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Pattern Recognition for Wireless Sensor Networks

Mohamed Baqer mbaq1@student.monash.edu.au. Pattern Recognition for Wireless Sensor Networks. 24 May 2007. Outline. Sensor Networks Energy Conservation Patterns and Sensor Networks Application So What’s the Big Deal? Challenges of Event Recognition in Sensor Networks

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Pattern Recognition for Wireless Sensor Networks

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  1. Mohamed Baqer mbaq1@student.monash.edu.au Pattern Recognition for Wireless Sensor Networks 24 May 2007

  2. Outline • Sensor Networks • Energy Conservation • Patterns and Sensor Networks • Application • So What’s the Big Deal? • Challenges of Event Recognition in Sensor Networks • Event Recognition for Sensor Networks • Voting Graph Neuron • VGN Model • Voting and Consensus • Sleeping Mode • Example • SGSIA • Summary

  3. Sensor Networks • Random vs. deterministic deployment • Long term deployment • Dynamic infrastructure • Unattended operations • Scale

  4. Energy Conservation • Scheduling-based • Operation mode (transmitting, receiving, idle, sleeping) • In-network Processing-based • Aggregation • Compression • Beamforming • CSIP

  5. Patterns and Sensor Networks • Spatio-temporal event patterns • Pattern collection • continuously • periodically • Even-driven • User-driven • hybrid

  6. Application: Structural Health Monitoring • SHM replace visual inspection • Applied for • Predict • Detect • monitor structures for damages

  7. So, What’s the Big Deal? • Can’t centralised servers (base station / sink node) perform pattern recognition for sensor networks? • Geographically dispersed sensory data • Require global information • Communication overhead • Offline detection

  8. Challenges of Event Recognition in Sensor Networks • Global vs. local • Constraint resources • Dynamic infrastructure • Energy efficiency • Scalable

  9. Event Recognition for Sensor Networks • Template matching • Distributed artificial intelligence • Cooperative distributed problem solving

  10. Voting Graph Neuron Model • Storage • Communication

  11. VGN algorithm • Votes vectors: • Local match • Use: • Local processing, information exchange and decision fusion • Consensus • Cooperatively negotiating by casting votes • Cast and rebuild vote vectors

  12. Sleep Mode • Committee members enter into sleep mode to conserve their energy • Who may go into the sleep mode? • Committee members that already cast their vote • Committee members with identical votes • When do identical vote vectors get created? • Initialisation stage • Negotiation stage

  13. Example Input sensory pattern Committee negotiation process Colour map of the negotiation process

  14. Comparison results of the difference in the pattern matching performance for committee storing random patterns and alphabet character patterns

  15. SGSIA • In-network Data Processing for Secure Grid-Sensor Integration Architecture • Provide timely and accurate responses to data acquisition requests intended for WSNs • Data processing at the sensor nodes to filter raw sensory data • Optimal and selective forwarding of grid-generated queries to the appropriate sensor networks. • Grid proxy: interface, QoS, cashing • Gateway (base station): managing, fuse, translate

  16. SGSIA

  17. Summary • Ambient intelligence • Decentralised in-network pattern recognition • Scalability • Adaptability

  18. Questions mbaq1@student.monash.edu.au

  19. Acknowledgment Zubair Baig And my supervisor Asad Khan

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