Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery

TL;DR

This paper introduces an energy-efficient sensor censoring scheme for distributed sparse signal recovery in wireless sensor networks, optimizing data transmission based on local inference to reduce energy use while maintaining data quality.

Contribution

It proposes a novel ternary censoring protocol with a closed-form optimal rule and a modified l_1-minimization algorithm for improved sparse signal reconstruction.

Findings

Reduced communication cost through censoring protocol

Enhanced signal recovery accuracy with the modified algorithm

Performance guarantees based on restricted isometry property

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 l_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.