OpenSHS: Open Smart Home Simulator

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).

Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling

“…A comprehensive review of simulation tools has been reported previously within the literature [13,16]. These tools can be split into model-based and interactive approaches.…”

SIMADL: Simulated Activities of Daily Living Dataset

“…OpenSHS uses the concept of a context which is a specific time-frame of interest to the researcher to be simulated [25]. In this work, we have chosen to simulate the interactions of the participants in different contexts.…”

“…Since we record the activities of the participants in real-time, every simulation will be different and will contain unique information. OpenSHS provides an aggregation algorithm that uses all the real-time recorded simulations to generate a new and random dataset but in a controlled manner [25].…”

A data-driven situation-aware framework for predictive analysis in smart environments

“…To generate data for our research, we use the simulator proposed in [13] In this phase, as shown in Figure 8.1, we build the virtual environment, import the smart devices, assign labels to activities and design the contexts. Using Blender, we designed the 3D smart home consisting of a bedroom, a bathroom, a living room, a kitchen and an office.…”

“…In this research, we propose a data-driven situation-aware framework for predictive analysis of big situation data in smart environments. We generate situation data using a smart home simulation tool called OpenSHS [13] [14]. The collected sensor data has two features that may cause problems in further processing: first, high dimensionality and second, not all of the data is useful for predictive analysis.…”