LoRaWAN‐implemented node localisation based on received signal strength indicator
نویسندگان
چکیده
Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands actively connected devices (nodes) to monitor the environment processes. In most cases, location information arguably plays a critical role and is desirable. this regard, physical characteristics communication channel can be leveraged provide feasible affordable node localisation solution. This paper presents an evaluation performance LoRaWAN Received Signal Strength Indicator (RSSI)-based in sandstorm environment. The authors employ machine learning algorithms, Support Vector Regression Gaussian Process Regression, which turn high variance RSSI due frequency hopping feature advantage, creating unique signatures representing different locations. work, features are used as input fingerprints into models. proposed method reduces complexity when compared GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, impact LoRa spreading factor kernel function on developed models have been studied. Experimental results show SVR-enhanced fingerprint yields significant improvement performance.
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ژورنال
عنوان ژورنال: IET wireless sensor systems
سال: 2022
ISSN: ['2043-6386', '2043-6394']
DOI: https://doi.org/10.1049/wss2.12039