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Related Experiment Video

Updated: Jun 4, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

Optimized task allocation in SDN-enabled 5 G IoMT networks using fog computing.

Reza Mohammadi1

  • 1Computer Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. r.mohammadi@basu.ac.ir.

BMC Medical Informatics and Decision Making
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized task allocation framework for 5G Internet of Medical Things (IoMT) networks. It enhances real-time health monitoring by reducing latency and energy consumption using fog computing and Software-Defined Networking (SDN).

Keywords:
5GFog computingIoMTSDN

Related Experiment Videos

Last Updated: Jun 4, 2026

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Health Informatics

Background:

  • The Internet of Medical Things (IoMT) uses 5G User Equipment (UE) for real-time patient monitoring, but faces latency, resource, and energy challenges.
  • Meeting 5G's ultra-reliable low-latency communication (URLLC) requirements in healthcare is critical for effective IoMT deployment.

Purpose of the Study:

  • To propose an optimized task allocation framework for SDN-enabled 5G IoMT networks integrating fog computing.
  • To minimize latency, load imbalance, and energy consumption in IoMT task processing at the network edge.

Main Methods:

  • Developed a multi-objective optimization model for task allocation, solved using a heuristic approach for NP-hard problems.
  • Integrated fog computing for edge processing and utilized Software-Defined Networking (SDN) for centralized control and routing.
  • Evaluated the framework using NS3, Mininet, and Ryu simulations, comparing against Round Robin and Nearest Fog Server baselines.

Main Results:

  • Demonstrated significant reductions in latency, deadline violations, and energy consumption compared to baseline methods.
  • Achieved improved load distribution across fog nodes, enhancing overall network efficiency.
  • The proposed framework effectively enhances Quality of Service (QoS), scalability, and sustainability in IoMT systems.

Conclusions:

  • The optimized task allocation framework, leveraging SDN and fog computing, provides a robust solution for next-generation healthcare systems.
  • This approach addresses critical challenges in 5G IoMT, improving performance and reliability for real-time health monitoring.