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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Dynamic Coverage and Clustering: A Maximum Entropy Approach
verfasst von : Carolyn Beck, Srinivasa Salapaka, Puneet Sharma, Yunwen Xu
Erschienen in: Distributed Decision Making and Control
Verlag: Springer London
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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.