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research-article

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

Authors:

Terrence Fernando,

May BassaninoAuthors Info & Claims

UCC '17 Companion: Companion Proceedings of the10th International Conference on Utility and Cloud Computing

Pages 221 - 226

https://doi.org/10.1145/3147234.3148096

Published: 05 December 2017 Publication History

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.

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Digital Library

Cited By

Iram SAl-Aqrabi HShakeel HFarid HRiaz MHill RVethathir PAlsboui T(2023)An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy ConsumptionIEEE Access10.1109/ACCESS.2023.328714511(76300-76320)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3287145
Iram SShakeel HFarid HHill RFernando T(2023)A web-based visual analytics platform to explore smart houses energy data for stakeholders: A case study of houses in the area of manchesterEnergy and Buildings10.1016/j.enbuild.2023.113342(113342)Online publication date: Jul-2023
https://doi.org/10.1016/j.enbuild.2023.113342
Shakeel HIram SAl-Aqrabi HAlsboui THill R(2022)A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization FrameworkIEEE Access10.1109/ACCESS.2022.320511510(96581-96601)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3205115
Show More Cited By

Index Terms

Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency
1. Computer systems organization

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Published In

cover image ACM Conferences

UCC '17 Companion: Companion Proceedings of the10th International Conference on Utility and Cloud Computing

December 2017

252 pages

ISBN:9781450351959

DOI:10.1145/3147234

General Chairs:
Ashiq Anjum
University of Derby, UK
,
Alan Sill
Texas Tech University, USA
,
Program Chairs:
Geoffrey Fox
Indiana University, USA
,
Yong Chen
Texas Tech University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE TCSC: IEEE Technical Committee on Scalable Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 December 2017

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UCC '17

Sponsor:

SIGARCH
IEEE TCSC

UCC '17: 10th International Conference on Utility and Cloud Computing

December 5 - 8, 2017

Texas, Austin, USA

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Overall Acceptance Rate 38 of 125 submissions, 30%

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Cited By

Iram SAl-Aqrabi HShakeel HFarid HRiaz MHill RVethathir PAlsboui T(2023)An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy ConsumptionIEEE Access10.1109/ACCESS.2023.328714511(76300-76320)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3287145
Iram SShakeel HFarid HHill RFernando T(2023)A web-based visual analytics platform to explore smart houses energy data for stakeholders: A case study of houses in the area of manchesterEnergy and Buildings10.1016/j.enbuild.2023.113342(113342)Online publication date: Jul-2023
https://doi.org/10.1016/j.enbuild.2023.113342
Shakeel HIram SAl-Aqrabi HAlsboui THill R(2022)A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization FrameworkIEEE Access10.1109/ACCESS.2022.320511510(96581-96601)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3205115
Spoladore DMahroo ATrombetta ASacco M(2019)ComfOnt: A Semantic Framework for Indoor Comfort and Energy Saving In Smart HomesElectronics10.3390/electronics81214498:12(1449)Online publication date: 1-Dec-2019
https://doi.org/10.3390/electronics8121449
Iram SFernando THill R(2018)Connecting to Smart Cities: Analyzing Energy Times Series to Visualize Monthly Electricity Peak Load in Residential BuildingsProceedings of the Future Technologies Conference (FTC) 201810.1007/978-3-030-02686-8_26(333-342)Online publication date: 18-Oct-2018
https://doi.org/10.1007/978-3-030-02686-8_26

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