Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags
With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2018-12-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814018808682 |
| _version_ | 1818143559589560320 |
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| author | Quangang Wen Yanchun Liang Chunguo Wu Adriano Tavares Xiaosong Han |
| author_facet | Quangang Wen Yanchun Liang Chunguo Wu Adriano Tavares Xiaosong Han |
| author_sort | Quangang Wen |
| collection | DOAJ |
| description | With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system. |
| first_indexed | 2024-12-11T11:33:36Z |
| format | Article |
| id | doaj.art-f68a3b16596c44f08a50cdbec89e6512 |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-11T11:33:36Z |
| publishDate | 2018-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-f68a3b16596c44f08a50cdbec89e65122022-12-22T01:08:49ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-12-011010.1177/1687814018808682Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tagsQuangang Wen0Yanchun Liang1Chunguo Wu2Adriano Tavares3Xiaosong Han4Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, ChinaZhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, ChinaZhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, ChinaDepartment of Industrial Electronics, University of Minho, Guimarães, PortugalKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaWith the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.https://doi.org/10.1177/1687814018808682 |
| spellingShingle | Quangang Wen Yanchun Liang Chunguo Wu Adriano Tavares Xiaosong Han Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags Advances in Mechanical Engineering |
| title | Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags |
| title_full | Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags |
| title_fullStr | Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags |
| title_full_unstemmed | Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags |
| title_short | Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags |
| title_sort | indoor localization algorithm based on artificial neural network and radio frequency identification reference tags |
| url | https://doi.org/10.1177/1687814018808682 |
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