Crynodeb
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.
| Iaith wreiddiol | Saesneg |
|---|---|
| Tudalennau (o-i) | 12-18 |
| Nifer y tudalennau | 7 |
| Cyfnodolyn | IEEE Network |
| Cyfrol | 32 |
| Rhif cyhoeddi | 4 |
| Dyddiad ar-lein cynnar | 31 Gorff 2016 |
| Dynodwyr Gwrthrych Digidol (DOIs) | |
| Statws | Cyhoeddwyd - Awst 2018 |