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```					 Content Distribution in VANETs using Network Coding:
Evaluation of the Generation Selection
Algorithms

Alexander Afanasyev

Tutors: Seung-Hoon Lee, Uichin Lee

March 2, 2009
Content Distribution in
   Applications
Content Distribution Challenges

◦ High mobility (i.e., highly dynamic networks)
◦ Error-prone channel (due to obstacles, multi-path fading, etc.)
CarTorrent: BitTorrent-like Cooperative
Content Distribution in VANETs

A file is divided into pieces
Web
Server

Exchange pieces via Vehicle-to-Vehicle Communications

Not useful!

      Problem: Peer & Piece selection
 coupon collection problem

Using Network Coding: CodeTorrent

A file is divided into pieces
Web
Server

1 more?

Any linearly independent coded packet is helpful


Network Coding Problem
Single Generation

5/10/50 Generations

   Delay without O/H
◦ Small # of generations is a better choice
◦ Larger # of generations  more severe coupon collection problem
   Solution: divide a file into small generations
◦ Problem: too many generation causes a coupon
collection problem
◦ Conflicting goals: maximizing benefits of NC vs.
minimizing coding O/H

50MB
   Solution: divide a file into small generations
◦ Problem: too many generation causes a coupon
collection problem
◦ Conflicting goals: maximizing benefits of NC vs.
minimizing coding O/H

10MB x 5

1           4
What is optimal strategy for
Global:             Gen1   Gen2   Gen3
(neighbor status)

Request to ??

Local:              Gen1   Gen2   Gen3
(my status)

   Checking neighbor rank improve chances of linearly
independent block, but
◦ Low-rank cars can also have valuable blocks

   Back to the BitTorrent problem of piece/generation selection
◦ Local status based decision (i.e., the least/the most
◦ Neighbor status based decision?
◦ Random?
Generation Selection Strategies

Virtual “Global”
Completeness Vector

Global Min: Gen 4
Global Max: Gen 3
Random: Random
Sequential: Gen 1
Simulation Setup
   Communications
◦ 802.11b; 11Mbps + Two-ray ground propagation
   Mobility
◦ Random Waypoint model w/ speed range of [0,20] m/s
◦ Westwood area map: 2400m*2400m
   Nodes                                  Westwood area map
◦ 3 APs: file sources
◦ 200 nodes/40% interest level:
◦ 50 megabyte file
◦ 10 generations
Generation Progress

Global Min              Local Min

Neighbor Min              Random
Overall Progress

Neighbor-aware
strategy improves
at the beginning of
Local-aware and
random strategies
has smaller tail

* c onfidence interval is c alculated with probability 95% using 8 s imulations
Finishing times histogram

Conclusions:
 Network-aware strategy has long tail of finishing times

 Local and random strategies behave almost as good as global status-aware
Generation Progress

Global Max                Local Max

Neighbor Max               Sequential
Overall Progress

!!! Neighbor-aware
strategy outperforms
local and global one

* c onfidence interval is c alculated with probability 95% using 8 s imulations
Finishing times histogram

Conclusions:
 Network-aware strategy outperforms other strategies

 Average finishing time for global/local max strategies 1.5 times worse than
neighborhood status aware policy
Overall progress of the best strategies

Conclusions:
 Neighbor-aware generation choosing considerably improves chances for
helpful block (linearly independent) at the beginning
Finishing times histogram

Conclusions:
 Neighbor-aware strategies have on average 20% worse finishing times than
local max strategy
Interesting Facts
Rank
   Checking generation rank of    checking
the available generation
greatly improves performance
for neighbor status aware                   No rank
strategies                                   check

   Integer vector gossiping            Bool
performance
Int
vector
Conclusion
   Generation selection strategy in multi-generation
   Local status aware strategies (local-min, random) have the
best finishing performance
   Neighbor status aware strategies have the best start-up
performance
   It is important to check rank for neighbor status aware
strategies

   Future work
◦ Investigate performance of combined strategies
◦ Check performance using different node mobility models

```
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