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.
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 language | English |
|---|---|
| Pages (from-to) | 12-18 |
| Number of pages | 7 |
| Journal | IEEE Network |
| Volume | 32 |
| Issue number | 4 |
| Early online date | 31 Jul 2016 |
| DOIs | |
| Publication status | Published - Aug 2018 |
Keywords
- sensors
- Task analysis
- machine learning
- servers
- robustness
- Edge computing
- computer architecture