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Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks.

Taiping Cui1,2, Yuyu Hu1,2, Bin Shen1,2

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Nan-An District, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|November 17, 2019
PubMed
Summary

This study introduces a mobile edge computing (MEC) approach for vehicle platoons to manage computation-intensive tasks. The proposed method optimizes task offloading, significantly reducing energy consumption and enhancing efficiency.

Keywords:
Lyapunov optimizationmobile edge computingtask offloadingvehicular platooning

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Area of Science:

  • Vehicular communication networks
  • Distributed computing systems
  • Mobile edge computing (MEC)

Background:

  • Vehicle terminals have limited resources, hindering computation-intensive applications and causing delays and energy drain.
  • Mobile edge computing (MEC) extends computation and storage to the network edge, enabling ultra-low latency services.
  • Vehicle platooning presents unique challenges for resource management due to coordinated vehicle movement.

Purpose of the Study:

  • To propose an efficient task offloading strategy for MEC-assisted vehicle platooning.
  • To address the limitations of vehicle terminal computation resources.
  • To minimize energy consumption and maximize offloading efficiency in vehicular environments.

Main Methods:

  • Developed a task offloading approach for MEC-assisted vehicle platooning.
  • Employed the Lyapunov optimization algorithm to ensure task queue stability.
  • Dynamically adjusted offloading decisions based on task data parameters.

Main Results:

  • The proposed approach effectively reduces energy consumption for task execution.
  • Significantly improved offloading efficiency compared to existing algorithms.
  • Demonstrated superior performance against shortest queue waiting time and full MEC offloading methods.

Conclusions:

  • The proposed MEC-assisted task offloading strategy is effective for vehicle platooning.
  • Optimized offloading decisions enhance system performance and resource utilization.
  • This approach offers a viable solution for computation-intensive tasks in vehicular networks.