Intelligent Energy-Efficient GNSS-Assisted and LoRa-Based Positioning for Wildlife Tracking

Abstract

The Internet of Things (IoT), together with low power wide area network (LPWAN) technologies, have revolutionized wildlife monitoring and tracking systems. The research in this article has been motivated by the need of an adequate tracking solution based on LoRaWAN technology to study the population of the yellow-legged gull at Sálvora Island, Atlantic Islands of Galicia National Park. The main contribution is an intelligent approach that estimates the positions from LoRa signal features [received signal strength indicator (RSSI) and signal-to-noise ratio (SNR)] and trajectory information from previous positions, combined with as less frequent GNSS information as possible. By doing so, we achieve a good compromise between energy consumption, sampling rate, and application-level estimation accuracy. The results show that the approach achieves satisfactory performance for sampling frequencies according to the biological problems of interest, minimizing recharging cycles and, thus maximizing the duration of monitoring sessions. Specifically, the combination of previous GNSS positions and LoRa radio indicators within an intelligent framework can improve energy efficiency for extended periods with sporadic power-intensive GNSS position updates.