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Multi-attribute, Energy Optimal Sensor Fusion in Hurricane Model Simulations

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.

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Multi-attribute, Energy Optimal Sensor Fusion in Hurricane Model Simulations

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  1. Multi-attribute, Energy Optimal Sensor Fusion in Hurricane Model Simulations Marlon J Fuentes Bennie Lewis Spring 2008 Advance Topics in Wireless Networks

  2. OVERVIEW Project description Related works Implementation Challenges and problems Experiment results Demonstration Conclusion

  3. 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

  4. PROJECT OBJECTIVE Develop a sensor fusion and buffering algorithm optimize the value of transmitted observations Optimize the use a fixed energy budge

  5. PROJECT GOALS Learn how to use YAES Learn from existing Hurricane simulators and data fusion techniques Implement data fusion for our application

  6. 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

  7. 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

  8. IMPLEMENTATION - ALGORITHM Collect data from hurricane observations Use autonomous level 1 processing fusion technique Temporal and spatial data fusion

  9. IMPLEMENTATION - SIMULATION Design in Eclipse YAES User Interface Hurricane track data is loaded from a file Data fusion algorithm

  10. IMPLEMENTATION CONT.

  11. IMPLEMENTATION CONT.

  12. IMPLEMENTATION CONT.

  13. ARCHITECTURE AND DESIGN

  14. 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

  15. EXPERIMENTAL RESULTSTOTAL VS FUSED BSERVATIONS Utility = Fused Transmission / Total Observations Utility = 1/20 = 0.05

  16. 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

  17. CONCLUSION Project Overview Goals Implementation Challenges and problems Experiment results

  18. Demonstration / Questions

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