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A Solar-powered, TDMA Distributed Wireless Network for Trace-gas Monitoring

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. Motivation.

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A Solar-powered, TDMA Distributed Wireless Network for Trace-gas Monitoring

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  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

  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. • 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 http://www.coas.oregonstate.edu/research/po/satellite.gif

  4. Chamber measurement of CO2 exchange Accumulation chamber & TDLAS node LI-COR Flux Chamber • 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

  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

  6. Requirements for Trace Gas Sensor Networks • A trace gas sensor for networks must provide: • Small size/portability • Low unit/capital cost • Low maintenance and operating costs • Robust construction • Low power consumption • High sensitivity (ppb) • High selectivity to trace gas species • Wireless networking capability • Ease of mass production Sensors work autonomously in the field Base Station Radio Range Sensors

  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 Herriottmulti-pass cell • 2 μm VCSEL & InGaAsphotodetector • Custom electronics board • Drives instrument and communications • Powered by either Li-Ion or 12-V battery for solar applications • Total power consumption < 1W • 2x to 10x less than commercial sensors Detector Laser CO2 Controlling Electronics

  8. 2 μm VCSEL & CO2 Absorption Spectrum • Low power vertical cavity surface emitting laser (VCSEL) • Consumes ~5 mW power • VCSEL temperature tuning 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 CO2 absorption lines P=1 atm Atmospheric Concentration, HITRAN/GEISA Source: HITRAN 2000 database

  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

  10. Custom Control and Acquisition Board Direct Digital Synthesizer TEC driver MCU 8MHz Modulated Current Driver Lock-In Amplifier + Front End 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

  11. Wireless Communications Interface • Commercial XbowTelosB 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

  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 1 Node 3 Node 2

  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 • ~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 Node 2

  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

  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

  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.

  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 gradualcompared 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 dip. 7 AM 12 AM 2:15 PM

  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.

  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

  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 openPHOTONSsystems An innovation award from The Keller Center for Innovation in Engineering Education National Science Foundation Grant No. 0903661 “Nanotechnology for Clean Energy IGERT”

  21. Questions?

  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

  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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