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

Prasannjeet Singh, Mehdi Saman Azari, Francesco Vitale, Francesco Flammini (), Nicola Mazzocca, Mauro Caporuscio and Johan Thornadtsson
Additional contact information
Prasannjeet Singh: Linnaeus University
Mehdi Saman Azari: Linnaeus University
Francesco Vitale: University of Naples Federico II
Francesco Flammini: Linnaeus University
Nicola Mazzocca: University of Naples Federico II
Mauro Caporuscio: Linnaeus University
Johan Thornadtsson: Sigma Technology

Environment Systems and Decisions, 2022, vol. 42, issue 2, 234-250

Abstract: 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.

Keywords: Self-repair; Self-diagnostics; Resilience; Data driven; Anomaly detection; Cyber-physical systems (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10669-022-09859-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:envsyd:v:42:y:2022:i:2:d:10.1007_s10669-022-09859-x

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10669

DOI: 10.1007/s10669-022-09859-x

Access Statistics for this article

More articles in Environment Systems and Decisions from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().