Transcript Native and Introduced Earthworms in the Jug Bay Wetlands Sanctuary

Building and End-to-end System
for Long Term Soil Monitoring
Katalin Szlávecz, Alex Szalay, Andreas Terzis,
Razvan Musaloiu-E., Sam Small, Josh Cogan,
Randal Burns
The Johns Hopkins University
Jim Gray, Stuart Ozer
Microsoft Research
Motivation for Building
a Sensor Network
Monitoring: background data, trends =>
• Soil animal activity/metabolic processes
depend on moisture, temperature
• Frequent visits disturb the sites
• Soil respiration, trace gas fluxes
• Better input for terrestrial hydrology models
• CS: Build and learn from a deployed system
Spatio-temporal Heterogeneity of the
Soil Ecosystem
Heterogeneity
• Sampling problem
• Scaling problem
• Large scale estimates?
Capturing Heterogeneity at Mesoscale:
Wireless Sensor Networks
• Small computers with radio
transmitter
• Each connected to multiple sensors
(moisture, air and soil temperature,
light)
• Automatic data upload
Architecture
Network Design
• Ten mote network
• Each mote
– samples every min
– data stored in FLASH
– status every 2 min,
wait for data request
• Single hop network
– Gateway connected
to campus network
2m
8m
2m
From Raw Data to Useful Quantities
Calibrations
in the Lab
Mote Resistor
Calibration
Temperature sensor
Voltage
Reference voltage
Voltage
Moisture sensor
Voltage
A/D units
A/D units
A/D units
Resistance
Temperature Sensor
Calibration
Soil
Temperature
Moisture Sensor
Calibration
Resistance
Water Potential
Light Intensity
A/D units
Air Temperature
A/D units
Temperature
Conversion
CPU clock
UTC DateTime
Air Temperature
Celsius
Soil Water Potential->
Volumetric Conversion
Water Content
Volumetric
Current Status Olin Deployment
• Operating since Sep 2005
• Over 8M data points
• Winding down
Database/Datacube
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SQL Server 2005 database
Rich metadata stored in DB
Adopted from astronomy: NVO
Data access through web services
Graphical interface
DataCube under construction
(multidimensional summary of data)
Online Data Access
Sensor Datacube Dimension Model
all
year
all
season
Season of Year
Site
week
Week of Season
Patch
day
Day of Season
all
sensor
hour
Hour of Day
all
category
tenMinute
all
depth
Measurement
Lessons Learned:
Wireless Sensor Networks
• Network lifetime is predictable 
• Nodes continue operate despite large
environmental fluctuations 
– Waterproofing is still an issue
Bathtub test
Lessons Learned:
Wireless Sensor Networks II
• Single-hop network: transmission range is
considerably shorter than in lab due to foliage
– Relay node helps 
• Low level programming is still required 
• Importance of sensor uniformity is essential
– Switch to Echo sensors 
Lessons Learned: Data Systems
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We got real data, end-to-end ! 
Sensors respond to environmental changes 
Database from off-the-shelf components 
Getting high level summaries : DataCube 
• We need a fully automated pipeline: the current
two manual steps are still too labor intensive 
Additional Deployments I
Leakin Park
Urban forest, BES permanent plot
Since March 06
Additional Deployments II
Baltimore Polytechnic High School
Two days ago
Integration of Sensor Data into
Baltimore Ecosystem Study Projects
• Urban-rural gradient studies
• Water and Carbon Cycling
– 200 node network at Cub Hill
• Ecology of invasive species
– Less fluctuating? More refuges?
– Light composition – onset of
reproduction
• Spatio-temporal patterns of soil C and
N cycling
– Attachment of additional gas sensors
Neighborhood Scale Heterogeneity:
Cub Hill
CO2 Flux tower
• Many different land
use /land
management types
• Different soil
conditions, soil
communities
• Plan: to deploy 200
motes in summer 06
Maps by E. Ellis and D. Cilento,
Dept. of Geography, UMBC
Acknowledgement
• Microsoft Research
• The Gordon and Betty Moore Foundation
• Seaver Foundation
• Gordon Bell
• Allison Smykel, Katy Juhaszova