Compressive Data Gathering in Wireless Sensor Networks Based on Random Path

Abstract

For the data gathering problem of Wireless Sensor Networks (WSNS), a compressive data gathering based on random path is proposed. The basic idea of the proposed algorithm is to reduce communication cost of data gathering by random path and compressive sensing. Firstly, neighborhood nodes are selected to be effective projection nodes by random path. Then the measurements are obtained by sensing data of these nodes, and they are transmitted to sink by shortest routing strategy. Finally, communication energy consumption of the proposed algorithm is analyzed and sensing data of each node is be reconstructed by measurement matrix. For effective projection nodes are selected by random path, each measurement can be transmitted to sink by only one path. Compared with data gathering methods based on compressive sensing, the proposed methods can remarkably reduce communication energy consumption of data gathering, and can effectively extend the network lifetime. Our experimental results validate the effectiveness of the proposed algorithm.