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| Title | A Multi-Objective Localization Algorithm With Real Average Distance In Wsn |
|---|---|
| ID_Doc | 2828 |
| Authors | Wang P.; Du L.; Cui Z.; Cai X.; Xie L. |
| Year | 2019 |
| Published | Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 |
| DOI | http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00019 |
| Abstract | Location information is an essential requirement in wireless sensor networks (WSN), many wireless sensor applications would be limited without location information. Distance vector-hop (DV-Hop), as a typical path-dependence algorithm, utilizes the connectivity and multi-hop transmission for estimated positioning. However, a large positioning error is caused because of its simple and rough estimated method. In this work, we analyze the defects of the existing traditional single-objective and multi-objective model based on DV-Hop algorithm, and provide a multi-objective localization model based on the real average estimated distance of all beacon nodes. Then, we solve it using the multi-objective optimization algorithm (NSGA-II), and named it RAMGA-DV-Hop. Eventually, we tested it with different aspects, and the simulation results confirm our method significantly outperforms the DV-Hop and OCS-LC-DV-Hop. Moreover, compare with existing multi-objective localization algorithms, such as NSGAII-DV-Hop, its overall performance also has advantages. © 2019 IEEE. |
| Author Keywords | multi-objective localization model; NSGA-II; real average estimated distance; wireless sensor networks |
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