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<p>The Internet of Things (IoT) is a complex network connected to heterogeneous IoT enabled devices. However, the potential of failure at different levels, such as IoT devices, networks, links, processing, and storage components, is a significant challenge that could compromise performance in IoT-enabled systems. This field has revolutionized numerous industries by enabling seamless connectivity and intelligent automation. The proliferation of IoT devices has also introduced fault detection and correction challenges, which are critical to ensuring system reliability and efficiency. In this context, the authors proposed an Autonomous Fault Detection and Recovery System (AFDRS-IoT) for IoT-enabled networks explicitly tailored for IoT environments. The hybrid Z-Test Score fault detection method categorizes faults and detects them efficiently. The fault recovery methods involve correcting and rerouting using tabu optimization. The proposed AFDRS-IoT system is compared to state-of-the-art algorithms. It performs better in terms of fault detection accuracy, false positive rate, and false alarm rate up to 30%, up to 60%, and up to 45% respectively.</p>

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Afdrs-iot: an autonomous fault detection and recovery systems for IoT networks

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Vishnu Kumar Prajapati,
  • T. P. Sharma,
  • Lalit Kumar Awasthi

摘要

The Internet of Things (IoT) is a complex network connected to heterogeneous IoT enabled devices. However, the potential of failure at different levels, such as IoT devices, networks, links, processing, and storage components, is a significant challenge that could compromise performance in IoT-enabled systems. This field has revolutionized numerous industries by enabling seamless connectivity and intelligent automation. The proliferation of IoT devices has also introduced fault detection and correction challenges, which are critical to ensuring system reliability and efficiency. In this context, the authors proposed an Autonomous Fault Detection and Recovery System (AFDRS-IoT) for IoT-enabled networks explicitly tailored for IoT environments. The hybrid Z-Test Score fault detection method categorizes faults and detects them efficiently. The fault recovery methods involve correcting and rerouting using tabu optimization. The proposed AFDRS-IoT system is compared to state-of-the-art algorithms. It performs better in terms of fault detection accuracy, false positive rate, and false alarm rate up to 30%, up to 60%, and up to 45% respectively.