City Research Online

Abstract

The digitization of manufacturing through the introduction of Industrie 4.0 technologies creates additional business opportunities and technical challenges. The integration of such technologies on legacy production machinery can upgrade them to become part of the digital and smart manufacturing environment. A typical example is that of industrial monitoring and maintenance, which can benefit from internet of things (IoT) solutions. This paper presents the development of an-IoT-enabled monitoring solution for machine tools as part of a remote maintenance approach. While the technical challenges pertaining to the development and integration of such solutions in a manufacturing environment have been the subject of relevant research in the literature, the corresponding new security challenges arising from the introduction of such technologies have not received equal attention. Failure to adequately handle such issues is a key barrier to the adoption of such solutions by industry. This paper aims to assess and classify the security aspects of integrating IoT technology with monitoring systems in manufacturing environments and propose a systematic view of relevant vulnerabilities and threats by taking an IoT architecture point of view. Our analysis has led to proposing a novel modular approach for secure IoT-enabled monitoring for legacy production machinery. The introduced approach is implemented on a case study of machine tool monitoring, highlighting key findings and issues for further research.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. APMS 2017. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-66923-6_46.
Subjects:	H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology

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