Wi-Fi fingerprinting based on collaborative confidence level training
Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerpri...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33124/ |
| _version_ | 1848794562644934656 |
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| author | Jing, Hao Pinchin, James Hill, Chris Moore, Terry |
| author_facet | Jing, Hao Pinchin, James Hill, Chris Moore, Terry |
| author_sort | Jing, Hao |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%. |
| first_indexed | 2025-11-14T19:18:10Z |
| format | Article |
| id | nottingham-33124 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:18:10Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-331242020-05-04T17:58:23Z https://eprints.nottingham.ac.uk/33124/ Wi-Fi fingerprinting based on collaborative confidence level training Jing, Hao Pinchin, James Hill, Chris Moore, Terry Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%. Elsevier 2016-08-01 Article PeerReviewed Jing, Hao, Pinchin, James, Hill, Chris and Moore, Terry (2016) Wi-Fi fingerprinting based on collaborative confidence level training. Pervasive and Mobile Computing, 30 . pp. 32-44. ISSN 1574-1192 Indoor positioning; Wi-Fi fingerprinting; Collaborative positioning http://dx.doi.org/10.1016/j.pmcj.2015.10.005 doi:10.1016/j.pmcj.2015.10.005 doi:10.1016/j.pmcj.2015.10.005 |
| spellingShingle | Indoor positioning; Wi-Fi fingerprinting; Collaborative positioning Jing, Hao Pinchin, James Hill, Chris Moore, Terry Wi-Fi fingerprinting based on collaborative confidence level training |
| title | Wi-Fi fingerprinting based on collaborative confidence level training |
| title_full | Wi-Fi fingerprinting based on collaborative confidence level training |
| title_fullStr | Wi-Fi fingerprinting based on collaborative confidence level training |
| title_full_unstemmed | Wi-Fi fingerprinting based on collaborative confidence level training |
| title_short | Wi-Fi fingerprinting based on collaborative confidence level training |
| title_sort | wi-fi fingerprinting based on collaborative confidence level training |
| topic | Indoor positioning; Wi-Fi fingerprinting; Collaborative positioning |
| url | https://eprints.nottingham.ac.uk/33124/ https://eprints.nottingham.ac.uk/33124/ https://eprints.nottingham.ac.uk/33124/ |