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OpenSHS: Open Smart Home Simulator.

Nasser Alshammari1,2, Talal Alshammari3,4, Mohamed Sedky5

  • 1Staffordshire University, College Road, ST4 2DE Stoke-on-Trent, UK. nasser.alshammari@research.staffs.ac.uk.

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|May 5, 2017
PubMed
Summary

Researchers developed OpenSHS, a 3D smart home simulator for generating datasets for the Internet of Things (IoT) and machine learning. Its hybrid approach and replication algorithm efficiently create large, representative smart home datasets.

Keywords:
internet of thingsmachine learningsimulationsmart homevisualisation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Smart Home Technology

Background:

  • Generating realistic datasets for smart home research, particularly for the Internet of Things (IoT) and machine learning applications, is challenging and time-consuming.
  • Existing simulation methods often lack flexibility or require significant manual effort, hindering model development and evaluation.

Purpose of the Study:

  • To introduce OpenSHS, a novel hybrid, open-source, cross-platform 3D smart home simulator designed for efficient and scalable smart home dataset generation.
  • To provide researchers with a flexible tool for testing and evaluating IoT and machine learning models in simulated smart home environments.

Main Methods:

  • OpenSHS employs a hybrid approach combining interactive and model-based simulation techniques.
  • A replication algorithm is implemented to extend initial datasets, ensuring logical event order and enabling the generation of large, representative datasets.
  • The tool features an extensible library of smart devices and a three-phase dataset generation process: design, simulation, and aggregation.

Main Results:

  • OpenSHS successfully reduces the time and effort required for simulated smart home dataset generation.
  • The replication algorithm effectively expands datasets without compromising event order, facilitating the creation of large-scale, representative datasets.
  • Initial usability testing using the System Usability Scale (SUS) indicated a positive user experience.

Conclusions:

  • OpenSHS offers a valuable, efficient, and user-friendly solution for researchers in the IoT and machine learning fields needing simulated smart home data.
  • The simulator's hybrid approach, replication algorithm, and extensible device library make it a versatile tool for current and future smart home research.