# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
###### 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 (times-tamps, 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. big data; environmental parameters; dataset; northern Saudi Arabia +
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Therefore, collecting environmental datasets is essential for monitoring environmental conditions and gaining knowledge of the environment's state in any country, including Saudi Arabia. Deserts, mountains, coastal regions, and marine environments are only a few of the ecosystems that may be found in Saudi Arabia. As a result, collecting data on the environment in Saudi Arabia is essential for various reasons. To begin, it might help identify environmental problems such as pollution, the degradation of habitats, conditions affecting healthcare, and the implications of climate change. Scientists and officials can comprehend the environment's condition better and make informed decisions about how to handle environmental challenges if they acquire data on the quality of the air and water, changes in the environment, and the distribution of animals. Second, collecting data on the environment may assist in keeping track of the movement toward environmental objectives and targets. Researchers may improve their knowledge of the dynamics of ecosystems, their ability to anticipate changes in the environment, and their ability to recommend solutions to environmental issues by using environmental data. For instance, Saudi Arabia has vowed to drastically decrease its emissions of greenhouse gases by 2030 drastically and to significantly raise the percentage of its total energy supply that comes from renewable sources. Collecting energy use, emissions, and renewable energy generation data may help track progress toward these objectives and identify areas where extra action may be required.