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A context aware framework for mobile crowd-sensing
conference contribution
posted on 2017-01-01, 00:00 authored by Ali Hassani, P D Haghighi, P P Jayaraman, Arkady ZaslavskyArkady ZaslavskyContext awareness plays ever increasing role in Mobile Crowd-Sensing (MCS), which relies on sensing capabilities of mobile devices to collect real-time user data and related context. The paper proposes a MCS framework for valuable data collection in order to enable smart applications. The paper also addresses a key challenge in MCS on how to reduce energy consumption in order to encourage user participation. The paper argues that to optimize task allocation costs, it is important for a given query to select the most appropriate participants according to the context of the device, the participant, and the sensing task. Context awareness can significantly reduce the sensing and communication costs. Yet to incorporate context awareness into MCS, there is a need for a standard and overarching context model. This paper proposes a multi-dimensional context model to capture related contextual information in the MCS domain, and incorporate it into a context-aware MCS framework to improve energy efficiency and support task allocation. The paper concludes with discussing implementation and evaluation of the proposed approach.
History
Event
French Association for Context. Conference (10th : 2017 : Paris, France)Volume
LNAI 10257Series
French Association for Context ConferencePagination
557 - 568Publisher
SpringerLocation
Paris, FrancePlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-06-20End date
2017-06-23ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319578361Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AGEditor/Contributor(s)
P Brézillon, R Turner, C PencoTitle of proceedings
CONTEXT 2017 : Proceedings of the 10th International and Interdisciplinary Conference on Modeling and Using ContextUsage metrics
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