Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks

TL;DR

This paper studies decentralized hypothesis testing in energy harvesting sensor networks, modeling energy dynamics with queues, and proposes a decision design considering long-term energy management to optimize detection performance.

Contribution

It introduces a novel formulation of decentralized detection with energy harvesting sensors, incorporating battery modeling and long-term energy management strategies.

Findings

Performance varies with battery capacity.

Energy management impacts detection accuracy.

Design guidelines for energy harvesting sensor networks.

Abstract

We consider the problem of decentralized hypothesis testing in a network of energy harvesting sensors, where sensors make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center. The fusion center makes a decision about the present hypothesis using the aggregate received data during a time interval. We explicitly consider a scenario under which the messages are sent through parallel access channels towards the fusion center. To avoid limited lifetime issues, we assume each sensor is capable of harvesting all the energy it needs for the communication from the environment. Each sensor has an energy buffer (battery) to save its harvested energy for use in other time intervals. Our key contribution is to formulate the problem of decentralized detection in a sensor network with energy harvesting devices. Our analysis is based on a queuing-theoretic model for the battery and we propose a sensor decision design method by considering long term energy management at the sensors. We show how the performance of the system changes for different battery capacities. We then numerically show how our findings can be used in the design of sensor networks with energy harvesting sensors.