Download Matching Data Dissemination Algorithms to Application Requirements

Matching Data Dissemination
Algorithms to Application
Requirements
John Heidermann, Fabio Silva, Deborah Estrin
Presented By: Bryan Wong
Outline
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Introduction
Problem Description
Diffusion Routing Algorithms
Evaluation
Conclusion
Introduction
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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
Problem Description
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How can diffusion address applicationspecific requirements?
Robustness Requirements
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Applications must be robust to change:
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Wireless links come and go
Nodes fail or move
How can communication be robust but also
efficient for many different applications?
Application Requirements
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Sensor network applications have different
needs
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Different traffic patterns (many-to-one, many-tomany, one-to-many, one-to-one)
Different data rates (fixed and variable, frequent
and infrequent)
Solution
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Match routing algorithms to application
requirements
Multiple Diffusion Routing
Algorithms
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Two-Phase Pull Diffusion
One Phase Pull Diffusion
Push Diffusion
GEAR
Two-phase pull diffusion
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Initial diffusion implementation
Periodically floods data sink’s interests and
exploratory data
GEAR
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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
Push Diffusion
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Reverses the roles in the publish/subscribe
API
Floods only exploratory data messages
One-phase pull diffusion
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Subscriber based system that avoids one of
the two phases of flooding in two-phase pull
Only floods interests
No exploratory messages
Sample Applications
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Push reduces message count by ~60% compared to two phase pull
Sample Applications
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GEAR reduces message count by ~40%
Systematic Evaluation
Systematic Evaluation
Systematic Evaluation
Systematic Evaluation
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One-phase pull is best with many sources,
few sinks
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Push works best with many sinks and few
sources
Conclusions
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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
References
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http://www.cens.ucla.edu/Education/RR_Post
ers/Research%20Review/015_Silva.pdf