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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Naeem, M. Xue, S. Lee, D.C. |
| Copyright Year | 2009 |
| Description | Author affiliation: School of Engineering Science at Simon Fraser University, Burnaby, BC V5A 1S6 Canada (Naeem, M.; Xue, S.; Lee, D.C.) |
| 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. |
| Starting Page | 396 |
| Ending Page | 401 |
| File Size | 571035 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424445219 |
| DOI | 10.1109/ISCIT.2009.5341219 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wireless sensor networks Energy consumption Cross-Entropy Optimization Parameter estimation Monte Carlo methods Upper bound Signal processing algorithms Optimization methods Machine learning Traveling salesman problems Entropy Sensor Selection |
| Content Type | Text |
| Resource Type | Article |
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