Hybrid Social Spider and Grey Wolf Optimization for Enhanced Lifetime in Wireless Sensor Network

Wireless sensor Networks is an energy limited network employed in environmental monitoring applications. Energy efficiency is the most significant consideration in all aspects of WSN operations. This work aims at developing a new metaheuristic algorithm that integrates the Improved Social Spider Optimization (ISSO) and Improved Grey Wolf Optimization (IGWO) to the WSN with better performance and energy consumption. The extended life of the network and better overall network performance are attributed to the hybrid algorithm in the selection of cluster heads and routing paths. With the combined strengths of ISSO and IGWO in the proposed system, exploration and exploitation are effectively performed for the search space and accuracy of the optimization process. The current cluster head is chosen using various parameters such as residual energy, node degree, and distance from the base station, while the routing path is chosen with parameters such as distance, energy of nodes, and transmission data rates. This two-fold strategy helps to select the most energy efficient and reliable links for data transmission. This work presented a novel solution to the energy efficiency problem that affects WSN, which enabled an effective optimization technique for different applications. The developed system can be considered as the improvement of the WSN mathematical model in a way that brings about increased performance and dependability, which promotes the use of these networks in actual applications. It presents an effective solution towards the achievement of energy efficiency hurdles inherent to WSNs, thus introducing a reliable optimization technique that can be applied in different scenarios.