{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:53:50Z","timestamp":1774896830807,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201110"],"award-info":[{"award-number":["62201110"]}]},{"name":"National Natural Science Foundation of China","award":["CSTB2022NSCQ-MSX1385"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1385"]}]},{"name":"National Natural Science Foundation of China","award":["CSTB2022NSCQ-MSX0895"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0895"]}]},{"name":"National Natural Science Foundation of China","award":["KJQN202200648"],"award-info":[{"award-number":["KJQN202200648"]}]},{"name":"Chongqing Natural Science Foundation Project","award":["62201110"],"award-info":[{"award-number":["62201110"]}]},{"name":"Chongqing Natural Science Foundation Project","award":["CSTB2022NSCQ-MSX1385"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1385"]}]},{"name":"Chongqing Natural Science Foundation Project","award":["CSTB2022NSCQ-MSX0895"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0895"]}]},{"name":"Chongqing Natural Science Foundation Project","award":["KJQN202200648"],"award-info":[{"award-number":["KJQN202200648"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["62201110"],"award-info":[{"award-number":["62201110"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["CSTB2022NSCQ-MSX1385"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1385"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["CSTB2022NSCQ-MSX0895"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0895"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202200648"],"award-info":[{"award-number":["KJQN202200648"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE\u2019s) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique\u2019s accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path\u2013Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path\u2013Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.<\/jats:p>","DOI":"10.3390\/s22239054","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T03:48:12Z","timestamp":1669175292000},"page":"9054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5007-8517","authenticated-orcid":false,"given":"Wilford","family":"Arigye","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China"}]},{"given":"Qiaolin","family":"Pu","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China"}]},{"given":"Mu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China"}]},{"given":"Waqas","family":"Khalid","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China"}]},{"given":"Muhammad Junaid","family":"Tahir","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MCOM.2002.1024422","article-title":"A Survey on Sensor Networks","volume":"40","author":"Akyildiz","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_2","unstructured":"Popov, L. (2008). I Nav: A Hybrid Approach to WiFi Localization and Tracking of Mobile Devices. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_3","unstructured":"Pace Frost, G., Lachow, I., Frelinger, D., Fossum, D., Wassem, D., and Pinto, M. (1995). The global positioning system. GPS History, Chronology, and Budgets, RAND Corporation. Chapter 1."},{"key":"ref_4","unstructured":"Dana, P. (1999). Global Positioning System Overview, Department of Geography University of Colorado."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/s140100927","article-title":"Precise point positioning with the beidou navigation satellite system","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_6","unstructured":"Polischuk, G.M., Kozlov, V.I., Ilitchov, V.V., Kozlov, A.G., Bartenev, V.A., Kossenko, V.E., Anphimov, N.A., Revnivykh, S.G., Pisarev, S.B., and Tyulyakov, A.E. (2002, January 3\u20135). The Global Navigation System Glonass: Development and Usage in the 21st Century. Proceedings of the 34th Annual Precise Time and Time Interval (PTTI) Meeting, Reston, Virginia."},{"key":"ref_7","unstructured":"Kaplan, E., and Hegarty, C. (2005). CH 2005 Understanding GPS: Principles and Applications, Artech House."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/TMC.2011.216","article-title":"Received-signal-strength-based indoor positioning using compressive sensing","volume":"11","author":"Feng","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.4236\/cn.2013.52B007","article-title":"RSSI-based Algorithm for Indoor Localization","volume":"5","author":"Zhu","year":"2013","journal-title":"Commun. Netw. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, T., Xia, H., Liu, S., and Qiao, Y. (2019). Probability-based indoor positioning algorithm using ibeacons. Sensors, 19.","DOI":"10.3390\/s19235226"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10092","DOI":"10.1109\/ACCESS.2018.2798918","article-title":"A Multimodal fingerprint-based indoor positiong system for Airports","volume":"6","author":"Molina","year":"2018","journal-title":"IEEE Access."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MSP.2014.2332611","article-title":"Location-Aware Communications for 5G networks: How location information can improve scalability, latency, and robustness of 5G","volume":"31","author":"Muppirisetty","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/MCOM.2017.1600655","article-title":"High-Efficiency Device Positioning and Location-Aware Communications in Dense 5G Networks","volume":"55","author":"Koivisto","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_14","unstructured":"Pedersen, T., Fleury, B.H., COST Action C415104, and IRACON (2022, September 17). Whitepaper on New Localization Methods for 5G Wireless Systems and the Internet-of-Things. COST Action CA1510. IRACON. Available online: http:\/\/www.ircon.org\/wp-content\/uploads\/2018\/03\/IRACON-WP2.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/MCOM.2018.1701057","article-title":"Benefits of Positioning-Aided Communication Technology in High-Frequency Industrial IoT","volume":"56","author":"Lohan","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ren, A., Zhou, F., Rahman, A., Wang, X., Zhao, N., and Yang, X. (2017, January 25\u201326). A study of indoor positioning based on UWB base-station configurations. Proceedings of the 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2017.8054352"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/COMST.2016.2637663","article-title":"A survey of selected indoor positioning methods for smartphones","volume":"19","author":"Davidson","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amr, M.N., El Attar, H., El Azeem, M.A., and El Badawy, H. (2021). An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model. Sensors, 21.","DOI":"10.3390\/s21030719"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1109\/TIE.2010.2055774","article-title":"A standalone RFID indoor positioning system using passive tags","volume":"58","author":"Saab","year":"2011","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, C., Mu, Q., Zhang, Z., Jin, Y., Wang, Z., and Shi, G. (2016, January 19\u201322). Indoor positioning system based on inertial MEMS sensors: Design and realization. Proceedings of the 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Chengdu, China.","DOI":"10.1109\/CYBER.2016.7574852"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1109\/TIE.2015.2499251","article-title":"A positioning system based on low-frequency magnetic fields","volume":"63","author":"Pasku","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qi, J., and Liu, G.P. (2017). A robust high-accuracy ultrasound indoor positioning system based on a wireless sensor network. Sensors, 17.","DOI":"10.3390\/s17112554"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/TCE.2008.4637573","article-title":"Vision-based location positioning using augmented reality for indoor navigation","volume":"54","author":"Kim","year":"2008","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Raharijaona, T., Mawonou, R., Nguyen, T.V., Colonnier, F., Boyron, M., Diperi, J., and Viollet, S. (2017). Local positioning system using flickering infrared LEDs. Sensors, 17.","DOI":"10.3390\/s17112518"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tian, Y., Shigaki, D., Wang, W., and Ahn, C.J. (2017, January 17\u201320). A weighted least-squares method using received signal strength measurements for WLAN indoor positioning system. Proceedings of the 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC), Bali, Indonesia.","DOI":"10.1109\/WPMC.2017.8301829"},{"key":"ref_26","unstructured":"(2020, January 15). Available online: https:\/\/developer.android.com\/reference\/android\/net\/wifi\/wifiinfo."},{"key":"ref_27","unstructured":"(2020, January 15). Available online: https:\/\/docs.microsoft.com\/enus\/uwp\/api\/windows.devices.wifi.wifi-availablenetwork."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1109\/JSEN.2013.2257731","article-title":"DuRT: Dual RSSI trend based localization for wireless sensor networks","volume":"13","author":"Sahu","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hou, Y., YNG, X., and Abbasi, Q.H. (2018). Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices. Sensors, 18.","DOI":"10.3390\/s18113698"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Subramanian, A.P., Deshpande, P., Gao, J., and Das, S.R. (2008, January 13\u201318). Drive-by localization of roadside WiFi networks. Proceedings of the INFOCOM 2008, The 27th Conference on Computer Communications, Phoenix, Arizona.","DOI":"10.1109\/INFOCOM.2008.122"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/7.993253","article-title":"Comparisons of error characteristics between TOA and TDOA positioning","volume":"38","author":"Shin","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_32","first-page":"626","article-title":"Super-resolution TOA estimation with diversity for indoor geolocation","volume":"8","author":"Li","year":"2004","journal-title":"IEEE Trans. Wirel. Commun. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/LCOMM.2004.835319","article-title":"The Cramer-Rao bounds of hybrid TOA\/RSSI and TDOA\/RSSI location estimation schemes","volume":"8","author":"Catovic","year":"2004","journal-title":"IEEE Commun. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/LCOMM.2014.2320939","article-title":"Energy-efficient RSSI-based localization for wireless sensor networks","volume":"18","author":"Yaghoubi","year":"2014","journal-title":"IEEE Commun. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TVT.2014.2334397","article-title":"RSSI-based localization in wireless sensor networks using convex relaxation: Noncooperative and cooperative schemes","volume":"64","author":"Tomic","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shi, Y., Shi, W., Liu, X., and Xiao, X. (2020). An RSSI classification and tracing algorithm to improve trilateration-based positioning. Sensors, 20.","DOI":"10.3390\/s20154244"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bullmann, M., Fetzer, T., Ebner, F., Ebner, M., Deinzer, F., and Grzegorzek, M. (2020). Comparison of 2.4 GHz WiFi FTM and RSSI-based indoor positioning methods in realistic scenarios. Sensors, 20.","DOI":"10.3390\/s20164515"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shum KC, Y., Cheng, Q.J., Ng JK, Y., and Ng, D. (2011, January 22\u201325). A signal strength based location estimation algorithm within a wireless network. Proceedings of the 2011 IEEE International Conference on Advanced Information Networking and Applications, Biopolis, Singapore.","DOI":"10.1109\/AINA.2011.80"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s11277-012-0682-7","article-title":"Performance analysis of received signal strength fingerprinting based distributed location estimation system for indoor wlan","volume":"70","author":"Shukla","year":"2013","journal-title":"Wirel. Pers. Commun."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"116753","DOI":"10.1109\/ACCESS.2019.2935192","article-title":"Wireless Communications Through Reconfigurable Intelligent Surfaces","volume":"7","author":"Basar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wu, C., and Liu, Y. (2012, January 22\u201326). Locating in fingerprint space: Wireless indoor localization with little human intervation. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, Istanbul, Turkey.","DOI":"10.1145\/2348543.2348578"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MPRV.2014.24","article-title":"Building a practical Wi-Fi-based indoor navigation system","volume":"13","author":"Han","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Youssef, M., and Agrawala, A. (2005, January 6\u20138). The Horus WLAN location determination system. Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, Seattle, WA, USA.","DOI":"10.1145\/1067170.1067193"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, L., Shen, G., Zhao, C., Moscibroda, T., Lin, H.J., and Zhao, F. (2014, January 11). Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service. Proceedings of the 2014 ACM MobiCom, Maui, HI, USA.","DOI":"10.1145\/2639108.2639118"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alraih, S., Alhammadi, A., Shayea, I., and Al-Samman, A.M. (2017, January 18\u201320). Improving Accuracy in Indoor Localization System Using Fingerprining Technique. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC.2017.8190985"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13673-019-0168-7","article-title":"Random forest and WiFi fingerprint-based indoor location recognition system using smart watch","volume":"9","author":"Lee","year":"2019","journal-title":"Hum. Cent. Comput. Inform. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.future.2018.06.030","article-title":"Smartphone based intelligent indoor positioning using fuzzy logic","volume":"89","author":"Orujov","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_48","unstructured":"Chen, Y., and Kobayashi, H. (May, January 8). Signal strength based indoor geolocation. Proceedings of the 2002 IEEE International Conference on Communications, New York, NY, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Krishnan, P., Krishnakumar, A., Ju, W.H., Mallows, C., and Gamt, S. (2004). A system for LEASE: Location estimation assisted by stationary emitters for indoor RF wireless networks. INFOCOM 2004, Proceedings of the Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, Hong Kong, China, 7\u201311 March 2004, IEEE.","DOI":"10.1109\/INFCOM.2004.1356987"},{"key":"ref_50","unstructured":"Battiti, R., Le, N.T., and Villani, A. (2002). Location-Aware Computing: A Neural Network Model for Determining Location in Wireless LANs, University of Trento."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"012116","DOI":"10.1088\/1757-899X\/881\/1\/012116","article-title":"Investigating the Access Point height for an indoor IOT Servies","volume":"881","author":"Burhan","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_52","first-page":"775","article-title":"RADAR: An in-building RF-based user location and tracking system","volume":"Volume 2","author":"Bahl","year":"2000","journal-title":"Proceedings of the 19th Joint Conference of the IEEE Computer and Communications Societies"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/35.983917","article-title":"Indoor geolocation science and technology","volume":"40","author":"Pahlavan","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s12243-009-0122-1","article-title":"Indoor Wi-Fi positioning: Techniques and systems","volume":"64","author":"Lassabe","year":"2009","journal-title":"Ann. Telecommun."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.1109\/TWC.2008.070373","article-title":"A novel algorithm for multipath fingerprinting in indoor wlan environments","volume":"7","author":"Fang","year":"2008","journal-title":"IEEE Trans Wirel."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TSMCC.2007.905750","article-title":"Survey of wireless indoor positioning techniques and systems","volume":"37","author":"Liu","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"ref_57","unstructured":"Kirshnakumar, A., and Krishnan, P. (2005, January 19\u201322). The theory and practice of signal strength based location estimation. Proceedings of the 2005 International Conference on Callaborative Computing: Networking, Applications and Worksharing, San Jose, CA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1550147719884893","DOI":"10.1177\/1550147719884893","article-title":"A three-dimensional pattern recognition localization system based on a Bayesian graphical model","volume":"16","author":"Alhammadi","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_59","unstructured":"Madigan, D., Einahrawy, E., Martin, R.P., Ju, W.H., Krishnan, P., and Krishnakumar, A.S. (2005, January 13\u201317). Bayesian indoor positioning systems. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Miami, FL, USA."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kubota, R., Tagashira, S., Arakawa, Y., Kitasuka, T., and Fukuda, A. (2013, January 25\u201328). Efficient Survey Database Construction Using Location Fingerprinting Interpolation. Proceedings of the IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), Barcelona, Spain.","DOI":"10.1109\/AINA.2013.53"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Widyawan Klepal, M. (2007, January 22\u201322). Influence of predicted and measured fingerprint no the accuracy of RSSI-based indoor location system. Proceedings of the 2007 4th Workshop on Positioning, Navigation and Communication, Hannover, Germany.","DOI":"10.1109\/WPNC.2007.353626"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Borelli, A., and Monti, C. (2004, January 20\u201324). Channel models for ieee 802.11b indoor system design. Proceedings of the 2004 IEEE International Conference on Communications, Paris, France.","DOI":"10.1109\/ICC.2004.1313233"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/25.704854","article-title":"A new empirical model for indoor propagation prediction","volume":"47","author":"Cheung","year":"1998","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1016003126882","article-title":"A probabilistic approach to WLAN user location estimation","volume":"9","author":"Roos","year":"2002","journal-title":"Int. J. Wirel. Inf. Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9054\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:22Z","timestamp":1760145862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9054"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,22]]},"references-count":64,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239054"],"URL":"https:\/\/doi.org\/10.3390\/s22239054","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,22]]}}}