# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 5 Discussion
When the desired illuminance level decreases, the optimization of the dimming level of each luminaire becomes important to energy savings. As the proposed lighting control system is based on open-loop controller in which control rules are designed to determine the dimming level, when the system is applied to a new commercial building, schedules (occupant pattern), artificial light contribution to the value of ambient light sensor, the partition for daylight harvesting and harvesting rules in Equation (3) need to be modified in a supplied software. Compared with closed-loop controller, open-loop controller in this system is less precise and less flexible. However, in wireless networks, an increase in settling time of luminaires due to packet losses was found when using a central and close-loop controller [9]. Contrarily, settling time in the proposed system is less as a result of the distributed and open-loop controller. Moreover, the proposed system is easier to deploy and cheaper in comparison to a wired system, especially for building retrofits. A sensing module and a LED driver are attached to a LED luminaire if the luminaire is nondimmable. Otherwise, only a sensing module is needed. However, when the proposed system is deployed in a public place, an important challenge is the security of ZigBee protocol used in the WSN for lighting control and countermeasures for attacks to ZigBee networks need to be devised [44; 45]. The experimental results show that the proposed smart lighting system can realize occupancy-driven and daylight harvesting lighting control and reduce energy consumption in practical environments. Nevertheless, the following limitations need to be underlined. First, in the scenario of the metro station, there are no standards on occupancy-based lighting control. In addition, the impact of the proposed smart lighting system on passengers' safety and comfortability is not quantitatively studied in this work. The lighting controller can be improved by combining dimming levels and user reactions. Second, in the scenario of the office room, the rule base, which is the core of the lighting controller, is designed only considering the location of luminaries, schedule, and occupancy, but more factors related to the energy consumption, such as seasons, hours, and weather information, should be analyzed and integrated into lighting control rules. Third, control designs that account for electric blinds are not included in the proposed system. Blinds are often used to reduce glare and improve thermal comfort. Meanwhile, the use of blinds has an effect on the illuminance of daylight. Integrated lighting control algorithms that take into consideration illuminance, light uniformity, and temperature are needed to minimize energy consumption. Fourth, the cost of the proposed system is not quantitatively analyzed, which includes cost in design, manufacture, installation, and maintenance. Whether achieved energy savings would quickly cover the cost of the smart lighting system is important for the development of the system. Our future work will address the mentioned problems and will focus on improving the proposed smart lighting system using the Internet of Things (IoT) and deep learning techniques. Conceptualization, Y.C. and C.F. ; methodology, Y.C. and J.Y. ; software, C.F. ; validation, Y.C. and L.Z. ; formal analysis, Y.C. and C.F. ; investigation, Y.C. ; resources, C.F. ; data curation, J.Y. ; writing--original draft preparation, Y.C. ; writing--review and editing, L.Z. and J.Y. ; visualization, C.F. ; supervision, J.Y. ; project administration, J.Y. ; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 71601047 and China Postdoctoral Science Foundation, grant number 2015M581706. The authors declare no conflict of interest.