# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
###### Abstract
To effectively apply machine learning algorithms for various applications, it is necessary to have a dataset representative of the problem domain for training and testing purposes. The dataset demonstrates the potential for evaluating and validating the proposed method's accuracy. Although many real-world datasets are publicly accessible, some lack adequate physical factors such as temperature, humidity, pressure, and altitude. It reads data from sensors that can be noisy, has poor network conditions, and is subject to several unknown variables, such as missing data and faulty sensors. Consequently, it is prone to errors that affect the accuracy of the data. As seen from the previous discussion, there are some efforts either from the authorities or researchers in environmental data collection in Saudi Arabia. However, the focus was on the outdoor environment. There is a lack of indoor environment data collection; to our knowledge, no dataset is already available for researchers. Therefore, this paper is an essential effort toward the indoor dataset availability in the Saudi Arabia environment, especially in the northern area. The dataset was generated using the Arduino integrated development environment (IDE), an open-source electronics platform that uses hardware and software. It includes several sensors on a board that can generate data using light, temperature, humidity, and other sensors. The dataset was generated from real sensors to measure physical variables in a home for one month. The dataset contains five features representing temperature, humidity, pressure, light, and altitude. It records measurements of physical variables in real time. To summarize, this paper makes several significant contributions to the field of environmental data collection, specifically within the context of indoor environments in Saudi Arabia:  
* **Environmental dataset:** This manuscript presents significant contributions to the domain of environmental data collection, particularly in the realm of indoor environments in Saudi Arabia. The dataset is publicly available at PLOMS Press [11]. * **Addressing a Research Gap:** The paper aims to fill a gap in the existing research and data collection efforts in Saudi Arabia, which have predominantly concentrated on outdoor settings. This manuscript introduces one of the nation's initial datasets of indoor environments. * **Regional Specificity:** The dataset holds significant value for research endeavors that concentrate on the northern region of Saudi Arabia, owing to its distinct environmental features. * **Use of Arduino IDE:** The data was acquired through the use of the Arduino Integrated Development Environment (IDE), an open-source electronics platform that integrates both hardware and software. The implementation of this platform signifies a novel method for collecting indoor environmental data. * **Wide Range of Variables:** The dataset comprises measurements from various sensors that capture data on light, temperature, humidity, pressure, and altitude. The diverse range of data points can offer a comprehensive overview of the indoor environment. * **Real-time Data Collection:** Real-time data collection was conducted by generating a dataset from sensor readings taken over one month. The collected data provides a rich temporal perspective for analysis. * **Practical Application:** This dataset holds significant value for researchers investigating indoor environments, their effects on health and well-being, and the advancement of smart home technologies. It is suitable for training, testing, and validating machine learning algorithms within these domains. The paper is structured as follows. Section 2 presents the related work in the literature. Section 3 clarifies the architecture and how to use the Arduino IDE and sensor devices to measure physical variables and generate datasets. Section 4 presents the methodology for acquiring the data from sensor devices to generate datasets. Section 5 describes the datasets.