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

Abstract

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.

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

“…However, Kim and Kim [98] proposed DL to achieve efficient load-balancing in IoT. e use of DL was proposed for heterogeneous data processing, vehicle platoon control, path planning, predicting driver behavior, and security to reduce the cloud's communication traffic load and computational load [99,100]. DNN is ANN with multiple hidden layers in which the hidden layer will train based on the previously hidden layers [24].…”

Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions

“…Unlike DL architectures, other artificial intelligence (AI) techniques like the shallow neural network, support vector machine, fuzzy systems, random forest, and k-nearest neighbor typically witness deteriorating performance as the amount of data increases, which makes them unfit for BDA. As discussed in Ali et al [3], support vector machine has the challenge of dealing with fast authentication mechanism for large-scale IoV architecture. Fuzzy system has the limitation of dealing with IoV multimedia communications.…”

“…e IoT has been extended to the Internet of Vehicles (IoV) [2] due to incorporation of intelligent transportation systems for enhanced services [3]. e IoV allows vehicles to communicate with their internal and external environments.…”

Machine Learning Using Big Data Link Stability Based Node Observation for IoT Security

“…Support Vector Regression (SVR), regression methods, and memory modules are examples of frequently utilized logistic regression. Classification, from the other side, works to discontinuous target value [7][8][9]. Classification algorithms that are commonly include using K-nearest neighbour, regression analysis, as well as Support Vector Machine (SVM).…”