{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:08:42Z","timestamp":1774159722456,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004768","name":"Universiti Teknikal Malaysia Melaka","doi-asserted-by":"publisher","award":["PJP\/2018\/FKEKK(3B)\/S01615"],"award-info":[{"award-number":["PJP\/2018\/FKEKK(3B)\/S01615"]}],"id":[{"id":"10.13039\/501100004768","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005417","name":"Universiti Teknologi Malaysia","doi-asserted-by":"publisher","award":["14J64 and 4F966"],"award-info":[{"award-number":["14J64 and 4F966"]}],"id":[{"id":"10.13039\/501100005417","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and\/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.<\/jats:p>","DOI":"10.3390\/s19102397","type":"journal-article","created":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T23:07:27Z","timestamp":1558912047000},"page":"2397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6746-6011","authenticated-orcid":false,"given":"Ahmed Salih","family":"AL-Khaleefa","sequence":"first","affiliation":[{"name":"Broadband and Networking (BBNET) Research Group, Centre for Telecommunication and Research Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohd Riduan","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Broadband and Networking (BBNET) Research Group, Centre for Telecommunication and Research Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azmi Awang Md","family":"Isa","sequence":"additional","affiliation":[{"name":"Broadband and Networking (BBNET) Research Group, Centre for Telecommunication and Research Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mona Riza Mohd","family":"Esa","sequence":"additional","affiliation":[{"name":"Institute of High Voltage and High Current (IVAT), School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor Bharu, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazan","family":"Aljeroudi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, International Islamic University of Malaysia (IIUM), Selangor 53100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Ahmed","family":"Jubair","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza Firsandaya","family":"Malik","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Universitas Sriwijaya (UNSRI), Inderalaya, Sumatera Selatan 30151, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MNET.2013.6616116","article-title":"Cloud-Enabled Wireless Body Area Networks for Pervasive Healthcare","volume":"27","author":"Wan","year":"2013","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ali, M.U., Hur, S., and Park, Y. (2017). LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning. Sensors, 17.","DOI":"10.3390\/s17061213"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.protcy.2014.10.248","article-title":"GuideMe\u2014A Tourist Guide with a Recommender System and Social Interaction","volume":"17","author":"Umanets","year":"2014","journal-title":"Procedia Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, S., Fidler, S., and Urtasun, R. (2015, January 7\u201313). Lost Shopping! Monocular Localization in Large Indoor Spaces. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.309"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, H., Ding, Y., Li, P., Wang, R., and Li, Y. (2017). An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor. Sensors, 17.","DOI":"10.3390\/s17081806"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Azizyan, M., Constandache, I., and Choudhury, R.R. (2009, January 21\u201323). SurroundSense: Mobile Phone Localization Via Ambience Fingerprinting. Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, Beijing, China.","DOI":"10.1145\/1614320.1614350"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.3390\/s120302798","article-title":"Artificial Neural Network for Location Estimation in Wireless Communication Systems","volume":"12","author":"Chen","year":"2012","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ibrahim, M., and Youssef, M. (2011, January 5\u20139). A Hidden Markov Model for Localization Using Low-End GSM Cell Phones. Proceedings of the IEEE International Conference on Communications (ICC), Kyoto, Japan.","DOI":"10.1109\/icc.2011.5962993"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14809","DOI":"10.3390\/s150614809","article-title":"A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization","volume":"15","author":"Quinteiro","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00521-014-1714-x","article-title":"Feature Adaptive Online Sequential Extreme Learning Machine for Lifelong Indoor Localization","volume":"27","author":"Jiang","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.3390\/s150101804","article-title":"A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine","volume":"15","author":"Zou","year":"2015","journal-title":"Sensors"},{"key":"ref_12","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. Proceedings of the IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, Hungary."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Salamah, A.H., Tamazin, M., Sharkas, M.A., and Khedr, M. (2016, January 4\u20137). An Enhanced WiFi Indoor Localization System Based on Machine Learning. Proceedings of the Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain.","DOI":"10.1109\/IPIN.2016.7743586"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","article-title":"Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes","volume":"17","author":"Huang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4959","DOI":"10.1109\/TIP.2016.2598679","article-title":"Robust Visual Knowledge Transfer Via Extreme Learning Machine Based Domain Adaptation","volume":"25","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TIM.2014.2367775","article-title":"Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems","volume":"64","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TCYB.2016.2523538","article-title":"Blind Domain Adaptation with Augmented Extreme Learning Machine Features","volume":"47","author":"Uzair","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jain, V., and Learned-Miller, E. (2011, January 20\u201325). Online Domain Adaptation of a Pre-Trained Cascade of Classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA.","DOI":"10.1109\/CVPR.2011.5995317"},{"key":"ref_20","unstructured":"Lu, X., Long, Y., Zou, H., Yu, C., and Xie, L. (2014, January 21\u201324). Robust Extreme Learning Machine for Regression Problems with its Application to WIFI Based Indoor Positioning System. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Reims, France."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/TCYB.2015.2399420","article-title":"Robust Extreme Learning Machine with Its Application to Indoor Positioning","volume":"46","author":"Lu","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6116","DOI":"10.1109\/ACCESS.2018.2791579","article-title":"Pedestrian Dead-Reckoning Indoor Localization Based on Os-Elm","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"AL-Khaleefa, A.S., Ahmad, M.R., Isa, A.A.M., AL-Saffar, A., Esa, M.R.M., and Malik, R.F. (2019). MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System. Information, 10.","DOI":"10.3390\/info10040146"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"AL-Khaleefa, A.S., Ahmad, M.R., Isa, A.A.M., Esa, M.R.M., AL-Saffar, A., and Hassan, M.H. (2019). Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine. Appl. Sci., 9.","DOI":"10.3390\/app9050895"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54769","DOI":"10.1109\/ACCESS.2018.2870754","article-title":"Infinite-Term Memory Classifier for Wi-Fi Localization Based on Dynamic Wi-Fi Simulator","volume":"6","author":"Ahmad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"232","article-title":"On-Line Sequential Extreme Learning Machine","volume":"2005","author":"Huang","year":"2005","journal-title":"Comput. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neunet.2012.04.002","article-title":"A Comparative Analysis of Support Vector Machines and Extreme Learning Machines","volume":"33","author":"Liu","year":"2012","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Torres-Sospedra, J., Montoliu, R., Mart\u00ednez-Us\u00f3, A., Avariento, J.P., Arnau, T.J., Benedito-Bordonau, M., and Huerta, J. (2014, January 27\u201330). UJIIndoorLoc: A New Multi-Building and Multi-Floor Database for WLAN Fingerprint-Based Indoor Localization Problems. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea.","DOI":"10.1109\/IPIN.2014.7275492"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lohan, S.E., Torres-Sospedra, J., Lepp\u00e4koski, H., Richter, P., Peng, Z., and Huerta, J. (2017). Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. Data, 2.","DOI":"10.3390\/data2040032"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:06Z","timestamp":1760187306000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,25]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102397"],"URL":"https:\/\/doi.org\/10.3390\/s19102397","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,25]]}}}