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Trajectory-Based Task Allocation for Reliable Mobile Crowd Sensing Systems
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-7153-6705
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-4722-0823
(Decisions, Networks and Analytics (DNA))
2015 (English)In: Proceedings - 15th IEEE International Conference on Data Mining Workshop, Institute of Electrical and Electronics Engineers (IEEE), 2015, Vol. 1, 398-406 p., 7395697Conference paper (Refereed)
Abstract [en]

Mobile crowd sensing (MCS) is as a promising people-centric sensing paradigm which allows ordinary citizens to contribute sensing data using mobile communication devices. In this paper we study correlation between users’ mobility and their role as contributors in MCS applications. We propose a new trajectory-based approach for task allocation in MCS environments and model participants’ spatio-temporal competences by analyzing their mobile traces. By allocating MCS tasks only to participant who are familiar with the target location we significantly increase the reliability of contributed data and reduce total communication cost. We introduce novel metric to estimate participants’ competence to conduct MCS tasks and propose fair ranking approach allowing newcomers to compete with experienced senior contributors. Additionally, we group similar expert contributors and thus open up new possibilities for physical collaboration between them. We evaluate our work using GeoLife trajectory dataset and the experimental results show the advantages of our approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. Vol. 1, 398-406 p., 7395697
Keyword [en]
mobile crowd sensing, mobile crowdsourcing, mobility profiling, participatory sensing, task allocation
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-188127DOI: 10.1109/ICDMW.2015.90ISI: 000380556700054ScopusID: 2-s2.0-84964777811ISBN: 978-1-4673-8492-6OAI: oai:DiVA.org:kth-188127DiVA: diva2:933464
Conference
15th IEEE International Conference on Data Mining Workshop, ICDMW 2015; Atlantic City; United States; 14 November 2015 through 17 November 2015
Note

QC 20160623

Available from: 2016-06-05 Created: 2016-06-05 Last updated: 2016-09-05Bibliographically approved

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Mrazovic, PetarMatskin, Mihhail
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ReferencesLink to record
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