The Application of Sensor Networks to Lighting Control

Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

“…Any of the three PIR sensors alone cannot provide a full coverage of this small office space. Similar findings were reported by others [48]. This is partly because of the room geometry and the furniture layout.…”

“…x Dong et al [16] x RH, temperature, light, CO2 Polese [75] Image processing occupancy sensor Labeodan et al [39,76] Chair sensors Jazizadeh and Becerik-Gerber [77] Light intensity sensors x Liao et al [78] Beam-break sensors Meyn et al [79] Camera, CO2 Tiller et al [48] x Wired SN Dedesko et al [81] Beam-break sensors, CO2 Erickson et al [82] Wireless Camera Erickson et al [17] Wireless Camera Harle et al [83] Ultrasonic 3D tracking Benezeth et al [84] Camera Hailemariam et al [29] RH, temperature, light, AC current Sensor array Attar et al [85] RH, temperature, light, AC current Sensor array Han et al [86] RH, temperature Han et al [87] CO2, PIR, RH Pandharipande and Caicedo [88] Ultrasound array sensor Agarwal et al [21] PIR + door sensor x Weekly et al [89] Particulate matter sensor Yavari et al [90] Doppler Radar Arora et al [54] Illuminance, RH, PIR, power consumption, door contact, CO2 Gunay et al [45] x PIR, door contact, CO2 Philipose et al [91] x Ramoser et al [92] x Brackney et al [93] Image processing occupancy sensor Milenkovic and Amft [94] PIR, ultrasound range finders, and plug-in equipment load Layered hidden Markov models were trained with 30 h worth of data for presence detection and with 50 h worth of data for people count detection. The models were shown to detect presence accurately.…”

“…The advantage of implicit occupancy sensing is that these sensors are already present for other purposes, are powered and capable of communication so that they can be accessed by the building control systems, and thus come at little or no incremental cost. Although these individual channels might have limited accuracy independently, their aggregated data may result in higher accuracy, and certainly more robustness, than any one high-end sensor[16,48,56,58,60].…”

“…Fuzzy Inference approach by Ekwevugbe et al[62]; -Belief Network based approaches by Dodier et al[46] and by Tiller et al[48];-Bayesian Networks by Harris and Cahill[71] and Zhao et al[68];-Support Vector Machine (SVM) by Zhen et al[73];…”

Application of teletraffic engineering modelling techniques for studying smart lighting systems for energy saving

“…18 For example, the National Lighting Product Information Program pointed out that half of a small sample of motion sensor detectors did not respond to a movement occurring within the coverage area claimed for the device. 7 In order to improve the accuracy of occupancy detection, Dodier et al 19 proposed using sensor belief networks based on Bayesian probability theory to determine occupancy and a similar idea was presented by Tiller et al 20 However, the effect of timeout settings on energy saving was not mentioned. Guo et al 21 and Tiller et al 22 proposed a data processing and evaluation framework for application to a lighting control sensor network.…”