{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:13:17Z","timestamp":1791479597177,"version":"4.3.4"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009543","name":"Pontificia Universidad Javeriana","doi-asserted-by":"publisher","award":["9426"],"award-info":[{"award-number":["9426"]}],"id":[{"id":"10.13039\/501100009543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009543","name":"Pontificia Universidad Javeriana","doi-asserted-by":"publisher","award":["CUP H24I17000070001"],"award-info":[{"award-number":["CUP H24I17000070001"]}],"id":[{"id":"10.13039\/501100009543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Italian MIUR, PRIN 2017 Project \u201cFluidware\u201d","award":["9426"],"award-info":[{"award-number":["9426"]}]},{"name":"Italian MIUR, PRIN 2017 Project \u201cFluidware\u201d","award":["CUP H24I17000070001"],"award-info":[{"award-number":["CUP H24I17000070001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Positioning systems have gained paramount importance for many different productive sector; however, traditional systems such as Global Positioning System (GPS) have failed to offer accurate and scalable solutions for indoor positioning requirements. Nowadays, alternative solutions such as fingerprinting allow the recognition of the characteristic signature of a location based on RF signal acquisition. In this work, a machine learning (ML) approach has been considered in order to classify the RSSI information acquired by multiple scanning stations from TAG broadcasting messages. TinyML has been considered for this project, as it is a rapidly growing technological paradigm that aims to assist the design and implementation of ML mechanisms in resource-constrained embedded devices. Hence, this paper presents the design, implementation, and deployment of embedded devices capable of communicating and sending information to a central system that determines the location of objects in a defined environment. A neural network (deep learning) is trained and deployed on the edge, allowing the multiple external error factors that affect the accuracy of traditional position estimation algorithms to be considered. Edge Impulse is selected as the main platform for data standardization, pre-processing, model training, evaluation, and deployment. The final deployed system is capable of classifying real data from the installed TAGs, achieving a classification accuracy of 88%, which can be increased to 94% when a post-processing stage is implemented.<\/jats:p>","DOI":"10.3390\/s23031542","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T01:36:59Z","timestamp":1675215419000},"page":"1542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A TinyML Deep Learning Approach for Indoor Tracking of Assets"],"prefix":"10.3390","volume":"23","author":[{"given":"Diego","family":"Avellaneda","sequence":"first","affiliation":[{"name":"School of Engineering, Electronics Engineering Department, Pontificia Universidad Javeriana, Bogot\u00e1 110231, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9866-4416","authenticated-orcid":false,"given":"Diego","family":"Mendez","sequence":"additional","affiliation":[{"name":"School of Engineering, Electronics Engineering Department, Pontificia Universidad Javeriana, Bogot\u00e1 110231, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-891X","authenticated-orcid":false,"given":"Giancarlo","family":"Fortino","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kohne, M., and Sieck, J. (2014, January 18\u201320). Location-Based Services with iBeacon Technology. Proceedings of the 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation, Madrid, Spain.","DOI":"10.1109\/AIMS.2014.58"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1109\/TIFS.2022.3214074","article-title":"Eddystone-EID: Secure and Private Infrastructural Protocol for BLE Beacons","volume":"17","author":"David","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sesyuk, A., Ioannou, S., and Raspopoulos, M. (2022). A Survey of 3D Indoor Localization Systems and Technologies. Sensors, 22.","DOI":"10.3390\/s22239380"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110698","DOI":"10.1109\/ACCESS.2019.2933921","article-title":"A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.future.2020.03.043","article-title":"An effective random statistical method for Indoor Positioning System using WiFi fingerprinting","volume":"109","author":"Ninh","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Shum, L.C., Faieghi, R., Borsook, T., Faruk, T., Kassam, S., Nabavi, H., Spasojevic, S., Tung, J., Khan, S.S., and Iaboni, A. (2022). Indoor Location Data for Tracking Human Behaviours: A Scoping Review. Sensors, 22.","DOI":"10.3390\/s22031220"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TSMC.2020.3042898","article-title":"Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools","volume":"51","author":"Fortino","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, M., Zhao, L., Tan, D., and Tong, X. (2019). BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching. Sensors, 19.","DOI":"10.3390\/s19224859"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ter\u00e1n, M., Aranda, J., Carrillo, H., Mendez, D., and Parra, C. (2017, January 16\u201318). IoT-based system for indoor location using bluetooth low energy. Proceedings of the 2017 IEEE Colombian Conference on Communications and Computing (COLCOM), Cartagena, Colombia.","DOI":"10.1109\/ColComCon.2017.8088211"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.apm.2018.06.031","article-title":"Indoor positioning system based on BLE location fingerprinting with classification approach","volume":"62","author":"Pu","year":"2018","journal-title":"Appl. Math. Model."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Savaglio, C., and Fortino, G. (2021). A Simulation-Driven Methodology for IoT Data Mining Based on Edge Computing. ACM Trans. Internet Technol., 21.","DOI":"10.1145\/3402444"},{"key":"ref_13","unstructured":"Springs, C., Mendelson, E., Springs, C., and Data, R.U.S.A. (2023, January 05). Indoor and Outdoor Navigation and Local Base Services Application Utilizing RF Bluetooth Beacons. Available online: https:\/\/patents.google.com\/patent\/US9204251B1\/en."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Zhang, X., Zhou, P., and Wang, S. (2019, January 28\u201330). Research on Indoor Position Fingerprint Location Based on Machine Learning combined Particle Filter. Proceedings of the 2019 2nd International Conference on Safety Produce Informatization (IICSPI), Chongqing, China.","DOI":"10.1109\/IICSPI48186.2019.9095921"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kudoh, E., and Karino, K. (2019, January 2\u20135). Location Estimation Applying Machine Learning Using Multiple Items of Sensed Information in Indoor Environments. Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia.","DOI":"10.1109\/ICUFN.2019.8806108"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, Z. (2021, January 3\u20135). Research on Radio Frequency Finerprint Licalization based on Machine Learning. Proceedings of the 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China.","DOI":"10.1109\/MLBDBI54094.2021.00008"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1109\/TASE.2016.2543242","article-title":"Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings","volume":"13","author":"Lin","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_18","unstructured":"Estimote Inc (2023, January 02). Estimote Products. Available online: https:\/\/estimote.com\/products\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ji, M., Kim, J., Jeon, J., and Cho, Y. (2015, January 1\u20133). Analysis of positioning accuracy corresponding to the number of BLE beacons in indoor positioning system. Proceedings of the 2015 17th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Republic of Korea.","DOI":"10.1109\/ICACT.2015.7224764"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Phutcharoen, K., Chamchoy, M., and Supanakoon, P. (2020, January 11\u201314). Accuracy Study of Indoor Positioning with Bluetooth Low Energy Beacons. Proceedings of the 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pattaya, Thailand.","DOI":"10.1109\/ECTIDAMTNCON48261.2020.9090691"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Altaf Khattak, S.B., Nasralla, M.M., Esmail, M.A., Mostafa, H., and Jia, M. (2022). WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach. Sensors, 22.","DOI":"10.3390\/s22145236"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117095","DOI":"10.1016\/j.eswa.2022.117095","article-title":"Performance analysis of fingerprinting indoor positioning methods with BLE","volume":"202","author":"Aranda","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ng, P.C., Spachos, P., She, J., and Plataniotis, K. (2022). A Kernel Method to Nonlinear Location Estimation with RSS-based Fingerprint. IEEE Trans. Mob. Comput.","DOI":"10.1109\/TMC.2022.3162612"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Avellenada, D., Mendez, D., and Fortino, G. (2022, January 12\u201315). BLE-based Indoor Positioning Platform Utilizing Edge Tiny Machine Learning. Proceedings of the 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), Falerna, Italy.","DOI":"10.1109\/DASC\/PiCom\/CBDCom\/Cy55231.2022.9927866"},{"key":"ref_25","unstructured":"Diego Avellaneda (2023, January 02). Scanio Raspberry Pi and ESP32. Available online: https:\/\/github.com\/diegoavellanedat17\/embedded-systems-3."},{"key":"ref_26","unstructured":"Team, Global Tag (2023, January 02). BLUETOOTH LOW ENERGY (BLE) OVERVIEW. Available online: https:\/\/www.global-tag.com\/bluetooth-low-energy-ble-overview\/."},{"key":"ref_27","unstructured":"Texas Instruments (2023, January 02). SWRA475A Application Report-Bluetooth Low Energy Beacons. Available online: https:\/\/www.ti.com\/lit\/an\/swra475a\/swra475a.pdf?ts=1672676696367."},{"key":"ref_28","unstructured":"Sihai Corporation (2023, January 05). HM Bluetooth Module Datasheet. Technical Report. Available online: https:\/\/www.rhydolabz.com\/documents\/37\/datasheet%20HM-10.pdf."},{"key":"ref_29","unstructured":"Zach Shelby (2023, January 02). Edge Impulse Advanced ML Online. Available online: https:\/\/www.edgeimpulse.com\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.comnet.2015.12.015","article-title":"Sample Size Determination Algorithm for fingerprint-based indoor localization systems","volume":"101","author":"Kanaris","year":"2016","journal-title":"Comput. Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wang, P., Chen, H., and Zhang, L. (2019). Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression. Sensors, 19.","DOI":"10.3390\/s19112508"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:22Z","timestamp":1760120362000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031542"],"URL":"https:\/\/doi.org\/10.3390\/s23031542","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,31]]}}}