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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.

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building and end to end system for long term soil monitoring

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
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
heterogeneity
Heterogeneity
  • Sampling problem
  • Scaling problem
  • Large scale estimates?
capturing heterogeneity at mesoscale wireless sensor networks
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
network design

2m

2m

8m

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
from raw data to useful quantities

Temperature SensorCalibration

Calibrationsin the Lab

Mote Resistor Calibration

Temperature sensorA/D units

SoilTemperature

Resistance

Reference voltageA/D units

Voltage

Moisture SensorCalibration

Moisture sensorA/D units

Voltage

Resistance

Water Potential

Light IntensityA/D units

Voltage

Air TemperatureA/D units

CPU clock

TemperatureConversion

Soil Water Potential->Volumetric Conversion

UTC DateTime

Air TemperatureCelsius

Water ContentVolumetric

From Raw Data to Useful Quantities
current status olin deployment
Current Status Olin Deployment
  • Operating since Sep 2005
  • Over 8M data points
  • Winding down
database datacube
Database/Datacube
  • 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)
sensor datacube dimension model

all

year

Season of Year

season

all

Week of Season

week

Site

day

Patch

Day of Season

Hour of Day

hour

all

sensor

tenMinute

all

category

Measurement

all

depth

Sensor Datacube Dimension Model
lessons learned wireless sensor networks
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
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
Lessons Learned: Data Systems
  • 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
Additional Deployments I
  • Leakin Park
  • Urban forest, BES permanent plot
    • Since March 06
slide18

Additional Deployments II

  • Baltimore Polytechnic High School
    • Two days ago
integration of sensor data into baltimore ecosystem study projects
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
Many different land use /land management types

Different soil conditions, soil communities

Plan: to deploy 200 motes in summer 06

Neighborhood Scale Heterogeneity: Cub Hill

CO2 Flux tower

Maps by E. Ellis and D. Cilento,

Dept. of Geography, UMBC

acknowledgement
Acknowledgement
  • Microsoft Research
  • The Gordon and Betty Moore Foundation
  • Seaver Foundation
  • Gordon Bell
  • Allison Smykel, Katy Juhaszova
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