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Related Experiment Video

Updated: Sep 27, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks.

Saeid Jahandar1, Lida Kouhalvandi2, Ibraheem Shayea1

  • 1Electronics and Communication Engineering Department, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34467, Turkey.

Sensors (Basel, Switzerland)
|April 12, 2022
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Summary
This summary is machine-generated.

This study introduces new algorithms for multi-access edge computing (MEC) in 5G networks to optimize task offloading decisions. The proposed methods effectively manage user mobility and reduce handover costs, improving overall system performance.

Keywords:
fifth generation (5G)handover (HO)mobility managementmulti-access edge computing (MEC)sixth generation (6G)task offloading (TO)

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Area of Science:

  • Computer Science
  • Telecommunications Engineering

Background:

  • Multi-access Edge Computing (MEC) is crucial for 5G networks, enabling low-latency applications by processing data near users.
  • Ultra-dense 5G networks present challenges in managing user mobility and frequent handovers in MEC environments.

Purpose of the Study:

  • To develop algorithms for optimizing task offloading decisions in MEC networks, considering handover costs.
  • To enhance MEC performance by balancing handover, delay, and energy consumption for online task offloading.

Main Methods:

  • Proposed two online task offloading algorithms: one utilizing user and base station (BS) information, and another employing a BS-learning approach based on observed costs.
  • Incorporated handover cost into optimization functions for decision-making under unknown future tasks.

Main Results:

  • The proposed algorithms demonstrated superior performance compared to existing literature and baseline scenarios.
  • Simulation results confirmed the effectiveness of the developed methods in improving overall system performance in MEC-enabled 5G networks.

Conclusions:

  • The developed algorithms provide an effective solution for managing user mobility and optimizing task offloading in dense MEC networks.
  • The research contributes to efficient resource management in 5G MEC by considering critical factors like handover, delay, and energy costs.