Leveraging reputation for enhanced decision accuracy in vehicle-to-vehicle communications under limited infrastructure
Vehicle-to-Vehicle (V2V) networking enhances transportation safety and efficiency by enabling vehicles to share alerts. However, malicious vehicles may inject false messages, leading to disputes. While certificates help ensure security, Certificate Revocation List (CRLs) may be outdated in low-connectivity areas, making it hard to verify conflicting reports. Reputation systems, using a pre-signature scheme, can aid decision-making even in infrastructure-limited environments. In this paper, we provide the mechanisms to use reputation in areas with low/no connectivity, whilst allowing for pseudonymous certificates to verify message authenticity without breaking privacy. The approach is integrated into the existing Security Credential Management System (SCMS), a framework for managing digital certificates for secure V2V communication. Our simulations evaluate the security performance of our proposed mechanism, with offline available reputation, against plain SCMS certificate management that rely solely on CRL to block malicious vehicles. The results are achieved by integrating vehicular simulation tools like SUMO, OMNeT++, and Veins, to evaluate the V2V communications in each system under two conditions (Accident and No accident) ensuring a comprehensive system evaluation. The proposed scheme improves accuracy in decision-making with conflicting information by 36% in accidents and 44.4% in No-Accident situations in a rural environment.
Authors
- Steven Furnell (ORCID: https://orcid.org/0000-0003-0984-7542)
- Dimah Almani (ORCID: https://orcid.org/0000-0001-6319-4840)
- Tim Muller (ORCID: https://orcid.org/0000-0003-1263-4230)
Institutions
- University of Nottingham (GB)
- Shaqra University (SA)
Publication Details
- Journal
- Vehicular Communications
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1016/j.vehcom.2025.100927
- Primary Topic
- Vehicular Ad Hoc Networks (VANETs)
- Type
- article
- Field-Weighted Citation Impact
- 0.00