Wireless Sensor and Actuator Networks for Lighting Energy ...

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utilizing a variant of a Kalman filter [73]. The performance of the methodology was verified by monitoring the temperature in a gas turbine power plant. Durrant-Whyte et al. derived a decentralized form of Kalman filter for multiple sensors to perform sensor fusion locally with limited communication among the sensors [74]. The efficiency of this decentralized sensor fusion architecture largely depends on the communication topology. Alag et al. used a modified Kalman filtering approach for real time sensor validation and PADF method for sensor fusion on the longitudinal control of automated vehicles [75]. Data from the sonar, radar and optical sensors mounted on each car were fused for accurate longitudinal distance between the cars. Bernardin et al. developed a human hand grasp recognition technique by fusing a grasp-type measure from finger joint angles and contact-point information from tactile sensors with a hidden Markov model [76]. The HMM was trained using expectation maximization (EM) algorithm and was able to attain 90% recognition accuracy with very little training data for twelve grasp classes. Zhou et al. proposed a maximum likelihood approach for fusion of multiple sensor data with correlated noise and unknown scaling factor mapping the sensor readings to physical representations [77]. The approach was based on a parametric modeling of the noise covariance and formulated in the transformed noise subspace. Wu et al. built a sensor fusion architecture based on Dempster-Shafer theory for context-aware computing applications by fusing together the evidence from both video and audio sensors [78]. Goebel et al. developed a fuzzy logic based architecture for real time sensor validation and fusion called FUSVAF. The performance of this algorithm was demonstrated on vehicle-following tasks on automated highways [79], and on the control of gas turbine power plants [80]. 35
The emergence of wireless sensor networks presents the need of sensor validation and fusion for extracting pertinent information from massively deployed yet disturbance-prone sensor nodes. Durrant-Whyte et al. extended the decentralized Kalman filter for sensor fusion derived in [74] so that sensor fusion can be performed locally on each sensor node without a fully connected network topology [81]. Yuan et al. discussed the trade-off between fusing large amounts of sensor data and the incurred latency when data are propagated towards the sink, and proposed a multi-level fusion synchronization protocol [82]. The protocol synchronized the fusion processes of the nodes at different levels in the data propagation tree so that maximum numbers of sensor data (or fused data) can get fused along the way towards the sink while minimizing the overall latency. Kumar et al. developed an architectural framework for distributed data fusion called DFuse, which consists of a data fusion application programming interface (API) and a distributed algorithm for energy-aware role assignment [83]. DFuse was implemented and evaluated on a network of personal digital assistant (PDA) with wireless capabilities; however, it can not yet be deployed to the more resource-limited wireless mote platforms. Jiang et al. developed a fusion algorithm based on a likelihood ratio-based test (LRT) scheme for fusing binary decisions made locally by each sensor node and transmitted through a fading (noisy) channel [84]. Various models of fading channels were investigated and the optimal LRT was derived under each scenario for data fusion. 36
Page 1 and 2: Wireless Sensor and Actuator Networ
Page 3 and 4: Abstract Wireless Sensor and Actuat
Page 5 and 6: To my wife for having faith in me a
Page 7 and 8: Table of Contents Chapter 1 Introdu
Page 9 and 10: 6.4.2 Lighting Optimization Algorit
Page 11 and 12: 10.2.1 Theoretical Contributions...
Page 13 and 14: List of Figures Figure 1-1 Research
Page 15 and 16: Figure 7-8 Voltage divider. .......
Page 17 and 18: Figure 9-31 Energy savings vs. payb
Page 19 and 20: The integration of wireless sensor
Page 21 and 22: esulting lighting is optimal in the
Page 23 and 24: acquisition and wireless transmissi
Page 25 and 26: a central base computer for sensor
Page 27 and 28: optimal light settings were in turn
Page 29 and 30: possibility of wireless actuator ne
Page 31 and 32: Chapter 2 Motivation The motivation
Page 33 and 34: [21]. Lighting accounts for 30% of
Page 35 and 36: Table 2-1 summarizes each of the co
Page 37 and 38: from light level tuning impossible
Page 39 and 40: used in the progress of this resear
Page 41 and 42: are expected to autonomously config
Page 43 and 44: office, and industrial applications
Page 45 and 46: Chapter 3 Related Research and Lite
Page 47 and 48: Although radio frequency is a suppo
Page 49 and 50: channels with different capabilitie
Page 51: In practice, fusion of sensor data
Page 55 and 56: A is empty f A (x) = 0, x X (3.1)
Page 57 and 58: Convex combination: the relation Th
Page 59 and 60: where no crisp boundary can be defi
Page 61 and 62: developed by Dantzig [87]. The ineq
Page 63 and 64: sensor network is far more valuable
Page 65 and 66: Figure 4-1 Mote-FVF algorithm archi
Page 67 and 68: Figure 4-2 Gaussian correlation cur
Page 69 and 70: predicted reading. Note that the of
Page 71 and 72: fuzzification and m for the defuzz
Page 73 and 74: mote sensors were arranged in a 3-b
Page 75 and 76: Figure 4-6 Mote-FVF with median val
Page 77 and 78: Figure 4-8 Comparison of variations
Page 79 and 80: The daylighting system application
Page 81 and 82: The key components are the predicti
Page 83 and 84: Figure 5-3 Prediction performance o
Page 85 and 86: Figure 5-5 Prediction performance o
Page 87 and 88: Among them, adaptive Wiener filteri
Page 89 and 90: even though the sensed environment
Page 91 and 92: 5.3 Simulation and Experiment Resul
Page 93 and 94: Figure 5-11 Adaptive sensing with d
Page 95 and 96: Figure 5-13 Damped adaptive sensing
Page 97 and 98: Chapter 6 Optimal Lighting Actuatio
Page 99 and 100: of the workplane level illuminance
Page 101 and 102: office with K luminaires, for examp
Page 103 and 104:
E sub = e pq e rs e xy 1
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optimizer for a consistency check.
Page 107 and 108:
Figure 6-4 Pseudo code of the light
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simulation was to show that the opt
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It is obvious from Figure 6-6 and F
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Chapter 7 Wireless-Enabled Lighting
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determined to be 0-2000 lux. Conseq
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discrete outputs from the motes spa
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actuation technologies, and additio
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Figure 7-8 Voltage divider. Since t
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Given the 256-level dimming capabil
Page 126 and 127:
Figure 7-11 Prototyping Luminaire S
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to its simplicity. Although coded w
Page 130 and 131:
The second experiment was executed
Page 132 and 133:
Chapter 8 System Verification on Hu
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commercial system was randomized fo
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8.2.1 Hardware Implementation in Sm
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eference point on the desktop was s
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commands were received. In addition
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were provided with three wireless p
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(a) Figure 8-6 Sensor placement of
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lower meter readings was that the m
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(2) Subtle and invisible shadows ma
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function keys are. The function key
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deliberately perturbed the lights d
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Table 8-2 Summary of the test on pr
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Current Level on the investigator
Page 158 and 159:
Chapter 9 System Implementation and
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Figure 9-1 Floor plan of the small
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Figure 9-3 System operation diagram
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campus-wide lighting maintenance, w
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message is addressed. The destinati
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Both the actuation and the grouping
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The construction or updating of the
Page 172 and 173:
determines if this message is for u
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Figure 9-12 Lighting preset setting
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Figure 9-13 Average hourly percent
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9.4 Daylight Response The second ph
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Furthermore, the structure allows t
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Figure 9-16 Daylight harvesting tes
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Figure 9-18 Sensor readings in the
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then gradually dimmed. After reachi
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Page 190 and 191:
Seven-occupant case with simulated
Page 192 and 193:
Page 194 and 195:
under significant daylight. Again,
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newly calculated light settings may
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Annual energy consumption = ( Numbe
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Figure 9-30 Energy savings vs. payb
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have partially contributed to the h
Page 204 and 205:
$40 per unit and the system achieve
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performance of the current research
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sensors. The research in [14] prese
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Other than optimizing the energy co
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lighting configuration in the offic
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communications. The adaptive sensin
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integration of the research system
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will broaden the application to dif
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system could be reallocated to oper
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entities could be tremendously crit
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[7] J. A. Veitch and G. R. Newsham,
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[20] K. A. Karmel, High Performance
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[34] P. Levis, S. Madden, J. Polast
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International Workshop on Wireless
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[57] LonMark International, "Fact S
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[71] S. Sahni and X. Xu, "Algorithm
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Proceedings of the 1st Internationa
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[99] SPOT: Sensor Placement + Optim
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[113] Advance Transformer Technical
Page 242 and 243:
A.2 Circuit Schematics of Photosens
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A.4 Circuit Schematics of the Secon
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Appendix B Human Subject Test B.1 C
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(a) Sensor placement of subject No.
Page 250 and 251:
B.2 Comparison of System Performanc
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B.3 Summary of the Responses from t
Page 254 and 255:
B.4 Human Subject Test Approval Let
Page 256 and 257:
B.5 Human Subject Test Protocol Nar
Page 258 and 259:
241
Page 260 and 261:
243
Page 262 and 263:
245
Page 264 and 265:
247
Page 266 and 267:
249
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B.7 Human Subject Test Questionnair
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253
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B.8 Human Subject Test Interview Sc
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Luminaire surface depreciation: 1.0
Page 276 and 277:
Table C-1 Daylight reception durati
Page 278:
At the rate of 9.62 cents per kWh a
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