Big Data Management and Analytics Framework for IoT- Enabled Smart Buildings

Bashir, Muhammad Rizwan

Big Data Management and Analytics Framework for IoT- Enabled Smart Buildings

Bashir, Muhammad Rizwan

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Bashir, Muhammad Rizwan
dc.date.accessioned	2023-07-12T02:24:36Z
dc.date.available	2023-07-12T02:24:36Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/171460
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The Internet of Things (IoT) is transforming how people communicate with each other and their environment. There is an increasing interest in IoT and its applications in various domains. One of the most significant applications of IoT is in the development and implementation of smart cities. In today’s world, not only is the demand to build new buildings as smart buildings, but the focus is also to transform existing buildings into smart buildings. Smart buildings can have hundreds or thousands of IoT sensors which generate huge amounts of data (also known as big data). The effective management and analysis of this big data is a huge challenge. The focus of this thesis is to address this challenge of efficiently and effectively managing and analysing big data generated by IoT sensors in smart buildings. This research proposes the IBDMA (Integrated Big Data Management and Analytics) framework to address the challenge of the effective and efficient management of big data generated by IoT sensors deployed in smart buildings. The IBDMA framework is developed using the design science research (DSR) method. The IBDMA framework consists of a reference architecture and a metamodel. The framework has five conceptual level elements namely i) people, ii) process, iii) technology, iv) information, and v) facility. The reference architecture provides an architecture for ingesting, storing, and analysing the IoT data as well as controlling various facilities of the smart building in an automated way. The metamodel provides details of all the elements within the smart building that enable the management and analysis of big data and to identify the relationship between these elements. The proposed IBDMA framework is evaluated by industry experts using an empirical evaluation comprising practical use cases. The results of the evaluation indicate that the proposed IBDMA framework can be considered reasonable for the efficient and effective management of data generated by IoT sensors in the context of smart buildings. The evaluation results indicate that the IBDMA framework is generic and can be scaled to different organisational contexts to enable the management and analysis of big data. The IBDMA framework is intended for use in IoT, 5G, the cloud and big data professionals as well as academics as a coherent framework with a reference architecture and a metamodel for the management and analysis of data generated by IoT sensors in smart buildings.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/171460/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2022 Muhammad Rizwan Bashir
dc.rights	au.edu.uts.lib/cph
dc.title	Big Data Management and Analytics Framework for IoT- Enabled Smart Buildings	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The Internet of Things (IoT) is transforming how people communicate with each other and their environment. There is an increasing interest in IoT and its applications in various domains. One of the most significant applications of IoT is in the development and implementation of smart cities. In today’s world, not only is the demand to build new buildings as smart buildings, but the focus is also to transform existing buildings into smart buildings. Smart buildings can have hundreds or thousands of IoT sensors which generate huge amounts of data (also known as big data). The effective management and analysis of this big data is a huge challenge. The focus of this thesis is to address this challenge of efficiently and effectively managing and analysing big data generated by IoT sensors in smart buildings. This research proposes the IBDMA (Integrated Big Data Management and Analytics) framework to address the challenge of the effective and efficient management of big data generated by IoT sensors deployed in smart buildings. The IBDMA framework is developed using the design science research (DSR) method. The IBDMA framework consists of a reference architecture and a metamodel. The framework has five conceptual level elements namely i) people, ii) process, iii) technology, iv) information, and v) facility. The reference architecture provides an architecture for ingesting, storing, and analysing the IoT data as well as controlling various facilities of the smart building in an automated way. The metamodel provides details of all the elements within the smart building that enable the management and analysis of big data and to identify the relationship between these elements. The proposed IBDMA framework is evaluated by industry experts using an empirical evaluation comprising practical use cases. The results of the evaluation indicate that the proposed IBDMA framework can be considered reasonable for the efficient and effective management of data generated by IoT sensors in the context of smart buildings. The evaluation results indicate that the IBDMA framework is generic and can be scaled to different organisational contexts to enable the management and analysis of big data. The IBDMA framework is intended for use in IoT, 5G, the cloud and big data professionals as well as academics as a coherent framework with a reference architecture and a metamodel for the management and analysis of data generated by IoT sensors in smart buildings.

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