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Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery

Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery. Xin Liu Computer Science Dept. University of California, Davis Collaborators: Qingfeng Huang & Ying Zhang , PARC. Objective. Simple, reliable, and efficient on-demand information discovery mechanisms.

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Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery

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  1. Comb, Needle, and Haystacks:Balancing Push and Pull for Information Discovery Xin Liu Computer Science Dept. University of California, Davis Collaborators: Qingfeng Huang & Ying Zhang, PARC

  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. Related Work • D. Braginsky and D. Estrin, “Rumor routing algorithm for sensor networks”, WSNA, 2002. • J. Heidemann, F. Silva, and D. Estrin, “Matching data dissemination algorithms to application requirements”, SENSYS 2003. • ACQUIRE, IDSQ, SRT, GHT, DIMENSIONS, DIM, GRAB, gossip, flooding-based, agent-based, geo-routing, … ACM Sensys

  9. Application Scenarios • On-demand information query • Any node can be the query entry node • Queries may be generated at anytime • Events can happen anywhere and anytime • Examples: • Firefighters query information in the field • Surveillance • Sensor nodes know their locations ACM Sensys

  10. Event When an Event Happens ACM Sensys

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

  12. Tuning Comb-Needle ACM Sensys

  13. 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

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

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

  16. Strategies for Improving Reliability • Local enhancement • Interleaved mesh • Routing update • Spatial diversity • Correlated failures • Enhance and balance query success rate at different geo-locations ACM Sensys

  17. Spatial Diversity x Event Query ACM Sensys

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

  19. Simulation Simulator: Prowler ACM Sensys

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

  23. Simulation • Regular grid • Communication cost: hop counts • No node failure • Adaptive scheme ACM Sensys

  24. Event & Query Frequencies ACM Sensys

  25. Tracking the Ideal Inter-Spike Spacing ACM Sensys

  26. 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

  27. 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

  28. 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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