Energy-Efficient Node Localization in Time-Varying UAV-RIS-Assisted and Cluster-Based IoT Networks

Abstract

This paper proposes a novel method for energy-efficient node localization in time-varying Internet of Things (IoT) networks. The method utilizes Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs) over cluster-based IoT networks, resulting in improved localization accuracy and Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). First, the proposed method computes the approximate coordinates of the User Equipments (UEs) through trilateration, utilizing a dataset comprising the coordinates of anchor nodes and Received Signal Strength (RSS) between UE-RIS pairs. Subsequently, K-means clustering is applied to efficiently group UEs based on their spatial proximity, leading to optimal RIS requirements. To further enhance the localization precision of the UEs, a Reinforcement Learning (RL) algorithm with a collision avoidance mechanism is employed over UAVs mounted with RIS. This innovative approach dynamically relocates a UAV-RIS pair to a maximum SINR position over the cluster. To compute the SINR value over a spatial location in the network, a novel approach is proposed herein, which utilizes a radio map of the network. Subsequently, the relocation of the UAV-RIS pair is followed by a novel method for computing the optimal phases of RIS elements, maximizing SINR at the BS. The final step involves Capon beamforming, strategically applied to antenna elements at the BS, resulting in further SINR improvement at the BS. The holistic integration of trilateration, clustering, RL, and beamforming collectively contributes to a system that achieves energy-efficiency, accurate localization, and enhanced SINR at BS. Experimental results demonstrate the effectiveness of the proposed methods, showcasing their potential for application in real-world scenarios where energy consumption and localization accuracy are critical considerations. To validate the significance of the proposed methods’ utilization, the proposed methods’ performance is also compared with that of existing methods.