Experiences and Challenges

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• Experiences and Challenges
• in Campaign Style Deployments
• using Wireless Sensor Networks
• Jayant Gupchup, Scott Pitz, Douglas Carlson ,
• Chih-Han Chang, Michael Bernard,
• Andreas Terzis, Alex Szalay, Katalin Szlavecz
  
• Department of Computer Science, Johns Hopkins
  University
• Department of Physics and Astronomy, Johns
  Hopkins University
• Department of Earth and Planetary Sciences, Johns
  Hopkins University

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Campaign Style Deployment
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Requirements / Constraints

• No access to line power or internet
• Collect data at a high rate (e.g. 30s)
• Deployment order of days to weeks
• Researchers require access to data in field

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A Typical Sensor Network
7 Ah
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Differences / Challenges

• On-site decisions
• Ad hoc hardware reconfigurations
• Dealing with high data rates in the field (using
  a netbook)
• Use of high power sensors
• Vaisala CO2 sensors
• Driven by a car battery

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Deployment Details - I

• Location Quito, Ecuador
• Goal
• Understand tropical soil respiration
• Duration 16 days

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Deployment Details -II

• 30s Sampling Interval
• Data retrieved over the air using a netbook
• 20 Sampling locations
• 12 Soil CO2
• 8 Soil Temperature Moisture
• Each CO2 Location
• 3 depths (12 locations, 3 depths 36 sensors)
• Vaisala GMT 220 Series
• CO2 Powered by 12 V / 45 Ah Car Battery

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A CO2 Set-Up
3 m
3 m
3 m
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Under The Hood
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Power Consumption

• Lead acid car batteries 12V / 45Ah
• Each battery serviced 9 CO2 sensors
• Total current draw 1A
• Lasted 36 hours after recharge

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Power Cycling Motivation

• Batteries needed replacement/recharged every
  other day
• Carried every other day for 3 Km
• 12V/40Ah car battery weighs 14Kg
• Power Cycling!
• Warm up time 15 min

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Some Data
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Ad Hoc Replacements

• At 11 locations, sensors lacked range to sense
  the phenomenon
• Distribution of CO2 hardware
• 10000 ppm 26
• 20000 ppm 12
• 30000 ppm 3
• 100000 ppm 3
• Researchers placed sensors initially
• Reconfigured sensors if range was not good enough
• Final data calibration requires accurate
  metadata
• sensor type and date of reconfiguration
• Motivates need for self-Identifying sensors
  (Dallas 1-wire protocol)

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High Data Rates

• Decisions in field are driven by the data
• Researchers used netbook to download and view
  data
• 10 days of 30s sampling 576000 rows
• Spreadsheet and word processing software unable
  to handle this volume
• Researchers stopped looking
• Downsample Provide low-resolution view in the
  field

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System Performance
Automated parts from existing system worked well.
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Conclusions

• WSN technology is mature enough to be driven by
  scientists
• Challenges / Lessons Learned
• Power cycling for high power sensors
• Self-Identifying sensor design
• Researchers require low-resolution data in field

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Credit

• NSF- MIRTHE, NSF- IDBR
• Microsoft Research
• Betty and Gordon Moore Foundation

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Questions