The emulation and simulation of Internet of Things devices for Building Information Modelling (BIM)

Arthur, Steven, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133 and Lark, Robert ORCID: https://orcid.org/0000-0002-1796-5321 2018. The emulation and simulation of Internet of Things devices for Building Information Modelling (BIM). Presented at: EG-ICE 2018: 25th EG-ICE International Workshop of the European Group for Intelligent Computing in Engineering, Lausanne, Switzerland, 10-13 June 2018. Published in: Smith, Ian F. C. and Domer, Bernd eds. Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part II. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.10864 Cham: Springer, pp. 325-338. 10.1007/978-3-319-91638-5_18

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Abstract

The most significant recent development in the AEC industry has been the adoption of Building Information Modelling (BIM), but its full potential is far from being fully exploited. The Internet of Things (IoT) provides a rich source of new data for BIM. BIM can provide a framework for the organization and analysis of IoT data. Each IoT solution can require thousands of sensors creating a continuous stream of varied data. A connection between BIM and the IoT throughout the lifecycle would result in many new possibilities. This paper examines emulating or simulating large numbers of IoT devices to explore the potential of effectively linking BIM with the IoT. With emulation, the complete outwardly observable behavior of real historical IoT devices is mimicked and matched. The alternative to emulation is simulation where an abstract model of the IoT network is created programmatically using rules. Emulating and simulating devices reduces the barriers to carrying out the development required to enable BIM to utilize the full potential of the IoT.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Springer
ISBN:	978-3-319-91638-5
ISSN:	0302-9743
Last Modified:	22 Nov 2022 10:17
URI:	https://orca.cardiff.ac.uk/id/eprint/112198

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