Reference : Energy Efficient Data Collection in Opportunistic Mobile Crowdsensing Architectures f...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/30106
Energy Efficient Data Collection in Opportunistic Mobile Crowdsensing Architectures for Smart Cities
English
Capponi, Andrea mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Fiandrino, Claudio [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Kliazovich, Dzmitry [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Giordano, Stefano [University of Pisa > Department of information engineering]
May-2017
3rd IEEE INFOCOM Workshop on Smart Cites and Urban Computing
Yes
International
3rd IEEE INFOCOM Workshop on Smart Cites and Urban Computing
May 2017
Atlanta
GA
[en] Mobile Crowdsensing ; Smart Cities ; Opportunistic Sensing
[en] Smart cities employ latest information and communication technologies to enhance services for citizens. Sensing is essential to monitor current status of infrastructures and the environment. In Mobile Crowdsensing (MCS), citizens participate in the sensing process contributing data with their mobile devices such as smartphones, tablets and wearables. To be effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and energy-efficient framework for data collection in opportunistic MCS architectures. Opportunistic sensing systems require minimal intervention from the user side as sensing decisions are application- or device-driven. The proposed framework minimizes the cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. We evaluate performance of the framework with simulations, performed in a real urban environment and with a large number of participants. The simulation results verify cost-effectiveness of the framework and assess efficiency of the data generation process.
http://hdl.handle.net/10993/30106

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
energy-efficient-data.pdfAuthor preprint2.01 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.