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Updated: Dec 15, 2025

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VNF Chain Placement for Large Scale IoT of Intelligent Transportation.

Xing Wu1,2, Jing Duan1, Mingyu Zhong1

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Sensors (Basel, Switzerland)
|July 12, 2020
PubMed
Summary

This study introduces Border VNF Chain Placement (BVCP), a new method for dynamic intelligent transportation networks. BVCP efficiently places virtual network functions in the Internet of Things (IoT) by dividing network graphs, outperforming existing solutions.

Keywords:
border nodeintelligent transportationplacementsubgraphvirtual network function

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Area of Science:

  • Computer Science
  • Network Engineering
  • Transportation Systems

Background:

  • Intelligent transportation systems (ITS) leverage the Internet of Things (IoT) for enhanced traffic management, safety, and reduced environmental impact.
  • A key challenge in ITS is the dynamic nature of network topology due to the constant movement of vehicles and pedestrians, necessitating frequent service chain reconfigurations.
  • Current Virtual Network Function (VNF) chain placement methods struggle with rapidly changing network topologies, leading to inefficient resource utilization and computational overhead.

Purpose of the Study:

  • To address the limitations of existing VNF placement methods in dynamic ITS environments.
  • To propose a novel and efficient VNF chain placement strategy tailored for large-scale IoT-enabled intelligent transportation networks.
  • To improve the adaptability and resource efficiency of VNF placement in rapidly evolving network topologies.

Main Methods:

  • Developed a novel VNF placement method named BVCP (Border VNF Chain Placement).
  • Represented the intelligent transportation network topology as a graph and divided it into multiple subgraphs.
  • Exploited border hypervisors within these subgraphs to optimize VNF chain placement.

Main Results:

  • The proposed BVCP method demonstrated superior performance compared to state-of-the-art VNF placement techniques.
  • BVCP achieved higher efficiency in VNF chain placement within large-scale IoT environments characteristic of intelligent transportation.
  • The method effectively handles the dynamic topology changes inherent in mobile edge nodes.

Conclusions:

  • BVCP offers a significant improvement for VNF chain placement in dynamic and large-scale IoT networks, particularly for intelligent transportation.
  • The subgraph division and border hypervisor exploitation strategy effectively mitigates the inefficiencies of traditional methods.
  • This approach enhances the scalability and resource management of intelligent transportation systems.