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.