Unbalanced Expander Based Compressive Data Gathering in Clustered Wireless Sensor Networks

作者：Li, Xiangling; Tao, Xiaofeng^*; Mao, Guoqiang

来源：IEEE Access, 2017, 5: 7553-7566.

DOI：10.1109/ACCESS.2017.2696745

• Compressive sensing
• data gathering
• unbalanced expander graph
• sparse binary matrix
• wireless sensor networks

Conventional compressive sensing-based data gathering (CS-DG) algorithms require a large number of sensors for each compressive sensing measurement, thereby resulting in high energy consumption in clustered wireless sensor networks (WSNs). To solve this problem, we propose a novel energy-efficient CS-DG algorithm, which exploits the better reconstruction accuracy of the adjacency matrix of an unbalanced expander graph. In the proposed CS-DG algorithm, each measurement is the sum of a few sensory data, which are jointly determined by random sampling and random walks. Through theoretical analysis, we prove that the constructed M x N sparse binary sensing matrix is the adjacency matrix of a (k, epsilon) unbalanced expander graph when M = O (k log N/k) and t = O(N-c/(kq)) for WSNs with N-c clusters, where 0 <= q <= 1 and N-c > k. Simulation results show our proposed CS-DG has better performance than existing algorithms in terms of reconstruction accuracy and energy consumption. When hybrid energy-efficient distributed clustering algorithm is used, to achieve the same reconstruction accuracy, our proposed CS-DG can save energy by at least 27.8%.

• 出版日期2017
• 单位北京邮电大学