Syndrome-based Fusion Rules in Heterogeneous Distributed Quickest Change Detection

TL;DR

This paper introduces syndrome-based fusion rules for heterogeneous distributed quickest change detection, providing bounds on detection delay, analyzing a weighted voting rule, and demonstrating improved performance through simulations.

Contribution

It proposes a novel syndrome-based framework for fusion rules in HetDQCD, offering performance bounds and an efficient method to optimize weighted voting rules.

Findings

Syndrome-based fusion rules encompass all deterministic rules.

Bounds on detection delay are derived using the Hasse diagram.

Weighted voting rule outperforms existing rules in simulations.

Abstract

In this paper, the heterogeneous distributed quickest change detection (HetDQCD) with 1-bit non-anonymous feedback is studied. The concept of syndromes is introduced and the family of syndrome-based fusion rules is proposed, which encompasses all deterministic fusion rules as special cases. Through the Hasse diagram of syndromes, upper and lower bounds on the second-order performance of expected detection delay as a function of average run length to false alarm are provided. An interesting instance, the weighted voting rule previously proposed in our prior work, is then revisited, for which an efficient pruning method for breadth-first search in the Hasse diagram is proposed to analyze the performance. This in turn assists in the design of the weight threshold in the weighted voting rule. Simulation results corroborate that our analysis is instrumental in identifying a proper design for the weighted voting rule, demonstrating consistent superiority over both the anonymous voting rule and the group selection rule in HetDQCD.