{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T06:32:05Z","timestamp":1787898725220,"version":"build-2784847793"},"reference-count":99,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Programme Erasmus+, Knowledge Alliances","award":["621639-EPP-1-2020-1-IT-EPPKA2-KA"],"award-info":[{"award-number":["621639-EPP-1-2020-1-IT-EPPKA2-KA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.<\/jats:p>","DOI":"10.3390\/s22134925","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T22:43:28Z","timestamp":1656542608000},"page":"4925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques"],"prefix":"10.3390","volume":"22","author":[{"given":"Daniele","family":"Atzeni","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Bacciu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele","family":"Mazzei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5646-7388","authenticated-orcid":false,"given":"Giuseppe","family":"Prencipe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","unstructured":"Tzeng, C.L. (2020). Global Wi-Fi Enabled Devices Shipment Forecast, 2020\u20132024, Market Intelligence & Consulting Institute (MIC)."},{"key":"ref_2","unstructured":"Barnett, T., Jain, S., Andra, U., and Khurana, T. (2018). Cisco visual networking index (vni) complete forecast update, 2017\u20132022. Americas\/EMEAR Cisco Knowledge Network (CKN) Presentation, EMEAR Cisco Knowledge Network (CKN)."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Varghese, A., and Tandur, D. (2014, January 27\u201329). Wireless requirements and challenges in Industry 4.0. Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysuru, India.","DOI":"10.1109\/IC3I.2014.7019732"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MWC.2006.1678163","article-title":"MIMO-OFDM wireless systems: Basics, perspectives, and challenges","volume":"13","author":"Bolcskei","year":"2006","journal-title":"IEEE Wirel. Commun."},{"key":"ref_5","first-page":"1","article-title":"WiFi sensing with channel state information: A survey","volume":"52","author":"Ma","year":"2019","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_6","first-page":"1","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_7","first-page":"1","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"Sutskever","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_8","unstructured":"Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., and Graepel, T. (2017). Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wallach, H.M. (2006, January 25\u201329). Topic modeling: Beyond bag-of-words. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143967"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.iedeen.2017.06.002","article-title":"Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis","volume":"24","author":"Amado","year":"2018","journal-title":"Eur. Res. Manag. Bus. Econ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1007\/s12559-020-09799-1","article-title":"Analyzing social robotics research with natural language processing techniques","volume":"13","author":"Mazzei","year":"2021","journal-title":"Cogn. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bellavista-Parent, V., Torres-Sospedra, J., and Perez-Navarro, A. (December, January 29). New trends in indoor positioning based on WiFi and machine learning: A systematic review. Proceedings of the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Virtual.","DOI":"10.1109\/IPIN51156.2021.9662521"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10846-021-01327-z","article-title":"A survey of machine learning techniques for indoor localization and navigation systems","volume":"101","author":"Roy","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"214945","DOI":"10.1109\/ACCESS.2020.3039271","article-title":"A survey of machine learning for indoor positioning","volume":"8","author":"Nessa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MCOM.2017.1700082","article-title":"A survey on behavior recognition using WiFi channel state information","volume":"55","author":"Yousefi","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127150","DOI":"10.1109\/ACCESS.2021.3111083","article-title":"Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview","volume":"9","author":"Singh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/coin.12441","article-title":"A systematic review on machine learning for fall detection system","volume":"37","author":"Rastogi","year":"2021","journal-title":"Comput. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1254","DOI":"10.1002\/widm.1254","article-title":"Recent trends in machine learning for human activity recognition\u2014A survey","volume":"8","author":"Roy","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13317","DOI":"10.1109\/ACCESS.2018.2812887","article-title":"Smart home based on WiFi sensing: A survey","volume":"6","author":"Jiang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, L., Wang, L., Liu, J., and Zhou, W. (2016, January 16\u201318). A survey on motion detection using WiFi signals. Proceedings of the 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Hefei, China.","DOI":"10.1109\/MSN.2016.040"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10115-018-1186-x","article-title":"Analyzing large-scale human mobility data: A survey of machine learning methods and applications","volume":"58","author":"Toch","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bithas, P.S., Michailidis, E.T., Nomikos, N., Vouyioukas, D., and Kanatas, A.G. (2019). A survey on machine-learning techniques for UAV-based communications. Sensors, 19.","DOI":"10.3390\/s19235170"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szott, S., Kosek-Szott, K., Gaw\u0142owicz, P., G\u00f3mez, J.T., Bellalta, B., Zubow, A., and Dressler, F. (2021). WiFi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning. arXiv.","DOI":"10.1109\/COMST.2022.3179242"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10776-020-00501-8","article-title":"Evolution and impact of Wi-Fi technology and applications: A historical perspective","volume":"28","author":"Pahlavan","year":"2021","journal-title":"Int. J. Wirel. Inf. Networks"},{"key":"ref_25","unstructured":"Poole, I., and Wi-Fi\/WLAN Channels, Frequencies, Bands & Bandwidths (2022, February 15). Adrio Communications Ltd. Available online: https:\/\/www.radioelectronics.com\/info\/wireless\/wi-fi\/80211-channels-number-frequencies-bandwidth.php."},{"key":"ref_26","unstructured":"(2007). IEEE Standard for Information Technology-Telecommunications and Information Exchange between Systems-Local and Metropolitan Area Networks-Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications (Standard No. IEEE Std 802.11)."},{"key":"ref_27","unstructured":"Mitchell, B. (2020). 802.11 Standards Explained: 802.11 ax, 802.11 ac, 802.11 b\/g\/n, 802.11 a, Lifewire."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Freudiger, J. (2015, January 22\u201326). How talkative is your mobile device? An experimental study of Wi-Fi probe requests. Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, New York, NY, USA.","DOI":"10.1145\/2766498.2766517"},{"key":"ref_29","unstructured":"IEEE Standards Association (2018). Guidelines for Use of Extended Unique Identifier (EUI), Organizationally Unique Identifier (OUI), and Company ID (CID), IEEE."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Vattapparamban, E., \u00c7iftler, B.S., G\u00fcven\u00e7, I., Akkaya, K., and Kadri, A. (2016, January 23\u201327). Indoor occupancy tracking in smart buildings using passive sniffing of probe requests. Proceedings of the 2016 IEEE International Conference on Communications Workshops (ICC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICCW.2016.7503761"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1109\/JSEN.2022.3160700","article-title":"DuLoc: Dual-Channel Convolutional Neural Network Based on Channel State Information for Indoor Localization","volume":"22","author":"Song","year":"2022","journal-title":"IEEE Sensors J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hao, Z., Duan, Y., Dang, X., Liu, Y., and Zhang, D. (2020). Wi-SL: Contactless fine-grained gesture recognition uses channel state information. Sensors, 20.","DOI":"10.3390\/s20144025"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6636665","DOI":"10.1155\/2021\/6636665","article-title":"A Survey of User Authentication Based on Channel State Information","volume":"2021","author":"Wang","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cunningham, P., Cord, M., and Delany, S.J. (2008). Supervised learning. Machine Learning Techniques for Multimedia, Springer.","DOI":"10.1007\/978-3-540-75171-7_2"},{"key":"ref_36","first-page":"1","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume":"31","author":"Zhang","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","unstructured":"Ghahramani, Z. (2003, January 2\u201314). Unsupervised learning. Proceedings of the Summer School on Machine Learning, Tubingen, Germany."},{"key":"ref_38","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_39","unstructured":"Pinaya, W.H.L., Vieira, S., Garcia-Dias, R., and Mechelli, A. (2020). Autoencoders. Machine Learning, Elsevier."},{"key":"ref_40","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_45","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_46","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105906","DOI":"10.1016\/j.ijsu.2021.105906","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"88","author":"Page","year":"2021","journal-title":"Int. J. Surg."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1742-5581-3-1","article-title":"Scopus database: A review","volume":"3","author":"Burnham","year":"2006","journal-title":"Biomed. Digit. Libr."},{"key":"ref_53","unstructured":"(2022, March 20). Web of Science. Available online: https:\/\/www.webofscience.com\/wos\/woscc\/basic-search."},{"key":"ref_54","unstructured":"(2022, March 20). IEEE Xplore Digital Library. Available online: https:\/\/ieeexplore.ieee.org\/Xplore\/home.jsp."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/JSAC.2012.120401","article-title":"Femtocells: Past, present, and future","volume":"30","author":"Andrews","year":"2012","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_56","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_57","unstructured":"Ferris, B., Fox, D., and Lawrence, N.D. (2007, January 6\u201312). Wifi-slam using gaussian process latent variable models. Proceedings of the IJCAI, Hyderabad, India."},{"key":"ref_58","unstructured":"Pan, S.J., Kwok, J.T., and Yang, Q. (2008, January 22\u201324). Transfer learning via dimensionality reduction. Proceedings of the AAAI, Stanford, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Dimatteo, S., Hui, P., Han, B., and Li, V.O. (2011, January 17\u201322). Cellular traffic offloading through WiFi networks. Proceedings of the 2011 IEEE 8th International Conference on Mobile ad hoc and Sensor Systems, Washington, DC, USA.","DOI":"10.1109\/MASS.2011.26"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/COMST.2015.2402161","article-title":"Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset","volume":"18","author":"Kolias","year":"2015","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhao, M., Li, T., Abu Alsheikh, M., Tian, Y., Zhao, H., Torralba, A., and Katabi, D. (2018, January 18\u201322). Through-wall human pose estimation using radio signals. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00768"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1080\/01638539809545028","article-title":"An introduction to latent semantic analysis","volume":"25","author":"Landauer","year":"1998","journal-title":"Discourse Process."},{"key":"ref_63","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","unstructured":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv."},{"key":"ref_65","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_67","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"205","DOI":"10.21105\/joss.00205","article-title":"hdbscan: Hierarchical density based clustering","volume":"2","author":"McInnes","year":"2017","journal-title":"J. Open Source Softw."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jpdc.2021.10.008","article-title":"Algorithms for addressing line-of-sight issues in mmWave WiFi networks using access point mobility","volume":"160","author":"Jian","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Seeram, S.S.S.G., Reddy, A.Y., Basil, N., Suman, A.V.S., Anuraj, K., and Poorna, S. (2022). Performance Comparison of Machine Learning Algorithms in Symbol Detection Using OFDM. Inventive Communication and Computational Technologies, Springer.","DOI":"10.1007\/978-981-16-5529-6_36"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kunarak, S., and Duangchan, T. (2021, January 23\u201325). Vertical Handover Decision based on Hybrid Artificial Neural Networks in HetNets of 5G. Proceedings of the 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Korea.","DOI":"10.1109\/TENSYMP52854.2021.9550952"},{"key":"ref_73","unstructured":"Urban, R., and Drexler, P. (2014, January 25\u201328). Intelligent Channel Assignment for WI-FI System Based on Reinforcement Learning. Proceedings of the PIERS Proceedings, Guangzhou, China."},{"key":"ref_74","first-page":"2668","article-title":"Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks-A Survey","volume":"15","author":"Huang","year":"2021","journal-title":"KSII Trans. Internet Inf. Syst. TIIS"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, H., Zhang, D., Wang, Y., and Wang, Y. (2016, January 18\u201321). A survey on wi-fi based contactless activity recognition. Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC\/ATC\/ScalCom\/CBDCom\/IoP\/SmartWorld), Toulouse, France.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0170"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Basri, C., and El Khadimi, A. (October, January 29). Survey on indoor localization system and recent advances of WIFI fingerprinting technique. Proceedings of the 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco.","DOI":"10.1109\/ICMCS.2016.7905633"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1049\/iet-com.2019.1059","article-title":"Survey on WiFi-based indoor positioning techniques","volume":"14","author":"Liu","year":"2020","journal-title":"IET Commun."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103281","DOI":"10.1016\/j.engappai.2019.103281","article-title":"Device free human gesture recognition using Wi-Fi CSI: A survey","volume":"87","author":"Ahmed","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2017.01.127","article-title":"Mobile crowd sensing of human-like intelligence using social sensors: A survey","volume":"279","author":"Xu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_80","unstructured":"Khan, U.M., Kabir, Z., and Hassan, S.A. (2017, January 26\u201330). Wireless health monitoring using passive WiFi sensing. Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Mauldin, T.R., Canby, M.E., Metsis, V., Ngu, A.H., and Rivera, C.C. (2018). SmartFall: A smartwatch-based fall detection system using deep learning. Sensors, 18.","DOI":"10.3390\/s18103363"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2018.09.003","article-title":"Mental health monitoring with multimodal sensing and machine learning: A survey","volume":"51","author":"Riegler","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge machine learning for ai-enabled iot devices: A review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/TPDS.2012.104","article-title":"Detection and localization of multiple spoofing attackers in wireless networks","volume":"24","author":"Yang","year":"2012","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.compeleceng.2015.10.010","article-title":"A client-side detection mechanism for evil twins","volume":"59","author":"Hsu","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Liu, H., and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci., 9.","DOI":"10.3390\/app9204396"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TIFS.2015.2478741","article-title":"Analyzing android encrypted network traffic to identify user actions","volume":"11","author":"Conti","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Shi, C., Liu, J., Liu, H., and Chen, Y. (2017, January 10\u201314). Smart user authentication through actuation of daily activities leveraging WiFi-enabled IoT. Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Chennai, India.","DOI":"10.1145\/3084041.3084061"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Fang, Y., Deng, Z., Xue, C., Jiao, J., Zeng, H., Zheng, R., and Lu, S. (2015, January 13\u201315). Application of an improved K nearest neighbor algorithm in WiFi indoor positioning. Proceedings of the China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III, Xi\u2019an, China.","DOI":"10.1007\/978-3-662-46632-2_45"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1109\/JIOT.2015.2495229","article-title":"A feature-scaling-based k-nearest neighbor algorithm for indoor positioning systems","volume":"3","author":"Li","year":"2015","journal-title":"IEEE Internet Things J."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.3390\/su14063312","article-title":"Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing","volume":"14","author":"Stadnicka","year":"2022","journal-title":"Sustainability"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"33256","DOI":"10.1109\/ACCESS.2019.2903487","article-title":"Deep learning-based indoor localization using received signal strength and channel state information","volume":"7","author":"Hsieh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_93","unstructured":"Aun, Y., Gan, M.L., and Khaw, Y.M.J. (August, January 30). Automatic Attendance Taking: A Proof of Concept on Privacy Concerns in 802.11 MAC Address Probing. Proceedings of the International Conference on Advances in Cyber Security, Penang, Malaysia."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"107970","DOI":"10.1016\/j.comnet.2021.107970","article-title":"IEEE 802.11 CSI randomization to preserve location privacy: An empirical evaluation in different scenarios","volume":"191","author":"Cominelli","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Gu, X., Wu, W., Gu, X., Ling, Z., Yang, M., and Song, A. (2020). Probe request based device identification attack and defense. Sensors, 20.","DOI":"10.3390\/s20164620"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Uras, M., Cossu, R., Ferrara, E., Bagdasar, O., Liotta, A., and Atzori, L. (2020, January 14\u201316). Wifi probes sniffing: An artificial intelligence based approach for mac addresses de-randomization. Proceedings of the 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Pisa, Italy.","DOI":"10.1109\/CAMAD50429.2020.9209257"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0194-3","article-title":"Detecting high indoor crowd density with Wi-Fi localization: A statistical mechanics approach","volume":"6","author":"Georgievska","year":"2019","journal-title":"J. Big Data"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"4442","DOI":"10.1109\/JIOT.2020.2972062","article-title":"Understanding crowd behaviors in a social event by passive wifi sensing and data mining","volume":"7","author":"Zhou","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Jamil, S., Khan, S., Basalamah, A., and Lbath, A. (2016, January 2\u201316). Classifying smartphone screen ON\/OFF state based on wifi probe patterns. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2971377"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4925\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:45Z","timestamp":1760139645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":99,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134925"],"URL":"https:\/\/doi.org\/10.3390\/s22134925","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}