Entropy-Based Framework for Dynamic Coverage and Clustering Problems

We propose a computationally efficient framework to solve a large class of dynamic coverage and clustering problems, ranging from those that arise from deployment of mobile sensor networks to classification of cellular data for diagnosing cancer stages. This framework provides the ability to identif...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2012-01, Vol.57 (1), p.135-150
Main Authors: Sharma, P., Salapaka, S. M., Beck, C. L.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Biological and medical sciences
Clustering
Clustering algorithms
Clusters
Combinatorial analysis
Computational efficiency
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Control system synthesis
Control theory. Systems
Cost function
Data processing. List processing. Character string processing
Dynamics
Exact sciences and technology
Heuristic algorithms
Maximum entropy
Maximum entropy principle (MEP)
Medical sciences
Memory organisation. Data processing
Mobile communication
Partitioning algorithms
Software
Studies
Tracking
Tumors
Vehicle dynamics
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Summary:We propose a computationally efficient framework to solve a large class of dynamic coverage and clustering problems, ranging from those that arise from deployment of mobile sensor networks to classification of cellular data for diagnosing cancer stages. This framework provides the ability to identify natural clusters in the underlying data set. In particular, we define the problem of minimizing instantaneous coverage as a combinatorial optimization problem in a Maximum Entropy Principle (MEP) framework that we formulate specifically for the dynamic setting, and which allows us to address inherent tradeoffs such as those between the resolution of the identified clusters and computational cost. The proposed MEP framework addresses both the coverage and the tracking aspects of these problems. Locating cluster centers of swarms of moving objects and tracking them is cast as a control design problem ensuring that the algorithm achieves progressively better coverage with time. Simulation results are presented that highlight the features of this framework; these results demonstrate that the proposed algorithm attains target coverage costs five to seven times faster than related frame-by-frame methods.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2011.2166713