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

Area of Science:

• Computer Science
• Electrical Engineering
• Transportation Systems

Background:

• The 6G Internet of Vehicles (IoV) vision integrates vehicles into a mobile Internet of Things (IoT)-oriented wireless sensor network (WSN).
• 5G and mobile edge computing enable real-time connectivity and massive access for IoV.
• IoT-oriented WSNs are vital for intelligent transportation systems, offering cost-effective traffic monitoring.

Purpose of the Study:

• To survey state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring.
• To highlight challenges in sensor placement for urban WSN deployment.
• To advocate for data-driven methodologies to enhance sensor deployment and traffic modeling accuracy.

Main Methods:

• Literature review of model-based sensor selection techniques.
• Analysis of sensor placement challenges in urban traffic monitoring.
• Conceptual advocacy for data-driven approaches in WSN deployment.

Main Results:

• Current model-based techniques for sensor selection face significant challenges, particularly concerning optimal sensor placement.
• Data-driven methodologies show promise for improving the efficacy of sensor deployment.
• Enhanced sensor deployment leads to more accurate traffic modeling.

Conclusions:

• Efficient sensor selection is a critical, complex problem in 6G IoV traffic monitoring.
• Data-driven approaches are essential for advancing adaptive transportation systems within the IoV paradigm.
• Future research should focus on developing and implementing data-driven solutions for sensor selection and traffic modeling.