Cluster Head Selection using the OA-PU Algorithm in the IoT

Providing better performance and energy optimization in the Internet of Things (IoT) in a network environment typically presents numerous challenges. So, here clustering is perhaps the most popular strategy for increasing the lifetime of an IoT, which immediately leads to a more robust routing procedure. This procedure entails grouping sensor nodes into clusters and assigning appropriate cluster heads to each cluster. The purpose of this research is to develop a novel clustering strategy that uses a new hybrid algorithm called Overtaker Assisted Political Update to select cluster heads (OA-PU). This CH selection takes four factors into account: energy, distance, cluster radius, and time. The hybrid algorithm is composed of the Fusion Rider Optimization Algorithm (F-ROA) and a political optimization algorithm (PO) inspired by nature. Fusion-ROA is based on groups of riders attempting to reach a goal, whereas PO is a cutting-edge meta-heuristic optimization approach for global as well as structural analysis performance prediction in the IoT environment. In terms of the evaluation of alive nodes, cost function analysis, as well as energy analysis, the proposed OA-PU outperforms conventional approaches, finally, the optimal system is developed and lifetime of network is increased.