Multi-attribute, Energy Optimal Sensor Fusion in Hurricane Model

SimulationsMarlon J FuentesBennie LewisSpring 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 RESULTSTOTAL VS FUSED BSERVATIONS

20001720

1160

2200

100 86 58 110

0

500

1000

1500

2000

2500

Andrew F rances J eanne Katrina

Total

F used

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