Distributed Detection over Time Varying Networks: Large Deviations Analysis

TL;DR

This paper uses large deviations theory to analyze the asymptotic performance of a distributed detection algorithm in sensor networks with time-varying connectivity, showing it approaches centralized optimal detection rates.

Contribution

It provides a theoretical large deviations analysis demonstrating that running consensus detection in dynamic networks asymptotically matches centralized detection performance.

Findings

Detection error probability decays exponentially at the Chernoff rate.

Performance approaches that of an optimal centralized detector over time.

Validates running consensus as effective in time-varying networks.

Abstract

We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation type algorithm, recently proposed. At each time step k, the state at each sensor is updated by a local averaging of the sensor's own state and the states of its neighbors (consensus) and by accounting for the new observations (innovation). We assume Gaussian, spatially correlated observations. We allow the underlying network be time varying, provided that the graph that collects the union of links that are online at least once over a finite time window is connected. This paper shows through large deviations that, under stated assumptions on the network connectivity and sensors' observations, the running consensus detection asymptotically approaches in performance the optimal centralized detection. That is, the Bayes probability of detection error (with the running consensus detector) decays exponentially to zero as k goes to infinity at the Chernoff information rate-the best achievable rate of the asymptotically optimal centralized detector.