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The exponential growth of the Internet of Things (IoT) has placed significant demands on the network infrastructure, particularly in ensuring the quality of service for real-time applications. This review investigates using Software-Defined Networking (SDN) and Artificial Intelligence (AI) as complementary approaches to address the quality-of-service challenges in IoT ecosystems. SDN offers centralized control, enabling dynamic resource management, while AI enhances decision-making by predicting network conditions and optimizing traffic routing. A comprehensive analysis compares the performance of traditional IoT systems, SDN-based IoT, and AI + SDN-based IoT regarding crucial Quality-of-Service (QoS) parameters: latency, energy efficiency, throughput, and packet loss. Our results demonstrate that the AI + SDN approach significantly outperforms the other models, making it an ideal solution for mission-critical applications like healthcare and smart cities. The combined AI + SDN framework provides superior adaptability, reliability, and efficiency, setting the foundation for future IoT deployments.

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Optimizing Quality of Service in IoT Ecosystems: A Review of Adaptive Intelligence and Software-Defined Networks for Efficient Task Offloading and Resource Allocation

  • 会议论文

  • 发表日期：

    2025年

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  • Lydia D. Isaac,
  • V. Mohanraj

摘要

The exponential growth of the Internet of Things (IoT) has placed significant demands on the network infrastructure, particularly in ensuring the quality of service for real-time applications. This review investigates using Software-Defined Networking (SDN) and Artificial Intelligence (AI) as complementary approaches to address the quality-of-service challenges in IoT ecosystems. SDN offers centralized control, enabling dynamic resource management, while AI enhances decision-making by predicting network conditions and optimizing traffic routing. A comprehensive analysis compares the performance of traditional IoT systems, SDN-based IoT, and AI + SDN-based IoT regarding crucial Quality-of-Service (QoS) parameters: latency, energy efficiency, throughput, and packet loss. Our results demonstrate that the AI + SDN approach significantly outperforms the other models, making it an ideal solution for mission-critical applications like healthcare and smart cities. The combined AI + SDN framework provides superior adaptability, reliability, and efficiency, setting the foundation for future IoT deployments.