# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 2 Related Works  
### Occupancy Sensors for Smart Lighting Control  
Occupancy sensing has been widely studied, and many sensing techniques have been proposed, including PIR sensors [18, 19], microwave Doppler sensors [20], ultrasound sensors [21], and Wi-Fi sensors [22]. PIR sensors have been widely used in the field of occupancy detection because they are fast and cost effective [23]. Specifically, a PIR sensor can detect radiations at a wavelength of approximately 10 microns, which is the peak wavelength of the heat energy emitted by humans [24], and its output is represented by zero or one corresponding to the unoccupied state or occupied state, respectively. Although a PIR sensor can achieve an accuracy rate of 98.4%, it often generates false-negative detection errors when occupants move slightly [23], and also fails to detect humans in hot weather. A microwave Doppler sensor utilizes the Doppler effect to analyze the frequency difference between transmitted and received signals [25]. It can not only detect the movement of objects but also monitor small movement. As microwave sensors have high sensitivity, they suffer from false alarms due to slight vibration or fast moving of objects in the air. Ultrasonic sensors are similar to microwave sensors except that high-frequency sound waves are used to detect movement according to the Doppler effect [26]. Ultrasonic sensors are highly suitable for detecting occupancy behind obstructions, so occupancy on adjacent hallways and outside of windows is also detected, which leads to false-positive errors [24]. Wi-Fi has also been proposed for occupancy sensing, especially for indoor positioning. The Wi-Fi access points detect the occupancy presence using the Wi-Fi signals transmitted by mobile devices. The disadvantage of this approach is that occupants are required to carry their mobile devices all the time [22]. Other sensors, including CO\({}_{2}\) sensors [27] and camera [28], can also be utilized for occupancy detection. However, there is a long latency period between the presence of people and CO\({}_{2}\) sensor responses, while the camera-based approaches require additional lighting and have high computational overload, along with privacy concerns. Merging data from different sensors can provide better results as different types of sensors compensate each other [29]. Recently, the integration of multiple sensors has been suggested to improve the performance of occupancy detection [30]. [PERSON] et al. [31] used a radial basis function (RBF) neural network whose inputs were preprocessed sensor data including temperature, humidity, CO\({}_{2}\), light, sound, and motion to estimate occupancy for demand-driven HVAC operations. [PERSON] et al. [32] estimated occupancy considering motion detection, power consumption, CO\({}_{2}\) concentration sensors, microphone, or door/window positions, and selected decision tree technique as the prediction model. [PERSON] et al. [33] presented a real-time occupancy estimation method based on a Conditional Random Field (CRF) model, which combined information from sensors such as door counters, acoustic, PIR motion, and CO\({}_{2}\) sensors, and applied the model to four real-life spaces, i.e., rest area, meeting room kitchen, and office, with different combinations of sensors. However, the performance of these fusion methods is influenced by the similarity between training samples and test samples. For example, the detection rate was 64.83% for cross-estimation compared with 87.62% for self-estimation in [31]. [PERSON] et al. [25] designed a real-time pedestrian detection method where a PIR sensor was used at the ambient temperatures of less than 40 \({}^{\circ}\)C, and at the higher temperatures, it switched to a microwave sensor. The reason for such design was to avoid detection errors of PIR sensors at high temperatures while taking advantage of the low power consumption of PIR sensors.