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Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA

Qiliang Xu1, Guo Zhang1, Jianping Wang1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|July 11, 2023
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Summary
This summary is machine-generated.

This study introduces a cloud-edge-end collaborative computing network for the Internet of Vehicles. The proposed strategy optimizes task offloading, significantly reducing delay and energy consumption for connected vehicles.

Keywords:
Internet of VehiclesM-TSAcollaborative computingcomputational offloading

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Area of Science:

  • Computer Science
  • Networking
  • Artificial Intelligence

Background:

  • In-vehicle terminals in the Internet of Vehicles (IoV) face limitations in computing power, leading to high delay and energy consumption for complex tasks.
  • Traditional cloud computing introduces significant latency for IoV applications due to data transmission.
  • Mobile Edge Computing (MEC) offers lower latency but has constrained resources, potentially increasing task processing delays with high demand.

Purpose of the Study:

  • To address the computational challenges in the Internet of Vehicles by proposing a novel cloud-edge-end collaborative computing network.
  • To develop an efficient computational offloading strategy that balances task requirements with available distributed resources.
  • To enhance the performance of IoV systems by minimizing task processing delay and energy consumption.

Main Methods:

  • A cloud-edge-end collaborative computing system model was constructed for the Internet of Vehicles.
  • A computational offloading strategy was developed, integrating the M-TSA algorithm with task prioritization and offloading node prediction.
  • The proposed strategy was evaluated through comparative experiments using simulated real-world vehicle task instances.

Main Results:

  • The proposed vehicle computing network effectively utilizes distributed computing resources from cloud servers, edge servers, service vehicles, and task vehicles.
  • The M-TSA-based offloading strategy significantly improved the utility of task offloading compared to existing methods.
  • Experimental results demonstrated a substantial reduction in offloading delay and energy consumption for IoV tasks.

Conclusions:

  • The cloud-edge-end collaborative computing approach is a viable solution for overcoming the limitations of in-vehicle terminals in IoV.
  • The developed computational offloading strategy effectively manages resources and optimizes task execution in a dynamic vehicular environment.
  • The proposed network and strategy offer a superior approach for enhancing the performance and efficiency of Internet of Vehicles applications.