A Novel Localization Technique in LoRa-Based Low-Power Relay Using Machine Learning

Wireless sensor network (WSN) position is required in many applications and the global positioning system (GPS) is not compatible with battery life management issues. In this article, we propose an autonomous, low-cost and energy-efficient localization solution based on long-range radio wireless network. Our design uses a low-power relay system to extend the coverage of a typical LoRaWAN gateway to improve the accuracy of received signal strength indicator (RSSI)-based approaches. We then apply artificial intelligence (AI) techniques, mainly regression algorithms, to obtain accurate location estimates of the target node. The proposed machine learning-multilateration (ML-MTL) algorithm-based localization approach better meets the demands of Internet of Things (IoT) applications and could achieve accuracy comparable to more intricate and energy intensive methods like Angle of Arrival (AoA), Time of Arrival (ToA), and Time Difference of Arrival (TDoA).