Cluster head selection algorithm in vehicular Ad Hoc networks

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Cluster Head Selection Algorithm in Vehicular Ad
Hoc Networks
BOUCHRA MARZAK, HICHAM TOUMI,
MOHAMED TALEA
Laboratory of information processing
Hassan II –Casablanca University
Casablanca, Morocco
Marzak8bouchra@gmail.com, toumi.doc@gmail.com,
taleamohamed@yahoo.fr
ELHABIB BENLAHMAR
Laboratory of Information Technology and Modeling
Hassan II –Casablanca University
Casablanca, Morocco
h.benlahmer@gmail.com
Abstract—Intelligent Transportation Systems (ITS)
applications were first deployed in Vehicular Ad-Hoc Network
VANET. The main goal of these applications is to provide
vehicles in the network, and they are useful information about
the state of road traffic. Moreover, they are numerous articles
have been published with improvements ITS. VANET must be
capable to communicate in any environment irrespective of
traffic densities and vehicle locations. The application of
clustering algorithm is effective in VANET because algorithm
makes it more robust and scalable network. However, due to the
high mobility of nodes, it is difficult to obtain stable clusters.
Consequently, many packets are dropped and the overhead due
to route repairs or failure notifications increases significantly,
leading to low delivery ratios and high transmission delays. In
this work, we propose a model that calculates the value of stable
nodes, we use YATES algorithm. This mechanism is designed to
overcome the stability of cluster.
Keywords—VANET; YATES algorithm; clustering; stability
I. INTRODUCTION
VANET was developed when it became possible to connect
several mobile vehicles without relying on pre-existing
communication infrastructures. These networks have currently
become the subject of increased attention from manufacturers
and researchers, due to their potential for improving road
safety and offering assistance to drivers. In recent years, the
inter-vehicle communication has attracted many researchers in
the world. The vehicle-to- vehicle communication (V2V) [1]
enables new services for vehicles and creates many
opportunities for improving road safety. Vehicles networks
have characteristics that are often in the form of multi-hop
network similar to those of mobile ad-hoc network MANET.
Network topology changes frequently due to the high mobility
of nodes. Numerous articles have been published with new
proposals solution that improves existing methods, algorithm,
simulation results etc. VANET use the RSU, or wireless that
improves the ubiquitous communication services across the
moving vehicles.
Cluster-based approaches have been applied in VANETs
because the clusters reduce the overhead and delay. They also
solve the scalability problem, provide efficient resource
consumption and load balance in large scale networks [2].
The clustering algorithm creates a hierarchy in the network.
The communication can be divided into cluster member to
cluster-head and cluster-head to cluster-head communications.
Each cluster has at least one cluster-head (CH) that is selected
or elected by other cluster nodes (CN). Cluster size varies from
one cluster to another and is mostly dependent on the
transmission range of the wireless communication device that a
node uses [3].
Cluster stability is an important goal that clustering
algorithms which try to achieve and to be considered as a
measure of performance of a clustering algorithm. In this paper
we propose a model which seeks to determine the value of
stability of nodes from the distance, probability, and the
difference in speed parameters using the Yates algorithm [4].
The proposed model possesses a better cluster stability where
stability is defined by long CH duration, long cluster member
duration, and low rate of CH change.
The remainder of this paper is organized as follows.
Section 2 discusses related works on routing protocols for
VANETs. Yates’ algorithm is presented in section 3. Section 4
presents the proposed approach. Finally, sections 5 conclude
the paper with a summary of the presented approach and
discuss our future work.
II. RELATED WORK
Several works are reported in the literature that deals with
ad hoc networks and their applicability in VANETs. Clustering
is a process of grouping nodes according to some rules. These
rules differ from one algorithm to another and are the key
factors to build stable clusters. Cluster stability can be defined
in different ways [5]:
The work given in [6] proposes a new VANET cluster
formation algorithm that tends to group vehicles showing
similar mobility patterns in one cluster. This algorithm takes
into account the speed difference among vehicles as well as the
position and the direction during the cluster formation process.
The degree of the speed difference between adjacent vehicles is
the key criterion for the construction of the structure relatively
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stable combination. The algorithm increases the cluster lifetime
and reduces vehicle transitions between clusters.
Algorithm VWCA [7] optimizes the CH election procedure
so that a more stable network can be obtained. Small clusters
may decrease the stability of network because there-affiliation
of network increases. They use distrust value, number of
neighbors based on dynamic transmission range and direction
of vehicles in weighted clustering. The VWCA algorithm has
five steps to choose it’s required. The VWCA technique
mainly focuses on improving the CH duration, member ship
duration and security. Entropy is used for determining the
stability of the entire vehicle. VWCA can reduce the number of
overheads created by high speed vehicles and increase network
connectivity when electing CH.
The so-called Aggregate Local Mobility [8] measure the
criterion triggering cluster re-organization. It incorporates a
contention–based scheme to prevent over-eager re-organization
of clusters when two CH accidentally get in each other’s range
for a short period of time. The node’s decision regarding its
status change is based on its perception of the aggregate local
mobility (ALM). The CH with the lower ALM maintains its
state, while the other changes it. Another difference is to
prevent a regular member (an MN node) from immediately
changing its status to CH when it stops receiving beacons from
its last CH and there is no other CH in the neighborhood. When
something like that happens, the MN node will first go to the
UN state. This change postpones the creation of a new CH,
which could trigger unnecessary re-clustering, and also gives
time to the MN node to detect another CH that it can subscribe
to. In this paper [9] present a hybrid cooperative traffic
information system to provide traffic data to drivers and other
suppliants. The hybrid approach is a scalable mechanism that
makes efficient use of the number of equipped vehicles moving
in a road segment to optimally estimate the total traffic density.
He provided a new clustering method, a cluster chaining
technique, and also a traffic generalization method.
Another approach [10] presents a distributed mobility-
based clustering algorithm for VANETs called APROVE. The
algorithm finds clusters that minimize both the relative
mobility and the distance from each CH to its cluster members.
Each node in the network transmits the responsibility and
availability messages to its neighbors, and then makes a
decision on clustering independently. The objective of the
algorithm is to create a stable group, a long-term average
cluster member, long CH medium and low average cluster
variation. APROVE is robust to channel error, and it exhibits
reasonable overhead.
Protocols mentioned above do not solve the problems of
stability groups. We propose a model that calculates the value
of stability to organize the cluster network. The CH is elected
on the stability of model-based if its factor belongs to a subset
determined.
III. YATES ALGORITHM
Yates’ algorithm [4] is a process used to compute the
estimates of main effects and of the interactions in a factorial
experiment. They consider a factorial experiment with j factors
which each one have two levels. This experiment isʹ௝.
The algorithm is presented in a table in which the first
column we find all the combinations of the levels of different
factors in a standard order. The second column contains the
observations of all the combinations. Before performing the
Yates algorithm, the data should be arranged in "Yates order".
Given j factors, the jth column consists of ʹ௝ିଵ minus signs
(i.e., the low level of the factor) followed by ʹ௝ିଵ plus signs
(i.e., the high level of the factor) [11].
A. Position of the problem.
Multiple linear regressions are a method of analysis of
quantitative data. To highlight the link may exist between a
variable called Y explained it is intended, and many other
variables X1, X2..., Xk called explanatory. We are interested in
this article said linear models, that is to say the models of the
type (1):
Y =D0 +D1X1 + D2X2 + ... +DkXk + H
In which D0, D1, D2,..., Dk: are random variables and His a
random variable taking the name of the error factor.
B. The matrix of experience: Yates’ algorithm
For k variables, the matrix of experience has k columns
and ʹ௞ lines. More generally:
x All columns start with -1.
x The -1 and +1 every ʹ௞ିଵ line to the jth column.
The estimated coefficient of the model is given by (2):
ܣመൌଵ
௡ܻܺ (2)
Each estimate of the model coefficient is equal to the
algebraic sum of the experimental responses Yᵢ affected signs
of the column of the matrix X corresponding to Xᵢ factor
divided by the number of experiments.
IV. PROPOSED APPROACH
Cluster stability can be defined in different ways, our
approach calculates the value of stability from the distance,
difference in speed and probability parameters. CH is chosen
with great value stability.
We assume that all vehicles are equipped both with a
positioning system GPS or Navigation System (NS) through
which each one can acquire information about its current
location. And an IEEE 802.11p compliant radio transceiver
through which it can communicate with the other vehicles [8].
Our algorithm works in a distributed manner, in which each
node can run the clustering algorithm. When a vehicle wants to
find a CH, it sends a message "Request" to all its neighbors if it
does not receive a response, it starts the group formation
process. Vehicles moving in the same direction are grouped in
the same cluster.

Each vehicle send the Hello message to its node group, the
message contains: ID, the speed V, and position. Thereafter, it
puts the parameters in an array of neighborhood and
determines:
x The distance between the vehicle N itself and its
neighbor Z:
ܦேǡ௓ ൌටሺݔேെݔ௓ሻଶ൅ሺݕேെݕ௓ሻ;ሺ͵ሻ
With (x, y) is the location of the node.
x The difference in speed between the vehicle N itself
and its neighbor ∆V [2]:
οܸேǡ௓ ൌȁܸேെܸ௓ȁሺͶሻ
x The Probability to be a cluster-head:
ܲൌܧ൅ʹכ݀݁݊ݏ݅ݐݕ݋݂ݐ݄݁݊݋݀݁൅ܸ (5) [12]
With E is the energy consumption of the node for
transmitting / receiving [12] a packet and V speed.
ܧൌܭכܧ
௖௢௡ሺ͸ሻሾͳʹሿ
With ܧ௖௢௡ is the energy consumption of the wireless send-
receive circuit [12].
x The stability ܵேǡ௓ factor must be calculated for all
neighbor vehicles Z in the transmission range:
ܵேǡ௓ ൌߙଵ൅ߙଶכܦேǡ௓ ൅ߙଷכοܸேǡ௓ ൅ߙସכܲ൅ߙହכܦேǡ௓
כοܸேǡ௓൅ߙ଺כܦேǡ௓כܲ൅ߙ଻כܲכοܸேǡ௓
൅ߙ଼כܦேǡ௓ כܲכοܸேǡ௓൅ߝሺ͹ሻ
Where:
Ƚଵ,Ƚଶ,Ƚଷ, Ƚସ, Ƚହ, Ƚ଺, Ƚ଻, Ƚ଼are called real coefficients of
the model.
อߙଵ
ڭ
ߙ଼อൌሺܺ௧ǤܺሻିଵǤሺܵǤܺሻ௧ (8)
Where S the observed output and X is the effects of matrix.
Constraints of the experiment used to vary each of the three
factors (the distance D, the difference in speed between the
nodes ∆V and probability) in the following ranges:
low level : -1
high level:+1
ܦேǡ௓
near
far
οܸேǡ௓
low
high
P
low
high
We are interested in a plan ʹଷ. The matrix X has 4
columns and ʹଷlines. It alternates between -1
and +1 all lines in the second column, both lines to
the third column, all four lines for the fourth, etc.
x The factor of stability S:
All vehicles exchanged values of stability (9) with vehicles
in its cluster. CH is the vehicle with the maximum value of
stability. ܵൌσܵேǡ௓ሺͻሻ
V. CONCLUSION
Clustering routing protocols are suitable for VANET.
These protocols are not limited by a fixed infrastructure. It can
be deployed in a wireless environment. The clustering
algorithm integrates the centralized management approach
stable cluster and data transmission.
This study justifies the possibility of applying the YATES
algorithm as a promising solution for the development of the
routing system. In this paper, we proposed a clustering
algorithm that calculates the stability of each node by using
distance, difference in speed and probability parameters. The
proposed algorithm can meet the needs of existing clustering
routing protocols in terms of creating stable canals between
different nodes.
The work proposed in this paper is part of a broader
approach in which our clustering algorithm is based on our
model.
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