Smart indoor lighting systems use occupancy and light sensor data to adapt artificial lighting in accordance with changing occupancy and daylight conditions. Such systems can be designed to reduce lighting energy consumption significantly. However, these systems cannot account for individual user preferences at the workplace in real time. We propose a sensor-driven, human-in-the-loop lighting system that incorporates user feedback in addition to occupancy and light sensor inputs. In this system, luminaires transmit unique visible light communication identifier signals. By processing the image captured by a smartphone camera, a user obtains two pieces of information: visible light communication identifiers of luminaires in the vicinity and average image pixel value. A control algorithm is designed that incorporates these user inputs along with occupancy and light sensor inputs to determine the dimming levels of the luminaires to achieve illumination levels acceptable to users. We compare the performance of the proposed lighting control system with a sensor-driven lighting control system in an office test bed.@en

Indoor lighting systems need to be designed to balance energy consumption and the visual comfort of occupants. Achieving this goal with multiple luminaires and sensors is, however, not a simple problem. Lighting control systems need to adjust the dimming levels of luminaires in real time based on occupancy conditions and external daylight changes. We propose a system based on visible light communication to monitor and control artificial lighting in a robust manner. In our system, luminaires modulate the emitted light to broadcast basic control information while fulfilling their main purpose of illumination. Based on the broadcasted information, luminaires use collocated light sensors to estimate the optical channel gains and daylight contributions. This information is then used in a control algorithm to determine the dimming levels of luminaires under specified illumination constraints. We provide an analytical framework, simulations, and an empirical evaluation of our approach in an office space. We compare our method with a state-of-art lighting control system that uses radio transceivers to communicate information, and thus, cannot monitor optical channels in real time. Our results show that while the radio-based system may underilluminate and even oscillate around the desired illumination due to large reflectance changes in the environment, our method provides stable illumination close to desired levels.
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