Distributed Detection in Coexisting Large-scale Sensor Networks

TL;DR

This paper develops and analyzes simple, robust detectors for signal detection in large-scale coexisting wireless sensor networks, demonstrating their near-optimal performance through theoretical analysis and simulations.

Contribution

Introduces a mixed-fractional lower order moment detector that is computationally simple, robust, and nearly matches maximum likelihood detector performance in coexisting sensor networks.

Findings

The FLOM detector performs close to ML detector in simulations.

The proposed detectors are robust to parameter estimation errors.

Asymptotic performance analysis supports the effectiveness of simple detectors.

Abstract

This paper considers signal detection in coexisting wireless sensor networks (WSNs). We characterize the aggregate signal and interference from a Poisson random field of nodes and define a binary hypothesis testing problem to detect a signal in the presence of interference. For the testing problem, we introduce the maximum likelihood (ML) detector and simpler alternatives. The proposed mixed-fractional lower order moment (FLOM) detector is computationally simple and close to the ML performance, and robust to estimation errors in system parameters. We also derived asymptotic theoretical performances for the proposed simple detectors. Monte-Carlo simulations are used to supplement our analytical results and compare the performance of the receivers.