Dynamic Coverage and Clustering: A Maximum Entropy Approach

Abstract:

In this talk we present a computational framework 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 classification of cellular data for diagnosing cancer stages. This framework provides the ability to identify natural clusters in an underlying dataset, and allows us to address 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 cluster centers and their associated velocity fields is cast as a control design problem that ensures the algorithm achieves progressively better coverage with time.

Biography:Dr. Carolyn Beck is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering (IESE) at the University of Illinois at Urbana-Champaign. She joined the department in August 1999. Prior to this she was a faculty member of the Department of Electrical Engineering at the University of Pittsburgh from September 1996 through July 1999. She was a post-doctoral research assistant at Lund Institute of Technology in Sweden for the first half of 1996, after receiving her Ph.D. in Electrical Engineering from the California Institute of Technology in January 1996, in the area of control theory. From 1985 through 1989, Dr. Beck was a Research and Development Engineer for Hewlett-Packard in Santa Clara, CA.