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Balancing Push and Pull for Efficient Information Discovery in Sensor Networks

This research presents a framework for on-demand information discovery using a balancing mechanism of push and pull strategies. The study introduces the Comb-Needle structure, which optimizes query success rates in a uniform network where events and queries can occur anywhere and anytime. Our experiments explore the optimal balance of query frequency relative to event frequency, enhancing reliability and energy efficiency in sparse networks. Future work will expand on these adaptive schemes and their applications in hierarchical structures for improved data aggregation.

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Balancing Push and Pull for Efficient Information Discovery in Sensor Networks

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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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