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<p>The Internet of Medical Things (IoMT) leverages 5 G-connected User Equipment (UE) to enable real-time health monitoring, transmitting patient data to hospital clouds for processing. However, this approach faces challenges in latency, resource efficiency, and energy use, critical for meeting 5 G’s ultra-reliable low-latency communication (URLLC) demands in healthcare. This paper proposes an optimized task allocation framework for SDN-enabled 5 G IoMT networks, integrating fog computing to process tasks closer to the edge. Patient-generated tasks defined by data size, CPU cycles, and deadlines are intercepted by a dedicated fog broker, which applies a multi-objective optimization model to minimize latency (transmission and processing), load imbalance, and energy consumption across fog nodes. A heuristic solves this NP-hard problem, after which the SDN controller installs optimal routing paths between UEs and selected fog nodes. We evaluate the framework using NS3, Mininet, and Ryu simulations, comparing it against Round Robin and Nearest Fog Server baselines. Results show reductions in latency, deadline violations, and energy consumption, alongside improved load distribution. By harnessing SDN’s centralized control and fog computing’s proximity, our approach enhances QoS, scalability, and sustainability in IoMT, offering a robust solution for next generation healthcare systems.</p>

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

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  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Reza Mohammadi

摘要

The Internet of Medical Things (IoMT) leverages 5 G-connected User Equipment (UE) to enable real-time health monitoring, transmitting patient data to hospital clouds for processing. However, this approach faces challenges in latency, resource efficiency, and energy use, critical for meeting 5 G’s ultra-reliable low-latency communication (URLLC) demands in healthcare. This paper proposes an optimized task allocation framework for SDN-enabled 5 G IoMT networks, integrating fog computing to process tasks closer to the edge. Patient-generated tasks defined by data size, CPU cycles, and deadlines are intercepted by a dedicated fog broker, which applies a multi-objective optimization model to minimize latency (transmission and processing), load imbalance, and energy consumption across fog nodes. A heuristic solves this NP-hard problem, after which the SDN controller installs optimal routing paths between UEs and selected fog nodes. We evaluate the framework using NS3, Mininet, and Ryu simulations, comparing it against Round Robin and Nearest Fog Server baselines. Results show reductions in latency, deadline violations, and energy consumption, alongside improved load distribution. By harnessing SDN’s centralized control and fog computing’s proximity, our approach enhances QoS, scalability, and sustainability in IoMT, offering a robust solution for next generation healthcare systems.