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Article type: Research Article
Authors: Annamalai, Tamizhselvia; * | Liju Anton, J.b | Yoganathan, P.c
Affiliations: [a] St. Joseph’s College of Engineering, Chennai, India | [b] Student, University of Florida, Gainesville, FL, United States | [c] Member Technical staff, Zoho Corporation, Chennai, India
Correspondence: [*] Corresponding author: Tamizhselvi Annamalai, Associate Professor, St.Joseph’s College of Engineering, Chennai, India. E-mail: [email protected].
Abstract: Intelligent transport system is a greatly emerging technology in recent years. The stability and reliability of these systems is very important. In vehicular ad-hoc networks (VANET), the data transmission process can be improved by employing clustering process. The nodes can be clustered in order to utilize the maximum bandwidth of the network and improving network stability. In VANETs it is to introduce road safety and driver safety. In addition, security is a major concern and the malicious nodes need to be accurately detected. Several kinds of attacks can present in the VANETs. Hence an efficient authentication method and trust aware method is essentially required. In this work, stability assured CNN based trust aware clustering and authenticated transmission is introduced. For data authentication quantum cryptography technique is employed. In clustering process, trust degree of nodes is computed, vehicle speed is observed, direction of vehicle and distance among nodes are taken. In addition, for ensuring more safety, the critical data transmission is given higher priority. Therefore in clustering, data criticality parameter is also considered. For cluster formation, convolution neural network is employed. After the clustering process, the quantum cryptography based authentication is implemented for vehicle units and road side units. Data among these units are transmitted with quantum channel encryption key. Then simulation results are observed for validating the proposed protocol.
Keywords: Vehicular ad-hoc networks, wireless communication, routing protocols, cryptography, convolution neural network
DOI: 10.3233/JIFS-220460
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8073-8087, 2022
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