A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks
Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/5/1890 |
| _version_ | 1797542188738936832 |
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| author | Zhongliang Deng Shihao Tang Xiwen Deng Lu Yin Jingrong Liu |
| author_facet | Zhongliang Deng Shihao Tang Xiwen Deng Lu Yin Jingrong Liu |
| author_sort | Zhongliang Deng |
| collection | DOAJ |
| description | Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable. |
| first_indexed | 2024-03-10T13:26:09Z |
| format | Article |
| id | doaj.art-751c16df6f6e474b8b405fc98fc4d90d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:26:09Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-751c16df6f6e474b8b405fc98fc4d90d2023-11-21T09:37:13ZengMDPI AGSensors1424-82202021-03-01215189010.3390/s21051890A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor NetworksZhongliang Deng0Shihao Tang1Xiwen Deng2Lu Yin3Jingrong Liu4School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaLocation information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable.https://www.mdpi.com/1424-8220/21/5/1890cooperative localizationlocation source optimizationfuzzy comprehensive evaluationDCPCRLB |
| spellingShingle | Zhongliang Deng Shihao Tang Xiwen Deng Lu Yin Jingrong Liu A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks Sensors cooperative localization location source optimization fuzzy comprehensive evaluation DCPCRLB |
| title | A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks |
| title_full | A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks |
| title_fullStr | A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks |
| title_full_unstemmed | A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks |
| title_short | A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks |
| title_sort | novel location source optimization algorithm for low anchor node density wireless sensor networks |
| topic | cooperative localization location source optimization fuzzy comprehensive evaluation DCPCRLB |
| url | https://www.mdpi.com/1424-8220/21/5/1890 |
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