# Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

## TL;DR

This paper proposes an optimal power adaptation scheme for energy-harvesting sensors in distributed detection networks, maximizing detection performance while considering finite battery capacity and energy harvesting dynamics.

## Contribution

It introduces a novel optimal power map that adapts sensor transmit power based on battery and channel states, improving detection performance in energy-harvesting wireless sensor networks.

## Key findings

- Optimal power map derived considering energy arrival and battery state.
- Simulation shows improved detection performance with proposed power adaptation.
- Performance varies with system parameters like energy arrival rate and battery capacity.

## Abstract

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.