Reference : Demo Abstract: Human Mobility Profiling Using Privacy-Friendly Wi-Fi and Activity Traces

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/28400

Title :	Demo Abstract: Human Mobility Profiling Using Privacy-Friendly Wi-Fi and Activity Traces
Language :	English
Author, co-author :	Faye, Sébastien [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Tahirou, Ibrahim [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	14-Nov-2016
Main document title :	Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016)
Peer reviewed :	Yes
Audience :	International
Event name :	The 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016)
Event date :	from 14-11-2016 to 16-11-2016
Event organizer :	ACM
Event place (city) :	Stanford
Event country :	USA
Keywords :	[en] Human Mobility Profiling ; Sensing Systems ; Graph Theory
Abstract :	[en] Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a user's mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user's profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information.
Research centres :	SnT
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/28400
DOI :	10.1145/2994551.2996530
Other URL :	http://dl.acm.org/citation.cfm?id=2996530
FnR project :	FnR ; FNR5825301 > Thomas Engel > MAMBA > MultimodAl MoBility Assistance > 01/04/2014 > 31/03/2017 > 2013

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Limited access final_paper.pdf Publisher postprint 266.26 kB Request a copy

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map