Next-Generation Smart Buildings: Federated Intelligence for Personalized, Sustainable, and Scalable Environments
In an era of increasing complexity in managing environments efficiently and sustainably, there is a growing need for intelligent systems capable of optimizing resources in real time while maintaining user comfort and well-being. This research proposes the development of an adaptive, energy-efficient framework for smart building management, with a particular focus on university environments. By integrating environmental sensors, physiological monitoring, and advanced machine learning models, the system can dynamically adjust to occupancy and user needs without compromising energy efficiency. The use of low-power, distributed devices in combination with federated learning ensures scalable, privacy-preserving data processing across heterogeneous systems. Real-time data analysis, supported by next-generation 5G infrastructure, enables highly responsive environmental adaptation. Beyond academic settings, the proposed approach also addresses broader societal challenges, such as supporting elderly individuals in care settings, helping to mitigate the risks of isolation and improve their overall quality of life.
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