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Target Tracking with Sensor Networks

Target Tracking with Sensor Networks. Chao Gui Networks Lab. Seminar Oct 3, 2003. Agenda. Background Frisbee: A Networks Model for Target Tracking Applications A Cooperative Tracking Algorithm Performance Study. Wireless sensor networks. Wireless sensor node power supply sensors

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Target Tracking with Sensor Networks

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

  2. Agenda • Background • Frisbee: A Networks Model for Target Tracking Applications • A Cooperative Tracking Algorithm • Performance Study

  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. 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. 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. Agenda • Background • Frisbee: A Networks Model for Target Tracking Applications • A Cooperative Tracking Algorithm • Performance Study

  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. Frisbee: A Networks Model for Target Tracking Applications

  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. Agenda • Background • Frisbee: A Networks Model for Target Tracking Applications • A Cooperative Tracking Algorithm • Performance Study

  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. e e R 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 • 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. 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. 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. 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. A Cooperative Tracking Algorithm • Better weights: • Proportional weight. • Logarithmic weight.

  17. Agenda • Background • Frisbee: A Networks Model for Target Tracking Applications • A Cooperative Tracking Algorithm • Performance Study

  18. Simulation Results 100 sensors. Target moving in straight line with speed 1 R/s.

  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

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