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Quantitative failure analysis for IoT systems: an integrated model-based framework

Author

Listed:
  • Alhassan Abdulhamid

    (University of Bradford)

  • Sohag Kabir

    (University of Bradford)

  • Ibrahim Ghafir

    (University of Bradford)

  • Ci Lei

    (University of Bradford)

Abstract

The widespread adoption of Internet of Things (IoT) systems emphasises the need for dependable performance. However, challenges emerge due to the inadequacy of informal failure analysis models in evaluating the complex and distributed nature of IoT designs. These challenges can lead to inconsistencies, complicated system modifications, and extended development timelines. This paper presents a Model-Based Systems Engineering (MBSE) approach to create an integrated quantitative analysis framework for assessing the reliability of IoT systems. This approach simplifies the modelling and analysis of failure behaviours in IoT design, linking failure models to system design using standard engineering frameworks. Based on descriptions of IoT systems, the integrated approach establishes a traceability link between the system description and the analysable artefacts generated, such as stochastic Fault Tree Analysis (FTA) and Bayesian network (BN) models, which are essential for effective failure analysis of IoT systems. The approach demonstrates how to quantify system-level failures, identify and prioritise the weakest links in the design, and show how targeted modifications can improve the overall reliability of IoT systems. An illustrative case study on IoT is included to highlight the effectiveness and practical applicability of the proposed framework.

Suggested Citation

  • Alhassan Abdulhamid & Sohag Kabir & Ibrahim Ghafir & Ci Lei, 2025. "Quantitative failure analysis for IoT systems: an integrated model-based framework," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 845-867, February.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02700-5
    DOI: 10.1007/s13198-024-02700-5
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    References listed on IDEAS

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    1. Huang, Jia & You, Jian-Xin & Liu, Hu-Chen & Song, Ming-Shun, 2020. "Failure mode and effect analysis improvement: A systematic literature review and future research agenda," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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