Open Access

ARTICLE

Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication

Muhammad Adnan Khan^1,2, Ahmed Abu-Khadrah³, Shahan Yamin Siddiqui⁴, Taher M. Ghazal^5,6, Tauqeer Faiz⁶, Munir Ahmad⁷, Sang-Woong Lee^2,*

1 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
2 Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, Korea
3 College of Computing & Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
4 School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan
5 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia
6 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
7 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan

* Corresponding Author: Sang-Woong Lee. Email:

Computers, Materials & Continua 2022, 73(2), 3319-3331. https://doi.org/10.32604/cmc.2022.023392

Received 06 September 2021; Accepted 30 March 2022; Issue published 16 June 2022

Abstract

Vehicular ad-hoc networks (VANETs) are mobile networks that use and transfer data with vehicles as the network nodes. Thus, VANETs are essentially mobile ad-hoc networks (MANETs). They allow all the nodes to communicate and connect with one another. One of the main requirements in a VANET is to provide self-decision capability to the vehicles. Cognitive memory, which stores all the previous routes, is used by the vehicles to choose the optimal route. In networks, communication is crucial. In cellular-based vehicle-to-everything (CV2X) communication, vital information is shared using the cooperative awareness message (CAM) that is broadcast by each vehicle. Resources are allocated in a distributed manner, which is known as Mode 4 communication. The support vector machine (SVM) algorithm is used in the SVM-CV2X-M4 system proposed in this study. The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2X-M4 system. The results show that the proposed system achieves an accuracy of 99.6%. Thus, the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

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Keywords

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