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Using log analytics and process mining to enable self-healing in the Internet of Things

Author

Listed:
  • Prasannjeet Singh

    (Linnaeus University)

  • Mehdi Saman Azari

    (Linnaeus University)

  • Francesco Vitale

    (University of Naples Federico II)

  • Francesco Flammini

    (Linnaeus University
    Mälardalen University)

  • Nicola Mazzocca

    (University of Naples Federico II)

  • Mauro Caporuscio

    (Linnaeus University)

  • Johan Thornadtsson

    (Sigma Technology)

Abstract

The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT.

Suggested Citation

  • Prasannjeet Singh & Mehdi Saman Azari & Francesco Vitale & Francesco Flammini & Nicola Mazzocca & Mauro Caporuscio & Johan Thornadtsson, 2022. "Using log analytics and process mining to enable self-healing in the Internet of Things," Environment Systems and Decisions, Springer, vol. 42(2), pages 234-250, June.
    • Handle: RePEc:spr:envsyd:v:42:y:2022:i:2:d:10.1007_s10669-022-09859-x
    DOI: 10.1007/s10669-022-09859-x
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