Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

Abstract

To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified 1  -minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.

The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

“…(3) Initial the temperature t 0 ; (4) Cooling rate α; (5) e outer-loop iteration out max ; (6) e inner-loop iteration inner max . (7) S * ⟵ S, t ⟵ t 0 (8) Iteration: (9) While i < out max do (10) for j < inner max do (11) S′ ⟵ random neighbor of a random neighborhood k, S′ ∈ N H (S)…”

“…CS technology can reduce the hardware requirements, further reduce the sampling rate, improve the signal restoration quality, and save the cost of signal processing and transmission. Currently, CS has been widely used in wireless sensor networks [5,6], information theory [7], image processing [8][9][10], earth science, optical/microwave imaging, pattern recognition [11], wireless communications [12,13], atmosphere, geology, and other fields. CS theory is mainly divided into three aspects: (1) sparse representation; (2) uncorrelated sampling; (3) sparse reconstruction.…”

Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey

“…In this subsection, we review the sensor management work that exploits sparsity aware techniques. Sensor management for jointly sparse signal recovery in WSNs is addressed in [229] when sparse measurement vectors are used to compress observations at distributed nodes. The goal is to design a ternary protocol at the sensors to decide whether to transmit the compressed measurement vector, transmit a 1-bit hard decision, or not transmit on a error criterion defined in terms of the overlap between the signal support and the support of the measurement vector.…”