{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:51:53Z","timestamp":1767340313004,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T00:00:00Z","timestamp":1612224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51774282"],"award-info":[{"award-number":["51774282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.<\/jats:p>","DOI":"10.3390\/s21031015","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T13:01:12Z","timestamp":1612270872000},"page":"1015","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-4256","authenticated-orcid":false,"given":"Yuqing","family":"Yin","sequence":"first","affiliation":[{"name":"China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, China"},{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2651-3432","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, China"},{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peihao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Chen","sequence":"additional","affiliation":[{"name":"China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, China"},{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Niu","sequence":"additional","affiliation":[{"name":"China Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou 221116, China"},{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shu, Y., Shin, K.G., He, T., and Chen, J. (2015, January 7\u201311). Last-mile navigation using smartphones. Proceedings of the ACM 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790099"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Daud Kamal, M., Tahir, A., Babar Kamal, M., Moeen, F., and Naeem, M.A. (2020). A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors. Sensors, 20.","DOI":"10.3390\/s20226495"},{"key":"ref_3","first-page":"8471503","article-title":"The Encountered Problems and Solutions in the Development of Coal Mine Rescue Robot","volume":"2018","author":"Wang","year":"2018","journal-title":"J. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TMC.2016.2557792","article-title":"Wifall: Device-free fall detection by wireless networks","volume":"16","author":"Wang","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, X., Li, S., Zhang, D., Xiong, J., Wang, Y., and Mei, H. (2016, January 12\u201316). Dynamic-music: Accurate device-free indoor localization. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971665"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chang, L., Xiong, J., Wang, Y., Chen, X., Hu, J., and Fang, D. (2017, January 5\u20138). iUpdater: Low cost RSS fingerprints updating for device-free localization. Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA.","DOI":"10.1109\/ICDCS.2017.216"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Guo, R., Qin, D., Zhao, M., and Wang, X. (2020). Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration. Sensors, 20.","DOI":"10.3390\/s20102818"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/2829988.2787487","article-title":"Spotfi: Decimeter level localization using wifi","volume":"Volume 45","author":"Kotaru","year":"2015","journal-title":"ACM SIGCOMM Computer Communication Review"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/8.127405","article-title":"914 MHz path loss prediction models for indoor wireless communications in multifloored buildings","volume":"40","author":"Seidel","year":"1992","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/LAWP.2013.2293578","article-title":"Third-order channel propagation model-based indoor adaptive localization algorithm for wireless sensor networks","volume":"12","author":"Tian","year":"2013","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, K., Xiao, J., Yi, Y., Gao, M., and Ni, L.M. (2012, January 25\u201330). Fila: Fine-grained indoor localization. Proceedings of the IEEE INFOCOM, Orlando, FL, USA.","DOI":"10.1109\/INFCOM.2012.6195606"},{"key":"ref_12","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/JIOT.2016.2558659","article-title":"CSI phase fingerprinting for indoor localization with a deep learning approach","volume":"3","author":"Wang","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2550","DOI":"10.1109\/TMC.2018.2812746","article-title":"Low human-effort, device-free localization with fine-grained subcarrier information","volume":"17","author":"Wang","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abbas, M., Elhamshary, M., Rizk, H., Torki, M., and Youssef, M. (2019, January 11\u201315). WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. Proceedings of the IEEE PerCom, Kyoto, Japan.","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, B., Liu, X., Yu, B., Jia, R., and Gan, X. (2019). An improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance. Sensors, 19.","DOI":"10.3390\/s19102300"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TII.2017.2750240","article-title":"Toward low-overhead fingerprint-based indoor localization via transfer learning: Design, implementation, and evaluation","volume":"14","author":"Liu","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_18","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 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925444"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, J., Wu, K., Yi, Y., Wang, L., and Ni, L.M. (2013, January 8\u201311). Pilot: Passive device-free indoor localization using channel state information. Proceedings of the 2013 IEEE 33rd International Conference on Distributed Computing Systems, Philadelphia, PA, USA.","DOI":"10.1109\/ICDCS.2013.49"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"256162","DOI":"10.1155\/2015\/256162","article-title":"Enhancing the performance of indoor device-free passive localization","volume":"11","author":"Yang","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5681","DOI":"10.1109\/TIE.2014.2301714","article-title":"Lightweight robust device-free localization in wireless networks","volume":"61","author":"Wang","year":"2014","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/TPDS.2012.134","article-title":"Rass: A real-time, accurate, and scalable system for tracking transceiver-free objects","volume":"24","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Youssef, M., Mah, M., and Agrawala, A. (2007, January 9\u201314). Challenges: Device-free passive localization for wireless environments. Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking, Montreal, QC, Canada.","DOI":"10.1145\/1287853.1287880"},{"key":"ref_24","first-page":"159","article-title":"Predictable 802.11 packet delivery from wireless channel measurements","volume":"41","author":"Halperin","year":"2011","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/2543581.2543592","article-title":"From RSSI to CSI: Indoor localization via channel response","volume":"46","author":"Yang","year":"2013","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., and Sen, R. (2012, January 22\u201326). Zee: Zero-effort crowdsourcing for indoor localization. Proceedings of the 18th ACM Annual International Conference on Mobile Computing and Networking, Istanbul, Turkey.","DOI":"10.1145\/2348543.2348580"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18066","DOI":"10.1109\/ACCESS.2017.2749516","article-title":"ConFi: Convolutional neural networks based indoor Wi-Fi localization using channel state information","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","article-title":"Integrating structured biological data by kernel maximum mean discrepancy","volume":"22","author":"Borgwardt","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/insr.12243","article-title":"Bayesian model averaging: A systematic review and conceptual classification","volume":"86","author":"Fragoso","year":"2018","journal-title":"Int. Stat. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/1925861.1925870","article-title":"Tool release: Gathering 802.11 n traces with channel state information","volume":"41","author":"Halperin","year":"2011","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_32","unstructured":"Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012, January 16\u201321). Geodesic flow kernel for unsupervised domain adaptation. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Schulz, M., Link, J., Gringoli, F., and Hollick, M. (2018, January 10\u201315). Shadow Wi-Fi: Teaching Smartphones to Transmit Raw Signals and to Extract Channel State Information to Implement Practical Covert Channels over Wi-Fi. Proceedings of the ACM 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany.","DOI":"10.1145\/3210240.3210333"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/1015\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:00Z","timestamp":1760159940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/1015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,2]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21031015"],"URL":"https:\/\/doi.org\/10.3390\/s21031015","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,2,2]]}}}