Cross-Entropy optimization for sensor selection problems

Author

Naeem, M. ; Xue, S. ; Lee, D.C.

Author_Institution

Sch. of Eng. Sci., Simon Fraser Univ., Burnaby, BC, Canada

fYear

2009

fDate

28-30 Sept. 2009

Firstpage

396

Lastpage

401

Abstract

In this paper, we apply the Cross-Entropy optimization (CEO) to the problem of selecting k sensors from a set of m sensors for the purpose of minimizing the error in parameter estimation. The computational complexity of finding an optimal subset through exhaustive search can grow exponentially with the numbers (m and k) of sensors. The CEO is a generalized Monte Carlo technique to solve combinatorial optimization problems. The CEO method updates its parameters from the superior samples at the previous iterations. The performance of proposed CEO-based sensor selection algorithm is better than existing sensor selection algorithm, and its effectiveness is verified through simulation results.

Keywords

Monte Carlo methods; combinatorial mathematics; distributed sensors; entropy; error statistics; iterative methods; minimisation; parameter estimation; sampling methods; search problems; set theory; CEO-based sensor selection algorithm; combinatorial optimization problem; computational complexity; cross-entropy optimization; error minimization; generalized Monte Carlo technique; optimal subset search; parameter estimation; sample iteration; sensor network localization problem; Energy consumption; Entropy; Machine learning; Monte Carlo methods; Optimization methods; Parameter estimation; Signal processing algorithms; Traveling salesman problems; Upper bound; Wireless sensor networks; Cross-Entropy Optimization; Sensor Selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology, 2009. ISCIT 2009. 9th International Symposium on

Conference_Location

Icheon

Print_ISBN

978-1-4244-4521-9

Electronic_ISBN

978-1-4244-4522-6

Type

conf

DOI

10.1109/ISCIT.2009.5341219

Filename

5341219