## 6 Conclusions  
This study successfully demonstrates that environmental conditions in university libraries play a crucial role in influencing user productivity, satisfaction, and work efficiency. By leveraging an IoT-driven approach combined with machine learning-based predictive modeling, this research provides valuable insights into how various environmental factors impact user experience. The findings confirm that a holistic approach to sensor data processing enhances predictive accuracy and reliability, with the combined sensor model outperforming models trained on single-factor data. Among the tested machine learning algorithms, KNNs emerged as the most efficient model, achieving the highest F1 score (0.9904) while maintaining lower memory consumption and faster training times compared to Random Forest. This makes KNNs the preferredmodel for real-time implementation in smart campus environments. Additionally, the fog-cloud architecture proposed in this study was identified as the most suitable computational framework for smart campus applications. This hybrid architecture offers several key advantages, including the following:  
Scalability: The smart campus project is planned to start with the smart library application and gradually expand to different campuses and various buildings (faculties, cafeterias, administrative buildings, etc.) over time. Such growth at this scale could lead to overloading and performance issues on central servers in a traditional cloud-based system. However, the fog-cloud architecture addresses this issue by providing local data processing capacity at each campus and even within each building. As new sensors, devices, and buildings are added to the system, fog nodes can also be added, allowing the system to scale horizontally. Low latency: In a smart campus environment, many applications require real-time or near-real-time response times. For example, scenarios such as automatically adjusting lighting and ventilation based on the occupancy rate in the library, providing instant notifications based on sound levels in classrooms, or detecting events that require rapid intervention in emergencies (such as a fire alarm) can be adversely affected by delays at the millisecond level, potentially negatively impacting the user experience or creating security risks. Fog computing ensures low latency, which is critical for such applications, by processing data at locations close to the source (e.g., at a fog node within the library). Bandwidth efficiency: Within the scope of the smart campus project, data will be continuously collected from a large number of sensors (e.g., temperature, humidity, light, sound, images, etc.). Sending all of these raw data to the central cloud would result in unnecessary bandwidth consumption and high costs. In the fog-cloud architecture, however, data are primarily processed in the fog layer. In this layer, irrelevant data are filtered, and data are compressed and summarized, or only critical changes (e.g., a sudden increase in temperature, exceeding a certain sound threshold) are sent to the cloud. This significantly reduces network traffic and ensures a more efficient use of bandwidth. Data privacy and security: Smart campus applications often involve personal data or sensitive information. For example, camera footage used to determine occupancy levels in the library or sensor data tracking student movements could raise privacy concerns. Fog computing enhances data privacy and security by processing such sensitive data at local fog nodes, preventing them from leaving the campus. Data can be stored in the fog layer in an encrypted form and made accessible only to authorized individuals. Additionally, local firewalls and intrusion detection systems can be implemented in the fog layer to provide extra protection against cyberattacks. Advantages of the hybrid approach: The proposed architecture combines the benefits of both fog computing (local, real-time processing) and cloud computing (centralized, big data analytics, and long-term storage).