Energy efficient information gathering in wireless sensor networks using compressive sensing

Wireless sensor networks are resource constrained with less memory space, limited power supply, processing speed and availability of bandwidth for communication. Information gathering is the fundamental task of wireless sensor networks. However due to enormous deployment of sensors, a large amount of data is generated by these sensor networks. Processing and transportation of such a huge data increase energy consumption of sensor nodes along with increase in network traffic. Analysis and storage of large data becomes complex. Compressive sensing provides a new paradigm for efficient information gathering in wireless sensor networks. Compressive sensing (CS) generates a sparse signal of few nonzero samples from the original signal at sub Nyquist sampling rate where reconstruction of original signal is possible even with few sparse samples. Thus, the necessary and more accurate information can be obtained from the data gathered by wireless sensor networks with less number of samples. In this paper we investigate the effect of different sampling rate at cluster head depending upon their location from the sink. Hence, we can save significant energy by varying overall sampling rate at the cluster head in wireless sensor network.