Exploring cross-domain data dependencies for smart homes to improve energy efficiency

Iram, S, Fernando, TP ORCID: https://orcid.org/0000-0001-5321-9071 and Bassanino, MN 2017, Exploring cross-domain data dependencies for smart homes to improve energy efficiency , in: 10th International Conference on Utility and Cloud Computing, 5 - 8 December 2017, Austin, Texas, USA.

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Over the past decade, the idea of smart homes has been conceived as a potential solution to counter energy crises or to at least mitigate its intensive destructive consequences in the residential building sector. Smart homes have emerged as one of the applications of Internet of Things (IoT) that enabled the use of technology to automate and customize home services with reference to users’ preferences. However, the concept of smart homes is still not fully matured due to the weak handling of diverse datasets that can be exploited to promote more adaptive, personalised, and context aware capabilities. Furthermore, instead of just deploying integrated automated services in the homes, the focus should be to bring the concerns of potential stakeholders into consideration. In this paper, we have exploited the concepts of ontologies to capture all sorts of data (classes and their subclasses) that belong to one domain based on stakeholders’ requirements analysis. We have also explored their significant associations with other datasets from another domain. In addition, this research work provides an insight about what sorts of interdependencies exist between different datasets across different ontological models such as Smart homes ontology model and ICT ontology model.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of the Built Environment > Centre for Urban Processes, Resilient Infrastructures & Sustainable Environments
Journal or Publication Title: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing
Publisher: Association for Computing Machinery
Related URLs:
Depositing User: TP Fernando
Date Deposited: 18 Jan 2018 13:58
Last Modified: 15 Feb 2022 22:49
URI: https://usir.salford.ac.uk/id/eprint/44993

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