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With the rapid expansion of IoT due to advancements like 5G, accurately forecasting IoT connections is essential for effective infrastructure planning. This paper presents a novel prediction model that integrates neural networks and macroeconomic factors, surpassing traditional single-factor methods. By combining a backpropagation neural network (BPNN) with macroeconomic indicators like GDP and digital economic output, our approach offers enhanced predictive accuracy and relevance to industry demands.

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A Novel Approach to Predicting IoT Connections Using Neural Networks and Macro-Economic Factors

  • 会议论文

  • 发表日期：

    2026年

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  • Shen Chong,
  • Bixian Zhang,
  • Longcun Wang,
  • Trong-The Nguyen,
  • Thi-Kien Dao,
  • Haohan Zhao

摘要

With the rapid expansion of IoT due to advancements like 5G, accurately forecasting IoT connections is essential for effective infrastructure planning. This paper presents a novel prediction model that integrates neural networks and macroeconomic factors, surpassing traditional single-factor methods. By combining a backpropagation neural network (BPNN) with macroeconomic indicators like GDP and digital economic output, our approach offers enhanced predictive accuracy and relevance to industry demands.