Collaborative sparse signal recovery in hierarchical wireless sensor networks

• Published in: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing pp. 125 - 128
• Main Authors: Qing Ling, Zhi Tian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2009
• Subjects: Clustering algorithms; Collaboration; Collaborative work; Decision making; Large-scale systems; Monitoring; Noise measurement; Sensor phenomena and characterization; Spread spectrum communication; Wireless sensor networks
• ISBN: 1424451795; 9781424451791
• DOI: 10.1109/CAMSAP.2009.5413322

This paper investigates the design choices and implementation schemes for information fusion among cluster heads in a large-scale hierarchical wireless sensor network. Two main issues addressed are: whether to choose centralized processing with aid of a fusion center or decentralized collaboration among cluster heads, and for the latter choice, how to collaborate. Based on a sparse signal recovery problem arising from an environmental monitoring application, we propose a decentralized collaborative decision-making algorithm for cluster heads, and compare it with the centralized scheme. Our observation is: when the number of sensors within each cluster is quite large to induce a large amount of data, and the cluster heads are subject to multi-hop communications due to limited communication range, the collaborative algorithm is superior to the centralized one in terms of communication load and energy efficiency.