Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

TLDR: This paper investigates how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account and discovers that a simple form of measurement matrix has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology.

Abstract: We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks The network capacity has been proven to increase proportionally to the sparsity of sensor readings In this paper, we further address two key problems in the CDG framework First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account Excitingly, we discover that a simple form of measurement matrix [I R] has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it Owing to the inherent flexibility of CS principle, the proposed CDG framework is able to utilize various sparsity patterns despite of a simple and unified data gathering process In particular, we present approaches for adapting CS decoder to utilize cross-domain sparsity (eg temporal-frequency and spatial-frequency) We carry out simulation experiments over both synthesized and real sensor data The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost