Department of Electronics Engineering, Sudan University of Sci...

Recently interest in Internet of Vehicles IoV technologies has significantly emerged due to the substantial development in the smart automobile industries Internet of Vehicles technology enables vehicles to communicate with public networks and interact with the surrounding environment It also allows vehicles to exchange and collect information about other vehicles and roads IoV is introduced to enhance road users experience by reducing road congestion improving traffic management and ensuring the road safety The promised applications of smart vehicles and IoV systems face many challenges such as big data collection in IoV and distribution to attractive vehicles and humans Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything V2X One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues Machine learning ML is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues For example ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications challenges This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning DRL approaches for the Internet of Vehicles IoV The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow The proposed analytical model considers the Markov decision process MDP and ML in offloading the decision process of different task flows of the IoV network control cycle In the paper we focused on buffer and energy aware in ML-enabled Quality of Experience QoE optimization where many recent related research and methods were analyzed compared and discussed The IoV edge computing and fog-based identity authentication and security mechanism were presented as well Finally future directions and potential solutions for secure ML IoV and V2X were highlighted

Department of Electronics Engineering, Sudan University of Science and Technology, Khartoum, Sudan

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Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applicatio...