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Smart city article details
| Title | A Secured Privacy-Preserving Multifactor Approach For Autonomous Vehicles Using Blockchain Technology |
|---|---|
| ID_Doc | 4518 |
| Authors | Sarika S.; Prabakeran S. |
| Year | 2025 |
| Published | Recent Advances in Electrical and Electronic Engineering, 18, 4 |
| DOI | http://dx.doi.org/10.2174/0123520965281391231212045852 |
| Abstract | Background: Smart vehicles are connected to the Internet of Things (IoT), bringing with them the potential to transform human existence in so-called "smart cities." Intelligent vehicle architecture revolves around the Vehicular-AdhocNetwork (VANET). The goal of a VANET is to make driving more pleasant. VANETs' message-sharing capabilities contribute to improved traffic management, reduced congestion, and safer driving. However, VANETs' usefulness could be diminished by the spread of fraudulent or erroneous messages. Methods: For better road safety and less congestion, it guarantees secure and accurate communication between vehicles and between vehicles and infrastructure. The security and privacy of a VANET, however, can be compromised by threats, including denial-of-service (DoS), replay, and Sybil attacks. These problems can cause a rogue node to send out faulty data throughout the sys-tem. We introduce a biometrics-blockchain (BBC) approach to ensure the safety of information exchanged between vehicles in a VANET and to preserve archival data in a tried-and-true envi-ronment. To protect the anonymity of users, the suggested framework makes use of biometric data to verify the identity of the sender. Results: As a result, the proposed BBC scheme creates a safe and reliable environment for vehicles in VANET, with the added benefit of identity tracing capabilities. To prove the effectiveness of the proposed framework, simulations were run in the urban mobility models OMNeT++, veins, and SUMO. Packet-delivery-rate (PDR), packet-loss-rate(PLR), and computing cost (CC) were used to assess the framework's efficiency. Conclusion: The outcomes highlighted the superiority of our innovative model over conventional methods, such as PDR slightly increased to 8-10%, PLR decreased to 20-25%, and CC also reduced to 15-20% compared to state-of-art models. © 2025 Bentham Science Publishers. |
| Author Keywords | blockchain; intelligent vehicles; IoV; privacy protection; trust management; Vehicular networks |
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