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<p>The rapid expansion of the Internet of Things (IoT) has sparked a pressing need for innovative solutions to manage network traffic and ensure Quality of Service (QoS). Drawing inspiration from the intrinsic factors that influence device functionalities, this article introduces a novel approach to tackle these challenges by integrating underlying factors into QoS policies. By recognizing the diverse influences behind device functionalities, our approach optimizes network performance in the face of the challenges faced in managing Quality of Service (QoS) in IoT networks, particularly in scenarios where diverse devices with varying requirements coexist. Evaluation of our approach showcases remarkable advantages, with the Monte Carlo Control (MCC) algorithm demonstrating superior performance. Surpassing traditional methods such as Q-learning and dynamic programming, MCC achieves peak efficiency in just 50 episodes. Statistical analysis further validates MCC’s superiority, with throughput reaching 730 packets/second and latency reduced to 50&#xa0;ms, outperforming Q-learning and dynamic programming. These findings underscore the pivotal role of these underlying factors in enhancing IoT QoS, leading to enhanced system performance and user experience optimization. Through the implementation of our approach, we envision a future where IoT networks seamlessly accommodate diverse influences, thereby enabling unprecedented scalability and reliability in IoT deployments. By harnessing the power of these underlying factors, we pave the way for transformative advancements in IoT infrastructure and propel towards a future of enhanced connectivity and efficiency.</p>

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Incorporating device category into QoS traffic management policy using Monte Carlo control in IoT gateways

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Imane Chakour,
  • Sajida Mhammedi,
  • Cherki Daoui,
  • Mohamed Baslam

摘要

The rapid expansion of the Internet of Things (IoT) has sparked a pressing need for innovative solutions to manage network traffic and ensure Quality of Service (QoS). Drawing inspiration from the intrinsic factors that influence device functionalities, this article introduces a novel approach to tackle these challenges by integrating underlying factors into QoS policies. By recognizing the diverse influences behind device functionalities, our approach optimizes network performance in the face of the challenges faced in managing Quality of Service (QoS) in IoT networks, particularly in scenarios where diverse devices with varying requirements coexist. Evaluation of our approach showcases remarkable advantages, with the Monte Carlo Control (MCC) algorithm demonstrating superior performance. Surpassing traditional methods such as Q-learning and dynamic programming, MCC achieves peak efficiency in just 50 episodes. Statistical analysis further validates MCC’s superiority, with throughput reaching 730 packets/second and latency reduced to 50 ms, outperforming Q-learning and dynamic programming. These findings underscore the pivotal role of these underlying factors in enhancing IoT QoS, leading to enhanced system performance and user experience optimization. Through the implementation of our approach, we envision a future where IoT networks seamlessly accommodate diverse influences, thereby enabling unprecedented scalability and reliability in IoT deployments. By harnessing the power of these underlying factors, we pave the way for transformative advancements in IoT infrastructure and propel towards a future of enhanced connectivity and efficiency.