title: Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets.
abstract: This paper presents a statistical-mechanics-inspired procedure for optimization of the sensor field configuration to detect mobile targets. The key idea is to capture the low-dimensional behavior of the sensor field configurations across the Pareto front in a multiobjective scenario for optimal sensor deployment, where the nondominated points are concentrated within a small region of the large-dimensional decision space. The sensor distribution is constructed using location-dependent energy-like functions and intensive temperature-like parameters in the sense of statistical mechanics. This low-dimensional representation is shown to permit rapid optimization of the sensor field distribution on a high-fidelity simulation test bed of distributed sensor networks.
authors.affiliation: Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USA. kum162@psu.edu
authors.forename: Kushal
authors.initials: K
authors.lastname: Mukherjee
authors.forename: Shalabh
authors.initials: S
authors.lastname: Gupta
authors.forename: Asok
authors.initials: A
authors.lastname: Ray
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Wettergren
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers