Asymptotic Properties of One-Bit Distributed Detection with Ordered Transmissions

TL;DR

This paper demonstrates that in large sensor networks, a single ordered transmission based on local statistics suffices for asymptotically perfect detection, significantly reducing communication needs.

Contribution

It introduces a one-bit detection scheme using ordered transmissions that achieves asymptotic consistency with minimal communication, extending previous work on sensor network detection.

Findings

A single transmission can achieve arbitrarily small detection errors in large networks.

Theoretical bounds on error convergence rate for log-likelihood based systems.

Validation through simulations on various network models and applications.

Abstract

Consider a sensor network made of remote nodes connected to a common fusion center. In a recent work Blum and Sadler [1] propose the idea of ordered transmissions -sensors with more informative samples deliver their messages first- and prove that optimal detection performance can be achieved using only a subset of the total messages. Taking to one extreme this approach, we show that just a single delivering allows making the detection errors as small as desired, for a sufficiently large network size: a one-bit detection scheme can be asymptotically consistent. The transmission ordering is based on the modulus of some local statistic (MO system). We derive analytical results proving the asymptotic consistency and, for the particular case that the local statistic is the log-likelihood (\ell-MO system), we also obtain a bound on the error convergence rate. All the theorems are proved under the general setup of random number of sensors. Computer experiments corroborate the analysis and address typical examples of applications including: non-homogeneous Poisson-deployed networks, detection by per-sensor censoring, monitoring of energy-constrained phenomenon.