Reference : Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Sma...

	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/10993/34424

Title :	Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications
Language :	English
Author, co-author :	Tomasoni, Mattia [University of Trento > Dipartimento di Ingegneria e Scienza dell’Informazione]
	Capponi, Andrea [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) >]
Publication date :	Mar-2018
Main document title :	The 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE Mobile Cloud 2018)
Peer reviewed :	Yes
Audience :	International
Event name :	The 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE Mobile Cloud 2018)
Event date :	March 2018
Event place (city) :	Bamberg
Event country :	Germany
Keywords :	[en] Mobile Crowdsensing ; Data Collection Framework ; Energy Efficiency
Abstract :	[en] Mobile crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. In MCS, citizens actively participate in the sensing process by contributing data with their smartphones, tablets, wearables and other mobile devices to a collector. As citizens sustain costs while contributing data, i.e., the energy spent from the batteries for sensing and reporting, devising energy efficient data collection frameworks (DCFs) is essential. In this work, we compare the energy efficiency of several DCFs through CrowdSenSim, which allows to perform large-scale simulation experiments in realistic urban environments. Specifically, the DCFs under analysis differ one with each other by the data reporting mechanism implemented and the signaling between users and the collector needed for sensing and reporting decisions. Results reveal that the key criterion differentiating DCFs' energy consumption is the data reporting mechanism. In principle, continuous reporting to the collector should be more energy consuming than probabilistic reporting. However, DCFs with continuous reporting that implement mechanisms to block sensing and data delivery after a certain amount of contribution are more effective in harvesting data from the crowd.
Permalink :	http://hdl.handle.net/10993/34424

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