Graph-based Joint Signal / Power Restoration for Energy Harvesting Wireless Sensor Networks

TL;DR

This paper introduces a graph-based joint signal and power restoration method for energy harvesting wireless sensor networks, addressing an under-determined inverse problem with priors on signal smoothness and sparsity, demonstrating low error and superior performance.

Contribution

It proposes a novel algorithm combining graph signal smoothness and sparsity priors to jointly recover signals and unknown power levels in energy harvesting WSNs.

Findings

Achieves very low reconstruction errors.

Outperforms conventional schemes.

Effective in sparse, energy-harvesting scenarios.

Abstract

The design of energy and spectrally efficient Wireless Sensor Networks (WSN) is crucial to support the upcoming expansion of IoT/M2M mobile data traffic. In this work, we consider an energy harvesting WSN where sensor data are periodically reported to a Fusion Center (FC) by a sparse set of active sensors. Unlike most existing works, the transmit power levels of each sensor are assumed to be unknown at the FC in this distributed setting. We address the inverse problem of joint signal / power restoration at the FC- a challenging under-determined separation problem. To regularize the ill-posed problem, we assume both a graph-signal smoothness prior (signal is smooth with respect to a graph modeling spatial correlation among sensors) and a sparsity power prior for the two unknown variables. We design an efficient algorithm by alternately fixing one variable and solving for the other until convergence. Specifically, when the signal is fixed, we solve for the power vector using Simplex pivoting in linear programming (LP) to iteratively identify sparse feasible solutions, locally minimizing an objective. Simulation results show that our proposal can achieve very low reconstruction errors and outperform conventional schemes under reasonable assumptions