Clustering is one of the effective approaches for prolonging the lifetime of a wireless sensor network and increasing its scalability. In current clustering protocols, load balancing is achieved by rotating the costly role of the cluster head among the sensors. To achieve this, the network operation is divided into fixed time durations called rounds. Network nodes are clustered for one round and are reclustered for the next round, i.e., round-based policy. Using this policy, loads of nodes are somewhat balanced. However, the imposed overhead from consecutive reclusterings wastes the energy resource of network nodes. Although many attempts have been made to introduce energy-efficient clustering protocols, the reclustering overhead still remains a serious drawback of these protocols. To mitigate this problem, this paper proposes a fuzzy-based hyper round policy (FHRP) to efficiently and flexibly schedule the clusteringtask. In FHRP, instead of every round, clustering is performed at the beginning of every Hyper Round (HR), which is composed of many rounds. During the network lifetime, the length of an HR is not fixed and is computed using a fuzzy inference system. The node’s residual energy and its distance from the sink are used as the inputs of this fuzzy system and the HR length is its output. Thus, the nodes’ situation is taken into account for determining the reclustering time. Simulation results reveal the effectiveness of FHRP in reducing the clustering energy overhead, lengthening the network lifetime, and conserving the network nodes’ energy.

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