Structurefree Data Aggregation

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Structure-freeData Aggregation

• Kaiwei Fan, Sha Liu, and Prasun Sinha
  (speaker)The Ohio State UniversityDept of
  Computer Science and Engineering

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Outline

• Introduction
• Structure-free Data Aggregation
• Simulation Results
• Experiments on a testbed
• Conclusion

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Introduction

• Data Aggregation
• In-network processing
• Reduces communication cost
• Approaches
• Static Structure
• LEACH, TWC 02
• PEGASIS, TPDS 02
• Dynamic Structure
• Directed Diffusion, Mobicom 00
• DCTC, Infocom 04

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Static Structure

• Pros
• Low maintenance cost
• Good for unchanging traffic pattern
• Cons
• Unsuitable for event triggered network
• Long link-stretch
• Long delay

sink
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Static Structure

• Pros
• Low maintenance cost
• Good for unchanging traffic pattern
• Cons
• Unsuitable for event triggered network
• Long link-stretch
• Long delay

sink
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Dynamic Structure

• Pros
• Reduces communication cost
• Cons
• High maintenance overhead

sink
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Structure-free Data Aggregation

• Challenge
• Routing who is the next hop?
• Waiting who should wait for whom?
• Approach
• Spatial Convergence
• Temporal Convergence
• Solution
• Data Aware Anycast
• Randomized Delay

Routing?
Waiting?
sink
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Data Aware Anycast

• Improve Spatial Convergence
• Anycast
• One-to-Any forwarding scheme
• Anycast for Immediate Aggregation
• To neighbor nodes having packets for aggregation
• Keep Anycasting for Immediate Aggregation

sink
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Data Aware Anycast

• 50 nodes in 200mx200m

sink
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Data Aware Anycast

• Forward to Sink
• To neighbor nodes closer to the sink
• Using Anycast for possible Immediate Aggregation

sink
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Data Aware Anycast

• Forwarding and CTS replying priority
• Class A Nodes for Immediate Aggregation
• Class B Nodes closer to the sink
• Class C Otherwise, do not reply

CTS slot
mini-slot
Class B
Class A
RTS
Sender
CTS
Class A Nbr
Canceled CTS
Class A Nbr
Canceled CTS
Class B Nbr
Class C Nbr
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Randomized Waiting

• Improve Temporal Convergence
• Naive Waiting Approach
• Use delay based on proximity to sink (closer to
  sink gt higher delay)
• Long delay for nodes close to the sink in case
  the event is near the sink
• Our Approach Random Delay at Sources

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Analysis

• Y Number of hops a packet is forwarded before
  being aggregated
• Assumptions
• Each node has k choices for next
• hops closer to sink
• All n nodes have packets to send
• EY
• x random delay in 0,1 picked up by a node
• dh random delay chosen by a node h hops away
  from sink
• Total Number of Transmissions

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Analysis vs. Simulation

• Results matches up to 40 hops
• Gap increases as network size increases
• Reason transmission delay is ignored in analysis
  

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Simulation Results

• Evaluated Protocols
• Opportunistic (OP)
• Optimum Aggregation Tree (AT)
• Data Aware Anycast (DAA)
• Randomized Waiting (RW)
• DAARW

• Evaluated Metric
• Normalized Number of Transmissions
• Parameters Studied
• Maximum Delay
• Event Size
• Aggregation Function
• Network Size

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Simulation Results Maximum delay

• Configuration
• 33 x 33 grid network
• event moves at 10m/s
• event radius 200m
• 140 nodes triggered by the event
• data rate 0.2 pkt/s
• data payload 50 bytes
• AT-2 Aggregation tree approach with varying
  delay
• DAARW improve OP by 70

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Simulation Results Maximum delay

• AT is sensitive to delay
• AT has best performance with highest delay

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Simulation Results Event Size

• Configuration
• event radius 50m 300m
• 8 260 nodes triggered by the event
• event radius 200m
• Key Observations
• DAARW is much better than OP
• DAARW is close to AT (optimal tree)

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Simulation Results Aggregation Ratio

• Configuration
• Aggregation Ratio ?0 1
• Packet sizemax(50, 50 (1-?) n)
• Max packet size400 bytes
• Key Observation
• DAARW performs better than AT
• Following the best tree is not optimum if the
  packet size is limited

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Simulation Results Network Size

• event distance to the sink 300m 700m
• event radius 200m
• Key Observation
• Improvement is higher for events farther from the
  sink

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Experiment Randomized Waiting

• Linear network with 5 sources and 1 sink
• 0.2 pkt/s
• data payload 29 bytes
• Key Observation
• Delay as low as 0.1 is sufficient for optimizing
  performance

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Conclusion

• Data Aware Anycast for Spatial Convergence
• Randomized Waiting for Temporal Convergence
• Efficient Aggregation without a Structure
• High Aggregation
• No maintenance overhead