A game theory‐based clustering and multi‐hop routing scheme in wireless sensor networks for energy minimization

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.