A Blockchain-Assisted Road Condition Monitoring Scheme With Privacy-Preserving for VANETs

Connected vehicular cloud computing applies cloud computing to Vehicular Ad Hoc Networks (VANETs), which can provide road condition monitoring services for intelligent transportation systems. However, it brings new security and efficiency challenges to VANETs. Specifically, malicious vehicles may impersonate legitimate vehicles to tamper with or forge road conditions. Cloud servers are not fully trusted entities, the vehicle information, driver information, as well as road conditions uploaded to the cloud may be leaked to an unauthorized party. Moreover, small devices such as on-board units and roadside units have limited computational resources, and thus require light-weight security mechanisms. To address these challenges, this article proposes a blockchain-assisted road condition monitoring scheme with privacy-preserving. It prevents the leakage of sensitive vehicle identity with pseudonyms, protects the privacy of road conditions through encryption, identifies malicious vehicle impersonation attacks with source authentication, and detects dishonest behaviors of cloud servers with blockchain supervision. Our scheme does not rely on expensive bilinear pairing operations, which significantly reduces the computational overheads. Security analysis shows that our proposal can provide unforgeability of authentication request/response, privacy of road conditions, resistance to reply attacks, anonymity of vehicle, and traceability of malicious vehicles. Performance analysis demonstrates that our work has lower computational and communication costs than other existing schemes.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-08-26
DOI
https://doi.org/10.1109/tvt.2024.3450078
Citations
4
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.06

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Blockchain-Assisted Road Condition Monitoring Scheme With Privacy-Preserving for VANETs

Lemei Da, Yong Ding, Wei Hu, Beibei Li et al.
4 citations
IEEE Transactions on Vehicular Technology
Traffic Prediction and Management Techniques
1.06
article

A Blockchain-Assisted Road Condition Monitoring Scheme With Privacy-Preserving for VANETs

Lemei Da, Yong Ding, Wei Hu, Beibei Li, Yujue Wang, Dan Zhu
article en
4 citations

Abstract

Connected vehicular cloud computing applies cloud computing to Vehicular Ad Hoc Networks (VANETs), which can provide road condition monitoring services for intelligent transportation systems. However, it brings new security and efficiency challenges to VANETs. Specifically, malicious vehicles may impersonate legitimate vehicles to tamper with or forge road conditions. Cloud servers are not fully trusted entities, the vehicle information, driver information, as well as road conditions uploaded to the cloud may be leaked to an unauthorized party. Moreover, small devices such as on-board units and roadside units have limited computational resources, and thus require light-weight security mechanisms. To address these challenges, this article proposes a blockchain-assisted road condition monitoring scheme with privacy-preserving. It prevents the leakage of sensitive vehicle identity with pseudonyms, protects the privacy of road conditions through encryption, identifies malicious vehicle impersonation attacks with source authentication, and detects dishonest behaviors of cloud servers with blockchain supervision. Our scheme does not rely on expensive bilinear pairing operations, which significantly reduces the computational overheads. Security analysis shows that our proposal can provide unforgeability of authentication request/response, privacy of road conditions, resistance to reply attacks, anonymity of vehicle, and traceability of malicious vehicles. Performance analysis demonstrates that our work has lower computational and communication costs than other existing schemes.

IEEE Transactions on Vehicular TechnologyVol. 73(12)
Northwestern Polytechnical University (CN), Guilin University of Electronic Technology (CN), Beihang University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Sustainable cities and communities
Openalex Percentile: Top 25%
Traffic Prediction and Management Techniques
1.06
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.