{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:47:23Z","timestamp":1760150843592,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology of Taiwan","doi-asserted-by":"publisher","award":["MOST 109-2221-E-305-004-MY3"],"award-info":[{"award-number":["MOST 109-2221-E-305-004-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user\u2019s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.<\/jats:p>","DOI":"10.3390\/s22030776","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T22:51:06Z","timestamp":1642719066000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2784-9616","authenticated-orcid":false,"given":"Yuh-Shyan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 237, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0386-2231","authenticated-orcid":false,"given":"Chih-Shun","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Information Management, Shih Hsin University, No. 1, Ln. 17, Sec. 1, Muzha Rd., Wenshan District, Taipei City 116, Taiwan"}]},{"given":"Ren-Shao","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 237, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tzitzis, A., Megalou, S., Siachalou, S., Tsardoulias, E., and Yioultsis, T. (2019, January 25\u201327). 3D Localization of RFID Tags with a Single Antenna by a Moving Robot and Phase ReLock. Proceedings of the IEEE International Conference on RFID Technology and Applications, Pisa, Italy.","DOI":"10.1109\/RFID-TA.2019.8892256"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhu, M., Xiao, B., and Qiu, Y. (2018, January 11\u201313). The Improved Fingerprint-Based Indoor Localization with RFID\/PDR\/MM Technologies. Proceedings of the IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS 2018), Singapore.","DOI":"10.1109\/PADSW.2018.8644602"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cheng, S., Wang, S., Guan, W., Xu, H., and Li, P. (2020). 3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning. Sensors, 20.","DOI":"10.3390\/s20092731"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ha, G.Y., Seo, S.B., Oh, H.S., and Jeon, W.S. (2019, January 16\u201318). LoRa ToA-Based Localization Using Fingerprint Method. Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC 2019), Jeju Island, Korea.","DOI":"10.1109\/ICTC46691.2019.8939702"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Abbas, M., Elhamshary, M., and Rizk, H. (2019, January 11\u201315). WiDeep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2019), Kyoto, Japan.","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Anzum, N., Afroze, S.F., and Rahman, A. (2018, January 20\u201324). Zone-Based Indoor Localization Using Neural Networks: A View from a Real Testbed. Proceedings of the IEEE International Conference on Communications (ICC 2018), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422182"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chang, R.Y., Liu, S.J., and Cheng, Y.K. (2018, January 9\u201313). Device-Free Indoor Localization Using Wi-Fi Channel State Information for Internet of Things. Proceedings of the IEEE Global Communications Conference (GLOBECOM 2018), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647261"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jiang, H., Peng, C., and Sun, J. (2019, January 20\u201324). Deep Belief Network for Fingerprinting-Based RFID Indoor Localization. Proceedings of the IEEE International Conference on Communications (ICC 2019), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761800"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Wang, H. (2019, January 11\u201313). Fingerprinting-based Indoor Localization with Relation Learning Network. Proceedings of the IEEE\/CIC International Conference on Communications in China (ICCC 2019), Changchun, China.","DOI":"10.1109\/ICCChina.2019.8855882"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hsu, C.S., Chen, Y.S., Juang, T.Y., and Wu, Y.T. (2018, January 5\u20138). An Adaptive Wi-Fi Indoor Localization Scheme using Deep Learning. Proceedings of the IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP 2018), Auckland, New Zealand.","DOI":"10.1109\/APCAP.2018.8538191"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117768","DOI":"10.1109\/ACCESS.2019.2936600","article-title":"RSS-AOA-Based Localization via Mixed Semi-Definite and Second-Order Cone Relaxation in 3-D Wireless Sensor Networks","volume":"7","author":"Qi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cramariuc, A., Huttunen, H., and Lohan, E.S. (2016, January 28\u201330). Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings. Proceedings of the International Conference on Localization and GNSS (ICL-GNSS 2016), Barcelona, Spain.","DOI":"10.1109\/ICL-GNSS.2016.7533846"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zanca, G., Zorzi, F., Zanella, A., and Zorzi, M. (2008, January 1). Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. Proceedings of the Workshop on Real-World Wireless Sensor Networks, Glasgow, UK.","DOI":"10.1145\/1435473.1435475"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, G., and Tseng, P. (2018, January 5\u20138). A Deep Neural Network Based Indoor Positioning Method Using Channel State Information. Proceedings of the International Conference on Computing, Networking and Communications (ICCNC 2018), Maui, HI, USA.","DOI":"10.1109\/ICCNC.2018.8390298"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.1109\/TVT.2017.2774103","article-title":"Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization","volume":"67","author":"Yan","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mari, S.K., Kiong, L.C., and Loong, H.K. (2018, January 26\u201328). A Hybrid Trilateration and Fingerprinting Approach for Indoor Localization Based on WiFi. Proceedings of the Fourth International Conference on Advances in Computing, Communication and Automation (ICACCA 2018), Subang Jaya, Malaysia.","DOI":"10.1109\/ICACCAF.2018.8776729"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zou, H., Zhou, Y., Jiang, H., Huang, B., Xie, L., and Spanos, C. (2017, January 19\u201322). Adaptive Localization in Dynamic Indoor Environments by Transfer Kernel Learning. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC 2017), San Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925444"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1109\/TVT.2018.2794322","article-title":"Joint Azimuth, Elevation, and Delay Estimation for 3-D Indoor Localization","volume":"67","author":"Wen","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.23919\/JCC.2020.01.005","article-title":"TOA-based NLOS error mitigation algorithm for 3D indoor localization","volume":"17","author":"Wang","year":"2020","journal-title":"China Commun."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Marques, N., Meneses, F., and Moreira, A. (2012, January 13\u201315). Combining similarity functions and majority rules for multi-building, multi-floor, WiFi positioning. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia.","DOI":"10.1109\/IPIN.2012.6418937"},{"key":"ref_21","unstructured":"Chen, D.G., Wang, H.Y., and Tsang, E.C. (2008, January 12\u201315). Generalized Mercer theorem and its application to feature space related to indefinite kernels. Proceedings of the International Conference on Machine Learning and Cybernetics, Kunming, China."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:36Z","timestamp":1760133876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030776"],"URL":"https:\/\/doi.org\/10.3390\/s22030776","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,1,20]]}}}