Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability
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 facilita...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1416 |
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| author | Marco Fabris Riccardo Ceccato Andrea Zanella |
| author_facet | Marco Fabris Riccardo Ceccato Andrea Zanella |
| author_sort | Marco Fabris |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e958c455eab46e5a8fb3333502bd5d8 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3e958c455eab46e5a8fb3333502bd5d82025-08-20T02:53:22ZengMDPI AGSensors1424-82202025-02-01255141610.3390/s25051416Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network ObservabilityMarco Fabris0Riccardo Ceccato1Andrea Zanella2Department of Information Engineering, University of Padova, Via Gradenigo 6B, 35131 Padua, ItalyDepartment of Civil, Environmental and Architectural Engineering, University of Padova, Via Marzolo 9, 35131 Padua, ItalyDepartment of Information Engineering, University of Padova, Via Gradenigo 6B, 35131 Padua, ItalyThe 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.https://www.mdpi.com/1424-8220/25/5/1416sensor selectiontraffic monitoringsystem observabilitywireless sensor networksurban networkssmart city |
| spellingShingle | Marco Fabris Riccardo Ceccato Andrea Zanella Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability Sensors sensor selection traffic monitoring system observability wireless sensor networks urban networks smart city |
| title | Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability |
| title_full | Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability |
| title_fullStr | Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability |
| title_full_unstemmed | Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability |
| title_short | Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques Leveraging Network Observability |
| title_sort | efficient sensors selection for traffic flow monitoring an overview of model based techniques leveraging network observability |
| topic | sensor selection traffic monitoring system observability wireless sensor networks urban networks smart city |
| url | https://www.mdpi.com/1424-8220/25/5/1416 |
| work_keys_str_mv | AT marcofabris efficientsensorsselectionfortrafficflowmonitoringanoverviewofmodelbasedtechniquesleveragingnetworkobservability AT riccardoceccato efficientsensorsselectionfortrafficflowmonitoringanoverviewofmodelbasedtechniquesleveragingnetworkobservability AT andreazanella efficientsensorsselectionfortrafficflowmonitoringanoverviewofmodelbasedtechniquesleveragingnetworkobservability |