A context aware framework for mobile crowd-sensing

Alireza Hassani, Pari Delir Haghighi, Prem Parkash Jayaraman, Arkady Zaslavsky

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    3 Citations (Scopus)


    Context 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.

    Original languageEnglish
    Title of host publicationModeling and Using Context
    Subtitle of host publication10th International and Interdisciplinary Conference, CONTEXT 2017, Paris, France, June 20–23, 2017, Proceedings
    EditorsPatrick Brézillon, Carlo Penco, Roy Turner
    Place of PublicationCham, Switzerland
    Number of pages12
    ISBN (Electronic)9783319578378
    ISBN (Print)9783319578361
    Publication statusPublished - 2017
    EventInternational and Interdisciplinary Conference on Modelling and Using Context 2017 - Paris, France
    Duration: 20 Jun 201723 Jun 2017
    Conference number: 10th
    https://link.springer.com/book/10.1007/978-3-319-57837-8 (Proceeidngs)

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceInternational and Interdisciplinary Conference on Modelling and Using Context 2017
    Abbreviated titleCONTEXT 2017
    Internet address


    • Context model
    • Context-aware computing
    • Crowd-sensing
    • Mobile

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