Wireless Sensor and Actuator Networks for Lighting Energy ...

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too sensitive to failures. Parameters m e and m were tuned so as to optimize the response of the algorithm to the lighting environment. m e , m , m dif and m mov were tuned separately for the two majority voting schemes. The constant scaling factor was chosen to be large in order not to result in an obvious lag in the fused value by weighting the previous predicted value too heavily. 4.4.3 Simulation and Real-time Testing Results Figure 4-6 shows the real-time implementation of the mote-FVF algorithm using the median value majority voting scheme on the six-mote sensor network. The small cross signs are the raw sensed data from each mote, the dashed line is the true illuminance measured by the high fidelity light meter, and the solid line is the fused value. The figure reveals that the algorithm accurately followed the sensed data and reflected the illuminance pertinently with a maximum error of 3.36% regardless of the lag in the transient mode. Similarly, the algorithm with Gaussian correlation majority voting scheme run off-line on the same set of data, shown in Figure 4-7, revealed a maximum error of 4.01%. These errors include calibration errors when mapping raw digital readings to units of lux and the variation of illuminance in the spatial position on the testbed. From a physical point of view, the 4.01% error represents about a 30 lux difference, to which a human being is insensitive. Furthermore, the choice of majority voting mechanism didn’t seem to have a noticeable impact on the performance of the algorithm. 57
Figure 4-6 Mote-FVF with median value majority voting scheme. Figure 4-7 Mote-FVF with Gaussian correlation majority voting scheme. A comparison of several different variations of the sensor validation and fusion algorithm is shown in Figure 4-8. Part of the data set gathered in the real-time testing was used to run off-line simulations for four configurations, and some modifications were introduced to the data to simulate possible sensor failures. Plot (a) shows the 58
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Wireless Sensor and Actuator Networ
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Abstract Wireless Sensor and Actuat
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To my wife for having faith in me a
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Table of Contents Chapter 1 Introdu
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6.4.2 Lighting Optimization Algorit
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10.2.1 Theoretical Contributions...
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List of Figures Figure 1-1 Research
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Figure 7-8 Voltage divider. .......
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Figure 9-31 Energy savings vs. payb
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The integration of wireless sensor
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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 and 52: In practice, fusion of sensor data
Page 53 and 54: The emergence of wireless sensor ne
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: mote sensors were arranged in a 3-b
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
Page 105 and 106: optimizer for a consistency check.
Page 107 and 108: Figure 6-4 Pseudo code of the light
Page 109 and 110: simulation was to show that the opt
Page 111 and 112: It is obvious from Figure 6-6 and F
Page 113 and 114: Chapter 7 Wireless-Enabled Lighting
Page 115 and 116: determined to be 0-2000 lux. Conseq
Page 117 and 118: discrete outputs from the motes spa
Page 119 and 120: actuation technologies, and additio
Page 122 and 123: Figure 7-8 Voltage divider. Since t
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Given the 256-level dimming capabil
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Figure 7-11 Prototyping Luminaire S
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to its simplicity. Although coded w
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The second experiment was executed
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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
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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
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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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Seven-occupant case with simulated
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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
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$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
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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.
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B.2 Comparison of System Performanc
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B.3 Summary of the Responses from t
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B.4 Human Subject Test Approval Let
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B.5 Human Subject Test Protocol Nar
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241
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243
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245
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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
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Table C-1 Daylight reception durati
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At the rate of 9.62 cents per kWh a
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