With the rapid advancement of smart city infrastructure, vehicle-to-network (V2N) communication has emerged as a crucial technology to enable intelligent transportation systems (ITS). The investigation of new methods to improve V2N communications is sparked by the growing need for high-speed and dependable communications in vehicular networks. To achieve ultra-reliable low latency communication (URLLC) for V2N scenarios, we propose a smart meter (SM)-based cognitive network (CN) architecture for V2N communications. Our scheme makes use of SMs' available underutilized time resources to let them serve as distributed access points (APs) for V2N communications to increase reliability and decrease latency. We propose and investigate two algorithms for efficiently associating vehicles with the appropriate SMs. Extensive simulations are carried out for comprehensive performance evaluation of our proposed architecture and algorithms under diverse system scenarios. Performance is investigated with particular emphasis on communication latency and reliability, which are also compared with the conventional base station (BS)-based V2N architecture for further validation. The results highlight the value of incorporating SMs into the current infrastructure and open the door for future ITSs to utilize more effective and dependable V2N communications.

翻译：随着智慧城市基础设施的快速发展，车辆与网络（V2N）通信已成为实现智能交通系统（ITS）的关键技术。车载网络对高速可靠通信需求的日益增长，催生了改进V2N通信新方法的研究。为在V2N场景中实现超可靠低延迟通信（URLLC），我们提出了一种基于智能电表（SM）的认知网络（CN）架构。该方案利用智能电表中未充分利用的时间资源，使其作为分布式接入点（AP）服务于V2N通信，从而提高可靠性并降低延迟。我们提出并研究了两种用于高效关联车辆与合适智能电表的算法。通过在不同系统场景下开展大规模仿真，对所提架构与算法进行了全面性能评估。仿真重点分析了通信延迟与可靠性，并与传统基于基站（BS）的V2N架构进行对比验证。结果凸显了将智能电表集成至现有基础设施的价值，为未来ITS利用更高效可靠的V2N通信开辟了道路。