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PODS: an Ecological Microsensor Network

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  1. PODS: an Ecological Microsensor Network Edo Biagioni, ICS Kim Bridges, Botany Brian Chee, ICS and many more!

  2. Overview • Introduction • Interpreting Spatial and Temporal Environmental Information • Early Deployment • Technical Details: Wireless Communications and Routing

  3. Part 1 Interpreting Spatial and TemporalEnvironmental Information

  4. 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

  5. Data Collection • 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?

  6. 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

  7. 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?

  8. Who needs the Information? • Scientists (botanists) • High-School Students • Virtual Tourists • Farmers

  9. 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

  10. 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

  11. Picture of weather data, from web • http://weather.yahoo.com/graphics/satellite/east_usa.html

  12. Simple Map Blue: rain Big Blue: recent rain Cyan: cool, dry Red: warm, dry http://red2.ics.hawaii.edu/cgi-bin/location

  13. 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 Graphs vs. Maps

  14. 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

  15. Better Map Blue: rain Red: temperature Yellow: sunlight Plant population Not (yet) automated on the web…

  16. 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

  17. 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

  18. 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

  19. MIT Media Lab Computer Radio Network Software TephraNet UH PODS Enclosures Sensors Power Field Site (Study Problem)

  20. Hawaii VolcanoesNational Park Hawaii VolcanoesObservatory Kilauea Crater Halemaumau SouthwestRift Zone Chain of Craters Highway

  21. SW Rift Zone

  22. Rainforest Rainfall Gradient Desert

  23. Hawaii VolcanoesObservatory Silene Study Area Southwest Rift Zone

  24. Silene hawaiiensis

  25. light wind (bend) temperature Computer& Radio humidity Batteries Rock Enclosures Internal: voltage

  26. Michael Lurvey rockmaker Inner mold: Latex & gauze Outer mold: Plaster of Paris Casting: pretinted “bondo”

  27. MIT TephraNet

  28. Hawaii VolcanoesObservatory Silene Study Area Southwest Rift Zone

  29. 300 feet 6 to 10 feet 100 feet

  30. Computer& Radio light `Ohia BranchEnclosures Batteries foam spacer temperature

  31. 6” PVC pipe Laser-printed texture Waterproof spray coating “Bondo” caps

  32. Battery Pack Spacers

  33. Transmission directions comm distance Deployment Layout Redundancy considerations

  34. Deployment Positioning Accuracy ~20 feet Wide Area Augmentation System

  35. HawaiiVolcanoes Observatory FieldDeployment Silene hawaiiensis

  36. Recent Lessons Keep it small! Manufacturing, shipping, deployment

  37. 1 2 3 March 2001 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Working against a deadline is important