Real-Time Scheduling for Internet of Vehicles

车联网实时调度

• 批准号: RGPIN-2017-05578
• 负责人: Lu, Ning
• 金额: $ 1.75万
• 依托单位: Thompson Rivers University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637362
• 关键词: Real; Time; Scheduling; Internet; Vehicles

Internet of Vehicles (IoV) is envisioned to enable next-generation vehicular applications, such as preventive maintenance/diagnostics, feedback-based routing, speed and fuel management, and advanced active safety, among others. As a mission-critical system, IoV is required to guarantee the Quality of Service (QoS) requirements of various services/applications, especially for real-time services with deadline constraints either at packet level or application level. Serving real-time applications is therefore considered as a key component of IoV and many other cyber-physical systems. Despite significant research effort on IoV technology, a mature science to support real-time applications of high-confidence IoV is still missing, and traditional analysis tools/algorithms are unable to cope with the full complexity of IoV or adequately predict system behavior, due to great challenges that arise from the high mobility and intrinsic heterogeneity of such systems.

车联网（IoV）被设想为实现下一代车辆应用，例如预防性维护/诊断、基于反馈的路由、速度和燃料管理以及先进的主动安全等。作为一个关键任务系统，车联网需要保证各种服务/应用的服务质量（QoS）要求，特别是对于在分组级或应用级具有截止期限约束的实时服务。因此，为实时应用提供服务被认为是车联网和许多其他网络物理系统的关键组成部分。尽管对车联网技术进行了大量研究，但仍然缺乏支持高置信度车联网实时应用的成熟科学，传统的分析工具/算法无法科普车联网的全部复杂性或充分预测系统行为，这是由于此类系统的高移动性和内在异构性带来的巨大挑战。