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Title Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications
ID_Doc 41461
Authors Tomasoni, M; Capponi, A; Fiandrino, C; Kliazovich, D; Granelli, F; Bouvry, P
Title Profiling Energy Efficiency of Mobile Crowdsensing Data Collection Frameworks for Smart City Applications
Year 2018
Published
DOI 10.1109/MobileCloud.2018.00009
Abstract 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.
Author Keywords
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Conference Proceedings Citation Index - Science (CPCI-S)
EID WOS:000494645200001
WoS Category Computer Science, Theory & Methods; Telecommunications
Research Area Computer Science; Telecommunications
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