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

Xin LiuComputer Science Dept.

University of California, Davis

Collaborators: Qingfeng Huang & Ying Zhang, PARC

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Objective

Simple, reliable, and efficient on-demand information discovery mechanisms

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Where are the tanks?

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Pull-based Strategy

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Pull-based Cont’d

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Push-based Strategy

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Comb-Needle Structure

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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, …

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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

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When an Event Happens

Event

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When a Query is Generated

Event

Query

Event

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Tuning Comb-Needle

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The Spectrum of Push and Pull

Pull Push

Global pull +Local push

Global push +Local pull

Push & Pull

Inter-spike spacing increases

Reverse comb

Relative query frequency increases

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Reverse Comb

Query

Event

When query frequency > event frequency

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Mid-term Review

Basic idea: balancing push and pull

Preview: Reliability Random network An adaptive scheme

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Strategies for Improving Reliability

Local enhancement Interleaved mesh Routing update

Spatial diversity Correlated failures Enhance and balance query success rate at

different geo-locations

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Spatial Diversity

Query

xEvent

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Random Network

Constrained geographical flooding Needles and combs have certain widths

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Simulation

Simulator: Prowler

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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

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Simulation

Regular grid Communication cost: hop counts No node failure Adaptive scheme

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Event & Query Frequencies

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Tracking the Ideal Inter-Spike Spacing

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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

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Summary

Adapt to system changes Can be applied in hierarchical structures

Pull Push

Global pull +Local push

Global push +Local pull

Push & Pull

Relative query frequency increases

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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