Source code of the agent-based simulator of smart beds with Internet-of-Things for exploring big data analytics called ABS-BedIoT

Published: 16-10-2017| Version 1 | DOI: 10.17632/42c3xcp3f7.1
Contributors:
Iván García-Magariño,
Raquel Lacuesta,
Jaime Lloret

Description

This dataset contains the source code of an agent-based simulator (ABS) of smart beds with Internet-of-Things for exploring big data analytics called ABS-BedIoT. This ABS allows practitioner to simulate different types of sleeper that adopt different kinds of sleeping postures during the night. Each posture is composed of an orientation (i.e. frontal or lateral) and a pose (i.e. neck bent, body bent, spine S, and normal pose). The sleeper is tracked by a grid of pressure sensors of a smart bed. Practitioners can test different ways of analyzing the big data from the simulated signals of the sensors of the smart bed. They can either implement a different kind of online analysis implementing another Analyzer agent, or analyze the big data logs generated by the simulator with an independent tool. This dataset includes some examples of the generated logs for each kind of implemented sleeper. The implemented sleeper kinds are bad sleeper, healthy sleeper and restless sleeper. However, practitioners can implement new kinds of sleepers by extending either the “Stochastic Sleeper Agent” class or the “Sleeper Agent” class. The development of this ABS has followed the Process for developing Efficient ABSs (PEABS). The stochastic behavior of some agents are based on the Technique for developing ABS Apps and Online tools with Nondeterministic Decisions (TABASOND). The current simulator has been implemented with Unity 3D engine, version 5.5.1. This dataset supports the research of the authors about ABS-BedIoT. The work about this dataset has been accepted for publication by the IEEE Access journal conditioned to certain changes. If other researchers use the current dataset, they can credit the work of the authors by citing the upcoming article in the IEEE Access journal about ABS-BedIoT. In case the researchers just reuse some components related with either PEABS or TABSAOND, they can cite any of the corresponding articles. Reference about ABS-BedIoT: García-Magariño, I., Lacuesta, R., Lloret, J. (2017). Agent-based simulation of smart beds with Internet-of-Things for exploring big data analytics. IEEE Access, In Press. References about TABSAOND and PEABS: García-Magariño, I., Palacios-Navarro, G., Lacuesta, R. (2017). TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions. Simulation Modelling Practice and Theory, 77, 84-107. García-Magariño, I., Gómez-Rodríguez, A., González-Moreno, J. C., Palacios-Navarro, G. (2015). PEABS: a Process for developing Efficient Agent-Based Simulators. Engineering Applications of Artificial Intelligence, 46, 104-112.

Files

Steps to reproduce

Make sure you have installed Unity version 5.5.1. If you have not installed it, you can obtain any Unity version from its Download Archive web page. It may be compatible with other versions of Unity although this has not been tested. Download and unzip the “BedIoT.zip” file. Open the project of the unzipped folder from the Unity editor. Press the “Play” button to run the simulator. All the source code and other elements are accessible from the opened project. You can also download any of the example logs for exploring offline big data analytics. These logs are plain text, and can be opened with any text editor.