1. Wireless Sensor Network for
Environmental CO2 Monitoring
Clinton J. Smith1*, Wen Wang1, Stephen So1,2, and Gerard Wysocki1**
1) Dept. of Electrical Engineering, Princeton University, Princeton, NJ 08544
2) Sentinel Photonics Inc., Monmouth Junction, NJ 08852, USA
* clintons@princeton.edu; ** gwysocki@princeton.edu
Motivation Sensor Node Long-Term Performance Two-Node Sensor Network Monitoring
The CO2 impact on the greenhouse gas effect requires global and In-Lab Activity
local monitoring capability which would greatly benefit from
availability of sensors that are lightweight, portable, robust, highly
0.5m Base
sensitive, and selective. Station 3.3m
For study of the Carbon Cycle, these sensors should also be low-
FL Sensor
power/battery operated and capable of being wirelessly networked
and autonomous. GL Sensor
Large area wireless networks of laser-based trace-gas sensors will
provide high spatial resolution of real time concentration data with
unprecedented sensitivity and selectivity to the target molecular
species.
These sensors are expected to maintain a high degree of long-term
stability in the field, despite changing environmental conditions.
Background CO2 concentration time series show the degree to which temperature
We have built a laser spectroscopic sensor for CO2 detection and correction improves sensor-node performance. Allan deviation
demonstrated its performance in preliminary laboratory and field calculation of long term concentration measurements allows quantifying Laboratory activity can be interpreted from this data
tests [1]. the sensor long-term stability. Adaptive linear regression to remove set.
sensor temperature dependence enables potentially indefinite assurance The high concentration events near 0 hours as shown by the
The sensor achieves higher long-term sensitivity by locking to
of short term (<10 sec.) sensitivity. FL Sensor are caused by the operator working at the base
the targeted absorption line. station to configure the WSN.
We identified environmental temperature as a main source of drift Multi-Node Long-Term Cross-Correlation Performance The baseline reflects the overall activity in the room while
and applied a linear regression technique to correct for it. the high short-time concentration spikes refer to
To demonstrate wireless sensor network (WSN) capability, both a individuals working in the vicinity of the sensor.
two-node and a three-node network similar to [2] for long-term Both at the beginning (hour 0) and at the end (hour 8) the
real-time monitoring of CO2 have been investigated. CO2 concentration exhibits a low baseline level
Base corresponding to low human activity in the lab.
The multi-node sensor cross-correlation was calculated. Station
Wireless Sensor Network & Deployment Summary and Future Directions
Node 1 Node 2 Node 3
E-QUAD at Princeton University We have demonstrated a two and a three sensor-node
350 m range directional
antennas are used network for long-term real-time CO2 concentration
monitoring.
Sensor node cross-correlation is comparable to that of
commercial products
A temperature correction technique was demonstrated
Test Sight: Crop field in Princeton, NJ
to remove the temperature impact on sensor drift.
Future directions:
Three calibrated sensor nodes are placed in one box and sample A large area WSN is being deployed in the field for long-
Three locations selected to monitor term trace-gas monitoring.
approximately the same air sample. Continuous CO2 measurement was
coupled local environments:
performed over a period of 10 hours. The cross-node correlation R2 values
1. Adjacent to the local road: car traffic
CO2 sensor-node. The
2. In the inner courtyard: local between the three nodes while line-locking (120 min. – 550 min.) are: References:
total size is less than [1] C. J. Smith, S. So, and G. Wysocki, "Low-Power Portable Laser Spectroscopic Sensor
vegetation
that of a shoebox. Node1, Node2 = 0.87 Standard deviation among the for Atmospheric CO2Monitoring," in Conference on Laser Electro-Optics:
3. On the roof of the building Applications, OSA Technical Digest (CD) (Optical Society of America, 2010), paper
Acknowledgements: This material is based upon work supported by the National Science Foundation
Node1, Node3 = 0.94 nodes ranges from 2 to 6 ppmv JThB4.
under Grant No. EEC-0540832, an NSF MRI award #0723190 for the openPHOTONS systems and
National Science Foundation Grant No. 0903661 “Nanotechnology for Clean Energy IGERT.” Node2, Node3 = 0.93 [2] S. So, A. A. Sani, Z. Lin, F. Tittel, and G. Wysocki, "Demo abstract: Laser-based trace-
gas chemical sensors for distributed wireless sensor networks," in Information Processing