1 / 49

Sensor Network Applications for Environmental Monitoring

Sensor Network Applications for Environmental Monitoring. Carla Ellis SAMSI 11-Sept-07. Survey of Deployments. Two in detail: Redwoods and ZebraNet Others Great Duck Island TurtleNet James Reserve Forest Volcanos & earthquakes Aquatic observing systems Localization, real-time tracking.

una
Télécharger la présentation

Sensor Network Applications for Environmental Monitoring

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sensor Network Applications for Environmental Monitoring Carla Ellis SAMSI 11-Sept-07

  2. Survey of Deployments • Two in detail: Redwoods and ZebraNet • Others • Great Duck Island • TurtleNet • James Reserve Forest • Volcanos & earthquakes • Aquatic observing systems • Localization, real-time tracking

  3. Great Duck Island: Petrel MonitoringUCB • Goal: build ecological models for breeding preferences of Leach’s Storm Petrel • Burrow (nest) occupancy during incubation • Differences in the micro-climates of active vs. inactive burrows • Environmental conditions during 7 month breeding season • Inconspicuous Operation • Reduce the “observer effect” • Unattended, off-the-grid operation • Sensor network • 26 burrow motes deployed • 12 weather station motes deployed (+2 for monitoring the insides of the base station case) Burrow Occupancy Detector

  4. TurtleNet (Corner, Umass) "Wetness" is a measure of current in the water sensor. This graph shows that the turtle came out of the water to sun itself for only brief periods and went back into the colder water. Mica2Dot hardware, GPS, Solar cells on the backs ofsnapping turtles.

  5. James Reserve Forest (CENS) • Heterogeneous • Robotics • Imaging • Full motion cameras • In nesting boxes • Time lapse images • Microclimate array& soil moisture

  6. Volcano Monitoring (Welsh, Harvard) • Motes with seismic sensors deployed on active volcano in Ecuador • Science dictates: high fidelity during events, large spatial separation, time synchronization. • Nature of the application allows triggered data collection rather than continuous.

  7. Aquatic Observing Systems (CENS)

  8. Macroscope in RedwoodsSenSys 05 Tolle et al UC Berkeley Intel Research Berkeley

  9. Dense temporal and spatial data collection 44 days from Apr 27 to Jun 10 33 sensor nodes Sampling every 5 minutes Temperature, relative humidity, PAR Deployment Up a Tree

  10. Mica2Dot node from Crossbow 4MHz processor 433 MHz radio, 40 Kbps 512 KB Flash Sensors Packaging Sensor Node Platform & Package

  11. TASK Software • Duty cycling – node on 4 sec every 5 min • Time synchronization • Tree route discovery between gateway and nodes • TinyDB data collection and querying • Data logging in Flash as backup

  12. Temporal Distributions

  13. Temporal Distributions

  14. Spatial Distributions

  15. Subtracting Timestamp Mean

  16. Subtracting Timestamp Mean

  17. One Day in the Life of a Tree

  18. One Day in the Life of a Tree

  19. Visualizing Change

  20. Visualizing Change

  21. Once battery voltage falls, temperature reading goes bad Opportunity to automatically reject outliers Outliers & Battery

  22. Performance of the Network:Data Transmitted

  23. Performance of the Network:Data Transmitted

  24. Logged Data

  25. Both are good – compensate for the other’s failures Flash running out of space but transmissions continue Transmissions stopped but Flash retains those data points Both Logging & Transmission

  26. Wildlife Tracking – ZebraNetAsplos 02 Juang et al Princeton

  27. Long-term & wide ranging zebra herd migration tracking Associated with data on feeding behavior, heart-rate, body temp. Biological Goal

  28. Why a Wireless Sensor Network Approach? • Traditional radio collars – coarse grain information • Sensor nodes (GPS), not networked – usually must retrieve collar to download stored data • Satellite tracking – high energy costs, low bitrate

  29. A Day in the Life of a Zebra • Social structure can be exploited • Plains zebra form tight-knit harems (1 male, multiple females). Collar 1 individual and track the group • Sometimes form loose herds of multiple harems, often at watering holes • Drink water on a daily basis • Mostly moving 24 hours a day

  30. Mobility Model

  31. Collar Design GPS samples every 3 minutes Detailed activity logs for 3 min every hr 1 year of operation 3-5 lb weight limit

  32. Energy and Weight Measurements

  33. Vandalism is a problem for deploying an array of fixed antennas or base stations Base station sporadically available Drive-by Mobile Base Station

  34. zebraA 10101 11101 10001 10001 10000 zebraB 10010 11111 10001 10000 Peer to Peer System Design

  35. zebraA 10101 11101 10001 10001 10000 zebraA 10101 11101 10001 10001 10000 zebraB 10010 11111 10001 10000 zebraB 10010 11111 10001 10000 Peer to Peer System Design

  36. zebraA 10101 11101 10001 10001 10000 zebraA 10101 11101 10001 10001 10000 zebraB 10010 11111 10001 10000 zebraB 10010 11111 10001 10000 Peer to Peer System Design

  37. zebraA 10101 11101 10001 10001 10000 zebraB 10010 11111 10001 10000 Peer to Peer System Design

  38. Implications of Collar Design • GPS provides precise synchronized clock • For avoiding short-range network collisions • Assume 5 days battery life between recharging • Need 13.5AH to sample (6KB/day), search for peers (6hr/day), search for base station (3 hr/day), and transmitting 640KB of data. • 640KB Flash = 300 days of data compressed, 110 days uncompressed • Need to accommodate redundancy of data stored from other nodes

  39. Homing Success Rate • Fraction of data successfully delivered to base station (goal to eventually get 100% data reported) • Simulation study (single radio): • Flooding protocol – share data with everyone encountered • History protocol – send to “best” peer discovered based on their previous success in delivering to base • Direct protocol – not peer-to-peer, just to base

  40. Simulation Results: Ideal

  41. Results with Constrained Storage(10 collar days)

  42. Results with Constrained Bandwidth (12kps) Short-range, flooding best Long-range, history best

  43. Energy (unconstrained case; normalized to direct)

  44. Final Design Choices • Storage viewed as effectively infinite • 2 radios: • one short-range, do flooding • other long-range, direct

  45. Summary of Challenges

  46. Energy in battery powered nodes. • Constrain lifetime of nodes, if not recharged • Energy harvesting, weight of solar collectors • Duty cycling necessary -> clock synchronization • Data delivery • Missing data • Connectivity • Routing issues • Unsynchronized duty cycles • Collisions • Dead nodes • Outliers • Calibration of sensors

  47. Hierarchy, heterogeneity, mobility • Robotics, actuation • Packaging • Weather effects = dead nodes • Weatherproofing – gets in the way of sensors • How to deal with massive amounts of data • Infrastructure • System behavior monitoring • Interactive remote control (retasking)

  48. Breakouts • Form 3 or 4 ad hoc multi-disciplinary groups (outside comfort zone: mix ECE+stat+CS+bio) • Discuss one of two topics • Research question you might address with Duke Forest data • Research study you might design from scratch, its requirements and challenges. • Report back at end of class (elect a spokesperson)

More Related