On the Implementation of Compressive Sensing on Wireless Sensor Network

Abstract

Compressive sensing (CS) is applied to enable real time data transmission in a wireless sensor network by significantly reduce the local computation and sensor data volume that needs to be transmitted over wireless channels to a remote fusion center. By exploiting the sparse structure of commonly used signals in Wireless Sensor Network (WSN) applications, a Compressed Sensing on WSN (CS-WSN) framework is proposed. This is accomplished by (i) random sub-sampling of data collected at sensor node, (ii) transmitting only the sign-bit of the data samples over wireless channels. It is shown that this CS-WSN framework is capable of delivering similar performance as conventional local data compression method while greatly reduce the data volume and local computation. This proposed scheme is validated using a prototype wireless sensor network test bed. Preliminary experimental results clearly validate the superior performance of this proposed scheme.