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40.1 Simulated kalman filter optimization algorithm for maximization.pdf Download (109kB) | Preview |
Abstract
Simulated Kalman Filter (SKF) is a population based optimization algorithm inspired by the Kalman filtering method. It had been successfully used for optimization of many engineering problems. In this work SKF is applied for wireless sensor networks (WSN) coverage optimization problem, where the objective is to maximize the area covered by the sensors in a region of interest. Coverage is an important issue in WSN. It is used as one of the measurement metric for a WSN’s quality of service. Many metaheuristics algorithms had been applied to solve this problem. Here, SKF is tested over several WSN and found to be able to perform better than particle swarm optimization (PSO) and genetic algorithm (GA) in improving WSN coverage.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Binary sensing model; Simulated Kalman filter; Wireless sensor networks; Coverage |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Faculty of Manufacturing Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 23 Oct 2019 08:13 |
| Last Modified: | 23 Oct 2019 08:13 |
| URI: | http://umpir.ump.edu.my/id/eprint/24943 |
| Download Statistic: | View Download Statistics |
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