Dataset Open Access

Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network

Pei Tian; Fengxu Yang; Xiaoyuan Ma; Carlo Alberto Boano; Xin Tian; Ye Liu; Jianming Wei


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Pei Tian</dc:creator>
  <dc:creator>Fengxu Yang</dc:creator>
  <dc:creator>Xiaoyuan Ma</dc:creator>
  <dc:creator>Carlo Alberto Boano</dc:creator>
  <dc:creator>Xin Tian</dc:creator>
  <dc:creator>Ye Liu</dc:creator>
  <dc:creator>Jianming Wei</dc:creator>
  <dc:date>2021-09-17</dc:date>
  <dc:description>This repository contains the long-term connectivity and link quality dataset collected on ChirpBox over 4 months (May -- September 2021) in the city of Shanghai, China. 

In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a Jupyter notebook --  dataset.ipynb for dataset visualization.

List of files:


	dataset_03052021_15092021.csv

	
		The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.
	
	
	data_analysis.py
	
		The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).
	
	
	metadata_processing.py
	
		The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is dataset_03052021_15092021.csv.
	
	
	dataset.ipynb 
	
		The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in data_analysis.py and metadata_processing.py.
	
	
	topology_map.png
	
		The node deployment map used to create topology figures. A usage example is Figure 1 shown in the notebook dataset.ipynb.
	
	
	dataset_metadata.zip
	
		The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.
	
	
	README.md
	
		The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.
	
	
</dc:description>
  <dc:identifier>https://zenodo.org/record/5594944</dc:identifier>
  <dc:identifier>10.5281/zenodo.5594944</dc:identifier>
  <dc:identifier>oai:zenodo.org:5594944</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.4736501</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>LoRa</dc:subject>
  <dc:subject>ChirpBox</dc:subject>
  <dc:subject>Connectivity</dc:subject>
  <dc:subject>Link quality</dc:subject>
  <dc:subject>Internet of Things</dc:subject>
  <dc:subject>Physical layer settings</dc:subject>
  <dc:subject>SX1276</dc:subject>
  <dc:subject>Temperature</dc:subject>
  <dc:subject>Weather</dc:subject>
  <dc:title>Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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