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

13. 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

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

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 UH Computer Radio Network Software Enclosures Sensors Power Field Site (Study Problem) TephraNet PODS

20. Kilauea Crater Halemaumau Hawaii Volcanoes Observatory Southwest Rift Zone Hawaii Volcanoes National Park Chain of Craters Highway

21. SW Rift Zone

22. Rainfall Gradient Desert Rainforest

23. Silene Study Area Hawaii Volcanoes Observatory Southwest Rift Zone

24. Silene hawaiiensis

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

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

29. MIT TephraNet

30. Silene Study Area Hawaii Volcanoes Observatory Southwest Rift Zone

31. 300 feet 100 feet 6 to 10 feet

33. `Ohia Branch Enclosures temperature light Batteries Computer & Radio foam spacer

34. 6” PVC pipe Laser-printed texture Waterproof spray coating “Bondo” caps 6” PVC pipe Laser-printed texture Waterproof spray coating “Bondo” caps

35. Battery Pack Spacers

36. comm distance Deployment Layout Redundancy considerations Transmission directions

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

40. Silene hawaiiensis Field Deployment Hawaii Volcanoes Observatory

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

50. Working against a deadline is important 123 45678910 11121314151617 18192021222324 25262728293031 March 2001

51. Multiple designs provide flexibility

52. Large numbers require special planning

53. Collaboration pushed a prototype into a system Using a real problem added great focus

54. University of Hawaii Network simulations 802.11 communications Enclosure design and fabrication Sensor design Camera testing and deployment Remote node administration

55. Part 3: Energy Efficient Wireless Routing Routing Existing Algorithms: Geographic, Gradient Gradient Backtrace Routing Geometric Routing

56. 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

57. 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 X Y Z 5 8 B 19 17

58. 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

59. Geographic Routing Send to the neighbor that’s closest to the destination Very scalable, no global information needed Fails on dead ends X Z Y B WH K

60. 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

61. 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

62. 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

63. 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, datahttp://www.botany.hawaii.edu/pods/ http://www.ics.hawaii.edu/~esb/pods/ slides, papers esb@hawaii.edu