Structure-free
Data Aggregation
Kaiwei Fan, Sha Liu, and Prasun Sinha (speaker)
The Ohio State University
Dept of Computer Science and Engineering
Outline
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Introduction
Structure-free Data Aggregation
Simulation Results
Experiments on a testbed
Conclusion
Introduction
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Data Aggregation
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In-network processing
Reduces communication cost
Approaches
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Static Structure
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[LEACH, TWC ’02]
[PEGASIS, TPDS ’02]
Dynamic Structure
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[Directed Diffusion, Mobicom ‘00]
[DCTC, Infocom ‘04]
Static Structure
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Pros
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Low maintenance cost
Good for unchanging
traffic pattern
Cons
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Unsuitable for event
triggered network
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Long link-stretch
Long delay
sink
Static Structure
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Pros
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Low maintenance cost
Good for unchanging
traffic pattern
Cons
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Unsuitable for event
triggered network
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Long link-stretch
Long delay
sink
Dynamic Structure
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Pros
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Reduces communication
cost
Cons
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High maintenance
overhead
sink
Structure-free Data Aggregation
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Challenge
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Approach
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Routing: who is the next hop?
Waiting: who should wait for
whom?
Spatial Convergence
Temporal Convergence
Routing?
Waiting?
Solution
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Data Aware Anycast
Randomized Delay
sink
Data Aware Anycast
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Improve Spatial Convergence
Anycast
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One-to-Any forwarding scheme
Anycast for Immediate Aggregation
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To neighbor nodes having packets for
aggregation
Keep Anycasting for Immediate
Aggregation
sink
Data Aware Anycast
50 nodes in 200mx200m
sink
Data Aware Anycast
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Forward to Sink
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To neighbor nodes closer to the sink
Using Anycast for possible Immediate
Aggregation
sink
Data Aware Anycast
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Forwarding and CTS replying priority
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Class A: Nodes for Immediate Aggregation
Class B: Nodes closer to the sink
Class C: Otherwise, do not reply
mini-slot
CTS slot
Class A Class B
Sender
Class A Nbr
Class A Nbr
Class B Nbr
Class C Nbr
RTS
CTS
Canceled CTS
Canceled CTS
Randomized Waiting
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Improve Temporal Convergence
Naive Waiting Approach
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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
Analysis
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Y:
Number of hops a packet is forwarded before being
aggregated
Assumptions:
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Each node has k choices for next
Sink
hops closer to sink
All n nodes have packets to send
1
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……
E[Y] = 0 [ E (Y | d h  x)]dx
h=n/k
 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 =
n/k
h 1
n n
k  E[Y ]  (n  1) H k ( )   n log n

k k
h 1 i  0
Analysis vs. Simulation
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Results matches up
to 40 hops
Gap increases as
network size
increases
Reason: transmission
delay is ignored in
analysis
Simulation Results
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Evaluated Protocols
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Opportunistic (OP)
Optimum Aggregation
Tree (AT)
Data Aware Anycast (DAA)
Randomized Waiting (RW)
DAA+RW
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Evaluated Metric
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Normalized Number of
Transmissions
Number of Total Transmissi ons
Units of Received Informatio n
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Parameters Studied
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Maximum Delay
Event Size
Aggregation Function
Network Size
Simulation Results – Maximum delay
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Configuration
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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
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AT-2: Aggregation tree
approach with varying
delay
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DAA+RW improve OP by
70%
Simulation Results – Maximum delay
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AT is sensitive to delay
AT has best performance
with highest delay
Simulation Results – Event Size
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Configuration
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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)
Simulation Results – Aggregation Ratio
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Configuration
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Aggregation Ratio ρ:
0~1
Packet size:
max(50, 50* (1-ρ)* n)
Max packet size:
400 bytes
Key Observation
 DAA+RW performs
better than AT
 Following the best
tree is not optimum if
the packet size is
limited
Simulation Results – Network Size
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event distance to the
sink: 300m ~ 700m
event radius: 200m
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Key Observation
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Improvement is higher
for events farther from
the sink
Experiment – Randomized Waiting
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Linear network with 5
sources and 1 sink
0.2 pkt/s
data payload: 29 bytes
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Key Observation
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Delay as low as 0.1 is
sufficient for optimizing
performance
Conclusion
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Data Aware Anycast for Spatial Convergence
Randomized Waiting for Temporal Convergence
Efficient Aggregation without a Structure
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High Aggregation
No maintenance overhead