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<p>The Internet of Things (IoT) is revolutionizing Information and Communication Technology (ICT) by transforming everyday objects into web-enabled, interconnected devices. As IoT adoption grows, the proliferation of vendors and backend systems has introduced diverse data protocols, creating challenges in integrating hybrid frameworks and application protocols, especially in terms of scalability. To address these issues, this study proposes a scalable IoT data interoperability framework tailored for real-time applications. It leverages a model-driven architecture (MDA) and a novel metamodeling technique that combines feature profiling with transformation mechanisms to enhance device scalability and integration across heterogeneous environments. The framework is validated using a smart building power consumption dataset comprising 60,215 instances. Experimental results show that the proposed method outperforms existing solutions, achieving a low delay of 5.23 ms, with high classification precision (92.62%), specificity (92.22%), and sensitivity (92.52%). It also demonstrates strong statistical performance, with a mean absolute error (MAE) of 3.82% and a root mean square error (RMSE) of 1.68%, along with improved reliability (87.50%) and stability (73.8%).</p>

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IoT-inspired scalability assessment for interoperable applications

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Renu Sharma,
  • Anil Sharma

摘要

The Internet of Things (IoT) is revolutionizing Information and Communication Technology (ICT) by transforming everyday objects into web-enabled, interconnected devices. As IoT adoption grows, the proliferation of vendors and backend systems has introduced diverse data protocols, creating challenges in integrating hybrid frameworks and application protocols, especially in terms of scalability. To address these issues, this study proposes a scalable IoT data interoperability framework tailored for real-time applications. It leverages a model-driven architecture (MDA) and a novel metamodeling technique that combines feature profiling with transformation mechanisms to enhance device scalability and integration across heterogeneous environments. The framework is validated using a smart building power consumption dataset comprising 60,215 instances. Experimental results show that the proposed method outperforms existing solutions, achieving a low delay of 5.23 ms, with high classification precision (92.62%), specificity (92.22%), and sensitivity (92.52%). It also demonstrates strong statistical performance, with a mean absolute error (MAE) of 3.82% and a root mean square error (RMSE) of 1.68%, along with improved reliability (87.50%) and stability (73.8%).