Target Tracking with Sensor Networks

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Target Tracking with Sensor Networks

• Chao Gui
• Networks Lab. Seminar
• Oct 3, 2003

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Agenda

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

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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

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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

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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

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Agenda

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

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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.

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Frisbee A Networks Model for Target Tracking
Applications
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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

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Agenda

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

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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

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A Cooperative Tracking Algorithm

• Sensor detection model
• Object always detected in rage R-e
• Object never detected out of range Re
• Object possibly detected in range R-e, Re
• 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 dont have to be circular.

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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 objects 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.

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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

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

• Better weights
• Proportional weight.
• Logarithmic weight.

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Agenda

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

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