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Efficient Routing Protocols for Vehicular Communication Networks Katsaros Konstantinos PhD Student Supervisor Dr M Dianati Cosupervisor Prof R Tafazolli T ransfer Presentation ID: 557037

konstantinos katsaros information routing katsaros konstantinos routing information layer position based network location vehicular cross delay pdr communications model

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Slide1

Reliable and

Efficient Routing Protocols for Vehicular Communication Networks

Katsaros Konstantinos PhD Student

Supervisor: Dr. M. Dianati Co-supervisor: Prof. R. Tafazolli

T

ransfer PresentationSlide2

Outline

Introduction

Scope, Objectives, ChallengesRouting in VANETsTaxonomy, Forwarding techniques, Recovery strategies

, Cross-layeringAchievements so farProposed CLWPR (System model, design characteristics)

Performance evaluation

Future plan

Konstantinos Katsaros

2Slide3

Scope

Intelligent Transportation Systems (ITS)Application of Information and Communication Technologies for future transport systems

In order to:Improve safety and traffic managementProvide infotainment services. Vehicular Communications is an important part of ITS.

Cellular (3G, LTE) and Dedicated Short Range Communications (IEEE 802.11p / WAVE)

Konstantinos Katsaros

3Slide4

VANETs: Challenges & Opportunities

Are a category of Mobile Ad-hoc Networks (MANETs) with specific characteristics:Less strict energy and computational constraints

Highly dynamicPredictable mobility patterns

High density of nodes

Konstantinos Katsaros

4Slide5

Objectives of this work

To design reliable and efficient routing protocols by exploiting:Position and mobility information in order to increase efficiencyPHY and MAC information in order to increase reliability

To design a Location Service that can provide position information for the routing protocols

Konstantinos Katsaros

5Slide6

BACKGROUND

Overview of routing and forwarding protocols for MANETs and VANETs

Konstantinos Katsaros

6Slide7

Routing Taxonomy

Advantages

Disadvantages

Routing Protocols

for VANETs

Topology Based

Proactive

Do not flood entire network

Fast

path selection

Overhead to maintain tables

Reactive

Do not maintain routing

tables

Initial

delay for route discovery

Flood a route request

Hybrid

Combination of proactive

and reactive in different

operation stages

Hierarchical

Exploit clusters with similar characteristics

Overhead to maintain clusters

Flooding

Low

complexity, high data reception

Flood entire

network

Position Based

With

out Navigation

Rely on local

information only

Need a

location service (LS), more prone to local maximum problem

With NavigationExploit mobility of nodes, less prone to local maximumNeed a LS, increased overhead due to enhanced beaconing

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7Slide8

Position-based Forwarding without Navigation

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8

S

3

5

1

2

D

4

6

7

8

Greedy Forwarding

Most Forward in Radius

Nearest Forwarding Progress

Compass

Random Positive ProgressSlide9

Local Maximum Problem & Recovery Techniques

Konstantinos Katsaros

9

S

D

Recovery

strategies

:

Drop packet

Enhanced Greedy (random retransmission once)

Carry-n-Forward

Coloring

Left hand rule

Perimeter routingSlide10

Position-based Forwarding with Navigation

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10

“Anchor” points at junctions with coordinator nodes

Enhanced beacon messages with velocity/heading

Position prediction policy (dead reckoning)

Estimation of link lifetime

Vehicle traffic information (max velocity, traffic density)

Recovery From Local

Maximum

Re-route

using different anchor points (with or without deletion)Slide11

Cross-Layer Optimization of Routing Protocols

Network layer with PHY and MAC: Use channel/link quality information for routing

decisionNetwork layer with Transport and Application

: Provide different levels of priorities on packets

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11Slide12

CROSS-LAYER POSITION BASED ROUTING (CLWPR)

Proposed routing protocol: system model and design characteristics

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12Slide13

System Model

Important Assumptions:Position and navigation information are available (e.g., using GPS)Nodes are equipped with the IEEE 802.11p based communication facility

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13Slide14

Main Features of CLWPR

Unicast, multi-hop, cross-layer, opportunistic routingNeighbor discovery based on periodic 1-hop “HELLO” messages“HELLO” message content: position, velocity, heading, road id, node utilization, MAC information, number of cached packets

total size 52

bytesUse of position prediction and “curvemetric” distanceUse of SNIR information from “HELLO” messagesEmploy carry-n-forward strategy for local-maximum

Combine metrics in a weighting function used for forwarding decisions

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14Slide15

Weighting Function for Next Hop Selection

The node with the

least

weight will be selected

Currently f

i

weights are fixed – open issue to optimize them or use adaptive values

 

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15Slide16

PERFORMANCE EVALUATION

Simulation setup, initial results, performance analysis and comparison

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16Slide17

Simulations Setup

Performance metricsPacket Deliver Ratio (PDR), End-to-End Delay, network overhead.

Use ns-3 for simulations5x5 grid network, 200 and 100 vehicles scenarios10 concurrent vehicle-to-vehicle connections UDP

packets (512 Bytes) with 2 sec intervalIEEE 802.11p, 3Mbps, RTS/CTS enabledTwo-Ray-Ground model

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17Slide18

Comparison with GPSR

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Increased PDR

Reduced end-to-end delay

Increased overhead due to larger HELLO messagesSlide19

Impact of HELLO interval and prediction

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19

Prediction improves PDR

More frequent HELLO increases PDR

Network overhead could be reduced by increasing HELLO interval for the same PDR threshold.Slide20

Influence of navigation

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20

Navigation improves PDR

Increasing weight of navigation information has positive effect in higher vehicle speedsSlide21

Influence of SNIR

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SNIR information reduces end-to-end delay

Due to propagation model used, not big improvements

Expect more when shadowing is includedSlide22

Influence of

Carry-n-Forward

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22

Increased PDR with time of caching

Increased end-to-end delay with time of cachingSlide23

FUTURE WORK

CWPR optimization, proposed location service, impact assessment and security issues

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23Slide24

Future Work (1)

CLWPR OptimizationUse realistic propagation modelOptimize all weighting parametersLocation Service (a)

RSUs as distributed databaseCo-operation between nodesReduce number and latency of queries

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24Slide25

Future Work (2)

Location Service (b) – heterogeneous networkUse of UMTS technologies for control and signaling to provide location service

Impact AssessmentAsses impact of ITS applications on network reliabilitySecurity IssuesAnalyze potential threats on reliability of vehicular networks, specially for Location services

Konstantinos Katsaros

25Slide26

Work Plan

Konstantinos Katsaros

26Slide27

Publications

Current:K. Katsaros, et al. “CLWPR - A novel cross-layer optimized position based routing protocol for VANETs

", in IEEE Vehicular Networking Conference, pp. 200-207, 2011K. Katsaros, et al. “

Application of Vehicular Communications for Improving the Efficiency of Traffic in Urban Areas", accepted in Wireless Communications and Mobile Computing, 2011.K. Katsaros, et al. ”Performance Analysis of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform

", in Proceedings of

IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

,

pp. 918 - 923, 2011Planned:

Survey Paper on routing protocols for VANETsConf. paper @ NS-3 Workshop in SIMUTools 2012, regarding the architecture and implementation (Nov. ‘11)Journal article @ JSAC on Vehicular Communications extending CLWPR paper (Feb. ‘12)

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27Slide28

Questions

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28

Email:

K.Katsaros@surrey.ac.uk

www:

info.ee.surrey.ac.uk/Personal/K.Katsaros/Slide29

Current work

Propagation Loss Model for urban environment, initial results

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29Slide30

Winner B1 model for urban V2V

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30

[1]

IST-WINNER D1.1.2 P. Kyösti, et al., "WINNER II Channel Models", September 2007

.

Available

at: https://www.ist-winner.org/WINNER2-Deliverables/D1.1.2v1.1.pdf

Use propagation models from [1] taking into account buildings and shadowing with LOS and NLOS componentsSlide31

TwoRayGround Vs. Winner in network graph / connections

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31Slide32

TwoRayGround Vs. Winner in PDR

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32Slide33

Cross-Layer Designs (1)

Network layer with PHY and MAC: Use channel/link quality information for routing decision

Link Residual TimeSNR info for MuiltiPoint Relay selectionMAC layer position information for prediction

MAC retransmissionsDeReHQ [1]: Delay, Reliability and Hop countPROMPT [2]: Delay aware routing and robust MACMAC collaboration for heterogeneous networks

[1]

Z.

Niu

, W. Yao, Q. Ni, and Y. Song, “Study on QoS Support in 802.11e-based Multi-hop Vehicular Wireless Ad Hoc Networks,” in IEEE

International Conference on Networking, Sensing and Control, pp. 705 –710, 2007.

[2] B. Jarupan and E. Ekici, “PROMPT: A cross-layer position-based communication protocol for delay-aware vehicular access networks,” Ad Hoc Networks, vol

. 8, pp. 489–505, July 2010.

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33Slide34

Cross-Layer Designs (2)

Network layer with transport and Application: Provide different levels of priorities on packetsVTP (Vehicular Transport Protocol)

Optimization of TCP and GPSR with vehicle mobility (adaptive beacon interval)Network layer with multiple layersJoint MAC, Network and Transport [1]

Konstantinos Katsaros

34

[1]

L. Zhou, B.

Zheng

, B. Geller, a. Wei, S.

Xu

, and Y. Li, “Cross-layer rate control, medium access control and routing design in cooperative

VANET”, Computer

Communications, vol. 31, pp. 2870–2882, July 2008Slide35

Location Services

Flooding based: All nodes host itProactive: DREAM

Reactive: LAR, MALM (mobility assisted)Rendezvous based: Some nodes host itQuorum: divide node set into two subsets (update and query)

Hashing (according to node ID or location): define server nodes using a hash functionRLSMP (Region-based Location Service Management Protocol) and MG-LSM (Mobile Group Location Service Management) designed for VANETs utilizing mobility information

Konstantinos Katsaros

35