{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:26:31Z","timestamp":1760239591933,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Research Fund Program of LIESMARS","award":["19P03"],"award-info":[{"award-number":["19P03"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971341"],"award-info":[{"award-number":["41971341"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Project of the National Natural Science Foundation of Guangdong Province","award":["2019A1515010748","2019A1515011872"],"award-info":[{"award-number":["2019A1515010748","2019A1515011872"]}]},{"name":"New Teacher Research Project of Shenzhen University","award":["2019056"],"award-info":[{"award-number":["2019056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.<\/jats:p>","DOI":"10.3390\/ijgi9120714","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T20:06:09Z","timestamp":1606853169000},"page":"714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints"],"prefix":"10.3390","volume":"9","author":[{"given":"Yankun","family":"Wang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"},{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renzhong","family":"Guo","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixi","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjun","family":"Tang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Big Data System Computing Technology & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University& Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqun","family":"Xiu","sequence":"additional","affiliation":[{"name":"Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xia, S., Liu, Y., Yuan, G., Zhu, M., and Wang, Z. (2017). Indoor Fingerprint Positioning Based on Wi-Fi: An Overview. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050135"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Mainetti, L., Patrono, L., and Sergi, I. (2014, January 17\u201319). A survey on indoor positioning systems. Proceedings of the 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia.","DOI":"10.1109\/SOFTCOM.2014.7039067"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1007\/s11277-019-06696-1","article-title":"Indoor Positioning Algorithm Fusing Multi-Source Information","volume":"109","author":"Tang","year":"2019","journal-title":"Wireless Pers. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"122428","DOI":"10.1109\/ACCESS.2019.2937464","article-title":"A Novel Clustering Algorithm for Wi-Fi Indoor Positioning","volume":"7","author":"Ren","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leca, C.L., Nicolaescu, I., and Ciotirnae, P. (2020). Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning. Sensors, 20.","DOI":"10.3390\/s20020427"},{"key":"ref_7","first-page":"25","article-title":"Evolution of indoor positioning technologies: A survey","volume":"4","author":"Brena","year":"2017","journal-title":"J. Sens."},{"key":"ref_8","unstructured":"Zafari, F., Gkelias, A., and Leung, K.K. (2017). A Survey of Indoor Localization Systems and Technologies. arXiv."},{"key":"ref_9","unstructured":"Ge, X., and Qu, Z. (2016, January 26\u201328). Optimization WI-FI indoor positioning KNN algorithm location-based fingerprint. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7143","DOI":"10.1109\/JSEN.2017.2749762","article-title":"Improved Wi-Fi indoor positioning based on particle swarm optimization","volume":"17","author":"Chen","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/JSYST.2018.2823358","article-title":"Indoor Localization with a Single Wi-Fi Access Point Based on OFDM-MIMO","volume":"13","author":"Han","year":"2019","journal-title":"IEEE Syst. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/JSYST.2016.2525814","article-title":"A map-assisted Wi-Fi AP placement algorithm enabling mobile device\u2019s indoor positioning","volume":"11","author":"Du","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31738","DOI":"10.1109\/ACCESS.2019.2902564","article-title":"Improving indoor fingerprinting positioning with affinity propagation clustering and weighted centroid fingerprint","volume":"7","author":"Subedi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6909","DOI":"10.1109\/JIOT.2019.2912808","article-title":"An online radio map update scheme for Wi-Fi fingerprint-based localization","volume":"6","author":"Huang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/LCOMM.2016.2608351","article-title":"A profile-matching method for wireless positioning","volume":"20","author":"Li","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, M., Chen, R., Li, D., Chen, Y., Guo, G., Cao, Z., and Pan, Y. (2017). Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach. Sensors, 17.","DOI":"10.3390\/s17122847"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24595","DOI":"10.3390\/s150924595","article-title":"Integrated WiFi\/PDR\/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_18","unstructured":"Altintas, B., and Serif, T. (2011, January 27\u201329). Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering. Proceedings of the 17th European Wireless 2011-Sustainable Wireless Technologies, Vienna, Austria."},{"key":"ref_19","first-page":"1","article-title":"Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering","volume":"2015","author":"Hu","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_20","unstructured":"Lee, C.W., Lin, T.N., Fang, S.H., and Chou, Y.C. (2013, January 8\u201311). A novel clustering-based approach of indoor location fingerprinting. Proceedings of the IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5311","DOI":"10.3390\/s150305311","article-title":"Fast Fingerprint Database Maintenance for Indoor Positioning Based on UGV SLAM","volume":"15","author":"Tang","year":"2015","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, W., Wang, W., Li, Q., Chang, Q., and Hou, H. (2016). A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI. Sensors, 16.","DOI":"10.3390\/s16030410"},{"key":"ref_23","first-page":"1","article-title":"Received signal strength-based localization for large space indoor environments","volume":"13","author":"Wang","year":"2017","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"130791","DOI":"10.1109\/ACCESS.2019.2940629","article-title":"A Sparse Manifold Learning Approach to Robust Indoor Positioning Based on Wi-Fi RSS Fingerprinting","volume":"7","author":"Shen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/LCOMM.2012.020212.111992","article-title":"Voronoi Tessellation Based Interpolation Method for Wi-Fi Radio Map Construction","volume":"16","author":"Lee","year":"2012","journal-title":"IEEE Comm. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3351","DOI":"10.1109\/JSEN.2018.2789431","article-title":"Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons","volume":"1","author":"Zuo","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., and Pang, Y. (2018). Indoor Positioning Algorithm Based on the Improved RSSI Distance Model. Sensors, 18.","DOI":"10.3390\/s18092820"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21377","DOI":"10.3390\/s150921377","article-title":"Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System","volume":"15","author":"Jan","year":"2015","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104462","DOI":"10.1109\/ACCESS.2019.2932024","article-title":"New Reconstructed Database for Cost Reduction in Indoor Fingerprinting Localization","volume":"7","author":"Moghtadaiee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/TVT.2015.2397598","article-title":"Distance-based interpolation and extrapolation methods for RSS-based localization with indoor wireless signals","volume":"64","author":"Talvitie","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bi, J.X., Wang, Y.J., Li, Z.K., Xu, S.L., Zhou, J.P., Sun, M., and Si, M.H. (2019). Fast Radio Map Construction by using Adaptive Path Loss Model Interpolation in Large-Scale Building. Sensors, 19.","DOI":"10.3390\/s19030712"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kram, S., Nickel, C., Seitz, J., Patino-Studencka, L., and Thielecke, J. (2017, January 10\u201312). Spatial interpolation of Wi-Fi RSS fingerprints using model-based universal kriging. Proceedings of the 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany.","DOI":"10.1109\/SDF.2017.8126382"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10283","DOI":"10.1109\/JSEN.2020.2989411","article-title":"Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map","volume":"20","author":"Dinh","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Brennan, J., and Martin, E. (2002). Foundations for a formalism of nearness. Australian Joint Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/3-540-36187-1_7"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Fan, H., and Chen, R. (2017). Indoors Locality Positioning Using Cognitive Distances and Directions. Sensors, 17.","DOI":"10.3390\/s17122828"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.compenvurbsys.2006.02.003","article-title":"Location-based services and GIS in perspective","volume":"30","author":"Jiang","year":"2006","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1080\/13658810802247114","article-title":"Positioning localities based on spatial assertions","volume":"23","author":"Liu","year":"2009","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.jvlc.2012.04.002","article-title":"Probability issues in locality descriptions based on Voronoi neighbor relationship","volume":"23","author":"Gong","year":"2012","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.cageo.2011.09.003","article-title":"A vector-based algorithm to generate and update multiplicatively weighted Voronoi diagrams for points, polylines, and polygons","volume":"42","author":"Gong","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, Y., Fan, H., Chen, R., Li, H., Wang, L., Zhao, K., and Du, W. (2018). Positioning Locality Using Cognitive Directions Based on Indoor Landmark Reference System. Sensors, 18.","DOI":"10.3390\/s18041049"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"34794","DOI":"10.1109\/ACCESS.2019.2958939","article-title":"Indoors Positioning Based on Spatial Relationships in Locality Description","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/714\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:40:20Z","timestamp":1760179220000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["ijgi9120714"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9120714","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,12,1]]}}}