1. University of Virginia Self-Organizing Wireless Sensor Networks in Action A Case Study Computer Science University of Virginia Jack Stankovic

2. University of Virginia Outline Pull everything together Type of summary Emphasize various “new” points –Really must build the system in real environments –Simple solutions (often) might be best –Interactions of solutions are very important –Fault Tolerance and Self-Healing –Classification –Issues with speed of targets, multiple targets –Scaling

3. University of Virginia Ad Hoc Wireless Sensor Networks Sensors Actuators CPUs/Memory Radio Minimal capacity 1000s Self-organize Reliable Abstraction

4. University of Virginia Mica2 and Mica2Dot ATMega 128L 8-bit, 8MHz, 4KB EEPROM, 4KB RAM, 128KB flash Chipcon CC100 multichannel radio (Manchester encoding, FSK). Up to 500-1000ft. Reality 50-100 feet when on the ground!

5. University of Virginia Sensor Board

6. University of Virginia VigilNet - Power Aware Surveillance Acoustic Magnetometer Four 90 degree motion sensors XSM motes - Crossbow

7. University of Virginia Requirements Develop an operational self-organizing sensor network of size 1000 for rare event area Cover an area of 1000m x 100m Stealthy Lifetime 3-6 months (with complicated system) Timely detection, track and classification – Large or small vehicle – Person, person with weapon Wakeup other devices when necessary – Extend the lifetime of those devices as well Exhibit self-healing capabilities

8. University of Virginia VigilNet Power Aware Surveillance Application –Field Test Scenarios and Overall Performance –Technical Details »Power Management - performance »Group Management - performance »Three Tier Filter and Classification Scheme – performance »Walking GPS - performance

9. University of Virginia 1. An unmanned plane (UAV) deploys motes 2. Motes establish an sensor network with power management 3.Sensor network detects vehicles and wakes up the sensor nodes Zzz... Energy Efficient Surveillance System Sentry

10. University of Virginia Tripwire-based Surveillance Self-organize (partition) sensor network into multiple sections (one per base station). Turn off all the nodes in dormant sections. Apply sentry-based power management in tripwire sections Flexible scheduling, sections rotate to balance energy. Road Dormant Active DormantActive Dormant

11. University of Virginia Architecture Overview

12. University of Virginia Overview Code – About 40,000 lines of code and 600 files – About 30 Middleware services provided Operates with a network of 200+ nodes over areas such as 500m x 50m – 10 Phases – MacDill AFB – Avon Park – Berkeley – UVA – Congress

13. University of Virginia Field Test Layout 2 0 1 Tent 200 XSM Motes 3 Bases (Tripwires) 300 by 200 Meters in T-shape Inter-tripwire communication Via 802.11 wireless LAN 300 meters, 30 motes each line, 4 non-uniform lines 200M200M

14. University of Virginia Field Test Scenarios Phase I – Initialization (self organizing) – Multiple stages (7) » Each step time based (real-time bounds) » No massive acknowledgements – Re-initialize periodically – rotation » Self-healing » Power load balancing – Understand status of network

15. University of Virginia Time-Driven System Operation

16. University of Virginia Results of Actual Test

17. University of Virginia Field Test Scenarios Phase II – Track and Classify Persons –Person walking, running and walking again –Compute velocity Phase III – Tracking and Classify Vehicles at various speeds –10 mph –20 mph –30 mph –50 mph

18. University of Virginia Field Test System Layout 0 Tent 200 XSM Motes 1 Base (Tripwire) 300 by 200 Meters in T-shape 300 meters, 30 motes each line, 4 non-uniform lines 200M200M A B C D

19. University of Virginia Field Test Scenarios Phase IV – Tracking multiple targets (people, vehicles, and then people and vehicles) –3 crossing people –Vehicle followed by person –2 vehicles following each other about 50 meters apart

20. University of Virginia Field Test Layout 0 Tent 200 XSM Motes 1 Base (Tripwire) 300 by 200 Meters in T-shape 300 meters, 30 motes each line, 4 non-uniform lines 200M200M A B C D

21. University of Virginia Field Test Scenarios Phase V – Tripwire Partitions Created –Set system parameters –Activate 2 additional base stations –Reset system (a rotation)

22. University of Virginia Results of Actual Test

23. University of Virginia Field Test Scenarios Phase VI - Activate and Deactivate tripwire sections Phase VII – Tracking with multiple tripwires –Person in dormant zone not detected then moves into active zone –Person first in active zone and moves into dormant zone –Vehicle at 30 mph

24. University of Virginia Field Test Scenarios Phase VIII – Fault Tolerance with base mote failure –Turn off base mote 2 –Rotate system –Nodes all reconfigure into 2 zones Phase IX – Fault Tolerance with mote failures –For all above tests about 15% of nodes were dead –Turned off an additional 12 motes all near the T intersection –Vehicle at 30mph –Person Phase X – activate remote IR cameras and exfiltrate data to command and control center via satellites

25. University of Virginia High Level Performance All tests worked correctly False Alarms –No false positives –1 False negative A few times classified a person as a vehicle –High Wind

26. University of Virginia Technical Details Two sets of motes on either side of the path. One node at the end designated as the base node.

27. University of Virginia Neighbor Discovery Every node periodically broadcasts HELLO messages. Communication at sensing range. Asymmetric Detection Protocol

28. University of Virginia Reality - Radio Irregularity Radio Communication Range

29. University of Virginia Impact on Routing Impact on: –Path-Reversal technique –Multi-Round technique –Used in AODV, DSR, LAR Impact on Path-Reversal Technique Route Discovery Using Multi- Round Technique

30. University of Virginia Asymmetric Detection Protocol Explicit asymmetric communication detection and then use in routing protocol –Adapt over time and/or as conditions change Such a solution in VigilNet –Exchange neighbor tables –I’m in your table and your in mine -> symmetric link –Retry multiple times for statistical result

31. University of Virginia Tripwire-based Surveillance Create tripwires –Nodes attach to nearest base station based on distance (not hops) One per base station Road Dormant Active DormantActive Dormant

32. University of Virginia Sentry-Based Power Management (SBPM) Two classes of nodes: sentries and non-sentries – Sentries are awake – Non-sentries can sleep Sentries – Provide coarse monitoring & backbone communication network – Sentries “wake up” non-sentries for finer sensing Sentry rotation – Even energy distribution – Prolong system life 1 4 3 2

33. University of Virginia SBPM - Illustration Sentry Declaration Phase Communication at sensing range.

34. University of Virginia SBPM - Illustration Sentry Declaration Phase Other nodes send SENTRY_DECLARE message as backoff expires (function of remaining energy).

35. University of Virginia SBPM - Illustration Sentry Declaration Phase Other nodes send SENTRY_DECLARE message as backoff expires.

36. University of Virginia SBPM - Illustration Backbone Creation Flooding initiates at base.

37. University of Virginia SBPM - Illustration Build spanning tree.

38. University of Virginia SBPM - Illustration Final result might look like this. Build second parent tree for robustness

39. University of Virginia SBPM - Illustration Backbone Repair

40. University of Virginia Area Only Wake-Up Power management – non-sentries go to sleep Upon detection of event all non-sentries in an area are awakened. Non-sentry powered-on Non-sentry powered-off Sentry

41. University of Virginia Power Management Sentry Tripwire Area only wakeup

42. University of Virginia Lifetime Analysis Network Life Time Number of Tripwires (10 regions, 30% sentry, 7 day life) 4321 2 AA Batteries50 days70 days105 days210 days 4 AA Batteries100 days140 days210 days420 days

43. University of Virginia Sentry Duty-Cycle Scheduling A common period p and duty-cycle β is chosen for all sentries, while starting times T start are randomly selected Non-sentries Sentries Target Trace A B C D E A B C D E t t t t t Awake Sleeping p02p

44. University of Virginia Lifetime Analysis Network Life Time Number of Tripwires (10 regions, 30% sentry, 7 day life) 10421 2 AA Batteries Sentries Awake 21 days50 days 105 days 210 days Sentries with Duty Cycles 50 days125 days 250 days 500 days 4 AA Batteries Sentries Awake 42 days100 days 210 days 420 days Sentries with Duty Cycles 100 days250 days 500 days 1000 days

45. University of Virginia Group Management IR Camera

46. University of Virginia Group Management IR Camera

47. University of Virginia DETECTION DELAY (S)CLASSIFICATION DELAY (S) VELOCITY DELAY (S) REPORTED VELOCITY (MPH) ACTUAL VELOCITY (MPH) 2.73.2 25.0/10.9N/A 1.83.2 24.6N/A 1.72.73.217.6N/A 3.84.85.39.3N/A 1.72.72.811.110 2.63.13.618.520 1.92.4 23.020 2.62.93.212.712 0.92.5 22.120 4.58.1 6.2N/A Detection/Classification/Velocity Delay

48. University of Virginia Sensing Realities Sensor fusion –Handle noise, missing reports, drift, environmental conditions, characteristics of sensors, etc. –Compute confidence –Minimize false alarms –On minimum capacity devices (but utilize multiple devices)

49. University of Virginia 3 -Tier Filter & Classification Group Base mote Report Performing base level classification Group leader, performing group level classification Normal mote, performing sensor (mote) level classification

50. University of Virginia Acoustic Sensing Three Cars Initial Calibration No Detection Detection when Energy Crosses Standard Deviation

51. University of Virginia DOA controls minimal aggregation degree to reduce false alarms Second Tier: Group Aggregation Awareness Range Detection Range Node Member Follower Leader

52. University of Virginia System Issues: False alarms Probability of false positives reduces as DOA increases Probability of false negatives increases as DOA increases With DOA = 3 we had zero false alarms The DOA parameter can be tuned based on sensing range and the density with which motes are deployed Impact of DOA on False Alarms Spatial-temporal correlated data aggregation can effectively reduce false alarms

53. University of Virginia THIRD TIER At base station –Maintain history of track –Further reduce false alarms by checking for anomalies –Compute velocity –Perform classification

54. University of Virginia GPS Mote assembly: –Garmin eTrex Legend GPS device (WAAS enabled) –MICA2 mote –helmet, RS232 cable, board, wristband –Memory size: 17 Kbytes (code), 600 Bytes (data) Sensor Node: –Mica2, XSM –Memory: 1 Kbytes (code), data: 120 bytes Localization – Walking GPS

55. University of Virginia Walking GPS Evaluation First deployment type: sensor motes turned on at the place of deployment, right before being deployed Localization error: 0.8 meters Standard deviation: 0.5 meters Second deployment type: sensor motes turned on all the time. Localization error: 1.5 meters Standard deviation: 0.8 meters

56. University of Virginia Summary Surveillance Application in Action –One message: must build complete systems and use them in realistic settings with real world realities –30 modules synthesized – a complete system –Scale via tripwires –Robust to faults –Novel technology »Power management »Group management »Asymmetric communication detection –Simple localization based on manual deployment – but it works