Data-aided Sensing for Distributed Detection

TL;DR

This paper introduces a data-aided sensing method using J-divergence for efficient distributed detection in wireless sensor networks with correlated measurements, enabling quicker and more reliable decisions with fewer sensors.

Contribution

It proposes a novel node selection criterion based on J-divergence for faster and more reliable distributed detection under delay constraints.

Findings

J-divergence based DAS reduces the number of sensors needed for reliable detection.

The method achieves faster decision-making with shorter delays.

Simulation confirms improved performance over existing approaches.

Abstract

In this paper, we study data-aided sensing (DAS) for distributed detection in wireless sensor networks (WSNs) when sensors' measurements are correlated. In particular, we derive a node selection criterion based on the J-divergence in DAS for reliable decision subject to a decision delay constraint. Based on the proposed J-divergence based DAS, the nodes can be selected to rapidly increase the log-likelihood ratio (LLR), which leads to a reliable decision with a smaller number of the sensors that upload measurements for a shorter decision delay. From simulation results, it is confirmed that the J-divergence based DAS can provide a reliable decision with a smaller number of sensors compared to other approaches.