Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing

Jonathan Rodriguez, Zhenyu Zhou, Haijun Liao, Bo Gu, Kazi Mohammed Saidul Huq, Shahid Mumtaz

Research output: Contribution to journalArticlepeer-review


The emergence of MCS technologies provides a cost-efficient solution to accommodate large-scale sensing tasks. However, despite the potential benefits of MCS, there are several critical issues that remain to be solved, such as lack
of incentive-compatible mechanisms for recruiting participants, lack of data validation, and high traffic load and latency. This motivates us to
develop robust mobile crowd sensing (RMCS), a framework that integrates deep learning based data validation and edge computing based local processing. First, we present a comprehensive state-of-the-art literature review. Then, the conceptual design architecture of RMCS and practical implementations are described in detail. Next, a case study of smart transportation is provided to
demonstrate the feasibility of the proposed RMCS framework. Finally, we identify several open issues and conclude the article.
Original languageEnglish
Pages (from-to)12-18
Number of pages7
JournalIEEE Network
Issue number4
Early online date31 Jul 2016
Publication statusPublished - Aug 2018


  • sensors
  • Task analysis
  • machine learning
  • servers
  • robustness
  • Edge computing
  • computer architecture


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