1. Optimizing CASCADE Data Aggregation for VANETs Khaled Ibrahim and Michele C. Weigle Department of Computer Science, Old Dominion University MASS 2008

2. Outline Introduction Related Work CASCADE （ Cluster-based Accurate Syntactic Compression fo Aggregated Data in VANETs ） Motivation and Goal Proposed algorithm Analysis Conclusion

3. Introduction Vehicular Ad-hoc Networks (VANETs) have been proposed to provide drivers with advance notification of traffic congestion using wireless communication. The more vehicles participating in the VANET the more messages are sent a frame size is finite Recently, data aggregation in VANETs has much attention to reduce the frame size. let a single frame to carry large number of information about vehicles

4. Related Work - CASCADE CASCADE GLOBECOM Workshops, 2008 IEEE This paper proposed a method for accurate aggregation of highway traffic information in VANETs. let a single frame to carry large number of information about vehicles. CASCADE uses compression to let frame can carry more traffic information without losing accuracy.

5. Related Work - CASCADE Assumptions Each vehicle is equipped with a GPS. Each vehicle is also pre-assigned a public key’s certificate, used for authentication. Cluster width is 16 m, cluster length is 64 m, the local view is 1600 m

6. Related Work - CASCADE Each vehicle broadcasts a primary frame contains the its primary record every 300-400 ms. The primary frame contains following: … Time-to-live （ TTL ）

7. Related Work - CASCADE A vehicle's local view is made up of primary records representing vehicles a certain distance ahead. （ local view = 1.6km ） A Vehicle ‘s Local view a

8. Related Work - CASCADE As primary frames are received To reduce the data size, the vehicle will grouped the records into their corresponding clusters, based on their distance from the receiving vehicle. aa

9. Related Work - CASCADE To achieve compression Each vehicle is represented by the difference between it and the cluster center. cluster

10. Related Work - CASCADE The total bits used to localize a vehicle is reduce to 16 bits. cluster 4m 64m

11. Related Work - CASCADE The primary data for each vehicle (location and speed) is represented in 136 bits (17 bytes) while the compact data for each vehicle is represented in at most 16 bits. …

12. Related Work - CASCADE Once the Compact records for all vehicles in the local view have been created, an aggregated cluster record （ ACR ） is form for each cluster.

13. Related Work - CASCADE Once the ACRs are constructed, they are concatenated into a aggregated frame and sent via broadcast. 100

14. Motivation The distance covered by the local view depends upon the number of vehicle records that can fit in a single IEEE 802.11 frame (2312 bytes). Local view

15. Motivation If the value of L c or W c is risen, the compact record size for a vehicle will increase the data The vehicle information in a frame will reduce, the local view is smaller cluster Local view

16. Goal In IEEE 802.11 frame size constraint, we determine the optimal cluster size To maximize the local view length.

17. Proposed algorithm ACR_1ACR_2…ACR_M CR_1CR_2…CR_K In determining the optimal cluster size, we strive to find an appropriate trade-off that will minimize the aggregated frame size and maximize the local view length. ACR: Aggregated Cluster Record （ the cluster data in local view ） CR: Compact record （ the vehicle data in a cluster ）

18. Proposed algorithm Aggregated frame Size Aggregated Cluster Record （ ACR ） Size We assume that the width of one lane is 4 m and that the average vehicle length is 5 m. LCLC WCWC

19. Proposed algorithm Aggregated frame Size Compact Record （ CR ） Size cluster

20. Proposed algorithm The Aggregated frame size function LCLC Length WCWC

21. Proposed algorithm In std. frame size constraint LCLC WCWC ACR.count

22. Proposed algorithm LCLC Length WCWC

23. Proposed algorithm

24. Analysis In our analysis, we consider four different cluster lengths （ 62m, 126m, 254m, 510m ） and three different cluster widths （ 1 lane, 2lanes, 4lanes ） 6 bits7 bits8 bits9 bits

25. Analysis For each traffic density, as the cluster dimensions change, the associated local view will change. We consider 53 vehicles/km as low density, 66 vehicles/km as medium density, and 90 vehicles/km as high density. N is total number of vehicles in the local view M is the number of clusters in the local view K is the maximum number of vehicles per cluster

26. Analysis Implies that increasing the cluster width to more than 4 lanes will provide no benefit

27. Analysis We calculate the aggregated frame size for various cluster sizes and also considering different traffic densities. Each vertical line represents the possible frame sizes for the specific cluster dimension.

28. Conclusion In our analysis, we determined that a cluster size 16 m wide and 126 m long would provide the best trade-off between frame size and local view length.

29. Thank you