Distributed and Self-adaptive Cluster-head Selection Algorithm for Hierarchical Wireless Sensor Networks

In the hierarchical wireless sensor network (WSN), selecting cluster head (CH) is important issue to increase the network energy efficiency, scalability and lifetime. For the sake of balancing energy expenditure of sensor nodes and improving the performance of routing, We propose a distributed and self-adaptive cluster-head selection algorithm. Based on the hierarchical agglomerative clustering (HAC) method, the algorithm uses the qualitative connectivity data as input data, and tailor simple numerical methods to generate a cluster tree. From such clustering sequence, the CH and the backup CH can be quickly selected without extra message exchanges. Simulation results demonstrate that this method is effective and self-adaptive, which can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime.