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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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.5524137", 
  "language": "eng", 
  "title": "Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network", 
  "issued": {
    "date-parts": [
      [
        2021, 
        9, 
        17
      ]
    ]
  }, 
  "abstract": "<p>This repository contains the long-term connectivity and link quality&nbsp;dataset collected on <a href=\"https://chirpbox.github.io/\">ChirpBox</a>&nbsp;over 4&nbsp;months&nbsp;(May&nbsp;--&nbsp;September&nbsp;2021)&nbsp;in&nbsp;the&nbsp;city&nbsp;of&nbsp;Shanghai,&nbsp;China.&nbsp;</p>\n\n<p>In&nbsp;addition&nbsp;to&nbsp;the&nbsp;dataset&nbsp;itself,&nbsp;we&nbsp;provide&nbsp;evaluation&nbsp;scripts&nbsp;for&nbsp;data&nbsp;analysis&nbsp;and&nbsp;visualization,&nbsp;in&nbsp;order&nbsp;to&nbsp;facilitate&nbsp;data&nbsp;exploration&nbsp;and&nbsp;re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook --&nbsp;</em>&nbsp;<strong>dataset.ipynb</strong> for dataset visualization. Please check the <a href=\"https://nbviewer.jupyter.org/urls/zenodo.org/record/5524137/files/dataset.ipynb/%3Fdownload%3D1\">notebook viewer</a> for a preview.</p>\n\n<p><strong>List of files:</strong></p>\n\n<ol>\n\t<li><em>dataset_03052021_15092021.csv</em>\n\n\t<ul>\n\t\t<li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li>\n\t</ul>\n\t</li>\n\t<li><em>data_analysis.py</em>\n\t<ul>\n\t\t<li>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).</li>\n\t</ul>\n\t</li>\n\t<li><em>metadata_processing.py</em>\n\t<ul>\n\t\t<li>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&nbsp;<strong>dataset_03052021_15092021.csv</strong>.</li>\n\t</ul>\n\t</li>\n\t<li><em>dataset.ipynb&nbsp;</em>\n\t<ul>\n\t\t<li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in&nbsp;<strong>data_analysis.py</strong>&nbsp;and&nbsp;<strong>metadata_processing.py</strong>.</li>\n\t</ul>\n\t</li>\n\t<li><em>topology_map.png</em>\n\t<ul>\n\t\t<li>The node deployment map used to create topology figures. A usage example is&nbsp;<strong>Figure 1</strong>&nbsp;shown in the notebook&nbsp;<strong>dataset.ipynb</strong>.</li>\n\t</ul>\n\t</li>\n\t<li><em>dataset_metadata.zip</em>\n\t<ul>\n\t\t<li>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.</li>\n\t</ul>\n\t</li>\n\t<li><em>README.md</em>\n\t<ul>\n\t\t<li>The&nbsp;README.md&nbsp;explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li>\n\t</ul>\n\t</li>\n</ol>", 
  "author": [
    {
      "family": "Pei Tian"
    }, 
    {
      "family": "Fengxu Yang"
    }, 
    {
      "family": "Xiaoyuan Ma"
    }, 
    {
      "family": "Carlo Alberto Boano"
    }, 
    {
      "family": "Xin Tian"
    }, 
    {
      "family": "Ye Liu"
    }, 
    {
      "family": "Jianming Wei"
    }
  ], 
  "version": "v1.0.9", 
  "type": "dataset", 
  "id": "5524137"
}
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