Decentralized Detection in Energy Harvesting Wireless Sensor Networks

TL;DR

This paper studies decentralized hypothesis testing with energy harvesting sensors, modeling their energy constraints with a queue-theoretic approach and analyzing how these constraints affect detection performance.

Contribution

It introduces a long-term energy management policy for energy harvesting sensors in decentralized detection and analyzes its impact using a queue-based model.

Findings

Performance degrades compared to energy-unconstrained sensors

Energy management policies improve detection accuracy

Battery dynamics significantly influence detection performance

Abstract

We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy unconstrained case.