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RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity
Azin Moradbeikie1,2,3, Mojtaba Zare4,5, Ahmad Keshavarz3
1CiTin - Centro de Interface Tecnológico Industrial, Inovarcos, 4970-786 Arcos de Valdevez, Portugal.
Data in Brief
|February 13, 2024
Summary
This study introduces a dataset of Received Signal Strength Indicator (RSSI) measurements from a LoRaWAN network in a harsh industrial harbor environment. The data aids in developing and evaluating signal-based device localization techniques for challenging settings.
Area of Science:
- Engineering
- Computer Science
- Wireless Communication
Background:
- Precise device localization is crucial for industrial applications, with signal features offering an alternative to Global Navigation Satellite Systems (GNSS).
- LoRaWAN networks provide long-range, low-cost, and low-power communication suitable for industrial IoT, but Received Signal Strength (RSS) is sensitive to dynamic environments.
- Harsh industrial settings amplify RSS sensitivity due to environmental factors like temperature, humidity, and noise, posing challenges for accurate localization.
Purpose of the Study:
- To present a comprehensive dataset of Received Signal Strength Indicator (RSSI) measurements in a challenging industrial harbor environment.
- To enable research into the environmental effects on RSSI and its impact on device localization accuracy.
- To facilitate the development and evaluation of RSSI-based localization algorithms for industrial IoT.
Main Methods:
- Collected RSSI and Signal-to-Noise Ratio (SNR) measurements using three LoRaWAN gateways and one mobile end node equipped with GPS for location data.
- Deployed two fixed end nodes transmitting at regular intervals to capture additional data points.
- Recorded measurements including timestamps, gateway IDs, and end node IDs for each transmitted packet.
Main Results:
- A comprehensive dataset of LoRaWAN RSSI and SNR measurements in a dynamic industrial harbor environment was compiled.
- The dataset captures the influence of environmental dynamics on signal strength, crucial for localization accuracy.
- Provides a valuable resource for researchers to develop and test localization algorithms.
Conclusions:
- The collected dataset is essential for advancing RSSI-based localization in harsh industrial environments.
- Further research can leverage this data to overcome challenges posed by dynamic environmental factors.
- Enables exploration of opportunities for robust and accurate device localization using LoRaWAN signals.
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