@Article{cmc.2022.023392,
AUTHOR = {Muhammad Adnan Khan, Ahmed Abu-Khadrah, Shahan Yamin Siddiqui, Taher M. Ghazal, Tauqeer Faiz, Munir Ahmad, Sang-Woong Lee},
TITLE = {Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {73},
YEAR = {2022},
NUMBER = {2},
PAGES = {3319--3331},
URL = {http://www.techscience.com/cmc/v73n2/48313},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2022.023392}
}