A Novel Method for Energy Efficient Clustering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a crucial role in various applications, including environmental monitoring, industrial automation, and healthcare. However, optimizing WSNs for efficient resource utilization, energy conservation, and reliable data transmission remains a challenging task due to the dynamic nature of the network environment and resource-constrained sensor nodes. In this study, we propose a Hybrid Firefly Genetic Algorithm (HFGA) for optimizing WSN performance. The HFGA combines the strengths of the firefly algorithm’s global search capabilities and the genetic algorithm’s local search and optimization efficiency. By integrating these two evolutionary algorithms, the HFGA aims to achieve superior performance in terms of energy efficiency, network coverage, and convergence speed. We evaluate the effectiveness of the proposed HFGA through extensive simulation experiments in various WSN scenarios. The results demonstrate that the HFGA outperforms traditional optimization approaches and baseline algorithms in optimizing WSN performance metrics. Furthermore, we discuss the practical implications and future research directions for deploying the HFGA in real-world WSN applications. Overall, this study contributes to advancing WSN optimization techniques and enhancing the reliability and efficiency of WSN deployments.