Matching Data Dissemination Algorithms to Application Requirements

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Matching Data Dissemination Algorithms to
Application Requirements

• John Heidermann, Fabio Silva, Deborah Estrin
• Presented By Bryan Wong

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Outline

• Introduction
• Problem Description
• Diffusion Routing Algorithms
• Evaluation
• Conclusion

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Introduction

• Data dissemination algorithms are application
  specific
• Reduces communications costs by replacing
  communication with computation in the network
• As number of protocols and sophistication of
  applications grows, choice of communication
  algorithm becomes a problem

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Problem Description

• How can diffusion address application-specific
  requirements?

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Robustness Requirements

• Applications must be robust to change
• Wireless links come and go
• Nodes fail or move
• How can communication be robust but also
  efficient for many different applications?

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Application Requirements

• Sensor network applications have different needs
• Different traffic patterns (many-to-one,
  many-to-many, one-to-many, one-to-one)
• Different data rates (fixed and variable,
  frequent and infrequent)

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Solution

• Match routing algorithms to application
  requirements

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Multiple Diffusion Routing Algorithms

• Two-Phase Pull Diffusion
• One Phase Pull Diffusion
• Push Diffusion
• GEAR

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Two-phase pull diffusion

• Initial diffusion implementation
• Periodically floods data sinks interests and
  exploratory data

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GEAR

• Adds support for geographically scoped queries
• If nodes know their locations, then geographic
  queries can influence data dissemination
• Replaces network wide communication with
  geographically constrained communication

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Push Diffusion

• Reverses the roles in the publish/subscribe API
• Floods only exploratory data messages

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One-phase pull diffusion

• Subscriber based system that avoids one of the
  two phases of flooding in two-phase pull
• Only floods interests
• No exploratory messages

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Sample Applications

• Push reduces message count by 60 compared to
  two phase pull

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Sample Applications

• GEAR reduces message count by 40

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Systematic Evaluation
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Systematic Evaluation
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Systematic Evaluation
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Systematic Evaluation

• One-phase pull is best with many sources, few
  sinks
• Push works best with many sinks and few sources

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Conclusions

• The break even point between the two algorithms
  depends upon specific control message frequency
  as well as application data rates
• For networks with more than a few dozen nodes,
  the benefits of geographically-scoped queries can
  outweigh other algorithmic choices

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References

• http//www.cens.ucla.edu/Education/RR_Posters/Rese
  arch20Review/015_Silva.pdf