Vehicles with abundant sensors and sophisticated communication capabilities have contributed to the emergency of vehicular crowdsensing systems. Vehicular crowdsensing is becoming a popular paradigm to collect a variety of traffic event-reports in intelligent transportation research. However, event-reports trustworthiness and drivers’ privacy are under the threats of the openness of sensing paradigms. This paper proposes TPSense, a lightweight fog-assisted vehicular crowdsensing framework, which guarantees data trustworthiness and users’ privacy. Firstly, we convert the data trustworthiness evaluation problem into a maximum likelihood estimation one, and solve it through expectation maximization algorithm. Secondly, blind signature technology is employed to generate a pseudonym to replace the vehicle’s real identity for the sake of drivers’ privacy protection. Our framework is assessed through simulations on both synthetic and real-world mobility traces. Results have shown that TPSense outshines existing schemes in event-reports trustworthiness evaluation and the reliability of vehicles.

具有丰富传感器和先进通信能力的车辆为车辆人群感应系统的紧急状况做出了贡献。在智能交通研究中，车辆大众感知已成为一种流行的范例，可以收集各种交通事件报告。但是，事件报告的可信赖性和驾驶员的隐私受到感知范式开放性的威胁。本文提出了TPSense，这是一种轻量级的雾辅助车载人群感知框架，可确保数据的可信度和用户的隐私。首先，将数据可信度评估问题转化为最大似然估计问题，并通过期望最大化算法进行求解。其次，为了保护驾驶员的隐私，采用了盲签名技术来生成化名来代替车辆的真实身份。我们的框架是通过模拟合成和真实流动性轨迹进行评估的。结果表明，TPSense在事件报告可信度评估和车辆可靠性方面优于现有方案。