Improving data transmission in the vanet using multi criteria decision making method based on fuzzy logic

International Journal in Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.6, November 2013

IMPROVING DATA TRANSMISSION IN THE VANET
USING MULTI-CRITERIA DECISION MAKING
METHOD BASED ON FUZZY LOGIC
Javad Badali 1 Mortaza Mokhtari Nazarlou2 parvin fartut3
1,2,3

Department of Computer, maku Branch, Islamic Azad University, maku, Iran

ABSTRACT
In vehicular ad-hoc networks the packets are sent using multi-hop methods and the receiving limit of a
message is gradually extended, but the exponential increment of the number of nodes re-broadcasting a
message results in broadcast storm problem in data broadcasting in this case. Some characteristics like
high speed of nodes, rapid topological changes and repetitive discontinuities have made it difficult to
design an efficient broadcasting protocol for these networks.
We have offered a novel fuzzy method based on multi-criteria decision-making (MCDM) for prioritizing the
vehicles in selection of the most proper neighbor to broadcast data in this paper. With using this fuzzy
method, the most proper vehicles participate in data broadcasting. The results of simulation using NS show
that because of selecting the neighboring vehicles with high priority in data broadcasting, the speed of
sending the packs is increased and the network load is considerably decreased. This method also
considerably decreases broadcasting traffic.

KEYWORDS
VANET, Data Dissemination, Broadcast Storm, Fuzzy Decision Making

1. INTRODUCTION
VANETs are subset of MANET known as new generation of ad-hoc networks [4,6]. In order to
establish the communication VANET, each vehicle is as a node which can act both as receiver
and sender and hereby broadcast different information between the vehicles. In these networks,
the vehicles are equipped with wireless terminals with standards like DSRC with sending limit
extendable up to 1000m. Because of limited radio range of each node in VANETs, it is required
to re-broadcast the received broadcasted message for the neighbors. This type of sending is called
multi-hop and requires routing algorithms. Routing in VANETs is very complicated and difficult
because of some characteristics like high dynamism, high speed of vehicles and high broadcasting
scale of information and the old routing methods are not sufficient in these networks.
In multi-hop sending, the received limit of a message is gradually extended; but in this case the
exponential increasing of the number of nodes re-broadcasting the message brings the problem of
broadcast storm in broadcasting of information. The following cases can be mentioned among the
important characteristics of VANETs [2]:




VANETs have dynamic topology but they are geographically limited.
These networks have potentially wide scale in broadcasting of information
They mostly suffer from desultoriness

DOI:10.5121/ijfcst.2013.3606

61







The density of network is variable and is a function of traffic of vehicles
Topology of the network highly depends on the treatment of the driver
VANETs have very low diameter in comparison with public networks
Existence of obstacles in urban environments in the networks can disturb routing

The vehicle-to-vehicle communications in VANET networks, data and message exchange
between the nodes (vehicle) is done in two ways as follows:



Vehicle-to-vehicle communications (V2V)
Vehicle-to-road side equipment communications (V2R)

Many studies are performed on V2V and V2R communications and it has been determined that
VANETs with V2V-based data communication have some priorities over V2R-based VANETs
[14] and that is why we have concentrated on V2V communications. Considering the importance
of creating a dedicated frequency band for wireless communication in VANET networks, the
Federal Communications Commission of the USA (FCC) has dedicated a 75MHz bandwidth in
5.9GHz for Dedicated Short-Range Communications. The channel dedication method in this band
is briefly shown in figure 1.The frequency limit is divided into 7 channels with 10MHz
bandwidth; out of which 1 channel is mainly dedicated to increase the safety coefficient of roads
and the remained 6 channels are dedicated for service-welfare applications [3].

Figure1. Method of Dedicating Frequency Band & Division of Its Channels for DSRC/WAVE [14]

The messages exchanged in V2V networks for safety application are divided into two categories
in terms of their nature and schedule of broadcasting [15]:
Periodic messages alternatively broadcasted by each vehicle: These periodic messages contain the
motional status of each vehicle in the route. By analyzing these messages in each vehicle, we can
obtain complete data about the status of the rout. In other words, these messages are called
beacon.
Emergency messages (event driven): these messages are issued from time to time and in case of
hazardous events in the rout in order to warn other vehicles. In case of receiving emergency
messages, we can prevent from more damages by making proper decisions without losing the
time.
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On the whole, some problems like weakening of the received signal level, saturation of channel
loading, high mobility network nodes and broadcast storm should be overcame in order to have a
reliable and safe communication in VANETs. The problem of Broadcast Storm in these networks
occurs because of some characteristics like high mobility of nodes, high scale and structure in
broadcasting and we try to solve these problems in this paper.
In this paper, a fuzzy method is proposed to select the vehicles participating in broadcasting,
which is compatible with special characteristics of VANE networks. As fuzzy inference systems
and fuzzy theories in general have an important role in optimization and prioritization process,
this paper wants to describe the application of fuzzy decision-making in improvement of
broadcast storm. The fuzzy theory in general and fuzzy sets will be described in section3 to get
more familiar with fuzzy theory.

2. R ELATED WORK
Different methods are described in order to solve the problem of Broadcast Storm in VANETs.
One of the methods used to solve this problem in MANET Networks is that it limits the number
of steps coursed in one multi-hop sending by defining some thresholds [9]. Some changes are
made in these networks to generalize these methods to VANETs. For example, from the received
signal strength (RSS) of a message, one part is used as a criterion for decision-making for rebroadcasting as follows:

pij  1 

RSSij  RSS min
RSS max  RSS MIN

(1)

In Equation (1), RSSmin is the minimum receivable signal level and RSSmax is the maximum sent
signal strength. For each message reaching from node i to j, the received signal strength is
calculated and placed in RSSij. The pij variable is re-broadcasting probability of the received
message. The higher the level of received signal, the closer it is to the source node and the
received message will be re-broadcasted with less probability in this case. This will finally result
in reduction of broadcast storm near the sender [12].
In another method, all message-receiving nodes don’t re-broadcast it and only the receiver in
extreme point of the destination should re-broadcast it for other nodes. Of course, we will need a
communication with location-finding equipment such as GPS in order to implement this protocol.
Request to Broadcast/Clear to Broadcast (RTB/CTB) hand-shale messages are used in this
protocol [7].
In another method, repeating method is used to send emergency messages. In this definition if an
accident occurs, emergency warning messages (EWM) are sent with a high rate and then the rate
of sending is decreased [13].As a simple method to discover broadcast storm, each node
investigates the sequence number of its received packs and calculates the receiving rate of packs
based on it. If the receiving rate is decreased in the present time in comparison with the previous
times, then the problem of hidden node and collision of frames occur in network. So, it is better to
increase the waiting time to occupy the channel. This is done by increasing the contention
windows. In this case, by decreasing the competition for sending and decreasing of network
loading, the rate of collision is decreased and receiving rate of broadcasting frames is increased.
Here the estimated receipt rate in communication between two nodes in the receiver is calculated
according to the following accumulated equation:
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Est Re cpt    Est Re cpt 1  (1   )  Sample Re cp

(2)
Alpha coefficient in equation 2 is used to change the important of receiving rate in the past in
comparison with the present in order to calculate the estimated receipt rate. The average local
receipt rate is calculated from all sending nodes for the receiver according to equation 3 [1].

Local Re cpRate  

Est Re cp
Nodes

(3)

Another solution for decreasing the flooding traffic for discovering the route is optimal routing
method. Clustering routing is one of the routing methods which is called CBRP (Cluster Based
Routing Protocol) and decreases broadcast storm. In clustering methods, the moving nodes are
divided into different categories and are placed next to each other in one cluster according to
special rules. In one cluster, the nodes can have different roles such as head cluster, gate and
ordinary nodes. In this method, the clusters are determined and then some criteria like node's
attribute, cluster and head cluster are provided. Ei criterion is considered to determine head cluster
which is calculated as follows:
d
Ei  e t



t  0, Tmax



(4)

t is the time of presence in road and d is speed deviation rate. Each node with bigger E is selected
as head cluster. That is, a vehicle moving for a longer time in the road with lower speed deviation
is selected. All nodes broadcast Hello messages alternatively with specific intervals. The
messages contain all data of the neighboring table and the adjacent cluster table.

3. F UZZY THEORY
Fuzzy systems are based on rules or knowledge. The heart of a fuzzy system is a database which
is formed of fuzzy if-then, conclusion and decision-making rules. Fuzzy decision-making is an
action of decision-making, some words of which are determined using continuous functions. Here
some advantages of fuzzy logic are mentioned [11].
-

Simple conceptual understanding
Flexibility
Production of complex non-linear mathematical methods
Logical and rational justifiability
Possibility of designing based on experimental theories of experts

Unlike its apparent similarity with probabilities, Fuzzy theory is a theory independent of
probabilities. Using of fuzzy systems in two following states is very important:
1- In studying of very complex systems
2- Under conditions where access to an approximate but very rapid method is preferred

3.1. Fuzzy Sets
Global set in fuzzy sets is all accessible data about a problem. Membership functions are
expresses with 0 or 1 in classic sets and this means that: is the considered component a member
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of the set or not. But membership functions in fuzzy sets allocate an amount between 1 and 0 to
themselves.
If the members of global set are discontinuous in fuzzy theory, the fuzzy theory is defined as
follows:
(5)
Fuzzification Stages of sets are done in four stages:
1- First, the minimum and maximum amounts of global set are determined.
2- The basic verbal amounts are determined. For example, we can use verbal amounts such as:
Very Low, Low, Medium, Lower Medium, Higher Medium, High and Very High.
3- The space between minimum and maximum of global set is divided into proper sections.
This division can be linear or non-linear. The number of these sections can be or cannot
be equal to the number of verbal amounts.
4- Proper membership functions for members of global set are define to represent the verbal
amount.
The best method for obtaining the membership functions is using of mathematical equations,
because the neutrality is kept by using this method. Several mathematical functions are suggested
for this purpose, among which three functions including Triangular, Bell-Shape and Gaussian are
used more than others.
The main characteristics of Gaussian and Bell-Shape functions is that they are closer to the
thinking method of human and the advantages of triangular function is that we can cite more
theoretic reasoning to prove the theories[8]. Therefore, we will use this method in the present
paper.
The following equation should be also used to determine the membership degree of members in
global set in a fuzzy quantity which follows a triangular function:

 0
x  a



A  ( c, a , b )   c  a
bx

b  c
 0


, xa
, axc
, cxb
, xb

(6)

3.2. Fuzzy Decision-Making
Multi-Criteria Decision Making (MCDM) is the most common modeling method in decisionmaking problems which tries to model the decision-making problem under condition where the
number of objections and attributes of decision-making is more than one. The aim of MCDM
technique is to design and help decision-making process to distinguish the most proper solution
according to the will of the decision maker about the problem. MCDM is divided into two
categories: MODM (Multi-Objective Decision Making) and MADM (Multi-Attributable Decision
Making) [5]. Methods of solving multi-objective decisions-making problem can be also easily
used for fuzzy numbers such that calculations are done on membership functions of fuzzy sets
65


instead of numerical amounts. In this state of fuzzy sets, the criteria showing the importance of
objectives (weight of criteria) and fuzzy sets of optimality show also the rate of optimality.

3.3. Multi-Criteria Decision-Making with Multi-Objective Decision-Making View
(MODM)
One of the methods for solving multi-criteria decision-making problems is analyzing it as a multiobjective problem, such that each criterion is considered as an objective where we want to reach
[10]. In multi-objective decision-making, we want to a set of important objectives by selecting the
best alternative. In these issues, we consider that we have a set of objectives as O= {o1 , o2, o3, ….,
on} and a set of alternatives A= {A1, A2, A3, … , An}. We decide to reach these objectives. In
other words, we want to reach o1, o2,… and on. If we show the decision with D, so we have:
D = o1 ô2ô3 ^…ôn
That is, o1, o2,… and on objectives are reached. The rate of reaching to jth objective is shown as
Dij. In other word, the rate of reaching to jth objective is equal to Dij. If the importance of jth
objective is Bj, then selecting ith alternative will fulfill this need as aij.
And using maximum method for gathering we can reach the Dij.
Dij  max( B , aij )

(7)

Considering the combination (and) in reaching the set of objectives, the rate of decision for
ithalternative is obtained as follows:

D ( Ai )  min

n
n
Dij  min
max( B , aij )
j 1
j 1

 





(8)

4. R ESEARCH METHOD
Our Proposed method in this paper is prioritization method for data broadcasting in urban VANE
which acts in accordance with Multi-Criteria Decision-Making (MCDM) with Multi-Objective
Decision-Making method (MODM) in fuzzy theory.
In the suggested method, we assume that the data about speed, the distance between the nodes
and the number of neighbors of each node are kept in a table. We have used GPS to determine the
location and distance between the vehicles and we have also taken advantage of beacon messages
to calculate the number of neighbors. The characteristics of the suggested protocol are as follows:


Fuzzy Variable of Vehicle's Speed:

The criterion of speed in urban environments for each vehicle are Vmax and the minimum speed
is Vmin. We consider a fuzzy set of {very high, high, higher medium, medium, lower medium,
low, very low} for vehicle's speed. We consider the "medium speed" for speed criterion weight.


Fuzzy Variable of the Number of Neighbors:

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We also consider a fuzzy set including seven members {very high, high, higher medium, medium,
lower medium, low, very low} for the number of neighbors. About the criterion weight of the
number of neighbors, "the highest number of neighbors “is considered.


Fuzzy Variable of Distance between Vehicles:

We consider a fuzzy set of {very high, high, higher medium, medium, lower medium, low, very
low} for the distance between vehicles. We use the equation in GPS to calculate the criterion
weight of optimal distance. We select the weight "very high “for distance criterion.
After calculating the total amounts of criteria membership for every single vehicle, we start to
calculate fuzzy decision-making in each vehicle according to equations. After calculation, we use
the gravity center method for defuzzification of the obtained amounts. When broadcasting the
data, each vehicle like Vi prioritizes the neighboring node according to the updated data of the
neighboring nodes and fuzzy decision-making and selects the node with high priority as the next
step to send the data. This method will considerably decrease the broadcasting traffic and will
increase its efficiency.
The aim of the suggested method is to select a node with highest number of neighbors and
average speed and the possible farthest distance. The node having the three criterion mentioned
above with high weights is selected as optimal node for data broadcasting. If the link between the
present node and the node with high priority is broken for some reasons such as high speed, going
beyond 1000m sending limit and increased loading on network, then a node with the next priority
order will be selected for data broadcasting. The priority level of each node will be created by
multi-objective decision-making in a set according to the order priorities. In the neighboring table
of each node, the data about the neighboring nodes such as distance, number of neighbors and
speed are mentioned. Each node prioritizes these nodes with fuzzy decision-making. When a
vehicle wants to broadcast data, a vehicle with highest priority is selected according to the
available prioritization and that vehicle will be used to transmit the data.

5. SIMULATION
Simulation environment is 4000×4000m and the maximum sending range of each node is 1000m.
The packets are sent with fixed size of 128 bytes and fixed rate of 4 pkts/sec. The maximum
number of packets which can be sent in each section is 6000 packs and can be received by 20
destinations. The calculation method of FMRBS in VANET was compared with other mobile
models. The criteria evaluated in simulations include the following cases:
Packets dissemination Speed (m/s):is equal to the distance passed by a pack divided by delay
Load Generated per Broadcast Packet: The number of bits sent for broadcasting of a pack in the
whole network
The Average Data Packet Receipt: expresses the rate of vehicles that received the broadcasted
pack to the total packs
Methods compared with our suggested protocol include: UMB (Urban Multi-Hop Broadcast
Protocol) and 802.11-Distance.

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5.1 Simulation Results
- Packets dissemination Speed:

Average packet dissemination speed (m/s)

In FMRBS, as packets are not sent to all neighbors to broadcast the date and only the vehicles
with high priority are selected as the next step, the broadcasting speed of packs is more than that
in other methods.

Average packet generation rate of each vehicle (packets/s)

Figure 2. Packets dissemination Speed

- Load Generated per Broadcast Packet

Average load generated for each broadcast
packet (bits)

In broadcasting of data using FMRBS method, instead of sending the pack to all neighbors only
the neighboring vehicles with higher priority are selected and the load on network in sending the
packs considerably decreases.


Figure 3. Load Generated per Broadcast Packet

- The Average of Data Pack Receipt
As all vehicles are not involved in data broadcasting, the vehicles with less neighbors and low
speed cannot receive a pack. The results of simulation show that this rate is very low and is
almost equal to other methods.

68

Average success percentage (%)



Figure 4. The Average of Data Pack Receipt

6.C ONCLUSION
A new method based on Fuzzy Multi-Criteria Decision Making (MCDM) for vehicle-2-vehcile
networks, called FMRBS, to solve the problem of Broadcast Storm in this paper. The next node
for sending emergency method is selected using fuzzy rules in this method. The parameters of
vehicle's speed, the number of neighbors and distance between vehicles are used as parameters
effective on determining the priority of vehicles for data broadcasting. The vehicle with high
priority is used as the next step in data broadcasting.
In simulation, the FMRBS method was compared with UMB and 802.11-distance methods. The
results of simulation show that because of selecting the most proper neighboring nodes as the next
stage in data broadcasting, the speed of sending the packs is increased and the network load is
considerably decreased. Also, because of high reliability of paths, sending the control packs is
considerably increased.

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