Data in Brief (Apr 2024)
RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity
Abstract
Enabling precise device localization is a critical requirement for the future of the industry. Leveraging signal features for location determination has emerged as a leading approach and a good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long-range coverage area. LoRaWAN (a low power and wide area networking protocol built on top of the LoRa radio modulation technique) is one of the most common communication networks that can provide coverage with low implementation cost and power consumption [1]. Among various signal features that can be used for localization, Received Signal Strength (RSS) gets more attention because of its low-cost deployment. However, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic setting. To evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRa gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via GPS and transmits it as the LoRa message. In addition, to provide more insight into the effect of the dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages at fixed time intervals, including their unique IDs. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, along with timestamps. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in a dynamic and harsh environment.
