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). 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. In this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. This paper's 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 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赋能的车联网（IoV）有望革命性地改变移动性和连接性，将车辆整合到面向移动物联网（IoT）的无线传感器网络（WSN）中。5G技术与移动边缘计算通过促进实时连接、赋能大规模互联网接入，进一步支撑这一愿景。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发挥着关键作用，为交通监控与管控提供了经济可行的替代方案。本文贡献体现在两个方面：（i）系统梳理了面向交通流监测中高效传感器选择的现有模型化技术，重点关注传感器布设难题；（ii）倡导采用数据驱动方法论以提升传感器部署效能与交通建模精度。进一步思考强调了数据驱动方法对构建适应IoV范式的自适应交通系统的重要性。