# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 5 Discussion  
In this study, a smart lighting system is proposed to control the indoor illuminance level. The proposed system includes distributed wireless sensing networks and a sensing module consisting of occupancy sensors and ambient light sensors. The proposed system is verified by the experiments in two scenarios, i.e., a metro station and an office room, and the results show that the proposed system is robust enough in practical environments. The performance of occupancy detection of the proposed system is also verified experimentally. The energy savings of the proposed system are also evaluated in the scenario of the metro station, where energy consumption is recorded, and in the scenario of the office room, where power consumption is calculated using dimming levels. In the scenario of the metro station, the relative energy saving of the proposed system is about 45% compared to the normal lighting system. The energy savings are achieved by dimming the LEDs down to a 10% level when no occupant is detected. The results show that the people flow density influences the value of the energy savings. [PERSON] et al. [40] presented a similar lighting control strategy designed for street lighting infrastructure and achieved 60-77% savings compared to a 100% dimming level at the dark time of the day. However, their savings were based on the simulation results obtained using the presence data and sunrise and sunset times from the weather data bank. On the contrary, in this study, a comparison experiment is conducted and the energy consumption is measured in a real scenario. Moreover, the considered people flow density is also different because our system was installed in a metro station in China while theirs was installed on a pedestrian road in Helsinki. Besides energy savings, when designing the lighting system, it is important to consider the user experience, including safety and comfort. For instance, in this study, the illuminated area is expanded so that a user cannot detect the change in lighting level around him/her. This increases the system cost and reduces the interest in investing the new technologies. This problem can be resolved by careful selection of the location where the smart lighting system is installed. For instance, metro stations with a low passenger flow density are more suitable for the installation of the proposed system than those with a moderate or high passenger flow density. Moreover, the sensing module can be extended to include other sensor types, such as Bluetooth, to provide new functions and reduce the cost. The results show that in the scenario of the office room, the energy savings were influenced by multiple factors, including weather, desired illuminance level, location of worktables, and the number of staff in the room. The power consumption savings were about 36%, which was lower than the power savings of 40% that was reported in [43] at the same desired illuminance level,  
Figure 16: Lighting power consumptions during office hours and overtime hours. i.e., 500 lx. There are two possible reasons for this difference. First, the overall conditions of the office room for daylight harvesting were not favorable, as 45% of workables were in the middle of the floor plan where the distance to windows was far, and the illuminance of daylight was low. Second, the open-loop lighting controller was used, and the rule base was designed for this controller. Compared to the closed-loop controller, the advantages of the controller used in this work are less amount of computation, faster reaction to the change in environmental conditions, and lower cost of manufacturing and maintenance. However, the main drawback is a lack of optimization of energy consumption. For instance, the PZ and SZ for daylight harvesting are unchanging in this work, which makes it impossible to fully utilize daylight as space in a room getting more sunlight varies with time from sunrise to sunset.