MEMS ‘Smart Dust Motes’ for Designing, Monitoring & Enabling

Efficient Lighting

MICRO Project Industry Sponsor General Electric Company; Global

Research Center

Professor Alice Agogino, Faculty AdvisorJessica Granderson, Ph.D. Student

Johnnie Kim, B.S. StudentYao-Jung Wen, Ph.D. Student

Rebekah Yozell-Epstein, M.S. Student

Commercial Lighting

• Electrical Consumption and Savings Potential

• Advanced Commercial Control Technologies- Up to 45% energy savings possible with

occupant and light sensors- Limited adoption in commercial

building sector

Commercial Lighting

• Problems With Advanced Control Technologies– Uncertainty is not considered --> sensor

signals, estimation, target maintenance– Time is not considered, lost savings

through demand reduction– All occupants are treated the same– Wires, retro-fit and commissioning

Intelligent Decision-Making with Motes

• An intelligent decision algorithm allows:validation of sensor signalsuncertainty in illuminance estimationdifferences in preference and perceptionpeak load reduction/demand response

• Smart dust motes potentially offer: wireless sensing at the work surface, increased sensing density, simpler retro-fitting and commissioning, wireless actuation, and an increased number of control points

BEST Lab Energy Research

• Characterization, validation, and fusion of mote signals

• Modeling the decision space for automatic dimming in large commercial office spaces (cubicles)

• Benchmarking a specific decision space for switching and occupancy patterns, proposed smart lighting design

• Determination of occupant preferences and perceptions for a specific decision space

Modeling the Decision Space

• Goal is a model that can balance occupant preferences and perceptions with real-time electricity prices in daylighting decisions

• Hierarchical problem breakdown– Local validation of sensor signals– Regional fusion of sensed data, actuation– Global optimization of regional decisions

Regional Influence Diagram

Immediate Work

• Regional Decision-Making – Balance occupant preferences – Empirical occupant testing without

windows to control for the effects of natural light

– Incorporation of electricity prices for demand-responsive load shedding

Regional Decisions (no windows)

Future Work

• Daylighting decisions– Glare, blinds– Natural/artificial light contributions – Contrast

• Design of a global value function– Optimal combination of regional

decisions

Features of Sensor Validation and Fusion for Sensor Networks• Purpose

– Provide reliable information of current environment for decision-making

– Feed appropriate value back to the control system

• Main Idea– Fuse sensor of the same kind into one or

more reliable virtual sensor– Fuse disparate sensors

Research Goals

• Characterize mote sensors• Find and construct the most suitable

sensor validation and fusion algorithm for sensor networks

• Build algorithm for sensor locating based on the result of sensor validation and fusion.

Purpose of Sensor Validation

• Noise rejection• Fault detection

– Sensor failure– Process failure– System failure

• Ultimate purposeTo provide the most reliable data for fusing

Methodology for Sensor Validation

1. Signal check2. Absolute limits

check3. System

performance limits check

4. Expected behavior check

5. Empirical correlation check

Performance limits check

Sensed data

Expect behavior check

Correlation check

Absolute limits check

Signal output check

Fusion procedure

Previous value

Sensor feature

Possible Methodology for Sensor Fusion

• Fuzzy Approach• Kalman filter• Bayesian network• Neural network

Sensor Fusion and Validation

Calculate fused value using oldpredicted value for validation gate and incoming readings

Calculate new predicted value using fused value

Fused value

Sensor readings

Controller

Decision-making system

Supervisory controller

Sensor Validation

Sensor Fusion

Sensor Readings

Diagnosis

Machine LevelController

Algorithm for sensor validation and fusion

Architecture for Sensor Validation and Sensor Fusion

The MoteProcessor and Radio Platform

• Atmega 128L processor (4MHz)• 916MHz transceiver• 100 feet maximum radio range• 40Kbits/sec data rate

The MoteSensor Board

The MoteSensor Board

MicrophonePanasonic WM-62A

ThermistorPanasonic

ERT-J1VR103J

Light SensorClairex

CL9P4L

MagnetometerHoneywellHmc1002

AccelerometerAnalog Devices

ADXL202JE

BuzzerSirius

PS14T40A(missing)

The MoteOther Accessories

• Basic SensorboardThis board has two sensors:temperaturephotoand is capable of integrating other kinds of sensors on it.

• Interface BoardProgramming each mote platform via parallel port.Aggregation of sensor network data onto a PC via serial port.

Example IAnalyzing of Old Cory Hall Data

Mote node_id 6174

Mote Location and Environment

Example IAnalyzing of Old Cory Hall Data

Mote node_id 6174

Mote Location and Environment

Example I (contd.)Analyzing of Old Cory Hall Data

Mote node_id 6174

Light Readings and Temperature readings5/24/01~5/31/01

Example I (contd.)Analyzing of Old Cory Hall Data

Mote node_id 6174

Light Readings and Temperature readings5/24/01~5/31/01

Possible failure of light sensor

Possible failure of both light and temperature sensor

Example IIAnalyzing of Old Cory Hall Data

Mote node_id 6190 & 6191 in Room 490

Sensor Readings in Cory Hall 4905/17/01~5/22/01

Example II (Contd.)Analyzing of Old Cory Hall Data

Mote node_id 6190 & 6191 in Room 490

Fusion of Light Reading of 5/17 Using Dr. Goebel’s FUSVAF Algorithm

Potential Difficulties: Validation and Fusion

• There is not a specific sensor on the sensor board for sensing occupancy

• Error of mapping sensor signals to physical readings due to the non-linearity and sensitivity of each sensor element

• The sampled data for the same time stamps might be received at different time due to wireless communication

• Only one sensor per board functions at any given time

Plans for the Next Two Months

• Setup the software and hardware to actuate the smart motes on hand

• Characterize the motes signals• Collect data of target office space using

one or several motes• Characterize motes failure patterns for

individual motes• Build algorithms for feature

identification and extraction• Search for the accurate and efficient way

to sense occupancy

Plans for the Next Six Months

• Build up mote sensor networks in the target office space

• Benchmark test the networks• Characterize motes failure patterns

for mote networks• Evaluate appropriate validation and

fusion algorithms• Determine best locations for motes

Plans for the Future

• Implement the mote validation and fusion algorithm to real time validating and fusing

• Refine the mote validation and fusion algorithm

• Evaluate the possibility of using motes to actuate dimming ballast directly

Benchmarking Research Goals

• Verify the need for a smart lighting system based on human interactions with their environment

• Develop design guidelines for a smart lighting system

• Propose a smart lighting system for the BEST Lab, (6102 Etch.)

Benchmarking Research Deliverables

• Benchmark the current switching and occupancy patterns in the BEST Lab

• Discuss potential energy savings based on the results of this benchmarking

• Perform a usability study to determine user preferences with respect to smart lighting

• Propose a system that will personalize lighting based on occupancy and save on electricity costs

Occupancy in Work Area

Average Total Occupancy vs. Time of Day

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Time of day (military time)

Ave

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occ

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eopl

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WednesdayThursdayFridaySaturdaySundayMondayTuesday

Occupancy in Conference Area

Average Conference Area Occupancy

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Switching Patterns in BEST Lab

Switching Patterns

-20.0

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40.0

60.0

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100.0

120.0

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Time of Day (military time)

Prob

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Will

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MondayTuesdayWednesdayThursdayFridaySaturdaySunday

Potential Energy Savings

• Calculate current energy usage in lab• Calculate energy usage for lights only

being used when and where they are needed

• Compare current and potential costs

Usability Issues

• What level of manual control and override will users need to feel comfortable with the system?

• How will users enter personal lighting preferences into the system and when (initially or once a problem is detected)?

Occupant Preferences and Perceptions

• Goal: Determine the illuminance ranges over which occupants perceive the lighting at their desk to be– too bright, – too dark,– or just right

Empirical Preference Testing

• Method: Perform multiple tests on individuals at their respective workstations

• Equipment:– 4-light fluorescent shop light– Dimmable electronic ballast– 0-10 VDC source– PVC Piping framework

Experiment flowchart

0-10 V variable DC

Dimmable electronic ballast

Variable illuminance

User’s perception

Experimental Setup

• A desktop apparatus that provides lighting 6-8 ft. directly above the work surface

6-8 ft.

Light Fixturing Detail

4-light fixture

chain connections

Future Energy Work

• Extension to intelligence HVAC control

• Agent-based technology for actuation

• Further personalization for individual spaces