3.9 Article

Artificial intelligence in cyber physical systems

期刊

AI & SOCIETY
卷 36, 期 3, 页码 783-796

出版社

SPRINGER
DOI: 10.1007/s00146-020-01049-0

关键词

Artificial cognition; Industrial internet of things; Cyber physical systems; Industry 4; 0; Artificial intelligence; Anomaly detection

资金

  1. EPSRC [EP/N02334X/1, EP/S035362/1] Funding Source: UKRI

向作者/读者索取更多资源

This article conducts a literature review of current and future challenges in the use of artificial intelligence in cyber physical systems, focusing on the conceptual framework for increasing resilience and decision-making evolution. The methodology of taxonomic analysis is applied to complex IoT interconnected systems. Increased attention is given to proposals on IoT models in academic and technical papers.
This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution isinevitable and autonomousbecause of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

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