CARES: Context-Aware Trust Estimation for Realtime Crowdsensing Services in Vehicular Edge Networks

A growing number of smart vehicles makes it possible to envision a crowdsensing service where vehicles can share video data of their surroundings for seeking out traffic conditions and car accidents ahead. However, the service may need to deal with situations that malicious vehicles propagate false information to divert other vehicles away to arrive at the destinations earlier or lead them to dangerous locations. This paper proposes a context-aware trust estimation scheme that can allow roadside units in a vehicular edge network to provide real-time crowdsensing services in a reliable manner by selectively using information from trustworthy sources. Our proposed scheme is novel in that its trust estimation does not require any prior knowledge towards vehicles on roads but quickly obtains the accurate trust value of each vehicle by leveraging transfer learning and its Q-learning based dynamic adjustment scheme autonomously estimates trust levels of incoming vehicles with the aim of detecting malicious vehicles and accordingly filtering out untrustworthy input from them. Based on an extensive simulation study, we prove that the proposed scheme outperforms existing ones in terms of malicious vehicle detection accuracy.