Vehicular Computation Offloading for Real-time Applications

实时应用的车辆计算卸载

• 批准号: RGPIN-2018-06499
• 负责人: Zhao, Dongmei
• 金额: $ 2.4万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669765
• 关键词: Vehicular; Computation; Offloading; Real; time

With the ever increasing expectation for intelligent transportation, very powerful computing capabilities are required to improve road safety, enhance transport efficiency, and enrich the travel experience. For example, video clips captured by many cameras in and around vehicles are processed for avoiding collisions, interactive maps and augmented reality are embedded in many transportation-related applications, and games with high resolution virtual reality are common in the mobile devices of passengers. These computation-intensive applications are often time critical, due to road safety or their interactive nature. As a result, supporting these services and applications may greatly exceed the processing capability of single vehicles or mobile devices.******The concepts of mobile cloud computation and computation offloading enable vehicles and mobile devices to offload part or all the computation load to other devices that help speed up the executions. As a result, the traditional vehicular communication systems are being transformed from mainly providing communication services for drivers and passengers to an integrated system of communications, computing, and control. Powerful cloud servers may be accessed by vehicular users through wireless networks and the Internet, but they are more suitable for delay-tolerable services due to the relatively long delay to access the servers. For time-sensitive applications, servers should be deployed close to the vehicular users to reduce the communication delay. ******If road side units and cellular base stations are available along a road, dedicated edge servers can be installed and co-located with these infrastructure devices. However, it is unlikely that this fixed infrastructure can cover all roads in the near future due to possible low capital returns, complicated design issues, unpredictable communication and computation demands, and other reasons. On the other hand, future smart vehicles are likely to have, or can be equipped with, relatively powerful computation units, and can either help other devices or collaborate together in processing complicated tasks. Given the large number of smart devices on road and their wide geographical distribution, their aggregate computing capability can be enormous. The objective of this research is to study how to effectively integrate the available computing resources at pre-installed computing devices and moving vehicles along roads, and efficiently utilize these resources with the support of the existing vehicular communication networks to provide safe and convenient transportation and enriched travel experiences. ******The new ideas and knowledge generated from this research will promote a great market opportunity for various road and travel related products and applications, which will be of strong interest to automobile industry, wireless network providers and users, and software industry.**

随着对智能运输的期望，需要非常强大的计算能力，以提高道路安全性，提高运输效率并丰富旅行体验。例如，在许多与运输相关的应用中嵌入了许多相机捕获的视频剪辑，以避免碰撞，交互式地图和增强现实，并且具有高分辨率虚拟现实的游戏在乘客的移动设备中很常见。由于道路安全性或它们的互动性质，这些计算密集型应用通常至关重要。结果，支持这些服务和应用程序可能会大大超过单车或移动设备的处理能力。到其他有助于加快执行速度的设备。结果，传统的车辆通信系统正在从主要为驾驶员和乘客提供通信服务转变为通信，计算和控制系统的集成系统。车辆用户可以通过无线网络和Internet访问强大的云服务器，但是由于访问服务器的延迟相对较长，因此它们更适合延迟倾斜服务。对于时间敏感的应用程序，应将服务器部署在车辆用户附近，以减少通信延迟。 ******如果路边单元和蜂窝基站在道路上可用，则可以使用这些基础设施设备安装专用的边缘服务器。但是，由于可能的低资本收益，复杂的设计问题，不可预测的沟通和计算需求以及其他原因，这种固定的基础设施不可能在不久的将来涵盖所有道路。另一方面，未来的智能车辆可能会拥有或可以配备相对功能强大的计算单元，并且可以帮助其他设备或共同处理复杂的任务。鉴于道路上有大量的智能设备及其广泛的地理分布，它们的总计算能力可能是巨大的。这项研究的目的是研究如何有效地整合预装的计算设备和沿道路上的车辆的可用计算资源，并在现有的车辆通信网络的支持下有效利用这些资源，以提供安全便捷的运输和丰富的运输方式旅行经验。 ******这项研究产生的新想法和知识将为各种道路和旅行相关的产品和应用提供巨大的市场机会，这将引起汽车行业，无线网络提供商和用户以及软件行业的浓厚兴趣。**