Clustering and Classification of IoT-Based Environmental Data Using Machine Learning Techniques

Ali Subhi Alhumaima¹, Waleed Khalid Al-Zubaidi¹, El-Sayed M. El-Kenawy^2,3,*, Marwa M. Eid^4,5

¹Electronic Computer Centre, University of Diyala, Diyala, Iraq

²Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

³Applied Science Research Center. Applied Science Private University, Amman, Jordan

⁴Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt

⁵Jadara Research Center, Jadara University, Irbid 21110, Jordan

Emails: alhumaimaali@uodiyala.edu.iq; waleed300@uodiyala.edu.iq; skenawy@ieee.org; mmm@ieee.org

Abstract

In this study, we present an integrated approach to IoT-based environmental data analysis using a collection of unsupervised-learning techniques. We employed KMeans clustering in particular to identify natural groupings in environmental and behavioral features such as air quality, noise level, temperature, stress level, sleeping hours, and mood score. We then trained a Decision Tree classifier to predict and interpret cluster membership from raw sensor readings. The data of more than 30,000 observations in indoor school environments has multifaceted relationships between environmental factors and psychological well-being. KMeans consistently detected three environmental-behavioral states, and the Decision Tree classifier performed 87% classification accuracy, which indicated extremely high predictability power in addition to interpretability. The results indicated that sleep duration, air, and stress were the main factors for cluster discrimination. The hybrid model introduces the potential of observing real-time environmental and mental states for applications in smart cities. The approach is scalable, interpretable, and usable in IoT settings for proactivity-enabled wellness management.

Keywords: IoT Sensor Data; Environmental Monitoring; KMeans Clustering; Decision Tree Classification; Behavioral Analysis; Air Quality; Stress Prediction; Machine Learning, Data Mining