TY - CHAP
T1 - Dynamic coverage and clustering
T2 - A Maximum Entropy approach
AU - Beck, Carolyn
AU - Salapaka, Srinivasa
AU - Sharma, Puneet
AU - Xu, Yunwen
N1 - Publisher Copyright:
© 2012, Springer London.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028830387&partnerID=8YFLogxK
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U2 - 10.1007/978-1-4471-2265-4_10
DO - 10.1007/978-1-4471-2265-4_10
M3 - Chapter
AN - SCOPUS:85028830387
SN - 9781447122647
T3 - Lecture Notes in Control and Information Sciences
SP - 215
EP - 243
BT - Distributed Decision Making and Control
PB - Springer
ER -