Quickest Change Detection in Sensor Networks
by
Venugopal V. Veeravalli
ECE Department and Coordinated Science Lab, University of Illinois at Urbana-Champaign
Coauthors: Alexander Tartakovsky, USC

The problem of detecting an abrupt change in a system based on stochastic observations of the system has a variety of applications, including critical infrastructure monitoring, quality control engineering, and surveillance. The centralized version of this problem, where all the information about the change is available at a single location, is well-understood and has been solved under a variety of criteria since the seminal works of Page and Shiryaev. This talk will cover recent results on the quickest change detection problem in the context of sensor networks, where the information available for decision-making is decentralized. The information is obtained through measurements taken at a set of distributed sensors, and a central entity (fusion center) must detect the change as soon as possible based on information received from the sensors. Two scenarios will be studied. In the first scenario, the sensors send quantized versions of their observations to a fusion center, where the change detection is performed based on all the sensor messages. In the second scenario, the sensors perform local change detection and send decisions to the fusion center for combining. The decentralized procedures for the latter scenario have the same first order asymptotic performance as the centralized procedure that has access to all of the sensor observations. However, numerical results for nominal values of false alarm rates indicate that even binary quantization of the sensor data can outperform tests based on local change detection. Problems with composite hypotheses will also be considered, in which case the tests based on quantized data have even higher performance than the tests with local decisions. These results point to the need for further research on designing procedures that perform local detection at the sensors.

Date received: March 14, 2007