Download - Structure-free 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
Class B
Canceled CTS
Canceled CTS
RTS
CTS
Sender
Class A Nbr
Class B Nbr
Class C Nbr
Class A Nbr
CTS slotmini-slot
Class A
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Randomized Waiting
Improve Temporal Convergence Naive Waiting Approach
Use delay based on proximity to sink (closer to sink => 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
E[Y] = 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 =
dxxdYE h )]|([1
0
… …Sink
h=n/k
kn
h
h
ik nn
k
n
k
nHnYEk
/
1
1
0
log)()1(][
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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) DAA+RW
Evaluated Metric Normalized Number of
Transmissions
Parameters Studied Maximum Delay Event Size Aggregation Function Network Size
nInformatioReceivedofUnits
onsTransmissiTotalofNumber
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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 t
he event data rate: 0.2 pkt/s data payload: 50 bytes
AT-2: Aggregation tree approach with varying delay
DAA+RW 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 DAA+RW is much
better than OP DAA+RW is close
to AT (optimal tree)
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Simulation Results – Aggregation Ratio
Configuration Aggregation Ratio ρ:
0 ~ 1 Packet size:
max(50, 50* (1-ρ)* n) Max packet size:
400 bytes
Key Observation DAA+RW performs be
tter than AT Following the best tre
e 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 suff
icient 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