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QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing.

Sidra Abid Syed1, Munaf Rashid2, Samreen Hussain3

  • 1Department of Biomedical Engineering, Faculty of ESTM, Ziauddin University, Karachi 74600, Pakistan.

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|January 11, 2022
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Summary
This summary is machine-generated.

This study introduces a new system for vehicular networks that uses fog computing to speed up message delivery and reduce errors. The proposed Quality of Service (QoS) aware and fault tolerance-based software-defined vehicular network (QAFT-SDVN) significantly improves communication efficiency.

Keywords:
fault-tolerancepriority basis schedulingquality of serviceresponse timesafety/non-safety messagesvehicular ad-hoc network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Telecommunications

Background:

  • Vehicular ad-hoc networks (VANETs) face challenges in Quality of Service (QoS) due to reliance on cloud computing, leading to increased response times.
  • Existing VANET communication systems require further research to overcome limitations like high latency and message failure rates.

Purpose of the Study:

  • To propose a novel Quality of Service (QoS) aware and fault tolerance-based software-defined vehicular network (QAFT-SDVN) utilizing cloud-fog computing.
  • To enhance the efficiency and reliability of vehicular communication systems by addressing response time and message failure issues.

Main Methods:

  • Implemented a hybrid cloud-fog computing model where Software-Defined Network (SDN) nodes are situated on fog nodes for message processing.
  • Developed heuristic algorithms for message prioritization based on message nature, deadline, and size, categorizing them into safety and non-safety.
  • Integrated a fault tolerance mechanism to re-transmit undelivered messages.

Main Results:

  • Reduced response time for safety and non-safety messages by 50% through the use of fog nodes for the SDN controller.
  • Decreased execution time for messages by up to 4%.
  • Lowered the task failure ratio by 15% to 23.3% compared to existing models.

Conclusions:

  • The proposed QAFT-SDVN model effectively reduces response and execution times in vehicular networks by leveraging fog computing.
  • The integrated fault tolerance mechanism significantly improves message delivery reliability.
  • The findings demonstrate the potential of cloud-fog computing in enhancing the performance of software-defined vehicular networks.