Performance analysis of proactive congestion control techniques for
VANETs
Geluvaraj B1, Nagaraj SR2
PG Student, CSE Dept, Nitte Meenakshi Institute of Technology, Bangalore, India
E-mail: geluvaraj999@gmail.com
2
Asst. Prof. Dept. of CSE, Nitte Meenakshi Institute of Technology, Bangalore, India, nagarajsr027@gmail.com.
1

Abstract: A Vehicular Ad-Hoc Networks (VANETs) are
technology that uses moving cars as nodes in a network to
create a mobile network. VANET turns every participating
car into a wireless router, allowing cars of each other to
connect and create a network with a wide range.[1]
Congestion control remains the major concern for VANET
application due to its characteristics such as bandwidth
limitation, fast change of topology and lack of central
coordination. solutions for these are based on packet
generation rate, transmit power control, utility function,
carrier sense threshold or a combination of them. In this
paper, the existing congestion control approach namely,
proactive, Besides, we propose and implement an algorithm
by which carrier sense (CS) threshold or Max Beaconing
Load (MBL) value can be assigned dynamically for
fine-tuning the Distributed Fair transmits Power Adjustment
for VANETs (D-FPAV) congestion control approach. In
addition to optimal channel bandwidth usage, the proposed
algorithm can be used in any situation considering traffic and
non-traffic conditions.[6][7]
Key words: Beacon messages, Congestion control,
Event-driven messages, IEEE 802.11p, VANET, Vehicular
networks, CSMA/CA,
INTRODUCTION
A wireless ad hoc network is a decentralized wireless
network. The network is ad hoc because it does not rely on a
Pre existing infrastructure (such as routers in wired network
or access points in infrastructure/managed wireless
networks).Instead, each node participates in routing by
forwarding message for other nodes, and so the determination
of which node forwards the message is made dynamically
based on the network connectivity. Vehicular ad hoc network
is a type of wireless ad hoc network. VANETS are important
component of the Intelligent Transportation Systems
(ITS).Vehicles communicate with each other via
Inter-Vehicle Communication (IVC) as well as with roadside
unit (RSU)/ Base Station via Roadside-to-Vehicle
Communication (RVC). The optimal goal is that vehicular ad
hoc networks will contribute to safer and more efficient roads
in the future by providing timely information to drivers and
concerned authorities. The main goal of VANET is providing
safety and comfort for passengers. A special electronic device
will be placed inside each vehicle which will provide Ad-Hoc
This special device is called on-board unit (OBU). This
network tends to operate without any infrastructure or legacy
client and server communication. Each vehicle equipped with
VANET device will be a node in the Ad- Hoc network and
can receive and relay others messages through the wireless
network. Collision warning, road sign alarms and in-place
traffic view will give the driver essential tools to decide the
best path along the way. The Federal Communication
Commission (FCC) assigned a frequency spectrum to
VANETs wireless communication. a dedicated short range
communications (DSRC) was established by the commission.
For providing public safety and private application, the DSRC
as communication service employs the 5.850-5.925 GHz. The
IEEE establishes a working group for Wireless Access in
Vehicular Environments (WAVE) standard or IEEE 802.11p
to provide DSRC for VANETs communication. The design of
DSRC is a system which has numerous channels. The FCC
categorize this spectrum into seven channels of 10 Mhz.
Service Channels (SCH) comprise six of these channels and
the remaining one is known as Control Channel (CCH). The
CCH channel is used for safety messages, however,
WAVE-mode short messages and non-safety services are
anticipate to be supplied from the SCH channels. There are
many challenges in VANET that have to be resolved to offer
reliable services such as routing, security, and quality of
service. Due to many issues such as Inaccurate State of
Information, dynamically Varying Network Topology,
Absence of Central Coordination, Hidden Terminal Problem,
Limited Resource Availability Error Prone Shared Radio
Channel, and Insecure Medium, therefore, supporting Quality
of Service (QoS) is a challenging task.[1][2][5][6]
Fig 1: An example for vanets
Congestion control is one of the solutions which
will be highlighted in this paper. congestion problem in
VANETs is explained as well as related work which proposed
to solve the mentioned problem and its shortages. Congestion
control classes which are proactive, reactive and hybrid as
well as their characteristics and taxonomy of congestion
control algorithms in VANETs is illustrated and about
D-FPAV congestion control algorithm. dynamic D-FPAV
algorithm for solving the mentioned shortages of D-FPAV
algorithm presents the simulation results which performed to
evaluate the effectiveness of our proposed dynamic
congestion control algorithm includes the conclusion of this
paper as well future work.
About proactive Congestion Control Algorithms for
VANETs
The congestion control mechanisms deriving from their
decision to adjust the transmission parameters. The proactive
congestion control which employs models that try to estimate
transmission parameters based on information such as the
number of nodes in the vicinity and data generation patterns.
Such transmission parameters will not lead to congested
channel conditions; meanwhile it provides the desired
application-level performance. In particular, such
mechanisms typically employ a system model to estimate the
channel load under a given set of transmission parameters,
and making the use of optimization algorithms to determine
the maximum transmit power and/or rate setting that will
adhere to a maximum congestion.
Proactive approaches are very appealing for vehicular
environments where radio communications are primarily used
for safety applications. The performance of such safety
applications would be seriously threatened by congested
channel conditions. In vehicular networks to ensure that all
vehicles in the network have similar opportunities to
communicate with nearby nodes. In fact, if congestion control
were obtained by sacrificing; say, a specific node in the
network is forced to set its transmission power to a very low
value, this node would not have a chance to communicate with
nodes in its surrounding which will consequently impair
application-level performance. Most importantly, in
safety-related applications, every vehicle in the network
should be able to receive fresh information about the status of
the other vehicles in its surrounding, along with
communicating its own status to the surrounding vehicles. For
this reason, fairness becomes a major design goal in
safety-related applications. As for prioritization, providing a
strict prioritization of different classes of packets is an
important requirement for vehicular networking, which is
partly addressed in the drafted IEEE 802.11p.The main
objective of this paper is fine-tuning the D-FPAV algorithm,
which is a proactive congestion control algorithm.
Distributed fairness power adjustment protocol for
vanets’s D-FPAV Algorithm
The D-FPAV approach is presented, which is a proactive
distributed congestion control in vehicular environments.
D-FPAV achieves congestion control by varying the node
transmission power, where a node’s transmit power setting
depends on predictions of application-layer traffic and the
observed number of vehicles in the surrounding. The
following designs goals are reached through this algorithm
which employs transmit power control. Congestion control:
through periodic beacon exchange the load on the medium
produced is limited by congestion control. Fairness:
Maximize the smallest amount of transmit power value over
all transmission power levels which are allocated to nodes.
This shapes the vehicular network under Constraint 1.
Prioritization: assign higher priority to event-driven
emergency messages when compared to the priority of
periodic beacons. Solving the problem in the following lines
in a fully distributed environment is the purpose of D FPAV .
Beaconing Max-Min T x Power Problem (BMMT x P) is
defined as a Given set of nodes N = (u1, u2, ....., un) in R = [0,
1] and a value for the MBL, determine a PA, i.e, PA, in a way
that the minimum power of transmit that the nods employed
for beaconing is maximized and the experience load on the
network at the nodes stays under the MBL.
Where, PA is the set of all possible PAs. The following
elements builds the D-FPAV: 1) implementing the algorithm
of FPAV at every node with the collected information from
the beacons which was received; 2) swapping transmit power
control values which are locally computed among vehicles in
the surroundings; and 3) choosing the lowest power level
among all those computer locally and by surrounding
vehicles. The D-FPAV algorithm is summarized in below.
D-FPAV Algorithm: (algorithm for node ui)
INPUT: all the nodes’ status in CSMAX (i)
OUTPUT: assigning a power, PA (i), for node ui, such that
the resulting power assigned is optimal
BMMT x P solution
Based on the nodes’ status in CSMAX (i),
Compute the maximum common t x power level Pi such that
the MBL threshold is not violated at any node in CSMAX (i)
Broadcast Pi to all nodes in CSMAX (i)
Receive the messages with the power level
from nodes uj such that ui ∈ CSMAX (j); store the
received values in Pj
Compute the final power level:
PA (i) = min {Pi, min j: ui ∈ CSMAX (j){ Pj }}
Our methodology for fine-tuning the D-FPAV algorithm is to
employ dynamic MBL value instead of a fixed value. The
implementation is based on the combination of transmitting
power control and message generation rate. Using Dynamic
MBL value makes the algorithm adjustable based on traffic or
non-traffic and event-driven or non-event-driven message
conditions. The conditions on the streets and highways can be
classified into two main categories; when there is traffic and
when there is no traffic. Heavy traffic in the streets and
highways can be detected from beacons information and
based on vehicles speed. Based on above mentioned
conditions, four different states are generated, namely,
non-traffic and event-driven, non-traffic and non
event-driven, traffic and event-driven, traffic and
non-event-driven. However, the last state may not be
generated due to the fact that the event-driven message is
issued in the case of abnormal conditions. Thus, piggybacked
beacons generation rate and MBL value can be decreased to 1
out 15 (instead of 1 out of 10) and bandwidth/3, respectively,
when there is traffic or MBL value can be increased up to the
maximum bandwidth of the channel when there is no traffic
and no event-driven messages around and also, it can be set to
2 × bandwidth/3 when there is no traffic although there are
event-driven messages around. Vehicles topology (location)
will change slowly due to heavy traffic in the streets. In this
situation, using the proposed approach can decrease the
number of piggybacked beacons and consequently, the
control channel overhead, which is already mentioned, can be
reduced. Moreover, needless to say that traffic happens when
there is an abnormal condition in a street. Therefore, in this
situation, event-driven messages should have higher priority
than beacon messages. Through the proposed methodology,
more bandwidth will be reserved for transmitting
event-driven messages in the case of traffic. As a result, the
probability of receiving event-driven messages will be raised
as well as their reception range.
always moving. This is because a road with smooth traffic can
become heavily congested in a few seconds, and vehicles in
heavy traffic areas cannot send at the same power as those in
light areas.
Procedure: find Traffic (for node ui)
According to the status of the nodes in CSMAX (i) and
neighbour table of
Compute the neighbour vehicle speed
If 80% of neighbour vehicles’ speed < 30km/h
Then there is traffic in the highway (street) and return true
Else return false
1)Executing the FPAV algorithm at each node with the
information gathered from received beacons.
2Exchanging the locally computed transmit power control
values among surrounding vehicles.
3) Selecting the minimum power level among the one locally
computed and those computed by the surrounding vehicles.
Procedure: dynamic MBL (for node ui)
If find Traffic = true
Piggyback the information every 15 beacons
Return MBL = Bandwidth/3
If find Traffic = false and no event-driven
Piggyback the information every 10 beacons
Return MBL = Bandwidth
If find Traffic = false and event-driven
Piggyback the information every 10 beacons
Return MBL = 2*Bandwidth/3
Simulation Experiment And Result Analysis
The effect of MBL value on existing D-FPAV algorithm
should be investigated, prior to implementation, in order to
find the best value for each condition of our proposed
algorithm. Therefore its effect will be investigated via
simulation.
The FPAV protocol is widely recognized for controlling
channel load in a fair manner. In this scheme, every node uses
a localized algorithm based on a “water filling” approach as
proposed by All the nodes increase their transmit power
simultaneously to the same maximum power, while the
constraint on the beaconing network load MBL is not
violated.
Each node collects the received information, compares the
maximum power reached by its neighbors, and then sends at a
power value higher than the maximum reached by its
neighbors. However, this fairness is not appropriate in a
highly mobile network like VANET. where vehicles are
DFPAV protocol flowchart:
D-FPAV is based on the following factors:
i. Development of Simulation Platform:
1.Road topology with more number of vehicle is created
using vanet mobisim output of vanet is fed as input to ns2 for
mobility of nodes. transmission of packets is done using
normal scheme and parameters such as end to end delay,
packet delivery ratio, network overhead is
.
2. Road topology with more number of vehicle is created
using vanet mobisim output of vanet is fed as input to ns2 for
mobility of nodes. transmission of packets is done using
proposed d-fpav algorithm
[a proactive distributed
congestion control in vehicular environments]
and
parameters such as end to end delay,packet delivery ratio,
network overhead is calculated.
Experiment
In the evaluation of the wireless communications, the aspect
of the using suitable models and their accurate configuration
plays a significant role. Since NS-2 is an extensively used
network simulator is employed as the simulator in our paper.
NS-2.33 version is the network simulator which was used for
our experiment. To consider a real-life scenario as well as a
dynamic network topology, a scenario has been used which
has 7km long with relatively considerable traffic density.50
vehicles travel at speed of not less than 4m/s (=14.5km/h) and
not more than 33m/s (=120km/h) which is the highest speed in
many countries
There are several other parameters which are
designed to perform the scenarios in the simulation. The
packet generation rate which is selected for beacons is 10
packets per second which is seen as a suitable value in order to
provide accurate data for the safety system. The sizes of
packets for all beacons are sat at 500 Bytes. In D-FPAV
implementation, MBL value will be assigned to different
fixed values such as 0.5Mbps, 1Mbps, 1.5Mbps, 2Mbps,
2.5Mbps, and 3Mbps to investigate the MBL effect on this
algorithm as well as to find the best value for each condition
of our proposed algorithm in methodology section. In the CS
range, rather than the number of nodes, the MBL threshold is
vented in relation to megabits per second. Nevertheless, both
measures that are the packet size and the packet generation
rate (assumed to be the same for all the nodes) are equal and
recognized.The maximum CS range, considering these
configuration parameters, is 664 meter. Table 2 shows the
most important configuration parameters which used in these
simulations
So as the sending rate increases delay decreases
Simulation results and Graphs:
1.The 1st graph where comparison is done with the parameter
Network overhead for FPAV and DFPAV protocol for 50
nodes
Configuration parameters in our simulation:
Parameters
Message generation rate of
beacon and event-driven
Size message
Communication range
Radio propogation model
Vehicle density
Number of vehicles
802.11p data rate
DFPAV algorithm
MBL
Communication protocol
Simulation time
Number of lanes
Minimum node speed
Maximum node speed
Value
10 messages per second
500 bytes
500 m
Nakagami model
20 vehicles per km
100
3Mb/s
FPAV algorithm
0.5Mb/s, 1Mb/s, 1.5Mb/s,
2Mb/s, 2.5Mb/s, 3Mb/s
UDP
200s
Not defined
4m/s (=14.5km/h)
33m/s (=120km/h)
2. 2nd Graph is a comparison of the packet through put for
the FPAV and DFPAV protocol for 50 nodes
Simulation results and Graphs:
When sending rate is 10 see the changes in the parameters
results:
So like that sending rate for: 50
3. Packet delivery ratio: u can see the gradual
difference of packet delivery ration between the 2
protocols
CONCLUSION AND OPEN ISSUES
4.Nodes drop:
5.Packets sent:
In this paper, a new efficient system for safety measures in
VANETs. The vehicular networks which uses the IEEE
802.11p and active-safety communication will consist of two
types of messages: 1) periodic beacon messages and 2)
event-driven emergence messages. The channel saturation
can “easily” occur due to the load caused by beacon message
transmissions. Simply increasing the rate or power will just
make the channel conditions worse. In these conditions, both
types of messages might not be received where they are
needed. D-FPAV is a transmit power control approach based
on a strict fairness criterion that can maximize the minimum
value over all transmission power levels. The EMDV
approach provides for robust and effective information
dissemination of emergency information with help of nearby
base station. We proposed an algorithm based on the
combination of transmit power control and message
generation rate to solve the problems. Our algorithm is based
on dynamic MBL assignment regarding to streets or highway
conditions (traffic and non-traffic conditions). To find the
best MBL value for each condition, D-FPAV algorithm is
implemented in network simulator NS-2 and different values
are assigned to MBL. Best MBL values for each condition are
found and consequently, are explained in results section based
on the obtained results from NS-2. After modifying the
D-FPAV algorithm by adding our proposed algorithm to it via
simulating in NS2, the dynamic MBL assignment effects in
this algorithm have been investigated. The obtained results
lead us to prove our claim of solving the mentioned problems.
Our results showed that dynamic D-FPAV has better
throughput and message reception probability than fixed
D-FPAV by considering the reception of beacon and
event-driven message. Furthermore, in addition to
considering the effectiveness of dynamic D-FPAV algorithm,
different scenarios should be simulated such as high speed
vehicles which would be remained for the future work.
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