Wireless Sensor and Actuator Networks for Lighting Energy ...

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Figure 7-11 Prototyping Luminaire Structure. 7.4.2 Desktop Illuminance Regulation with Distributed Mote-FVF Algorithm A desktop illuminance regulation system is realized on the prototyping structure by integrating the wireless mote photosensors, the wireless ballast actuation module, and the mote-FVF algorithm. An experiment was designed to demonstrate the feasibility of embedding the developed sensor fusion algorithm in each mote sensor. The mote-FVF algorithm described in Chapter 4 has shown promise in extracting pertinent sensor information from a cluster of sensors while isolating disturbed or faulty sensors. However, it may create heavy wireless network traffic and significant energy drain if all the sensor nodes have to transmit readings to a designated central unit, which could be at a distance, for validation and fusion. If a cluster of mote sensors can perform 109
low-power, short-distance communication and execute intra-network sensor fusion, then only a single fused reading has to be transmitted to a central processing unit for control purposes from each cluster at each time stamp. The mote-FVF algorithm is believed to be lean enough in its current form for a battery powered mote platform to carry out the computations with its available resources. Experiment setup Six mote photosensors, each of which was programmed with a cluster leader electing logic and the mote-FVF algorithm, were placed on the testbed under the prototyping luminaire structure. These six mote sensors form a cluster and should generate one fused sensor reading each time. Upon being powered up, a timer also starts in each of the motes. If the timer expires in a mote, it claims itself as the cluster leader, requests sensor readings from its fellow motes, executes the mote-FVF algorithm, and sends out the fused readings. If a mote is asked for readings by another sensor before its timer expires, it sends out its reading, resets the timer and becomes a cluster member. This simple cluster leader electing logic guarantees that there is always one leader responsible for performing sensor fusion. More sophisticated logic may be adopted to select a cluster leader, such as one considering the remaining energy in each mote, but is out of the scope of this test. The mote-FVF algorithm, which was originally coded in Matlab ® , was rewritten in nesC as a function and incorporated into TinyOS, the operating system of the motes. The majority-voting scheme required in the validation unit of the mote-FVF algorithm was implemented with the median value approach due 110
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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
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acquisition and wireless transmissi
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a central base computer for sensor
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optimal light settings were in turn
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possibility of wireless actuator ne
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Chapter 2 Motivation The motivation
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[21]. Lighting accounts for 30% of
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Table 2-1 summarizes each of the co
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from light level tuning impossible
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used in the progress of this resear
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are expected to autonomously config
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office, and industrial applications
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Chapter 3 Related Research and Lite
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Although radio frequency is a suppo
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channels with different capabilitie
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In practice, fusion of sensor data
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The emergence of wireless sensor ne
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A is empty f A (x) = 0, x X (3.1)
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Convex combination: the relation Th
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where no crisp boundary can be defi
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developed by Dantzig [87]. The ineq
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sensor network is far more valuable
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Figure 4-1 Mote-FVF algorithm archi
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Figure 4-2 Gaussian correlation cur
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predicted reading. Note that the of
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fuzzification and m for the defuzz
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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
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
Page 124 and 125: Given the 256-level dimming capabil
Page 128 and 129: 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
Page 134 and 135: commercial system was randomized fo
Page 136 and 137: 8.2.1 Hardware Implementation in Sm
Page 138 and 139: eference point on the desktop was s
Page 140 and 141: commands were received. In addition
Page 142 and 143: were provided with three wireless p
Page 144 and 145: (a) Figure 8-6 Sensor placement of
Page 146 and 147: lower meter readings was that the m
Page 148 and 149: (2) Subtle and invisible shadows ma
Page 150 and 151: function keys are. The function key
Page 152 and 153: deliberately perturbed the lights d
Page 154 and 155: Table 8-2 Summary of the test on pr
Page 156 and 157: Current Level on the investigator
Page 158 and 159: Chapter 9 System Implementation and
Page 160 and 161: Figure 9-1 Floor plan of the small
Page 162 and 163: Figure 9-3 System operation diagram
Page 164 and 165: campus-wide lighting maintenance, w
Page 166 and 167: message is addressed. The destinati
Page 168 and 169: Both the actuation and the grouping
Page 170 and 171: The construction or updating of the
Page 172 and 173: determines if this message is for u
Page 174 and 175: 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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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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