Enhancing energy efficient dynamic load balanced clustering protocol using Dynamic Genetic Algorithm in MANET

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303
37
Enhancing energy efficient dynamic load balanced
clustering protocol using Dynamic Genetic Algorithm
in MANET
T.Dhivya1
1
M.E communication and Networking
National Engineering College, Kovilpatti,
dhivyathiru17@email.com
M.Kaliappan2
2
Assistant Professor, IT Department
National Engineering College, Kovilpatti
kalsrajan@yahoo.co.in
Abstract— Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc networks.
MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that
leads to dynamic load balanced clustering problem (DLBCP). It is necessary to have an effective clustering algorithm for
adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load
balance and energy metric in GA to solve the DLBCP. It is important to select the energy efficient cluster head for
maintaining the cluster structure and balance the load effectively. Elitism based Immigrants Genetic algorithm (EIGA) and
Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster
head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the
population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency
for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed
schemes increases the network life time and reduces the energy consumption.
Index Terms— MANET, DLBCP, Genetic Algorithm (GA), Dynamic GA, Immigrants scheme, Memory scheme.
——————————  ——————————
1 INTRODUCTION
In Mobile Ad hoc Network (MANET), it has more number of
nodes and its topology changes randomly and dynamically due to
its characteristics [2]. So clustering of nodes is very essential in
MANET. The information about the nodes and links are stored
within each cluster. Due to the random movement of nodes in
MANET, Scalability problem arises. So Clustering [3] is the most
efficient one to solve this problem in MANETs. And also
clustering is useful for efficient routing and for topology control.
For effective cluster structure, the selection of cluster head is
more important in a dynamically changing environment. Genetic
Algorithm (GA) techniques is used to find the best and optimal
cluster head to each cluster.
Genetic Algorithm is an optimization heuristics technique to
find the optimal solution. This technique will select the best one
from the group of members by using the operations like selection,
mutation and crossover. Genetic Algorithm is not a deterministic
technique so it is used in the dynamic environments to select the
possible solutions. In the genetic algorithm,each one represents a
possible solution. In this paper, this technique is used to select the
cluster head dynamically .Cluster heads are selected based on the
distance and energy of each node in network.
Due to the movement of nodes in the MANET, it leads to
Dynamic Load Balanced Clustering Problem (DLBCP). Dynamic
genetic algorithm is used to solve the DLBCP. In a Dynamic
genetic Algorithm, the series of Dynamic schemes are used to
manage the dynamic network environments and to maintain the
populations. The dynamic GA schemes include immigrants
schemes and memory related schemes are mainly used to
maintain the population and to store the information about an
environment respectively. The contributions of this work are
summarized as follows.
 We designed a Dynamic optimization problem into
dynamic load balanced clustering problem (DLBCP). So
Dynamic GA is used to solve this problem
 Cluster head selection is done by using the genetic
Algorithm operations such as selection, fitness function,
mutation and crossover. In this work we have considered
the parameters like distance and energy to select the
cluster head and to balance the load.
 We used two dynamic schemes like Elitism based
Immigrants Genetic Algorithm (EIGA) and Memory
Enhanced Genetic Algorithm (MEGA) to solve this
problem.
 We examine their performances on the DLBCP. The
results show that MEGA out performs the EIGA.
The rest of this paper contains the following sections. In section 2
the related works are discussed. The Network model and DLBCP
problem are discussed in the section 3. In section 4 we had
presented the pursued GA for the static load balanced clustering
problem. The dynamic GA schemes are described and also it is
used to solve the DLBCP in the section 5. In section 6 the
experimental analysis and the performance of the networks are
evaluated. Conclusion of this paper was presented in the section 7
2 RELATED WORKS
Priyanka Goyal et al. [1] described the fundamental problems
of ad hoc network by giving its related research background
including the concept, features, status, and vulnerabilities of
MANET. Due to severe challenges, the special features of

38
MANET bring this technology great opportunistic together. This
paper presents an overview and the study of the routing
protocols. Also include the several challenging issues, emerging
application and the future trends of MANET.
Aarti et al.[2] discussed MANET and its characteristics,
challenges, advantages, application, security goals, various types
of security attacks in its routing protocols. Security attack can
divided as a active or passive attacks. Due to dynamic topology,
distributed operation and limited bandwidth MANET is more
vulnerable to many attacks.
Hui Cheng et al [4], developed the dynamic load balanced
clustering problem (DLBCP) into a dynamic optimization
problem.In this, they designed to use a series of dynamic genetic
algorithms (GAs) to solve the DLBCP in MANETs. They earned
good results by using memory related GAs than using standard
GAs. In this GAs are not used to solve the dynamic multi-metric
clustering problem.
Shengxiang Yang et al [5], addressed the static shortest path
(SP) problem .The SP routing problem in MANETs turns out to
be a dynamic optimization problem due to the characteristics of
MANETs.They achieved better performance by using memory
related GAs than using standard GAs. GA approaches are not
applied to multicasting routing problem in dynamic network
environments
Bhaskar Nandi et al [6], focussed on the implementation of
Weighted Clustering Algorithm with the help of Genetic
Algorithm (GA). They applied genetic algorithms (GA) as an
optimization technique to improve the performance of cluster
head election procedure. They had achieved that the cluster head
will be minimized by incrementing the transmission range So GA
does not provide a optimal solution, when they decrease the
transmission range.
Bo Peng and Lei Li [7], presented a new adaptive genetic
simulated annealing algorithm (AGSAA) for QoS multicast
routing. Their experiments, mainly test the convergence ability,
the convergence speed, and the least-cost of achieving solutions
in each generation. The result indicates that the AGSAA provides
a significant improvement for obtaining a global optimum
solution .The simulation results show that this AGSAA algorithm
has fast convergence speed.
Abin Paul, preetha k.g [8], described a novel method to deal
the dynamic nature of ad hoc networks. Their main idea is to
cluster the networks and to use an „avoidance strategy‟ for the
clusters up to a particular threshold. Topology tracing is done
by flooding which consumes much of the network resources. In
this they didn‟t use the efficient method to trace the networks.
Ting Lu and Jie Zhu [9], Proposed an energy efficient genetic
algorithm to find the delay-constrained multicast tree and reduce
the total energy consumption of the tree. Power-aware
multicasting was proposed to reduce the power consumption. A
series of experiments was performed to verify the convergence
performance, SR and running time of the proposed algorithm.
Syed Zohaib Hussain Zahidi et al [10], proposed the
development of an improved ILP formulation of the Clustering
Problem. There have been several improvements in the
performance of Integer Linear Programming (ILP) and Boolean
Satisfiability (SAT) solvers in modeling complex engineering
problems. One such problem is the Clustering Problem in Mobile
Ad-Hoc Networks (MANETs).SAT solvers performed well for
small scale networks. It takes more time to find optimal solution
as the network gets bigger.
Peng Zhao,Xiny et al[11], developed an LVC algorithm to
construct a hierarchical network and to eliminate unidirectional
links to explore the advantages of high-power nodes, Power
heterogeneity is common in MANETs. They presented a loose-
virtual-clustering-based (LVC) routing protocol for power
heterogeneous (LRPH) MANETs. They achieved better
performance by using LVC based Routing Protocol than other
existing protocols. Energy issues are not focused.
Ibukunola.A et al [12], described an energy function model
base on Geographic Adaptive Fidelity (GAF), which is one of the
best known topology management schemes used in saving energy
consumption in ad-hoc wireless networks. They showed meta-
heuristics based optimization methods are effective and useful for
minimizing energy consumption.
Jie Yang, Nei Kato et al. [13] focused on the issue of
certificate revocation to isolate attackers from further
participating in network activities. They proposed a cluster-based
certificate revocation with vindication capability (CCRVC)
scheme to solve the problem of false accusation. The proposed
CCRVC scheme is more effective and efficient in revoking
certificates of malicious attacker nodes, reducing revocation time,
and improving the accuracy and reliability of certificate
revocation.
John M.Shea, Joseph P.Macker (14), developed a new method
for determining the number of clusters based on the eigenvalues
of a normalized graph Laplacian matrix. REQ achieves much
more stable way to select the number of clusters. In this they
showed that the effectiveness of using REQ is used to select the
number of clusters. This method is not suitable for updating the
clustering in a distributed manner as the network evolves over
time.
From these works we had concluded that it is essential to
design dynamic genetic algorithm to solve the multi-metric
clustering problem.
3 DYNAMIC LOAD BALANCED CLUSTERING
PROBLEM
In this we developed an MANET design and then defined the
Dynamic load balanced clustering problem (DLBCP). After the
creation of nodes, due to the characteristics of MANET the
problem turns to be DLBCP. We had developed the MANET ant
it works within a transmission range of about 250m. After the
creation of nodes we had defined a set of clusters and each cluster
head will send the data to the nodes which are in the transmission
range of 250m. And then cluster head is selected by considering
the parameters like distance and energy to balance the load. To
find the minimum deviation of each node the following formula
is used.
 

m
j
CHjCH ii
dd
m 1
21
 (1)
where represents the cluster head degree cj that is the number

39
of cluster members served by cluster head cj and represents
the average number of cluster members served by the cluster
head. To find the energy efficient node the following formula is
used.
ideal
E
rx
E
tx
E
T
E  (2)
where,
Etx and Erx is the energy consumed when the packet is
transmitted and received.
Then, we selected the minimum standard deviation node as well
as the energy efficient node as a cluster head
4 PURSUED GA FOR THE DLBCP
In this section we had developed the factors for DLBCP. The
GA consists of various operations like genetic representations,
population initialization, fitness function, selection scheme,
crossover and mutation. In this work, the pursued GA was
developed to solve the DLBCP. In the dynamically changing
environment the clustering problem will offer different sequences
of cluster head for each environmental change. The node degree
and the number of cluster members are used as the parameters in
the fitness function. These two parameters are not dependent of
other environmental components. The crossover and mutation
operation can also be well designed, after these operations the
repeated node IDs will not be appearing in the chromosomes.
4.1 Genetic representations
A random permutation of node IDs are used to generate a
random set of cluster heads. In this work, random permutation of
node IDs are used to represent a chromosome.
Population :{n1,n2,n3,n4,n5,n6,n7,n8} Where n1…,n8 represents
the number of nodes in the network
Gene: Represents each node in network, i.e.{n1},{n2},…,{n8}
Chromosome: Set of permutation nodes of nodes
i.e.{n4,n3,n8,n7,n1,n6,n2,n5}
• The permutations are calculated by using the formula:
npr = n!/ (n-r)!
where
n represents the total number of nodes
r represents the elements taken from the given set of n.
4.2 Population Initialization
In a GA, each chromosome corresponds to a potential
solution. The initial population Q is composed of a certain
number, denoted as q, of chromosomes. To explore the genetic
diversity, in our algorithms, for each chromosome, the
corresponding permutation of node IDs is randomly generated
(Hui Cheng, Shengxiang Yang , Jiannong Cao , 2013).
The initial population is generated by the following method.
Step 1 : start (k = 0).
Step 2: generate chromosome Chrk: create a random permutation
of the entire node IDs and derive the corresponding cluster head
set CHk.
Step 3: k = k + 1. If k < q, go to Step 2, otherwise, stop.
Thus, the initial population Q = {Chro , Chr1, . . .,Chrq_ 1} is
obtained. The problem we have investigated is a combinatorial
optimization problem and is called DLBCP.
4.3 Fitness Function
In this section we accurately evaluated its quality (i.e., fitness
value), which is determined by the fitness function. In our
algorithms, we aim to find the set of cluster heads, which can
build up the load balanced cluster structure, that is, each cluster
head has the efficient energy to balance the load. For each round
the cluster head is elected by finding the minimum deviation and
the energy consumption of each node. The load is balanced by
using the energy consumption of each cluster head.
Therefore, from the set of nodes in the network, we had
chosen the one with the least standard deviation as well as the
energy efficient node as the cluster head.
The fitness value of chromosome Chri(representing the cluster
head CHi), denoted as F(Chri), is given by:
 
1
1
2)(
11)(




m
j i
CH
d
j
d
mi
CHi
ChrF  (3)
After applying this equation to each node the result will be
F(chri)={CH1,CH2,……..,CHn}
F(Chri) represents the fitness value of chromosome
Chri(representing the cluster head CHi)
4.5 Selection scheme
Selection plays a main role in improving the average quality
of the population by passing the high quality chromosomes to the
next generation. The selection of chromosome is based on the
fitness value. We adopt the scheme of pairwise tournament
selection without replacement (Chang Wook Ahn,and R. S.
Ramakrishna,2002) as it is simple and effective.
4.6 Crossover and Mutation.
Crossover and mutation are two vital genetic operators.
Crossover is used to generate two offspring chromosomes from
two parent chromosomes. From different parts of the two parent
chromosomes all the genes in each offspring chromosome are
acquired. Mutations are used to generate an offspring
chromosome from only one parent chromosome. This is done by
changing the values of some genes.
5 DYNAMIC GA SCHEMES
5.1 GA with immigrants schemes
The most well-known advance to create the new arrivals
seems to create it erratically. In this we used arbitrarily
accomplished the individuals to replace the worst individuals of
the population in each generation. The random immigrant scheme
performs well in occasional environments and large changes in
the location of the optimal.
5.2 The Elitism-Based Immigrants Scheme
In a continuously changing environment, the introduced
random immigrants [16] may turn away the searching force of the
GA during each environment before a change occurs and hence
may degrade the performance. On the other hand, if the
environment only changes slightly in terms of cruelty of changes,

40
random immigrants may not have any actual effect even when a
change occurs because individuals in the previous environment
may still be quite fit in the new environment.
Based on the above concern, an immigrants advance, called
elitism-based immigrants [4], was proposed for GAs to address
DLBCPs. Within the EIGA, for each generation t, after the
normal genetic operations (i.e., selection and recombination), the
best E (t - 1) from previous generation is used as the base to
create the new arrivals. From E (t - 1), a set of rein individuals are
iteratively generated by mutating E(t - 1) with a probability pm
i
,
where n is the population size and rei is the ratio of the number of
elitism-based immigrants to the population size.
Then,the generated individuals will act as immigrants and
replace the worst individuals in the current population. It uses the
best one from previous population to guide the immigrants
toward the present environment, which is likely to recover the
presentation of GAs in dynamic environments.
5.3 GA with memory schemes
In this paper, the memory approach [14] was used to enhance
the GA‟s performance in dynamic environments in a variety of
ways. The memory approach works by storing useful information
from the current environment, either implicitly through redundant
representations or explicitly by storing good solutions of the
current population in an extra memory space. And the stored
information can be restarted later in the new environment.
For example, for the explicit memory scheme, when the
current environment changes, old solutions in the new
environment will be reactivated and hence may adapt the GA to
the new environment more directly. For the memory
management, since the memory space is usually restricted, it is
essential to inform memory solutions, when it full, for making
room for new ones. A universal approach is to pick one memory
point to be removed for that best individual from the populations
or to be moved forward it. As to which memory point should be
chosen for updating, there are several memory replacement
approaches.
For example, replacing the least important one with respect to
the age, contribution to diversity and fitness, replacing the one
with least contribution to memory variance, replacing the most
similar one if the new individual is better, or replacing the less fit
one of a pair of memory points that have the minimum distance
among all pairs.
5.4 Memory Enhanced GA
The GA with the memory scheme, called memory-
enhanced GA (MEGA)[15] which uses a memory of size m =
0.1* n. The memory in MEGA is re-evaluated every generation
to detect environmental changes. If an environmental change is
identified, then the memory is added with the current population
and the best n - m individuals are selected as a provisional
population to experience genetic operations for a new population
while the memory remains unchanged.
The memory is also revised according to the population just
before the environmental change in order to store the most
important information to an environment in the memory, every
time an environmental change is identified. If any of the
erratically initialized points still exists in the memory, the best
individual of the current population will change one of them
unpredictably when the memory is to update.
5.5 Pseudo code for MEGA
t:=0, tM := rand(5,10) and ET = 1 joule
initialize population P(0) and memory M(0) randomly
repeat
evaluate population P(t) and memory M(t)
replace the worst individual in P(t) by the elite E(t-1) from P(t-
1)
Elite’(t-1) =(Elite(t-1)) > ET
If change detected then
P”(t) := retrieve Best Members From (P(t), M(t))
Else P’(t) := P(t)
// time to update memory
If t= tM || change detected then
If t = tM then
Bp(t) := retrieve best member from (P`(t))
If change detected then Bp(t) := E`(t-1)
If still any random point in memory then,
replace a random point in memory with Bp(t)
else
If t=tM then
Find the memory point CM(t) closest to Bp(t)
If f(Bp(t)) > f(CM(t)) then
CM(t) := Bp(t)
TM := t+rand(5,10)
// standard genetic operations
P``(t) := select for reproduction (P’(t))
Crossover(P``(t),pc)
Mutate(P``(t),pm)
P(t+1) := P``(t)
Until the termination condition is met // e.g.,t>tmax
Where,
P(t) & m(t) are the population and memory respectively
P’(t) is the best member which is retrieved from p(t) and m(t)
Bp(t) is the best member which is retrieved from P’(t)
CM(t) is the memory point closest to Bp(t).
6 PERFORMANCE EVALUATIONS
Performance of the network can be evaluated through a
number of nodes changes in the network and the metrics used
in network. Several metrics can be defined to grade the
performance of a technology against the elements of wireless
networking. In this paper, we had considered the following
metrics
 Packet delivery ratio
 Throughput
 Routing over head
 End-to-end Delay
 Total energy
since packets are sent from the source and destination by
using the efficient clustering structures.
.

41
Fig. 1. Graph for Packet Delivery Ratio
Fig. 1. Packet Delivery Ratio vs. no of nodes
fig.1. Shows that the above graph is plotted across the number
of nodes and the packet delivery ratio (PDR). Normally the value
of PDR will get increased when we compared with the existing
methods. In this graph, it shows that the packet delivery ratio
increased for the proposed model since it stores, the best
individuals in the memory when compared to the existing
methods. In proposed method it shows that up to 95% it gets
increased.
Fig. 2. Average delay vs. no of nodes
fig.2. Shows that the graph is plotted across the number of
nodes and the average delay in the network. When we are
increasing the value of nodes the delay will also get increased.
Normally the value of delay will get decreased when compared
with the existing method. In this graph the average delay will get
decreased in the proposed method when compared with the
existing methods.
Fig.3. Routing overhead vs. no of nodes
nodes and the routing overheads in the network.when increasing
the no of nodes the routing overheads are also increases.From the
graph it has been shown that the routing overhead decreased in
the proposed method when compared with the existing methods.
Fig. 4. Graph for Throughput
Fig. 4. Throughput vs. no of nodes
nodes in the network and the throughput.Normally the value of
throughput will get increased in the proposed model since it
sends the more amonut of data in a time when compared to the
existing onee.From the graph it has been shown that the value of
throughput was increased in the proposed work when compared
to the existing one.

42
Fig. 5.Total energy Consumption vs. no of nodes
fig. 5. Shows that the graph is plotted across the number of
nodes and the total energy.This graph shows that the total energy
consumption of nodes decreases in memory enhanced genetic
algorithm(MEGA) since it stores, the best individuals in the
memory when compared to the elitism based immigrants genetic
algorithm(EIGA).
Fig.6.Number of packets vs.delay
fig. 6. Shows that the graph is plotted across the number of
packets and delay.This graph shows that the delay increases when
we are increasing the number of packets. But when we compared
with the existing methods, the delay was decreased in the
proposed scheme (EEDLBCP_MEGA).
7 CONCLUSION
A novel general model of dynamic network optimization
problem is developed. By considering the domain knowledge of
network clustering, we develop a two dynamics handling
schemes like EIGA and MEGA. For the selection of cluster head
the distance and energy parameters are considered and finally the
cluster head is elected by using this Dynamic GAs. By using
Memory Enhanced GA (MEGA) we earned a better performance
than Immigrants based GA (EIGA).The efficiency and
effectiveness of dynamic GAs have also been verified with the
existing ones.
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