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

Explore the effective strategies of push and pull mechanisms in information discovery, focusing on simple and reliable on-demand solutions. The research, conducted at the Computer Science Dept., University of California, Davis in collaboration with PARC, delves into optimal structures like Comb-Needle to address the challenges. The study offers insights on improving reliability, spatial diversity, and adaptive schemes to enhance query success rates varying geographically. Simulation results using the Prowler simulator show promising outcomes, indicating potential gains in success ratios with optimized energy consumption. The research envisions future work in random networks, data aggregation, and compression integration, aiming for comprehensive communication models.

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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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