A less computationally complex clustering algorithm based on dynamic K-means for increasing lifetime of wireless sensor networks

Abstract

Clustering in wireless sensor networks is a critical issue based on network lifetime, energy efficiency, connectivity and scalability. Sensor nodes are capable to collect data from any geographical region using routing protocol. This research endeavours to design a less computationally time complex clustering algorithm for hierarchical homogeneous wireless sensor network to extend network lifetime. It forms optimal number of clusters and reduces data communication span of sensor nodes using dynamic K-means algorithm. Selection of suitable cluster head is based on ratio of remaining energy of sensor node to its distance from centre of cluster. The simulation results prove that algorithm that has been presented achieves better energy efficiency when compared to other hierarchical homogeneous cluster based algorithms. It increases network lifetime, number of alive nodes per round, data delivered to base station, time of first node, middle node and last node to die for scalable situations in terms of node density and size of sensing region.