Enhancing Low-Power Indoor Localization through Characterization of Reflectors in Wireless Channels

In the realm of modern wireless communication systems for the Internet of Things (IoT), the demand for both prolonged battery life and precise indoor localization is escalating. Bluetooth Low Energy (BLE) is proposed as the wireless technology most suitable for a novel indoor localization system. This article addressed the usage of BLE in wireless sensor networks and discussed the challenges in achieving long battery life and precise indoor localization in IoT devices. The accuracy of localization is impacted by multipath fading, induced by reflectors in indoor channels. The proposed low-energy method characterized the wireless channel by using a Recurrent Neural Network; power delay profiles (PDPs) and novel ray-tracing algorithms. The method predicts reflector sites by de-convolving the temporal representation and determining the signal phase. Ray tracing works well for analyzing RF reflectors in indoor environments as it can mimic multipath propagation. The method validated reflectors using MATLAB and then generated low-energy PDPs for different indoor environments.