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Wireless Sensor Networks (WSN) is the fundamental technology for the Internet of Things (IoT). It is a network formed from several sensor nodes to sense the changes in the environment. The nodes are battery powered that performs sensing and transmission of information to other nodes in the network. Thus, the energy of the sensor node plays a crucial role in WSN. Thus, intelligent models are anticipated to solve the network problems by optimizing or minimizing the mechanism inorder to improve the energy efficiency. In this paper, a combined meta-heuristic approach called Grey Wolf Optimization based Game theoretical Approach (GWOGA) is proposed that helps for clustering to find the best solutions for selection of aggregation points and this optimal selection of aggregation points lead the nodes to maximize its battery/lifetime. Experimental and simulation analysis shows that the GWOGA outperforms the existing models and retains the lifetime of the network.

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Summary Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

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