A Genetic-Algorithm Based Mobile Sensor Network Deployment Algorithm EE382C: Embedded Software Systems Final Report
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| Název: | A Genetic-Algorithm Based Mobile Sensor Network Deployment Algorithm EE382C: Embedded Software Systems Final Report |
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| Autoři: | Yulai Suen |
| Přispěvatelé: | The Pennsylvania State University CiteSeerX Archives |
| Zdroj: | http://users.ece.utexas.edu/~bevans/courses/ee382c/projects/spring04/YuklaiSuen/FinalReport.pdf. |
| Sbírka: | CiteSeerX |
| Popis: | This paper describes a genetic-algorithm (GA) based deployment algorithm of mobile sensor network. The algorithm is designed for real-time online deployment for maximum coverage of the environment. The paper presents the details on the algorithm and the implementation, including the major components in our design: recombination, mutation, and the fitness function. The algorithm considers power metrics of the nodes for real-time planning of the next movement. The algorithm was implemented with Java Genetics Algorithm Package [4] and simulated with Network Simulator 2 [5] for performance evaluation. The simulation showed that the algorithm helped the network to avoid local maxima in coverage. 1. |
| Druh dokumentu: | text |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.9709; http://users.ece.utexas.edu/~bevans/courses/ee382c/projects/spring04/YuklaiSuen/FinalReport.pdf |
| Dostupnost: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.9709 http://users.ece.utexas.edu/~bevans/courses/ee382c/projects/spring04/YuklaiSuen/FinalReport.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Přístupové číslo: | edsbas.EA41E63B |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | This paper describes a genetic-algorithm (GA) based deployment algorithm of mobile sensor network. The algorithm is designed for real-time online deployment for maximum coverage of the environment. The paper presents the details on the algorithm and the implementation, including the major components in our design: recombination, mutation, and the fitness function. The algorithm considers power metrics of the nodes for real-time planning of the next movement. The algorithm was implemented with Java Genetics Algorithm Package [4] and simulated with Network Simulator 2 [5] for performance evaluation. The simulation showed that the algorithm helped the network to avoid local maxima in coverage. 1. |
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