CatchUp: A Data Aggregation Scheme for VANETs

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Catch-Up A Data Aggregation Scheme for VANETs

• Bo Yu, Jiayu Gong, Cheng-Zhong Xu
• Dept. of ECE, Wayne State Univ.
• ACM VANET08

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Outline

• Introduction
• Related Work
• Motivation
• Aggregation Scheme
• Analysis
• Simulation
• Conclusion

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Introduction

• Traffic Information Dissemination
• Each vehicle periodically detects the traffic
  conditions around it, and then, forwards the
  information to vehicles following behind it
• Redundant Data Limited Bandwidth
• Multiple redundant copies for the same traffic
  status
• Consuming a considerable amount of bandwidth

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Data Aggregation

• A useful technique to reduce data redundancy and
  improve communication efficiency
• Two aspects
• Routing-related (our focus)
• How two reports can meet each other at the same
  time at the same node
• Data-related
• Coding, calculation, and compression of
  aggregatable data

v1
v3
r1 (30mph)
r3?r1 r2 ((3035)/232.5mph)
v2
r2 (35mph)
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Related Work

• Structured Aggregation
• A routing structure, forwarding tree, is
  maintained to ensure reports can be forwarded to
  the same node at the same time
• Widely used in sensor networks, but infeasible in
  VANETs

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Related Work (Cont.)

• Structureless Aggregation
• Randomized Waiting
• Wait for a random period before forwarding to the
  next hop
• During the waiting period, more reports can be
  received and aggregated
• Periodical Waiting
• TrafficView, SOTIS
• Wait for a fixed period before forwarding to the
  next hop
• An arising question how long should it wait to
  achieve better aggregation performance?

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Motivation

• Two Properties of VANETs
• Channel Eavesdropping
• Every node is able to receive reports being
  transmitted in the channel and log them into its
  local database
• Traffic Information is not delay-sensitive
• Even a delay of tens of seconds is still
  acceptable

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Motivation (Cont.)

• Determine waiting time based on local
  observations of individual vehicles
• Challenge outdated and incomplete knowledge

r1 is ahead, so r2 should speed up and catch up
with r1
r1
r2
v1
v2
v3
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Distributed MDP Model

• s world state
• o observation
• b internal state
• a action
• SE State Estimator
• ? Decision Maker

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Distributed MDP Model (Cont.)

• action a WALK, RUN
• the propagation speed of a report (how fast
  shall we propagate a report)
• can be transformed into two different delays
  before forwarding to the next hop
• observation o eavesdropped reports
• an observation is a tuple ltreport, time_stamp,
  action(WALK/RUN)gt
• internal state b estimated position of a report
• b(r,pt) the probability that report r is at
  position p at time t

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Expected Future Reward

• Objective
• To find a policy which maximizes the expected
  future reward
• Policy p
• a sequence of actions to be performed for a given
  report in the future
• Expected Future Reward
• 
  ,
• ? - a future discount factor
• wt - the expected reward at time t (the saved
  communication overhead due to aggregation of the
  reports)

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Expected Future Reward (Cont.)

• Virtual Report r0
• To encourage some reports to speed up, but the
  others to slow down
• Is supposed to be always following the current
  report
• Can be configured according to the average
  frequency of the event source
• Internal States
• (r0,r1 ,r2 ,r2,)
• Total Expected Reward

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Decision Tree

• To find the optimal policy p(a0,a1,a2,)

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Other Issues

• Report Overtaking

r1r2
r1r2
r2
v1
v2
v3
v4
r1
X
r1
r1
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Other Issues (Cont.)

• How to judge whether a report is contained by
  another aggregated report
• r1 ? r3 ? where r3r1r2
• Bloom Filter
• is a space-efficient probabilistic data structure
  that is used to test whether an element is a
  member of a set

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Property

• All reports from a given road section and from a
  given time period can be aggregated into an
  overview report
• The convergence time upper bound
• The convergence distance upper bound

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Simulation

• Based on NS2 and GrooveNet
• Compared to Randomized Waiting

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Results

• CATCHUP(100,1000) - walking speed at 100m/s,
  running speed at 1000m/s
• CATCHUP(200,2000) - walking speed at 200m/s,
  running speed at 2000m/s
• For CATCHUP, the aggregation operations mainly
  reside within the first 5 km.
• For Randomized Waiting, the aggregation
  operations are distributed all over the
  propagation distance.

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Results (Cont.)

• Before running, scale them to the same total
  delay
• For CATCHUP, the delay mainly reside within the
  first 4 km
• CATCHUP trades increased delay for reduced
  communication overhead
• For Randomized Waiting, the delay is linear

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Conclusion

• We studied the adaptive control of forwarding
  delay in data aggregation in VANETs
• Aggregation is a tradeoff between delay and
  communication overhead
• We make the delay more controllable in a manner
  that a report has a better chance to be
  aggregated with other reports

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THANKS!
Bo Yu, Jiayu Gong, Cheng-Zhong Xu Dept. of ECE,
Wayne State Univ. ACM VANET08