Intelligent mobility management in emerging vehicular networks

车载网络是 新兴的网络，使车辆之间的连接成为可能， 将其与道路基础设施连接起来。在这样的网络中管理移动性提出了几个 我们希望在本提案中解决的挑战。这些网络使得 实现不同类型的应用：安全应用，以避免 碰撞和道路工程，例如，自动化的实时应用 驾驶辅助、智能交通系统（ITS）应用，以管理交通 并建议绕道，舒适的应用，如自动收费 高速公路上的支付、与在线多媒体内容的连接等。 为了提高车辆的运行效率， 以及几乎连续的连接。的 我们在此提出的研究计划的重点是智能移动管理， 新兴的汽车网络。它涵盖了广泛的领域，如移动的网络， 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， 交通堵塞。 的主要目标 研究计划是设计智能方法，算法和工具，以有效地 通过交通拥堵预测来真实的实时管理车辆移动性， 检测与控制从长远来看，这项研究计划旨在提出 下一代自组织车辆的移动性管理体系结构 基于机器学习的网络，具有预测能力， 或减少交通挤塞。为达致这个目标，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和理解相关信息， 用户，以推断战略行动和决策，以检测，控制和 预测交通拥堵，提供更好的服务质量， 改善道路使用者的体验。 移动性 新兴车辆网络中的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， 这些网络。事实上，车载网络将使各种 应用：道路安全应用，高效运输应用， 监控应用。

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.