Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks. (6th March 2016)

• Record Type: Journal Article
• Authors: Wang, Deqing; Xu, Ru; Hu, Xiaoyi; Su, Wei
• Other Names: Ferrari Gianluigi Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2016)
• Publication Date: 2016-03-06
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2016/8197606
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Energy-efficient data aggregation is important for underwater acoustic sensor networks due to its energy constrained character. In this paper, we propose a kind of energy-efficient data aggregation scheme to reduce communication cost and to prolong network lifetime based on distributed compressed sensing theory. First, we introduce a distributed compressed sensing model for a cluster-based underwater acoustic sensor network in which spatial and temporal correlations are both considered. Second, two schemes, namely, BUTM-DCS (block upper triangular matrix DCS) and BDM-DCS (block diagonal matrix DCS), are proposed based on the design of observation matrix with strictly restricted isometric property. Both schemes take multihop underwater acoustic communication cost into account. Finally, a distributed compressed sensing reconstruction algorithm, DCS-SOMP (Simultaneous Orthogonal Matching Pursuit for DCS), is adopted to recover raw sensor readings at the fusion center. We performed simulation experiments on both the synthesized and real sensor readings. The results demonstrate that the new data aggregation schemes can reduce energy cost by more than 95 percent compared with conventional data aggregation schemes when the cluster number is 20.