{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:35:13Z","timestamp":1771702513085,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)","doi-asserted-by":"publisher","award":["2021R1F1A1059915"],"award-info":[{"award-number":["2021R1F1A1059915"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Catholic University of Korea, Research Fund","award":["2021R1F1A1059915"],"award-info":[{"award-number":["2021R1F1A1059915"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Location-based indoor applications with high quality of services require a reliable, accurate, and low-cost position prediction for target device(s). The widespread availability of WiFi received signal strength indicator (RSSI) makes it a suitable candidate for indoor localization. However, traditional WiFi RSSI fingerprinting schemes perform poorly due to dynamic indoor mobile channel conditions including multipath fading, non-line-of-sight path loss, and so forth. Recently, machine learning (ML) or deep learning (DL)-based fingerprinting schemes are often used as an alternative, overcoming such issues. This paper presents an extreme gradient boosting-based ML indoor localization scheme, simply termed as XGBLoc, that accurately classifies (or detects) the positions of mobile devices in multi-floor multi-building indoor environments. XGBLoc not only effectively reduces the RSSI dataset dimensionality but trains itself using structured synthetic labels (also termed as relational labels), rather than conventional independent labels, that classify such complex and hierarchical indoor environments well. We numerically evaluate the proposed scheme on the publicly available datasets and prove its superiority over existing ML or DL-based schemes in terms of classification and regression performance.<\/jats:p>","DOI":"10.3390\/s22176629","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:19:01Z","timestamp":1662077941000},"page":"6629","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["XGBLoc: XGBoost-Based Indoor Localization in Multi-Building Multi-Floor Environments"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-1608","authenticated-orcid":false,"given":"Navneet","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si 14662, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5294-6154","authenticated-orcid":false,"given":"Sangho","family":"Choe","sequence":"additional","affiliation":[{"name":"Department of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si 14662, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0264-9714","authenticated-orcid":false,"given":"Rajiv","family":"Punmiya","sequence":"additional","affiliation":[{"name":"Department of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si 14662, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9800-4795","authenticated-orcid":false,"given":"Navneesh","family":"Kaur","sequence":"additional","affiliation":[{"name":"Center for Distance and Virtual Learning, University of Hyderabad, Hyderabad 500046, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11277-021-08209-5","article-title":"A review of indoor localization techniques and wireless technologies","volume":"119","author":"Obeidat","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/COMST.2015.2464084","article-title":"Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons","volume":"18","author":"He","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"116348","DOI":"10.1109\/ACCESS.2019.2935225","article-title":"A probabilistic approach for WiFi fingerprint localization in severely dynamic indoor environments","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/COMST.2019.2911558","article-title":"A survey of indoor localization systems and technologies","volume":"21","author":"Zafari","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Niu, J., Wang, B., Cheng, L., and Rodrigues, J.J.P.C. (2015, January 8\u201312). WicLoc: An Indoor Localization System Based on WiFi Fingerprints and Crowdsourcing. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.","DOI":"10.1109\/ICC.2015.7248785"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/JPROC.2018.2823500","article-title":"Localization via visible light systems","volume":"106","author":"Keskin","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1109\/TMC.2018.2888973","article-title":"Visible light localization using conventional light fixtures and smartphones","volume":"18","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1109\/JSEN.2017.2706303","article-title":"Fusing similarity-based sequence and dead reckoning for indoor positioning without training","volume":"17","author":"Liu","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Soltanaghaei, E., Kalyanaraman, A., and Whitehouse, K. (2018, January 10\u201315). Multipath Triangulation: Decimeter-Level WiFi Localization and Orientation with a Single Unaided Receiver. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany.","DOI":"10.1145\/3210240.3210347"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/COMST.2016.2632427","article-title":"Recent advances in indoor localization: A survey on theoretical approaches and applications","volume":"19","author":"Yassin","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1109\/COMST.2020.3014304","article-title":"Indoor intelligent fingerprint-based localization: Principles, approaches and challenges","volume":"22","author":"Zhu","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127150","DOI":"10.1109\/ACCESS.2021.3111083","article-title":"Machine learning based indoor localization using Wi-Fi RSSI fingerprints: An overview","volume":"9","author":"Singh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110698","DOI":"10.1109\/ACCESS.2019.2933921","article-title":"A novel convolutional neural network based indoor localization framework with WiFi fingerprinting","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1109\/TSG.2019.2892595","article-title":"Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing","volume":"10","author":"Punmiya","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nbt0308-303","article-title":"What is principal component analysis?","volume":"26","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wall, M.E., Rechtsteiner, A., and Rocha, L.M. (2003). Singular Value Decomposition and Principal Component Analysis. A Practical Approach to Microarray Data Analysis, Springer.","DOI":"10.1007\/0-306-47815-3_5"},{"key":"ref_18","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 2014 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea.","DOI":"10.1109\/IPIN.2014.7275492"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lohan, E.S., 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"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Montoliu, R., Sansano, E., Torres-Sospedra, J., and Belmonte, O. (2017, January 18\u201321). IndoorLoc Platform: A Public Repository for Comparing and Evaluating Indoor Positioning Systems. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115940"},{"key":"ref_21","unstructured":"Ge, X., and Qu, Z. (2016, January 26\u201328). Optimization WiFi Indoor Positioning KNN Algorithm Location-Based Fingerprint. Proceedings of the 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1109\/JIOT.2018.2864607","article-title":"Experimental analysis on weight K-nearest neighbor indoor fingerprint positioning","volume":"6","author":"Hu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, S., Guo, J., Wang, W., and Hu, J. (2018, January 22\u201323). Indoor 2.5 D positioning of WiFi based on SVM. Proceedings of the 2018 IEEE Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Wuhan, China.","DOI":"10.1109\/UPINLBS.2018.8559903"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Se\u00e7kin, A.\u00c7., and Co\u015fkun, A. (2019). Hierarchical fusion of machine learning algorithms in indoor positioning and localization. Appl. Sci., 9.","DOI":"10.3390\/app9183665"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tang, Z., Li, S., Kim, K.S., and Smith, J. (2022). Multi-output Gaussian process-based data augmentation for multi-building and multi-floor indoor localization. arXiv.","DOI":"10.1109\/ICCWorkshops53468.2022.9814616"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahmed Elesawi, A.E., and Kim, K.S. (2021, January 21\u201324). Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks. Proceedings of the 2021 9th International Symposium on Computing and Networking Workshops (CANDARW), Matsue, Japan.","DOI":"10.1109\/CANDARW53999.2021.00038"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Laska, M., and Blankenbach, J. (2021). DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization. Sensors, 21.","DOI":"10.3390\/s21062000"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jang, J.W., and Hong, S.N. (2018, January 3\u20136). Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network. Proceedings of the 10th IEEE International Conference on Ubiquitous and Future Networks (ICUFN), Prague, Czech Republic.","DOI":"10.1109\/ICUFN.2018.8436598"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26024","DOI":"10.1109\/ACCESS.2022.3156579","article-title":"Multi-task neural network for position estimation in large-scale indoor environments","volume":"10","author":"Laska","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","article-title":"Tabular data: Deep learning is not all you need","volume":"81","author":"Armon","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_31","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1145\/7474.7475","article-title":"A logical design methodology for relational databases using the extended entity-relationship model","volume":"18","author":"Teorey","year":"1986","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yoo, J., and Park, J. (2019). Indoor localization based on Wi-Fi received signal strength indicators: Feature extraction, mobile fingerprinting, and trajectory learning. Appl. Sci., 9.","DOI":"10.3390\/app9183930"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qin, F., Zuo, T., and Wang, X. (2021). CCpos: WiFi fingerprint indoor positioning system based on CDAE-CNN. Sensors, 21.","DOI":"10.3390\/s21041114"},{"key":"ref_36","unstructured":"Shlens, J. (2014). A tutorial on principal component analysis. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Berkvens, R., Weyn, M., and Peremans, H. (2015, January 13\u201316). Localization Performance Quantification by Conditional Entropy. Proceedings of the 2015 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Banff, AB, Canada.","DOI":"10.1109\/IPIN.2015.7346969"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9263","DOI":"10.1016\/j.eswa.2015.08.013","article-title":"Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems","volume":"42","author":"Montoliu","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_40","unstructured":"Nowicki, M., and Wietrzykowski, J. Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning. Proceedings of the International Conference Automation."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41044-018-0031-2","article-title":"A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting","volume":"3","author":"Kim","year":"2018","journal-title":"Big Data Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"38251","DOI":"10.1109\/ACCESS.2018.2852658","article-title":"HybLoc: Hybrid indoor Wi-Fi localization using soft clustering-based random decision forest ensembles","volume":"6","author":"Akram","year":"2018","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6629\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:08Z","timestamp":1760142128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":42,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176629"],"URL":"https:\/\/doi.org\/10.3390\/s22176629","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]}}}