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Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.

Tao Huang1,2, Siyu Yan3,4, Fan Yang5,6

  • 1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. htao@bupt.edu.cn.

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Summary

This study introduces a new architecture for software-defined vehicular sensor networks in agriculture. It enhances network stability and survivability during controller connection loss, crucial for efficient precision agriculture.

Keywords:
Open vSwitchSDN-based vehicular sensor networks in agricultureconnection statenetworking survivabilityself-learning

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Area of Science:

  • Computer Science
  • Agricultural Engineering
  • Network Engineering

Background:

  • Software-defined networking (SDN) offers efficiency in vehicular sensor networks for precision agriculture.
  • Controller node disconnection in wireless SDN environments disrupts network control and data flow.
  • Dynamic topology and unstable signals pose challenges to SDN-based vehicular sensor network stability.

Purpose of the Study:

  • To propose a novel SDN architecture for vehicular sensor networks to minimize performance loss from controller connection interruption.
  • To enhance the survivability and stability of SDN-based vehicular sensor networks in agricultural applications.

Main Methods:

  • Designed a connection state detection and self-learning mechanism for SDN controller resilience.
  • Developed prototypes using extended Open vSwitch and Ryu.
  • Evaluated network performance under controller connection loss scenarios.

Main Results:

  • Achieved controller connection loss recovery time under 100 ms.
  • Maintained real-time rule updating and stable throughput.
  • Demonstrated enhanced network survivability and stability.

Conclusions:

  • The proposed architecture effectively mitigates performance penalties associated with controller connection loss in SDN-based vehicular sensor networks.
  • This approach significantly improves the reliability of precision agriculture systems relying on autonomous vehicles and sensor networks.