2,892 research outputs found
Unstructured sequential testing in sensor networks
We consider the problem of quickly detecting a signal in a sensor network
when the subset of sensors in which signal may be present is completely
unknown. We formulate this problem as a sequential hypothesis testing problem
with a simple null (signal is absent everywhere) and a composite alternative
(signal is present somewhere). We introduce a novel class of scalable
sequential tests which, for any subset of affected sensors, minimize the
expected sample size for a decision asymptotically, that is as the error
probabilities go to 0. Moreover, we propose sequential tests that require
minimal transmission activity from the sensors to the fusion center, while
preserving this asymptotic optimality property.Comment: 6 two-column pages, To appear in the Proceedings 2013 IEEE Conference
on Decision and Control, Firenze, Italy, December 201
Distributed Change Detection via Average Consensus over Networks
Distributed change-point detection has been a fundamental problem when
performing real-time monitoring using sensor-networks. We propose a distributed
detection algorithm, where each sensor only exchanges CUSUM statistic with
their neighbors based on the average consensus scheme, and an alarm is raised
when local consensus statistic exceeds a pre-specified global threshold. We
provide theoretical performance bounds showing that the performance of the
fully distributed scheme can match the centralized algorithms under some mild
conditions. Numerical experiments demonstrate the good performance of the
algorithm especially in detecting asynchronous changes.Comment: 15 pages, 8 figure
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