Data-Efficient Minimax Quickest Change Detection in a Decentralized System

TL;DR

This paper introduces a data-efficient, decentralized quickest change detection algorithm that adaptively samples sensor data and minimizes communication costs, achieving asymptotic optimality under false alarm constraints.

Contribution

It proposes a novel adaptive sampling and communication strategy for decentralized change detection, ensuring asymptotic optimality with reduced observation and communication costs.

Findings

Algorithm is asymptotically optimal as false alarm rate approaches zero.

Adaptive sampling reduces observation costs before change.

Occasional binary communication conserves bandwidth.

Abstract

A sensor network is considered where a sequence of random variables is observed at each sensor. At each time step, a processed version of the observations is transmitted from the sensors to a common node called the fusion center. At some unknown point in time the distribution of the observations at all the sensor nodes changes. The objective is to detect this change in distribution as quickly as possible, subject to constraints on the false alarm rate and the cost of observations taken at each sensor. Minimax problem formulations are proposed for the above problem. A data-efficient algorithm is proposed in which an adaptive sampling strategy is used at each sensor to control the cost of observations used before change. To conserve the cost of communication an occasional binary digit is transmitted from each sensor to the fusion center. It is shown that the proposed algorithm is globally asymptotically optimal for the proposed formulations, as the false alarm rate goes to zero.