Reference : A Survey on Mobile Crowdsensing Systems: Challenges, Solutions and Opportunities
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39438
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions and Opportunities
English
Capponi, Andrea mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Fiandrino, Claudio [IMDEA Networks Institute]
Kantarci, Burak [University of Ottawa > School of Electrical Engineering and Computer Science]
Foschini, Luca [University of Bologna > DISI]
Kliazovich, Dzmitry [ExaMotive]
Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
In press
IEEE Communications Surveys and Tutorials
Institute of Electrical and Electronics Engineers
Yes
International
1553-877X
[en] Mobile crowdsensing ; Urban sensing ; Opportunistic sensing ; Participatory sensing
[en] Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns.
Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.
http://hdl.handle.net/10993/39438

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