Active Safety Evaluation In Car-To-Car Networks
CHAPTER 1
INTRODUCTION
The main objective of C2C (Car-to-Car) networks is to exchange information
between vehicles and use it to decrease the number of possible road accidents. Wireless
access in vehicular environments (WAVE) defines enhancements to 802.11 to support intervehicle communications [6]. These communications mainly exchange data between vehicles
and between vehicles and roadside infrastructure (RSU). IEEE 802.11p is the draft based on
802.11 in 5,9GHz band that describes the functions and services required for C2C. 802.11p
physical layer is based on IEEE 802.11a and MAC layer is based on genericIEEE 802.11. The
spectrum of 802.11p will be divided in 7channels of 10MHz to provide separate safety and
non-safety car-to-car services [13]. To exchange information between vehicles, the
simulation platform consists of NS-2, Summand TraCI, with vehicles driving in one scenario,
sending, receiving and forwarding data. At some point, a vehicle breaks down and
broadcasts a warning message. Vehicles that receive this message should seek new routes.
In the first part of this paper we provide a brief description of the software and
hardware used for simulations. In the second part, the scenario and propagation model is
analyzed and the final results are presented.
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CHAPTER 2
RELATED WORK
There are various traffic and network simulators to simulate car-to-car networks.
The objective is to exchange information to influence the vehicle behavior in the mobility
model. TraNS [2] is the first framework that implements TraCI [3] to interconnect SUMO [4]
and NS-2 [5] but uses old implementation of TraCI and can only be used with old versions of
SUMO and NS-2. Other simulators can exchange information and influence the vehicle
behavior but are commercial (VISSIM [14], CARISMA [15], etc.). VISSIM is a microscopic,
time-discrete simulator. Its functionality can only be customized by adjusting parameters.
Since VISSIM provides certain programmable interfaces, it can well be coupled with other
programs. However, the simulator costs a high license fee. CARISMA traffic simulator,
developed by BMW, has been written as a simulator for relatively small scenarios. The
simulator works on a time-discrete basis and updates vehicles positions and directions
every second. We have chosen SUMO because it is a high performing traffic simulator, an
open source and a microscopic, time-discrete simulator. In our simulations, we have used
802.11p [1] network implementation. IEEE 802.11p implements vehicular requirements that
ensure higher reliability and lower latency and interferences. Real simulations in car-to-car
networks are very expensive, therefore, we need simulators to study this type of
communications. Simulations have been carried out in several studies to simulate car-tocar-networks [2],[3] and [7],[10],[11] to evaluate network performance, throughput, delay,
and routing protocols in car-to-car networks. Many studies show important information
about network performance in car-to-car networks but, in our case, applying the studies
cited above, we show statistical values about how an accident is reported in our simulation
platform and how the information is received by other cars (number of hops, time of
reception, velocity of vehicles at the moment of reception, etc.). Thus, we can determine
which scenario performs car-to- car communications.
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CHAPTER 3
SIMULATIONS
For simulations, we have used the NS-2.34 network simulator, Simulation of Urban
Mobility (SUMO) and TraCI to interconnect SUMO and NS-2. TraCI is designed to interlink
road traffic and network simulators and thus control the behavior of vehicles in simulations.
The reason for combining SUMO and NS-2 with TRACI is that they are free software and
they can be adapted to our needs through python scripts. The traffic and network
simulators interconnected generate realistic simulations of vehicular adhoc networks
(VANETs). That is, if a car sends information reporting an accident, the neighbors may
receive it and make their own decisions (changing route, velocity….)
TraCI uses TCP-based client/server architecture that makes that SUMO and NS-2
update information in real-time (alert messages, route update, etc.). After starting the
SUMO application, NS-2 connects to it by setting up a TCP connection to the appointed
SUMO port. The client application sends commands to SUMO to control the simulation run,
to influence single vehicle's behavior or to ask for environmental details. SUMO answers
with a Status response to each command and additional results that depend on the given
command.
In order to improve network simulations, we will use 802.11p with Mac802_11Ext
and WirelessPhy_Ext that are MAC and PHY layer extensions from 802.11a to
802.11p.These extensions perform the noise modeling, capture effect, multiple modulation
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schemes, which is of benefit to communication between vehicles. With respect to the
Nakagami propagation model, we should remark that it is the most appropriate model
because it has more configurable parameters, i.e., it is possible to model a low or high
fading channel. The chosen routing protocol is AODV and simulations were made with an
Intel Quad-Core processor with 4GB RAM and with Debian x64 operating system.
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CHAPTER 4
SIMULATION MODEL
We will simulate two scenarios: (i) a highway scenario and (ii) a Manhattan scenario.
In both scenarios, we will define three traffic patterns: high traffic, medium traffic and low
traffic. The propagation model used in all situations is the Nakagami model and the fading
parameters were adapted for each situation. The accident message will be sent in a period
of one second and with udp data.
A. Highway scenario
The highway scenario is a 25 km long region with two lanes in each direction. In this
scenario we want to see the utility of car-to-car communications to reduce in time the
number of warning messages about highway accidents taking place at high speeds. We use
the Nakagami propagation model without fading since this scenario is normally
characterized by direct line-of-sight visibility.
B. Manhattan scenario
In this scenario, we attempt to represent a section of Manhattan that can withstand
lots of traffic and receive strong signal attenuation due to buildings. The streets have only
two lanes in each direction to create traffic congestion.
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CHAPTER 5
RESULTS
In this study we have analyzed the following parameters parsing traces generated
from our simulation platform (Figure 1).
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In Figure 2 we can see how within the first 1600 meters all messages are received in
one hop and 70% of the packages within the 2000 meters. On the highway, there is line of
sight and the message can be propagated much farther in few hops.
In Figure 3 we can see how the accident message is spread to more vehicles as time goes
by.
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Active Safety Evaluation In Car-To-Car Networks
In Figure 5 we can see the overall number of vehicles of the simulation to receive
the accident message and the number of hops. All vehicles have received the alert message
in no more than six hops.
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Active Safety Evaluation In Car-To-Car Networks
Figure 6. Number of hops of the message to be received
Figure 6 shows how in the Manhattan scenario the accident message is received
within the first 400 meters only. With low traffic or high fading, it is possible that only
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Active Safety Evaluation In Car-To-Car Networks
nearby vehicles could communicate between them. As we can see, we need more than 10
hops to cover an area greater than 3500m.
In Figure 7 it can be observed that in the city the accident message reaches less than
80% of the vehicles.
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Active Safety Evaluation In Car-To-Car Networks
We can see in Figure 8 and Figure 9 how the number of vehicles receiving the
accident message decreases considerably with respect to highway. In highway, all packages
were received in up to 6 hops while in the city some vehicles have received the message in
12 hops. The significant difference is that in the highway characterized by low traffic, line of
sight and no fading the 28% of the alert messages were received in one hop, while in the
city with low traffic and fading only a 3% of the alert messages were received in the first
hop.
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CHAPTER 6
CONCLUSIONS
The results obtained in the highway scenario indicate the 100% reception of
messages in the context of low and high traffic conditions because of the line of sight and
because of the fact that the Nakagami propagation model works like a Free Space.
However, in the Manhattan scenario the reception is of 80% in low traffic conditions
because in the city there are obstructions between the transmitter and the receiver.
As shown in Figure 2, in the highway scenario the accident message arrives at 8800
meters in 6 hops. On the contrary, as shown in Figure 6, in the Manhattan scenario with low
traffic conditions, some messages arrive to others cars at 3000 meters in equal or more
than 10 hops. In Figure 5 we can see that on the highway the 28% of the vehicles receives
the accident signal in one hop and in Manhattan less than 5% receives the message in the
first hop.
The results obtained are satisfactory even in low traffic conditions. In the worst case
the accident message has been sent to 80% of the vehicles in a city scenario with a
maximum distance of 3000 meters.
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CHAPTER 7
FUTURE ENHANCEMENTS
In situations of low traffic or high fading it is possible that nearby vehicles cannot
communicate between them in the first moment and the solution may be to install RSU
units in “black spots” like tunnels, mountain or village roads with low traffic.
This study has proved that it is necessary to add new features in the network layer
and use them to improve car-to car communications, performing routing algorithms based
on geographical positions to reduce and optimize the packet delivery time.
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CHAPTER 8
REFERENCES
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[11] Torrent-Moreno M.,Festag A., and Hartenstein H., “System Design for Information
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networks (LCN), Florida, USA, 2006.
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