Structure-freeData Aggregation

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

Outline

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

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]

Static Structure

Pros Low maintenance cost Good for unchanging

traffic pattern Cons

Unsuitable for event triggered network Long link-stretch Long delay sink

Static Structure

Pros Low maintenance cost Good for unchanging

traffic pattern Cons

Unsuitable for event triggered network Long link-stretch Long delay sink

Dynamic Structure

Pros Reduces communication

cost Cons

High maintenance overhead

sink

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

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

Data Aware Anycast

50 nodes in 200mx200m

sink

Data Aware Anycast

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

Aggregation

sink

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

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

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(][

Analysis vs. Simulation

Results matches up to 40 hops

Gap increases as network size increases

Reason: transmission delay is ignored in analysis

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

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%

Simulation Results – Maximum delay

AT is sensitive to delay AT has best performance

with highest delay

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)

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

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

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

Conclusion

Data Aware Anycast for Spatial Convergence Randomized Waiting for Temporal Convergence Efficient Aggregation without a Structure

High Aggregation No maintenance overhead