Reference : Characterizing User Mobility Using Mobile Sensing Systems
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/31971
Characterizing User Mobility Using Mobile Sensing Systems
English
Faye, Sébastien mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bronzi, Walter mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Tahirou, Ibrahim mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2017
International Journal of Distributed Sensor Networks
SAGE Journals
13
8
Yes (verified by ORBilu)
1550-1477
1550-1477
[en] Mobile computing ; Sensing systems ; Human mobility profiling
[en] Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Networking Research Group (NetLab)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/31971
10.1177/1550147717726310
http://journals.sagepub.com/doi/10.1177/1550147717726310
FnR ; FNR5825301 > Thomas Engel > MAMBA > Multimodal Mobility Assistance > 01/04/2014 > 31/03/2017 > 2013

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