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<p>The Internet of Things (IoT) is a fundamental technology for emerging 5G networks, enabling data exchange among interconnected devices. However, the rapid expansion of IoT-based smart cities increases data traffic and collision probability. This paper presents a novel adaptive IEEE 802.11ah MAC protocol that integrates artificial intelligence (AI) to mitigate data collisions. Two mathematical models are derived to analyze collision probability in the Restricted Access Window (RAW) and Periodic RAW (PRAW) access mechanisms. Moreover, six machine learning (ML) algorithms are applied to classify IoT traffic patterns and map them to appropriate access channels. The proposed protocol achieves an 80–90% reduction in collision probability, with the decision tree (DT) algorithm attaining 99.45% testing accuracy and 1.066&#xa0;s processing time.</p>

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A novel adaptive IEEE 802.11ah MAC protocol using machine learning for data collision reduction in smart cities IoT networks

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Amin S. Ibrahim,
  • Ahmed M. Abbas,
  • Saeed Mohsen

摘要

The Internet of Things (IoT) is a fundamental technology for emerging 5G networks, enabling data exchange among interconnected devices. However, the rapid expansion of IoT-based smart cities increases data traffic and collision probability. This paper presents a novel adaptive IEEE 802.11ah MAC protocol that integrates artificial intelligence (AI) to mitigate data collisions. Two mathematical models are derived to analyze collision probability in the Restricted Access Window (RAW) and Periodic RAW (PRAW) access mechanisms. Moreover, six machine learning (ML) algorithms are applied to classify IoT traffic patterns and map them to appropriate access channels. The proposed protocol achieves an 80–90% reduction in collision probability, with the decision tree (DT) algorithm attaining 99.45% testing accuracy and 1.066 s processing time.