Data Gathering in Wireless Sensor Networks Based on Reshuffling Cluster Compressed Sensing

Abstract

The existing compressed sensing (CS) based data gathering (CSDG) methods in wireless sensor networks (WSNs) usually assume that the sensed data are sparse or compressible. However, the sparsity of raw sensed data in some case is not straightforward. In this paper, we present reshuffling cluster compressed sensing based data gathering (RCCSDG) method to achieve both energy efficiency and reconstruction accuracy in WSNs. By incorporating CS into the cluster protocol, RCCSDG is able to reduce the energy consumption and support larger networks. Moreover, the sparsity of raw sensed data can be greatly improved by reshuffling pretreatment. A theoretical analysis to energy consumption of cluster head is performed, and the cost of the pretreatment is small enough to be neglected. Based on these natures, the raw sensed data can be recovered from fewer samples. Also, considering the sensed data to be of excellent temporal stability in a short time, we reshuffle them just one time in this stable period to further reduce the energy consumption of WSNs. In addition, the delay of RCCSDG is analyzed based on TDMA 2 scheduling scheme. We carry out simulations on real sensor datasets. The results show that the RCCSDG can effectively compress the data transmission and decrease energy consumption of WSNs while ensuring the reconstruction accuracy.

Data Gathering Techniques for Wireless Sensor Networks: A Comparison

“…The first category, named compression-oriented, is focused on maximizing network lifetime by taking advantage of data compression techniques [3][4][5][6][7][8][9][10]. In particular, [3,4] analyze different lossless compression schemes for WSNs exploiting the temporal correlation in the sampled signals; in [5,6], the authors exploit spatial correlation by using distributed source coding techniques based on the Slepian-Wolf theorem; finally, [7][8][9][10] investigate the fundamental limits of data gathering techniques based on the new paradigm of compressive sensing [11,12]. A comprehensive review of existing data compression approaches in WSNs is provided in [13].…”