Reliable and Efficient Routing Protocols for Vehicular

Communication Networks

Katsaros Konstantinos PhD Student

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

Transfer Presentation

Outline Introduction

Scope, Objectives, ChallengesRouting in VANETs

Taxonomy, Forwarding techniques, Recovery strategies, Cross-layering

Achievements so farProposed CLWPR (System model, design

characteristics)Performance evaluation

Future plan

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Scope• Intelligent Transportation Systems (ITS)

– Application of Information and Communication Technologies for future transport systems

– In order to:• Improve safety and traffic management• Provide infotainment services.

• Vehicular Communications is an important part of ITS.– Cellular (3G, LTE) and Dedicated Short Range

Communications (IEEE 802.11p / WAVE)

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VANETs: Challenges & Opportunities

• Are a category of Mobile Ad-hoc Networks (MANETs) with specific characteristics:

– Less strict energy and computational constraints

– Highly dynamic

– Predictable mobility patterns

– High density of nodes

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Objectives of this work• To design reliable and efficient routing

protocols by exploiting:– Position and mobility information in order to

increase efficiency– PHY and MAC information in order to

increase reliability• To design a Location Service

– that can provide position information for the routing protocols

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BACKGROUND

Overview of routing and forwarding protocols for MANETs and VANETs

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Routing TaxonomyAdvantages Disadvantages

Routing Protocols for

VANETs

Topology Based

Proactive Do not flood entire networkFast 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

Without Navigation

Rely on local information only

Need a location service (LS), more prone to local

maximum problem

With Navigation

Exploit mobility of nodes, less prone to local

maximum

Need a LS, increased overhead due to

enhanced beaconing

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Position-based Forwarding without Navigation

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S

3

51

2D

4

6 7

8Greedy Forwarding

Most Forward in Radius

Nearest Forwarding ProgressCompass

Random Positive Progress

Local Maximum Problem & Recovery Techniques

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S D

Recovery strategies: Drop packet Enhanced Greedy (random

retransmission once) Carry-n-Forward Coloring Left hand rule Perimeter routing

Position-based Forwarding with Navigation

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1. “Anchor” points at junctions with coordinator nodes

2. Enhanced beacon messages with velocity/heading

3. Position prediction policy (dead reckoning)

4. Estimation of link lifetime5. Vehicle traffic information (max

velocity, traffic density)

Recovery From Local MaximumRe-route using different anchor points (with or without deletion)

Cross-Layer Optimization of Routing Protocols

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

• Network layer with Transport and Application: Provide different levels of priorities on packets

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CROSS-LAYER POSITION BASED ROUTING (CLWPR)

Proposed routing protocol: system model and design characteristics

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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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Main Features of CLWPR• Unicast, multi-hop, cross-layer, opportunistic routing• Neighbor 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 52bytes

• Use of position prediction and “curvemetric” distance• Use of SNIR information from “HELLO” messages• Employ carry-n-forward strategy for local-maximum• Combine metrics in a weighting function used for

forwarding decisions

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Weighting Function for Next Hop Selection

The node with the least weight will be selected Currently fi weights are fixed – open issue to

optimize them or use adaptive values

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PERFORMANCE EVALUATION

Simulation setup, initial results, performance analysis and comparison

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Simulations Setup• Performance metrics

– Packet Deliver Ratio (PDR), – End-to-End Delay, – network overhead.

• Use ns-3 for simulations• 5x5 grid network, • 200 and 100 vehicles scenarios• 10 concurrent vehicle-to-vehicle connections • UDP packets (512 Bytes) with 2 sec interval• IEEE 802.11p, 3Mbps, RTS/CTS enabled• Two-Ray-Ground model

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Comparison with GPSR

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Increased PDR Reduced end-to-end delay—Increased overhead due to larger HELLO messages

Impact of HELLO interval and prediction

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Prediction improves PDR More frequent HELLO

increases PDR Network overhead could

be reduced by increasing HELLO interval for the same PDR threshold.

Influence of navigation

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Navigation improves PDR Increasing weight of

navigation information has positive effect in higher vehicle speeds

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 included

Influence of Carry-n-Forward

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Increased PDR with time of caching—Increased end-to-end delay with time of caching

FUTURE WORK

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

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Future Work (1)• CLWPR Optimization

– Use realistic propagation model– Optimize all weighting parameters

• Location Service (a)– RSUs as distributed database– Co-operation between nodes

• Reduce number and latency of queries

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Future Work (2)• Location Service (b) – heterogeneous network

– Use of UMTS technologies for control and signaling to provide location service

• Impact Assessment– Asses impact of ITS applications on network

reliability• Security Issues

– Analyze potential threats on reliability of vehicular networks, specially for Location services

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Work Plan

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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, 2011

– K. 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, 2011

• Planned:– Survey Paper on routing protocols for VANETs– Conf. 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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QUESTIONS

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Email: K.Katsaros@surrey.ac.ukwww: info.ee.surrey.ac.uk/Personal/K.Katsaros/

Current work

Propagation Loss Model for urban environment, initial results

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Winner B1 model for urban V2V

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[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 components

TwoRayGround Vs. Winner in network graph / connections

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TwoRayGround Vs. Winner in PDR

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0 5 10 15 20 25 30 350

10

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70

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100

PDR Vs. Velocity

TRG-BENCH Winner-BENCH TRG-PREDICT Winner-PREDICT

Node Average Velocity (m/s)

Pa

ck

et

De

live

ry R

ati

o (

5)

Cross-Layer Designs (1)• Network layer with PHY and MAC: Use

channel/link quality information for routing decision– Link Residual Time– SNR info for MuiltiPoint Relay selection– MAC layer position information for prediction– MAC retransmissions– DeReHQ [1]: Delay, Reliability and Hop count– PROMPT [2]: Delay aware routing and robust

MAC– MAC 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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Cross-Layer Designs (2)• Network layer with transport and

Application: Provide different levels of priorities on packets– VTP (Vehicular Transport Protocol)– Optimization of TCP and GPSR with vehicle

mobility (adaptive beacon interval)• Network layer with multiple layers

– Joint MAC, Network and Transport [1]

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[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 2008

Location Services• Flooding based: All nodes host it

– Proactive: DREAM– Reactive: LAR, MALM (mobility assisted)

• Rendezvous based: Some nodes host it– Quorum: divide node set into two subsets (update and

query)– Hashing (according to node ID or location): define server

nodes using a hash function– RLSMP (Region-based Location Service Management

Protocol) and MG-LSM (Mobile Group Location Service Management) designed for VANETs utilizing mobility information

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