Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

Alshammari, Talal and Ramadan, Rabie A. and Ahmad, Aakash (2023) Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia. Applied Sciences, 13 (12): 7326. ISSN 2076-3417

Abstract

The advancement of the Internet of Things applications (technologies and enabling platforms), consisting of software and hardware (e.g., sensors, actuators, etc.), allows healthcare providers and users to analyze and measure physical environments at home or hospital. The measured physical environment parameters contribute to improving healthcare in real time. Researchers in this domain require existing representative datasets to develop machine-learning techniques to learn physical variables from the surrounding environments. The available environmental datasets are rare and need too much effort to be generated. To our knowledge, it has been noticed that no datasets are available for some countries, including Saudi Arabia. Therefore, this paper presents one of the first environmental data generated in Saudi Arabia’s environment. The advantage of this dataset is to encourage researchers to investigate the effectiveness of machine learning in such an environment. The collected data will also help utilize the machine learning and deep learning algorithms in smart home and health care applications based on the Saudi Arabia environment. Saudi Arabia has a special environment in each session, especially in the northern area where we work, where it is too hot in the summer and cold in the winter. Therefore, environmental data measurements in both sessions are important for the research community, especially those working in smart and healthcare environments. The dataset is generated based on the indoor environment from six sensors (timestamps, light, temperature, humidity, pressure, and altitude sensors). The room data were collected for 31 days in July 2022, acquiring 8910 records. The datasets include six columns of different data types that represent sensor values. During the experiment, the sensors captured the data every 5 min, storing them in a comma-separated value file. The data are already validated and publicly available at PLOMS Press and can be applied for training, testing, and validating machine learning algorithms. This is the first dataset developed by the authors for the research community for such an environment, and other datasets will follow it in different environments and places.