{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:47:39Z","timestamp":1767772059914,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"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 possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users\u2019 devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users\u2019 mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.<\/jats:p>","DOI":"10.3390\/s21196397","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3683-7158","authenticated-orcid":false,"given":"Michele","family":"Girolami","sequence":"first","affiliation":[{"name":"Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1491-6450","authenticated-orcid":false,"given":"Dimitri","family":"Belli","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-9478","authenticated-orcid":false,"given":"Stefano","family":"Chessa","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy"},{"name":"Department of Computer Science, University of Pisa, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9062-3647","authenticated-orcid":false,"given":"Luca","family":"Foschini","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (DISI), University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1109\/COMST.2019.2914030","article-title":"A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities","volume":"21","author":"Capponi","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MCOM.2011.6069707","article-title":"Mobile crowdsensing: Current state and future challenges","volume":"49","author":"Ganti","year":"2011","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alvear, O., Calafate, C.T., Cano, J.-C., and Manzoni, P. (2018). Crowdsensing in smart cities: Overview platforms and environment sensing issues. Sensors, 18.","DOI":"10.3390\/s18020460"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., and Oliveira, A. (2011). Smart cities and the future Internet: Towards cooperation frameworks for open innovation. The Future Internet assembly (LNCS 6656), Springer.","DOI":"10.1007\/978-3-642-20898-0_31"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JIOT.2014.2306328","article-title":"Internet of Things for smart cities","volume":"1","author":"Zanella","year":"2014","journal-title":"IEEE Internet Things J."},{"key":"ref_6","unstructured":"Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., and Laurila, J. (2010, January 13\u201316). Towards Rich Mobile Phone Datasets: Lausanne Data Collection Campaign. Proceedings of the ACM International Conference on Pervasive Services (ICPS 2010), Berlin, Germany."},{"key":"ref_7","unstructured":"Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T.-M.-T., Dousse, O., Eberle, J., and Miettinen, M. (2012, January 18\u201322). The Mobile Data Challenge: Big Data for Mobile Computing Research. Proceedings of the on the Mobile Data Challenge Workshop (MDC) in Conjunction with Pervasive, Newcastle, UK."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., and Estrin, D. (2010, January 1\u20133). A first look at traffic on smartphones. Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (IMC), Melbourne, Australia,.","DOI":"10.1145\/1879141.1879176"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/JIOT.2015.2409151","article-title":"A survey of incentive techniques for mobile crowd sensing","volume":"2","author":"Jaimes","year":"2015","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/MNET.2017.1500151","article-title":"QUOIN: Incentive mechanisms for crowd sensing networks","volume":"32","author":"Ota","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1016\/j.jss.2011.06.073","article-title":"A survey on privacy in mobile participatory sensing applications","volume":"84","author":"Christin","year":"2011","journal-title":"J. Syst. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/MNET.2018.1700331","article-title":"Task Assignment in Mobile Crowdsensing: Present and Future Directions","volume":"32","author":"Gong","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00779-005-0046-3","article-title":"Reality mining: Sensing complex social systems","volume":"10","author":"Eagle","year":"2006","journal-title":"J. Pers. Ubiquitous Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aslak, U., Rosvall, M., and Lehmann, S. (2018). Constrained information flows in temporal networks reveal intermittent communities. Phys. Rev. E, 97.","DOI":"10.1103\/PhysRevE.97.062312"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12752","DOI":"10.1073\/pnas.1821667116","article-title":"Quantifying the sensing power of vehicle fleets","volume":"116","author":"Anjomshoaa","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.cageo.2017.11.008","article-title":"Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data","volume":"111","author":"Wang","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1111\/tgis.12336","article-title":"The characteristics of asymmetric pedestrian behavior: A preliminary study using passive smartphone location data","volume":"22","author":"Malleson","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1038\/s41586-020-2909-1","article-title":"The scales of human mobility","volume":"587","author":"Alessandretti","year":"2020","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms9166","article-title":"Returners and explorers dichotomy in human mobility","volume":"6","author":"Pappalardo","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.01.001","article-title":"Human mobility: Models and applications","volume":"734","author":"Barbosa","year":"2018","journal-title":"Phys. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjds\/s13688-015-0059-8","article-title":"The effect of recency to human mobility","volume":"4","author":"Barbosa","year":"2015","journal-title":"EPJ Data Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., and Jin, D. (2018, January 23\u201327). Deepmove: Predicting human mobility with attentional recurrent networks. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186058"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1140\/epjds\/s13688-015-0046-0","article-title":"A survey of results on mobile phone datasets analysis","volume":"4","author":"Blondel","year":"2015","journal-title":"EPJ Data Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MCOM.2010.5560598","article-title":"A survey of mobile phone sensing","volume":"48","author":"Lane","year":"2010","journal-title":"IEEE Commun. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/SURV.2012.031412.00077","article-title":"Mobile phone sensing systems: A survey","volume":"15","author":"Khan","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","article-title":"Predicting taxi\u2013Passenger demand using streaming data","volume":"14","author":"Gama","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","unstructured":"Pappalardo, L., Ferres, L., Sacasa, M., Cattuto, C., and Bravo, L. (2020). An individual-level ground truth dataset for home location detection. arXiv, Available online: https:\/\/arxiv.org\/abs\/2010.08814."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2743025","article-title":"Trajectory Data Mining: An Overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Zhou, X. (2011). Location-Based Social Networks: Users. Computing with Spatial Trajectories, Springer.","DOI":"10.1007\/978-1-4614-1629-6_8"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., and Newth, D. (2015). Understanding Human Mobility from Twitter. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0131469"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Potort\u00ec, F., Crivello, A., Girolami, M., Traficante, E., and Barsocchi, P. (2016, January 4\u20137). Wi-Fi probes as digital crumbs for crowd localization. Proceedings of the 7th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Madrid, Spain.","DOI":"10.1109\/IPIN.2016.7743599"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. (2009, January 20\u201324). Mining interesting locations and travel sequences from GPS trajectories. Proceedings of the 18th international World Wide Web Conference (WWW 2009), Madrid, Spain.","DOI":"10.1145\/1526709.1526816"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W.-Y. (2008, January 21\u201324). Understanding Mobility Based on GPS Data. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409677"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.pmcj.2016.09.005","article-title":"Mobile crowd sensing management with the ParticipAct living lab","volume":"38","author":"Chessa","year":"2016","journal-title":"Pervasive Mob. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M.M., Larsen, J.E., and Lehmann, S. (2014). Measuring large-scale social networks with high resolution. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0095978"},{"key":"ref_36","unstructured":"Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2019). Scikit-mobility: A python library for the analysis, generation and risk assessment of mobility data. arXiv, Available online: https:\/\/arxiv.org\/abs\/1907.07062."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1126\/science.1177170","article-title":"Limits of Predictability in Human Mobility","volume":"327","author":"Song","year":"2010","journal-title":"Science"},{"key":"ref_38","first-page":"1","article-title":"Can co-location be used as a proxy for face-to-face contacts?","volume":"7","author":"Barrat","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/S0306-4379(00)00022-3","article-title":"Rock: A robust clustering algorithm for categorical attributes","volume":"25","author":"Guha","year":"2000","journal-title":"Inf. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","article-title":"Birds of a Feather: Homophily in Social Networks","volume":"27","author":"Mcpherson","year":"2001","journal-title":"Annu. Rev. Sociol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3172867","article-title":"Community discovery in dynamic networks: A survey","volume":"51","author":"Rossetti","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jnca.2018.02.011","article-title":"Community detection in networks: A multidisciplinary review","volume":"108","author":"Javed","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.procs.2019.04.042","article-title":"A comprehensive literature review on community detection: Approaches and applications","volume":"151","author":"Mohamed","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101231","DOI":"10.1016\/j.pmcj.2020.101231","article-title":"The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection","volume":"67","author":"Belli","year":"2020","journal-title":"Pervasive Mob. Comput."},{"key":"ref_45","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA."},{"key":"ref_46","unstructured":"David, A., and Vassilvitskii, S. (2007, January 7\u20139). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_47","unstructured":"Aslak, U., and Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. arXiv, Available online: https:\/\/arxiv.org\/abs\/2003.14370."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1007\/s10994-016-5582-8","article-title":"Tiles: An onlne algorithm for community discovery in dynamic social networks","volume":"106","author":"Rossetti","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cazabet, R., Amblard, F., and Hanachi, C. (2010, January 20\u201322). Detection of overlapping communities in dynamical social networks. Proceedings of the IEEE Second International Conference on Social Computing, Minneapolis, MN, USA.","DOI":"10.1109\/SocialCom.2010.51"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Edler, D., and Bohlin, L. (2017). Mapping higher-order network flows in memory and multilayer networks with infomap. Algorithms, 10.","DOI":"10.3390\/a10040112"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Phys. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1108\/IJPCC-07-2020-0081","article-title":"In defence of digital contact-tracing: Human rights, South Korea and Covid-19","volume":"16","author":"Ryan","year":"2020","journal-title":"Int. J. Pervasive Comput. Commun."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"eabb6936","DOI":"10.1126\/science.abb6936","article-title":"Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing","volume":"368","author":"Ferretti","year":"2020","journal-title":"Science"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jo, W., Chang, D., You, M., and Ghim, G.H. (2021). A social network analysis of the spread of COVID-19 in South Korea and policy implications. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-87837-0"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjds\/s13688-017-0129-1","article-title":"Understanding predictability and exploration in human mobility","volume":"7","author":"Cuttone","year":"2018","journal-title":"EPJ Data Sci."},{"key":"ref_56","unstructured":"Cintia, P., Pappalardo, L., Rinzivillo, S., Fadda, D., Boschi, T., Giannotti, F., Chiaromonte, F., Bonato, P., Fabbri, F., and Penone, F. (2020). The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy. arXiv, Available online: https:\/\/arxiv.org\/abs\/2006.03141."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"012008","DOI":"10.1088\/1742-6596\/1797\/1\/012008","article-title":"Containing COVID-19 Pandemic using Community Detection","volume":"1797","author":"Prabhu","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gibbs, H., Nightingale, E., Liu, Y., Cheshire, J., Danon, L., Smeeth, L., Pearson, C.A.B., Grundy, C., Kucharski, A.J., and Eggo, R.M. (2021). Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19. PLoS Comput. Biol., 17.","DOI":"10.1371\/journal.pcbi.1009162"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MCOM.2017.1600690CM","article-title":"Promoting cooperation by the social incentive mechanism in mobile crowdsensing","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Girolami, M., Chessa, S., Foschini, L., Ianniello, R., and Corradi, A. (2015, January 6\u20139). Social Amplification Factor for Mobile Crowd Sensing: The ParticipAct Experience. Proceedings of the IEEE International Symposium on Computers and Communications, Larnaca, Cyprus.","DOI":"10.1109\/ISCC.2015.7405544"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/COMST.2017.2705720","article-title":"On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration","volume":"19","author":"Taleb","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1109\/JIOT.2019.2957835","article-title":"A Probabilistic Model for the Deployment of Human-enabled Edge Computing in Massive Sensing Scenarios","volume":"7","author":"Belli","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Aslam, F., Aimin, W., Li, M., and Rehman, K.U. (2020). Innovation in the era of IoT and industry 5.0: Absolute innovation management (AIM) framework. Information, 11.","DOI":"10.3390\/info11020124"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Svertoka, E., Saafi, S., Rusu-Casandra, A., Burget, R., Marghescu, I., Hosek, J., and Ometov, A. (2021). Wearables for industrial work safety: A survey. Sensors, 21.","DOI":"10.3390\/s21113844"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Y., and Sun, Y. (2016, January 5\u20139). Accurate indoor localization based on crowd sensing. Proceedings of the 12th International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus.","DOI":"10.1109\/IWCMC.2016.7577143"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Coluccia, A., and Fascista, A. (2019). A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks. Sensors, 19.","DOI":"10.3390\/s19050988"},{"key":"ref_67","unstructured":"Luca, M., Barlacchi, G., Lepri, B., and Pappalardo, L. (2020). A Survey on Deep Learning for Human Mobility. arXiv, Available online: https:\/\/arxiv.org\/abs\/2012.02825."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"107226","DOI":"10.1016\/j.compeleceng.2021.107226","article-title":"IoT-based crowd monitoring system: Using SSD with transfer learning","volume":"93","author":"Ahmed","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Patrikakis, C.Z., Kogias, D.G., Chatzigeorgiou, C., Kalyvas, D., Katsadouros, E., and Giannousis, C. A method for measuring urban space density of people and deliver notification, with respect to privacy. Digest of Technical Papers, Proceedings of the IEEE International Conference on Consumer Electronics, Virtual Conference, 10\u201312 January 2021, Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/9427758.","DOI":"10.1109\/ICCE50685.2021.9427758"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:48Z","timestamp":1760166288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196397"],"URL":"https:\/\/doi.org\/10.3390\/s21196397","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,9,25]]}}}