Future Science Forum - Blue Oak Ranch Reserve

Transcription

DEPARTMENT OF
PRIMARY INDUSTRIES
Future Science Forum
Integration and Use of Embedded
Sensor Networks
Prof Michael Hamilton
Center for Embedded Network Sensing,
University of California
Digital Habitat Sensing
Center for Embedded Network Sensing (CENS)
Habitat Sensing Group
Michael Hamilton
James San Jacinto
Mountains Reserve
Richard Gump South
Pacific Research Station
Moorea, French Polynesia
Enabled by the National Science Foundation,
Science and Technology Center Program,
a $47.5 million, 10 year project initiated in 2002
UCLA
Computer Science: D. Estrin (PI), R. Muntz, S. Soatto
Electrical Engineering: J. Judy, W. Kaiser, G. Pottie, M.
Srivastava, K. Yao)
Mechanical and Aerospace Engineering: C.M. Ho
Civil and Environmental Engineering: T. Harmon, J. Wallace
Physiological Sciences and Biology: P. Rundel, C. Taylor
Earth and Space Sciences: P. Davis (co-PI), M. Kohler
Institute of the Environment: R. Turco
Education and Information: C. Borgman (co-PI), W. Sandoval
Management:
Director: Deborah Estrin
Chief Amin Officer: Bernie Dempsey
Education Coor: Sara Terheggen
Budget Analyst: David Jaquez
Admin Asst: Stacy Robinson
USC
CalTech
UC Riverside
Computer Science: A.Requicha
(co-PI), R. Govindan, G. Sukhatme
Electrical Engineering: C. Zhou
Marine Biology: D. Caron
Electrical Engineering: Y. C. Tai
Conservation Biology: M. Allen, M.
Hamilton (co-PI), J. Rotenberry
Cal State LA
Grades 7-12
JPL
Engineering, CEACREST
The Buckley School: K. Griffis
New Roads School: J.A. Wise
Center for Integrated Space
Microsystems: L. Alkalai, Manager
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The Digital
Digital Sensing
Sensing Potential
Potential
Integrated microsensors, low-power
processors, wireless
interfaces at very small
scale
Monitor “up close”
Monitoring and Controlling Enables spatially and
Natural Processes
temporally dense,
continuous monitoring
Microorganism Detection
Embedded
Networked
Sensing will reveal
previously
unobservable
phenomena
Monitoring and
Controlling Agricultural
Processes
Biological Conservation
Exploring
the Laboratory
benefits of sensor networks:
Habitat Sensing
Initial Test
Bed
Activities
our
first
experimental
test bed
Soil sensors can monitor
microbial respiration, nutrients,
moisture, fungi and soil
organisms
Dense sensor networks can
continuously track variation in
temperature, rainfall, soil
moisture, and nutrients in
farms and watersheds
Sensors can monitor without disturbance
plants and animals, and their immediate
surroundings
Sensor networks
networks can
can direct
direct
the movements and
activities of robotic
equipment, on both land and
in the lagoon
How is it done?
1. New sensors in and above soil/water
measure nutrients, pollutants, organisms
2. Wireless data communication among
sensors
3. Improved engineering new devices are
cheap, durable, easily mass produced
4. Low power using battery or solar
5. Powerful microprocessors detect
patterns in data streams, and combine
computing power for running sophisticated
algorithms
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Sensor Node Developments
Developments
LWIM III
AWAIRS I
UCLA, 1996
UCLA/RSC 1998
Geophone, RFM
Geophone, DS/SS
radio, PIC, star
Radio, strongARM,
network
Multi-hop networks
Sensor Mote
Medusa, MK-2
UCB, 2000
UCLA NESL
RFM radio,
2002
PIC
Applications
Applications in
in Watershed
Watershed management
management
“ba ckbone”
netwo rk
a dap ted f ro m
CA D W R web site
1. Terrestrial inputs (e.g., fertilizers) impact
ground water
2. Inputs and ground disturbances impact rivers,
lakes, and ocean (lagoon)
3. Great need for multi-scale monitoring of
nutrients and hydrology
Habitat Sensing Laboratory
Choosing
New
New Test
Test Sites
Sites
Initial Test Bed Activities
1. Small, tractable ecosystem
2. Easy access to many types of habitat,
agriculture and microclimates
3. Ability to follow complete watersheds (from
mountain to ocean)
4. Essential technological infrastructure (e.g.,
Broadband Internet access) and scientific
expertise (e.g., research stations)
5. Relatively protected natural systems for first
deployment from lab to field (e.g., a protected
lagoon provides a relatively controlled
environment)
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Applications
of Digital
Digital
Sensing
Sensing
1. Precision agriculture and chemical sensing
2. Monitoring micro-climate
3. Wildlife conservation
4. Robotic sensing (networked infomechanical systems)
California Agriculture-Environment
Agriculture-Environment Test
Test Project
Project
Farm Irrigation
Reused water –
high in Nitrates
Wastewater
treatment plant
Agricultural
Agricultural Application
Application -- California
California
Reused Water
Irrigation
Control Irrigation Input
Sensors
detect NO3
level
Protect
groundwater
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Sensor network
network calibration
calibration

Goal: sensor network error resiliency in heterogeneous soil
domain
Primarily off-the-shelf sensors
soil moisture
nitrate ISEs
Role of individual sensor noise, failure
In situ calibration vs lab calibration
Protocols for network calibration
Challenges
Challenges at
at the
the sensor
sensor level
level
air
Ion selective membranes need
water carry ions
Resiliency to long-term
environmental deployment
untested:
wetting and drying cycles
biofouling
material degradation
Challenges with network calibration
Complex, dynamic
media effects
in situ calibration
self-calibration
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Larger scale spatially
spatially distributed
distributed flow,
flow, transport
transport patterns
patterns
30 acre
circle
Too large for us to fully instrument
Sample randomly throughout
Employ simulation models to
interpolate between sensors
Geostatistical approaches to
parameterize models
Silty sand
clay
Rotating
pivot
Network
Network Calibration/Error
Calibration/Error Resiliency/Fault
Resiliency/Fault Tolerance
Tolerance
Too large for us to fully instrument
Sample randomly throughout
Employ simulation models to
interpolate between sensors
Geostatistical approaches to
parameterize models
Minimizing cost without
without cost
cost to
to research
research
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36
GroPoint 1
GroPoint 2
GroPoint 3
GroPoint 4
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32
30
28
Moisture
26
24
22
20
18
16
14
12
10
8
6
4
2
12:00 AM
8/14/2003
12:00 AM
8/21/2003
12:00 AM
8/28/2003
12:00 AM
9/4/2003
12:00 AM
9/11/2003
12:00 AM
9/18/2003
12:00 AM
9/25/2003
12:00 AM
10/2/2003
Date
heterogeneous sensor network
small numbers of “high end” sensors (e.g. TDR
probes)
large numbers of “low end” sensors (e.g.,
gypsum blocks)
collaborative sensing algorithms
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New sensor development: nitrate sensors

Current (off the shelf) technology
Cigar sized
Relatively expensive
Not resilient to field conditions
detection limit typically about 1 ppm
Small scale sensor
sensor network:
network: beneath
beneath aa single
single plant
plant
Many of the same problems
apply:
lab calibration vs in situ
self-calibration
sensor longevity
Form factor will be an issue
“mini” Echo?
“micro” Echo?
New sensor development: nitrate sensors
Scaling down to facilitate production
240
Carbon band electrodes (Microfabricated)
Carbon thickness: 200-250 nm
4.9 mm X 0.45 mm, S = 2.205 x-210
cm2
Voltage (mV)
200
160
2
J = 1.59 mA/cm
, Slope: 43.0 mV/dec.
2
J = 1.81 mA/cm
2
J = 2.27 mA/cm
2
J = 2.27 mA/cm
2
J = 2.72 mA/cm
2
J = 3.17 mA/cm
2
J = 3.63 mA/cm
120
80
40
0
1
2
3
4
5
6
-
-log(NO3 )
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Nitrate micro-electrodes
micro-electrodes
Microelectrode Sensor
‘Doped’ Microelectrode
mini-nitrate sensor--single fiber
-
Potentiometric Response for NO
Ion
3
320
Voltage (mV)
280
Electrochemical deposition (constant current conditions)
of polypyrrole dopped with nitrate
onto carbon fibers substrate
240
Carbon fibers, 7µm diameter each,
~ 20-30 fibers, 1.2 cm depth
200
160
2
3 days after deposition (Slope: 54.3 mV,
=R0.9999)
2
9 days after deposition (Slope: 54.4 mV,=R0.9999)
2
19 days after deposition (Slope: 52.6 mV,
=R0.9999)
120
80
5-6 ppm
1
2
3
4
5
6
-log(NO3-)
7 µm carbon
fiber
Nitrate micro-electrodes
micro-electrodes
7 µm carbon fiber (SGL) covered with epoxy
Plating onto cross section, S = 3.84 -7x cm
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Single filament
Voltage (mV)
200
160
120
2
iappl = 10 nA; appl
j = 26.0 mA/cm
, without stir., Slope: 45.4 mV
2
iappl = 5 nA; appl
j = 13.0 mA/cm, with stir., Slope: 50.4 mV
80
1
2
3
4
5
-log(NO3-)
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Fiber-based nitrate microsensors “in situ” (saturated sand)
40
point nitrate H 2O/[NO 3- ] =0.1 M
source
30
y
(cm) 20
12 3 4 5
10
0
10
20
30
50
40
60
x(cm)
80
70
90
100
Sensor 1 - 2 cm from the source
Sensors 2 - 5 cm from the source
Fiber-based nitrate microsensors “in situ” (box experiment)
2/10/04
Box Experiment
0.30
Source H
2O
Homemade elctrodes (Carbon fibers, 2 mm length, bunch of 100-150
, fibers)
Depth of source and probes 6 cm
Depthsand = (8.0 + 6.5)/2 = 7.25 cm; Flow rate = 18.0 mL/min
Flow velocity = 18.0 ml/min / (48 cm x 7.25 cm x 0.38) = 0.136 cm/min = 8.2 cm/h
0.27
Source NO
0.1 M
3
O
0-33 min - source 2H
0.24
Voltage, V
0
33_250 min - source NO
3 0.1 M
#1
#2
#3
#4
#5
0.21
0.18
0.15
For eluent (water) thick tube, for source
(H 3-) thin tube
2O/NO
0.12
Source (H2O or NO3- 0.1 M) ingection rate = 1.4 ml/min
0
File: exp_2_10_04
50
100
150
200
250
Time, min
New sensor development: Liquid Chromatographyon-a-Chip Based Ion Sensing -- Fabricated Device
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Soil - Root Ecology: monitoring microbial, fungal
and root respiration
1.
Online Microscope to monitor root/fungi
2.
Soil moisture, soil water potential, soil temperature,
CO 2 sensing
3.
Video recording triggered by changes in above ground
sensors and imaging criteria
Challenge: robustness of chemical microsensors
1) pick our battles: where should we invest in sensor
development?
2) get creative with form factor (take advantage of
micro-, and nanofabrication techniques
3) calibration/drift/degradation/longevity…how to
quantify and standardize?
4) …we have only just begun to address REAL
environmental conditions (wet-dry cycling, material
weathering, biofouling, etc)--a lot of work, but
“nonglamorous”
Applications
of Digital
Digital
Sensing
Sensing
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Habitat Sensing:
Dense Micro-climate Sensor Networks
Habitat Sensing:
Dense Micro-climate Sensor Networks
Patch
Network
Sensor Node
Sensor Patch
Gateway
Transit Network
Client Data Browsing
and Processing
Basestation
Base-Remote Link
Internet
Data Service
Applications
of Digital
Digital
Sensing
Sensing
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CMS and ESS Deployment Plan
Nesting Success and Micro-climate Factors
Biology: non-invasive direct species observations of reproductive success, behavior,
environmental effects, predation
Technology: video content analysis, triggered actuation, environmental sensors
Nest Box Microclimate and Proximity Sensors
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Video Content Analysis of Reproduction and Nesting Behavior
Video Content Analysis using principal components analysis: automated recognition of eggs and adult birds
Violet-green Swallow nestling mortality linked to micro-climate
variability, June 21, 2003
Time
Time of
of death
death
5pm
5pm 6/21/03
6/21/03
Applications
of Digital
Digital
Sensing
Sensing
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Mobile Sensor Systems:
Terrestrial Applications
Networked Infomechanical Systems
Networked mobile nodes
Sensing
Sampling
Energy logistics
Communication
Infrastructure Enables:
Deterministic, precise
motion
Proper node elevation
Mass transport at low
energy
System Ecology
Fixed nodes
Mobile nodes
Infrastructure
NIMS Progress:
Progress: Sensing
Sensing Uncertainty
Uncertainty
1
0.9
Detection Probability
Test Hypothesis
Constrained articulation will have large
impact on sensing uncertainty
Theoretical and Experimental Investigation
Distributed imagers
Linear motion
Distributed cylindrical obstacles
Confirm prediction
Articulation can have dramatic impact
on uncertainty
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
3
4
Number of Nodes
14
14
NIMS Progress:
Progress: Sampling
Sampling
72
62
52
42
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PAR Value
22
4.5
12
3.0
Vertical
Displacement 1.5
(m)
5.0
4.0
3.0
2.0
0.0
1.0
2
0.0
Horizontal Displacement (m)
NIMS Progress: Adaptive Sampling and
Task Allocation
Developed
Adaptive Sampling Strategy,
Architecture, and Algorithms
Emstar-based Node
Theoretical Analysis
Autonomous System with Task
Allocation in Lab
Publication at ICRA 2004
Acceptable
Error
+
Stratification
Policy
-
Mobile
Sampling
Piecewise
Estimation
Map
Estimation
Error
NIMS Physical
Physical Systems
Systems
NIMS First Prototype
Complete prototype field system end-toend operation including energy
harvesting Sept 03
Laboratory NIMS operational
In use for Adaptive Sampling and Task
Allocation
Sustainable NIMS system
Observatory grade sensor suite
Deployment at James Reserve in two
weeks
Deployments
Wind River Canopy Crane Research
Facility
James Reserve
Microclimate exploratory data
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Sustainable NIMS
NIMS

Field NIMS Target Capability

All Observatory Class Sensors

All Weather Continuous Operation

Microclimate

Imaging

Planned Spatially-Resolved Carbon Flux
Horizontal and Vertical Transport

High efficiency and high torque, high speed motor

Integral brake

Integral odometry
Vertical Transport

50m articulation with electromech cable system

Slip ring access

AC supply

Wireless link to vertical node
Vertical Node

Stargate

RH

Temperature

PAR

Acoustic Level
All Weather

Precipitation (sealed and shrouded components)

Low Temperature (internal camera system heater
capability)

High Temperature (precipitation – proof ventilators)
Power systems

Module, individually addressable power
Designed to

In Field Upgrade and Modification without Tools

Accommodate robust operation

Convenient installation
Center for Embedded Networked Sensing
Pioneering new ecological technologies
Building
Building Partnerships
Partnerships
with::
with
Agriculture
Agriculture
Aquaculture
Aquaculture
Public Health
Health
Environment
Environment
Tourism
Tourism
Marine resources
resources
Research
Research
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