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

2. 2 M. Baqer mbaq1@student.monash.edu.au 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. 3 M. Baqer mbaq1@student.monash.edu.au Sensor Networks Random vs. deterministic deployment Long term deployment Dynamic infrastructure Unattended operations Scale

4. 4 M. Baqer mbaq1@student.monash.edu.au Energy Conservation Scheduling-based –Operation mode (transmitting, receiving, idle, sleeping) In-network Processing-based –Aggregation –Compression –Beamforming –CSIP

5. 5 M. Baqer mbaq1@student.monash.edu.au Patterns and Sensor Networks Spatio-temporal event patterns Pattern collection –continuously –periodically –Even-driven –User-driven –hybrid

6. 6 M. Baqer mbaq1@student.monash.edu.au Application: Structural Health Monitoring SHM replace visual inspection Applied for – Predict – Detect – monitor structures for damages

7. 7 M. Baqer mbaq1@student.monash.edu.au 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. 8 M. Baqer mbaq1@student.monash.edu.au Challenges of Event Recognition in Sensor Networks Global vs. local Constraint resources Dynamic infrastructure Energy efficiency Scalable

9. 9 M. Baqer mbaq1@student.monash.edu.au Event Recognition for Sensor Networks Template matching Distributed artificial intelligence Cooperative distributed problem solving

10. 10 M. Baqer mbaq1@student.monash.edu.au Voting Graph Neuron Model Storage Communication

11. 11 M. Baqer mbaq1@student.monash.edu.au 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. 12 M. Baqer mbaq1@student.monash.edu.au 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. 13 M. Baqer mbaq1@student.monash.edu.au Example Input sensory patternCommittee negotiation process Colour map of the negotiation process

14. 14 M. Baqer mbaq1@student.monash.edu.au Comparison results of the difference in the pattern matching performance for committee storing random patterns and alphabet character patterns

15. 15 M. Baqer mbaq1@student.monash.edu.au 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. 16 M. Baqer mbaq1@student.monash.edu.au SGSIA

17. 17 M. Baqer mbaq1@student.monash.edu.au Summary Ambient intelligence Decentralised in-network pattern recognition Scalability Adaptability

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

19. 19 M. Baqer mbaq1@student.monash.edu.au Acknowledgment Zubair Baig And my supervisor Asad Khan