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

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.