Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network
Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2019/3478027 |
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| _version_ | 1832564208432054272 |
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| author | Zhirui Huang Lip Yee Por Tan Fong Ang Mohammad Hossein Anisi Mohammed Sani Adam |
| author_facet | Zhirui Huang Lip Yee Por Tan Fong Ang Mohammad Hossein Anisi Mohammed Sani Adam |
| author_sort | Zhirui Huang |
| collection | DOAJ |
| description | Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a method that uses fuzzy logic to combine both hardware-based and software-based metrics is proposed to improve the accuracy rate for evaluating a link quality. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. The Fuzzifier module is used to determine the degree to which input link quality metrics belong to each fuzzy set through proposed membership functions. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier module. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The result from the Defuzzifier module is then used to evaluate the link quality. A simulation conducted to compare the accuracy rates of the proposed method and those found in related works showed that the proposed method had higher accuracy rates for evaluating a link quality. |
| format | Article |
| id | doaj-art-80dac5892ca74092bd14ff1e5ee86ebd |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-80dac5892ca74092bd14ff1e5ee86ebd2025-02-03T01:11:35ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2019-01-01201910.1155/2019/34780273478027Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor NetworkZhirui Huang0Lip Yee Por1Tan Fong Ang2Mohammad Hossein Anisi3Mohammed Sani Adam4Faculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, MalaysiaSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, UKFaculty of Computer Science & Information Technology, University of Malaya, 50603 Kuala Lumpur, MalaysiaLink quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a method that uses fuzzy logic to combine both hardware-based and software-based metrics is proposed to improve the accuracy rate for evaluating a link quality. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. The Fuzzifier module is used to determine the degree to which input link quality metrics belong to each fuzzy set through proposed membership functions. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier module. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The result from the Defuzzifier module is then used to evaluate the link quality. A simulation conducted to compare the accuracy rates of the proposed method and those found in related works showed that the proposed method had higher accuracy rates for evaluating a link quality.http://dx.doi.org/10.1155/2019/3478027 |
| spellingShingle | Zhirui Huang Lip Yee Por Tan Fong Ang Mohammad Hossein Anisi Mohammed Sani Adam Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network Advances in Fuzzy Systems |
| title | Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network |
| title_full | Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network |
| title_fullStr | Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network |
| title_full_unstemmed | Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network |
| title_short | Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network |
| title_sort | improving the accuracy rate of link quality estimation using fuzzy logic in mobile wireless sensor network |
| url | http://dx.doi.org/10.1155/2019/3478027 |
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