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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S3
51
2D
4
6 7
8Greedy ForwardingMost Forward in
RadiusNearest Forwarding
ProgressCompassRandom 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 packetstotal 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: [email protected]: 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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PDR Vs. VelocityTRG-BENCH Winner-BENCH TRG-PREDICT Winner-PREDICT
Node Average Velocity (m/s)
Pack
et D
eliv
ery
Rat
io (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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