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EPIDEMIC DENSITY ADAPTIVE DATA DISSEMINATION EXPLOITING OPPOSITE LANE IN VANETS Irem Nizamoglu Computer Science & Engineering.

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Presentation on theme: "EPIDEMIC DENSITY ADAPTIVE DATA DISSEMINATION EXPLOITING OPPOSITE LANE IN VANETS Irem Nizamoglu Computer Science & Engineering."— Presentation transcript:

1 EPIDEMIC DENSITY ADAPTIVE DATA DISSEMINATION EXPLOITING OPPOSITE LANE IN VANETS Irem Nizamoglu Computer Science & Engineering

2 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

3 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

4 Motivation Increase the safety of passengers, Disseminating emergency packets or road condition information efficiently, Decreasing the fuel consumption and air pollution. Longest recorded traffic jam in the world (260 km)-Shangai/China.

5 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

6 Epidemic Protocols Probabilistic information dissemination which does not require any knowledge of the network topologies. Suitable for VANETs; Provides intelligence while reducing contentions and collisions. Not require infrastructure support. Fits well with the non-deterministic nature of VANETs (highly dynamic and unpredictable topology changes).

7 Epidemic Protocols ProtocolDisconnected Network Problem Reality of the traces Minimize Delay Edge-Aware [1] - ✔ - DV-CAST [2] ✔ - ✔ DAZL [3] ✔ -- EpiDOL ✔✔✔ [1] M. Nekovee, “Epidemic algorithms for reliable and efficient information dissemination in vehicular ad hoc networks,” Intelligent Transport Systems, IET, vol. 3, no. 2, pp. 104 –110, june [2] O. Tonguz, N. Wisitpongphan, and F. Bai, “Dv-cast: A distributed vehicular broadcast protocol for vehicular ad hoc networks,” Wireless Communications, IEEE, vol. 17, no. 2, pp. 47 –57, april [3] R. Meireles, P. Steenkiste, and J. Barros, “Dazl: Density-aware zone- based packet forwarding in vehicular networks,” in Vehicular Networking Conference (VNC), 2012 IEEE, pp. 234–241.

8 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

9 EpiDOL Goal: Maximize throughput while disseminating data in a certain area and keeping the overhead and delay below a certain level of threshold. Key properties: Defining flags for packet dissemination direction and vehicles’ movement direction, deciding intelligent transmission, Using opposite lane in an epidemic manner efficiently, Decreasing collision rate by using density adaptive probability functions p same, p opposite and p sameToOpp. Including range adaptivity feature that utilizes channel busy ratio and reception rate.

10 EpiDOL Performance Metrics: End-to-End Delay: Time taken for packet transmission from source to nodes in the range of dissemination distance. Throughput: Rate of successfully received packets by all nodes within dissemination distance. Opposite Lane: How many times opposite lane nodes resend the packets that are taken from the original side. Overhead: The number of duplicate packets received during the simulation.

11 EpiDOL df : direction flag of : original flag

12 EpiDOL

13 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

14 Parameter Optimization For density adaptive probability functions; However, as a result of the analysis best α value is different in the same and the opposite sides.

15 Parameter Optimization For the same directional probability best α same is chosen as 15 where; max throughput>90% such that eed<0.06 s & overhead<0.07.

16 Parameter Optimization For the opposite directional probability best α opposite is chosen as 21 where; max throughput>97% such that eed<0.08 s & overhead<0.1.

17 Parameter Optimization For calculation of P sameToOpposite, we need to specify backwardValue.

18 Parameter Optimization To achieve 90% throughput in lower densities. backwardValue > 9. Considering overhead values for several different vehicle densities, the optimum backwardValue is determined as 11.

19 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

20 Performance Results & Adaptivity Features Background Traffic: 1 KB sized FTP packets with 1, 0.1, 0.01 second frequency.

21 Performance Results & Adaptivity Features Background Traffic (con’t):

22 Performance Results & Adaptivity Features Range Adaptivity: Included a transmission range adaptivity feature to achieve the maximum possible throughput at different densities and data rates. Channel Busy Ratio (CBR): ratio of the busy time of the channel over all time. 0.4 < CBR < sec/packet0.5 sec/packet 1 sec/packet

23 Performance Results & Adaptivity Features Range Adaptivity (con’t): Limits are specified from previous graphs.

24 Performance Results & Adaptivity Features Range Adaptivity (con’t): Reception rate: successfully received packets in 1 second period of time. 1< Reception Rate < 1.5

25 Performance Results & Adaptivity Features Range Adaptivity (con’t): Between 1 and 1.5, we have high throughput.

26 Performance Results & Adaptivity Features Range Adaptivity (con’t):

27 Performance Results & Adaptivity Features Range Adaptivity (con’t):

28 Performance Results & Adaptivity Features Comparative Results: Compared EpiDOL and EpiDOL+Adaptivity with protocols in literature; DV-CAST, Edge-Aware and DAZL.

29 Performance Results & Adaptivity Features Comparative Results (con’t):

30 Outline Motivation Epidemic Protocols EpiDOL Parameter Optimization Performance Results & Adaptivity Features Conclusion

31 At low densities, achieved more than the %90 throughput. EpiDOL handled the disconnected network problem. At high densities, throughput achieved by EpiDOL is better than the others. Indicates that broadcast storm problem did not effect our protocol due to its probabilistic density adaptive functions.

32 Conclusion Unless the background traffic is heavy, EpiDOL is not significantly affected. The last version of the adaptivity function improves throughput %25 in high densities while comparing with raw EpiDOL. Future work; consider more complicated highway structures.

33 Publication I. Nizamoglu, S. C. Ergen and O. Ozkasap, "EpiDOL: Epidemic Density Adaptive Data Dissemination Exploiting Opposite Lane in VANETs", EUNICE Workshop on Advances in Communication Networking, August [pdf | link]pdf link In preparation to submission (Journal): Epidemic Density Adaptive Data Dissemination Exploiting Opposite Lane in Vanets

34 THANK YOU Irem Nizamoglu: Wireless Networks Laboratory:


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