Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability

Abstract

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.

Keywords: 6G; intelligent transportation systems; internet of things; internet of vehicles; sensor selection; smart city; system observability; traffic monitoring; urban networks; wireless sensor networks.

Figure 1

From left to right, the representations of traffic models at different scale levels: microscale [26], mesoscale [27], macroscale [27].

Figure 2

The segments highlighted in green indicate the best selection according to the metrics $\mathrm{rank}\left[{\mathcal{W}}_n\right]$ (left) and $\mathcal{K}\left[{\mathcal{W}}_n\right]$ (right) of $p^{★}=8$ roads among $p=22$ possible roads in the industrial zone of Padua, Italy. Whereas, blue segments indicate roads that are not selected. More on these simulations at https://thesis.unipd.it/handle/20.500.12608/74384 (accessed on 21 October 2024).