Experiences and Challengesin Campaign Style Deploymentsusing 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*

Campaign Style Deployment

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Quito

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

Gateway/Basestation

Stable Storage

…….7 Ah

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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Power ~ 4W

Deployment Details - I

• Location : Quito, Ecuador

• Goal :

Understand tropical soil respiration

• Duration : 16 days604/21/23 Jayant Gupchup

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

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3 m

3 m

3 m

Under The Hood

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Antenna

TelosB Mote

CO2 Sensors

Mote Battery

CO2 SensorPower Connector

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

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• 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

Some Data

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Power Loss Sensors lack range

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

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Automated parts from existing system worked well.

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

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