JWCN_V5_N1_RevP1 N. Subhashini M. Murugan Journal On Wireless Communication Networks 2320 - 2351 5 1 34 42 Wireless Sensor Network, Compressive Sensing, Shannon Theorem, Sparsity, Incoherence, Signal Reconstruction Compressive sensing outperforms the traditional limits of the sampling theory. Based on the principle of sparsity and incoherence, the Compressive sensing retrieves the original signal with the least number of samples compared to the conventional method. Wireless sensor network consists of a large number of sensor nodes or motes with varying size depending upon the application. The spatially distributed nodes transmit the data sensed from the field in cooperation with other nodes to the fusion center. If the monitoring field is wide, the data collected from the field is also large consuming more energy, bandwidth and capacity of the network. Increase in the energy consumption of the node results in the decrease in the lifetime of the node. Hence, to increase the lifetime of the node, the data traffic in the network is reduced by associating compressive sensing with the wireless sensor network. This paper deals with the variation of the performance metrics of wireless sensor networks, in the presence of compressive sensing. April - June 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=6023