RL-OGSA-EEOSP

In this article, a comprehensive and intelligent framework (RL-OGSA-EEOSP) is presented, with the primary goal of reducing energy consumption and extending network lifetime in Mobile Wireless Sensor Networks (MWSNs). It employed an improved Voronoi approach with inspired-RL for targeted node distribution to maximize network coverage, the GSA discrete algorithm with opposition-based learning to enhance exploration for clustering and cluster head selection, and the EEOSP algorithm to determine the next point of the mobile sink's movement. A multi-criteria re-clustering policy and routing based on sensor node trust were implemented to enhance the findings. The authors demonstrated the efficacy of the strategy in two ways. In the first phase, they showed that the suggested methodology is effective. They then demonstrated that it outperforms other methods and achieves considerable gains in energy efficiency, coverage area, and load balancing, thereby increasing network lifetime. The implemented code is available at https://github.com/R-askarizade/RL-OGSA-EEOSP.