Topic: Vehicular Networks
Team 6
R99922041 陳彥璋
R99922083 梁逸安
R99945051 洪晧瑜
CARS: Context-Aware Rate
Selection for Vehicular Networks
P. Shankar , T. Nadeem , J. Rosca , L. Iftode , Proc. IEEE ICNP ,
Oct. 2008 , pp. 1 – 12
Speaker: 陳彥璋
Outline
Introduction
 Key challenges
 Context Aware Rate Selection
 Performance
 Conclusion

Rate Selection

IEEE 802.11 allows multiple transmission
rate at the physical layer (PHY).
Low bitrate
High bitrate
Low link quality
Good
High error rate
High link quality
Underutilization
Good
Link Quality
High link quality.
Low link quality due to
long distance.
Outline
Introduction
 Key challenges
 Context Aware Rate Selection
 Performance
 Conclusion

Key challenges
1.
Rapid variations of the link quality.
 Mobility at vehicular speed.
Key challenges
2.
Few or no packets transmitted in
estimation window during infrequent
and bursty transmission.
 No past history to estimate link quality.
Key challenges
3.
Distinguish losses due to environment
from hidden-station induced collision.
 Loss due to hidden station:
rate↓ transmission time↑ contention↑
Outline
Introduction
 Key challenges
 Context Aware Rate Selection
 Performance
 Conclusion

Architecture
Past transmission
history.
Positions and speeds
of itself and its
neighbors.
Algorithm
for all rate do
Context information
Transmission rate
Packet length
Past frame transmission statistics
input
input
Ec
α
Eh
Packet error rate
Throughput
end for
(1-α)
α is assigned
based on the
vehicle speed.
Find the rate that maximize the throughput.
Algorithm
Ec
Empirical model.
Measurements from extensive outdoor vehicular experiments.
Use multivariate linear regression as the learning approach.
Eh
Exponentially weighted moving average (EWMA)
of past frame transmission statistics.
http://en.wikipedia.org/wiki/File:Exponential_moving_average_weights_N%3D15.png
Outline
Introduction
 Key challenges
 Context Aware Rate Selection
 Performance
 Conclusion

Experimental Result
Simulation Result
Outline
Introduction
 Key challenges
 Context Aware Rate Selection
 Performance
 Conclusion

Conclusion
Context Aware Rate Selection
 Use context information to perform fast
rate adaption in vehicular network.

Connectivity-Aware Routing
(CAR)
in Vehicular Ad Hoc Networks
Valery Naumov & Thomas R. Gross
ETH Zurich, Switzerland
IEEE INFOCOM 2007
speaker:梁逸安
Outline
Introduction
 Related Works (GPSR)
 Connection-Aware Routing (CAR)
 Simulation
 Conclusion

Introduction

Vehicular ad hoc networks (VANETs) using
802.11-based WLAN technology have
recently received considerable attention in
many projects

Several geographic routing (GR) protocols
use an idealized mechanism such that for
every originated data packet the true
position of the destination is known
Introduction

Another problem is that, all of the GR
protocols do not take into account if a path
between source and destination is populated.

This paper presents a novel position-based
routing scheme called Connectivity-Aware
Routing (CAR) to address these kind of
problems
Outline
Introduction
 Related Works (GPSR)
 Connection-Aware Routing (CAR)
 Simulation
 Conclusion

Greedy Perimeter Stateless Routing
Greedy Perimeter Stateless Routing

Perimeter Mode
Greedy Perimeter Stateless Routing
Outline
Introduction
 Related Works (GPSR)
 Connection-Aware Routing (CAR)
 Simulation
 Conclusion

Connection-Aware Routing (CAR)

The CAR protocol consists of four main
parts:
◦ (1) destination location and path discovery
◦ (2) data packet forwarding along the found
path
◦ (3) path maintenance with the help of guards
◦ (4) error recovery
Destination location discovery
A source broadcast a path discovery (PD)
 Each node forwarding the PD updates
some entries of PD packets
 If two velocity vectors’angle > 18°, anchor
is set.

Greedy forwarding over the
anchored path

A neighbor that is closer to the next anchor
point is chosen (greedy) , instead of destination.
Path maintenance

If an end node (source or destination) changes
position or direction, standing guard will be
activated to maintain the path.
Path maintenance

If end node changes direction against the
direction of communication, traveling
guard will be activated.
◦ A traveling guard runs as end node’s old
direction and speed, and reroute the packets
to the destination.
Path maintenance
Routing error recovery

The reason for routing error
◦ A temporary gap between vehicles

(1) Timeout algorithm
 When a node detects a gap – buffer the packets

(2) Walk-around error recovery
 When Timeout algorithm fail , do location discovery
 Whether the location discovery is successful, the
result will be reported to the source node.
Outline
Introduction
 Related Works (GPSR)
 Connection-Aware Routing (CAR)
 Simulation
 Conclusion

Simulation

Scenarios
◦ City
◦ Highway

Traffic density
◦ Low – less than 15 vehicles/km
◦ Medium – 30-40 vehicles/km
◦ High – more then 50 vehicles/km
SimulationPacket Delivery Ratio
SimulationAverage data packet delay
SimulationRouting overhead
Outline
Introduction
 Related Works (GPSR)
 Connection-Aware Routing (CAR)
 Simulation
 Conclusion

Conclusion

Address the populated problem about paths.

Path discovery & Anchor points
Path maintenance with guards
Error recovery



Higher performance and lower routing
overhead than GPSR
Delay-bounded Routing in
Vehicular Ad-hoc Networks
Antonios Skordylis, Niki Trigoni
Oxford University Computing Laboratory
ACM International Symposium on Mobile Ad hoc
Networking and Computing, 2008
Speaker : R99945051 洪晧瑜
Outline

Introduction
◦ VANETs
◦ Delay-bounded Routing
Objective and Model
 Algorithm

◦ D-Greedy
◦ D-MinCost
Evaluation and result
 Conclusion

Introduction

VANETs
◦ vehicles equipped with wireless transceivers that
will enable them to communicate with each other
form a special class of wireless network
• Delay-bounded Routing
◦ timely and bandwidth efficient data dissemination
from vehicles to an access point, given statistical
information about road traffic
◦ tradeoff :
timely data delivery v.s. low bandwidth utilization
Outline

Introduction
◦ VANETs
◦ Delay-bounded Routing
Objective and Model
 Algorithm

◦ D-Greedy
◦ D-MinCost
Evaluation and result
 Conclusion

Objective

Objective
◦ carry-and-forward algorithms leverage
knowledge of traffic statistics in an urban
setting
◦ enable timely delivery of messages from
vehicles to APs
◦ minimizing wireless transmissions/optimizing
bandwidth utilization
Model


Urban scenario
Vehicles (mobile nodes ):
◦ geographical position(GPS receiver )
◦ digital map(G(V,E)) : historical traffic statistics
 u: average speed,
 d: average vehicle density per road segment
◦ communication range: 250m

APs(stationary access points ):
◦ infrastructure nodes whose absolute location in known to
all vehicles

Message informations:
◦ tg : message generation time
◦ λ : time-to-live value, message delay threshold
Outline

Introduction
◦ VANETs
◦ Delay-bounded Routing
Objective and Model
 Algorithm

◦ D-Greedy
◦ D-MinCost
Evaluation and result
 Conclusion

Algorithm

Forwarding a message
◦ minimize the number of transmissions
◦ within the message-specific delay threshold

Alternate between two forwarding
strategies:
◦ Multihop Forwarding
◦ Data Muling

Algorithms
◦ D-Greedy
◦ D-MinCost
D-Greedy

Delay-bounded Greedy Forwarding
◦ No knowledge of global traffic conditions
◦ Available location information, ex. Node
speed
◦ Best path: shortest path
D-Greedy

Each vehicle maintains a neighbor list by
periodically broadcasting beacons
◦ id : unique vehicle identifier
◦ distToAP : the length of the shortest path
between the vehicle’s current location and the
location of the closest access point (Dijkstra)
TTL : delay threshold value for message
 distToInt : the remaining length, until the
next intersection, of the current street
segment e

D-Greedy:Formulation

Data Muling strategy
◦ DelDM ≤ Del

Multihop Forwarding strategy
◦ otherwise
Del : available delay budget
Del = TTL × distToInt/distToAP
 DelDM = distToInt/u

D-MinCost

Delay-Bounded Minimum Cost
Forwarding
◦ Knowledge of global traffic statistics
◦ ex. estimated values of average vehicle speed
u and density d for all edges of the street
graph G.
◦ bandwidth-efficient delay-constrained paths
D-MinCost

Annotate each edge with two metrics
◦ C : cost , #message transmissions along the
edge
◦ Del : delay , the time required to forward a
message along the edge
Graph
extension
D-MinCost

Data Muling strategy
Cost & Delay
◦ DelDM = l/u, CDM = 1
◦ l : the length of the edge
◦ u : the average vehicle speed along that edge

Multihop Forwarding strategy
◦
◦
◦
◦
◦
if l > R and d ≥ l/R
CMH = l/R, DelMH = CMH × q
R : communication range
d : the average vehicle density for the edge
q : the time required for the node to check its
neighbor list and identify the best next hop
D-MinCost:Path selection



The minimum cost path
The delay-constrained least-cost routing problem
is known to be NP-complete
Heuristics: the Delay Scaling Algorithm(DSA) [7]
◦ The AP that can be reached with the least cost.
◦ The exact min-cost path to that AP.
◦ The strategy that should be followed at each edge of
the path in order to adhere to the message’s remaining
delay budget.
AP
min-cost path
strategy
Outline

Introduction
◦ VANETs
◦ Delay-bounded Routing
Objective and Model
 Algorithm

◦ D-Greedy
◦ D-MinCost
Evaluation and result
 Conclusion

Evaluation and result

Compared with
◦ Epidemic routing : achieves optimal delay and
delivery ratio under our scenario
◦ MinDelay : a greedy delay minimizing scheme
inspired by [16]
Delivery Ratio
Transmitted Bytes
Message Delay
Strategy chosen
Outline

Introduction
◦ VANETs
◦ Delay-bounded Routing
Objective and Model
 Algorithm

◦ D-Greedy
◦ D-MinCost
Evaluation and result
 Conclusion

Conclusion

Two novel packet forwarding schemes :
◦ D-Greedy
◦ D-MinCost

Achievement
◦ Forwarding strategy
◦ Minimize communication cost
◦ Adhere to defined delay threshold

Outperform
◦ communication cost

Similar delivery ratio to Epidemic
Compared with
Epidemic
&MinDelay