Dynamic coverage and clustering: A Maximum Entropy approach

Carolyn Beck, Srinivasa Salapaka, Puneet Sharma, Yunwen Xu

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We present a computational framework we have recently developed for solving a large class of dynamic coverage and clustering problems, ranging from those that arise in the deployment of mobile sensor networks to the identification of ensemble spike trains in computational neuroscience applications. This framework provides for the identification of natural clusters in an underlying dataset, while addressing inherent tradeoffs such as those between cluster resolution and computational cost.More specifically, we define the problem of minimizing an instantaneous coverage metric as a combinatorial optimization problem in a Maximum Entropy Principle framework, which we formulate specifically for the dynamic setting. Locating and tracking dynamic cluster centers is cast as a control design problem that ensures the algorithm achieves progressively better coverage with time.

Original languageEnglish (US)
Title of host publicationDistributed Decision Making and Control
PublisherSpringer
Pages215-243
Number of pages29
ISBN (Print)9781447122647
DOIs
StatePublished - Jan 1 2012

Publication series

NameLecture Notes in Control and Information Sciences
Volume417
ISSN (Print)0170-8643

ASJC Scopus subject areas

  • Library and Information Sciences

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