Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation
In real-world large-scale deployments of indoor localization, Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain ad...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022069/ |
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| _version_ | 1841539961526943744 |
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| author | Zenghua ZHAO Yuefan TONG Jiayang CUI |
| author_facet | Zenghua ZHAO Yuefan TONG Jiayang CUI |
| author_sort | Zenghua ZHAO |
| collection | DOAJ |
| description | In real-world large-scale deployments of indoor localization, Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain adaptation theory of deep learning, the device type of the smartphone was taken as the domain, the task-related and device-independent Wi-Fi data features were extracted through adversarial training, and the learned source domain location information was transferred to the target domain.Pre-training and joint training were employed to improve model training stability and to accelerate convergence.The performance of DeviceTransfer was evaluated using four types of smartphones in two real-world indoor environments: a school building and a shopping mall.The experimental results show that DeviceTransfer effectively extracts device-independent Wi-Fi fingerprint features.Using only one type of phone to collect Wi-Fi fingerprints, online localization using other types still achieves high localization accuracy, thus reducing localization cost significantly. |
| format | Article |
| id | doaj-art-2d678152c1ef4091a34d6ca36df105a9 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2d678152c1ef4091a34d6ca36df105a92025-01-14T06:30:18ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-04-014314315359396928Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptationZenghua ZHAOYuefan TONGJiayang CUIIn real-world large-scale deployments of indoor localization, Wi-Fi fingerprinting approaches suffer from device diversity problem which impacts the localization accuracy significantly.A device-independent Wi-Fi fingerprint indoor localization model DeviceTransfer was proposed.Based on the domain adaptation theory of deep learning, the device type of the smartphone was taken as the domain, the task-related and device-independent Wi-Fi data features were extracted through adversarial training, and the learned source domain location information was transferred to the target domain.Pre-training and joint training were employed to improve model training stability and to accelerate convergence.The performance of DeviceTransfer was evaluated using four types of smartphones in two real-world indoor environments: a school building and a shopping mall.The experimental results show that DeviceTransfer effectively extracts device-independent Wi-Fi fingerprint features.Using only one type of phone to collect Wi-Fi fingerprints, online localization using other types still achieves high localization accuracy, thus reducing localization cost significantly.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022069/device diversityWi-Fi fingerprinting localizationindoor localizationdomain adaptationdeep learning |
| spellingShingle | Zenghua ZHAO Yuefan TONG Jiayang CUI Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation Tongxin xuebao device diversity Wi-Fi fingerprinting localization indoor localization domain adaptation deep learning |
| title | Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation |
| title_full | Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation |
| title_fullStr | Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation |
| title_full_unstemmed | Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation |
| title_short | Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation |
| title_sort | device independent wi fi fingerprinting indoor localization model based on domain adaptation |
| topic | device diversity Wi-Fi fingerprinting localization indoor localization domain adaptation deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022069/ |
| work_keys_str_mv | AT zenghuazhao deviceindependentwififingerprintingindoorlocalizationmodelbasedondomainadaptation AT yuefantong deviceindependentwififingerprintingindoorlocalizationmodelbasedondomainadaptation AT jiayangcui deviceindependentwififingerprintingindoorlocalizationmodelbasedondomainadaptation |