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In the rapidly expanding domain of the Internet of Things (IoT), optimizing Quality of Service (QoS) is essential to ensure seamless operation and enhance user experience. This paper introduces an innovative approach to QoS optimization through a three-layer architecture that integrates fuzzy logic with advanced machine learning algorithms. Our model dynamically adapts to fluctuating network conditions and evolving user needs, demonstrating substantial improvements in both service delivery and resource management. The experimental results highlight the effectiveness of the model, showing an increase in QoS 7and 11% in the perception layer for 100 and 1000 services, respectively, and an improvement 10% and 8.75% in the network layer for the same quantity of services. This approach establishes a foundation for more resilient and efficient IoT ecosystems.

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Toward a QoS Improvement in IoT by Combining Fuzzy Logic and Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Lagnfdi Oussama,
  • Myyara Marouane,
  • Darif Anouar

摘要

In the rapidly expanding domain of the Internet of Things (IoT), optimizing Quality of Service (QoS) is essential to ensure seamless operation and enhance user experience. This paper introduces an innovative approach to QoS optimization through a three-layer architecture that integrates fuzzy logic with advanced machine learning algorithms. Our model dynamically adapts to fluctuating network conditions and evolving user needs, demonstrating substantial improvements in both service delivery and resource management. The experimental results highlight the effectiveness of the model, showing an increase in QoS 7and 11% in the perception layer for 100 and 1000 services, respectively, and an improvement 10% and 8.75% in the network layer for the same quantity of services. This approach establishes a foundation for more resilient and efficient IoT ecosystems.