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

TL;DR

This paper reviews model-based sensor selection techniques for traffic flow monitoring in IoV-enabled networks, highlighting challenges and advocating for data-driven methods to improve sensor deployment and traffic modeling accuracy.

Contribution

It surveys current model-based approaches and emphasizes the need for data-driven methodologies to optimize sensor deployment in intelligent transportation systems.

Findings

Sensor placement is a complex challenge in urban traffic monitoring.

Data-driven approaches can significantly enhance sensor deployment efficiency.

Adaptive systems are crucial for future IoV-enabled transportation networks.

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.