TY - JOUR
T1 - Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets
AU - Mukherjee, Kushal
AU - Gupta, Shalabh
AU - Ray, Asok
AU - Wettergren, Thomas A.
N1 - Funding Information:
Manuscript received April 12, 2010; revised October 18, 2010; accepted November 9, 2010. Date of publication December 17, 2010; date of current version May 18, 2011. This work was supported in part by the U.S. Office of Naval Research under Grant N00014-09-1-0688 and in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant W911NF-07-1-0376. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. This paper was recommended by Associate Editor S. Ferrari.
PY - 2011/6
Y1 - 2011/6
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/79957483461
UR - https://www.scopus.com/inward/citedby.url?scp=79957483461&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2010.2092763
DO - 10.1109/TSMCB.2010.2092763
M3 - Article
C2 - 21172754
AN - SCOPUS:79957483461
SN - 1083-4419
VL - 41
SP - 783
EP - 791
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 3
M1 - 5669356
ER -