Abstract
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 of 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 dataefficient 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.
Original language  English (US) 

Pages (fromto)  148170 
Number of pages  23 
Journal  Sequential Analysis 
Volume  34 
Issue number  2 
DOIs  
State  Published  Apr 3 2015 
Keywords
 Asymptotic optimality
 Minimax
 Observation control
 Quickest change detection
 Sensor networks
ASJC Scopus subject areas
 Statistics and Probability
 Modeling and Simulation
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Dive into the research topics of 'DataEfficient Minimax Quickest Change Detection in a Decentralized System'. Together they form a unique fingerprint.Prizes

Abraham Wald Prize in Sequential Analysis
Veeravalli, Venugopal Varadachari (Recipient), 2016
Prize: Prize/Award