Comparative Analysis of MANET Routing Protocols and Cluster Head Selection Technique in MANET

Mr. Pratik Gite Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 5, Issue 5, ( Part -2) May 2015, pp.68-79
www.ijera.com 68 | P a g e
Comparative Analysis of MANET Routing Protocols and Cluster
Head Selection Technique in MANET
Mr. Pratik Gite1
, Dr. Sanjay Thakur2
1
Pacific Academy of Higher Education and Research University, Udaipur, (R.J.), India
2
Pacific Academy of Higher Education and Research University, Udaipur, (R.J.), India
Abstract
Mobile Ad-hoc Network is a kind of wireless network. It is a backbone of new generation advanced
communication technology. MANET is an ideal applicant for rescue and emergency situation due to its
independence of connected devices of fixed wires. This paper represents a work on trust based system in
MANET cluster that can be used to improve the performance of the network even in the existence of not trusted
nodes. In the cluster architecture, cluster head and gateway nodes form a communication for routing among
neighbouring clusters. But selection of cluster head is the important problem in dynamic Ad-hoc network
because cluster head work as coordinator in clustered architecture. In this work, some values have used
correspond to the threshold values of forward packet and dropped packet of each node within the network
cluster. These values have been used dynamically updated every time and the node is selected as cluster head. In
this technique of selecting the node as cluster head, the node which has maximum trusted value is elected as
cluster head and this information is updated in every node’s trusted table. After implementation of our desired
work, the proposed Dynamic Trust Evaluation of Cluster Head (DTE-CH) technique is analysed with traditional
routing protocols and traditional clustering technique viz. Highest Degree Algorithm. The simulation is done by
using network simulator software on the basis of different performance metrics throughput, packet delivery
ratio, routing overhead, packet drop, average end to end delay and remain energy. Simulation result presents that
proposed DTE-CH technique improves the performance of network as compare to most suitable existing AODV
MANET protocol based technique as well as traditional highest degree clustering technique.
Keywords— MANET, Routing Protocols, Highest Degree Clustering Algorithm, Proposed DTE-CH Algorithm
I. INTRODUCTION
Mobile Ad-hoc Network is a rapid configurable
network. In addition to that the mobility is an
essential property of the network [7]. Due to this
reason, the topology is dynamically created whenever
required [8]. Thus there are mainly two kinds of data
delivery approaches utilized for organization of
network. First using the routing protocols like DSDV,
AODV and DSR. Secondly, for efficiency clustering
approaches are used. This paper is concern about the
brief study of MANET routing protocols and
traditional clustering techniques. There are basically
two objectives of this paper. First is to analyse the
MANET routing protocols to select efficient protocol
for small network as well as large network. Second is
to evaluate the performance of network under
traditional highest degree clustering algorithm,
Proposed DTE-CH algorithm and traditional
MANET protocol.
II. DSDV, AODV AND DSR ROUTING
PROTOCOLS
DSDV: - Destination-Sequenced Distance-Vector
Routing (DSDV) is a table-driven routing scheme for
ad hoc mobile networks based on the Bellman-Ford
algorithm. The improvement made to the Bellman-
Ford algorithm includes freedom from loops in
routing tables by using sequence numbers. It was
developed by C. Perkins and P. Bhagwat in 1994.
The DSDV protocol can be used in Mobile Ad-hoc
Network environments by assuming that each
participating node acts as a router. Each node must
maintain a table that consists of all the possible
destinations. In this routing protocol, an entry of the
table contains the address identifier of a destination,
the shortest known distance metric to that destination
measured in hop counts and the address identifier of
the node that is the first hop on the shortest path to
the destination. Each mobile node in the system
maintains a routing table in which all the possible
destinations and the number of hops to them in the
network are recorded. A sequence number is also
associated with each route/path to the destination.
The route labelled with the highest sequence number
is always used. This also helps in identifying the stale
routes from the new ones, thereby avoiding the
formation of loops. Also, to minimize the traffic
generated, there are two types of packets in the
system. One is known as ―full dump‖, which is a
packet that carries all the information about a change.
However, at the time of occasional movement,
another type of packet called ―incremental‖ will be
RESEARCH ARTICLE OPEN ACCESS

used, which will carry just the changes, thereby,
increasing the overall efficiency of the system.
DSDV requires a regular update of its routing tables,
which uses up battery power and a small amount of
bandwidth even when the network is idle. Whenever
the topology of the network changes, a new sequence
number is necessary before the network re-converges;
thus, DSDV is not suitable for highly dynamic
networks [4].
DYNAMIC SOURCE ROUTING: Dynamic
Source Routing (DSR) is a routing protocol for
wireless mesh networks and is based on a method
known as source routing. It is similar to AODV in
that it forms a route on-demand when a transmitting
computer requests one. Except that each intermediate
node that broadcasts a route request packet adds its
own address identifier to a list carried in the packet.
The destination node generates a route reply message
that includes the list of addresses received in the
route request and transmits it back along this path to
the source. Route maintenance in DSR is
accomplished through the confirmations that nodes
generate when they can verify that the next node
successfully received a packet. These confirmations
can be link-layer acknowledgements, passive
acknowledgements or network-layer
acknowledgements specified by the DSR protocol.
However, it uses source routing instead of relying on
the routing table at each intermediate device. When a
node is not able to verify the successful reception of a
packet it tries to retransmit it. When a finite number
of retransmissions fail, the node generates a route
error message that specifies the problematic link,
transmitting it to the source node. When a node
requires a route to a destination, which it doesn’t
have in its route cache, it broadcasts a Route Request
(RREQ) message, which is flooded throughout the
network. The first RREQ message is a broadcast
query on neighbours without flooding. Each RREQ
packet is uniquely identified by the initiator’s address
and the request id. A node processes a route request
packet only if it has not already seen the packet and
its address is not present in the route record of the
packet [5]. This minimizes the number of route
requests propagated in the network. RREQ is replied
by the destination node or an intermediate node,
which knows the route, using the Route Reply (RREP)
message. The return route for the RREP message
may be one of the routes that exist in the route cache
(if it exists) or a list reversal of the nodes in the
RREQ packet if symmetrical routing is supported. In
other cases the node may initiate it owns route
discovery mechanism and piggyback the RREP
packet onto it. Thus the route may be considered
unidirectional or bidirectional. DSR doesn’t enforce
any use of periodic messages from the mobile hosts
for maintenance of routes. Instead it uses two types
of packets for route maintenance: Route Error (RERR)
packets and ACKs. Whenever a node encounters fatal
transmission errors so that the route becomes invalid,
the source receives a RERR message. ACK packets
are used to verify the correct operation of the route
links. This also serves as a passive acknowledgement
for the mobile node. DSR enables multiple routes to
be learnt for a particular destination. DSR does not
require any periodic update messages, thus avoiding
wastage of bandwidth [6].
AODV: AODV is essentially a combination of both
DSR and DSDV. It borrows the basic on-demand
mechanism of Route Discovery and Route
Maintenance from DSR, plus the use of hop-by-hop
routing, sequence numbers, and periodic beacons
from DSDV.
Basic Mechanisms
When a node S needs a route to some destination D,
it broad-casts a ROUTE REQUEST message to its
neighbours, including the last known sequence
number for that destination. The ROUTE REQUEST
is ﬂooded in a controlled manner through the network
until it reaches a node that has a route to the
destination. Each node that forwards the ROUTE
REQUEST creates a reverse route for itself back to
node S. When the ROUTE REQUEST reaches a node
with a route to D, that node generates a ROUTE
REPLY that contains the number of hops necessary
to reach D and the sequence number for D most
recently seen by the node generating the REPLY.
Each node that participates in forwarding is REPLY
back toward the originator of the ROUTE REQUEST
(node S), creates a forward route to D. The state
created in each node along the path from S to D is
hop-by-hop state; that is, each node remembers only
the next hop and not the entire route, as would be
done in source routing. The basic functionality of
AODV is shown in figure 1.
Figure 1 AODV Routing
In order to maintain routes, AODV normally
requires that each node periodically transmit a
HELLO message, with a default rate of once per
second. Failure to receive three consecutive HELLO
messages from a neighbour is taken as an indication
that the link to the neighbour in question is down.
Alternatively, the AODV specification briefly
suggests that a node may use physical layer or link

layer methods to detect link breakages to nodes that it
considers neighbours [10]. When a link goes down,
any upstream node that has recently forwarded
packets to a destination using that link is notiﬁed via
an UNSOLICITED ROUTE REPLY containing an
inﬁnite metric for that destination. Upon receipt of
such a ROUTE REPLY, a node must acquire a new
route to the destination using Route Discovery as
described above [10].
III. CLUSTERING TECHNIQUES
Wherever, Mobile Ad-hoc Networks (MANETs) are
created randomly by a set of mobile nodes which
cover others with the help of their transmission range.
Mobile Ad-hoc Network does not depend on fixed
infrastructures and they operate without any
centralized administration [11]. Mobile nodes are
using dynamic topologies as nodes are capable of
moving aggressively [12]. Routing protocols act as
the required steps in MANETs and assist
communication outside the physical wireless
coverage of the nodes. To provide connectivity, every
node needs cooperation from other nodes to distribute
packets to their destination by performing as a router
[13]. These protocols could function in either a
normal or hierarchical network environment [13]. In
the normal architecture, all nodes contribute in the
routing procedure. In the hierarchical design nodes
are separated into a number of clusters each of which
is managed by a cluster-head that manage decisions
for cluster members. In this environment only cluster
heads and gateway nodes, intermediate nodes
between two cluster-heads can participate in the
routing [14]. Figure 2 shows the inter cluster and
intra cluster communication between nodes.
Figure 2 Inter cluster and Intra Cluster
Communication between Nodes
Established MANET routing protocols assume that
all nodes in the network have no trust among each
other. This may cause the network exposed to
malicious attacks. The selfish and malicious nodes
are threat to network in order to save their own
batteries they do not transmit packets from other
nodes as instructed by the protocol [14]. Since the
communication reliability of a node exclusively
depends on a proper option of the path used to make
the destination, it is significantly important for a host
to know the reliability of the nodes forming the route
[15].
IV. VARIOUS CLUSTERING TECHNIQUES
In this section, various kinds of Ad-hoc Network
clustering approaches are discussed in detail with
their appropriate domain of classification [23]:
A. Connectivity Based Clustering
In connectivity based clustering approach, the
connectivity between nodes is the essential property
for cluster-head formation. The relevant approaches
of such technique are discussed in this section.
1. K-hop Connectivity ID Clustering Algorithm:
It combines two clustering algorithms namely
Lowest- ID and Highest-degree heuristics. At the
initiation, node starts a flooding process in which a
clustering request is sent to all nodes. If using only a
lower ID clusters then more clusters unnecessary
generated thus result is a set of cluster-heads
increases. Thus, result is a set of cluster-head
increases. On the other hand using only node
connectivity causes numerous of evaluation between
nodes. So, by combining both can limit on number of
clusters. The node having highest connectivity is
selected as the cluster-head, when number of hops
k=1, connectivity is same as node degree. K-CONID
generalizes connectivity for a k-hop neighbour-hood
and if degree of connectivity of two nodes is same
then priority node is selected with lowest ID. So,
every node maintains two parameters. These
parameters are degree of connectivity and lowest ID
[23].
2. Highest Connectivity Clustering Algorithm:
Each node broadcasts its id to nodes that are within
its transmission range. The node with maximum
number of neighbours is chosen as a cluster-head.
Since no cluster-heads are directly linked, only one
cluster-head is allowed per cluster. Any two nodes in
a cluster are at most two hops away since the cluster-
head is directly linked to each of its neighbours in
cluster. The systems also have some limitations
which are given in following manner [23]:
i) The system has a low rate of cluster-head change
but the throughput is low. Typically, each cluster
is assigned some resources which is shared
among the members of that cluster. (CH) are
shared between all its neighbours So, CH
becomes a bottleneck.
ii) As well as the maximum number of nodes in a
Cluster is unlimited.

iii) The re-affiliation count of nodes is high due to
node movements and as a result, the highest-
degree node may not be re-elected to be a
cluster-head even if it loose one neighbour.
3. Adaptive Cluster Load Balance Method:
In HCC clustering scheme, one cluster head can be
exhausted when it serves too many mobile hosts. It is
not desirable and CH becomes a bottleneck. So a new
approach is given. In hello message format, there is
an "Option" is available. If a sender node is a cluster
head, it will set the number of its dominated member
nodes as "Option" value. When a sender node is not a
cluster head or it is undecided, "Option" will be reset
to 0. When a CH's Hello message shows its
dominated nodes' number exceeds a threshold, no
new node will participate in this cluster. As a result,
this can eliminate the CH bottleneck phenomenon
and optimize the cluster structure. This algorithm can
get load balance between various clusters. Thus,
resource consumption and information transmission
is distributed to all clusters instead of few clusters
[23].
B. MOBILITY BASED CLUSTERING:
1. Mobility Based Metric for Clustering:
It proposes the partitions of an Ad-hoc Network into
D-hop clusters based on mobility metric. The clusters
are formed in such a way mobile nodes with low
speed relative to their neighbours become cluster-
heads. The aggregate mobility metric is computed
over a small time by calculating the difference of
relative mobility between a node and all its
neighbours. The only dissimilarity between Lowest-
ID and MOBIC is that it uses mobility metric for
cluster formation instead of ID information. In this
algorithm, received power of transmissions of two
successive Hello message is measured by each node
from every neighbour. First, the pair wise relative
mobility metrics is calculated and then aggregate
relative mobility metric is computed before
transmitting the next packet. In Hello message, every
node broadcasts its own mobility metric to its 1-hop
neighbor and it is stored in the neighbour table with a
timeout period. In such way, every node receives the
aggregate mobility from its neighbouring nodes and
compares its own mobility with its neighbors. The
node having the lowest mobility value amongst all its
neighbours is selected as cluster-head. The conditions
are [23]:
i) If a node with Cluster Member status having low
mobility into the range of another Cluster Head
node which having higher mobility, then re-
clustering is not done.
ii) If two nodes having status Cluster Head move
into each other’s range, re-clustering is deferred
for Cluster Contention Interval (CCI) to permit
for incidental contacts between passing nodes.
iii) If the nodes are in transmission range of each
other even after the Cluster Contention Interval
timer has expired, re-clustering is triggered and
the node with the lower mobility metric is
selected as cluster head.
iv) In case of particular scenarios, where the relative
mobility between nodes does not differ
drastically, the mobility metric gives better
results [23].
C. COMBINED WEIGHTED BASED
CLUSTERING
In such kind of clustering algorithm a normalized
weight is calculated for creating a cluster [23].
1. Weighted clustering algorithm (WCA):
The WCA is based on the use of a merged weight
metric. For cluster-head election the metrics used are
the number of neighbours, distance with all
neighbours, mobility and cumulative time for which
the node acts as the cluster-head. By rebroadcasting
each node knows the weight values of all other nodes
and information of other cluster-heads in the system.
So, there is high overhead induced by WCA. The
drawback of WCA is, if a node moves into an area it
is not covered by any cluster-head then the cluster
set-up procedure is invoked again which causes re-
affiliations. A Hello message comprises its ID and
position. Each node builds its neighbour list based on
the Hello messages received. Each node computes its
weight value by following algorithm.
a) Find the set of neighbours of each node v called
N(v). (e.g. if the distance between v and v' is less
than the transmission range of v then v' is
neighbour of v). Set dv, the degree of v.
b) Calculate the degree-difference for each node,
where pre-defined threshold is means the
number of nodes that a cluster-head can handle
ideally.
c) For every node, calculate the sum of the
distances Dv with all its neighbours. Then
compute the running average of the speed for
every node until current time T. This gives a
measure of mobility Mv where (Xt, Yt) defines
the position of the node v at instant.
Compute the cumulative time Pv during which a
node v acts as cluster-head. Pv indicates how
much battery power has been consumed, which
is assumed more for a cluster-head than an
ordinary node.

d) Calculate the combined Weight (Wv) for each
node v where
Where the node with the smallest Wv is elected as
cluster-head and then all ordinary nodes of the
selected cluster-head are not allowed to participate in
the election process
e) Repeat steps 2 to 5 for the remaining nodes
which are not yet selected as a cluster-head or
assigned to a cluster.
2. An Efficient Weighted Distributed Clustering
(CBMD):
It uses different weight function which takes into
consideration the parameters: connectivity (C),
residual battery power (B), average mobility (M), and
distance (D) of the nodes to choose locally optimal
cluster-heads. Advantages of these clustering
algorithms are load balancing between the clusters is
achieved and less number of clusters formed by
specifying the maximum and minimum number of
nodes that a cluster-head can ideally handle.
Furthermore, each mobile node starts to measure its
weight after n (small integer in order to minimize the
memory requirement) successive HELLO messages,
where the result specifies the accurate value for the
mobility and battery power. This algorithm is used to
elect optimal cluster-heads and divide optimal
number of clusters without degrading the whole
network performance, to satisfy the load balancing
between clusters, to maximize the cluster stability
and to reduce the communication overhead and
minimizing the explicit control messages caused by
cluster maintenance [23].
3. Distributed Weighted Clustering Algorithm:
It works same as WCA except that power
management and distributed cluster set up is done by
localizing configuration and reconfiguration of
clusters. The consumed battery power is a better
determine than the cumulative time during which the
node acts as a cluster-head that is used in WCA
because it reflects the actual amount of power usage.
If there is inadequate battery power then lifetime of
topology can be enhanced by switching the role of
the cluster-head to an ordinary node. Two situations
can invoke the cluster maintenance phase, when there
is node movement outside of its cluster boundary and
when there is excessive battery consumption at the
cluster-head. When an ordinary node moves outside
of its cluster boundary, it is needed to find a new
cluster-head to affiliate with. If it finds a new cluster-
head, it hands over to the new one cluster. If not, it
declares itself as a cluster-head. Each cluster-head
modified the amount of consumed battery power
when it sends and receives packets. If the amount of
consumed battery power becomes more than a pre-
defined threshold value then the cluster-head resigns
and becomes an ordinary node. This algorithm offers
better performance than WCA in terms of the number
of re-affiliations, end-to-end throughput, overheads
during the primary clustering set up phase, and the
lifespan of nodes [23].
V. PERFORMANCE ANALYSIS OF DSDV,
AODV AND DSR
To select best protocols for further analysis of
MANET environment with traditional clustering
techniques and newly proposed clustering technique,
the simulation based analysis of MANET routing
protocols is done by using network simulator
software on the basis of different performance
metrics. The results are discussed bellow.
THROUGHPUT: It is the amount of data per unit
time that is delivered from one node to another via a
communication link [4]. The throughput of the
network provides the information how the network
efficiently provides the services to their clients. That
is measured for finding the available bandwidth
consumption in terms of MBPS or KBPS. Efficient
routing protocols must have greater throughput.
Figure 3 Throughput with Number of Varying of
Nodes
The given figure 3 shows the throughput comparison
of DSDV, DSR and AODV routing protocols. In
order to show the performance of the AODV routing
protocol the red line is used, for demonstration of
DSDV green line is used and for DSR routing
protocol blue line is used. In this figure the X axis
represents the number of nodes in the network and
the Y axis shows the throughput of the network.
According to the obtained results the AODV routing
protocol provides much consistent and constant bit
rate as compared to DSDV and DSR routing protocol.
On the other hand the throughput of DSDV and DSR
routing protocol is much affected as the number of
nodes in the network increases and throughput of the

AODV is not so much affected as the number of node
increases and decreases in the network.
PACKET DELIVERY RATIO (PDR): The packet
delivery ratio is expressed as the percentage of
number of received packets by destination node to
number of packets sent by all the source nodes
within the period of simulation time [5]. It is the
essential performance metrics of routing protocols.
Higher the value of packet delivery ratio gives the
better results. The Packet Delivery Ratio comparison
of the DSDV, AODV and DSR routing protocols are
demonstrated using figure 4 with increasing number
of nodes.
Figure 4 PDR with Number of Varying of Nodes
In order to represent the performance of routing
protocols, the X axis contains number of nodes and
the corresponding percentages of delivered packets
are given using Y axis. According to the obtained
results the performance of DSR and AODV routing
protocol in terms of Packet Delivery Ratio is much
adoptable as compare to the DSDV for 20-nodes and
40-nodes scenario because in most of the experiments
both the protocol delivers similar amount of data. But
as the number node increases in the network AODV
perform better among the three protocols and DSR
does not provide very much efficiency as compare to
both in high number of nodes.
ROUTING OVERHEADS: Routing overheads is
the number of control packets generated by each
routing protocol during simulation. It is the internal
measure or efficiency of any routing protocols. Two
different routing protocols can use different amounts
of overheads depending on their internal efficiency.
If control and data traffic share the same channel and
channel capacity is limited then excessive control
traffic often impacts data routing performance
(throughput). If more control packets are sent by the
routing agents, then delay in the network will also
increase [2]. The routing overhead comparison of the
DSDV, AODV and DSR routing protocols with the
increasing number of nodes are given using figure 5.
In this figure, the amount of overhead is represented
using Y axis and the number of nodes shown using X
axis.
Figure 5 Overheads with Number of Varying
Nodes
According to the obtained results the performance of
DSR routing protocol is better than DSDV and
AODV routing protocols in terms of routing
overhead for 20, 40, 60, 80 and 100 nodes scenario.
Therefore, DSR is more adoptable for this
performance metrics in Mobile Ad-hoc Network
Routing. Basically DSDV sends periodically routing
information to one node to another in throughout the
network as maintain consistent network view and
AODV also needs to send some more routing packets
during the transmission and retransmissions of data
as compare to the DSR routing protocols. So, DSDV
and AODV having more routing overheads as
compare to the AODV routing protocol.
AVERAGE END TO END DELAY: The total
amount of time required to deliver data from source
to destination is given using end to end delay. It
therefore includes all the delays in the network such
as buffer queues, transmission time and delays
induced by routing activities and MAC control
exchanges [4]. Here the estimated delays of networks
are measured in terms of milliseconds and simulated
using Y axis as given figure 6.
Figure 6 Average End to End Delays with Number
of Varying Nodes

Additionally X axis shows the number of nodes in
network. According to the obtained simulation results,
the average end to end delay of DSDV, AODV and
DSR routing protocol is also increases and decreases
as the number of nodes increases and decreases in the
network. In the analysed scenario, it is found that, the
average end to end delay of AODV is maximum for
20, 40 and 60 nodes scenario and that of DSR is
moderate for 20 nodes and minimum for 40, 60, 80
and 100 nodes scenario. But in case of 80 and 100-
nodes average end to end delay of DSDV is
maximum and that of AODV is minimum. Basically,
DSDV sends periodic data exchange between the
neighbour nodes thus that increases the additional
packets in network and in AODV the delay is also
produces due to updating of routing tables
information in case of new route initiation in the
network.
PACKET DROP: The amount of packet failed
during the transmission of data is known as the
packet drop [5]. A packet is dropped in two cases: the
buffer is full when the packet needs to buffer and the
time that packet has been buffer exceeds the limit.
The figure 7 shows the packet drop of the network
where the comparative results among AODV, DSDV
and DSR routing is provided.
Figure 7 Packet Drop with Number of Varying
Nodes
The amount of packet drops in AODV and DSR
routing protocol is much similar from each other
because that is indirectly proportional to packet
delivery ratio. Thus if the packet delivery ratio is
higher than the packet drop ratio is less and vice
versa. According to the obtained results, the packet
drop for DSDV routing protocol is maximum and
that of AODV and DSR routing protocol is minimum
and much similar for 20, 40, 60, 80 and 100 nodes
scenario.
REMAIN ENERGY:
The amount of energy preserved during the
active sessions of communication is known as remain
energy. The Ad-hoc devices are developed with the
inbuilt energy sources therefore for each event in
network a fixed amount of energy is consumed from
the initial energy. Figure 8 shows the amount of
remain energy in different routing sessions over
increasing number of varying nodes.
Figure 8 Remain Energy with Number of Varying
of Nodes
In this figure, the percentage amount of remain
energy is given on Y axis and the X axis contains the
number of nodes in the network. According to the
evaluated results in terms of remain energy AODV
routing protocol is maximum and that of DSDV is
minimum. Basically, DSR consumes more energy as
compare to the DSDV and AODV routing protocol.
In the analysed scenario it is found that according to
the MANET property, low battery can affect the
network functioning thus the DSR is not much
effective for the long simulations. Additionally, the
DSR is not much supportive for the large network.
Subsection Conclusion: - This sub section provides
a brief study of the DSDV, AODV and DSR routing
protocols theoretically and through simulation.
Further, different performance issues associated with
these routing protocols are discussed in detail. In this
study it is analysed that, the key element of the entire
network functioning depends on the routing strategy.
Therefore, DSDV, AODV and DSR routing protocols
are analysed in terms of throughput, packet delivery
ratio, routing overhead, packet drop, average end to
end delay and remain energy. In order to obtain the
efficient and reliable communication for small and
large networks, the performance study made among
the AODV, DSDV and DSR routing protocols is
done. In this comparison AODV routing protocol is
found most reliable routing technique because this is
much flexible to support small networks as well as
large networks. In addition to that, in other
performance parameters such as throughput, packet
delivery ratio, remain energy, packet drop the AODV
routing protocol is much efficient than the DSDV and

DSR routing protocol for varying number of nodes
but the key factor is that AODV routing protocol is
also energy saving technology too, because of the
less control message exchanges and less periodic
message exchanges. Therefore, the AODV routing is
obtained as the most optimum routing protocol for
the remaining experimentations.
VI. DYNAMIC LEADER SELECTION TECHNIQUE
Various researchers have been carried out detailed
study on cluster based routing in MANET. Trust is
one of the main aspects these are considered while
developing routing protocols. Trust value of any node
represents its participation and reliability in network.
A node which is having very low trust among its
neighbour affect the MANET services provided such
as unreliable services, suddenly breakdown in
services and soon. These types of nodes are always
avoided by their neighbours. A node’s trust value is
decided by various factors. These factors include
dropped packet, forward packets and packet
receive by un-trusted nodes. Some researchers
proposed various scheme to calculate and analyse the
trust value of nodes. Some mechanism proposed a
method in which each node is chosen as cluster head
based on the influence of the trust value evaluated
by the neighbours, level of a node, stability and
battery level. Each node is treated as malicious node
or a normal node based on its earlier behaviour. For
cluster head determination, each node can propose
one of its neighbours as cluster head. The objective
of the proposed idea is the dynamic cluster head
appointment for reliable data transmission in trusted
MANET environment. In our approach, a cluster
head position is changing among the nodes at a
predefined interval. A node cannot acquire cluster
head position for along time. Position rotation among
the nodes make the cluster head trust value more
reliable and secure. If a node behaves as an un-
trusted nodes then it is dismissed from its position
and new cluster head selection mechanism selected
the most reliable and trusted node as cluster head.
When a node joins MANET, then its trust value is
assumed and assigned. Most of the time, this value is
equal to one. For calculating, the trust value our
approach maintains a table at each node is called trust
table. This table is maintained by every node and
must be updated at an interval in addition to their
neighbour tables. At each node, trust table includes
Node Id, Number of Packets Forwarded, Number
of Packets Dropped, Number of Packets Received.
These values are calculated on the basis of nodes
performance within the cluster and outside the cluster.
This cluster information table is updated after an
interval. Trust value of each node is calculated on the
basis of trust factors. These trust factors includes
some threshold values. These threshold values
depend on some dynamic behaviour of nodes within
the cluster.
Here and are the number of packet
forwarded and dropped within the cluster and outside
the cluster respectively. , , and are
received packet and sent packet from the nodes of the
same cluster to the nodes of same cluster or other
cluster and to the same cluster or other cluster
respectively. In our proposed work, we have used
some threshold values α and β. These values are
correspond to the threshold values of forward packets
and dropped packets of each node within the cluster.
These values are updated every time the node is
appointed as cluster head. In the process of selecting
the cluster head the node, the nodes which have
maximum trusted value is elected as cluster head and
this information is updated in every node’s trusted
table.
VII. PROPOSED DTE-CH SELECTION
ALGORITHM
The entire process of the cluster head selection on the
basis of new method is given using the steps.
Step1: Initialize the network with N nodes
Step 2: For each node in network enable and
initialize the trust tables
For i=1 to m
(Where m is highest value of node)
End for
Step 3: For i=1 to m (For each time interval T
Compute node’s trust values)

End for
Step 4: For i=1 to m
(Exchange the trust values and compare them for
cluster head selection
For each connected node)
If node trust value >neighbour trust values
Broadcast self as cluster head
End if
End for
VIII. SIMULATION RESULTS AND
ANALYSIS
The simulation results of newly proposed technique,
existing AODV routing protocol and highest degree
algorithm are shown in the following section in the
form of comparative graphs. The simulation analysis
of the protocols primarily focuses on a few
performance metrics. The following metrics are used
in this work for the performance evaluation of
traditional AODV Highest Degree Algorithm and
new proposed DTE-CH algorithm.
1. THROUGHPUT
The throughput is calculated at destination node
during entire simulation period. In this subsection,
throughput of proposed DTE-CH algorithm, AODV
routing protocol and Highest Degree Algorithm is
calculated for different number of nodes. The
variation of throughput with the number of nodes
under AODV, Highest Degree Algorithm and
proposed DTE-CH algorithm is shown in figure 9.
Figure 9 Throughput of AODV, Highest Degree
and DTE-CH Algorithm
According to the obtained results, the throughput of
the DTE-CH technique is maximum, AODV
protocol is minimum and Highest Degree
Algorithm is between the two. The key advantage of
the proposed DTE-CH technique is that, it would
work on the dynamic leader selection of cluster based
approach. Thus it improves the connectivity and data
delivery ratio as well as improves the network
performances and bandwidth consumption for
internal as well external communication scenario.
2. DATA PACKET DROP
The number of data packets that are not
successfully sent to the destination. Basically, it is
defined as the number of packets drop to the total
number of packet generated during the simulation
time. Lower the packet drop, lower would be the
delay in the network.
Figure 10 Drop of AODV, Highest Degree and
DTE-CH Algorithm
According to the obtained results, the packet drop
of existing AODV protocol is maximum, proposed
DTE-CH is minimum and highest degree algorithm
is between the two for number of varying nodes. As
per the given results, the proposed secure clustering
technique improves the connectivity as well as the
throughput thus the nodes optimized for
transmitting more data with more reliable
environment. Thus, the performance of the network
in terms of packet drop ratio is also improved as
compared to the traditional AODV routing protocol
and highest degree algorithm.
3. AVERAGE END TO END DELAY
Average end to end delay on network refers to the
time taken for a packet to be transmitted across a
network from source to destination device, this delay
is calculated using the below given formula. E2E
delay = receiving time – sending time. The
comparative network performance in terms of end to
end delay is given below:

Figure 11 Delays of AODV, Highest Degree and
DTE-CH Algorithm
According to the obtained results, the average end
to end delay of the existing AODV protocol is
maximum, proposed DTE-CH is minimum and that
of highest degree is between the two. Basically the
end to end delay reduces in the proposed technique
because the address of the cluster head and
destination nodes are known by the cluster heads thus
for identifying the node location needs less time and
that phenomena improves decision efficiency in
terms of end to end delay in cluster oriented network
as compared to the traditional way of routing.
4. ROUTING OVERHEAD
It is the total amount of control data packets
generated by each routing protocols throughout the
duration of simulation experiment.
Figure 12 Overhead of AODV, Highest Degree
and DTE-CH Algorithm
According to the obtained results, the routing
overheads of existing AODV protocol is maximum,
proposed DTE-CH is minimum and highest degree
algorithm is between the two.
5. PACKET DELIVERY RATIO
It is the ratio of the number of data packets received
by the destination node to the number of data
packets sent by the source node. It can be calculated
in terms of percentage (%).
Figure 13 PDR of AODV, Highest Degree and
DTE-CH Algorithm
According to the obtained results, the routing
overheads of existing AODV protocol is maximum,
algorithm is between the two for number of varying
nodes.
6. REMAIN ENERGY
The mobile nodes are developed with the limited
amount of energy source thus for each event in
network the significant amount of energy is
consumed. After completing the simulation the
amount of battery power remain is known as remain
energy.
Figure 14 Energy of AODV, Highest Degree and
DTE-CH Algorithm
According to the obtained results, the remaining
energy of existing AODV protocol is maximum,
algorithm is between the two.
IX. CONCLUSION AND FUTURE SCOPE
In MANET, mobile nodes are known to their
changing location. Therefore, frequent location
change of nodes affects the network performance. A
network needs to maintain the routing information
and restart all the routing process when a node
changes its location. A better selection mechanism
should be implanted to provide a better way to assign
a node to cluster head position. In this research work,
a new technique is developed that assign a node to a
cluster head position after a fair selection process.

This cluster head selection process is started every
time in the predefined interval or if cluster head node
behaves as a malicious node. A node which has
maximum trust value is elected as cluster head and
updated in all nodes cluster table. A trusted node
reputation value is required to be sustained and
improved. A not trusted node is a threat for a network
performance. So, it should be avoided and blocked by
the network. According to above discussed
simulation results, first performance analysis of
MANET routing protocols is done and best routing
protocols is selected for further experimentations
with highest degree algorithm based clustering
technique and proposed DTE-CH algorithm based
clustering technique for finding the effectiveness of
MANET performance in terms of throughput, packet
delivery ratio, routing overheads, average end to end
delay, packet drop and remain energy.
In the analysed scenario, it is found that the
throughput of proposed DTE-CH algorithm is better
than AODV routing protocol and highest degree
algorithm for 20-nodes, 40-nodes, 60-nodes, 80-
nodes and 100 nodes scenario. The Packet Loss of
proposed DTE-CH algorithm is minimum and
AODV Routing Protocol is maximum and that of
highest degree algorithm is between the two for 20,
40, 60, 80 and 100-Nodes and 100-Nodes scenario.
Simulation results of Average End to End Delay
shows that the AODV Routing Protocol has
maximum Average End to End Delay and proposed
DTE-Ch has minimum delay and highest degree is
between the two. Simulation results of routing
overhead shows that AODV Routing Protocol has
maximum routing overhead and proposed DTE-CH
has minimum routing overhead and highest degree
algorithm is between the two. According to above
simulation results, the packet drop of AODV is
maximum, proposed DTE-CH algorithm is minimum
and that of highest degree algorithm is between the
two for 20-nodes, 40-nodes, 60-nodes, 80-nodes and
100 nodes scenario. Simulation results of Remain
Energy shows that, AODV has maximum remain
energy, proposed DTE-CH algorithm has minimum
remain energy and that of highest degree algorithm is
between the two for 20-nodes, 40-nodes, 60-nodes,
80-nodes and 100 nodes scenario. In the analysed
scenario, it is found that our proposed protocol is
better in network cluster environment and provides a
better approach to elect cluster head in trust
environment. The given-technique is much promising
for electing efficient cluster heads thus that can be
extendable for the different security schemes for
wireless network technology. Therefore in near future
the given technique is implemented with additional
network characteristics for applying the security for
trusted management schemes.
REFERENCES
[1] J. Broch, D. A. Maltz, D. B. Johnson, Y.C.
Hu and J. Jetcheva, ―A Performance
Comparison of Multi-hop Wireless Ad-hoc
Network Routing Protocols‖, Proceedings of
the 4th Annual ACM/IEEE International
Conference on Mobile Computing and
Networking, PP. 85-97, ACM Press, 1998.
[2] A. Boukerche, ―Performance Comparison
and Analysis of Ad-hoc Network Routing
Algorithms‖, IEEE Conference on
Performance, Computing and
Communication, PP.171-178, April 2001.
[3] D. Chakeres and E. B. Royer, ―The Utility
of Hello Message for Determining Link
Connectivity‖, Proceedings of 5th
International Conference on Wireless
Personal Multimedia Communications
(WPMC) Vol. 2 , PP. 504-508, Hawaii,
2002.
[4] M. D. Naim Hider, M. D. Haque and M.
D. Shohidul, ―An Extensive Comparison
among DSDV, DSR and AODV Protocols in
MANET‖, International Journal of Computer
Application, Vol. 15, No.2, PP. 123-130,
February 2011.
[5] A. Iwata, C. Chiang, G. Pei, M. Gerla,
and T. Chen, "Routing Strategies for Ad-
hoc Wireless Networks", IEEE Conference
on Selected Areas in Communications, Vol.
17, No. 8, PP. 1369-1379, Japan, August
1999.
[6] Noor Mohammad Suryati, Usop Azizol
Abdullah, Ahamed Faisal Amri Abidin,
―Performance Evaluation of AODV, DSDV
and DSR Routing Protocols in Grid
Enviornment‖, International Journal of
Computer Science and Network Security,
Vol. 9, No. 7, PP. 45-53, July 2009.
[7] P. Johnson, T. Larson, N. Headman, B.
Mielczarek and M. Degmark, ―Scenario
Based Performance Analysis of Routing
Protocols for Mobile Ad-hoc Networks‖,
Mobicom’99, PP. 195-206, 1999.
[8] H. Zou, Q. Wei, ―Efficiency Evaluation and
Comparison of Routing Protocols in
MANETs‖, In Proceedings of ISISE-2008
Vol. 2, PP. 201-206, Japan, 2008.
[9] G. S. Mamatha and Dr. S. C. Sharma,
―Analyzing the MANET Variations,
Challenges, Capacity and Protocols Issues‖,
International Journal of Computer Science
& Engineering Survey (IJCSES), Vol.1,
No.1, PP. 10-16, August 2010.
[10] C. Mbarushimana, A. Shahrabi,
"Comparative Study of Reactive and

Proactive Routing Protocols Performance in
Mobile Ad-hoc Networks", 21st International
Conference on Advanced Information
Networking and Applications Workshops
(AINAW'07), IEEE Computer Society, PP.
57-588, March 2007.
[11] Haidar Safa, Hassan Artail and Diana
Tabet, ―A cluster-based trust-aware routing
protocol for Mobile Ad-hoc Networks‖ ,
Springer Wireless Network (2010), 16:969–
984, DOI 10.1007/s11276-009-0182-1.
[12] Fenye Bao, Ing-Ray Chen and Moonjeong
Chang, ―Hierarchical Trust Management
for Wireless Sensor Networks and Its
Application to Trust-Based Routing‖,
SAC’11, March 21-25, 2011, TaiChung,
Taiwan., ACM 1978-1-4503-0113-8/11/03
[13] Azzedine Boukerche, Yonglin Ren, and
Richard Werner, ―An Adaptive
Computational Trust Model for Mobile Ad-
hoc Networks‖, IWCMC’09, June 21–24,
2009, Leipzig, Germany, ACM 978-1-
60558-569-7/09/06.
[14] Wenjia Li, Anupam Joshi and Tim
Finin,‖ Automated Trust Management for
Mobile Ad-hoc Networks Using Support
Vector Machine‖, IEEE 978-0-7695-4436-
6/11, MURI FA9550-08-1-0265 2011DOI
10.1109/MDM.2011.21.
[15] Christos Papageorgiou, Tasos Dagiuklas
and Stavros Kotsopoulos, ―Dynamic trust
establishment in emergency ad hoc
networks‖, IWCMC ’09, June 21-24, 2009,
Leipzig, Germany. ACM 978-1-60558-569-
7/09/06.
[16] Pushpita Chatterjee, ―Trust Based
Clustering and Secure Routing Scheme for
Mobile Ad-hoc Networks‖, International
Journal of Computer Network and
Communication (IJCNC), Vol. 1, No. 2,
July 2009.
[17] A.Pravin Renold and R. Parthasarathy,
―Source based Trusted AODV Routing
Protocol for Mobile Ad-hoc Networks‖,
ICACCI’12, August 3-5, 2012, ACM 978-1-
4503-1196-0/12/08.
[18] S. Aruna and Dr. A. Subramani,
Comparative Study of Weighted Clustering
Algorithm for Mobile Ad-hoc Networks,
International Journal of Emerging
Technology and Advance Engineering,
ISSN 2250-2459, ISO 9001:2008, Certified
Journal Vol. 4, Issue 5, May 2014.
[19] Floriano De Rango and Salvatore Marano,
―Trust-based SAODV Protocol with
Intrusion Detection and Incentive
Cooperation in MANET‖, IWCMC’09, June
21–24, 2009, Leipzig, Germany.
[20] V. Preetha and Dr. K. Chitra, Clustering
and Cluster Head Selection Technique in
Mobile Ad-hoc Networks, International
Journal of Innovative Research in Computer
and Communication Engineering, ISSN
(Online): 2320-9801, ISSN (Print): 2320-
9798, Vol. 2, Issue 7, July 2014.
[21] N. Gupta, M. Shrivastava and A. Singh,
―Cluster Based On-Demand Routing
Protocol for Mobile Ad-hoc Network‖,
IJERT, Vol. 1, Issue No.3, 2012
[22] Marc Bechlar and Hans-Joachim Hof and
Denial Kraft and Frank Pahlke and I ars
Wolf ―A Cluster Based Security
Architecture for Ad-hoc Networks, in Proc
of IEEE INFOCOM 2004.
[23] Basant Kumar Verma and Dr. Binod
Kumar, ―A Survey On Cluster Based
Network For Ad-hoc Network‖,
International Journal of Research in
Computer Engineering and Electronics,
ISSN 2319-376X, Vol. 3, Issue 5, PP. 1-5,
Sep 2014.

Comparative Analysis of MANET Routing Protocols and Cluster Head Selection Technique in MANET

More Related Content

What's hot (19)

Viewers also liked (13)

Similar to Comparative Analysis of MANET Routing Protocols and Cluster Head Selection Technique in MANET (20)

Recently uploaded (20)

Comparative Analysis of MANET Routing Protocols and Cluster Head Selection Technique in MANET