such as: the device it was acquired with, the LOS/NLOS condition, the type of obstruction (if any), etc.</li>
<li><strong>unaligned_processed_data.csv</strong> -- CSV file which contains the data. Each row has the following fields: timestamp, true distance, measured distance, time of arrival index, channel impulse response (stored as a list), and the LOS/NLOS label.</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li><strong>split_train_test_val</strong> -- Datasets that were used to train and test the models from Section 4 from the paper. The datasets contain the same information as the directory <strong>parallel_measurements</strong>, only aligned to the TOA and randomized according to the procedure described in the paper. We include the generated sets to ensure the repeatability of our results.</li>
<li><strong>trained_models_error_prediction</strong> -- Models trained for error prediction that were used to obtain the results from Section 4 from the paper.</li>
</ul>
<p>We also provide code examples for reading the data, training and testing the models, and analyzing the data at the following repository:</p>
<p><a href="https://github.com/lauraflu/uwb-multiple-platforms">https://github.com/lauraflu/uwb-multiple-platforms</a></p>
<p>The accompanying code is subject to change in the case of bugs/errors.</p>
<p>For more information about how the measurements were acquired, please refer to the file <strong>documentation_dataset.pdf</strong>, which includes detailed information about each of the rooms, the device setup, the structure of the directories, etc.</p>
<p>For any questions, do not hesitate to contact the authors of the paper.</p>
<p><strong>Note</strong>: The dataset (in the <strong>parallel_measurements</strong> directory) contains measurements acquired with the devices at fixed locations and also "free movement" measurements, during which one of the devices was moved freely around a certain area. Therefore, free-movement recordings with the same name but from different devices were <em>not</em> acquired at exactly the same locations, only in the same rooms. The free-movement recordings were not used in the paper (because they do not contain ground truth distances), but we nevertheless include them in this dataset, as they can be useful to test future algorithms.</p>
https://doi.org/10.5281/zenodo.6984698
oai:zenodo.org:6984698
eng
Zenodo
https://zenodo.org/communities/a_wear
https://doi.org/10.5281/zenodo.6984697
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ultra-wideband
uwb
distance
channel impulse response
indoor localization
Dataset: Ultra-Wideband Ranging Measurements Acquired With Three Different Platforms (Qorvo, TDSR, 3db Access)
info:eu-repo/semantics/other
oai:zenodo.org:5948678
2022-06-14T13:51:08Z
user-a_wear
openaire_data
user-ipin
user-eu
Joaquin Torres-Sospedra
Fernando Aranda Polo
Felipe Parralejo
Vladimir Bellavista Parent
Fernando Alvarez
Antoni Pérez-Navarro
Antonio R. Jimenez
Fernando Seco
2021-12-02
<p>This package contains the datasets and supplementary materials used in the IPIN 2021 Competition.</p>
<p><strong>Contents:</strong></p>
<ul>
<li>IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition</li>
<li>01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors.</li>
<li>02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.</li>
<li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results.</li>
</ul>
<p><strong>Please, cite the following works when using the datasets included in this package:</strong></p>
<ul>
<li>Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678</li>
</ul>
We would like to thank GISS Research team at University of Extremadura for their support in colllecting the datasets and the ISTI-CNR for managing the competition and find sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable committment in organizing and promoting the IPIN competition and conference. This work has been done within the framework of the IPIN 2021 Conference in Lloret de Mar, organised by A. Pérez-Navarro, R. Montoliu and J. Torres-Sospedra.
Parts of this work were carried out with the financial support received from projects and grants:
- A-WEAR (H2020-MSCA-ITN-2018, Grant Agreement 813278)
- INSIGNIA (PTQ2018-009981)
- REPNIN+ network (TEC2017-90808-REDT)
- LORIS (TIN2012-38080-C04-04) - SmartLoc(CSIC-PIE Ref.201450E011)
- TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)
- MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)
https://doi.org/10.5281/zenodo.5948678
oai:zenodo.org:5948678
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://zenodo.org/communities/ipin
https://doi.org/10.5281/zenodo.5948677
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Indoor Positioning
IPIN Indoor Localisation Competition
Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site)
info:eu-repo/semantics/other
oai:zenodo.org:7936441
2023-05-16T14:27:11Z
user-a_wear
Rabia Qadar
Waleed Bin Qaim
Bo Tan
Jari Nurmi
2023-01-18
<p>In the last decade, the field of wireless optical communication has gathered immense interest due to its adoption in growing bandwidth-hungry underwater applications. The expensive and non-standardized on-field research measurements call for a reliable simulation tool that allows researchers to realistically design and assess the performance of Underwater Optical Communication (UOC) systems before conducting actual underwater experiments. In this paper, we present a UOC module as an extension to the network simulator ns-3. The module can study the impact of different water conditions on underwater optical networks from the physical layer to the network layer. The proposed UOC module realizes physical layer models of the UOC channels where the added noise and interference effects are modeled as Additive White Gaussian Noise (AWGN). Results show the capability of our module to facilitate large underwater optical network design and optimization. Since ns-3 is open-source software, the module has the flexibility and reusability to be further developed by the worldwide research community.</p>
https://doi.org/10.1109/VTC2022-Fall57202.2022.10012885
oai:zenodo.org:7936441
eng
Zenodo
https://doi.org/10.1109/VTC2022-Fall57202.2022.10012885
https://zenodo.org/communities/a_wear
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
VTC, IEEE 96th Vehicular Technology Conference, London, U.K., 26-29, September 2022
Underwater Optical Communication, Channel Models, Network Simulator, ns-3.
Underwater Optical Communication Module: An Extension to the ns-3 Network Simulator
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6228358
2022-02-25T01:50:12Z
user-a_wear
user-tau_wireless
openaire_data
user-eu
Pavel Masek
Martin Stusek
Ekaterina Svertoka
Jan Pospisil
Radim Burget
Elena Simona Lohan
Ion Marghescu
Jiri Hosek
Aleksandr Ometov
2022-02-22
<p>This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a></p>
<p>The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters:</p>
<ul>
<li>
<p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p>
</li>
<li>
<p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p>
</li>
<li>
<p>EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface.</p>
</li>
<li>
<p>ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970).</p>
</li>
<li>
<p>fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages.</p>
</li>
<li>
<p>port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved.</p>
</li>
<li>
<p>freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message.</p>
</li>
<li>
<p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p>
</li>
<li>
<p>dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate.</p>
</li>
<li>
<p>ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic.</p>
</li>
<li>
<p>gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW.</p>
<ul>
<li>
<p>rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>].</p>
</li>
<li>
<p>snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level.</p>
</li>
<li>
<p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p>
</li>
<li>
<p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p>
</li>
<li>
<p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p>
</li>
<li>
<p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p>
</li>
<li>
<p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p>
</li>
<li>
<p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p>
</li>
</ul>
</li>
<li>
<p>bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%).</p>
</li>
<li>
<p>data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements.</p>
</li>
<li>
<p>device_Lat—Latitude of the measurement point gathered from the GPS.</p>
</li>
<li>
<p>device_Lon—Longitude of the measurement point gathered from the GPS.</p>
</li>
</ul>
<p>The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.</p>
This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset's primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.
https://doi.org/10.5281/zenodo.6228358
oai:zenodo.org:6228358
eng
Zenodo
https://doi.org/10.3390/data6060062
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.6228357
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
industrial IoT
LPWAN
LoRaWAN
urban measurements
dataset
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
info:eu-repo/semantics/other
oai:zenodo.org:7020693
2022-08-25T05:55:01Z
user-a_wear
user-eu
Channa Asma
Ruggeri Giuseppe
Mammone Nadia
Ifrim Rares-Cristian I
Iera Antonio
Popescu Nirvana
2022-08-24
<p>The management of motor complications in Parkinson’s disease (PD) is an unmet need. This paper proposes an eHealth platform for Parkinson’s disease (PD) severity estimation using a cloud-based and deep learning (DL) approach. The system quantifies the hallmark symptoms of PD using motor signals of patients with PD (PwPD). In this study, the dataset named ”The Michael J. Fox Foundation-funded Levodopa Response Study” is used for the development and evaluation of computational methods focusing on severity estimation of motor function in response to the levodopa treatment. The data is derived from a wearable inertial device, named Shimmer 3, to collect motion data from a patient’s upper limb which is more affected by the disease during the performance of some standard activities selected by MDS-UPDRS III and at home while performing daily life activities (DLAs). Seventeen PwPD were enrolled from two clinical sites, who have varying degrees of motor impairment. An incorporated cloud-based framework is proposed where patients’ motion data is saved in MS Azure cloud where an automatic evaluation of patients’ motor activities in response to the levodopa dose is performed using continuous wavelet transform and CNN-based transfer learning approach. Experimental results show that the efficiency and the robustness of the proposed procedure are proven by 90.0% accuracy for tremor estimation and 86.4% for bradykinesia, with good performance in terms of sensitivity and specificity in each class. Index Terms—Parkinson’s disease, cloud computing, deep learning, severity estimation.</p>
https://doi.org/10.1109/COINS54846.2022.9854945
oai:zenodo.org:7020693
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Parkinson's Disease Severity Estimation using Deep Learning and Cloud Technology
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5091337
2022-04-15T14:51:42Z
user-a_wear
openaire_data
user-eu
Pascacio, Pavel
Torres-Sospedra, Joaquín
Jiménez, Antonio R.
Casteleyn Sven
2021-07-23
<p>Supplementary Materials for 'Mobile device-based Bluetooth Low Energy Database for range estimation in collaborative positioning'</p>
<p>This package contains a collaborative BLE-RSS database, which considers different collaborative set-ups involving five mobile devices. The database is composed of three main subdirectories (Raw-Data, Processed-Data, and Code). The Code directory contains the Matlab script files used to process the raw data, visualize the processed data, and usage examples of the data provided. Further information is provided in the Readme file.</p>
https://doi.org/10.5281/zenodo.5091337
oai:zenodo.org:5091337
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5091336
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Supplementary Materials for 'Mobile device-based Bluetooth Low Energy Database for range estimation in collaborative positioning'
info:eu-repo/semantics/other
oai:zenodo.org:6798302
2022-10-13T13:23:21Z
user-a_wear
openaire_data
user-eu
Bravenec, Tomáš
Torres-Sospedra, Joaquín
Gould, Michael
Frýza, Tomáš
2022-07-05
<p>Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"</p>
<p>This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
https://doi.org/10.5281/zenodo.6798302
oai:zenodo.org:6798302
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.6798301
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
probe requests
wi-fi
privacy
pcap
IPIN2021
Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"
info:eu-repo/semantics/other
oai:zenodo.org:4543285
2021-02-17T00:27:27Z
user-a_wear
user-tau_wireless
openaire_data
user-eu
Viktoriia Shubina
Sylvia Holcer
Michael Gould
Elena Simona Lohan
2020-09-23
<p>In the paper [1], we have provided a comprehensive overview of applicable solutions for proximity detection and contact tracing used to tackle the spread of the COVID-19 pandemic. On the webpage [2], we have provided the most recent findings of the existing solutions.</p>
<p>Structure description of JSON data format is attached in the file ReadMe.pdf</p>
<p>References:</p>
<p>[1] <a href="https://www.mdpi.com/2306-5729/5/4/87">https://www.mdpi.com/2306-5729/5/4/87</a></p>
<p>[2] <a href="https://sites.tuni.fi/survey-of-digital-solutions/">https://sites.tuni.fi/survey-of-digital-solutions/</a></p>
https://doi.org/10.5281/zenodo.4543285
oai:zenodo.org:4543285
eng
Zenodo
https://doi.org/10.3390/data5040087
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4543284
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
applications
contact tracing
COVID-19
dataset
Supplementary Dataset for "Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era"
info:eu-repo/semantics/other
oai:zenodo.org:7139890
2022-10-04T05:14:36Z
user-a_wear
user-eu
Ricci, Sara
Dzurenda, Petr
Casanova-Marqués, Raúl
Cika, Petr
2022-09-07
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1007/978-3-031-16168-1_7
oai:zenodo.org:7139890
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
BPM 2022, BPM 2022: Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum, Münster, Germany, 11-16 September 2022
Threshold Signature
Multi-signature
Blockchain
Secret Sharing
Paillier Cryptosystem
Schnorr Protocol
Threshold Signature for Privacy-preserving Blockchain
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4643668
2021-07-11T11:39:10Z
user-a_wear
user-tau_wireless
openaire_data
user-eu
Laura Flueratoru
Viktoriia Shubina
Dragoș Niculescu
Elena Simona Lohan
2021-07-07
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p>
<p><strong>Contents:</strong></p>
<ul>
<li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li>
<li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li>
<li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li>
</ul>
<p><strong>Results based on the data have been presented in the paper:</strong><br>
Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p>
<p><strong>To cite these data sets please use the following:</strong><br>
Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
https://doi.org/10.5281/zenodo.4643668
oai:zenodo.org:4643668
eng
Zenodo
https://doi.org/10.1109/JSEN.2021.3095710
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4643667
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Indoor navigation
Indoor localization
Indoor radio communication
Received signal strength indicator (RSSI)
Received Signal Strength (RSS)
Bluetooth
Bluetooth Low Energy (BLE)
Measurements
Fluctuations
Contact tracing
Proximity detection
Ranging
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
info:eu-repo/semantics/other
oai:zenodo.org:7307613
2022-11-09T14:26:25Z
user-a_wear
Bravenec, Tomáš
Torres-Sospedra, Joaquín
Gould, Michael
Frýza, Tomáš
2022-10-26
<p>The focus on privacy-related measures regarding wireless networks grew in last couple of years. This is especially important with technologies like Wi-Fi or Bluetooth, which are all around us and our smartphones use them not just for connection to the internet or other devices, but for localization purposes as well. In this paper, we analyze and evaluate probe request frames of 802.11 wireless protocol captured during the 11th international conference on Indoor Positioning and Indoor Navigation (IPIN)~2021. We explore the temporal occupancy of the conference space during four days of the conference as well as non-cooperatively track the presence of devices in the proximity of the session rooms using 802.11 management frames, with and without using MAC address randomization. We carried out this analysis without trying to identify/reveal the identity of the users or in any way reverse the MAC address randomization. As a result of the analysis, we detected that there are still many devices not adopting MAC randomization, because either it is not implemented, or users disabled it. In addition, many devices can be easily tracked despite employing MAC randomization.</p>
https://doi.org/10.1109/IPIN54987.2022.9918134
oai:zenodo.org:7307613
eng
Zenodo
https://zenodo.org/communities/a_wear
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IPIN, 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Beijing, China, 5-7 September 2022
MAC randomization
temporal analysis
privacy
probe requests
What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5547252
2021-10-04T13:48:33Z
user-a_wear
user-eu
Nadezhda Chukhno
Olga Chukhno
Sara Pizzi
Antonella Molinaro
Antonio Iera
Giuseppe Araniti
2021-10-01
<p>The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a low-complexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem.</p>
https://doi.org/10.1109/BMSB53066.2021.9547189
oai:zenodo.org:5547252
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
BMSB, 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting
Millimeter wave communication
multicast
D2D
wearable devices
blockage
machine learning
Unsupervised Learning for D2D-Assisted Multicast Scheduling in mmWave Networks
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5150210
2021-08-02T05:16:24Z
user-a_wear
user-eu
Asma Channa
Najeeb ur Rehman Malik
Nirvana Popescu
2021-07-26
<p>Alterations in gait cycle i.e stride rate show fractal dynamics and lower limb walking variability between healthy controls and the controls associated to any neurological disorder. The paper proposes a study in which these stride variability changes in neurological functioning linked with individuals with neurocognitive disorder and accumulation of changes happen in the human body with time called ageing. To validate this research work, we compared the participants' gait cycle in time series: 1) healthy young participants and healthy older adults 2) subjects with Parkinson's disease (PD) and disease-free subjects. Using the Detrended Fluctuation Analysis (DFA) method we computed α , a measure of a degree to which one stride time is compared with the previous and the consecutive intervals over a various time span. The scaling exponent α is exigently curtailed in older adults as compared to young healthy participants. The scaling exponent α is also lower in the subjects with PD compared with the disease-free participants. Moreover, α seems linearly correlated to the degree of functional impairment in subjects with PD. These findings demonstrate that stride time fluctuations are more arbitrary in elderly subjects and in subjects with PD. Abnormal fluctuations in the fractal properties of lower limb dynamics are clearly related to functioning in central nervous system control.</p>
https://doi.org/10.1109/CSCS52396.2021.00089
oai:zenodo.org:5150210
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Association of Stride Rate Variability and Altered Fractal Dynamics with Ageing and Neurological Functioning
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5158917
2021-08-04T13:48:19Z
user-a_wear
user-eu
Salwa Saafi
Gabor Fodor
Jiri Hosek
Sergey Andreev
2021-07-30
<p>Industrial digital transformation through efficient automation hinges largely on the deployment of communication infrastructures that meet the requirements of smart factory use cases. These infrastructures involve multiple devices that utilize different communication technologies to increase the overall operational efficiency. Rooting from the key implementation requirements of a smart factory environment, this article focuses on the role of cellular connectivity and wearable technology in enabling new industrial applications. Specifically, we shed light on a novel category of services - industrial mid-end wearable applications - by positioning their requirements among the 5G service classes. We then identify features that can complement cellular connectivity to further support the given requirements. More precisely, we review cellu-lar-network-aided device-to-device communications and reduced-capability devices. Our performance evaluation results justify the choice of these features and show that they can work in concert with cellular connectivity to enhance spectral efficiency and reliability in industrial mid-end wearable applications.</p>
https://doi.org/10.1109/MCOM.001.2000988
oai:zenodo.org:5158917
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Communications Magazine, 59(7), 61 - 67, (2021-07-30)
Smart factories
Cellular connectivity
Wearable technology
Industrial mid-end applications
5G NR features
D2D communications
NR RedCap
Cellular Connectivity and Wearable Technology Enablers for Industrial Mid-End Applications
info:eu-repo/semantics/article
oai:zenodo.org:7885806
2023-05-03T02:26:51Z
user-a_wear
user-eu
Fryza Tomas
Tomas Bravenec
Zdenek kohl
2023-05-02
<p>We present new approaches for determining occupancy in smart building management systems. The solutions can be applied dually, in civil and military areas, not only for economic management but also in crisis situations when it is necessary to ensure the safety or rescue of citizens. Examining the occupancy of university workplaces can lead to future improvements in safety and energy consumption. In addition to common PIR-based motion methods, our implementation uses communication between mobile devices and infrastructure in the form of probe requests from Wi-Fi packets. The data are captured using sniffers based on ESP32 microcontrollers, then processed using Python. Thanks to this, the total number of people (respectively mobile devices) in the building can be estimated. The achieved RMSE estimation error was evaluated for minimal, small, and medium-sized room scenarios, respectively. Aspects of the use of smart building technologies are also considered in detail from the military point of view.</p>
https://doi.org/10.1109/RADIOELEKTRONIKA57919.2023.10109085
oai:zenodo.org:7885806
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
MAREW, 2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA), Pardubice, Czech Republic, 19-20 April, 2023
occupancy detection
Wi-Fi
probe requests
crisis management
Security and Reliability of Room Occupancy Detection Using Probe Requests in Smart Buildings
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3530506
2021-09-13T17:06:14Z
user-a_wear
user-eu
Casanova-Marqués, Raúl
Hajny, Jan
2019-10-18
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.5281/zenodo.3530506
oai:zenodo.org:3530506
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3530505
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
URSI, XXXV Finnish URSI Convention on Radio Science, Tampere, Finland, 18 October 2019
privacy
localization
wearable devices
anonymous routing
Anonymous Communication Using Wearables and Constrained Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4498935
2021-02-04T12:27:21Z
user-a_wear
user-tau_wireless
user-eu
Pascacio, Pavel
Casteleyn. Sven
Torres-Sospedra, Joaquín
Lohan, Elena Simona
Nurmi, Jari
2021-02-02
<p>Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.</p>
https://doi.org/10.3390/s21031002
oai:zenodo.org:4498935
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau_wireless
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sensors, 21(3), (2021-02-02)
collaborative indoor positioning systems
positioning technique
positioning method
positioning technology
location-based services
systematic literature review
Collaborative Indoor Positioning Systems: A Systematic Review
info:eu-repo/semantics/article
oai:zenodo.org:6876775
2022-07-22T07:46:31Z
user-a_wear
user-eu
Salwa Saafi
Olga Vikhrova
Gabor Fodor
Jiri Hosek
Sergey Andreev
2022-07-13
<p>The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions, but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine-learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.</p>
https://doi.org/10.1109/MNET.104.2100351
oai:zenodo.org:6876775
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Network, 36(3), 183 - 190, (2022-07-13)
Maritime
6G
AI
Terrestrial/non-terrestrial networks
sustainability
Energy efficiency
AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networking
info:eu-repo/semantics/article
oai:zenodo.org:5504033
2021-09-14T01:48:23Z
user-a_wear
user-eu
Hajny, Jan
Dzurenda, Petr
Casanova-Marqués, Raúl
Malina, Lukas
2020-10-05
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR, http://www.a-wear.eu/).</p>
https://doi.org/10.1109/ICUMT51630.2020.9222243
oai:zenodo.org:5504033
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICUMT, 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Online, 5-7 October 2020
authenticity
confidentiality
integrity
privacy
constrained devices
Cryptographic Protocols for Confidentiality, Authenticity and Privacy on Constrained Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3818998
2020-05-13T20:20:40Z
user-a_wear
user-eu
Channa, Asma
Popescu, Nirvana
Ciobanu, Vlad
2020-05-09
<p>The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A- WEAR, <a href="http://www.a-wear.eu/">http://www.a-wear.eu/</a>). </p>
https://doi.org/10.3390/s20092713
oai:zenodo.org:3818998
eng
Zenodo
https://doi.org/10.3390/s20092713
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
wearable sensors; Parkinson's patients; Parkinson's disorder; neurocognitive disorder; rehabilitation exercises
Wearable Solutions for Patients with Parkinson's Disease and Neurocognitive Disorder: A Systematic Review
info:eu-repo/semantics/article
oai:zenodo.org:6595367
2022-05-31T01:50:29Z
user-a_wear
user-eu
Waleed Bin Qaim
Aleksandr Ometov
Claudia Campolo
Antonella Molinaro
Elena Simona Lohan
Jari Nurmi
2021-12-13
<p>The development of small form-factor handheld electronics is pacing the personal devices market, followed by the increasing number of various applications. Some of those applications also cover computation-hungry use-cases, such as image or video processing and compression, among others. Historically, wearable and handheld devices were not designed to execute computationally intensive operations for reasons ranging from limited battery capacity to radiated heat. Offloading computationally heavy tasks to a comparatively more powerful and less energy-dependent device can help prolong the battery lifetime of a wearable.<br>
This paper analyzes different task offloading scenarios from the wearable to a device located at the network edge. Such a device can be a smartphone paired with the wearable or an edge server co-located with the cellular base station. A comprehensive performance evaluation conducted under a wide variety of realistic settings in terms of task demands, processing capabilities, and data rate, unveils the circumstances in which offloading is convenient and when it is not, in terms of meaningful metrics.</p>
https://doi.org/10.1109/ICUMT54235.2021.9631613
oai:zenodo.org:6595367
eng
Zenodo
https://doi.org/10.1109/ICUMT54235.2021.9631613
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICUMT, 2021 IEEE 13th International Congress on Ultra Modern Telecommunications and Control Systems, Brno, Czech Republic, 25-27 October, 2021
Task Offloading
Edge
Computing
Wearables
Internet of Things
Understanding the Performance of Task Offloading for Wearables in a Two-Tier Edge Architecture
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3903103
2020-10-06T19:47:38Z
user-a_wear
user-eu
Joaquín Torres-Sospedra
Darwin
Germán M. Mendoza-Silva
Jari Nurmi
Yevgeny Koucheryavy
Joaquín
2020-06-04
<p>Preprint version of the paper entitled “k-Means Clustering and Wi-Fi Fingerprinting”, presented in the 2020 International Conference on Localization and GNSS (ICL-GNSS).<br>
<br>
Wi-Fi fingerprinting is a popular technique for In- door Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (includ- ing radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost –around 40% lower– than the original k-means.</p>
The authors gratefully acknowledge funding from Ministerio de Ciencia, In- novacio ́n y Universidades (INSIGNIA, PTQ2018-009981); European Union's H2020 Research and Innovation programme under the Marie Skłodowska- Curie grant agreement No.813278 (A-WEAR, http://www.a-wear.eu/); and Universitat Jaume I (PREDOC/2016/55).
https://doi.org/10.1109/ICL-GNSS49876.2020.9115419
oai:zenodo.org:3903103
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICL-GNSS, 2020 International Conference on Localization and GNSS, Tampere, Finland, 2-4 June 2020
Wi-Fi Fingerprinting; Clustering; RSS.
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7801798
2023-05-13T02:27:53Z
user-a_wear
openaire_data
user-eu
Tomas Bravenec
Joaquín Torres-Sospedra
Michael Gould
Tomas Fryza
2023-04-05
<p>This package contains an anonymized packets of 802.11 probe requests captured throughout March of 2023 at Universitat Jaume I. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).<br>
<br>
The dataset is usable for analyzis of Wi-Fi probe requests, presence detection, occupancy estimation or signal stability analyzis.</p>
https://doi.org/10.5281/zenodo.7801798
oai:zenodo.org:7801798
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7801797
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
probe requests
wi-fi
privacy
pcap
Supplementary Materials for "UJI Probes: Dataset of Wi-Fi Probe Requests"
info:eu-repo/semantics/other
oai:zenodo.org:5005839
2021-06-24T13:48:22Z
user-a_wear
user-eu
Pascacio, Pavel
Casteleyn, Sven
Torres-Sospedra, Joaquín
2021-06-15
<p>Positioning people indoors has known an exponential growth in the last few years, especially thanks to Bluetooth Low Energy (BLE) technology and the Received Signal Strength Indicator (RSSI) technique. This approach is available in wearable devices, is easy to implement and has energy consumption advantages. However, the relative distance calculation is inaccurate, as the strength of BLE signals significantly fluctuates in indoor environments. Typical coping mechanisms, such as path-loss propagation models, require mathematical modeling and time-consuming calibration, that depend on the environment. In this paper, we propose a novel distance estimator based on RSSI-fuzzy classification of the BLE signals. Fuzzy-logic improves the robustness and accuracy of RSSI-based estimators, does not require an explicit propagation model and is easy and intuitive to (graphically) tune (using basic statistical analysis). The estimator’s suitability and the feasibility to provide an easy implementation were experimentally demonstrated in two scenarios with real-world data. </p>
https://doi.org/10.1109/ICL-GNSS51451.2021.9452226
oai:zenodo.org:5005839
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Fuzzy-logic
Distance estimation
RSSI
BLE
Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7935096
2023-05-16T14:27:23Z
user-a_wear
user-tau-tltpos
user-eu
Torres-Sospedra, Joaquín
Quezada-Gaibor, Darwin
Nurmi, Jari
Koucheryavy, Yevgeni
Lohan, Simona
Huerta, Joaquín
2023-05-14
<p>Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with simi- lar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.</p>
https://doi.org/10.1109/JIOT.2022.3230913
oai:zenodo.org:7935096
eng
Zenodo
https://zenodo.org/communities/a_wear
https://zenodo.org/communities/tau-tltpos
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Internet of Things Journal, (2023-05-14)
k-means
Bluetooth low energy (BLE)
received signal strength (RSS)
Wi-Fi
affinity propagation
clustering
fingerprinting
indoor localization
Scalable and Efficient Clustering for Fingerprint-Based Positioning
info:eu-repo/semantics/article
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