A Novel Mobile CrowdSensing Architecture for Road Safety

Intelligent Transportation Systems have become an essential part of today’s transportation systems as they aim to enhance efficiency, safety and mobility. They rely particularly on various communication and sensing technologies to achieve their objectives. At this level, Mobile CrowdSensing presents a cost-efficient solution and provides interesting features for data collection which is a major component in ITS. However, it still faces some challenges such as lack of incentive mechanisms, data validation, privacy and security. These challenges motivate us to propose a Mobile CrowdSensing architecture for our future SI-CAR (Secure and Intelligent Crowdsensing Application for Road Safety) application that integrates deep learning-based data validation, edge computing-based local processing for data privacy and gamification based-incentive mechanism. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.