TY -的A2 -艾哈迈德,Fawad AU -阿里,Elmustafa赛义德AU -哈桑,穆罕默德Kamrul AU -哈桑,Rosilah AU -赛义德,拉希德a . AU -哈桑,莫娜Bakri盟——伊斯兰教,Shayla AU -纳Nazmus瓶非盟- Bevinakoppa Savitri PY - 2021 DA - 2021/03/13 TI -机器学习技术的安全车辆通信网络的车辆:最新进展和应用SP - 8868355六世- 2021 AB -最近,互联网的车辆的兴趣(IoV)技术已经明显出现了由于大量的智能汽车行业的发展。互联网的车辆的技术使车辆与公共网络通信,与周围的环境进行交互。它还允许车辆交换和收集关于其他车辆和道路信息。IoV介绍提高道路使用者的经验通过减少道路拥堵问题,提高交通管理,并确保道路安全。承诺的智能车辆和IoV应用系统面临许多挑战,如IoV大数据收集和分发给车辆和人类的吸引力。另一个挑战是实现快速、高效的许多不同车辆之间的通信和智能设备称为Vehicle-to-Everything (V2X)。之一,研究人员需要解决的重要问题是如何有效地处理大量的数据的隐私和车辆IoV系统。人工智能技术提供了许多聪明的解决方案,可以帮助IoV网络解决所有这些问题和问题。机器学习(ML)是效率最高的国家之一AI工具,被广泛用于解决所有提到的问题。例如,毫升可以用来避免交通事故分析的驾驶行为和环境遥感数据周围的环境。 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. SN - 1939-0114 UR - https://doi.org/10.1155/2021/8868355 DO - 10.1155/2021/8868355 JF - Security and Communication Networks PB - Hindawi KW - ER -