1 / 19

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

meghana-naidu
Download Presentation

Target Tracking with Sensor Networks

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. 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

More Related