Directed Diffusion II Matching Data Dissemination Algorithms to - - PowerPoint PPT Presentation

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Directed Diffusion II Matching Data Dissemination Algorithms to Application Requirements, John Heidemann, Fabio Silva, and Deborah Estrin, 2003 Impact of network density on data aggregation in wireless sensor networks, Chalermek

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Directed Diffusion II

  • Matching Data Dissemination Algorithms to

Application Requirements, John Heidemann, Fabio Silva, and Deborah Estrin, 2003

  • Impact of network density on data aggregation in

wireless sensor networks, Chalermek Intanagonwiwat, Deborah Estrin, Ramesh Govindan, and John Heidemann, 2001

Presented by: Gazihan Alankus gazihan@cse.wustl.edu 10/25/2004

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Introduction

  • Sensor networks require data dissemination

protocols that are designed for the application needs

  • No single protocol is optimal for all applications
  • Appropriate protocol must be chosen based on

the nature of the application

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Introduction

  • Choice of protocol greatly affects application

performance

Protocols Problems

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Outline

  • Directed diffusion and standard algorithms
  • Proposed new algorithms

– Push diffusion – Two-phase pull diffusion – Greedy aggregation

  • Experimental results
  • Conclusion
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Directed Diffusion

  • Sources and sinks

– Sinks subscribe to sources – Data flows from sources to sinks – Data-centric

  • Separate API and implementation

– New implementations for same application

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Directed Diffusion

  • Standard algorithms

– Two-phase pull – GEAR – Opportunistic data aggregation

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Directed Diffusion

  • Two-phase pull

Sink Source Network

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Directed Diffusion

  • Two-phase pull

Sink Source Network interest

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Directed Diffusion

  • Two-phase pull

Sink Source Network interest

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Directed Diffusion

  • Two-phase pull

Sink Source Network exploratory data

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Directed Diffusion

  • Two-phase pull

Sink Source Network exploratory data

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Directed Diffusion

  • Two-phase pull

Sink Source Network reinforcement

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Directed Diffusion

  • Two-phase pull

Sink Source Network data

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Directed Diffusion

  • GEAR

– Use geographic information instead of flooding – Flood in the destination region after reaching the

destination

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Directed Diffusion

  • Opportunistic data aggregation

– Aggregate data if similar data happen to meet at a

branching node

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Proposed Algorithms

  • Idea: Among sources and sinks, silence the group

that is more crowded in order to reduce traffic

– Push – One-phase pull

  • Idea: The chances of similar data meeting on

network is low, make it higher

– Greedy aggregation

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Proposed Algorithms

  • Push

– Active sources – Passive sinks – Less floods than two-phase pull

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Proposed Algorithms

  • Push

Sink Source Network

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Proposed Algorithms

  • Push

Sink Source Network exploratory data

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Proposed Algorithms

  • Push

Sink Source Network exploratory data

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Proposed Algorithms

  • Push

Sink Source Network reinforcement

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Proposed Algorithms

  • Push

Sink Source Network data

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Proposed Algorithms

  • Push

– Advantages

  • Less flooding
  • Good for many sinks

– Disadvantages

  • Sinks cannot make requests
  • Not ideal for few sinks
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Proposed Algorithms

  • One-phase pull

– No exploratory data messages – Path is formed based on interest messages

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Proposed Algorithms

  • One-phase pull

Sink Source Network

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Proposed Algorithms

  • One-phase pull

Sink Source Network interest

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Proposed Algorithms

  • One-phase pull

Sink Source Network interest

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Proposed Algorithms

  • One-phase pull

Sink Source Network data

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Proposed Algorithms

  • One-phase pull

– Advantages

  • Less flooding
  • Instant path

– Disadvantages

  • Link asymmetry
  • Path formed based on the quality of reverse path
  • Flow-id necessary
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Proposed Algorithms

  • Greedy aggregation

– Instead of expecting similar messages to meet,

  • verlap the paths and increase the chance

– Delay messages to increase the chance

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Proposed Algorithms

  • Greedy aggregation
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Proposed Algorithms

  • Greedy aggregation
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Proposed Algorithms

  • Greedy aggregation
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Proposed Algorithms

  • Greedy aggregation
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Proposed Algorithms

  • Greedy aggregation

– Advantages

  • More aggregation
  • Less traffic
  • Even though nodes delay messages, overall delay is less

than opportunistic aggregation

– Disadvantages

  • Dependant on the tree, does not tolerate dead nodes
  • Although spends less battery in overall, main branches of

tree spend more battery power than others

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Experimental Results

  • Push vs one-phase pull

– Push is bad for many sources, OPP is bad for many sinks

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Experimental Results

  • Push vs one-phase pull with 5 sinks

– Cost of push increases with number of sources, but better than OPP

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Experimental Results

  • Push vs one-phase pull – relative overhead

– Overhead drops with more sources, fixed overhead is shared

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Experimental Results

  • One-phase pull

– OPP is not good with many sinks

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Experimental Results

  • Push

– Overhead of (a) exploratory data and (b) reinforcements

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Experimental Results

  • Push with GEAR and OPP with gear

– Flooding not required with geographic information

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Experimental Results

  • Opportunistic vs greedy aggregation
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Summary

  • Push

– Does not require the flooded interest messages – Sources advertise their data – Interested sinks reinforce

  • Pros

– Without interest messages, there is less flooding

  • Cons

– Not best with few sinks

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Summary

  • One phase pull

– Does not require the flooded exploratory data – Does not require reinforcements – Sources use interest messages to find route

  • Pros

– Less flooding

  • Cons

– The route found is not always the best

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Summary

  • Greedy aggregation

– Routes from the same path and introduces delays in

  • rder to make more aggregation
  • Pros

– More aggregation, less overall delay

  • Cons

– Depends heavily on the existing tree

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Conclusion

  • Underlying dissemination algorithm can greatly

affect the performance of applications that use diffusion

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Critique

  • Explanation of some graphs are insufficient
  • More real-life experiments necessary
  • Since topologies are random, experiment results should

also include the topologies used. Ex: Using the exact same topology for different algorithms (not explicitly mentioned)

  • Experiments with asymmetric links would be interesting

for OPP and TPP

  • Power-oriented analysis is also necessary