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					 Multi-attribute, Energy
Optimal Sensor Fusion in
   Hurricane Model
      Simulations
       Marlon J Fuentes
         Bennie Lewis
         Spring 2008
   Advance Topics in Wireless
           Networks
OVERVIEW
 Project description
 Related works
 Implementation
 Challenges and problems
 Experiment results
 Demonstration
 Conclusion
PROJECT DESCRIPTION
 Implement a Wireless Sensor Network
 Collection of time stamped observation
       Wind speed, Barometric Pressure, etc
 Sensor nodes can buffer data collections
 Sensor nodes can perform data fusion
PROJECT OBJECTIVE
 Develop a sensor fusion and buffering
  algorithm
 optimize the value of transmitted
  observations
 Optimize the use a fixed energy budge
PROJECT GOALS
 Learn how to use YAES
 Learn from existing Hurricane simulators
  and data fusion techniques
 Implement data fusion for our application
RELATED WORK –
HURRICANES
   HURRAN model
       Uses historical hurricane data
       Lacks performance when no data is available
   CLIPPER models
       Use prior statistical data
       Suffer from biased data
   3D Models
       Use current data to render 3D model of storm
       Require large amount of data
RELATED WORK – FUSION
ALGORITHMS
   Level 1 processing fusion techniques
   Centralized
       Requires sensors to send raw data to central node
       Central node performs fusion
   Autonomous
       Data is collected and fused at sensor location
       Fused data is sent to central node
   Hybrid
       Determines which method is best suited
       Requires additional logic to make accurate determination
IMPLEMENTATION -
ALGORITHM
 Collect data from hurricane observations
 Use autonomous level 1 processing fusion
  technique
 Temporal and spatial data fusion
IMPLEMENTATION -
SIMULATION
 Design in Eclipse
 YAES
 User Interface
 Hurricane track data is loaded from a file
 Data fusion algorithm
IMPLEMENTATION CONT.
IMPLEMENTATION CONT.
IMPLEMENTATION CONT.
ARCHITECTURE AND DESIGN
CHALLENGES AND PROBLEMS
ENCOUNTERED
 Knowledge of sensor Networks
 Fusion algorithms
 YAES Learning curve
 Sending messages to the sink node
 GUI crashing the Simulator
 Nodes range symbol getting painted
  behind the image
EXPERIMENTAL RESULTS
TOTAL VS FUSED BSERVATIONS
   2500
                                        2200
          2000
   2000             1720

   1500
                                1160
                                                       Total
   1000
                                                       F us e
                                                       d
    500
             100           86      58      110
      0
          Andrew F rances J eanne       K atrina

   Utility = Fused Transmission / Total Observations

   Utility = 1/20 = 0.05
EXPERIMENTAL RESULTS
 Not dependent on historical data
 Not biased by statistical values
 Does not require extensive amount of data
 Reduces amount of transmissions required
  thus extending node power life
CONCLUSION
 Project Overview
 Goals
 Implementation
 Challenges and problems
 Experiment results
Demonstration /
  Questions

				
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posted:8/18/2012
language:Latin
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