Increasing Energy Efficiency in Sensor Networks: Blue Noise Sampling and Non-Convex Matrix Completion

TL;DR

This paper proposes a novel approach combining blue noise sampling with non-convex matrix completion to reduce energy consumption in sensor networks while maintaining accurate data reconstruction.

Contribution

It introduces a non-convex matrix completion method paired with blue noise sampling, improving data reconstruction over traditional random sampling strategies.

Findings

Blue noise sampling enhances reconstruction accuracy.

Non-convex algorithms outperform convex counterparts.

Energy savings achieved by partial sensor activation.

Abstract

The energy cost of a sensor network is dominated by the data acquisition and communication cost of individual sensors. At each sampling instant it is unnecessary to sample and communicate the data at all sensors since the data is highly redundant. We find that, if only a random subset of the sensors acquires and transmits the sample values, it is possible to estimate the sample values at all the sensors under certain realistic assumptions. Since only a subset of all the sensors is active at each sampling instant, the energy cost of the network is reduced over time. When the sensor nodes are assumed to lie on a regular rectangular grid, the problem can be recast as a low-rank matrix completion problem. Current theoretical work on matrix completion relies on purely random sampling strategies and convex estimation algorithms. In this work, we will empirically show that better reconstruction results are obtained when more sophisticated sampling schemes are used followed by non-convex matrix completion algorithms. We find that the proposed approach coupling blue-noise sampling with non-convex reconstruction algorithm, gives surprisingly good results.