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Matching Data Dissemination Algorithms to Application Requirements

This presentation discusses the problem of matching data dissemination algorithms to application requirements. It explores various diffusion routing algorithms and evaluates their effectiveness in addressing application-specific needs. The conclusion highlights the benefits of geographically-scoped queries in larger networks.

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Matching Data Dissemination Algorithms to Application Requirements

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  1. Matching Data Dissemination Algorithms to Application Requirements John Heidermann, Fabio Silva, Deborah Estrin Presented By: Bryan Wong

  2. Outline • Introduction • Problem Description • Diffusion Routing Algorithms • Evaluation • Conclusion

  3. 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

  4. Problem Description • How can diffusion address application-specific requirements?

  5. 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?

  6. 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)

  7. Solution • Match routing algorithms to application requirements

  8. Multiple Diffusion Routing Algorithms • Two-Phase Pull Diffusion • One Phase Pull Diffusion • Push Diffusion • GEAR

  9. Two-phase pull diffusion • Initial diffusion implementation • Periodically floods data sink’s interests and exploratory data

  10. 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

  11. Push Diffusion • Reverses the roles in the publish/subscribe API • Floods only exploratory data messages

  12. 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

  13. Sample Applications • Push reduces message count by ~60% compared to two phase pull

  14. Sample Applications • GEAR reduces message count by ~40%

  15. Systematic Evaluation

  16. Systematic Evaluation

  17. Systematic Evaluation

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

  19. 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

  20. References • http://www.cens.ucla.edu/Education/RR_Posters/Research%20Review/015_Silva.pdf

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