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Catch-Up: A Data Aggregation Scheme for VANETs

Catch-Up: A Data Aggregation Scheme for VANETs. Bo Yu , Jiayu Gong, Cheng-Zhong Xu Dept. of ECE, Wayne State Univ. ACM VANET’08. Outline. Introduction Related Work Motivation Aggregation Scheme Analysis Simulation Conclusion. Introduction. Traffic Information Dissemination

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Catch-Up: A Data Aggregation Scheme for VANETs

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  1. Catch-Up: A Data Aggregation Scheme for VANETs Bo Yu, Jiayu Gong, Cheng-Zhong Xu Dept. of ECE, Wayne State Univ. ACM VANET’08

  2. Outline • Introduction • Related Work • Motivation • Aggregation Scheme • Analysis • Simulation • Conclusion

  3. 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

  4. 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 ((30+35)/2=32.5mph) v2 r2 (35mph)

  5. 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

  6. 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?

  7. 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

  8. 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 • Determine waiting time based on local observations of individual vehicles • Challenge: outdated and incomplete knowledge r1 is ahead, so r2 should speed up and catch up with r1 r1 r2 v1 v3 v2

  9. Distributed MDP Model • s – world state • o – observation • b – internal state • a – action • SE – State Estimator • П – Decision Maker

  10. 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 <report, time_stamp, action(WALK/RUN)> • internal state b: estimated position of a report • b(r,pt) : the probability that report r is at position p at time t

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

  12. 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

  13. Decision Tree • To find the optimal policy π=(a0,a1,a2,…)

  14. Other Issues • Report Overtaking r1+r2 r1+r2 r2 v1 v2 v3 v4 r1 X r1 r1

  15. Other Issues (Cont.) • How to judge whether a report is contained by another aggregated report • r1 r3? where r3=r1+r2 • Bloom Filter • is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set

  16. 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:

  17. Simulation • Based on NS2 and GrooveNet • Compared to Randomized Waiting

  18. 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.

  19. 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

  20. 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

  21. THANKS! Bo Yu, Jiayu Gong, Cheng-Zhong Xu Dept. of ECE, Wayne State Univ. ACM VANET’08

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