Content:•Introduction•Literature Review•The mathematics of CS•Proposed Model.•Network capacity analysis.•Mathematical module.•Simulation result.•Result and discussion.•Conclusions & Future Scope•Bibliography•Publications
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Abstract—Main constraints of wireless sensor networks (WSN) are less memory space, limited power supply, processing speed and availability of bandwidth for communication. The most important challenge in wireless sensor networks is to design energy-efficient data gathering network which increases the life time of wireless sensor networks. In various studies, it is observed that processed data requires less power as compared to transmitting data in the wireless medium. Hence, it is more significant to apply compressed sensing algorithm at sensing node. Also, if Compressed Sensing (CS) is used with clustering method it will give significant power saving in wireless sensor networks (WSN). In this paper, we addressed comparison between energy saving obtained by changing random compressive sensing samples and without compressive sensing.
Abstract— Wireless sensor networks are mainly resource constrained with less memory space, limited power supply, processing speed and availability of bandwidth for communication. One of the most important challenges in wireless sensor networks is to design energy-efficient data gathering a network which increases the lifetime of wireless sensor networks. Due to an enormous deployment of sensors, a tremendous data isgenerated by these sensor networks. Processing and transportation of such a huge data increase the energy consumption of sensor nodes along with an increase in network traffic. It is observed that processed data requires less power as compared to transmitting data in the wireless medium. Hence, it is more significant to apply compressed sensing algorithm at sensing node. Compressive sensing (CS) technique generates a sparse signal of few nonzero samples from the original signal at sub-Nyquist sampling rate where reconstruction of the original signal is possible even with few sparse samples. Thus, all the necessary and more accurateinformation can be obtained from the data gathered by wireless sensor networks with less number of samples. In this paper, we compare three types of data gathering technique.