Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
10,
No.
1,
February
2020,
pp.
500
511
ISSN:
2088-8708,
DOI:
10.11591/ijece.v10i1.pp500-511
r
500
Ener
gy
efficient
intelligent
r
outing
in
WSN
using
dominant
genetic
algorithm
Shanthi
D
L
1
,
Dr
.
K
esha
v
a
Prasanna
2
1
Department
of
Information
Science
and
Engineering,
BMSIT
&
M,
Bang
alore,
India
2
Department
of
Computer
Science
and
Engineering,
CIT
,
T
umkur
,
India
Article
Inf
o
Article
history:
Recei
v
ed
Feb
14,
2019
Re
vised
Aug
19,
2019
Accepted
Aug
30,
2019
K
eyw
ords:
Ener
gy
ef
ficient
Genetic
algorithm
Heuristic
Mobility
Netw
ork
lifetime
ABSTRA
CT
In
the
current
era
of
wireless
sensor
netw
ork
de
v
elopment,
among
the
v
arious
challeng-
ing
issues,
the
life
enhancement
has
obtained
the
prime
interest.
Reason
is
clear
and
straight:
the
battery
operated
sensors
do
ha
v
e
limited
period
of
life
hence
to
k
eep
the
netw
ork
acti
v
e
as
much
as
possible,
life
of
netw
ork
should
be
lar
ger
.
T
o
enhance
the
life
of
the
netw
ork,
at
dif
ferent
le
v
el
dif
ferent
approaches
has
been
applied,
broadly
defining
the
proper
scheduling
of
sensors
and
defi
ning
the
ener
gy
ef
ficient
commu-
nication.
In
this
paper
heuristic
based
ener
gy
ef
ficient
communi
cation
approch
has
applied.
A
ne
w
de
v
elopment
in
the
Genetic
algorithm
has
presented
and
called
as
Dominant
Genetic
algorithm
t
o
determine
the
optimum
ener
gy
ef
ficient
routing
path
between
sensor
nodes
and
to
define
the
optimal
ener
gy
ef
ficient
traj
ectory
for
mobile
data
g
athering
node.
Dominanc
y
of
high
fitness
solution
has
included
in
the
Genetic
algorithm
because
of
its
natur
al
e
xistence.
The
proposed
solution
has
applied
the
con-
nection
oriented
crosso
v
er
and
mutation
operator
to
ma
intain
the
feasibility
of
gener
-
ated
solution.
The
proposed
solution
ha
s
applied
with
v
arious
simulation
e
xperiments
under
tw
o
dif
ferent
scenarios:
in
first
case
ener
gy
ef
ficient
routes
among
the
sensors
ha
v
e
e
xplored
to
deli
v
er
the
information
from
source
sens
or
to
the
sink
node
and
in
second
case,
ener
gy
ef
ficient
route
among
all
local
data
hubs
for
mobile
data
g
athering
node
has
obtained.
The
proposed
solution
performances
ha
v
e
been
analyzed
quantita-
ti
v
ely
and
analyt
ically
.
It
has
observ
ed
with
v
arious
e
xperimental
results
that
proposed
method
not
only
has
deli
v
ered
the
better
solution
b
ut
also
has
f
aster
con
v
er
gence
and
high
le
v
el
of
reliability
in
compared
to
con
v
entional
form
of
Genetic
algorithm.
Copyright
c
2020
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Shanthi
D
L,
Department
of
ISE,
BMS
Institute
of
T
echnology
,
Bang
alore-64,
India.
T
el:
+91-9449176450
Email:
gopalaiahshanthi@bmsit.in
1.
INTR
ODUCTION
The
inno
v
ations
and
progress
in
wireless
communication
and
micro-sensing
deli
v
ers
a
useful
means
to
observ
e
en
vironment,
ecological
system,
personal
health,
in
b
uilding
smart-homes,
military
surv
eillance,
v
ehicle
monitoring
and
so
on.
An
y
sensor
netw
ork
is
implemented
using
a
huge
number
of
tin
y
de
vices
that
are
limited
by
sensing,
processing,
transmitti
ng
abilities,
and
are
battery
po
wered.
Collect
ing
data
is
the
most
common
and
essential
tasks
in
sensor
netw
orks;
ef
fecti
v
eness
of
e
x
ecuting
data
collection
opera-
tion
defines
netw
ork
lifetime.
Due
to
the
inadequate
radio
resources
and
the
ener
gy
limitation
on
each
sensor
node,
it
is
v
ery
moti
v
ating
task
t
o
e
xtend
the
netw
ork
lifetime
while
maintaining
certain
data
collection
rate.
Se
v
eral
w
ays
are
used
to
transfer
the
sensed
data
from
sensor
nodes
to
the
base
station
for
processing
say
multi-hop.
Ener
gy
is
the
primary
apprehension
to
W
ireless
Sensor
Netw
orks
(WSNs)
for
all
its
transmis-
J
ournal
homepage:
http://iaescor
e
.com/journals/inde
x.php/IJECE
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
501
sion
and
reception
of
data
b
ut,
consumes
a
lot
of
ener
gy
to
synchronize
and
to
ensure
the
location
a
w
areness
of
the
sensors.
A
number
of
researchers
ha
v
e
suggested
mobility
as
a
solution
to
the
problem
of
data
g
athering
[1-7].
Contemporary
study
has
re
v
ealed
that
a
significant
decrease
in
communication
ener
gy
con-
sumption
using
controlled
mobility
in
WSN.
Example,
a
Mobile
Base
Station
(MBS)
can
go
from
place
to
place
in
the
sensing
area
to
collect
data
from
nodes
using
short-range
communications.
Introduction
of
mobile
nodes
in
to
WSN,
ener
gy
utilization
of
static
nodes
may
be
reduced.
Unscheduled
mobility
of
mobile
node
might
increase
latenc
y
in
data
collection.
Hence,
it
is
necessary
that
trajectory
obtained
by
MBS
should
be
optimal
in
the
sense
of
tra
v
el
distance
and
in
v
ested
ener
gy
.
Major
limiting
f
actor
of
a
WSN
is
its
node’
s
ener
gy
,
which
demands
the
design
of
an
ener
gy-ef
ficient
routing
protocol
that
increases
the
whole
system
performance.
In
this
paper
tw
o
dif
ferent
pers
pecti
v
es
of
ener
gy
sa
ving
scheme
in
WSN
has
presented.
In
the
firs
t
case
the
need
of
routing
path
between
tw
o
sensors
obtained
which
carry
the
minimum
e
xpenditure
of
ener
gy
while
in
other
case
the
trajectory
of
mobile
data
g
athering
has
been
defined
to
collect
the
data
from
dif
ferent
local
base
nodes
which
already
has
the
information
of
all
sensors
belonging
to
their
clusters.
It
is
assumed
that
in
the
first
case
there
is
no
mobility
observ
ed
among
t
he
sensors
while
in
the
second
case
cluster
heads
were
already
a
v
ailable.
Because
of
comple
xity
in
v
olv
ed
with
the
problem
is
NP
hard,
heuristic
approach
has
applied
to
achie
v
e
the
solution.
Among
the
v
arious
possibilities,
the
heuristic
approach
of
Genetic
algorithm
has
pro
v
ed
with
the
time
as
one
of
the
more
dominant
and
wide
applicable
method.
There
are
v
arious
issues
with
con
v
entional
approach
of
Genetic
algorithm
(CGA)
lik
e
unf
air
selection
of
parents
to
produce
of
fspring,
locus
of
crosso
v
er
point,
strate
gy
of
mutation
applied
and
more
importantly
balancing
between
e
xploration
ag
ainst
e
xploitation.
T
o
o
v
ercome
these
issues,
a
dominant
form
of
Genetic
al
g
or
ithm
has
proposed
which
pro
vide
f
air
opportunities
to
each
and
e
v
ery
parent
in
participation
of
of
fspring
creation
helps
in
e
xploration,
while
elite
members
of
the
population
deli
v
er
the
rule
of
dominanc
y
to
e
xploit
better
one.
A
f
air
selection
process
through
tournament
selection
has
applied
which
deli
v
er
the
number
of
opportunities
to
each
and
e
v
ery
one
to
pro
v
e
itself
rather
than
fitness
oriented
selection
which
causes
f
aster
con
v
er
gence
with
sub-optimal
solution.
From
simulation
e
xperiments
it
has
been
observ
ed
that
there
is
better
and
f
aster
routing
strate
gy
achie
v
ed
within
v
ery
less
number
of
iterations
which
mak
es
the
proposed
solution
v
ery
computation
ef
ficient
also.
2.
RELA
TED
W
ORK
In
the
direction
of
ener
gy
sa
ving
scheme
in
WSN,
there
were
number
of
dif
ferent
approaches
by
dif
ferent
researchers
ha
v
e
been
proposed
in
the
past.
In
[1],
using
the
theory
of
potential
in
ph
ysics,
an
Ener
gy-
Balanced
Routing
Protocol
(EBRP)
had
been
designed
by
crea
ting
a
mix
ed
virtual
potential
field
in
terms
of
depth,
ener
gy
density
,
and
residual
ener
gy
.
In
[2],
the
nodes
deplo
yed
in
WSN
uses
t
he
rene
w
able
ener
gy
generated
by
solar
panels
b
uilt
o
v
er
for
routing
and
sensing,
here
ener
gy-ef
ficient
routing
protocol
for
ener
gy
harv
esting
WSN
is
designed
and
implemented.
While
choosing
route
the
parameters
considered
are
trans-
mission
quality
,
ener
gy
depletion
and
ener
gy
w
asting
and
the
ef
fect
of
bit
error
rate
(BER).
T
o
maximize
this
WSN
feature,
the
data
and
message
deli
v
ery
routes
are
carefully
chosen
so
that
o
v
erall
ener
gy
consumption
is
minimized.
M.
Bayani
[3]
had
analyzed
a
detai
led
comparison
between
typical
WSN
protocols
and
their
impacts
o
v
er
the
WSN
lifetime
and
percei
v
ed
that
flat
and
cluster
-based
protocols
can
increase
WSN
lifetime
in
dif
ferent
w
ays.
Study
has
presented
in
[4]
o
v
er
basic
optimization
of
base-station
positioning
in
WSN
so
that
the
data
from
the
s
ensors
can
be
communicated
in
an
ener
gy-ef
ficient
manner
.
Chaonan
W
ang
[5]
had
proposed
a
prototypical
and
e
v
aluated
the
consistenc
y
and
lifet
ime
of
a
sensor
node
in
three
typical
set-ups,
pro
viding
precise
reliability
analysis
of
WSN
systems.
Substantial
impro
v
ement
in
WSN
lifetime
could
be
attained
by
int
roducing
standby
or
spare
nodes.
These
spare
nodes
are
substituted,
once
an
y
prime
(original)
node
is
depleted
with
ener
gy
.
Bilal
Ab
uBakr
[6]
had
proposed
the
LEA
CH-SM
protocol,
this
is
a
modi-
fied
form
of
Lo
w-Ener
gy
Adapti
v
e
Clust
ering
Hierarch
y
(LEA
CH)
desi
g
ne
d
by
pro
viding
an
optimum
spare
nodes
and
ener
gy
management
in
spares.
Mariam
Akbar
[7]
had
proposed
Balanced
Ener
gy-Ef
ficient
Netw
ork
Inte
grated
Super
Heterogeneous
for
heterogeneous
WSNs
to
impro
v
e
stability
,
lifetime
and
throughput.
In
the
direction
of
impro
ving
the
performance
of
WSN
with
respect
to
ener
gy
requisite
and
to
increase
net-
w
ork
lifetime
a
communication/computation
ener
gy
trade-of
f
need
to
be
analyzed
[8].
The
analysis
could
be
made
at
netw
ork-le
v
el
(i.e.,
all
nodes
in
the
netw
ork
use
the
same
strate
gy)
or
at
a
node-le
v
el
(i.e.,
sensor
nodes
do
not
necessarily
ha
v
e
identical
strate
gies).
Robert
M.Curry
[9]
had
e
xplored
a
number
of
research
methodolo-
Ener
gy
ef
ficient
intellig
ent
r
outing
in
WSN
using
...
(Shanthi
D
L)
Evaluation Warning : The document was created with Spire.PDF for Python.
502
r
ISSN:
2088-8708
gies
and
lee
w
ay
to
the
problem
that
includes
online
routing,
clustering
methods,
and
lifetime
maximization
on
specially
structured
netw
ork.
Based
on
Lagrange
relaxation
method,
[
1
2]
had
proposed
an
ener
gy
optimization
method
to
assure
delay
constraint.
An
objecti
v
e
function
has
been
proposed
in
terms
of
ener
gy
consumption
and
delay
and
also
defined
a
method
to
find
an
optimal
multiplier
for
that
objecti
v
e
function.
Based
on
Ant
colon
y
algorithm
optimal
path
for
routing
has
proposed
in
[13].
Jian
Shen
[14]
had
proposed
an
ener
gy-ef
ficient
centroid-based
routing
protocol
(EECRP)
for
WSN
assisted
IoT
to
get
better
performance
of
the
netw
ork.
Rumor
routing
is
another
typical
random
w
alk
routing
protocol
defined,
b
ut
the
problem,
is
not
scalable
and
can
lead
to
spiral
paths.
Hsiang-Hung
Liu
[15]
had
considered
straight-line
routing
(SLR)
to
decrease
the
ener
gy
utilization
of
sensor
nodes
in
WSNs.
Locality
of
sink
node
may
considerably
af
fect
the
ener
gy
dissipation
and
throughput
of
the
netw
ork.
Y
ah
ya
K
ord
T
amandani
[16]
had
gi
v
en
in
v
estig
ation
o
v
er
an
opti-
mum
position
for
the
sink
node
in
such
a
w
ay
that
the
sum
of
distances
from
all
the
sensor
nodes
to
the
sink
node
is
minimized.
An
Ener
gy
Ef
ficient
Connected
Co
v
erage
(EECC)
scheduling
is
made
use
to
e
xtend
the
lifetime
of
the
WSN
is
gi
v
en
in
[17].
Secure
and
ener
gy-ef
ficient
method
of
optimization
has
been
proposed
in
[18]
using
the
Dij-Huf
f
Method.
T
urki
A.
Alghamdi
[19]
had
proposed
a
WSN-based
multi-hop
netw
ork
infrastruc-
ture,
to
increase
netw
ork
lifetime
by
optimizing
the
routing
strate
gy
.
A
proposal
of
a
no
v
el
routing
architecture
has
sugges
ted
in
[20]
for
s
e
v
ere
en
vironment
monitoring
in
heterogeneous
WSN.
The
aim
w
as
to
impro
v
e
the
stability
period
and
netw
ork
lifetime
by
restriction
the
distance
between
the
sensor
nodes
and
the
g
ate
w
ay
node
by
mitig
ating
the
hot-spot
problem
in
the
netw
ork.
Random
projection
based
on
compressed
sensing
might
decrease
the
v
olume
of
data
communicated
in
a
WS
N,
and
ef
ficient
routing
could
ease
the
netw
ork
traf
fic.
Jianhua
Qiao
[21]
presented
a
Random
projection-Polar
coordinate-Chain
routing
(RPC)
scheme
to
de
v
elop
the
time
and
ener
gy
ef
ficient
protocol.
The
re
vie
w
in
[22]
had
presented
the
state
of
the
art
in
the
ener
gy
man-
agement
schemes,
and
the
a
v
ailable
challenges
in
the
area
of
WSN.
[23]
had
proposed
a
method
to
reduce
the
ener
gy
consumption
by
ener
gy
balancing
in
clusters
among
all
sensor
nodes
to
minimize
the
ener
gy
dissipation
during
netw
ork
communications.
Arun
L.Kakhandki
[24]
presented
a
distrib
uted
MA
C
and
transcei
v
er
opti-
mization
technique
for
selecti
v
e
hop
de
vice
selection
to
minimize
ener
gy
consumption
per
bit
and
maximize
the
lifetime
of
sensor
netw
ork.
The
study
in
[25]
presented
a
surv
e
y
approach
for
dif
ferent
aspects
in
v
olv
ed
with
Heterogeneous
W
ire-
less
Sensor
netw
ork
and
design
issues
for
routing
in
heterogeneous
en
vironment.
Hema
v
athi
P
[26]
had
applied
the
modified
v
ersion
of
Bacteria
F
oraging
Optimization
to
optimize
the
ener
gy
consumption
in
data
aggre
g
ation
process
in
WSN.
Basa
v
araj
G.N
[27]
had
applied
Lo
w
Latenc
y
and
Ener
gy
Ef
ficient
Routing
(LLEER)
design
for
heterogeneous
WSN
to
pro
vide
the
trade-of
f
between
ener
gy
ef
ficienc
y
and
latenc
y
requirement.
Chaitra
HV
[28]
had
presented
cluster
head
selection
based
life
enhancement
of
netw
ork
using
a
Multi-objecti
v
e
impe-
rialist
competiti
v
e
algorithm
(MOICA)
.
3.
PR
OPOSED
W
ORK
3.1.
Mathematical
modeling
of
pr
oblem
Mathematical
representation
of
the
objecti
v
e
function
can
be
defined
as
the
minimizat
ion
of
the
total
ener
gy
spends
o
v
er
the
considered
route
path.
In
graphical
model
of
simulated
net
w
o
r
k
of
WSN
G
(V
,
E),
Ener
gy
ef
ficient
routing
betwe
en
defined
sensor
nodes
(P
,Q)
can
be
consider
as
finding
a
number
of
possi-
ble
paths
f
P
i
j
i
2
f
0
;
1
;
::::
gg
sequentially
o
v
er
a
set
of
graphs
f
G
i
j
i
2
f
0
;
1
;
::::
gg
,
which
must
carry
the
minimum
ener
gy
cost
path
as
gi
v
en
in
equation
(1)
C
F
(
P
Q
)
=
M
in
8
<
:
X
l
2
P
i
(
s;r
)
C
F
l
9
=
;
(1)
The
optimal
routi
n
g
has
transformed
as
a
problem
of
optimization
where
objecti
v
e
w
as
to
m
inimize
the
total
in
v
ested
tra
v
el
cost
as
a
function
of
ener
gy
.
The
optimization
of
objecti
v
e
function
has
achie
v
ed
by
applying
a
modified
form
of
Genetic
algorithm
which
has
used
a
more
natural
mechanism
of
“Dominanc
y”
in
the
formation
of
ne
w
solution
population.
Genetic
algorithm
has
sho
wn
great
interest
by
a
number
of
researchers
[10,
11].
3.2.
Dominant
genetic
algorithm
A
solution
population
is
initialized
for
a
defined
source-destination
sensor
pair
which
uses
a
concept
of
connected
possible
solution
in
the
feasibility
domain
of
possible
paths.
An
equal
opportunity
Int
J
Elec
&
Comp
Eng,
V
ol.
10,
No.
1,
February
2020
:
500
–
511
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
503
for
each
parent
is
performed
through
uniform
distrib
ution
probability
in
the
process
of
of
fspring
formation.
This
step
is
v
ery
natural
and
pro
vides
t
he
f
acility
of
better
e
xploration
of
the
solution
domain.
One
point
crosso
v
er
operator
has
applied
which
carried
the
node
connected
feature,
where
a
set
of
common
nodes
in
both
parents
form
the
possible
locus
of
crosso
v
er
point.
If
there
were
more
tha
n
one
possible
locus
in
the
set,
a
uniform
random
process
has
applied
to
select
the
particular
one.
Such
kind
of
cross
o
v
er
m
aintain
the
e
xplored
solution
under
the
feasibility
domain
and
may
cause
the
change
i
n
the
le
ng
t
h
of
generat
ed
of
fspring.
A
dynamic
mutation
strate
gy
for
each
locus
of
of
fspring
has
under
lo
w
mutation
probability
t
o
maintain
the
di
v
ersity
is
used.
A
possible
candidate
of
mutation
is
the
member
from
a
set
of
all
possible
connected
sensor
nodes
from
the
just
pre
vious
sensor
.
Ag
ain
such
process
maintains
the
feasibility
of
e
xplored
solution.
Once
the
number
of
generated
of
fspring’
s
is
same
as
parent
population
size,
both
populations
are
mer
ged
to
form
a
selec-
tion
pool
where
a
tournament
select
ion
process
is
applied.
In
this
selection
process
for
each
member
a
number
of
opponent
members
are
selected
randomly
and
it
depends
upon
the
fitness
comparison
and
a
tournament
score
declared.
The
higher
scored
members
form
a
ne
w
population.
In
the
ne
w
population,
under
a
defined
range,
a
random
number
of
members
are
selected
as
elite
members
as
well
as
poorest
members
depending
upon
their
fitness.
Elite
member
generate
the
of
fsprings
and
the
y
replace
the
poorest
member
from
the
population
if
their
fitness
is
better
.
Such
kind
of
dominanc
y
is
v
ery
ob
vious
and
could
be
observ
ed
in
human
population
as
well.
This
dominanc
y
in
v
olv
ement
mak
es
population
more
fitter
as
well
as
increase
the
rate
of
con
v
e
gence
without
compromise
with
the
e
xploration
le
v
el.
The
concept
of
conditional
niche
has
also
applied
where
the
pre
vi-
ous
best
observ
ed
solution
replaces
the
weak
est
member
of
the
solution
if
the
fitness
allo
wed.
The
obtained
final
population
has
considere
d
as
t
h
e
ne
xt
generati
on
population
for
further
process.
The
funct
ional
block
diagram
of
proposed
algorithm
has
sho
wn
in
Figure
1.The
number
associated
with
the
edges
indicate
the
flo
w
of
process
sequentially
.
3.2.1.
Chr
omosome
r
epr
esentation
Initial
solution
population
has
defined
under
a
constraint
based
definition
of
nodes
connection
between
a
predefined
source-destination
pair
.
The
follo
wing
steps
are
used
in
the
formation
of
each
member
in
the
initial
population.
Pseudo
code
f
or
initial
solution
f
ormation:
1.
Initialization
of
Population
size
F
or
each
member:
fix
ed
Sr
&
Dt:
start
solution
as
S
[Sr]
2.
Find
Possible
neighbors
of
last
member
of
S,
say
NR
f
Ni,Nj,....Nm
g
3.
Add
a
node
from
set
NR
to
S:
[Sr
,Nj]
[NR
]
[Uniform
Random
Process]
4.
Go
to
step
2
if
the
last
node
of
[S(end)]
#
Dt.
3.2.2.
Connecti
vity
based
cr
osso
v
er
operator
and
mutation
In
the
considered
form
of
WSN,
normal
course
of
crosso
v
er
operation
is
not
possible
because
of
limited
connecti
vity
association
with
other
a
v
ailable
sensor
nodes.
As
in
the
case
of
normal
process
of
crosso
v
er
,
an
y
random
position
is
considered
as
point
of
crosso
v
er
,
obtained
of
fspring
may
carry
the
infeasibilit
y
and
it’
s
not
possible
to
recorrect
that
infeasibility
with
penalty
proce
ss.
In
the
proposed
form
of
crosso
v
er
same
locus
position
will
not
appear
,
instead
of
that
same
node
will
be
considered
as
the
position
of
crosso
v
er
as
sho
wn
in
Figure
2.
As
in
Figure
2
(a),
parents
P1
AND
P2
ha
v
e
tak
en
for
crosso
v
er
.
There
are
tw
o
attractors
a
v
ailable
in
chromosomes
(N3,
N2)
and
(N2,
N6),
where
the
first
position
(lik
e
N3)
is
the
position
from
first
parent
and
a
second
position
(N2)
is
the
position
from
second
parent.
Other
positions
are
not
allo
wed
to
crosso
v
er;
because
the
y
will
mak
e
the
solution
unfeasible.
It
is
also
observ
ed
that
crosso
v
er
can
cause
the
change
in
chromosome
length.
In
this
paper
,
possi
ble
domain
of
change
under
mutation
with
each
node
is
the
possible
number
of
nodes,
which
are
connected
with
their
neighbors
only
.
In
this
paper
,
possible
domain
of
change
under
mutation
with
each
node
is
the
possible
number
of
nodes,
which
are
connected
with
their
neighbor’
s
only
.
F
or
a
node
which
has
to
mutated
first
all
the
connected
nodes
ha
v
e
e
xplored
in
the
sensor
netw
ork
and
nodes
which
already
e
xisted
beside
in
the
solution,
discarded.
From
the
remaining
nodes,
through
uniform
random
selec
tion
process
a
node
has
selected
as
the
mutated
node.
F
or
e
xample
the
mutation
strate
gy
for
the
node
9th
of
the
of
fspring
O2
has
sho
wn
in
Figure
2(c).
First
for
the
node
9,
all
the
connected
nodes
ha
v
e
e
xplored
in
the
sensor
netw
ork
and
it
has
appeared
that
nodes
1,
8,
19,
17
and
4
are
the
connected
nodes.
Nodes
1
and
8
which
were
already
e
xisted
beside
in
the
solution
discarded
Ener
gy
ef
ficient
intellig
ent
r
outing
in
WSN
using
...
(Shanthi
D
L)
Evaluation Warning : The document was created with Spire.PDF for Python.
504
r
ISSN:
2088-8708
and
among
remaining
nodes
4,
17
and
19,
a
node
(for
e
xample
node
19
in
MO2)
has
selected
through
uniform
random
selection
process.
Figure
1.
Functional
representation
of
proposed
Dominant
Genetic
algorithm
(a)
(b)
(c)
Figure
2.
Connecti
vity
based
crosso
v
er
,
(a)
parents
selected
for
crosso
v
er
(b)
generated
of
fspring
after
crosso
v
er
(c)
Mutation
Strate
gy
Int
J
Elec
&
Comp
Eng,
V
ol.
10,
No.
1,
February
2020
:
500
–
511
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
505
3.3.
Adv
antages
of
pr
oposed
solution
The
applied
form
of
proposed
solution
is
ha
ving
numerous
adv
antages
in
terms
of
finding
the
ef
ficient
solution.
1.
Dynamic
length
of
solution
in
DGA
has
pro
vided
the
possibilities
of
high
le
v
el
of
e
xploration
as
well
as
computational
ease.
2.
Connected
nodes
cross-o
v
er
operation
only
e
xplore
ne
w
solution
and
ne
v
er
destro
y
the
de
v
eloped
so-
lution
(while
in
con
v
entional
Genetic
algorithm
crosso
v
er
operator
cause
of
construction
as
well
as
de-
struction
also
which
may
cause
of
more
number
of
iterations
to
appear
same
solution
or
may
not
deli
v
er
the
optimal
solution
at
all).
3.
Connected
node
mutation
strate
gy
causes
of
ne
w
solution
e
xploration
with
minimal
computational
cost.
4.
Routing
path
feasibility
correction
process
causes
of
another
process
to
increase
the
le
v
el
of
di
v
ersity
in
the
solution.
5.
Chance
of
e
v
ery
parent
equally
in
of
fspring
creation
cause
of
deeper
e
xploration
in
solution
space.
6.
Dominant
process
pro
vides
high
le
v
el
of
balancing
between
e
xploration
vs
e
xploitation.
Explored
Elite
of
fsprings
e
xploited
immediately
by
suppressing
the
weak
est
members
and
cause
of
f
aster
con
v
er
gence.
4.
SIMULA
TION
RESUL
T
T
w
o
dif
ferent
possibilities
of
r
o
ut
ing
scenarios
ha
v
e
e
xplored.
In
the
first
case
number
of
sensors
form
a
netw
ork
and
communication
tak
es
place
between
the
source
node
and
sink
node,
as
sho
wn
in
Figure
3
simulated
netw
ork
1
.
Such
a
scenario
needs
an
optimal
path
so
that
minimum
ener
gy
is
in
v
ested
o
v
er
the
communication.
In
second
case,
data
g
athering
node
mo
v
es
from
centroid
of
one
cluster
to
another
cluster
to
collect
the
data
and
after
visiting
all
the
clusters
it
will
return
back
to
the
starting
position,
as
sho
wn
in
Figure
7
simulated
netw
ork
2.
Both
cases
can
be
handled
as
problem
of
path
optimization.
4.1.
Case1.
netw
ork1
In
the
area
of
200
X
200
square
units
simulation
e
xperiment
has
done
in
MA
TLAB
en
vironment
by
random
placement
of
sensors
through
the
uniform
distrib
ution.
A
T
otal
of
50
sensors
ha
v
e
deplo
yed
and
with
the
communication
range
of
50
units,
connecti
vity
in
the
netw
ork
among
the
sensors
has
formed.
Rather
than
considering
the
direct
ph
ysical
Euclidean
distance
between
the
connected
sensors,
a
uniform
random
number
in
the
range
of
[10,
20]
has
selected
to
model
in
v
ested
ener
gy
with
the
irre
gularities
e
xisted
in
the
practical
geographical
en
vironment.
A
population
size
of
20
has
selected
wit
h
crosso
v
er
rate
as
1
if
feasibility
e
xist
otherwise
equal
to
0.
A
lo
w
probability
of
mutation
rate
0.1
has
considered
in
inheriting
the
parent
quality
.
F
or
a
gi
v
en
node
pair
,
the
p
r
ocess
has
iterated
upto
to
20
iterations
and
10
independent
trials
to
estimate
the
statistical
significance.
Performance
qualities
ha
v
e
measured
in
terms
of
cost
of
objecti
v
e
function
v
alue
as
well
as
time
of
solution
stability
.
Figure
4
sho
ws
lar
ge
change
of
con
v
er
gence
in
CGA
under
10
independent
trails
and
Figure
5
sho
ws
a
tight
con
v
er
gence
in
DGA
under
10
independent
trails
for
netw
ork
1.
Figure
6
sho
ws
a
mean
con
v
er
gence
for
CGA
and
DGA
for
10
independent
trails.
Statistically
an
impro
v
ement
in
the
performance
is
seen
in
DGA
compared
to
CGA
from
T
able
1
and
T
able
2
respecti
v
ely
.
Figure
3.
Simulated
netw
ork
1
with
obtained
route
between
nodes
Figure
4.
Con
v
er
gence
in
CGA
under
10
independent
trials
Ener
gy
ef
ficient
intellig
ent
r
outing
in
WSN
using
...
(Shanthi
D
L)
Evaluation Warning : The document was created with Spire.PDF for Python.
506
r
ISSN:
2088-8708
Figure
5.
Con
v
er
gence
in
DGA
under
10
independent
trials
Figure
6.
Mean
Con
v
er
gence
in
CGA
and
DGA
o
v
er
10
independent
trials
T
able
1.
Performance
of
CGA
and
DGA
under
10
independent
trials
T
rial
No.
CGA
DGA
P=20;
S=50;
C=50
Route
Cost
Iteration
Cost
Route
Cost
Iteration
Cost
1
103
5
89
6
2
92
7
84
6
3
86
13
79
7
4
108
4
79
2
5
100
6
79
2
6
91
5
79
2
7
84
4
79
2
8
95
11
79
2
9
95
6
79
2
10
103
3
79
2
T
able
2.
Statistical
beha
vior
of
performance
in
CGA
and
DGA
CGA
DGA
Route
Cost
Iteration
Cost
Route
Cost
Iteration
Cost
Best
84.0
3.0
79.0
2.0
W
orst
108.0
13.0
89.0
7.0
Mean
95.7
6.4
80.5
3.3
Std.De
v
.
7.78
3.2
3.4
2.1
4.2.
Case1.
netw
ork2
A
netw
ork
wit
h
dif
ferent
source
and
sink
node
set
up
is
considered
to
test
the
performacne
of
DGA
o
v
er
CGA
for
10
independent
trails
is
sho
wn
in
Figure
7.
It
has
observ
ed
with
Netw
ork
1
and
Netw
ork
2,
a
v
ery
sharp
benefit
is
obtained
with
DGA
compared
to
CGA.
Figure
8
sho
ws
a
lar
ge
change
in
the
co
v
er
gence
characteristics
with
CGA
while
Figure
9
with
DGA
follo
w
a
v
ery
tight
relation
among
the
dif
ferent
trials.
The
clear
dif
ference
of
con
v
er
gence
between
CGA
and
DGA
ha
v
e
sho
wn
in
Figure
10
for
neto
wrk2
o
v
er
10
independent
trails.
The
statistical
analysis
from
T
able
3
sho
ws
that
DGA
has
remarkably
f
aster
and
lo
wer
route
path
cost.
T
able
4
sho
ws
statistical
beha
vior
o
f
performance
in
CGA
and
DGA
for
best
,
w
orst
and
mean
cases
approximately
50%
increase.
In
both
cases
DGA
has
deli
v
ered
the
lo
wer
v
alue
of
route
cost
as
well
as
strong
reliability
in
performance.
The
computation
cost
is
about
2
to
3
iterations
in
DGA
while
CGA
has
tak
en
around
7
iterations.
Int
J
Elec
&
Comp
Eng,
V
ol.
10,
No.
1,
February
2020
:
500
–
511
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
507
Figure
7.
Simulated
netw
ork
2
with
obtained
route
between
nodes
Figure
8.
Con
v
er
gence
in
CGA
under
10
independent
trials
Figure
9.
Con
v
er
gence
in
DGA
under
10
independent
trials
Figure
10.
Mean
Con
v
er
gence
in
CGA
and
DGA
o
v
er
10
independent
trials
T
able
3.
Performance
of
CGA
and
DGA
under
10
independent
trials
T
rial
No.
CGA
DGA
P=20;
S=50;
C=50
Route
Cost
Iteration
Cost
Route
Cost
Iteration
Cost
1
133
14
125
8
2
169
7
122
6
3
120
8
122
2
4
130
14
122
2
5
132
7
119
4
6
158
5
119
2
7
122
7
119
2
8
131
6
119
2
9
135
6
119
2
10
128
3
119
2
T
able
4.
Statistical
beha
vior
of
performance
in
CGA
and
DGA
CGA
DGA
Route
Cost
Iteration
Cost
Route
Cost
Iteration
Cost
Best
120.0
3.0
119.0
2.0
W
orst
169.0
14.0
125.0
8.0
Mean
135.8
7.7
120.5
3.2
Std.De
v
.
15.5
3.6
2.1
2.5
Ener
gy
ef
ficient
intellig
ent
r
outing
in
WSN
using
...
(Shanthi
D
L)
Evaluation Warning : The document was created with Spire.PDF for Python.
508
r
ISSN:
2088-8708
4.3.
T
rajectory
of
data
gathering
node
A
simple
model
of
mobile
data
g
athering
in
WSN
has
sho
wn
in
Figure
11.
Here
each
cluster
is
ha
ving
a
cluster
head
which
has
all
the
information
of
cluster
center
.
Rather
than
getting
the
information
from
each
sensor
,
infor
mation
collection
by
data
g
athering
node
can
sa
v
e
lot
of
ener
gy
,
and
also
the
delay
in
information
transmission
can
completely
depend
upon
the
trajectory
quality
.
Hence
it
is
necessary;
the
selected
trajectory
should
be
optimal
and
f
aster
.
Figure
11.
Data
g
athering
from
centroid
sensor
node
by
mobile
base
node
The
simulated
netw
ork
consists
of
10
clusters
and
it
is
assumed
that
clusters
are
already
formed,
en-
er
gy
in
v
ested
to
find
the
trajectory
of
mobile
data
g
athering
node
is
measured
under
DGA
and
CGA
o
v
er
10
trails.
Experiments
are
carried
by
considering
tw
o
dif
ferent
starting
points
for
data
g
athering
node.
In
the
first
case
the
starting
point
for
mobile
data
g
athering
node
is
at
first
cluster
while
in
the
second
case
the
starting
point
is
at
se
v
enth
cluster
.
Figure
12
sho
ws
the
trajectory
con
v
er
gence
in
CGA
and
DGA
for
modile
node
starting
from
1st
cluster
,
and
Figure
13
gi
v
es
con
v
er
gence
for
mobile
node
starting
from
7th
cluster
.
The
performance
of
DGA
and
CGA
in
terms
of
in
v
ested
ener
gy
and
obtained
trajectory
for
mobile
node
starting
from
1st
cluster
is
sho
wn
in
T
able
5
and
starting
from
7th
cluster
in
T
able
6
respecti
v
ely
,
and
it
is
observ
ed
that
DGA
consumes
less
ener
gy
than
CGA.
Similarly
T
able
7
and
T
able
8
sho
ws
the
performance
of
DGA
and
CGA
with
mobile
node
starting
position
from
7th
cluster
.
It
has
observ
ed
that
v
ery
less
ener
gy
in
v
ested
trajectory
has
been
opted
by
DGA
compared
to
CGA.
The
performance
of
mean
con
v
er
gence
o
v
er
10
independent
trials
has
also
sho
wn
in
Figure
12
and
Figure
13
and
in
both
cases
a
significant
impro
v
ement
has
been
observ
ed
with
DGA
compared
to
CGA.
Figure
12.
T
rajectory
Con
v
er
gence
in
CGA
and
DGA
o
v
er
10
independent
trials
with
starting
position
in
1st
cluster
Figure
13.
Con
v
er
gence
in
CGA
and
DGA
o
v
er
10
independent
trials
with
starting
position
is
7th
cluster
Int
J
Elec
&
Comp
Eng,
V
ol.
10,
No.
1,
February
2020
:
500
–
511
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
509
T
able
5.
Performance
under
10
trials
by
DGA
T
rial
No.
Obtained
T
rajectory
by
DGA
In
v
ested
Ener
gy
1
1
6
7
8
5
3
10
2
9
4
1
818
2
1
4
9
2
10
3
5
8
7
6
1
818
3
1
5
3
9
4
2
10
6
7
8
1
836
4
1
4
9
2
10
3
5
8
7
6
1
818
5
1
4
9
2
10
3
5
8
7
6
1
818
6
1
6
7
8
5
3
10
2
9
4
1
818
7
1
4
9
2
10
3
5
8
7
6
1
818
8
1
4
9
3
5
7
8
2
10
6
1
825
9
1
4
9
2
8
7
6
10
3
5
1
826
10
1
6
10
2
4
9
3
5
7
8
1
836
Mean
823.1
Std.
De
v
.
7.46
T
able
6.
Performance
under
10
trials
by
CGA
T
rial
No.
Obtained
T
rajectory
by
CGA
In
v
ested
Ener
gy
1
1
3
5
8
7
6
10
2
9
4
1
832
2
1
4
5
3
9
2
10
6
7
8
1
863
3
1
2
9
10
6
7
8
5
3
4
1
862
4
1
4
2
9
3
5
7
8
10
6
1
855
5
1
8
7
5
3
9
4
2
10
6
1
836
6
1
4
9
2
10
3
5
8
7
6
1
818
7
1
5
3
9
4
2
10
6
7
8
1
836
8
1
4
2
9
3
5
7
8
10
6
1
855
9
1
4
9
2
10
6
7
8
5
3
1
832
10
1
2
8
7
5
3
4
9
10
6
1
860
Mean
844.9
Std.
De
v
.
15.87
T
able
7.
Performance
under
10
trials
by
CGA
T
rial
No.
Obtained
T
rajectory
by
CGA
In
v
ested
Ener
gy
1
7
6
1
4
9
2
10
3
5
8
7
818
2
7
8
2
9
4
1
5
3
10
6
7
826
3
7
8
2
9
4
1
6
10
3
5
7
826
4
7
5
3
9
4
1
6
10
2
8
7
825
5
7
6
10
2
4
9
3
5
1
8
7
836
6
7
6
1
5
3
10
9
4
2
8
7
846
7
7
8
2
4
9
10
3
5
1
6
7
846
8
7
6
10
2
4
9
3
5
1
8
7
836
9
7
6
10
2
1
4
9
3
5
8
7
830
10
7
5
3
9
2
4
1
6
10
8
7
855
Mean
834.4
Std.
De
v
.
11.6
T
able
8.
Performance
under
10
trials
by
DGA
T
rial
No.
Obtained
T
rajectory
by
DGA
In
v
ested
Ener
gy
1
7
8
2
10
6
1
4
9
3
5
7
825
2
7
8
2
9
4
1
5
3
10
6
7
826
3
7
6
1
4
9
2
10
3
5
8
7
818
4
7
6
10
3
5
1
4
9
2
8
7
826
5
7
6
1
4
9
2
10
3
5
8
7
818
6
7
6
1
4
9
2
10
3
5
8
7
818
7
7
6
1
4
9
2
10
3
5
8
7
818
8
7
6
10
3
5
1
4
9
2
8
7
826
9
7
8
1
6
10
2
4
9
3
5
7
836
10
7
5
3
9
4
1
6
10
2
8
7
825
Mean
823.6
Std.
De
v
.
5.7
Ener
gy
ef
ficient
intellig
ent
r
outing
in
WSN
using
...
(Shanthi
D
L)
Evaluation Warning : The document was created with Spire.PDF for Python.