Intelligent mobility management in emerging vehicular networks

车载网络是 新兴网络，使车辆之间的连接成为可能，并 将它们与道路基础设施连接起来。在这样的网络中管理移动性会带来几个问题 我们希望在这项提案中解决的挑战。这些网络使之成为可能 实现不同类型的应用程序：避免安全应用程序 例如，碰撞和道路工作，自动化的实时应用程序 驾驶辅助、智能交通系统(ITS)应用程序以管理交通 并建议在其他方面走弯路，舒适的应用程序，如自动收费 高速公路上的付款、连接到在线多媒体内容等 高效运行，车辆对计算能力的需求增加，因为 以及一种近乎连续的联系。这个 我们在此提出的研究计划集中在智能移动管理方面 新兴的车载网络。它覆盖了从移动网络到移动网络的广泛领域， 人工智能、机器学习、数据科学和数学 优化，以解决车辆造成的道路拥堵和 交通堵塞。 这项工作的主要目标是 研究计划是设计智能的方法、算法和工具，以有效地 通过交通拥堵预测实时管理车辆机动性， 检测和控制。从长远来看，这项研究计划旨在提出 一种适用于下一代自组织车辆的移动性管理体系结构 基于机器学习的具有预测能力的网络 交通拥堵还是为了减轻它。为了达到这一目标，我们建议 一种基于大数据特征的四个V的方法：数量、多样性、 准确性和可变性。我们的长期架构将允许收集、 从网络生成的数据中提取并理解相关信息 用户，以便推断战略行动和决策以检测、控制和 预测交通拥堵并提供更好的服务质量以及 改善了道路使用者的体验。 移动性 在新兴的车辆网络中管理是一个非常重要的问题 从科学研究和商业扩张的角度来看， 自动驾驶汽车。它提出了许多挑战，这些挑战限制了采用 这些网络。事实上，车载网络将使各种 应用：道路安全应用、高效交通应用、 监控应用程序。

Vehicular networks are emerging networks that make it possible to connect vehicles to one another and to link them with road infrastructures. Managing mobility in such networks raises several challenges we wish to address in this proposal. These networks make it possible to implement different types of applications: safety applications to avoid collisions and road work for example, real-time applications for automated driving assistance, intelligent transport system (ITS) applications to manage traffic and suggest detours amongst others, comfort applications such as automatic toll payments on motorways, connection to online multimedia content, etc. To function efficiently, the vehicle has an increased need for computing power as well as a nearly-continuous connection. The research program we hereby present focuses on intelligent mobility management in emerging vehicular networks. It covers areas as broad as mobile networks, artificial intelligence, machine learning, data science and mathematical optimization, in order to address road congestion created by vehicles and causing traffic jams. The main objective of this research program is to design intelligent methods, algorithms and tools to efficiently manage vehicular mobility in real time through traffic congestion prediction, detection and control. In the longer term, this research program aims at proposing a mobility management architecture for next-generation ad-hoc vehicular networks based on machine learning and endowed with the ability to predict traffic congestion or to reduce it. To achieve this objective, we propose an approach based on the four Vs that characterize big data: Volume, Variety, Veracity and Variability. Our long-term architecture will allow to collect, extract and understand relevant information from network generated data and users in order to infer strategic actions and decisions to detect, control and predict traffic congestion and provide better quality of service as well as an improved experience to road users. Mobility management in emerging vehicular networks is a problem of great importance both from the point of view of scientific research and the commercial expansion in autonomous vehicles. It raises many challenges that condition the adoption of these networks. In fact, vehicular networks will make possible various applications: road safety applications, efficient transport applications, surveillance applications.