OpenSHS: Open Smart Home Simulator.
OpenSHS: Open Smart Home Simulator.
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DOI：
10.3390/s17051003
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发表时间：
2017-05-02
期刊：
影响因子：
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通讯作者：
Bauer C
中科院分区：
文献类型：
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作者：
Alshammari N;Alshammari T;Sedky M;Champion J;Bauer C
本文开发了一种新的混合，开源，跨平台的3D智能家居模拟器，OpenSHS，用于数据集生成。OpenSHS为物联网（IoT）和机器学习领域的研究人员提供了测试和评估其模型的机会。遵循混合方法，OpenSHS结合了交互式和基于模型的方法的优势。这种方法减少了生成模拟智能家居数据集所需的时间和精力。我们设计了一个复制算法来扩展和扩展数据集。OpenSHS生成的一个小样本数据集可以在不影响事件逻辑顺序的情况下进行扩展。复制提供了用于生成大型代表性智能家居数据集的解决方案。我们已经建立了一个可扩展的智能设备库，便于模拟当前和未来的智能家居环境。我们的工具将数据集生成过程分为三个不同的阶段：第一个设计：研究人员通过建造房屋，导入智能设备和创建上下文来设计初始虚拟环境;第二个模拟：参与者模拟他/她的特定于上下文的事件;第三个聚合：研究人员应用复制算法来生成最终数据集。我们进行了一项研究，以评估我们的工具的易用性的系统可用性量表（SUS）。
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).