epidemic density adaptive data dissemination exploiting opposite lane in vanets irem nizamoglu...
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EPIDEMIC DENSITY ADAPTIVE DATA DISSEMINATION EXPLOITING OPPOSITE LANE IN VANETS
Irem Nizamoglu
Computer Science & Engineering
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
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.
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
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).
Epidemic Protocols
Protocol Disconnected 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 2009.[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 2010. [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.
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
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 psame, popposite and psameToOpp.
• Including range adaptivity feature that utilizes channel busy ratio and
reception rate.
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.
EpiDOL
df : direction flagof : original flag
EpiDOL
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
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.
Parameter Optimization
• For the same directional probability best αsame is chosen
as 15 where;
• max throughput>90% such that eed<0.06 s & overhead<0.07.
Parameter Optimization
• For the opposite directional probability best αopposite is
chosen as 21 where;
• max throughput>97% such that eed<0.08 s & overhead<0.1.
Parameter Optimization
• For calculation of PsameToOpposite, we need to specify
backwardValue.
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.
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
Performance Results & Adaptivity Features
• Background Traffic:
• 1 KB sized FTP packets with 1, 0.1, 0.01 second frequency.
Performance Results & Adaptivity Features
• Background Traffic (con’t):
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 < 0.7
0.3 sec/packet 0.5 sec/packet 1 sec/packet
Performance Results & Adaptivity Features
• Range Adaptivity (con’t):
• Limits are specified from previous graphs.
Performance Results & Adaptivity Features
• Range Adaptivity (con’t):
• Reception rate: successfully received packets in 1 second period of time.
• 1< Reception Rate < 1.5
Performance Results & Adaptivity Features
• Range Adaptivity (con’t):
• Between 1 and 1.5, we have high throughput.
Performance Results & Adaptivity Features
• Range Adaptivity (con’t):
Performance Results & Adaptivity Features
• Range Adaptivity (con’t):
Performance Results & Adaptivity Features
• Comparative Results:
• Compared EpiDOL and EpiDOL+Adaptivity with protocols in
literature; DV-CAST, Edge-Aware and DAZL.
Performance Results & Adaptivity Features
• Comparative Results (con’t):
Outline
• Motivation
• Epidemic Protocols
• EpiDOL
• Parameter Optimization
• Performance Results & Adaptivity Features
• Conclusion
Conclusion
• 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.
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.
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 2013. [pdf | link]
• In preparation to submission (Journal): Epidemic Density
Adaptive Data Dissemination Exploiting Opposite Lane in Vanets
