This document summarizes a presentation on developing a solar-powered wireless sensor network for monitoring trace gases like carbon dioxide (CO2). Key points: 1) The network uses tunable diode laser absorption spectroscopy sensors with herriott cells and wireless nodes to measure CO2 at various locations around Princeton University. 2) Field tests captured localized CO2 variations at different nodes and validated measurements against commercial sensors. 3) The autonomous solar-powered sensor nodes can help characterize diverse CO2 sources and sinks better than conventional techniques. 4) Future work includes implementing multi-hop networking for wider coverage and exploring 3G connections between nodes.

1. IPSN PhD Forum
April 7, 2013
A Solar-powered, TDMA Distributed Wireless
Network for Trace-gas Monitoring
Clinton J. Smith
Dept. of Electrical Engineering, Princeton University, Princeton, NJ 08544
pulse.princeton.edu

2. Motivation
• Carbon dioxide (CO2) is a major atmospheric greenhouse gas (GHG)
 Need to better understand the carbon cycle
 Quantify the exchange of CO2 between the surface of the earth and the
atmosphere
• Natural and manmade CO2 sources and sinks are both temporally and spatially
varied
 Natural variations in CO2 concentration range from 370 ppmv to 10,000 ppmv
 Global ambient CO2 concentration is ~390 ppmv
• Regulations to limit GHG emissions will lead to technology such as carbon
capture and sequestration (CCS)
 Requires monitoring for leak signals which are significantly smaller than the natural
background CO2 variations
• Characterization of diverse CO2 sources and sinks requires many measurement
sensors running continuously to accurately monitor
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3. Project Goal & Outline
The project goal:
• Develop a CO2 measurement technique consisting of a low-power
autonomous wireless sensor network with each node capable of
measuring local CO2 concentration changes in a footprint area of 1 m to
100 m radius. http://www.coas.oregonstate.edu/research/po/satellite.gif
Outline
• Existing technology cannot accurately monitor diverse CO2 sources and
sinks
• Requirements for trace gas sensor networks
• Overview of sensor node and network design
• Field deployment and measurements
• Conclusions and future directions
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4. Chamber measurement of CO2 exchange
• Chamber measurements are used for measuring concentration at the
smallest spatial scales of areas < 1 m3.
• Due to the size of the chamber measurement area, they result in
geographically sparse CO2 data points.
 Flow-through chamber designs can have errors of as much as ±15%
 In accumulation chamber designs, concentration gradients are degraded over
time as CO2 accumulates in the chamber
LI-COR Flux Chamber Accumulation chamber & TDLAS node
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5. Eddy Covariance measurement of CO2 exchange
• Eddy Covariance can measure the CO2 exchange of entire ecosystem
 Commonly used for spatial scales on the order of 100 m to several kilometers
 Uses micrometeorological theory to interpret the covariance between vertical
wind velocity and a scalar CO2 concentration measurement
 Sample at as much as 20 Hz, which enables great temporal resolution in
monitoring for low time-duration events
• Limitations with the Eddy Covariance method
 Most accurate during steady environmental conditions
 Measurement areas with uneven terrain, diverse vegetation, or buildings cause
errors to be introduced into the measurement
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6. Requirements for Trace Gas Sensor Networks
A trace gas sensor for networks Sensors work autonomously
must provide: in the field
Base Station
• Small size/portability
• Low unit/capital cost
• Low maintenance and operating
costs
• Robust construction
• Low power consumption
• High sensitivity (ppb) Radio
• High selectivity to trace gas Range
species
• Wireless networking capability
• Ease of mass production Sensors
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7. CO2 Sensor Node Design & Specifications
• Tunable diode laser absorption spectroscopy
(TDLAS)
• Housed within a NEMA enclosure for
environmental protection
 “Quasi-Open Path”
• 3.5 m path Herriott multi-pass cell
• 2 μm VCSEL & InGaAs photodetector
• Custom electronics board
 Drives instrument and communications nL
• Powered by either Li-Ion or 12-V battery for I  I 0e
solar applications Laser Detector
•
CO2
Total power consumption < 1W
 2x to 10x less than commercial sensors
Controlling
Electronics
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8. 2 μm VCSEL & CO2 Absorption Spectrum
• Low power vertical cavity
surface emitting laser
(VCSEL)
 Consumes ~5 mW P=1 atm
power Atmospheric
• VCSEL temperature tuning
Concentration,
HITRAN/GEISA
range of ~5 cm-1
• Absorption coefficients in
this range correspond to
~1% absorption over
3.5 m path
• Water absorption lines
have limited impact on nL
CO2 absorption lines I  I 0e
Source: HITRAN 2000 database 8

9. Wavelength Modulation Spectroscopy
• Wavelength Modulation Spectroscopy (WMS) used for greater noise
filtering  better sensitivity
 0.1 – 0.3 ppmv CO2 concentration sensitivity achieved in 1 second
measurement (~400 ppmv ambient)
• VCSEL is wavelength modulated at 10 kHz
 Via current modulation
 2nd harmonic peak value will be used for CO2 concentration measurement
• A lock-in amplifier is used to select and demodulate each harmonic
 WMS signal correlates linearly with gas concentration
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10. Custom Control and Acquisition Board
Direct Digital Synthesizer TEC driver
MCU
8MHz
Modulated
Current
Lock-In Amplifier + Front End Driver
So, S., Sani, A. A., Zhong, L., Tittel, F., and Wysocki, G. 2009. Demo abstract: Laser-based trace-gas chemical sensors for distributed wireless
sensor networks. In /Proceedings of the 2009 international Conference on information Processing in Sensor Networks/ (April 13 - 16, 2009).
Information Processing In Sensor Networks. IEEE Computer Society, Washington, DC, 427-428
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11. Wireless Communications Interface
• Commercial Xbow TelosB wireless interface card
 IEEE 802.15.4/ZigBee compliant communications
 Running TinyOS
• Communicates with acquisition & control board via UART
• Communicates with the base station PC via USB
 Labview used for control and data logging
http://moodle.utc.fr/file.php/498/SupportWeb/co/Module_RCSF_35.html
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12. Wireless network specifications
• TinyOS ActiveMessage used for transmission of data
 Single-hop only
 Transmission rates as fast as 250 kbps
 6 Hz transmission of sensor data packets (30 bytes each, ~1 kbps)
• MultiHopRouter, Tymo (Dynamic MANET On-demand implementation)
available for multihop
 Built on ActiveMessage protocol
 Node bandwidth is reduced due to aggregate bandwidth limit and increased
overhead
Base Station
TDMA with data update
every 15 seconds
Node 3 Node 1
Node 2
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13. Field Campaign Layout & Locations
Node 1
Node 3
Princeton University Engineering-Quad (E-Quad) building
LICOR
• Node 1 was deployed in the E-Quad courtyard
 ~0.5 m above the ground
• Node 2 was deployed to B-wing rooftop
Node 2  ~23.5 m above the ground.
• Node 3 was deployed at the northwest outside corner of
E-Quad near the intersection of Olden St. and a service
road leading to a parking lot
 ~1 m above the ground and ~1.5 m from the service road
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14. Solar Irradiance Calculations
• Calculations based on historical
Princeton, NJ solar irradiance data
 Found a 100 Ah battery with 35 W
panels is needed for areas of shade
(1/3 direct sunlight per day)
 Corresponds to 3 sq. ft. of solar panels
• Solar panel power is rated based on 1
kW/m2 irradiance
• Enabled Nodes 1 and 3 to be solar
powered indefinitely
• For comparison, Eddy Covariance
stations typically consume a minimum
of 12 W power
 Would require a minimum of 350 W
solar panels
 Corresponds to > 30 sq. ft. of solar
panels
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15. Field Campaign Measurements Over a Week
• 30 minute averages shown
• 5 minute rolling average σ is 1.6 – 3.7 ppmv (depending on the node)
• TDLAS measurements compared against commercial LI-COR Non-Dispersive
Infrared (NDIR) CO2 sensor
 Node 2 on rooftop
• Large changes such as diurnal cycles are common to all three nodes
• Node 3 is largely decoupled from Nodes 1 & 2
 Street corners have increased turbulence
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16. LI-COR and Node 2 Correlation
Perfect correlation
• All sensor nodes calibrated a priori with known CO2 concentration.
• Scatter plot of the LI-COR and TDLAS sensor Node 2, computed for Jan. 11
• The measurements are in good agreement
• A robust regression (with downweighting of outliers) between the two
measurements produces a slope of 0.9966 and an offset of 8.1 ppmv
 Approximately the calibration accuracy of the two instruments.
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17. Vignette of Jan. 11 Network Measurements
• The network is able to capture some of the
localized effects induced by the geometry of
the landscape
• The low wind speed (< 1m/s) and ustar (<.2
m/s) indicate low turbulence and hence less
mixing during this period.
• These conditions lead to a gradual build up of
CO2 (from approximately 11.4 to 11.6)
• At the courtyard, aided by low ventilation, the
buildup of CO2 is higher/more gradual
compared to other nodes.
• Sources and sinks vary from within the E-
Quad courtyard to out on the street
 The sharp dip a little past 11.5 UTC is only
visible at the Courtyard and Rooftop node.
 The Street Corner node does not pick up this
12 AM 7 AM 2:15 PM dip.
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18. Conclusion and Future Directions
• We built a solar-powered distributed wireless network for atmospheric trace
gas monitoring.
 Captured events on different time and spatial scales.
• The sensor nodes in the network were completely autonomous .
 Placed in areas such as street corners and courtyards where CO2 exchange is
difficult to quantify with conventional techniques .
• The sensor nodes were shown to have similar sensitivity on the 5 minute time
scale as the NDIR based eddy covariance CO2 sensors .
 Enabling reasonable comparison between the two technologies.
• Distributed wireless networks with many nodes could help fill in the gaps in
understanding carbon cycle sources and sinks in areas with heterogeneous
landscapes.
 Can complement the use of eddy covariance and measurement chambers in
quantifying environmental carbon exchange.
• Implement multi-hop and explore 3G transceivers for greater geographic
coverage.
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19. Acknowledgements
Advisor
Prof. Gerard Wysocki
Collaborators
Dr. Prathap Ramamurthy
Prof. Mohammed Amir Khan
Wen Wang
Dr. Stephen So
Prof. Mark A. Zondlo
Prof. Ellie Bou-Zeid
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20. Acknowledgements
This work was sponsored in part by:
The National Science Foundation’s MIRTHE Engineering Research Center
An NSF MRI award #0723190 for the openPHOTONS systems
An innovation award from The Keller Center for Innovation in Engineering
Education
National Science Foundation Grant No. 0903661 “Nanotechnology for Clean
Energy IGERT”
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21. Questions?
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22. TDLAS CO2 Sensor 3rd Harmonic Line Locking
• Overcome laser frequency drift from
temperature and electronics instability
• Control laser temperature so that 3rd
harmonic signal is near zero
 This corresponds to the maximum of the
2nd harmonic signal
Measure the CO2 concentration by
continuously monitoring the 2nd harmonic
signal value at the peak
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23. Vignette of Street Corner
• When the Node 3 data (near the street-corner) is examined with only 15
seconds of averaging, the influence of passing cars can be detected
• Direction of the tail-pipe and the size and model of the car correlate with the
degree of the increase in CO2 concentration
 Traditional internal combustion engine based cars with a tail-pipe facing the
direction of the sensor cause much higher concentration spikes than hybrid
vehicles (for which there is no measurable concentration change).
• Larger vehicles have a much greater impact on the local CO2 concentration.
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