Abstract
This paper presents a novel application of intelligent environmental management within a university residence, aiming to enhance the overall well-being and satisfaction of residents by dynamically addressing their comfort preferences. The proposed system leverages cutting-edge technologies such as Internet of Things (IoT) sensors, machine learning algorithms, and smart devices to create an adaptive and responsive living environment. Through real-time data collection and analysis, the system learns individual and collective comfort patterns, allowing for personalized adjustments to temperature, and other environmental factors. The study focuses on the development and implementation of the intelligent environment application, emphasizing user-centric design and seamless integration into daily life. Residents are empowered to set and modify their comfort preferences through a user-friendly interface, while the system continuously refines its understanding of these preferences over time. Additionally, the application considers energy efficiency and sustainability, contributing to a greener and more resource-conscious university residence. The paper discusses the technical architecture of the intelligent environment application, including the deployment of sensors, data processing pipelines, and the communication infrastructure. Furthermore, it addresses privacy concerns by outlining robust security measures and anonymization techniques to protect user data. In conclusion, this paper contributes to the growing body of research on intelligent environments by showcasing a practical application tailored to university residence settings. The presented system not only prioritizes resident comfort but also aligns with the broader goals of sustainability and resource optimization, making it a valuable addition to smart living solutions in educational institutions.
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Acknowledgments
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).
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Oliveira, P.F., Matos, P. (2024). An Intelligent Environment Application Case to Manage Comfort Preferences, at an University Residence. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_3
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DOI: https://doi.org/10.1007/978-3-031-62269-4_3
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