Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing
Abstract
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.
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- Authors
- Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J.
- Affiliations
- North China Electric Power University (CN)
- Kogakuin University (JP)
- Instituto de Telecomunicações (PT)
- Venue
- IEEE Network
- Published
- 2018-07
- Volume
- 32
- Issue
- 4
- Pages
- 54-60
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2887817291
- Cited by
- 260
- References
- 14
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 12 · 2023: 28 · 2022: 36 · 2021: 41 · 2020: 58 · 2019: 74 · 2018: 2
- FWCI
- 55.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Network (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- mobile crowdsensing, deep learning, edge computing, data validation, incentive mechanisms, participant recruitment, traffic load, latency, smart transportation
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