Data aggregation by enhanced squirrel search optimization algorithm for in wireless sensor networks

Wireless Sensor Network's are inherently power-constrained, with data transmission being a major source of energy depletion. Efficient data aggregation is therefore essential to minimize energy consumption and extend the network's operational lifetime. This paper introduces a novel hybrid meta-heuristic optimization algorithm that integrates the squirrel search algorithm (SSA) with the monarch butterfly optimization algorithm (MBOA) to optimize the clustering process and the selection of aggregation nodes. The hybrid algorithm leverages SSA's strengths in local search and MBOA's robust global exploration capabilities to overcome the limitations of traditional methods, such as premature convergence to local optima. By dynamically balancing exploitation and exploration, the proposed model ensures more effective cluster head selection, significantly reduces communication overhead, and enhances overall network stability. Simulation results demonstrate that the hybrid algorithm outperforms existing state of the art models in performance metrics including energy efficiency, aggregation delay, and network lifetime. The algorithm's adaptability to varying network conditions, coupled with its ability to maintain population diversity, positions it as a highly effective solution for improving the performance and reliability of WSNs.