A clustering approach for error beacon filtering in underwater wireless sensor networks
Underwater wireless sensor networks are the enabling technology for the aquatic environmental monitoring and exploring and have attracted much attention recently. Due to the highly hostile and unpredictable underwater environments, some beacon nodes tend to move or be damaged. Therefore, the unknown...
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| Format: | Article |
| Language: | English |
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Wiley
2016-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147716681793 |
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| _version_ | 1849473350122864640 |
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| author | Linfeng Liu Jingli Du Dongyue Guo |
| author_facet | Linfeng Liu Jingli Du Dongyue Guo |
| author_sort | Linfeng Liu |
| collection | DOAJ |
| description | Underwater wireless sensor networks are the enabling technology for the aquatic environmental monitoring and exploring and have attracted much attention recently. Due to the highly hostile and unpredictable underwater environments, some beacon nodes tend to move or be damaged. Therefore, the unknown nodes will be positioned with larger error, which abases the value of data collected by sensor nodes. In order to solve the beacon error problem, this article proposes an error beacon filtering algorithm based on K -means clustering. First, the coordinate of each beacon is calculated through an improved trilateration method, and then the beacon with the maximum positioning error is filtered out via the K -means clustering algorithm. The remaining beacons repeat the above processes until the distance error of each beacon does not exceed a preset threshold. The analysis of simulation results indicates that the error beacons can be accurately found and filter out through our proposed error beacon filtering algorithm (based on K -means clustering), and thus the localization accuracy is enhanced. Besides, error beacon filtering algorithm also has a provable low complexity. |
| format | Article |
| id | doaj-art-c92104d6296542e685e4080c6c3283aa |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c92104d6296542e685e4080c6c3283aa2025-08-20T03:24:11ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-12-011210.1177/1550147716681793A clustering approach for error beacon filtering in underwater wireless sensor networksLinfeng Liu0Jingli Du1Dongyue Guo2School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing, ChinaUnderwater wireless sensor networks are the enabling technology for the aquatic environmental monitoring and exploring and have attracted much attention recently. Due to the highly hostile and unpredictable underwater environments, some beacon nodes tend to move or be damaged. Therefore, the unknown nodes will be positioned with larger error, which abases the value of data collected by sensor nodes. In order to solve the beacon error problem, this article proposes an error beacon filtering algorithm based on K -means clustering. First, the coordinate of each beacon is calculated through an improved trilateration method, and then the beacon with the maximum positioning error is filtered out via the K -means clustering algorithm. The remaining beacons repeat the above processes until the distance error of each beacon does not exceed a preset threshold. The analysis of simulation results indicates that the error beacons can be accurately found and filter out through our proposed error beacon filtering algorithm (based on K -means clustering), and thus the localization accuracy is enhanced. Besides, error beacon filtering algorithm also has a provable low complexity.https://doi.org/10.1177/1550147716681793 |
| spellingShingle | Linfeng Liu Jingli Du Dongyue Guo A clustering approach for error beacon filtering in underwater wireless sensor networks International Journal of Distributed Sensor Networks |
| title | A clustering approach for error beacon filtering in underwater wireless sensor networks |
| title_full | A clustering approach for error beacon filtering in underwater wireless sensor networks |
| title_fullStr | A clustering approach for error beacon filtering in underwater wireless sensor networks |
| title_full_unstemmed | A clustering approach for error beacon filtering in underwater wireless sensor networks |
| title_short | A clustering approach for error beacon filtering in underwater wireless sensor networks |
| title_sort | clustering approach for error beacon filtering in underwater wireless sensor networks |
| url | https://doi.org/10.1177/1550147716681793 |
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