Machine Learning-Based Energy Optimization Routing Protocol For Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have recognized as a technology with great potential and promising prospects for many different applications, such as environmental monitoring, surveillance, and industrial automation. Energy efficiency is one of an indispensable concern in WSN s. It is not only enhancing their overall efficiency but also extends the network lifespan. Many efficient routings are proposed for WSNs. Considering all the proposed routing protocols, hierarchical routing schemes have emerged as a significant solution for effectively addressing the energy efficiency challenges in networks. However, most of proposed routing protocols are single-hop routing that have some limitation such as limited coverage, energy inefficiency, and scalability challenges. In this paper, Multi-hop Low Energy Adaptive Clustering Hierarchy (MHLEACH) protocol is proposed. MHLEACH protocol enhances the routing process through a multi-hop cluster-based approach by utilizing an optimization Ant Colony algorithm to efficiently determine the optimal shortest path to the base station. The proposed protocol has been simulated using MATLAB, and it has demonstrated promising results. It extended the stability period of the network by approximately %31.3 compared to the original LEACH.