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Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery Xin Liu Computer Science Dept. University of California, Davis Collaborators:

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Presentation on theme: "Comb, Needle, and Haystacks: Balancing Push and Pull for Information Discovery Xin Liu Computer Science Dept. University of California, Davis Collaborators:"— Presentation transcript:

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 11/4/2004 ACM Sensys2 Objective Simple, reliable, and efficient on-demand information discovery mechanisms

3 11/4/2004 ACM Sensys3 Where are the tanks?

4 11/4/2004 ACM Sensys4 Pull-based Strategy

5 11/4/2004 ACM Sensys5 Pull-based Cont’d

6 11/4/2004 ACM Sensys6 Push-based Strategy

7 11/4/2004 ACM Sensys7 Comb-Needle Structure

8 11/4/2004 ACM Sensys8 Related Work D. Braginsky and D. Estrin, “Rumor routing algorithm for sensor networks”, WSNA, J. Heidemann, F. Silva, and D. Estrin, “Matching data dissemination algorithms to application requirements”, SENSYS ACQUIRE, IDSQ, SRT, GHT, DIMENSIONS, DIM, GRAB, gossip, flooding-based, agent- based, geo-routing, …

9 11/4/2004 ACM Sensys9 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

10 11/4/2004 ACM Sensys10 When an Event Happens

11 11/4/2004 ACM Sensys11 When a Query is Generated Event Query Event

12 11/4/2004 ACM Sensys12 Tuning Comb-Needle

13 11/4/2004 ACM Sensys13 The Spectrum of Push and Pull PullPush Global pull +Local push Global push +Local pull Push & Pull Inter-spike spacing increases Reverse comb Relative query frequency increases

14 11/4/2004 ACM Sensys14 Reverse Comb When query frequency > event frequency

15 11/4/2004 ACM Sensys15 Mid-term Review Basic idea: balancing push and pull Preview: Reliability Random network An adaptive scheme

16 11/4/2004 ACM Sensys16 Strategies for Improving Reliability Local enhancement Interleaved mesh Routing update Spatial diversity Correlated failures Enhance and balance query success rate at different geo-locations

17 11/4/2004 ACM Sensys17 Spatial Diversity Query x Event

18 11/4/2004 ACM Sensys18 Random Network Constrained geographical flooding Needles and combs have certain widths

19 11/4/2004 ACM Sensys19 Simulation Simulator: Prowler

20 11/4/2004 ACM Sensys20

21 11/4/2004 ACM Sensys21

22 11/4/2004 ACM Sensys22 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

23 11/4/2004 ACM Sensys23 Simulation Regular grid Communication cost: hop counts No node failure Adaptive scheme

24 11/4/2004 ACM Sensys24 Event & Query Frequencies

25 11/4/2004 ACM Sensys25 Tracking the Ideal Inter-Spike Spacing

26 11/4/2004 ACM Sensys26 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

27 11/4/2004 ACM Sensys27 Summary Adapt to system changes Can be applied in hierarchical structures PullPush Global pull +Local push Global push +Local pull Push & Pull Relative query frequency increases

28 11/4/2004 ACM Sensys28 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


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