Sensor Selection and Power Allocation Strategies for Energy Harvesting Wireless Sensor Networks

TL;DR

This paper proposes two suboptimal strategies for joint sensor selection and power allocation in energy-harvesting wireless sensor networks to minimize reconstruction distortion, validated through simulations and benchmarking.

Contribution

It introduces the JSS-EH and SS-EH schemes for energy-efficient sensor selection and power allocation, addressing the non-convex optimization challenge.

Findings

JSS-EH iteratively finds stationary solutions for the non-convex problem.

SS-EH provides an analytical power allocation policy with sensor selection.

Performance benchmarks include distortion bounds and online schemes.

Abstract

In this paper, we investigate the problem of jointly selecting a predefined number of energy-harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to minimize the reconstruction distortion at the fusion center. This optimization problem is, unfortunately, non-convex. To circumvent that, we propose two suboptimal strategies: (i) a joint sensor selection and power allocation (JSS-EH) scheme that, we prove, is capable of iteratively finding a stationary solution of the original problem from a sequence of surrogate convex problems; and (ii) a separate sensor selection and power allocation (SS-EH) scheme, on which basis we can identify a sensible sensor selection and analytically find a power allocation policy by solving a convex problem. We also discuss the interplay between the two strategies. Performance in terms of reconstruction distortion, impact of initialization, actual subsets of selected sensors and computed power allocation policies, etc., is assessed by means of computer simulations. To that aim, an EH-agnostic sensor selection strategy, a lower bound on distortion, and an online version of the SS-EH and JSS-EH schemes are derived and used for benchmarking.