ReLeC-based Clustering and Multi-Objective Optimization for Efficient Energy Optimization in IoT Networks

Abstract The Internet of Things (IoT) is a rapidly growing field that involves the interconnection of various devices and objects through the internet. One of the major challenges in IoT is the efficient use of energy by the sensor nodes, which have limited battery life. In this context, clustering and routing techniques play a crucial role in optimizing energy consumption in wireless sensor networks. In this paper, we propose a novel protocol that combines the ReLeC clustering algorithm with multi-objective optimization to achieve efficient energy optimization in wireless sensor networks. The ReLeC protocol employs reinforcement learning-based clustering to optimize network topology and enhance energy efficiency. Multi-objective optimization is used to find a set of non-dominated solutions on the Pareto front, which enables a trade-off between competing objectives such as network lifetime, energy consumption, and data transmission quality. Simulation results show that the proposed protocol outperforms existing clustering and routing techniques in terms of energy efficiency, network lifetime, and data transmission quality. The proposed protocol can be implemented using MATLAB, making it easy to reproduce and adapt for different IoT applications.