PODS: an Ecological
Microsensor Network
Edo Biagioni, ICS
Kim Bridges, Botany
Brian Chee, ICS
and many more!
Overview
• Introduction
• Interpreting Spatial and Temporal
Environmental Information
• Early Deployment
• Technical Details: Wireless
Communications and Routing
Part 1
Interpreting Spatial and Temporal
Environmental Information
The Challenge
• Endangered plants grow in few locations
• Hawai'i has steep weather gradients: the
weather is different in nearby locations
• A single weather station doesn’t help, so
• Have many sensors (PODS)
• Make them unobtrusive: rock or log
• Resulting in lots of data
Data Collection
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Wind, Rain, Temperature, Light, Moisture
At each pod
Every 5 minutes to 1 hour, for years
Images at some of the pods
Networking challenge: getting the data
back without discharging the batteries
• How to make sense of all this data?
Spatial Patterns
• Wet and dry areas have different plants
• Cold and warm areas have different plants
• Where is the boundary? The boundary
will be different for different plant species
• Does cloud cover matter?
• Does wind matter? Pollinators, herbivores
Temporal Patterns
• Is this a warm summer? Winter?
• Is it a warm summer everywhere, or just in
some places?
• Does it rain more when it is warmer?
• What events cause flowering?
• How long does it take the plant to recover
after an herbivore passes?
Who needs the Information?
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Scientists (botanists)
High-School Students
Virtual Tourists
Farmers
What use is the Information?
• Study the plants, prevent decline
• Determine what is essential for the plant’s
survival: e.g., how will global warming
affect it?
• Locate alternative areas
• Watch what happens, instead of trying to
reconstruct what happened
• Capture rare phenomena
How is the data communicated?
• Graphs, maps, tables
• Tables unwieldy for large numbers of
PODS
• Graphs need many different scales
• Maps can help intuitive understanding
• Ultimately, need to find useful patterns
Picture of weather data, from web
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http://weather.yahoo.com/graphics/satellite/east_usa.html
Simple Map
Blue: rain
Big Blue: recent rain
Cyan: cool, dry
Red: warm, dry
http://red2.ics.hawaii.edu/cgi-bin/location
Graphs vs. Maps
• Graphs
• Good for recognition
of temporal patterns
• Can summarize a lot
of data very concisely
• Mostly for
homogeneous data
• Maps
• Good for recognition
of spatial patterns
• Can summarize a lot
of data very concisely
• Good for
heterogeneous data
Strategies
• Data Mining: search data for patterns, try
to match to plant distribution
• Machine Learning: try to predict new data.
If prediction is wrong, something
unpredicted (unpredictable!) is happening
• Better maps, incorporating lots of data
including images, but in a way that
supports intuitive analysis
Better Map
Blue: rain
Red: temperature
Yellow: sunlight
Plant population
Not (yet) automated on the web…
Where to go from here
• Plant “surveillance”: being there, remotely
• Data Collection is only the essential first
step
• Data Analysis must be supported by
appropriate tools
• Find out what really matters in the life of
an endangered plant
Part 2: Early Deployment
• Deployment of hybrid PODS
• Computer, radio, and some sensors built
by a team at MIT
• Enclosures, most sensors, and power built
by UH pods team
September
October
November
December
January
February
March
April
May
June
July
Complementary activities
Contact regarding a joint test
Design
Manufacturing
Field deployment
Redesign & manufacturing
Lab testing
Redeployment
Field testing
MIT Media Lab
Computer
TephraNet
Radio
Network Software
UH
PODS
Enclosures
Sensors
Power
Field Site (Study Problem)
Hawaii Volcanoes
National Park
Hawaii Volcanoes
Observatory
Kilauea Crater
Halemaumau
Southwest
Rift Zone
Chain of Craters Highway
SW Rift Zone
Rainforest
Desert
Hawaii Volcanoes
Observatory
Silene Study Area
Southwest Rift Zone
Silene hawaiiensis
Rock Enclosures
light
Internal: voltage
wind (bend)
temperature
humidity
Computer
& Radio
Batteries
Michael Lurvey
rockmaker
Inner mold: Latex & gauze
Outer mold: Plaster of Paris
Casting: pretinted “bondo”
MIT TephraNet
Hawaii Volcanoes
Observatory
Silene Study Area
Southwest Rift Zone
300 feet
6 to 10 feet
100 feet
light
`Ohia Branch
Enclosures
Computer
& Radio
foam spacer
temperature
6” PVC pipe
Laser-printed texture
Waterproof spray coating
“Bondo” caps
Battery Pack Spacers
Transmission directions
Deployment Layout
Redundancy considerations
Deployment Positioning
Accuracy ~20 feet
Wide Area Augmentation System
Hawaii
Volcanoes
Observatory
Field
Deployment
Silene
hawaiiensis
Recent Lessons
Keep it small!
Manufacturing, shipping, deployment
Working against a deadline is important
March 2001
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Multiple designs provide flexibility
Large numbers require special planning
Collaboration pushed a prototype
into a system
Using a real problem added great focus
University of Hawaii
Network simulations
802.11 communications
Enclosure design and fabrication
Sensor design
Camera testing and deployment
Remote node administration
Part 3: Energy Efficient Wireless
Routing
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Routing
Existing Algorithms: Geographic, Gradient
Gradient Backtrace Routing
Geometric Routing
Routing
• Automatically let the network discover how
to get from A to B
• Assume neighbors can communicate
• Distance-Vector Routing: if I can reach B
at distance d, I tell my neighbors
• If neighbor n (distance δ from me) can
reach B at distance d’, and d’ + δ < d, I
route packets for B via n
Distance-Vector Routing Example
• Router X has neighbors Y (distance 8) and
Z (distance 5)
• Y tells X it can reach B at distance 17, so
X sends to Y all packets for B
• Z now tells X it can reach B at distance 19,
so X sends to Z the packets for B
5
19
Z
B
X
17
8
Y
Wireless Routing
• Easy to broadcast to all our neighbors
• No “networks” in the IP sense
• Energy may be more important than other
considerations:
– Quick convergence and few messages
– Load balancing
– Suboptimal routes may be OK
– We can receive more than transmit, but
cannot receive for a long time
Geographic Routing
• Send to the neighbor that’s closest to the
destination
• Very scalable, no global information
needed
W
H
• Fails on dead ends
K
Z
B
X
Y
Geometric Routing
• Similar to Geographic routing, but has
some additional information
• Each node broadcasts where (in its
perimeter) it cannot reach
• This information can be summarized as a
polygon
• Scales well if there are only a few dead
ends
• Biagioni, Wei Chen, Shu Chen, 2001
Gradient Routing
• If everyone is sending to a base station
• Let the base station broadcast to its
neighbors
• And everyone forward the broadcast
(flooding), keeping track of the distance
• Send to the base station along the
steepest gradient
• Destination must initiate route
Gradient Backtrace Routing
• The source initiates the flooding
• The destination responds along the
gradient
• Sets up forward as well as reverse paths,
used for bidirectional communication
• Others can use partial paths to the source
or destination
• Shu Chen, Biagioni
Acknowledgements and Links
• Co-Principal Investigators: Kim Bridges, Brian
Chee
• Students and others: Shu Chen, Wei Chen,
Michael Lurvey, Dan Morton, Bryan Norman,
Fengxian Fan, and many more
• http://www.botany.hawaii.edu/pods/
pictures, data
• http://www.ics.hawaii.edu/~esb/pods/
slides, papers
• esb@hawaii.edu