Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM
Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Hea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9049345/ |
| _version_ | 1818330295615619072 |
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| author | Thi-Kien Dao Trong-The Nguyen Jeng-Shyang Pan Yu Qiao Quoc-Anh Lai |
| author_facet | Thi-Kien Dao Trong-The Nguyen Jeng-Shyang Pan Yu Qiao Quoc-Anh Lai |
| author_sort | Thi-Kien Dao |
| collection | DOAJ |
| description | Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Head (CH) is an essential demand for a successful WSN application. This paper proposes a new scheme of identifying collected information correctness for aggregating data in CHs in hierarchical WSN based on improving classification of Support vector machine (SVM). The optimal parameter SVM is implemented by an improved flower pollination algorithm (IFPA) to achieve classification accuracy. The collecting environmental information like temperature, humidity, etc., from sensor nodes to CHs that classify data fault, aggregate, and transfer them to the BS. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications. |
| first_indexed | 2024-12-13T13:01:41Z |
| format | Article |
| id | doaj.art-2a9a7b619f984f66b61135829d0901f6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:01:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2a9a7b619f984f66b61135829d0901f62022-12-21T23:44:58ZengIEEEIEEE Access2169-35362020-01-018610706108410.1109/ACCESS.2020.29832199049345Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVMThi-Kien Dao0https://orcid.org/0000-0002-2805-652XTrong-The Nguyen1https://orcid.org/0000-0002-6963-2626Jeng-Shyang Pan2Yu Qiao3Quoc-Anh Lai4Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, ChinaFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaDepartment of Pattern Recognition and Image Processing, Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, VietnamWireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Head (CH) is an essential demand for a successful WSN application. This paper proposes a new scheme of identifying collected information correctness for aggregating data in CHs in hierarchical WSN based on improving classification of Support vector machine (SVM). The optimal parameter SVM is implemented by an improved flower pollination algorithm (IFPA) to achieve classification accuracy. The collecting environmental information like temperature, humidity, etc., from sensor nodes to CHs that classify data fault, aggregate, and transfer them to the BS. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.https://ieeexplore.ieee.org/document/9049345/Wireless sensor networksupport vector machineidentification failure dataclassification |
| spellingShingle | Thi-Kien Dao Trong-The Nguyen Jeng-Shyang Pan Yu Qiao Quoc-Anh Lai Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM IEEE Access Wireless sensor network support vector machine identification failure data classification |
| title | Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM |
| title_full | Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM |
| title_fullStr | Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM |
| title_full_unstemmed | Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM |
| title_short | Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM |
| title_sort | identification failure data for cluster heads aggregation in wsn based on improving classification of svm |
| topic | Wireless sensor network support vector machine identification failure data classification |
| url | https://ieeexplore.ieee.org/document/9049345/ |
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