A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks
Wi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/4583147 |
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| _version_ | 1849699249995907072 |
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| author | Nan Li Jiabin Chen Yan Yuan Xiaochun Tian Yongqiang Han Mingzhe Xia |
| author_facet | Nan Li Jiabin Chen Yan Yuan Xiaochun Tian Yongqiang Han Mingzhe Xia |
| author_sort | Nan Li |
| collection | DOAJ |
| description | Wi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of indoor environment and the time delay caused by the time consumption to provide the position. In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). The clustering technique is adopted to reduce the maximum location error and enhance the prediction performance of PSO-ANN model. And the strong learning ability of PSO-ANN model enables the proposed system to adapt to the complicated indoor environment. Meanwhile, the fast learning and prediction speed of the PSO-ANN would greatly reduce the time consumption. Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed. |
| format | Article |
| id | doaj-art-3209730a15d34e23b97f94b5d4d1945a |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-3209730a15d34e23b97f94b5d4d1945a2025-08-20T03:18:39ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-03-011210.1155/2016/45831474583147A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural NetworksNan Li0Jiabin Chen1Yan Yuan2Xiaochun Tian3Yongqiang Han4Mingzhe Xia5 School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, China School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, China School of Computer Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, China School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, China School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, China School of Automation, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, ChinaWi-Fi based indoor localization system has attracted considerable attention due to the growing need for location based service (LBS) and the rapid development of mobile phones. However, most existing Wi-Fi based indoor positioning systems suffer from the low accuracy due to the dynamic variation of indoor environment and the time delay caused by the time consumption to provide the position. In this paper, we propose an indoor localization system using the affinity propagation (AP) clustering algorithm and the particle swarm optimization based artificial neural network (PSO-ANN). The clustering technique is adopted to reduce the maximum location error and enhance the prediction performance of PSO-ANN model. And the strong learning ability of PSO-ANN model enables the proposed system to adapt to the complicated indoor environment. Meanwhile, the fast learning and prediction speed of the PSO-ANN would greatly reduce the time consumption. Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.https://doi.org/10.1155/2016/4583147 |
| spellingShingle | Nan Li Jiabin Chen Yan Yuan Xiaochun Tian Yongqiang Han Mingzhe Xia A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks International Journal of Distributed Sensor Networks |
| title | A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks |
| title_full | A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks |
| title_fullStr | A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks |
| title_full_unstemmed | A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks |
| title_short | A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks |
| title_sort | wi fi indoor localization strategy using particle swarm optimization based artificial neural networks |
| url | https://doi.org/10.1155/2016/4583147 |
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