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Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment.

Tran Trong Khanh1, Tran Hoang Hai2, Md Delowar Hossain1

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

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Summary

This study introduces the Fu-SARSA algorithm for optimizing task offloading in 5G Internet of Things (IoT) networks. It effectively reduces task failure rates and service times by intelligently allocating resources across edge and cloud servers.

Keywords:
Internet of ThingsSARSAfuzzy logicmobile edge orchestratormulti-access edge computingtask offloading

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) devices in 5G networks generate substantial data traffic, demanding low latency and high computational power.
  • Existing task offloading strategies in Multi-Access Edge Computing (MEC) often lead to unbalanced server loads and high failure rates due to network congestion.

Purpose of the Study:

  • To propose a novel collaboration algorithm, Fu-SARSA, for efficient task offloading in heterogeneous 5G networks.
  • To minimize task failure rates and service times by optimizing resource allocation among local edge servers, cloud servers, and neighboring edge servers.

Main Methods:

  • Developed the Fu-SARSA algorithm, integrating a fuzzy-logic-based Mobile Edge Orchestrator (MEO) with State-Action-Reward-State-Action (SARSA) reinforcement learning.
  • Simulated performance using four application types: healthcare, Augmented Reality (AR), infotainment, and compute-intensive applications.

Main Results:

  • The Fu-SARSA algorithm demonstrated superior performance compared to existing methods.
  • Achieved significant reductions in both service time and task failure rates, particularly under high system load conditions.

Conclusions:

  • The proposed Fu-SARSA framework effectively addresses the challenges of task offloading in 5G MEC environments.
  • Intelligent resource allocation, including leveraging neighboring edge servers, is crucial for improving Quality of Service (QoS) in dynamic IoT networks.