1. 1 Target Tracking with Sensor Networks Chao Gui Networks Lab. Seminar Oct 3, 2003

2. University of California, Davis 2 Agenda Background Frisbee: A Networks Model for Target Tracking Applications A Cooperative Tracking Algorithm Performance Study

3. University of California, Davis 3 Wireless sensor networks Wireless sensor node  power supply  sensors  embedded processor  wireless link Many, cheap sensors  wireless  easy to install  intelligent  collaboration  low-power  long lifetime

4. University of California, Davis 4 Taxonomy of Sensor Networks SN Characteristics:  Sensor  Observer  Phenomenon SN Architecture  Infrastructure – sensors & their deployment (density, location, etc)  Network Protocol – communication between sensors and observer(s)  Application/Observer – translation between observer interest and network level implementation

5. University of California, Davis 5 Taxonomy of Sensor Networks Communication Models  Information delivery – dissemination of interests & delivery of interested data  Infrastructure – comm. needed to configure, maintain and optimize Data Delivery Models  Continuous  Event-driven  Observer-initiated Network Dynamics Models  Mobile observer  Mobile sensor  Mobile phenomenon

6. University of California, Davis 6 Agenda Background Frisbee: A Networks Model for Target Tracking Applications A Cooperative Tracking Algorithm Performance Study

7. University of California, Davis 7 Frisbee: A Networks Model for Target Tracking Applications Extend network life-time with given energy resource. “Interesting” events happen infrequently, and only take place at certain locations. Make the sensors sleep during the long interval of inactivity. When and where event occurs, only a limited zone of network is kept in full active state. For moving target, the active zone moves along.

8. University of California, Davis 8 Frisbee: A Networks Model for Target Tracking Applications

9. University of California, Davis 9 Issues with Frisbee Model Power savings with wake-up  Can be waked up by neighbors  Be able to form a “wakeup wavefront” that precedes the target Localized algorithm for defining the Frisbee boundary  Each node autonomously decide if it is in the current Frisbee  Adaptive fidelity

10. University of California, Davis 10 Agenda Background Frisbee: A Networks Model for Target Tracking Applications A Cooperative Tracking Algorithm Performance Study

11. University of California, Davis 11 Cooperative Tracking with SN Tracking – identify an object and determine its path over a period of time. Advantages  Easy deployment  Track multiple targets simultaneously Difficulties  Very limited resources  Work with local information  Timeliness of sensor data

12. University of California, Davis 12 A Cooperative Tracking Algorithm Sensor detection model  Object always detected in rage R-e  Object never detected out of range R+e  Object possibly detected in range [R-e, R+e]  e≈ 0.1R e e R Comments: Binary detection model is most simple and reliable. Traditional algorithms rely on more sophisticated model: determining the distance by AOS/AOA. Location resolution is the sensing range for one sensor, however, by combining multiple sensors, resolution is improved significantly. The sensing range don’t have to be circular.

13. University of California, Davis 13 A Cooperative Tracking Algorithm When the object enters the region where multiple sensors can detect it, its position is within the intersection of the overlapping sensing ranges. Algorithm:  Each node records the duration for which the object is in its range.  Neighboring nodes exchange these times and their locations.  For each point of time, the object’s estimated position is computed as the weighted average of the detecting nodes’ locations.  A line fitting algorithm is run on the resulting set of points.

14. University of California, Davis 14 A Cooperative Tracking Algorithm Weight assignment  Sensitive, affect the accuracy of tracking  Possible ways: Equal weight – Estimated object position is at the centroid of the sensing nodes’ locations Weight according to the distances to the object  The sensing node closer to the object should have higher weight

15. University of California, Davis 15 A Cooperative Tracking Algorithm Observation: Sensors that are closer to the path of the target will stay in sensor range for a longer duration.

16. University of California, Davis 16 A Cooperative Tracking Algorithm Better weights:  Proportional weight.  Logarithmic weight.

17. University of California, Davis 17 Agenda Background Frisbee: A Networks Model for Target Tracking Applications A Cooperative Tracking Algorithm Performance Study

18. University of California, Davis 18 Simulation Results 100 sensors. Target moving in straight line with speed 1 R/s.

19. University of California, Davis 19 References S. Tilak, N.B. Abu-Ghazaleh, W. Heinzelman, “A Taxonomy of Wireless Micro-Sensor Network Models”, Mobile Computing and Communications Review, Vol. 6, No. 2. A. Cerpa, J. Elson, M. Hamilton, J. Zhao, “Habitat Monitoring: Application Driver for Wireless Communications Technology”, First ACM Sigcomm Workshop on Data Communications in Latin America and the Caribbean, Apr. 2001 K. Mechitov, S. Sundresh, Y. Kwon, G. Agha, “Cooperative Tracking with Binary-Detection Sensor Networks,” Technical Report UIUCDCS-R-2003-2379, Computer Science, UIUC, Sept. 2003