1. Challenges and opportunities in the implementation
of wireless sensor networks for environmental
monitoring
Dr. Arturo Sánchez-Azofeifa, P.Eng., SM IEEE
Earth and Atmospheric Science Department
University of Alberta, Edmonton, Alberta, Canada
arturo.sanchez@ualberta.ca

2. Are we facing a change in our
environmental monitoring paradigm?
1. The science of environmental monitoring is evolving as well
as the technology that supports it.
• How do we conduct new synthesis?
2. Environmental monitoring science is facing a
disproportioned increase on data availability.
• How do we manage, analyze and visualize TBs of
information?
3. The way that we communicate and divulge environmental
monitoring data is also changing.
• How do we accurate communicate environmental trends
using clear language?

3. Evolution of Science Paradigms
1st Paradigm  Thousand years ago:
• Science was empirical and aimed to describe
natural phenomena.
2nd Paradigm  Last few hundred years:
•
2
.
The theoretical branch of a 4 G c2
using models, generalizations a 3 a2
3rd Paradigm Last few decades:
• A computational branch paradigm emerged
aimed at simulating complex phenomena
4th Paradigm Today:
• Data exploration (eScience): unify theory of
experiment, and simulation, taking into
consideration that:
– Data captured by instruments or
generated by simulator
– Processed by software
– Information/Knowledge stored in computer
– Scientist analyzes database / files
using data management and statistics

4. Wireless Sensor Networks (WSNs)
Great potential and numerous advantages over wired
ecosystem monitoring and part of the 4th Paradigm of Science
Purelink.ca Altenergymag.com

5. Interdisciplinary Nature of WSN research and
development
• Sensor design and field testing: hardware and software integration.
• Sensor power management: power optimization and harvesting.
• Network architecture design: optimal area coverage and architecture
design of network topology
• Advance cyber- infrastructure: intelligent data query and analyses.
• Integration into Environmental monitoring programs: ground and
remote sensing. From node - to site - to decision making.

6. WSN Design and Field Testing
Modification of WSNs
For specific environmental
applications
Land, Water, Life, and
Air applications

8. Australia’s field testing site:
Whroo, VIC. TERN Supersite

9. Sensor Power Management
1. Remote monitoring systems have to operate within a
limited energy budget
2. Need to manage the available resources
• Collect available energy (energy harvesting)
• Store excess energy (batteries)
• Be efficient (avoid energy waste)
3. Power management
• Extend life
• Reduce maintenance

10. Network Architecture Design and
Network Spatial Topologies

11. Advanced Cyber- Infrastructure
1. Upload your field data 3. Visualize and analyze your data
2. Data query (QC) 4. Retrieve selected data

12. PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR uE)
0
500
1000
1500
2000
2500
3000
6:47
7:18
7:49
8:20
8:52
9:23
9:54
10:25
10:57
11:28
11:59
12:30
13:01
13:32
14:03
14:34
15:05
15:36
16:07
16:38
17:09
17:40
18:11
18:42
19:13
Quantification of Spatial Variability of Micro-
IN
P9
P8
P7
P6
P5
P4
P3
P2
P1
REF
P12
P11
P10
Meteorological Variables at High Temporal Rates
TOW
TOW

13. Optimal VPD from 7-12 hPa
Daytime VPD > 10 at
Whroo Super Site
July-Sept 2%
Sept-Nov 42%
Ksj.mit.edu Nov-Feb 74%

14. Baseline Definition & Long Term
Monitoring: Ecosystem Succession Metrics
New approaches for data exploration and trend detection are necessary given
the large size of emerging datasets: Machine Learning, Probabilistic
distribution analysis, non-parametric statistics…

15. Baseline Definition & Long Term Monitoring:
Ecosystem Succession Metrics
Surface albedo
• Changes on micro-
meteorological variables
as a result of ecological
succession.
• Responses to
Ecological Restoration
Relative Humidity
• Key tool to monitor
changes on the
restoration of Oil sands.

16. Derived information: Event Detection (Brazil)
Normal Productivity
Insect attack
Storm event
Optical sensor network captures anomalous events in detail

17. Challenges on the development of WSNs
1. Remote outdoor durability
2. Component miniaturization
• Integration of Nanotechnology
3. Improved communication and coverage
4. Sub-second sampling and sampling on demand;
• Specialized software for event detection
5. Enhanced memory capacity
6. Advanced cyber-infrastructure --> cloud computing
data management
• Data analytics approaches for data analysis
in real time.

18. Nano Materials for water
Temperature & pH
Courtesy of Dr. Mike
Serpe, Chemistry
Department, UAlberta.

19. Thank
You!