Ayaz M., Yücel U., Ergün R. E., Eken S.
ACM TRANSACTIONS ON SENSOR NETWORKS, cilt.0, sa.--, ss.1-26, 2026 (Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
0
Sayı:
--
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Basım Tarihi:
2026
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Doi Numarası:
10.1145/3798095
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Dergi Adı:
ACM TRANSACTIONS ON SENSOR NETWORKS
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Derginin Tarandığı İndeksler:
Scopus, Compendex, INSPEC
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Sayfa Sayıları:
ss.1-26
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Kocaeli Üniversitesi Adresli:
Evet
Özet
This study presents a modular smart lighting system that employs an open-loop, daylight-based control strategy supported by artificial intelligence (AI). Unlike conventional closed-loop systems that rely on multiple indoor sensors and complex wiring, the proposed system adopts a distributed architecture in which a Master LED Luminaire (MLL) coordinates Slave LED Luminaires (SLLs). The MLL incorporates a Support Vector Regression (SVR)-based daylight prediction model that estimates indoor daylight availability using fixed architectural parameters such as room dimensions, window geometry, and surface reflectance together with real-time outdoor irradiance data. The system was implemented and validated in a public building under real operating conditions. Comparative evaluation with traditional on–off control and sensor-based closed-loop control indicated that, while the closed-loop approach achieved the lowest artificial lighting demand (56.4%), the proposed system required only slightly more (66.4%) yet offered clear advantages in scalability, ease of installation, and reduced infrastructure costs by eliminating the need for multiple sensors. Integration with IoT-enabled communication further allows real-time parameter updates and adaptive dimming control. The findings demonstrate that the proposed method provides a scalable and cost-effective retrofit solution, addressing the installation, maintenance, and adaptability challenges of conventional lighting control systems.