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Combs, Needles, and Haystacks: Balancing Push and Pull for Information Discovery

Combs, Needles, and Haystacks: Balancing Push and Pull for Information Discovery. Xin Liu Computer Science Dept. University of California, Davis Collaborators: Qingfeng Huang & Ying Zhang , PARC. Presented by Chien-Liang Fok on March 4, 2004 for CSE730. Objective.

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Combs, Needles, and Haystacks: Balancing Push and Pull for Information Discovery

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  1. Combs, Needles, and Haystacks:Balancing Push and Pull for Information Discovery Xin Liu Computer Science Dept. University of California, Davis Collaborators: Qingfeng Huang & Ying Zhang, PARC Presented by Chien-Liang Fok on March 4, 2004 for CSE730

  2. Objective Simple, reliable, and efficient on-demand information discovery mechanisms ACM Sensys

  3. Where are the tanks? ACM Sensys

  4. Pull-based Strategy ACM Sensys

  5. Pull-based Cont’d ACM Sensys

  6. Push-based Strategy ACM Sensys

  7. Comb-Needle Structure ACM Sensys

  8. Assumptions • Events: Anywhere & Anytime • Queries: Anywhere & Anytime • Global discovery-type • One shot • Network: Uniform • Examples: • Firefighters query information in the field • Surveillance • Sensor nodes know their locations ACM Sensys

  9. Event When an Event Happens ACM Sensys

  10. Event Event When a Query is Generated Query ACM Sensys

  11. Tuning Comb-Needle ACM Sensys

  12. Query Freq. < Event Freq. ACM Sensys

  13. Query Freq. < Event Freq. ACM Sensys

  14. Query Event Reverse Comb When query frequency > event frequency ACM Sensys

  15. Global pull +Local push Global push +Local pull Pull Push & Pull Push Relative query frequency increases The Spectrum of Push and Pull Reverse comb Inter-spike spacing increases ACM Sensys

  16. Mid-term Review • Basic idea: balancing push and pull • Preview: • Reliability • Random network • An adaptive scheme ACM Sensys

  17. Strategies for Improving Reliability • Local enhancement • Interleaved mesh (transient failures) • Routing update (permanent failures) • Spatial diversity • Correlated failures • Enhance and balance query success rate at different geo-locations • Two-level redundancy scheme • l=2s ACM Sensys

  18. Spatial Diversity x Diversify queryspatially using green arrows Event Query ACM Sensys

  19. Random Network • Constrained geographical flooding • Needles and combs have certain widths ACM Sensys

  20. Simulation Using Prowler • Transmission model: • Reception model: Threshold  • MAC layer: Simulates Berkeley Motes’ CSMA • Use Default radio model: • σa=0.45, σb=0.02, perror=0.05, =0.1 ACM Sensys

  21. Two Experiments • What is the optimal spacing of the comb & needle length given Fq and Fe? • What is the robustness of the protocol in a really sparse network? ACM Sensys

  22. Experiment 1 Results l=1, s=3 optimal l=1, s=3 optimal loptimal ~ ACM Sensys

  23. Experiment 2 Results Wider the CGF width  More Reliable  More Energy ACM Sensys

  24. Adaptive Scheme • Comb granularity depends on the query and event frequencies • Nodes estimate the query and event frequencies to guess s • Important to match needle length and inter-spike spacing • Allow asymmetric needle length • Comb rotates • Load balancing • Broadcast information of current inter-spike spacing ACM Sensys

  25. Simulation • 20x20 regular grid • Communication cost: hop counts • No node failure • Adaptive scheme ACM Sensys

  26. Event & Query Frequencies ACM Sensys

  27. Tracking the Ideal Inter-Spike Spacing ACM Sensys

  28. Simulation Results • Gain depends on the query and event frequencies • Even if needle length < inter-spike spacing, there is a chance of success. • Tradeoff between success ratio and cost • 99.33% success ratio and 99.64% power consumption compared to the ideal case ACM Sensys

  29. Global pull +Local push Global push +Local pull Pull Push & Pull Push Relative query frequency increases Summary • Adapt to system changes • Can be applied in hierarchical structures ACM Sensys

  30. Future work • Further study on random networks • Building a “comb-needle-like” structure without location information • Integrated with data aggregation and compression • Comprehensive models for communication costs Thank you! ACM Sensys

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