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Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic

Md Delowar Hossain1, Tangina Sultana1, Md Alamgir Hossain1

  • 1Department of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, Korea.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces a dynamic task offloading approach for vehicular edge computing (VEC) using non-cooperative games. The method optimizes offloading strategies to reduce response times and task failures in vehicular networks.

Keywords:
game theorymulti-access edge computingtask offloadingvehicular edge computing

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Area of Science:

  • Vehicular networking
  • Edge computing
  • Game theory applications

Background:

  • Vehicular edge computing (VEC) enhances vehicular networks (VNs) via task offloading.
  • Resource-constrained vehicles offload tasks to roadside units (RSUs).
  • High mobility and network overload pose challenges for real-time task processing in VEC, degrading performance.

Purpose of the Study:

  • To propose an efficient dynamic task offloading approach for VEC to address real-time processing challenges.
  • To enhance vehicular performance by minimizing response time and task failure rates.
  • To enable vehicles to independently decide optimal offloading strategies to MEC or cloud servers.

Main Methods:

  • A non-cooperative game (NGTO) framework is proposed for dynamic task offloading.
  • Vehicles employ a best response offloading strategy to achieve a stable equilibrium.
  • Task-offloading probabilities are dynamically adjusted to maximize individual vehicle utility.

Main Results:

  • The NGTO approach significantly reduces response time and task-failure rates.
  • Compared to Local RSU Computing (LRC), reductions were 47.6% and 54.6%, respectively.
  • Compared to random and collaborative offloading, reductions were substantial (up to 39.7%).

Conclusions:

  • The proposed NGTO scheme effectively addresses VEC challenges related to mobility and overload.
  • The strategy ensures performance guarantees by optimizing task offloading decisions.
  • This approach offers a stable and efficient solution for improving vehicular network performance.