Transcript Wireless Sensor Network Applications in Urban Tele

Community Environmental Networks for
Risk Identification & Management
Preparing an Interactive Decision-Making System…
Paul J. Croft, Feng Qi, Patricia Morreale
(Meteorology, GIS, Computer Science)
School of Environmental and Life Sciences
Undergraduate Meteorology Majors
CENRIM: Intent is to make decisions…
• Environmental &
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Related Monitoring
Real-time inquiry/query
Wireless Sensors
Automated
Adaptable (movable)
Multi-layered data
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Collect data
Display data
GIS mapping
Animation
Integrated analysis
Scenario development
SELS – CNAHS
Given
sufficient
information
Identify
developing
hazards
(and/or useful
applications)
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What sort of hazards/applications?
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High impact
Short duration
Limited area
Population
Energy
Economy
Health
Welfare
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SCENARIO DEVELOPMENT – EXAMPLES
Environmental Feature
Parameters to Sample
Heat Distribution
Energy consumption
Wind/Alternative Energy
CO2, CO, habitation
internal temperature,
external temperature
 Engineering of heating/cooling zones & timing
 Internal & External microclimate used as guide to green technology
 Seasonal variations & insulation strategies
 Alert to maintenance and/or physical discomfort or hazards
 Source/Sink and automated response system
Environmental Feature
Parameters to Sample
Air Quality
Transport Contaminants
Local Flooding or Severe
CO2, CO, traffic volume
wind speed, direction
water floats, rainfall rates
 Provide real-time monitoring, automated prompts
 Increased traffic volume; flow rates; pollutant pooling
 Alerts to authorities; traffic re-routing as needed
 Pre-alerts to authorities for advancing system or as forecast
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Prototype – Apply to KU Campus
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Relevant problems
Real applications
Student participation
Prototype deployment
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Wireless Sensor Network & more…
• Composed of low-cost, embedded sensors
• IRIS Mote 2.GHz (shown), 500 meter
range with 250 Kbps data rate
• www.crossbow.com
• www.sunspotworld.com
• Cross-check with others
• Develop/Create sensors
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Environmental Information Network
Prototype Development
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GIS Mapping Preparation
NJDEP Land
Use Database
Clip
Land Use Map7X7
ESRI GIS Data,
USA
NJDEP GIS Data
Warehouse
Clip
2006 Real Color Ortho imagery
Clip
Sensor
measurements
Clip
Imagery campus
Clip
Digitize
Imagery 7x7
Hospitals
Campus Buildings
Airports
Geocoding
Campus Trees
Point maps
Railroads
Campus Fields
Cemeteries
Flowline
Campus Sidewalks
Churches
Spatial
interpolation
Roads
Campus Sites
Golf Courses
Water Body
Parks
Farms
Contour maps
Roads
Water Body
Pressure
Humidity
Schools
Flowline
Temperature
Carbon
Dioxide
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Examining the “Local Neighborhood”
= weather platforms
= sensors
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NYC – Metropolitan area (most populated and urbanized location)
Sampling 7x15 mile area for data & observations/collection
Area selected for its diversity in landscapes (i.e. urban, rural, et cetera)
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Goal of Research: Visualization for decision-making and scenario building
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Visualize and analyze relationships between variables (Atmosphere, Land Use)
Seek understanding as to why the patterns exist/change
Examine local data versus WRF Model data for real time operations
Predict variations in space and time for application to decision-making process
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Site Selection – Specify Characteristics
Key locations selected to study modification of air temperature were based on
– Land Use Types
– Elevation
– City Population
 Data variations as related to
the local CWA landscapes
– Imperviousness
 These are forecast locations of
interest for verification by the
user & apply decision-making
process locally
– Satellite Images
 Sensor variations v. model v.
verification
 NDFD applications?
– Churches
– Cemeteries
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Sensor Deployment
Temp (C)
• HOBO data loggers used to record temperature 1.5 meters
above the ground, at the chosen sites
• Radiation shields constructed to reduce radiative effects
• Calibration in time/space of sensors
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Shield vs No Shield
Time
Shield Temp
No Shield Temp
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Scenario = “Hot/Dry Summer”
• Data collected August 3 – 5, 2010
• Temperature data every 5 min
• Data from 3 local stations used for
comparison
• Data from CWOP/Other sites in
the region
• Model data from the WRF EMS
platform in 6 hour increments
collected for comparative analysis
and for combining data sets for
decision-making purposes
Interactive Decision-Making System
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Tie-in with demographic information…
Elderly/sq. mile
1-650
651-1208
1209-1875
1876-3036
3036-4394
Median Household Income
10232 - 25000
25001 - 40000
40001 - 55000
55001 - 75000
75001 - 116088
Data Integration
Putting the Two Data Sets Together
Real Time
Observations
Methodology
WRF EMS
Methodology
How does it all “fit” together?
Specify Domain
and Parameters
.5’ Spatial Resolution
using NAM SPoRT data
Specify parameters
and domain of Desired
Location
Low
Income
Elderly
= Decision-Making
Model Processing
Convert netCDF to GRIB
within model program
Prioritize emergency
services based on
demographic map
Relation of
hotspots to
Land Use
7x15 mile study
area collecting
Temperature Data.
Create Shapefile of
station locations
Feed data collected from
stations and sensors
GRIB to GIS shapefile
using deGRIB program
Identify
hotspots
Pinpoint forecast
errors in model
GIS shapefile
to raster
Subtract model Data from Real
Time Observations with raster
calculator (or CDC data: NCAR
Re-Analysis and other datasets)
Creation of .dbf file for ArcGIS
to read data and relating each
set of readings to station
location
Climate Impacts
Interpolation of data to
create isotherms
Convert maps to same cell
size as model data.
Convert maps to same
cell size as model data.
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Verification: Compare, Contrast, Establish “Truth”
Sensor
WRF
Difference
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• The temperature difference map identifies weaknesses in operational model by
showing cool or warm spots; or by showing discrepancies in forecast conditions
• Identifying areas of warmer temperature essential for risk management of
emergency services to environments based on a scale of high or low priority
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What else may be done?
Alternative Hazards
Online System
• Data mining and analysis for spatial and temporal pattern
recognition & correlations; time series analysis
• Visualization for data discovery techniques, possible
“CAVE” use (supercomputer) to explore interactions
• Contour and additional map analyses for operational and
risk management use; planning and management
• Flash animation, uncertainty visualization, additional userdefined scenarios and tie-in socio-economic systems
• Societal risk factors, emergency management, empiricalclimatic investigations for resource allocation/expectation
• Vulnerabilities evaluation, natural and human systems
coupling/modeling; cost-effectiveness
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For example…
Day 1: Carbon Dioxide for 07/14/2009
Data
Day 2: Carbon Dioxide for 07/15/2009
Day 3: Carbon Dioxide for 07/21/2009
Contour Maps:
Patterns &
Features of
Interest;
Source/Sink
Day 4: Carbon Dioxide for 07/22/2009
• Carbon Dioxide
• Temperature
• Pressure
• Humidity
• Light
Day 5: Carbon Dioxide for 08/04/2009
Day 6: Carbon Dioxide for 08/05/2009
• Sound
• Water
Quality
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Kean “We Map It” for Operations (or Climate Impacts)
[Weather and Ecosystem Monitoring, Assessment, and Prediction for Integration and Training]
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Thank you for your time…
Acknowledgements
Kean Departments of Geology & Meteorology and Computer Science:
Faculty & Staff, Students and Majors, Alumni and Student Volunteers
Dean and/or College of Natural, Applied, and Health Sciences and the
School of Environmental and Life Sciences
Office of Research & Sponsored Programs through the RIA & SpF
Programs at Kean University
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