## 6 Conclusions
* This study was conducted in a library environment; however, it can be easily applied in the future to places where people gather in large numbers, such as offices, restaurants, and cafes. 4. Enhancing edge computing for real-time decision-making. The fog-cloud architecture proposed in this study ensures low latency and efficient bandwidth usage, but further improvements could be made as follows:* Deploying AI-driven edge computing: using tiny machine learning (TinyML) models directly on IoT sensor nodes to process data locally, reducing the need for cloud-based inference. * Smart dormitories: personalized environmental settings based on student preferences and biometric data to enhance comfort and well-being. * Blockchain integration for data security: implementing blockchain-based decentralized data management to enhance privacy and ensure secure, tamper-proof transactions between IoT devices. * Developing an AI-powered decision support system for university administrators. To maximize the impact of this research, an AI-driven decision support system could be developed, allowing university administrators to conduct the following:  
* Visualize and analyze real-time environmental data via interactive dashboards. * Automatically generate reports on student productivity trends based on historical sensor data. * Receive AI-driven recommendations for optimizing campus resource allocation and space utilization. The ultimate goal of future research in this area is to transform universities into fully intelligent, self-adaptive environments that seamlessly integrate AI, the IoT, and user-centric computing. By expanding and refining the proposed system, the next-generation smart campus will not only enhance user experience but also contribute to the broader goals of energy efficiency, sustainability, and data-driven decision-making in higher education institutions. Conceptualization, S.M. and E.K. ; methodology, S.M. and E.K. ; software, S.M. ; validation, S.M. ; formal analysis, S.M. and E.K. ; investigation, S.M. ; resources, S.M. and E.K. ; data curation, S.M. ; writing--original draft preparation, S.M. and E.K. ; writing--review and editing, E.K. ; visualization, S.M. ; supervision, E.K. ; project administration, S.M. and E.K. ; funding acquisition, none; data collection, S.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of T.C. Duzce University Scientific Research and Publication Ethics Committee (meeting number 11, decision number 2024/300, and date of approval: 4 October 2024). Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. All collected data were anonymized, and no personally identifiable information was retained. The data supporting the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy concerns. Specific details are kept confidential to protect the personal and sensitive information of participants. We sincerely thank the individuals and institutions that contributed to various stages of this research process. The language of this article has been reviewed using artificial intelligence technologies to enhance clarity and consistency. All data used in this research have been carefully anonymized to protect the privacy of participants, and necessary measures have been taken to prevent identity disclosure, ensuring compliance with ethical standards. We extend our special thanks to all participants who played a crucial role during the data collection phase in shaping the scope of this research and enhancing the reliability of the findings. We would like to emphasize that their contributions have helped us gain a deeper understanding of our research questions and obtain more robust results. The authors declare no conflicts of interest.