The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet of Things (IoT)-oriented wireless sensor network (WSN). Meanwhile, 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. Within this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. Efficient sensor selection thus represents a critical concern while deploying WSNs on urban networks. In this paper, we provide an overview of such a notably hard problem. The contribution is twofold: (i) surveying state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring, emphasizing challenges of sensor placement, and (ii) advocating for {the development of} data-driven methodologies to enhance sensor deployment efficacy and traffic modeling accuracy. Further considerations underscore the importance of data-driven approaches for adaptive transportation systems aligned with the IoV paradigm.

翻译：6G赋能的车辆物联网（IoV）的出现有望彻底改变移动性与连接性，将车辆集成到一个面向移动物联网（IoT）的无线传感器网络（WSN）中。与此同时，5G技术和移动边缘计算通过促进实时连接并支持海量互联网接入，进一步支撑了这一愿景。在此背景下，面向物联网的无线传感器网络在智能交通系统中扮演着关键角色，为交通监测与管理提供了经济高效的替代方案。因此，在城市网络中部署无线传感器网络时，高效的传感器选择成为一个至关重要的问题。本文针对这一公认的难题进行了综述。贡献主要体现在两个方面：（i）系统梳理了面向交通流监测的高效传感器选择的最新模型驱动技术，重点分析了传感器布局所面临的挑战；（ii）倡导发展数据驱动方法，以提升传感器部署效能与交通建模精度。进一步的讨论强调了数据驱动方法对于构建符合车辆物联网范式的自适应交通系统的重要性。