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 Temperature Sensor Calibration Soil Temperature Water Content Volumetric Soil Water Potential-> Volumetric Conversion Voltage Moisture sensor A/D units Reference voltage A/D units Temperature sensor A/D units CPU clock Air Temperature A/D units Light Intensity A/D units Temperature Conversion Air Temperature Celsius UTC DateTime Resistance Mote Resistor Calibration Moisture Sensor Calibration Water Potential Calibrations in the Lab

Current Status Olin Deployment Operating since Sep 2005 Over 8M data points Winding down

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

Measurement sensor hour day week season year all tenMinute depth category all Hour of Day Day of Season Week of Season Season of Year Patch Site all Sensor Datacube Dimension Model

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 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 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 CO 2 Flux tower

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