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Dynamic Coverage and Clustering: A Maximum Entropy Approach

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Distributed Decision Making and Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 417))

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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.

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Author information

Authors and Affiliations

  1. Coordinated Science Lab, University of Illinois at Urbana-Champaign, USA

    Carolyn Beck & Yunwen Xu

  2. Department of Industrial and Enterprise Systems Engineering, University of Illinois, 117 Transportation Building MC-238, 104 S. Mathews Ave., Urbana, IL, 61801, USA

    Carolyn Beck

  3. Coordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 61801

    Srinivasa Salapaka

  4. Department of Mechanical Science and Engineering, University of Illinois, 362c Mechanical Engineering Building, 1206 West Green Street, Urbana, IL, 61801, USA

    Srinivasa Salapaka

  5. Integrated Data Systems Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Puneet Sharma

  6. Department of Industrial and Enterprise Systems Engineering, University of Illinois, 1308WMain Street, Urbana, IL, 61801- 2307, USA

    Yunwen Xu

Authors
  1. Carolyn Beck
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  2. Srinivasa Salapaka
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  3. Puneet Sharma
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  4. Yunwen Xu
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Editor information

Editors and Affiliations

  1. Department of Automatic Control, Lund University, Ole Römers väg 1, 22100, Lund, Sweden

    Rolf Johansson

  2. Department of Automatic Control, Lund University, Ole Römers väg 1, 221 00, Lund, Sweden

    Anders Rantzer

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Beck, C., Salapaka, S., Sharma, P., Xu, Y. (2012). Dynamic Coverage and Clustering: A Maximum Entropy Approach. In: Johansson, R., Rantzer, A. (eds) Distributed Decision Making and Control. Lecture Notes in Control and Information Sciences, vol 417. Springer, London. https://doi.org/10.1007/978-1-4471-2265-4_10

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  • DOI: https://doi.org/10.1007/978-1-4471-2265-4_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2264-7

  • Online ISBN: 978-1-4471-2265-4

  • eBook Packages: EngineeringEngineering (R0)

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