Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency

Shamaila Iram, Terrence Fernando, May Bassanino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationUCC '17 Companion
Subtitle of host publicationCompanion Proceedings of the 10th International Conference on Utility and Cloud Computing
PublisherAssociation for Computing Machinery (ACM)
Pages221-226
Number of pages6
ISBN (Electronic)9781450351959
DOIs
Publication statusPublished - 5 Dec 2017
Event10th International Conference on Utility and Cloud Computing - AT&T Hotel and Conference Center, Austin, United States
Duration: 5 Dec 20178 Dec 2017
Conference number: 10
http://www.depts.ttu.edu/cac/conferences/ucc2017/ (Link to Conference Website)

Conference

Conference10th International Conference on Utility and Cloud Computing
Abbreviated titleUCC '17
CountryUnited States
CityAustin
Period5/12/178/12/17
Internet address

Fingerprint

Energy efficiency
Ontology
Internet of things

Cite this

Iram, S., Fernando, T., & Bassanino, M. (2017). Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency. In UCC '17 Companion: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing (pp. 221-226). Association for Computing Machinery (ACM). https://doi.org/10.1145/3147234.3148096
Iram, Shamaila ; Fernando, Terrence ; Bassanino, May. / Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency. UCC '17 Companion: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing. Association for Computing Machinery (ACM), 2017. pp. 221-226
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Iram, S, Fernando, T & Bassanino, M 2017, Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency. in UCC '17 Companion: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing. Association for Computing Machinery (ACM), pp. 221-226, 10th International Conference on Utility and Cloud Computing, Austin, United States, 5/12/17. https://doi.org/10.1145/3147234.3148096

Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency. / Iram, Shamaila; Fernando, Terrence; Bassanino, May.

UCC '17 Companion: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing. Association for Computing Machinery (ACM), 2017. p. 221-226.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Iram S, Fernando T, Bassanino M. Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency. In UCC '17 Companion: Companion Proceedings of the 10th International Conference on Utility and Cloud Computing. Association for Computing Machinery (ACM). 2017. p. 221-226 https://doi.org/10.1145/3147234.3148096