Clustering in Wireless Sensor Networks based on near optimal bi-partitions

Wireless Sensor Networks (WSNs) have recently become an area of attractive research interest. A WSN consists of low-cost, low power, and energy-constrained sensors responsible for monitoring and reporting a physical phenomenon to the sink node where the end-user can access the data. Reducing energy consumption and enlarging lifetime of the whole WSNs are the important challenges in these fields of applications. To deal with these challenges, clustering algorithms can be used. In this paper we present a new approach called the Spectral Classification based on Near Optimal Clustering in Wireless Sensor Networks (SCNOpC-WSNs). This protocol uses spectral graph theory in order to subdivide the network such that each cluster includes the highest inter-correlated sensors. Simulation results demonstrate that SCNOpC-WSNs distribute energy consumption more effectively among the sensors. Thus, the proposed approach enlarges the network lifetime by as much as 44.8% compared to LEACH.