Abstract
Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)-based node localisation in a sandstorm environment. The authors employ machine learning algorithms, Support Vector Regression and Gaussian Process Regression, which turn the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage, creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR-enhanced fingerprint yields the most significant improvement in node localisation performance.