Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering
Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K -nearest neighbors (WKNN), which calculates K -nearest neighboring points to a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/109642 |
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| _version_ | 1850216396035719168 |
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| author | Xuke Hu Jianga Shang Fuqiang Gu Qi Han |
| author_facet | Xuke Hu Jianga Shang Fuqiang Gu Qi Han |
| author_sort | Xuke Hu |
| collection | DOAJ |
| description | Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K -nearest neighbors (WKNN), which calculates K -nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems that there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close to the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio signal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering based on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the proposed approach does outperform existing approaches. |
| format | Article |
| id | doaj-art-3c03b60d0bc04d2684b516ecb75fb258 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-3c03b60d0bc04d2684b516ecb75fb2582025-08-20T02:08:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-01-011110.1155/2015/109642109642Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation ClusteringXuke Hu0Jianga Shang1Fuqiang Gu2Qi Han3 National Engineering Research Center for Geographic Information System, Wuhan 430074, China National Engineering Research Center for Geographic Information System, Wuhan 430074, China National Engineering Research Center for Geographic Information System, Wuhan 430074, China Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, CO 80401, USAIndoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K -nearest neighbors (WKNN), which calculates K -nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems that there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close to the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio signal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering based on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the proposed approach does outperform existing approaches.https://doi.org/10.1155/2015/109642 |
| spellingShingle | Xuke Hu Jianga Shang Fuqiang Gu Qi Han Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering International Journal of Distributed Sensor Networks |
| title | Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering |
| title_full | Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering |
| title_fullStr | Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering |
| title_full_unstemmed | Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering |
| title_short | Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering |
| title_sort | improving wi fi indoor positioning via ap sets similarity and semi supervised affinity propagation clustering |
| url | https://doi.org/10.1155/2015/109642 |
| work_keys_str_mv | AT xukehu improvingwifiindoorpositioningviaapsetssimilarityandsemisupervisedaffinitypropagationclustering AT jiangashang improvingwifiindoorpositioningviaapsetssimilarityandsemisupervisedaffinitypropagationclustering AT fuqianggu improvingwifiindoorpositioningviaapsetssimilarityandsemisupervisedaffinitypropagationclustering AT qihan improvingwifiindoorpositioningviaapsetssimilarityandsemisupervisedaffinitypropagationclustering |