AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication
VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by fact...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2145636 |
| Summary: | VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput. |
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| ISSN: | 0883-9514 1087-6545 |