A User-Centric Smart Library System: IoT-Driven Environmental Monitoring and ML-Based Optimization with Future Fog-Cloud Architecture

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dc.authoridKucukkulahli, Enver/0000-0002-0525-0477
dc.contributor.authorMammadov, Sarkan
dc.contributor.authorKucukkulahli, Enver
dc.date.accessioned2025-10-11T20:47:49Z
dc.date.available2025-10-11T20:47:49Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractUniversity libraries are essential academic spaces, yet existing smart systems often overlook user perception in environmental optimization. A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). Unlike prior works, our system fuses objective measurements and subjective input for personalized assessment. Environmental factors-including air quality, sound, temperature, humidity, and lighting-were monitored using microcontrollers and image processing. User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. KNNs achieved the highest F1 score (99.04%), validating the hybrid approach. A user interface analyzes environmental factors, identifying primary contributors to suboptimal conditions. A scalable fog-cloud architecture distributes computation between edge devices (fog) and cloud servers, optimizing resource management. Beyond libraries, the framework extends to other smart workspaces. By integrating the IoT, ML, and user-driven optimization, this study presents an adaptive decision support system, transforming libraries into intelligent, user-responsive environments.en_US
dc.identifier.doi10.3390/app15073792
dc.identifier.issn2076-3417
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-105002276789en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/app15073792
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21592
dc.identifier.volume15en_US
dc.identifier.wosWOS:001463702100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectuniversity libraryen_US
dc.subjectenvironmental qualityen_US
dc.subjectIoTen_US
dc.subjectmachine learningen_US
dc.subjectuser feedbacken_US
dc.subjectKNNen_US
dc.subjectfog-cloud architectureen_US
dc.titleA User-Centric Smart Library System: IoT-Driven Environmental Monitoring and ML-Based Optimization with Future Fog-Cloud Architectureen_US
dc.typeArticleen_US

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