A parallel WOA with two communication strategies applied in DV-Hop localization method
Abstract Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact posit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-02-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-020-01663-y |
| _version_ | 1811287153203740672 |
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| author | Qing-wei Chai Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Wei-min Zheng |
| author_facet | Qing-wei Chai Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Wei-min Zheng |
| author_sort | Qing-wei Chai |
| collection | DOAJ |
| description | Abstract Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results. |
| first_indexed | 2024-04-13T03:13:41Z |
| format | Article |
| id | doaj.art-f88e610dbf9f443e812f195664cc40fb |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-04-13T03:13:41Z |
| publishDate | 2020-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-f88e610dbf9f443e812f195664cc40fb2022-12-22T03:04:59ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-02-012020111010.1186/s13638-020-01663-yA parallel WOA with two communication strategies applied in DV-Hop localization methodQing-wei Chai0Shu-Chuan Chu1Jeng-Shyang Pan2Pei Hu3Wei-min Zheng4College of Computer Science and Engineering, Shandong University of Science and TechnologyCollege of Computer Science and Engineering, Shandong University of Science and TechnologyCollege of Computer Science and Engineering, Shandong University of Science and TechnologyCollege of Computer Science and Engineering, Shandong University of Science and TechnologyCollege of Computer Science and Engineering, Shandong University of Science and TechnologyAbstract Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.http://link.springer.com/article/10.1186/s13638-020-01663-yParallelWOACommunication strategyDV-Hop |
| spellingShingle | Qing-wei Chai Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Wei-min Zheng A parallel WOA with two communication strategies applied in DV-Hop localization method EURASIP Journal on Wireless Communications and Networking Parallel WOA Communication strategy DV-Hop |
| title | A parallel WOA with two communication strategies applied in DV-Hop localization method |
| title_full | A parallel WOA with two communication strategies applied in DV-Hop localization method |
| title_fullStr | A parallel WOA with two communication strategies applied in DV-Hop localization method |
| title_full_unstemmed | A parallel WOA with two communication strategies applied in DV-Hop localization method |
| title_short | A parallel WOA with two communication strategies applied in DV-Hop localization method |
| title_sort | parallel woa with two communication strategies applied in dv hop localization method |
| topic | Parallel WOA Communication strategy DV-Hop |
| url | http://link.springer.com/article/10.1186/s13638-020-01663-y |
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