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Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.

Summary Wireless sensor networks (WSNs) became very popular and still remains an active research because of diverse applications. Minimizing the energy dissipation and maximizing the network lifetime is an important design issue in WSN. Clustering is the widely used energy effective technique to lessen the overall energy consumption of WSN. An appropriate selection of cluster heads (CHs) and cluster size is a crucial process in clustered WSN. The CHs nearer to base station (BS) suffer from hot spot problem and die earlier than the normal lifetime. This paper contributes a five input fuzzy‐based unequal clustering protocol (F5NUCP) for selecting CHs and determining the appropriate cluster size to prevent the network from hot spot problem. Unequal clusters eliminates the hot spot problem by producing smaller clusters for the nodes located near the BS and larger clusters for the nodes located far away from the BS. F5NUCP uses a nonprobabilistic approach for selecting tentative CHs by introducing a back‐off timer where the timer value is set using the remaining energy of the node. The proposed method uses five input parameters, namely, remaining energy, distance to BS, distance to its neighboring nodes, link quality, and node degree. The output fuzzy parameters used in this research are cluster size and the probability of becoming CHs. The proposed method is implemented in MATLAB. The simulation results show that F5NUCP performs well when compared with LEACH, DEEC, TEEN, and EAUCF in terms of network lifetime and reduced energy consumption.

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