On Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks with Limited Fusion Center Feedback

TL;DR

This paper develops an adaptive power control strategy for energy-harvesting sensors in wireless networks to optimize detection performance, balancing energy harvesting and consumption with limited feedback.

Contribution

It introduces a near-optimal, low-complexity power control method that adapts sensor transmission based on battery state and quantized channel information.

Findings

Maximized detection metric using the proposed power control strategy.

Achieved effective energy management balancing harvesting and consumption.

Demonstrated low computational complexity of the hybrid search method.

Abstract

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), tasked with solving a binary distributed detection problem. Sensors communicate directly with the FC over orthogonal fading channels. Each sensor can harvest randomly arriving energy and store it in a battery. Also, it knows its quantized channel state information (CSI), acquired via a limited feedback channel from the FC. We propose a transmit power control strategy such that the J-divergence based detection metric is maximized, subject to an average transmit power per sensor constraint. The proposed strategy is parametrized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals, to strike a balance between the rates of energy harvesting and energy consumption for data transmission. This strategy allows each sensor to adapt its transmit power based on its battery state and its qunatized CSI. Finding the optimal strategy requires solving a non-convex optimization problem that is not differentiable with respect to the optimization variables.We propose near-optimal strategy based on hybrid search methods that have a low-computational complexity.