Sub-1 GHz Indoor RSSI-Based Localization: An Experimental Evaluation of Trilateration, Multilateration, and Machine Learning Fingerprinting Methods

As wireless radio frequency (RF)-based localization techniques continue to attract interest, a plethora of approaches including received signal strength (RSSI) trilateration and multi-lateration, phase, time-of-arrival, and machine learning models have been explored for indoor localization. However, there has been no comprehensive experimental investigations that compared the accuracy of these methods in a practical IoT wireless sensor network (WSN). Herein, we present a holistic evaluation of localization techniques in an indoor smart home environment, based on off-the-shelf 868/915 MHz transceivers. First, the hardware limitations such as the antenna and RSSI radiation patterns and the effects of multi-path reflections are experimentally investigated, identifying the optimal node placement. A practical RSSI recording and forwarding scheme is proposed and implemented using microcontroller units (MCUs), showing a frugal approach for Joint Sensing and Communication (JSAC), with under 420 ms cycle time. Using this testbed, we compare multi-lateration approaches for 3 and 4 receivers, in both line-of-sight (LOS) and non-line-of-sight (NLOS) links, achieving between 46% and 89% room prediction accuracy, with a minimum mean error of 1.49 m. A machine learning (ML)-based approach, using multinomial logistic regression, is then reported with a peak room classification accuracy of 97-100%, for 25-30 RSSI points. A comparison with state-of-the-art implementations is presented showing a high room localization accuracy at a low hardware complexity, demonstrating the feasibility of RSSI-only localization in resourceconstrained IoT networks.