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T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
4201
A Cluster based Routing Protocol for MANET using Mahalanobis
Distance based Clustering and Gravitational Search Algorithm
1 T.V.Suresh Kumar, 2Dr.Prabhu G Benakop
1 Research Scholar, Electronics and Communication Engineering, JNTUH Hyderabad, Telangana, India.
tv.sureshkumar18@gmail.com
2 Principal, Methodist College of Engg & Tech., Hyderabad, Telangana, India.
ABSTRACT
Mobile ad hoc network is generally an independent wireless
network in the mobile nodes are communicated through the
wireless links without using any centralized infrastructure.
Since, the dynamic topology of the network affects the routing
and data transmission between the nodes. In this research, an
effective clustering and routing methods are developed to
improve the MANET performances. The mahalanobis distance
based clustering algorithm is used to select an effective cluster
head from the network. Then gravitational search algorithm is
used to find a routing path from the source to the destination
over the cluster heads. The fitness function of gravitational
search algorithm considers the multi objective such as residual
energy, distance and node degree for optimizing the network
performances. The performance of the proposed method are
taken in terms of average packet delivery ratio, energy
consumption, average end to end delay and normalized control
overhead. The performance of the proposed method is
compared to with two exiting works such as FEER and
FHACO.The energy consumption of proposed method is 940J
at 300 data packets that is less when compared to the FEER and
FHACO.
Key words: Cluster head, gravitational search algorithm,
mahalanobis distance based clustering, mobile ad hoc network,
residual energy and routing path.
1. INTRODUCTION
Mobile Ad hoc Network (MANET) is a collection of self-
sustaining mobile nodes that communicates with other nodes
over the wireless connections. Since, the MANET is a one of
the wireless ad hoc network that contains various properties
such as self-healing, peer-to-peer and self forming network.
This MANET doesn’t requires any pre-existing infrastructure
to perform the communication among the mobile nodes [1] [2].
The mobile nodes in the dynamic mobile network is act as
router as well as host [3]. If the nodes are not in the
transmission range of the source node, that requires other nodes
for transmit the data to the respective destination [4]. The ad
hoc networks are used in various situations such as rescue
operations, emergency search, vehicular communications,
military applications and conferences [5]. The instability and
vulnerability are occurred in the node and link of MANET, due
to the irregular mobility, huge difference of received signal
strength and less battery power [6]. Additionally, the time
varying topology and node’s mobility causes higher overhead
while handling the changes of routes [7].
The node’s higher mobility in network environment creates the
frequent link failure. This causes the heavy packet losses over
the network. Moreover the network partition is created in the
network, due to the nodes mobility. The network partition
affects the packet transmitting rate [8]. The aforesaid
limitations are overcome by using an effective clustering and
routing techniques. The clustering technique is used to
minimize the traffic in the distributed MANET. The Cluster
Head (CH) selection is an important phase of the clustering
algorithm. Since, the CH selection is carried out by two
methods connectivity based and identity-based algorithm [9].
The information about the local link connectivity is important
consideration in the route establishment and maintenance of
MANET [10]. The design of routing protocol which adopts the
topology variation is important for supporting the Quality of
Service (QoS). Because, the frequent link break of high-speed
node environment affects the QoS performance [11]. For
example, the existing protocols used in the MANET are
specified as follows: binary particle swarm optimization
algorithm [12], election algorithm [13], power aware
heterogeneous AODV routing protocol [14] and loose-virtual-
clustering-based routing protocol [15]. The major contributions
of this research paper are given as follows:
A reliable data transmission is achieved by combining
both the clustering and routing techniques in the
ISSN 2278
-
3091
Volume 9, No.4, July – August 2020
International Journal of Advanced Trends in Computer Science and Engineering
Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse50942020.pdf
https://doi.org/10.30534/ijatcse/2020/05942020
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
4202
MANET. Since, the clustering technique is used to
minimize the traffic in the network.
The Mahalanobis Distance Based Clustering (MDBC)
algorithm is developed in the MANET to form the
appropriate clusters and to select the optimal CH from
the clusters. The main advantage of using MDBC in
MANET is that considers the covariance of the node’s
position while calculating the distance.
Additionally, the data transmission from the source to
the destination over the CHs are detected by using the
Gravitational Search Algorithm (GSA). Since, the
GSA considers three fitness values such as residual
energy, distance and node degree to overcome the
node/link failure.
The overall organization of the paper is given as follows:
the literature survey about the recent techniques related to the
MANET is given in the section 2. The proposed clustering and
routing algorithm using MDBC and GSA are clearly described
in the section 3. The performance analysis of the proposed
method is explained in the section 4. Finally the conclusion is
made in section5.
2. LITERATURE SURVEY
The literature survey about the recent techniques related to the
clustering and routing in MANET are explained in this section.
Pathak, S. and Jain, S [16] developed the Weight Based
Clustering (WBC) protocol to perform the routing in MANET.
This WBC considers two different parameters for clustering the
network such as node degree and bandwidth requirement. The
parameters considered in the WBC are used to derive the
weight function and node’s weight. Then the calculated
weights are used in the cluster generation and CH selection.
Moreover cluster merging is used in MANET for combining
two clusters when the clusters are too near in the network. The
stability of ensured by assigning the weight of the CH. This
work fails to analyze the energy consumption and delay during
the data transmission.
Robinson, Y.H., Krishnan, R.S., Julie, E.G., Kumar, R.
and Thong, P.H [17] presented the reliable routing method for
obtaining the QoS in MANET. The node’s signal strength from
the deployed nodes are used to compute the bandwidth
requirement. The delay and stability parameters helps to select
the appropriate route over the network. The conventional
broadcasting is incorporated in this reliable routing for
identifying the effective route which accomplished by neighbor
knowledge methods. The routing overhead is minimized by
using the Neighbor Knowledge-based Rebroadcast (NKR)
algorithm and Loose Virtual Clustering (LVC) algorithm. The
performance of the designed algorithm is degraded and QoS is
affected, due to the dynamic networks.
Rajashanthi, M. and Valarmathi, K [18] presented the
effective energy-saving mechanism which is integrated with
the MANET proactive routing protocol. An efficiency of the
data transmission is identified by using the energy-aware
routing method. Initially, the K-medoid clustering algorithm is
used for generating the clusters. Since, this clustering
minimizes the cost of data routing in large and dense networks.
Subsequently, the Opposition Genetic-based Fish Swarm
Optimization (OGFSO) algorithm is used to obtain the
multipath routing for minimizing the energy consumption. This
OGFSO causes higher delay during data transmission.
Rao, M. and Singh, N [19] developed the QoS based
routing algorithm for transmitting the data over the MANET.
Initially, the network is clustered by using the K-means
clustering method. Subsequently, the firefly optimization is
used for classifying the optimizing the grouped nodes to detect
the CHs from its own clusters. Additionally, the data
transmission between the nodes are carried out by using the
TDMA based MAC routing method. The developed K-means
with firefly and MAC routing is named as KF-MAC method.
This KF-MAC method minimizes the packet loss rate.
However, the delay is high, when the data flows under labelled
packet exchanging protocols.
Kandan, J.M. and Sabari, A [20] developed the Fuzzy
Hierarchy Ant Colony Optimization (FHACO) routing protocol
for selecting the appropriate CH. This selection of appropriate
CH is helps to control the data packet transmission in the
cluster. The weight of the nodes decides the normal nodes to
become a CH. The parameters used for calculating the weight
of the nodes are node degree, buffer size, energy and speed.
This FHACO increases the routing overhead and throughput
over the network. The probability distribution of ACO is
changed for each iteration.
3. PROPOSED METHOD
In this proposed method, the reliable data transmission is
carried out by using an effective clustering and routing
techniques. Initially, the MDBC is developed to group the
nodes in the network as well as it selects an effective CHs from
the respective clusters. The data from the normal nodes are
collected by its CH. Subsequently, the optimal path from the
source to the destination is identified by using the GSA
algorithm. The overall flowchart for the proposed method is
shown in the Figure 1.
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
4203
Figure 1: Flowchart of the proposed method
3.1. Mahalanobis distance based clustering
The MDBC is introduced in the proposed method for
improving the network performance. Since, the different
distance from the destination node causes unbalanced energy
consumption over the network. The mahalanobis distance from
the node to destination node is used to cluster the nodes in the
MANET. Additionally, the appropriate CHs are identified by
using this clustering. The threshold to select the candidate node
as CH is expressed in the equation (1).
()=
+ (1 − )
(1)
Where, is the random number among the 0 and 1; is the
number of nodes: probability of a candidate node become CH
is represented as ; the number or round is denoted as ; The
node’s initial energy and residual energy are represented as
and respectively. The maximum and minimum distance
from the node to destination node are denoted as and
. Moreover, the specifies the mahalanobis
distance which is expressed in the equation (2).
=(⃗ − ⃗)(⃗ − ⃗) (5)
Where, the andspecifies the location of the nodes. Then the
attributes of and axis are represented as ⃗ and ⃗
respectively. Besides, specifies the covariance among the
⃗and ⃗. An unwanted information during the distance
calculation is reduced by considering the correlation among the
⃗ and ⃗ in the mahalanobis distance. This helps to enhance the
accuracy while calculating the accuracy.
This MDBC algorithm clusters the network as well as
it selects an effective CH from its clusters. The location
information about the identified CHs are given to the GSA
based routing to identify the optimal routing path through the
MANET.
3.2. Gravitational search algorithm based routing
GSA is generally a population search optimization
algorithm which is motivated by the newton first law of
gravity. In GSA, the particles are attracted to other particles
based on the gravitational force that is directly proportional to
product of masses and inversely proportional to the distance
among the particles. The newton 2nd law states that each
particle acceleration is mainly depends on the applied force and
particle’s mass.
The swarm of agents considered in the GSA is and the
each agent of GSA is , 1 < < which specifies the
complete solution. The position and velocity of an each agent
are
and
, where 1 < <, the dimension of the search
space is represented as .
The th agent position, when there is a amount of
agents in the GSA is represented as =
,
,…,
, where
th agent position in dimension is denoted as
. Equation
(3) shows the force acted on the agent from th agent.
()=()()×()
()
()−
()
(3)
Where, active gravitational mass with respect to the th agent
is represented as (); passive gravitational mass with
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
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respect to the th agent is represented as (); gravitational
constant in time is represented as () and Euclidian distance
among the agent and is . Moreover, the total amount of
force used by the all agents on the agent at dimension in
time is represented in equation (4).
()=∑
, ×
() (4)
The gravitational and inertial masses for agent are
determined by using the fitness calculation given in the
equation (5). Here, the agent with less masses is considered as
less capable than the remaining agents.
()=()
()() (5)
Where, the th agent fitness function at time is represented as
(). Additionally, () and () are expressed in
equation (6) and (7) respectively. Since, () and ()
are defined for minimization problem.
()= min() ∈ {1,2,…,} (6)
()= max() ∈ {1,2, … ,} (7)
The following equation (8) shows the masses of the
GSA,
= = =,=()
∑()
(8)
The th agent acceleration in direction at time is defined
based on the motion law which is shown in the equation (9).
=
()
() (9)
Therefore, the agents with higher masses have minimum
fitness value which is considered as effective agents. The
agents with higher masses attracts the remaining masses.
Moreover, the forces from the higher mass and higher
acceleration affects the agents with less masses. The optimal
solution is discovered by updating the velocity and position of
the agent. The velocity and position of the agent in the GSA are
represented in the equation (10) and (11) respectively.
(+1)= ×
()+
(10)
(+1)=
(+ 1)+
(+1) (11)
3.2.1. Identification of optimal path for data
transmission
The th agent defines the complete solution in the GSA
algorithm and the agent denotes the CH’s optimal location
obtained from the distance based clustering. Here, the
dimension of all agent is identical to the amount of CHs.
Consider =
,
,… ,
which defines the agent in the
population, 1 < <, where the amount of CHs in the
network is ; amount of agents are represented as ;
component in the th agent is
, 1 < < specifies the
sensor’s coordinates.
3.2.1.1. Fitness function
The fitness function used in the GSA for identifying the
optimal path considers three different values such as residual
energy of the CH, distance and node degree.
a. Residual energy
The primary objective considered in the fitness function is
the residual energy of the CH (). The node with higher
energy has higher priority for transmitting/ receiving the data
from the cluster members. The first objective () is expressed
in the equation (12).
=∑
(12)
b. Distance
The distance is considered as secondary objective()
during route generation, which is given in equation (13). Here,
two kinds of differences are considered such as distance from
the CH to the Next Hop (NH) node ((,)) and
distance from the NH to the destination node ((,)),
where represents the destination node.
=
∑,(,)
(13)
c. Node degree
The node degree specifies the normal nodes present in the
NH node. The amount of energy consumption while receiving
the data is less, when the NH node’s node degree is less in the
network. The first objective () is expressed in the equation
(14).
=
∑
(14)
Where, the specifies the number of nodes in the respective
CH.
The computed objective values are conflicted each other, so
weighted sum approach is used and a specific weight value is
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
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assigned to each objective value. Here the multiple objectives
are converted into single objective which is shown in equation
(15).
=×+×+× (15)
Where ∑
= 1,∈(0,1)
Therefore, an optimal transmission path is identified by
using the GSA algorithm. After identifying the transmission
path, the data packets are transmitted from the source to the
destination.
In this proposed method, the CH’s residual energy is
monitored at each simulation time to avoid the route failure
through the network. The clustering algorithm is initiated again
in the network, when any of the CHs energy level goes beyond
the threshold level. Additionally, the faster convergence of the
GSA is used to obtain an optimal routing path. The nodes with
inadequate residual energy doesn’t considered in the routing
path. Because, the node failure during data transmission causes
the packet loss. After determining the routing path, the data
packets are transferred through the network.
4. RESULTS AND DISCUSSION
The experimental and comparative analysis of the proposed
method is described in this section. The implementation and
simulation of this MANET is carried out by using the Network
Simulator-2.35. In MANET, the MDBC is developed for
identifying the CHs through the network. Subsequently, the
GSA based algorithm is developed for creating the
transmission path from the source to the destination node.
Here, 100 nodes are considered to be deployed over the area of
1500× 1500. The specification parameters considered in
this proposed method is specified in the Table 1.
Table 1: Specification parameters
Parameters
Value
Area
1500m
×
1500m
Clustering algorithm
MDBC
Routing algorithm
GSA
Number of nodes
100
Number of packets
20
-
300
Mobility model
Random way point model
Speed of node
0
–
25 ms
Transmission range
250 m
Simulation time
50
–
500 s
4.1. Performance analysis
The performance analysis of the proposed method is taken
in terms of average Packet Delivery Ratio (PDR), energy
consumption, average End to End Delay (EED) and normalized
routing control overhead. Additionally, these performances are
compared with FEER [20] and FHACO [20]. This FEER [20]
and FHACO [20] are also implemented and simulated for the
specifications mentioned in the Table 1. The performances are
taken by varying the different number of data packets.
4.1.1. Packet delivery ratio
PDR is ratio between the amount of packets received in the
destination and amount of delivered packets by the source
node. Since, the PDR is mainly depends on the packets which
is transmitted and collected while delivering the packets.
Figure 2: Packet delivery ratio
Figure 2 shows the comparative analysis of PDR for
proposed method with FEER [20] and FHACO [20]. This PDR
comparison is made by varying the amount of packets
delivered through the network. From the Figure 2, concludes
that the proposed method higher PDR than the existing works.
For example, the PDR of the proposed method is 98% at
100data packets which is high when compared to the existing
works. The proposed method achieves the higher PDR by
considering the residual energy of the nodes in the fitness
function of GSA. The residual energy consideration is used to
avoid the node with lesser energy during the data transmission.
Because the node with less residual energy causes the node
failure which causes the packet loss through the network.
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
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4.1.2. Energy consumption
The calculation of energy consumption is depends on the
amount of energy consumed by the energy during the packet
routing in MANET. The energy consumption is the produce of
the sensor nodes, power and time. Moreover, the power and
time is considered in terms of watts and seconds respectively.
Figure 3: Energy consumption
The energy consumption of the proposed method with
FEER [20] and FHACO [20] is shown in the Figure 3. The
comparison shown in the Figure 3 is taken by varying the
number of packets from 30 to 300. From the Figure 3, knows
that the energy consumption of the proposed method is less
when compared to the FEER [20] and FHACO [20]. For the
instance, the energy consumption of the proposed method is
940J at 300 packets, which is less when compared to the
existing works. The MDBC used in the proposed method
precisely identifies the CH over the MANET. This helps to
reduce the unwanted energy consumption. Moreover, the
distance consideration in the GSA is used to detect the shortest
path over the MANET. The energy consumption of the network
is minimized by identifying the shortest path over the network.
4.1.3. Average end to end delay
EED is defined as the amount of time taken for transmitting
and receiving the data packets over the network.
Figure 4: Average end to end delay
Figure 4 shows the comparative analysis of average EED
for proposed method with FEER [20] and FHACO [20]. This
average EED comparison is made by varying the amount of
packets from 10 to 100. From the Figure 4, knows that the
proposed method less EED than the existing works. For
example, the average EED of the proposed method is 27.4ns at
100 data packets that is less when compared to the existing
works. The lesser delay over the MANET is achieved by
considering the distance factor in both the clustering and
routing while routing the data to the destination.
4.1.4. Normalized control overhead
Normalized Control overhead is defined as total amount of
unaffected packets are provided while transmitting the data
packets.
Figure 5: Normalized control overhead
T.V.Suresh Kumar et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 4201 – 4208
4207
The normalized control overhead of the proposed method with
FEER [20] and FHACO [20] is shown in the Figure 5. The
comparison shown in the Figure 5 is taken by varying the
number of packets from 10 to 100. From the Figure 5, knows
that the normalized control overhead of the proposed method is
less when compared to the FEER [20] and FHACO [20]. For
example, the overhead of the proposed method is 27 at 100 data
packets that is less than the existing works. The fitness function
values of GSA such as residual energy, distance and node
degree are used to minimize the losses while transmitting the
data packets. This helps to minimize the routing overhead in
the MANET.
5. CONCLUSION
In this paper, the MDBC algorithm and GSA routing is
combined for achieving the better data transmission through the
network. The MDBC algorithm used in this proposed method
considers the covariance during distance calculation which
helps to select the significant CH. Additionally, the faster
convergence of the GSA selects an optimal path from the
source to the destination. The GSA used in the proposed
method considers three values such as residual energy, distance
and node degree. Therefore the developed clustering and
routing overcome the issues of link/ node failure caused by the
dynamic network topology and less battery power. The
proposed method shows better performances than the FEER
and FHACO. The control overhead is 27 at 100 data packets
that is less than the FEER and FHACO methods.
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