An energy-efficient sensing matrix for wireless multimedia sensor networks

Abstract

A Wireless Multimedia Sensor Network (WMSN) make possible new surveillance applications in environments that traditional systems would not handle, including search and rescue operations after a disaster. However, WMSNs ought to perform under energy-constrained conditions that insist on novel compression methods to diminish bandwidth usage and extend network lifespan.

Compressed Sensing (CS) was presented as a means to achieve overcome the challenges faced by WMSNs. A sensing matrix is crucial to the compressed sensing framework. The sensing matrix can maintain the fidelity of a compressed signal, diminish the sampling rate obligation and improve the strength and performance of the recovery algorithm. A great number of measurement matrices have been proposed to either offer reduced computational complexity or good recovery performance, but only some have managed to accomplish both, and even fewer have been proven in a compelling manner. There are images that do not lend themselves to compression, and to maintain Quality of Service (QoS) expectations, adaptive sampling is essential.

Low-performance nodes are essential for making WMSN practical and flexible. Different low-performance nodes have been proposed in the literature, but the Telos Revision B (TelosB) sensor module (mote) can be used as a reference for energy-constrained applications. TelosB is a very low power wireless mote for research and experimentation. The design of sensing matrices has been influenced by practical considerations in WSN. The two major innovations have replaced floating point numbers with bipolar and binary entries and sparse sensing matrices.

The Deterministic Partial Canonical Identity (DPCI) matrix was presented to address the needs of an energy-constrained environment for WMSN. The choices of random number generators were discussed, and criteria were developed for selection. Complexity optimisation was undertaken to improve the time complexity of the construction. The DPCI was outperformed by the Deterministic Binary Block Diagonal (DBBD) and Binary Permuted Block Diagonal (BPBD) in terms of recovery performance but gave a substantial computational cost reduction. The DPCI gives a compelling balance between recovery performance and energy efficiency, benefiting energy-sensitive applications. A recovery performance prediction algorithm was also proposed to be used for an adaptive sampling scheme.