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Sensors 2017, 17(5), 1003; doi:10.3390/s17051003

OpenSHS: Open Smart Home Simulator

1
Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK
2
College of Information and Computer Science, Aljouf University, Sakaka 72388, Saudi Arabia
3
College of Computer Science and Engineering, University of Hail, Hail 53962, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Chatzigiannakis and Georgios Mylonas
Received: 20 February 2017 / Revised: 23 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
(This article belongs to the Special Issue Advances in Sensors for Sustainable Smart Cities and Smart Buildings)
View Full-Text   |   Download PDF [1522 KB, uploaded 2 May 2017]   |  

Abstract

This paper develops a new hybrid, open-source, cross-platform 3D smart home simulator, OpenSHS, for dataset generation. OpenSHS offers an opportunity for researchers in the field of the Internet of Things (IoT) and machine learning to test and evaluate their models. Following a hybrid approach, OpenSHS combines advantages from both interactive and model-based approaches. This approach reduces the time and efforts required to generate simulated smart home datasets. We have designed a replication algorithm for extending and expanding a dataset. A small sample dataset produced, by OpenSHS, can be extended without affecting the logical order of the events. The replication provides a solution for generating large representative smart home datasets. We have built an extensible library of smart devices that facilitates the simulation of current and future smart home environments. Our tool divides the dataset generation process into three distinct phases: first design: the researcher designs the initial virtual environment by building the home, importing smart devices and creating contexts; second, simulation: the participant simulates his/her context-specific events; and third, aggregation: the researcher applies the replication algorithm to generate the final dataset. We conducted a study to assess the ease of use of our tool on the System Usability Scale (SUS). View Full-Text
Keywords: smart home; simulation; internet of things; machine learning; visualisation smart home; simulation; internet of things; machine learning; visualisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alshammari, N.; Alshammari, T.; Sedky, M.; Champion, J.; Bauer, C. OpenSHS: Open Smart Home Simulator. Sensors 2017, 17, 1003.

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