Distributed Sequential Hypothesis Testing with Dependent Sensor Observations

TL;DR

This paper introduces a copula-based distributed sequential detection method for wireless sensor networks with dependent observations and noisy communication channels, demonstrating asymptotic optimality and effectiveness through numerical experiments.

Contribution

It proposes a novel copula-based sequential detection scheme that models spatial dependence and accounts for communication noise in sensor networks.

Findings

The proposed method achieves asymptotic optimality.

Numerical results confirm the effectiveness of the approach.

The scheme effectively handles dependent sensor observations and noisy channels.

Abstract

In this paper, we consider the problem of distributed sequential detection using wireless sensor networks (WSNs) in the presence of imperfect communication channels between the sensors and the fusion center (FC). We assume that sensor observations are spatially dependent. We propose a copula-based distributed sequential detection scheme that characterizes the spatial dependence. Specifically, each local sensor collects observations regarding the phenomenon of interest and forwards the information obtained to the FC over noisy channels. The FC fuses the received messages using a copula-based sequential test. Moreover, we show the asymptotic optimality of the proposed copula-based sequential test. Numerical experiments are conducted to demonstrate the effectiveness of our approach.