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

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The 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 like malicious vehicles propagating false information to divert other vehicles to arrive at destinations earlier or lead them to dangerous locations. This article 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 of 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 oncoming 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.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2022-11
Language
English
Article Type
Article
Citation

ACM TRANSACTIONS ON INTERNET TECHNOLOGY, v.22, no.4

ISSN
1533-5399
DOI
10.1145/3514243
URI
http://hdl.handle.net/10203/299644
Appears in Collection
CS-Journal Papers(저널논문)
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