A novel Routing-Algorithm for On-Demand information ... - About

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A novel Routing-Algorithm for On-Demand information ... - About

Donnerstag, 22. Juli 2010

A novel Routing-Algorithm for On-Demand

information-seeking Applications on the basis of

multi-hop C2X networks

by Björn Kunz

1


Structure

1.Motivation

2.Questions

3.Related Work

4.LTDAR - Local traffic-density aware

Routing

5.Evaluation Environment

6.Extensions

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Motivation

What are „push“ and „pull“

applications ?

Why do we need a new routing

algorithm ?

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The C2X Pull paradigm

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The C2X Pull paradigm

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The C2X Pull paradigm

• Current applications based on pushing information out

• „Pull“ applications actively ask / search for information

• Example: A navigation system asking at potential parking lots for free spaces

• Road-side units (RSU) at the parking lot holds the information and respond to

requests

• Node holding the wanted information not necessarily in transmission range

Routing to the position / area is needed

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Vehicular Adhoc Networks

• Special case of mobile adhoc networks

• Cars communicating with each other according to IEEE 802.11p

• In contrast to standard wireless networks very high node mobility leading to frequent

connection losses due to:

• Nodes moving at speeds of more than 130 km/h (at least on german highways)

• Buildings in urban environments

• Sparse / differences in node distribution

• => building a static route of forwarding nodes problematic

• => we want a more flexible routing algorithm optimized for the special needs of VANETs

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Research Questions

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Research Questions

1.Which suitable routing algorithms exist for On-Demand information seeking

applications on the basis of multi-hop C2X networks ?

2.Can the desired functionality be obtained by extending an existing routing

algorithm ?

3.How can the algorithm be made flexible enough for the usage with novel C2X

paradigms ?

4.Which is the most suitable routing algorithm for On-Demand „Pull“

information seeking applications using multi-hop C2X Networks and how can

the performance of the algorithm be evaluated ?

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Related Work

What algorithms are available ?

Can we improve them ?

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Current Algorithms and Research

• Many proposed algorithms

Only some look promising for C2X pull applications

• Differences in the assumptions on the knowledge available to the nodes

• GPS available ?

• a map available ?

• traffic-density data available ?

Algorithms that gave the most inspiration for our algorithm

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• Anchor-based Street and Traffic Aware Routing (A-STAR)

• Greedy Perimeter Stateless Routing with Movement Awareness

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Current Algorithms and Research

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Current Algorithms and Research

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Anchored-based Street and Traffic Aware Routing

(A-STAR)

• Relies on the following assumptions for every node:

• A map is available

• GPS is available

• Information on bus routes in a city available

• Basic idea is to identify main streets in a city by the number of bus routes

moving along streets

• Instead of a static route consisting of nodes a so-called anchored route

consisting of traffic crossings is used

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Greedy Perimeter Stateless Routing with

Movement Awareness GPSR-MA

• Relies on the following assumptions for every node:

• GPS is available

• Basic idea is based on the knowledge about the position of own neighbouring nodes

• Next-hop selected based on distance to the destination node

• Position of neighbouring nodes is estimated based on their last known position and

velocity vector

• Afterwards any node closer to the destination is a likely candidate

• Recovery Strategy based on sending the message around the perimeter

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LTDAR

Local traffic-density aware

routing

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LTDAR - Overview

• Tries to combine A-STAR and GPSR-MA

• Getting the best of two different types of routing algorithms

• Enhance it with additional beaconing data: The route the car will take

• => Dynamic weights for the Anchor-based approach

• => More accurate position estimation

• Hope to get the route stability of anchor-based routing approaches combined

with the speed of greedy-location based routing

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LTDAR - Algorithm outline

• Beaconing data is stored in two different lists:

• Neighbour list

• Street lookup list

• Five consecutive steps when routing a packet:

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1. Update Lists according to beaconed data (delete old entries)

2. Check if destination node is in Neighbour list

3. Compute anchored path to the destination node

4. Estimate new position of the nodes in the Neighbour list

5. Choose the neighbour which is closest to one of the anchors as next-hop

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LTDAR - Step 1

• Update already existing neighbouring nodes with new data and TTL or add new entries

• Update already existing data in Street lookup list with new data and TTL or add new

entries

• Delete old entries from Neighbour list and Street lookup list

• Update cost of the edges in the road network - minimum: half of the actual length of

the street

• Mainly two ideas for how much to „shorten“ the street:

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• Length(s) / (NumberCars(s) x 3.5m)

• Estimated average distance between the cars

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LTDAR - Step 3

• Compute anchored path between intersections closest to source and

destination

• Use A* for computing this path

• Cost: Length given in the road network

• Heuristic: Manhattan Distance

• Resulting path varies according to local knowledge on traffic density

• Anchored path can change!

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LTDAR - Step 4

• Estimate the positions using the given navigation information

• Distance(n) = (CurrentTime - TimeLastBeacon(n)) * Velocity(n)

• If the end of a road is reached use the next one to estimate the position

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LTDAR - Step 5

for every neighbour compute the anchor that is closest to him while still being

closer to the current forwarding node

• among those choose the best node

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Evaluation

Environment

How do we evaluate our

algorithm ?

Against which other algorithms

do we compare it ?

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Field Test vs. Simulation

pro

contra

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gives very accurate

results

algorithm employed on

real hardware

802.11p compliant

hardware very expensive

Need for more than just

a few equipped vehicles

pro

contra

easy and repeatable

can simulate hundreds of

network nodes

Implementation of

physical phenomena of

wireless transfer

Orchestration not easy to

achieve

27


Field Test vs. Simulation

pro

contra

Donnerstag, 22. Juli 2010

gives very accurate

results

algorithm employed on

real hardware

802.11p compliant

hardware very expensive

Need for more than just

a few equipped vehicles

pro

contra

easy and repeatable

can simulate hundreds of

network nodes

Implementation of

physical phenomena of

wireless transfer

Orchestration not easy to

achieve

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Simulation Environment

Network

Simulator

JiST/SWANS

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[5]

VSimRTI

Traffic

Simulator

SUMO

[4]

Application

Interface

by VSimRTI

[3]

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Proposed Scenario

• Use the road graph of Saarbrücken or part of it as a basis to conduct our experiments

• Implement the already mentioned „Parking Lot“ Application in VSimRTI

• Want to incorporate buildings into the pathloss model - possibly use the 3D model of

Saarbrücken done by group of Prof. Slusallek

• Test the algorithm with regard to the following performance measures:

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• Packet Delivery Ratio

• Average Delay

• Average number of hops

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Extensions

How could we possibly improve

the algorithm further ?

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Future Work

• Extend the algorithm with a recovery strategy

• Incorporate information about buildings in the movement awareness

component and test if this improves the overall effectiveness

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Questions ?

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References

• [1] Boon C. Seet, Genping Liu, Bu S. Lee, Chuan H. Foh and Keok K. Lee.

A-star: A mobile ad hoc routing strategy for metropolis vehicular communications. In Proc.

NETWORKING 2004, pages 989-999, 2004

• [2] Fabrizio Granelli, Giulia Boato, Dzmitry Kliazovich and Gianni Vernazza

Enhanced GPSR Routing in multi-hop vehicular communications through movement awareness,

Communications Letters, IEEE, 11:781-783, 2007

• [3] VSimRTI Homepage: http://www.dcaiti.tu-berlin.de/research/simulation

• [4] Sumo Homepage: sumo.sourceforge.net/

• [5] JiST/SWANS Homepage: Jist.ece.cornell.edu/

• [6] OpenStreetMap: http://www.openstreetmap.org/

• [7] eWorld: http://eworld.sourceforge.net/

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