Reference : Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collecti...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36942
Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks
English
Tomasoni, Mattia [University of Trento > Dipartimento di Ingegneria e Scienza dell’Informazione]
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]
Kliazovich, Dzmitry [ExaMotive]
Granelli, Fabrizio [University of Trento > Dipartimento di Ingegneria e Scienza dell’Informazione]
Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Oct-2018
Pervasive and Mobile Computing
Elsevier
Crowd-sensed Big Data for Internet of Things Services
Yes
International
1574-1192
1574-1192
[en] Mobile crowdsensing ; Energy consumption ; Data collection
[en] Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent
paradigms for urban sensing. The citizens actively participate in the sensing process by contributing
data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed
for sensing and reporting operations. Hence, devising energy efficient data collection frameworks
(DCF) is essential to foster participation. In this work, we investigate from an energy-perspective
the performance of different DCFs. Our methodology is as follows: (i) we developed an Android
application that implements the DCFs, (ii) we profiled the energy and network performance with a
power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for
large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The
amount of collected data, energy consumption and fairness are the performance indexes evaluated.
The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than
DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to
produce the same amount of data, the associated energy cost of different users can vary significantly.
http://hdl.handle.net/10993/36942

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
energy-matters.pdfAuthor preprint10.79 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.