Presented by Arturo Sanchez-Azofeifa at the 2nd ICOS Science Conference in Helsinki, Finland, 27-29 Sep 2016.

1. +
Autonomous Wireless Sensor Networks: from
development to long term implementation
Prof. Dr. Arturo Sanchez-Azofeifa1 (arturo.sanchez@ualberta.ca)
Saulo Castro1, Mauricio Vega-Araya2
1. Alberta Centre for Earth Observation Sciences (CEOS)
University of Alberta
2. Univesidad Nacional de Costa Rica

2. +

3. +
http://www.enviro-net.org/

4. +
Applications of some WSN in the
literature
Application Location #,sensor,nodes Institution Year Duration Variables
Links,between,weather,&,
hydrology:,catchment?
scale,monitoring Hawaii not,given U,Hawaii 2008 7,months
water:,pH,,temp,,
conductivity,,pO2,,
turbidity,,water,level
Soil,water,content
Almkerk,,
Netherlands 18
Twente,&,
Wageningen 2009 6,months
soil,moisture,
(Decagon)
Climate,,broadly Amazon UNAMA 2006
Petrel,nesting Maine 32
Intel,&,UC,
Berkeley 2002 7,months
light,,temp,,IR,,RH,,
barometric,pressure
Sediment Kansas 2 Kansas,State 2008 8,months opacity
Center?pivot,irrigation Texas 17 USDA 2008 1,month
IR,thermometer,for,
canopy,temp,,air,
temp,,RH,,solar,
radiation,,windspeed,,
rainfall
Traveling,irrigation Montana 5 USDA 2008 4,months
temp,,RH,,wind,
speed,,wind,direction
After MacGregor et al. 2013

5. +
• Measurements at many spatial and
temporal scales
• Changing data needs (usually sparse)
• Increased spatial coverage in
heterogeneous environments.
• Synchronized sampling across sensors.
• Real-time data retrieval capabilities.
• Landscape scale remote sensing
validation of biophysical products.
• Reduced human effort with increased
information output.
• Non-intrusive!
Advantages ofWSN in monitoring

6. + Network Topologies and spatial design

7. + Sensor Network Cyberinfrastructure
n Near real-time data management for Wireless
Sensor Networks
n Simplified data/trend visualization
n Data mining: web data/metadata for cross-
discipline social network research cooperation and
analysis

8. +

9. + Santa Rosa Environmental
Monitoring Super Site, Costa Rica

10. Santa Rosa National Park,
Environmental Monitoring
Super Site, Guanacaste,
Costa Rica:
• 10 billion data points/year
• CO2/H20 fluxes (vegetation and
soil)
• Hyperspectral canopy
observations
• Wireless Sensor Networks
• On-line/Real time
communication via satellite
technology
• Drone research
• Micro-Satellite testing site
(AlbertaSat)
• Atmospheric Sounding
calibration site
• NASA Calibration/Validation site
• Airborne and ground-based
LiDAR

11. +Santa Rosa Environmental Monitoring
Super Site: NEE and APAR from WSNs.
0
0.2
0.4
0.6
0.8
1
1.2
2013-161
2013-185
2013-209
2013-233
2013-257
2013-281
2013-305
2013-329
2013-353
2014-09
2014-33
2014-57
2014-81
2014-105
2014-129
2014-153
2014-177
2014-201
2014-225
2014-249
2014-273
2014-297
2014-321
2014-345
2015-01
2015-25
2015-49
2015-73
2015-97
2015-121
2015-145
2015-169
2015-193
2015-217
2015-241
2015-265
2015-289
2015-313
2015-337
2015-361
2016-017
2016-041
2016-065
2016-089
FPAR
Terra MODIS v Environet FPAR Raw
MODIS FPAR Raw
Environet FPAR Score

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0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.2 0.4 0.6 0.8 1
EnviroNetFPARWSNEstimate
MODIS FPAR Estimate
Santa Rosa Environmental Monitoring
Super Site: FPAR MODIS Comparison
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
EnvironetFPARWSNEstimate
MODIS FPAR Estimate
MODIS TERRA MODIS AQUA

13. +Santa Rosa Environmental Monitoring
Super Site: NEE and APAR from WSNs.

14. +
Long term deployment of a WSN:
Brazil, 10 –years (2006-2016).
Temperature/RH Photosynthetic Active Radiation (PAR)

15. +
Final remarks: Challenges with
Environmental WSNs
n Standardization
n Durability of Hardware
n Power Management
n Data Management!!!

16. +Final Remarks: Changes on the
environmental monitoring paradigm

17. +
Current fact finding
Analyze data in motion – before it is stored
Low latency paradigm, push model
Data driven – bring the data to the query
Historical fact finding
Find and analyze information stored on disk
Batch paradigm, pull model
Query-driven: submits queries to static data
Traditional Computing Stream Computing
Query Data Results Data Query Results
Stream computing – Analyzing data in motion

18. + Thank You!