Reliable and Efficient Routing
Protocols for Vehicular
Communication Networks
Transfer Presentation
Katsaros Konstantinos
PhD Student
Supervisor: Dr. M. Dianati
Co-supervisor: Prof. R. Tafazolli
Outline
 Introduction
Scope, Objectives, Challenges
 Routing in VANETs
Taxonomy, Forwarding techniques, Recovery
strategies, Cross-layering
 Achievements so far
Proposed 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 Taxonomy
Topology
Based
Advantages
Disadvantages
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
Combination of proactive and reactive in different
operation stages
Hybrid
Routing
Protocols for
VANETs
Hierarchical
Exploit clusters with similar
characteristics
Overhead to maintain
clusters
Flooding
Low complexity, high data
reception
Flood entire network
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
Position
Based
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Position-based Forwarding
without Navigation
6
1
5
7
2
S
D
3
4
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Random
Positive
Nearest
Most Forward
Forwarding
in
Greedy
Compass
Forwarding
Progress
Radius
8
Local Maximum Problem &
Recovery Techniques
Recovery strategies:
S
D
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 Drop packet
 Enhanced Greedy (random
retransmission once)
 Carry-n-Forward
 Coloring
 Left hand rule
 Perimeter routing
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Position-based Forwarding
with Navigation
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 lifetime
5. Vehicle traffic information (max
velocity, traffic density)
Recovery From Local Maximum
Re-route using different anchor
points (with or without deletion)
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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
𝑾𝒆𝒊𝒈𝒉𝒕
= 𝑓1 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝑓2 𝑁𝑜𝑟𝑚𝐴𝑛𝑔𝑙𝑒 + 𝑓3 𝑁𝑜𝑟𝑚𝑅𝑜𝑎𝑑
+ 𝑓4 𝑈𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝑓5 𝑀𝐴𝐶𝑖𝑛𝑓𝑜 + 𝑓6 𝐶𝑛𝐹𝑖𝑛𝑓𝑜
+ 𝑓7 𝑆𝑁𝐼𝑅𝑖𝑛𝑓𝑜
 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
 Increased PDR
 Reduced end-to-end delay
— Increased overhead due to larger HELLO messages
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Impact of HELLO interval and
prediction
 Prediction improves PDR
 More frequent HELLO
increases PDR
 Network overhead could
be reduced by increasing
HELLO interval for the
same PDR threshold.
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Influence of navigation
 Navigation improves PDR
 Increasing weight of
navigation information has
positive effect in higher
vehicle speeds
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Influence of SNIR
 SNIR information reduces
end-to-end delay
— Due to propagation model
used, not big improvements
 Expect more when
shadowing is included
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Influence of Carry-n-Forward
 Increased PDR with time of caching
— Increased end-to-end delay with time of caching
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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
Email: K.Katsaros@surrey.ac.uk
www: info.ee.surrey.ac.uk/Personal/K.Katsaros/
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Current work
Propagation Loss Model for urban
environment, initial results
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Winner B1 model for urban V2V
Use propagation
models from [1] taking
into account buildings
and shadowing with
LOS and NLOS
components
[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
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TwoRayGround Vs. Winner in
network graph / connections
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TwoRayGround Vs. Winner in PDR
PDR Vs. Velocity
TRG-BENCH
Winner-BENCH
TRG-PREDICT
Winner-PREDICT
100
Packet Delivery Ratio (5)
90
80
70
60
50
40
30
20
10
0
0
5
10
15
20
25
Node Average Velocity (m/s)
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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 delayaware 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]
[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
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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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