Vol.73, No.2, 2022, pp.3319-3331, doi:10.32604/cmc.2022.023392
Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication
  • Muhammad Adnan Khan1,2, Ahmed Abu-Khadrah3, Shahan Yamin Siddiqui4, Taher M. Ghazal5,6, Tauqeer Faiz6, Munir Ahmad7, Sang-Woong Lee2,*
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:
Received 06 September 2021; Accepted 30 March 2022; Issue published 16 June 2022
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.
Mode-4 communication; ad-hoc vehicular network; CV2X; support vector machine
Cite This Article
M. Adnan Khan, A. Abu-Khadrah, S. Yamin Siddiqui, T. M. Ghazal, T. Faiz et al., "Support-vector-machine-based adaptive scheduling in mode 4 communication," Computers, Materials & Continua, vol. 73, no.2, pp. 3319–3331, 2022.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.