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

arXiv: https://arxiv.org/abs/2404.08588 · HTML: https://mlsanity.com/paper/2404.08588/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2024.

## Built on by (most cited first, 4 of 4)

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- [Towards Scalable IoT Deployment for Visual Anomaly Detection via Efficient Compression](https://mlsanity.com/paper/2505.07119/) (2025)
- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)
- [Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/2605.14501/) (2026)

## Abstract (arXiv)

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.