{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:28:45Z","timestamp":1777487325881,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy\u2013complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.<\/jats:p>","DOI":"10.3390\/s21134605","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T22:02:04Z","timestamp":1625522524000},"page":"4605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7084-6210","authenticated-orcid":false,"given":"Ladislav","family":"Polak","sequence":"first","affiliation":[{"name":"Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082\/12, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanislav","family":"Rozum","sequence":"additional","affiliation":[{"name":"Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082\/12, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5418-4843","authenticated-orcid":false,"given":"Martin","family":"Slanina","sequence":"additional","affiliation":[{"name":"Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082\/12, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5749-8389","authenticated-orcid":false,"given":"Tomas","family":"Bravenec","sequence":"additional","affiliation":[{"name":"Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082\/12, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7313-6142","authenticated-orcid":false,"given":"Tomas","family":"Fryza","sequence":"additional","affiliation":[{"name":"Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082\/12, 616 00 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7355-327X","authenticated-orcid":false,"given":"Aggelos","family":"Pikrakis","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Piraeus, 185 34 Pireas, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/COMST.2019.2911558","article-title":"A Survey of Indoor Localization Systems and Technologies","volume":"21","author":"Zafari","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al-Ammar, M.A., Alhadhrami, S., Al-Salman, A., Alarifi, A., Al-Khalifa, H.S., Alnafessah, A., and Alsaleh, M. (2014, January 6\u20138). Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms. Proceedings of the 2014 International Conference on Cyberworlds, Santander, Spain.","DOI":"10.1109\/CW.2014.41"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Stavrou, V., Bardaki, C., Papakyriakopoulos, D., and Pramatari, K. (2019). An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons. Sensors, 19.","DOI":"10.3390\/s19204550"},{"key":"ref_4","first-page":"1","article-title":"A survey on wireless indoor localization from the device perspective","volume":"49","author":"Xiao","year":"2016","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/COMST.2016.2632427","article-title":"Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications","volume":"19","author":"Yassin","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Baert, M., Rossey, J., Shahid, A., and Hoebeke, J. (2018). The Bluetooth mesh standard: An overview and experimental evaluation. Sensors, 18.","DOI":"10.3390\/s18082409"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.adhoc.2019.102015","article-title":"Beyond beaconing: Emerging applications and challenges of BLE","volume":"97","author":"Yang","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_8","first-page":"1","article-title":"Improving indoor localization using Bluetooth low energy beacons","volume":"2016","author":"Kriz","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Neburka, J., Tlamsa, Z., Benes, V., Polak, L., Kaller, O., Bolecek, L., Sebesta, J., and Kratochvil, T. (2016, January 19\u201320). Study of the performance of RSSI based Bluetooth Smart indoor positioning. Proceedings of the 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA), Kosice, Slovakia.","DOI":"10.1109\/RADIOELEK.2016.7477344"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pelant, J., Tlamsa, Z., Benes, V., Polak, L., Kaller, O., Bolecek, L., Kufa, J., Sebesta, J., and Kratochvil, T. (2017, January 19\u201320). BLE device indoor localization based on RSS fingerprinting mapped by propagation modes. Proceedings of the 2017 27th International Conference Radioelektronika (RADIOELEKTRONIKA), Brno, Czech Republic.","DOI":"10.1109\/RADIOELEK.2017.7937584"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rozum, S., and Sebesta, J. (2018, January 19\u201320). SIMO RSS measurement in Bluetooth low power indoor positioning system. Proceedings of the 2018 28th International Conference Radioelektronika, Prague, Czech Republic.","DOI":"10.1109\/RADIOELEK.2018.8376391"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rozum, S., Kufa, J., and Polak, L. (2019, January 1\u20133). Bluetooth Low Power Portable Indoor Positioning System Using SIMO Approach. Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary.","DOI":"10.1109\/TSP.2019.8769114"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Giuliano, R., Cardarilli, G.C., Cesarini, C., Di Nunzio, L., Fallucchi, F., Fazzolari, R., Mazzenga, F., Re, M., and Vizzarri, A. (2020). Indoor localization system based on Bluetooth low energy for museum applications. Electronics, 9.","DOI":"10.3390\/electronics9061055"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Atashi, M., Malekzadeh, P., Salimibeni, M., Hajiakhondi-Meybodi, Z., Plataniotis, K.N., and Mohammadi, A. (2021, January 18\u201321). Orientation-matched multiple modeling for RSSI-based indoor localization via BLE sensors. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287489"},{"key":"ref_16","unstructured":"Ng, P.C., She, J., and Rong, R. (2020). Compressive RF Fingerprint Acquisition and Broadcasting for Dense BLE Networks. IEEE Trans. Mob. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Heyn, R., Kuhn, M., Schulten, H., Dumphart, G., Zwyssig, J., Trosch, F., and Wittneben, A. (May, January 28). User Tracking for Access Control with Bluetooth Low Energy. Proceedings of the 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia.","DOI":"10.1109\/VTCSpring.2019.8746465"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wisanmongkol, J., Klinkusoom, L., Sanpechuda, T., Kovavisaruch, L., and Kaemarungsi, K. (2019, January 25\u201327). Multipath Mitigation for RSSI-Based Bluetooth Low Energy Localization. Proceedings of the 2019 19th International Symposium on Communications and Information Technologies (ISCIT), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ISCIT.2019.8905164"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2021.107237","article-title":"BERT-ADLOC: A secure crowdsourced indoor localization system based on BLE fingerprints","volume":"104","author":"Sun","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abed, A., and Abdel-Qader, I. (2019). RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems. Appl. Sci., 9.","DOI":"10.3390\/app9153137"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sthapit, P., Gang, H.S., and Pyun, J.Y. (2018, January 24\u201326). Bluetooth Based Indoor Positioning Using Machine Learning Algorithms. Proceedings of the 2018 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), JeJu, Korea.","DOI":"10.1109\/ICCE-ASIA.2018.8552138"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duong, N.S., and Dinh, T.M. (2019, January 25\u201327). Indoor Localization with lightweight RSS Fingerprint using BLE iBeacon on iOS platform. Proceedings of the 2019 19th International Symposium on Communications and Information Technologies (ISCIT), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ISCIT.2019.8905160"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kajioka, S., Mori, T., Uchiya, T., Takumi, I., and Matsuo, H. (2014, January 7\u201310). Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon. Proceedings of the 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE), Tokyo, Japan.","DOI":"10.1109\/GCCE.2014.7031308"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Campa\u00f1a, F., Pinargote, A., Dom\u00ednguez, F., and Pel\u00e1ez, E. (2017, January 16\u201320). Towards an indoor navigation system using Bluetooth Low Energy Beacons. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.","DOI":"10.1109\/ETCM.2017.8247464"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iqbal, Z., Luo, D., Henry, P., Kazemifar, S., Rozario, T., Yan, Y., Westover, K., Lu, W., Nguyen, D., and Long, T. (2018). Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205392"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lovon-Melgarejo, J., Castillo-Cara, M., Orozco-Barbosa, L., and Garc\u00eda-Varea, I. (2017, January 8\u201310). Supervised learning algorithms for indoor localization fingerprinting using BLE4.0 beacons. Proceedings of the 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Arequipa, Peru.","DOI":"10.1109\/LA-CCI.2017.8285716"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.adhoc.2018.09.017","article-title":"GRNN and KF framework based real time target tracking using PSOC BLE and smartphone","volume":"84","author":"Jondhale","year":"2019","journal-title":"Ad Hoc Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Takayama, T., Umezawa, T., Komuro, N., and Osawa, N. (2018, January 22\u201323). An Indoor Positioning Method Based on Regression Models with Compound Location Fingerprints. Proceedings of the 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Wuhan, China.","DOI":"10.1109\/UPINLBS.2018.8559728"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8713","DOI":"10.1109\/ACCESS.2019.2890850","article-title":"Indoor Location Learning Over Wireless Fingerprinting System with Particle Markov Chain Model","volume":"7","author":"Sou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., Yang, J., Li, Y., Qi, L., and El-Sheimy, N. (2016). Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons. Sensors, 16.","DOI":"10.3390\/s16050596"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Theodoridis, S., Pikrakis, A., Koutroumbas, K., and Cavouras, D. (2010). Introduction to Pattern Recognition: A MATLAB Approach, Academic Press.","DOI":"10.1016\/B978-1-59749-272-0.50003-7"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Duong, S.N., Trinh, A.V.T., and Dinh, T.M. (2018, January 12\u201314). Bluetooth Low Energy Based Indoor Positioning on iOS Platform. Proceedings of the 2018 IEEE 12th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC), Hanoi, Vietnam.","DOI":"10.1109\/MCSoC2018.2018.00021"},{"key":"ref_33","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 Cybern. Part C Appl. Rev."},{"key":"ref_34","unstructured":"Telegies (2010). ETRX35x ZigBee Modules, Telegies. TG-ETRX35X-PM-010-103."},{"key":"ref_35","unstructured":"Laird Connectivity (2016). BL652 Series Bluetooth v5, Laird Connectivity. BL652-SA and BL652-SC, Version 2.2."},{"key":"ref_36","unstructured":"STMicroelectronics (2015). ARM-Based Microcontroller, STMicroelectronics. STM32F091xB and STM32F091x; STM32F091xB and STM32F091x; Rev. 3."},{"key":"ref_37","unstructured":"PulseLarsen Electronics (2007). Wireless External Antenna for 2.4 GHz Applications, Pulse Electronics. W1030; Version 1.1."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/10485252.2017.1404598","article-title":"Multiple predicting K-fold cross-validation for model selection","volume":"30","author":"Jung","year":"2017","journal-title":"J. Nonparametr. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fong-Mata, M.B., Garc\u00eda-Guerrero, E.E., Mej\u00eda-Medina, D.A., L\u00f3pez-Bonilla, O.R., Villarreal-G\u00f3mez, L.J., Zamora-Arellano, F., L\u00f3pez-Mancilla, D., and Inzunza-Gonz\u00e1lez, E. (2020). An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells\u2019 Criteria. Electronics, 9.","DOI":"10.3390\/electronics9111810"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TMC.2017.2703892","article-title":"Improve accuracy of fingerprinting localization with temporal correlation of the RSS","volume":"17","author":"Tian","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"10208","DOI":"10.1109\/JSEN.2018.2874453","article-title":"A soft range limited K-nearest neighbors algorithm for indoor localization enhancement","volume":"18","author":"Hoang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_42","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:14Z","timestamp":1760163974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134605"],"URL":"https:\/\/doi.org\/10.3390\/s21134605","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,5]]}}}