Building and end to end system for long term soil monitoring
This presentation is the property of its rightful owner.
Sponsored Links
1 / 21

Building and End-to-end System for Long Term Soil Monitoring PowerPoint PPT Presentation


  • 88 Views
  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

Building and End-to-end System for Long Term Soil Monitoring

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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


Spatio temporal heterogeneity of the soil ecosystem

Spatio-temporal Heterogeneity of the Soil Ecosystem


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


Architecture

Architecture


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)


Online data access

Online Data Access


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


Building and end to end system for long term soil monitoring

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


  • Login