A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks

A new method is proposed in this paper to improve Low Energy Adaptive Clustering Hierarchy (LEACH) by electing cluster heads according to the residual energy of the nodes dynamically. A sliding window is set up to adjust the electing probability and keep stable the expected number of the cluster heads using two parameters in this method, one is the initial energy information of the nodes and the other is the average energy information of those that have not already been cluster heads in the network. Meanwhile, the number of cluster heads which is fixed in the entire network lifetime in LEACH is modified to be a variable according to the number of the living nodes. Simulations show that the improvement for First Node Dies (FND) and Half of the Nodes Alive (HNA) is 41% and 36%, respectively over LEACH, 17% and 26% for Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection (LEACH-DCHS), 22% and 21% for Advanced Low Energy Adaptive Clustering Hierarchy (ALEACH).

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► A sliding window is set up to adjust the electing probability and manage to keep stable the expected number of the cluster heads.
► The number of cluster heads is modified to be a variable according to the number of the living nodes.
► The expected number of cluster heads per round in LEACH-SWDN algorithm is k.
► This method can balance the energy consumption and extend the network lifetime better.