{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:55:57Z","timestamp":1774450557109,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T00:00:00Z","timestamp":1715990400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62273298"],"award-info":[{"award-number":["62273298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2023203063"],"award-info":[{"award-number":["F2023203063"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Communication"],"published-print":{"date-parts":[[2024,9,10]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Indoor location based on Wi\u2010Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion\u2010based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two\u2010layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K\u2010nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two\u2010layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM.<\/jats:p>","DOI":"10.1002\/dac.5828","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T12:34:35Z","timestamp":1716035675000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Wi\u2010Fi fingerprint\u2010based indoor location: Multiple fingerprints and multiple classifiers with two\u2010layer fusion weights"],"prefix":"10.1002","volume":"37","author":[{"given":"Na","family":"Liu","sequence":"first","affiliation":[{"name":"School of Economics and Management Yanshan University Qinhuangdao China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3768-8788","authenticated-orcid":false,"given":"Zhixin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering Yanshan University Qinhuangdao China"}]},{"given":"Jie","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering Yanshan University Qinhuangdao China"}]},{"given":"Yazhou","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering Yanshan University Qinhuangdao China"}]},{"given":"Kit","family":"Yan Chan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences Curtin University Perth Western Australia Australia"}]}],"member":"311","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3115849"},{"issue":"11","key":"e_1_2_9_3_1","first-page":"2022","article-title":"Vision\u2010aided self\u2010calibration of a wireless propagation model for crowdsourcing\u2010based indoor localization","volume":"205","author":"He Y","journal-title":"Measurement"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2015.2430281"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2989387"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2014.2322771"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2021.9010044"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2853745"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3092255"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"BaiYB et al.A new algorithm for improving the tracking and positioning of cell of origin. In: 2015 International Association of Institutes of Navigation World Congress (IAIN);2015:1\u20106.","DOI":"10.1109\/IAIN.2015.7352267"},{"issue":"2","key":"e_1_2_9_11_1","first-page":"134","article-title":"How to select the best sensors for TDOA and TDOA\/AOA localization?","volume":"16","author":"Zhao Y","year":"2019","journal-title":"China Commun"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"WangA SongY.Improved SDS\u2010TWR ranging technology in UWB positioning. In: International Conference on Sensor Networks and Signal Processing (SNSP) 2018;2018:222\u2010225.","DOI":"10.1109\/SNSP.2018.00049"},{"issue":"11","key":"e_1_2_9_13_1","first-page":"2022","article-title":"NLOS identification using parallel deep learning model and time\u2010frequency information in UWB\u2010based positioning system","volume":"195","author":"Wei J","journal-title":"Measurement"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"YoussefM AbdallahM AgrawalaA.Multivariate analysis for probabilistic WLAN location determination systems. In: The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services;2005:353\u2010362.","DOI":"10.1109\/MOBIQUITOUS.2005.41"},{"key":"e_1_2_9_15_1","unstructured":"KimY ShinH ChaH.Smartphone\u2010based Wi\u2010Fi pedestrian\u2010tracking system tolerating the RSS variance problem. In: IEEE International Conference on Pervasive Computing and Communications Lugano;2022:11\u201019."},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3298603"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2731874"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"CuiS YinJ WangJ XuP.Data link fault location model based on machine learning. In: 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC);2021:619\u2010623.","DOI":"10.1109\/ICNISC54316.2021.00117"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2833029"},{"key":"e_1_2_9_20_1","unstructured":"XiaoL BehboodiA MatharR.Learning the localization function: machine learning approach to fingerprinting localization.2018. arXiv preprint arXiv: 1803.08153."},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3113837"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2312421"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2014.24"},{"key":"e_1_2_9_24_1","doi-asserted-by":"crossref","unstructured":"YoussefM AgrawalaA.Continuous space estimation for WLAN location determination systems. In: Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat No04EX969);2004:161\u2010166.","DOI":"10.1109\/ICCCN.2004.1401614"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2012.175"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"HonkavirtaV PeralaT Ali\u2010LoyttyS PicheR.A comparative survey of WLAN location fingerprinting methods. In: 2009 6th Workshop on Positioning Navigation and Communication;2009:243\u2010251.","DOI":"10.1109\/WPNC.2009.4907834"},{"key":"e_1_2_9_27_1","first-page":"1026","article-title":"WLAN location determination in e\u2010home via support vector classification","volume":"2","author":"Wu C","year":"2004","journal-title":"IEEE Int Confer Netw Sens Control"},{"key":"e_1_2_9_28_1","doi-asserted-by":"crossref","unstructured":"Nuno\u2010BarrawG Paez\u2010BarralloJM.Application of linear discriminant functions to location estimation. In: IEEE\/SP 13th Workshop on Statistical Signal Processing;2005:503\u2010508.","DOI":"10.1109\/SSP.2005.1628647"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/bltj.21649"},{"key":"e_1_2_9_30_1","doi-asserted-by":"crossref","unstructured":"KangN XiaoS.Research on map matching algorithm based on moving trajectory of underground personnel. In: International Conference on Intelligent Transportation Big Data and Smart City (ICITBS) 2020;2020:53\u201056.","DOI":"10.1109\/ICITBS49701.2020.00019"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2011.101211.101957"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2012.022112.120131"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.2021.04.004"},{"key":"e_1_2_9_34_1","doi-asserted-by":"crossref","unstructured":"ChabbarH ChamiM.Indoor localization using Wi\u2010Fi method based on Fingerprinting Technique. In: 2017 International Conference on Wireless Technologies Embedded and Intelligent Systems (WITS);2017:1\u20105.","DOI":"10.1109\/WITS.2017.7934613"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2010.2054063"},{"key":"e_1_2_9_36_1","doi-asserted-by":"crossref","unstructured":"AlshamiIH AhmadNA SahibuddinS.RSS certainty: an efficient solution for RSS variation due to device heterogeneity in WLAN fingerprinting\u2010based indoor positioning system. In: 2021 Palestinian International Conference on Information and Communication Technology (PICICT);2021:71\u201076.","DOI":"10.1109\/PICICT53635.2021.00024"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2365779"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2810601"},{"issue":"2","key":"e_1_2_9_39_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TSMCC.2008.2008099","article-title":"Resident location\u2010recognition algorithm using a Bayesian classifier in the PIR sensor\u2010based indoor location\u2010aware system","volume":"39","author":"Kim HH","year":"2019","journal-title":"IEEE Trans Syst, Man, Cybern Part C (Appl Rev)"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2874453"}],"container-title":["International Journal of Communication Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/dac.5828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T10:04:14Z","timestamp":1732010654000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/dac.5828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,18]]},"references-count":39,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,9,10]]}},"alternative-id":["10.1002\/dac.5828"],"URL":"https:\/\/doi.org\/10.1002\/dac.5828","archive":["Portico"],"relation":{},"ISSN":["1074-5351","1099-1131"],"issn-type":[{"value":"1074-5351","type":"print"},{"value":"1099-1131","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,18]]},"assertion":[{"value":"2023-10-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e5828"}}