WLAN environment for indoor localization
Forecasting; Indoor positioning systems; Motion compensation; Nearest neighbor search; Pattern recognition; Access point (APs); Correction algorithms; Indoor environment; Indoor localization; K nearest neighbor algorithm; K-nearest neighbors; Prediction accuracy; Wireless local area networks (WLAN)
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| _version_ | 1833412699549073408 |
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| author | Burhan M.F.B. Shiham N.S.M. Balasubramaniam N. Din N.M. |
| author2 | 57191487465 |
| author_facet | 57191487465 Burhan M.F.B. Shiham N.S.M. Balasubramaniam N. Din N.M. |
| author_sort | Burhan M.F.B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Forecasting; Indoor positioning systems; Motion compensation; Nearest neighbor search; Pattern recognition; Access point (APs); Correction algorithms; Indoor environment; Indoor localization; K nearest neighbor algorithm; K-nearest neighbors; Prediction accuracy; Wireless local area networks (WLAN) |
| format | Conference Paper |
| id | my.uniten.dspace-22422 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-224222023-05-29T14:00:53Z WLAN environment for indoor localization Burhan M.F.B. Shiham N.S.M. Balasubramaniam N. Din N.M. 57191487465 57191480082 26967610800 9335429400 Forecasting; Indoor positioning systems; Motion compensation; Nearest neighbor search; Pattern recognition; Access point (APs); Correction algorithms; Indoor environment; Indoor localization; K nearest neighbor algorithm; K-nearest neighbors; Prediction accuracy; Wireless local area networks (WLAN) This paper investigates the deployment of WLAN for indoor localization. K-Nearest Neighbor algorithm is adapted to predict the location of a user in an indoor environment. The accuracy of K-Nearest Neighbor in predicting user's location in an indoor environment is evaluated. As resistance in indoor environment such as walls and movement of objects adversely affect the performance of the algorithm, emphasis is placed on RSS sample vector fluctuation correction. Two simulations were carried out, one adapting the fluctuation correction algorithm and one without fluctuation correction algorithm. The results of the investigation shows that deployment of fluctuation correction algorithm improves the prediction accuracy. The number of access points (APs) deployed in the investigated area also contribute to the prediction accuracy. � 2014 IEEE. Final 2023-05-29T06:00:53Z 2023-05-29T06:00:53Z 2015 Conference Paper 10.1109/ICE2T.2014.7006225 2-s2.0-84990964646 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990964646&doi=10.1109%2fICE2T.2014.7006225&partnerID=40&md5=94b686a1cda280cc236c5b17c7de4bad https://irepository.uniten.edu.my/handle/123456789/22422 2014-August 7006225 89 93 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Burhan M.F.B. Shiham N.S.M. Balasubramaniam N. Din N.M. WLAN environment for indoor localization |
| title | WLAN environment for indoor localization |
| title_full | WLAN environment for indoor localization |
| title_fullStr | WLAN environment for indoor localization |
| title_full_unstemmed | WLAN environment for indoor localization |
| title_short | WLAN environment for indoor localization |
| title_sort | wlan environment for indoor localization |
| url_provider | http://dspace.uniten.edu.my/ |