A Sub-Clustering Algorithm Based on Spatial Data Correlation for Energy Conservation in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) have emerged as a promising solution for various applications due to their low cost and easy deployment. Typically, their limited power capability, i.e., battery powered, make WSNs encounter the challenge of extension of network lifetime. Many hierarchical protocols show better ability of energy efficiency in the literature. Besides, data reduction based on the correlation of sensed readings can efficiently reduce the amount of required transmissions. Therefore, we use a sub-clustering procedure based on spatial data correlation to further separate the hierarchical (clustered) architecture of a WSN. The proposed algorithm (2TC-cor) is composed of two procedures: the prediction model construction procedure and the sub-clustering procedure. The energy conservation benefits by the reduced transmissions, which are dependent on the prediction model. Also, the energy can be further conserved because of the representative mechanism of sub-clustering. As presented by simulation results, it shows that 2TC-cor can effectively conserve energy and monitor accurately the environment within an acceptable level.