论文标题
以信息为中心的车辆网络中的低延迟和新内容提供
Low-Latency and Fresh Content Provision in Information-Centric Vehicular Networks
论文作者
论文摘要
在本文中，考虑到与动态驾驶相关的上下文信息，从移动边缘缓存方面研究了以信息为中心的车辆网络（ICVN）的内容服务。为了提供低延迟的最新信息，设计了两个方案，用于在路边单元（RSUS）的缓存更新和内容交付。路边单元中心（RSUC）方案通过带宽分割来解速缓存更新和内容交付，其中缓存的内容项目以圆形旋转方式定期更新。请求自适应（REA）方案在用户请求中以某些概率更新了缓存的内容项。分析了两种提出的方案的性能，从而以封闭形式得出信息的平均信息（AOI）和服务潜伏期。令人惊讶的是，AOI延迟权衡并不总是存在，并且频繁的缓存更新会降低这两种性能。因此，进一步优化了RSUC和REA方案，以平衡AOI和潜伏期。对SUMO和OMNET ++模拟器进行了广泛的模拟，结果表明，所提出的方案可以将服务潜伏期最多减少80 \％，同时保证重载的ICVN中的内容新鲜度。
In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80\% while guaranteeing content freshness in heavily loaded ICVNs.
