{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:19:12Z","timestamp":1761621552513,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:00:00Z","timestamp":1605571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Today\u2019s applications and providers are very interested in knowing the social aspects of users in order to customize the services they provide and to be more effective. Among the others, the most frequented places and the paths to reach them are information that turns out to be very useful to define users\u2019 habits. The most exploited means to acquire positions and paths is the GPS sensor, however it has been shown how leveraging inertial data from installed sensors can lead to path identification. In this work, we present a Computationally Efficient algorithm to Reconstruct Vehicular Traces (CERT), a novel algorithm which computes the path traveled by a vehicle using accelerometer and magnetometer data. We show that by analyzing data obtained through the accelerometer and the magnetometer in vehicular scenarios, CERT achieves almost perfect identification for medium and small sized cities. Moreover, we show that the longer the path, the easier it is to recognize it. We also present results characterizing the privacy risks depending on the area of the world, since, as we show, urban dynamics play a key role in the path detection.<\/jats:p>","DOI":"10.3390\/info11110534","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T07:23:28Z","timestamp":1605597808000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of Social Aspects by Means of Inertial Sensor Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Luca","family":"Bedogni","sequence":"first","affiliation":[{"name":"Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, 41125 Modena MO, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4942-2453","authenticated-orcid":false,"given":"Giacomo","family":"Cabri","sequence":"additional","affiliation":[{"name":"Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, 41125 Modena MO, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s10291-014-0415-3","article-title":"Tightly coupled integration of GPS precise point positioning and MEMS-based inertial systems","volume":"19","year":"2015","journal-title":"GPS Solut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MSP.2017.25","article-title":"The Perils of User Tracking Using Zero-Permission Mobile Apps","volume":"15","author":"Narain","year":"2017","journal-title":"IEEE Secur. Priv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/TMSCS.2017.2751462","article-title":"PinMe: Tracking a Smartphone User around the World","volume":"4","author":"Mosenia","year":"2018","journal-title":"IEEE Trans. Multi-Scale Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Narain, S., Vo-Huu, T.D., Block, K., and Noubir, G. (2016, January 22\u201326). Inferring User Routes and Locations Using Zero-Permission Mobile Sensors. Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2016.31"},{"key":"ref_5","unstructured":"Christidis, P. (2020, November 11). EU Travel Survey on Demand for Innovative Transport Systems. Available online: https:\/\/data.europa.eu\/euodp\/data\/dataset\/jrc-tem-eu_travel_survey_2014_new_technologies."},{"key":"ref_6","unstructured":"Federal Highway Administration (FHWA) (2020, November 11). National Household Travel Survey, Available online: https:\/\/nhts.ornl.gov\/."},{"key":"ref_7","unstructured":"German DLR (2020, November 11). Mobility in Germany 2008. Available online: http:\/\/daten.clearingstelle-verkehr.de\/223\/."},{"key":"ref_8","unstructured":"UK Department of Transport (DfT) (2020, November 11). UK Natinoal Travel Survey 2016, Available online: https:\/\/www.gov.uk\/government\/collections\/national-travel-survey-statistics."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., and Tobe, Y. (2009). On the Anonymity of Home\/Work Location Pairs. Pervasive Computing, Springer.","DOI":"10.1007\/978-3-642-01516-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s007790170019","article-title":"Understanding and Using Context","volume":"5","author":"Dey","year":"2001","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ayu, M.A., Mantoro, T., Matin, A.F.A., and Basamh, S.S.O. (2011, January 20\u201322). Recognizing user activity based on accelerometer data from a mobile phone. Proceedings of the 2011 IEEE Symposium on Computers Informatics, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISCI.2011.5958987"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1002\/wcm.2702","article-title":"Context-aware Android applications through transportation mode detection techniques","volume":"16","author":"Bedogni","year":"2016","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/978-3-642-21257-4_36","article-title":"Human activity recognition from accelerometer data using a wearable device","volume":"6669 LNCS","author":"Casale","year":"2011","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1689239.1689243","article-title":"Using mobile phones to determine transportation modes","volume":"6","author":"Reddy","year":"2010","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_15","unstructured":"Chen, L., Yang, D., Nogueira, M., Wang, C., and Zhang, D. (2020, November 17). Data-Driven C-RAN Optimization Exploiting Traffic and Mobility Dynamics of Mobile Users. Available online: https:\/\/ieeexplore.ieee.org\/document\/8981890."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11265-012-0711-5","article-title":"Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals","volume":"71","author":"Collin","year":"2013","journal-title":"J. Signal Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cankaya, I.A., Koyun, A., Yigit, T., and Yuksel, A.S. (2015). Mobile indoor navigation system in iOS platform using augmented reality. 2015 9th International Conference on Application of Information and Communication Technologies (AICT), IEEE.","DOI":"10.1109\/ICAICT.2015.7338563"},{"key":"ref_18","first-page":"140","article-title":"Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass","volume":"47","author":"Rehman","year":"2016","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bedogni, L., Franzoso, F., and Bononi, L. (2016, January 4\u20138). A Self-Adapting Algorithm Based on Atmospheric Pressure to Localize Indoor Devices. Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA.","DOI":"10.1109\/GLOCOM.2016.7841545"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jimenez, A., Seco, F., Prieto, C., and Guevara, J. (2009). A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU. 2009 IEEE International Symposium on Intelligent Signal Processing, IEEE.","DOI":"10.1109\/WISP.2009.5286542"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1109\/JSEN.2014.2382568","article-title":"SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization","volume":"15","author":"Kang","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wahab, A.A., Khattab, A., and Fahmy, Y.A. (2013). Two-way TOA with limited dead reckoning for GPS-free vehicle localization using single RSU. 2013 13th International Conference on ITS Telecommunications (ITST), IEEE.","DOI":"10.1109\/ITST.2013.6685553"},{"key":"ref_23","unstructured":"Han, J., Owusu, E., Nguyen, L.T., Perrig, A., and Zhang, J. (2012, January 3\u20137). ACComplice: Location inference using accelerometers on smartphones. Proceedings of the 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012), Bangalore, India."},{"key":"ref_24","unstructured":"Sun, J., Liu, J., Fan, S., and Lu, X. (2015). Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization. China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jin, Y., Tan, H.X., and Soh, W.S. (2014). CIMLoc: A crowdsourcing indoor digital map construction system for localization. 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE.","DOI":"10.1109\/ISSNIP.2014.6827640"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aguilar Herrera, J.C., Hinkenjann, A., Ploger, P.G., and Maiero, J. (2013). Robust indoor localization using optimal fusion filter for sensors and map layout information. International Conference on Indoor Positioning and Indoor Navigation, IEEE.","DOI":"10.1109\/IPIN.2013.6817877"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Link, J.A.B., Smith, P., Viol, N., and Wehrle, K. (2011). FootPath: Accurate map-based indoor navigation using smartphones. 2011 International Conference on Indoor Positioning and Indoor Navigation, IEEE.","DOI":"10.1109\/IPIN.2011.6071934"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5464","DOI":"10.1109\/TVT.2015.2475608","article-title":"The Bologna ringway dataset: Improving road network conversion in SUMO and validating urban mobility via navigation services","volume":"64","author":"Bedogni","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luxen, D., and Vetter, C. (2011, January 1\u20134). Real-time routing with OpenStreetMap data. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems\u2014GIS \u201911, Chicago, IL, USA.","DOI":"10.1145\/2093973.2094062"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bedogni, L., and Bononi, L. (2019, January 11\u201315). Vehicular Route Identification Using Mobile Devices Integrated Sensors. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan.","DOI":"10.1109\/PERCOMW.2019.8730753"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0024-3795(88)90139-5","article-title":"An inequality for the Hadamard product of an M-matrix and an inverse M-matrix","volume":"101","author":"Fiedler","year":"1988","journal-title":"Linear Algebra Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/11\/534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:34:22Z","timestamp":1760178862000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/11\/534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,17]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["info11110534"],"URL":"https:\/\/doi.org\/10.3390\/info11110534","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,11,17]]}}}