Machine learning‐based LoRa localisation using multiple received signal features

Abstract

Low‐power localisation systems are crucial for machine‐to‐machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range‐based technique to estimate the distance of a target node from a LoRa gateway using machine‐learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range‐based distance mapping with trilateration and fingerprint‐based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF‐based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration‐based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint‐based direct location estimation approaches.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

“…The Internet of Things (IoT) is a rapidly growing field that involves connecting a wide range of devices to the Internet to enable communication and data exchange between them. IoT enables seamless integration of the physical and digital worlds, revolutionizing various domains such as healthcare, transportation, agriculture, and industrial automation [ 1 , 2 , 3 , 4 ]. In IoT connectivity, several technologies have emerged to address the diverse requirements of IoT applications.…”