TELKOM
NIKA
, Vol.14, No
.4, Dece
mbe
r
2016, pp. 13
68~137
5
ISSN: 1693-6
930,
accredited
A
by DIKTI, De
cree No: 58/DIK
T
I/Kep/2013
DOI
:
10.12928/TELKOMNIKA.v14i4.4013
1368
Re
cei
v
ed Ma
y 18, 201
6; Revi
sed Aug
u
st
8, 2016; Accepted Augu
st
22, 2016
Energy Efficient Error Rate Optimization Transmissio
n
in Wireless Sen
s
or Network
Sharada K.
A*
1
,
Siddaraju
2
1
Departme
n
t of Computer Sci
ence En
gi
neer
i
ng, JJT
Univer
sit
y
, Raj
a
stha
n
,
India
2
Departme
n
t of CSE, Dr. AIT
,
VT
U, Karnatak
a, India
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: sharad
asom
ashek
har2
0
1
1
@
gmai
l.com
1
, sidd
araj
u.ait@
gmail.c
o
m
2
A
b
st
r
a
ct
W
i
reless Se
ns
or Netw
ork is
a col
l
ectio
n
of ind
e
p
end
en
t nodes
an
d
create a
netw
o
rk for
mo
nitori
ng
pur
poses
in v
a
ri
ous sce
nar
ios
like
militar
y
oper
ation, env
iron
me
ntal
op
eratio
n
etc. W
S
N
netw
o
rk si
z
e
is
increas
ing v
e
r
y
rapid
l
y these
days, due
to l
a
rge n
e
tw
ork si
z
e
en
ergy co
n
s
umptio
n is als
o
incre
a
sed
an
d
it has s
m
al
l b
a
ttery, lif
etime of
netw
o
rk decre
ases d
ue to
e
a
r
ly deat
h of no
des a
nd it i
m
p
a
ct
the ov
eral
l syst
em p
e
rformanc
e. Clust
erin
g is
a tec
hni
qu
e fo
r en
hanc
e th
e
netw
o
rk lifeti
m
e in
WSN. Her
e
in this p
aper
w
e
propos
e a
new
multi-
obj
ective
ad
aptiv
e sw
arm opti
m
i
z
at
io
n (MASO) techniqu
e
for
clusteri
ng an
d
comp
utes the
maxi
mu
m nu
mb
er of cl
uste
rs,
w
h
ich is best suited for the netw
o
rk. Each
cluster
has c
l
u
s
ter he
ad
an
d
cluster
me
mb
ers a
nd
perfor
m
e
d
th
e task
of loc
a
l
infor
m
ation
extracti
o
n
.
Cluster he
ad g
a
thers
a
ll
th
e extract
ed i
n
for
m
ati
on fro
m
member
no
des
and
s
e
n
d
it to
the b
a
se stati
on,
w
here base st
ation p
e
rfor
me
d glo
bal i
n
for
m
ation extr
acti
o
n
from a
ll the
cluster he
ad n
odes a
nd g
e
n
e
r
ate
so
me
u
s
e
f
u
l
resu
l
t. In
MASO
te
ch
n
i
q
u
e
,
o
b
j
e
c
t i
s
u
s
e
d
to
fi
n
d
th
e
be
st gl
o
b
a
l
po
si
ti
on
fo
r th
e
n
o
d
e
and
compar
e w
i
th
existin
g
p
o
sitio
n
val
ue. If
new
valu
e is
better
than th
e ol
d v
a
lu
e, than
no
d
e
moves to
a
n
e
w
positi
on
and
o
b
ject u
p
d
a
te t
heir ta
bl
e for
this new
p
o
sit
i
on. W
e
are c
onsi
deri
ng
err
o
r pro
b
a
b
il
ity i
n
transmissio
n
of
data
packet
in
one
ho
p co
mmu
n
ic
ation.
H
e
re obta
i
n
ed th
e
results ar
e co
mp
are
d
w
i
th ot
her
researc
h
in ter
m
s of ov
eral
l n
e
tw
ork lifetime
and
effect
on n
e
tw
ork lifetime
w
hen the
si
z
e
of the netw
o
rk is
chan
ge
d. W
e
have fine tu
ned
the nod
e
’
s d
e
c
a
y rate and thr
oug
hp
ut of the netw
o
rk.
Ke
y
w
ords
: MASO, Global, Extraction, F
i
tne
s
s
Copy
right
©
2016 Un
ive
r
sita
s Ah
mad
Dah
l
an
. All rig
h
t
s r
ese
rved
.
1. Introduc
tion
Wirel
e
s
s
Se
n
s
or
N
e
two
r
k
(WS
N) i
s
a
c
o
llectio
n of
set of tiny sen
s
or no
de
s an
d ba
se
station. Sensor nod
es a
r
e
deployed in
an area fr
o
m
where we
want to gath
e
r the sen
s
i
t
ive
informatio
n from va
riou
s
appli
c
ation
s
l
i
ke milit
a
r
y b
a
ttle field, an
y environ
me
ntal data,
he
alth
operation etc.
A wireless
sen
s
o
r
nod
e con
s
i
s
ts
of memory whi
c
h
has capa
bility to process the
data an
d it
con
s
i
s
ts of t
r
ansmitte
r an
d re
ceive
r
, b
a
ttery etc. [1
]. Senso
r
no
des
are batt
e
ry
con
s
trai
ned, l
i
fetime of nodes i
s
dep
en
ds on
battery
usag
e, it is not re
cha
r
ge
able ag
ain a
n
d
again
and
sometime
s it
is n
o
t po
ssi
b
le to
remov
e
. Sensor
no
des die
s
afte
r thei
r u
s
ag
e
of
battery po
we
r and
be
com
e
useless, it affect
s
the overall syste
m
perfo
rma
n
c
e
a
nd
net
work
lifetime. Sensor nod
es
se
nse
s
the
sign
al and gath
e
rs it and proce
s
s the si
g
nal and tu
rn
it
into informati
on, and then
sen
d
it to the base
st
ation.
Base statio
n
which is re
si
ding in
side th
e
sen
s
o
r
field
or out
side th
e se
nsor n
e
twork, it
dep
e
nds
on lo
calit
y or situatio
n
.
Commu
nica
tion
betwe
en
sen
s
or no
de
s a
nd ba
se
stat
ion requi
re
s
a lot of e
nergy. Energy
consumption
fo
r
transmissio
n
of informatio
n by the se
nso
r
no
de
s depe
nd
s
on the
dista
n
ce betwe
en sen
s
or
node
s an
d b
a
se
station. I
f
distan
ce is
more it
requ
ires
more e
nergy fo
r the com
m
uni
ca
tion
and drain
s
ou
t the battery p
o
we
r. If we place the
ba
se
station nea
r the sen
s
o
r
net
work, it redu
ce
the battery powe
r
co
nsum
ption but it has som
e
dem
e
r
its nod
es which a
r
e ne
ar to base stati
o
n
dies e
a
rly a
s
comp
are to node
s, whi
c
h are far a
w
ay
from the ba
se station. So it create
s
hol
e
s
near the b
a
se station. it lead
s to cove
rage p
r
oble
m
near the b
a
se
stat
ion. In that sce
nario
actual info
rm
ation is misse
d
out from that parti
cul
a
r a
r
ea,
so it
imp
a
ct
s t
he sy
st
em perf
o
rma
n
ce
[2]. Now we
can
say. In
WSN sen
s
or
node
s
are
en
ergy
con
s
trai
ned. S
o
savi
ng o
r
red
u
ce
d the
con
s
um
ption
of energy in that are
a
is
challe
n
ge. Ma
ny techni
que
s are p
r
op
osed an
d re
se
a
r
ch
work i
s
still
going
on but
it is not satisf
ying t
he re
q
u
irem
ent of
energy savin
g
up to the m
a
rk.
To reduce the energy c
o
ns
umption and to impr
ove the overall s
y
s
t
em performanc
e
, a technique
calle
d clu
s
teri
ng of netwo
rk is use
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Energ
y
Efficient Erro
r Rat
e
Optim
i
zation Tran
sm
issi
on in Wirele
ss Sensor… (Shara
da K. A)
1369
Clu
s
terin
g
i
s
a me
ch
ani
sm to divide
the large
ne
twork into
n
u
mbe
r
of
small sub
netwo
rks call
ed clu
s
ters [3]. LEACH is very popul
ar clu
s
teri
ng
mecha
n
ism
and gives the
con
c
e
p
t of
cl
uster h
ead
a
nd
clu
s
ter m
e
mbe
r
s to
re
duce the
e
n
e
r
gy
con
s
um
ption. A
cl
uste
r i
s
con
s
i
s
t of Cluster
Hea
d
(CH) and cl
u
s
ter mem
bers, clu
s
ter me
mbers co
mm
unicate with CH
,they send da
ta to CH and
clu
s
ter he
ad
gathers or
co
llects all the data and the
n
send it to the
base statio
n, but LEACH p
r
otocol
ha
s
probl
em that
CH
com
m
uni
cate
s with b
a
s
e
station u
s
i
ng
singl
e h
op
o
n
ly cau
s
ing
more
en
ergy
loss
of
CH [
5
]. To
achiev
e b
e
tter
ene
rgy utilizatio
n
and
to create the
energy efficie
n
t netwo
rk, A
n
artificial i
n
telligen
ce
(AI)
techni
que i
s
use
d
in
side t
h
e
clu
s
ter. AI te
chniqu
e
works ba
se
d o
n
a
nalysi
s
of
pre
v
ious
data
an
d p
r
edi
cts the
future
data
[4],
Swarm o
p
tim
i
zation te
chni
que is in
spired by AI.
Sw
arm they are
work in grou
p if they do not
kno
w
wh
ere is the food they ju
st watch
their nearest
neighbo
r an
d go there. Here We are u
s
i
n
g
the co
ncept
of swarm
opt
imization i
n
a differ
ent
way and n
a
me
d as
multi-o
b
j
ective ad
apti
v
e
swarm o
p
timi
zation. Thi
s
algorith
m
is d
y
namic in
n
a
ture b
a
sed on
the situation
or en
ergy lev
e
l
of obje
c
t (nod
e) a
nd
ca
n ch
ange t
heir po
sition o
r
cl
u
s
t
e
r. Obj
e
ct
co
mpare thei
r
pre
s
ent
po
sition
and
glob
al be
st po
sition
to
che
c
k
whi
c
h i
s
b
e
st
and
m
o
ves to
that p
o
sition. P
r
ob
ability of pa
cket
failure o
r
tra
n
smi
ssi
on e
r
ror i
s
al
so
consi
dered for one ho
p co
mmuni
cation.
Here Autho
r
s
optimize
the overall energy
utilized
by
the wirel
e
ss sensor netwo
rk in communication.
Cal
c
ul
at
e
the pe
rcenta
ge of
nod
es live o
r
di
e
con
d
ition
s
b
a
s
ed
on
time,
Ch
eck the
effect of
ene
rgy
con
s
um
ption
and
nod
e’s e
nergy
de
cay
rate when
n
e
tw
ork si
ze
is i
n
crea
sed.
Rest of
the
pa
per
is o
r
ga
nized
as follo
ws: Seco
nd
se
ctio
n gives the
re
lated work in
WSN. In thi
r
d
se
ction
Net
w
ork
desi
gn with
prop
osed me
thod is di
scu
s
sed. Sect
io
n four de
scri
bes the
re
sul
t
s obtaine
d a
fter
simulat
i
o
n
an
d at
last
con
c
l
u
sio
n
is giv
e
n
.
2. Related Work
In literature review, Authors discu
s
sed
about
p
r
eviou
s
wo
rks d
one
in the field of WSN.
In [6] authors initialized t
he conc
ept
of virtual he
xagon
s in m
a
kin
g
clu
s
te
r which hel
ps in
avoiding ove
r
lappi
ng of n
ode
s of
ci
rcular
clu
s
ter.
Based
on th
e avera
ge di
stan
ce bet
ween
clu
s
ter
he
ad and clu
s
te
r
m
e
mbe
r
s, su
b-circle
can
be
made
in th
e
forme
d
virtu
a
l hexa
gon.
But
Authors n
o
t d
i
scusse
d
re
gardi
ng
ch
a
nnel
colli
si
o
n
con
d
ition
s
fo
r ma
king
sub-circle
and
virt
ual
hexago
n. Be
tter adju
s
tme
n
t of tran
sm
issi
on
rang
e
for saving
energy in [7
, 8] based
on
geog
rap
h
ical informatio
n a
nd ene
rgy m
odel is p
r
o
p
o
s
ed, he
re it choo
se
s the b
e
st tran
smi
s
sion
path for data.
A quantitative analysi
s
m
odel [9] for
the optimal tran
smissio
n
ran
ge pro
b
lem, they
use
thro
ugh
p
u
t as the o
p
timization
crite
r
ia a
nd
co
ncl
ude th
at the
optimal tran
smissi
on
po
we
r is
determi
ned
b
y
the net
work load, th
e nu
mber of n
ode
s a
nd th
e net
work
si
ze. It i
s
difficult task for
the re
se
arch
ers to maxim
i
ze the
net
work lifet
ime b
e
ca
use sen
s
or n
ode
s h
a
ve battery
whi
c
h
contai
ns limit
ed po
we
r. Du
ring exten
s
iv
e data tra
n
smissi
on, batt
e
ry drains
qui
ckly. To ove
r
come
this i
s
sue, a
r
ticle [18]
prop
ose
s
a ne
w
schem
e
whi
c
h
uses
optimal
sin
k
lo
catio
n
ba
sed
strate
gy.
Another m
e
th
od is p
r
e
s
e
n
ted mitigate th
e co
nge
stion
in network u
s
ing rel
a
y nod
es. In this
wo
rk
also, PSO b
a
se
d optimi
z
ation is u
s
e
d
fir optimal sink lo
cation
with the corresp
ondi
ng re
lay
node
s
whi
c
h
re
sult
s in
e
nergy
efficie
n
cy. Optim
a
l
de
sign
of
p
h
ysical, net
work (ro
u
ting)
and
medium a
ccess co
ntrol l
a
yers i
s
give
n. In
[10] ad-ho
c wi
rele
ss sen
s
or n
e
tworks (WS
N
s)
a
particl
e swa
r
m optimisation (PSO
) al
gorithm i
s
u
s
ed to
coll
e
c
tively estim
a
te a monit
o
red
para
m
eter
by sen
s
o
r
no
de
s. In the prop
ose
d
me
cha
n
i
sm eve
r
y se
nso
r
no
de i
s
itself a wi
rele
ss
sen
s
o
r
net
wo
rk a
nd is
equi
pped
with a
Modified Pa
rticle Swarm O
p
timization
(MPSO) algo
ri
thm
for estim
a
tion
of para
m
ete
r
of interest.
Nod
e
self
-lo
c
alizatio
n is a
n
impo
rtant issue in
wirel
e
ss
sen
s
o
r
net
wo
rks (WS
N
). T
o
solve thi
s
p
r
oble
m
, a
no
d
e
self
-localiza
t
ion algo
rithm
is p
r
opo
se
d.
A
modified pa
rticle swa
r
m o
p
timization (PSO) is
intro
duced to find out the loca
tion of unkno
wn
node
s [17].
A very simpl
e
fram
work
serves
as
a b
enchm
a
r
k to
a Multi Obje
ctive Genetic
Algorithm
(MO
C
A) fo
r t
he
sen
s
o
r
pl
acem
ent te
ch
nique,
whe
r
e
two
com
peti
ng o
b
jective
s
are
con
s
ide
r
ed
for the
sen
s
o
r
coverage
a
nd the
lifetime of the
net
work [1
1]. Th
ey give the
concept of vi
ce-
clu
s
ter he
ad. Whe
n
clu
s
ter head die
s
, vice cl
uste
r he
ad take the charg
e
. Comm
unication is n
o
t
discontin
ued
and e
a
ch tim
e
no n
eed to
elect the
ne
w clu
s
ter
hea
d. To imp
r
ove t
he effeci
en
cy of
the alg
o
rithm
as an
expoli
t
ing
search
p
hase a
gui
de
d search
me
t
hod i
s
embe
dded
in
a-ve
cto
r
Particle S
w
a
r
m Optimization (PSO
) al
gorithm, the
main aim fo
r this is to st
eer th
e search
towards the
d
e
sired
directi
on. In thi
s
propo
sed
st
rate
gy gradient
computation
of
the
Ja
cobi
na
is
exclud
ed
in d
e
termin
ation of
t
he co
rrespondi
ng de
si
red directio
n
i
n
the pa
ram
e
ter spa
c
e. Th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1368 – 137
5
1370
comp
one
nts
of the PSO
algorith
m
a
r
e
also
red
e
si
g
ned
acco
rdin
gly [12]. Base statio
n
kee
p
s
track
th
e re
co
rd of
resid
ual energy
of all node
s
an
d b
a
se
d o
n
e
n
e
r
gy level, ba
se statio
n
cho
o
se
the net
work.
For i
n
crea
sin
g
network life
t
ime multi-
n
o
de
cluste
rin
g
proto
c
ol i
s
gi
ven in
WSN f
o
r
data gathe
rin
g
pro
c
e
s
s [13]. In this wo
rk run
-
time is divided in to
time slot rou
nds. Ea
ch ro
und
start
s
with
a
sho
r
t re-clu
sterin
g p
h
a
s
e and
lo
n
ger data tran
sm
issi
on p
h
a
s
e.
In time ba
sed
proto
c
ol
syn
c
hroni
zatio
n
is need
ed in
a
netwo
rk
, so
at the same
time nod
e
can pe
rform re-
clu
s
terin
g
me
cha
n
ism a
s
well a
s
coo
p
e
rative me
ch
anism. In mo
st of the case we have seen
that most importan
c
e i
s
gi
ven to the energy co
ns
erv
a
tion but alo
ng with en
ergy con
s
ervati
on,
informatio
n retrieval task should al
so
be kept
in
mind. If we save lot of energy with
out
perfo
rming
i
n
formatio
n
re
trieval ta
sk a
nd n
o
t
sent to
the
sin
k
, In
that case m
o
tive of
WSN is
not
achi
eved. So total energy
con
s
um
ption
and inform
ation retri
e
val task both sh
ould go h
and
in
hand to serve the purp
o
se of WS
N. So here
a
u
thors motive is to red
u
c
e the en
ergy
con
s
um
ption
witho
u
t affe
cted th
e i
n
fo
rmation
retri
e
val p
r
o
c
e
s
s. In [15] a
u
thor u
s
ed
cl
o
ud
algorith
m
whi
c
h mi
nimize t
he
weight
of
particl
e,
he
re
all the
path
length i
s
con
s
ide
r
ed
in th
e
obje
c
tive fun
c
tion. Ant
col
ony an
d pa
rti
c
le
sw
arm op
timization
bot
h con
c
ept
used by th
e aut
hor
in [16] for po
sitive feedback ant colo
ny al
gorithm
i
s
u
s
ed, but maint
a
ining two dif
f
erent alg
o
rith
m
utilize more energy.
3. Proposed
Model
W
i
r
e
le
ss
Se
ns
o
r
Ne
tw
ork
c
o
me
s ac
ro
ss
th
e pr
o
b
l
em o
f
in
for
m
a
t
io
n
re
tr
ie
va
l in
mu
lti-
clu
s
ter
syste
m
beca
u
se of transmi
ssion
packet
failure or informati
on retrieval failure.
3.1. Net
w
o
r
k
Model
Followi
ng
b
a
si
c dia
g
ram
sh
ows
WS
N
with
clust
e
rs an
d com
m
unication
with base
station.
Figure 1. WS
N Net
w
ork M
odel
A wirele
ss S
ensor
Network is
basi
c
ally
comb
i
nation
of two thing
s
; one i
s
Ba
se station
and
se
con
d
i
s
sen
s
o
r
no
d
e
s. Are
a
whe
r
e
sen
s
o
r
no
des
are de
plo
y
ed kn
own a
s
sen
s
o
r
field
and
it can be given by
. Base station may resi
de ge
nerally
outside o
r
insid
e
the sensor
netwo
rk field
with height
m above fr
om cente
r
of area
. The
distan
ce fro
m
Cluste
r
membe
r
s to
cluster h
ead i
s
denoted by
and it is
.
1.
Senso
r
Node:
Senso
r
n
ode
s de
ployed in
an area to e
x
tract the info
rmation
about
the
target and
se
nd it to the base statio
n
2.
Base Statio
n: Base
statio
n
re
ceive
s
th
e
data from
clu
s
ter
hea
d’s a
nd p
r
o
c
e
s
s it
and
gene
rate u
s
ef
ul result.
3.
K is intermedi
ate hop di
sta
n
ce
Let
be num
ber of sen
s
o
r
nodes a
r
e d
eployed in an
area, whe
r
e
be the numb
e
r of
targets o
r
informatio
n and
,At a
time one sen
s
o
r
ca
n extract information from
one target.
No
w net
wo
rk is divid
ed in
two
clu
s
ter
and n
o
w wh
ole net
wo
rk i
s
divide
d int
o
num
be
r o
f
clu
s
ters. Each clu
s
ter h
a
s
its own
clu
s
te
r hea
d .So nu
mber of
clust
e
r he
ad is e
q
ual to
. Res
t
of
the nod
es in
side th
at clu
s
ter beh
aves
as
clu
s
ter m
e
mbe
r
s.
Clu
s
ter memb
ers
work i
s
to se
nse
the data and
send it to the clu
s
ter h
e
a
d
. Cluste
r he
ad also se
nses data o
r
ex
tract targ
et data
and collect all
data togethe
r and send it to the base
station or si
nk.
Here Net
w
ork model ch
ann
el
is noi
sy a
n
d
hen
ce
data
travel throu
gh noi
sy
cha
nnel. Data e
n
co
ded
by PCM
(Pulse cod
e
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Energ
y
Efficient Erro
r Rat
e
Optim
i
zation Tran
sm
issi
on in Wirele
ss Sensor… (Shara
da K. A)
1371
modulatio
n)
method a
nd
sen
d
it to the
sin
k
bit by bi
t through f
a
di
ng chan
nel.
PCM de
co
din
g
is
done at
the base
statio
n whe
r
e sin
k
e
x
tracts
th
e gl
obal info
rmat
ion received
by the cl
uste
r
head
s.
3.2. Success
ful Packe
t Tr
ansmission
or Targe
t Ex
trac
tion
Each ro
und
of transmi
ssi
on may not be su
cc
essfu
l
, so here Authors com
p
utes the
packet failure probability in co
mmuni
cation. Cluster
heads receives
the
data packet
s
from
clu
s
ter mem
bers locally and after re
cei
v
ing the dat
a, they extract
inform
ation su
ccessfully
by
removin
g
dup
licate
s
call
su
ccessful pa
cket transmi
ss
i
o
n or target e
x
traction. Rate of succe
s
sful
packet tran
smissi
on
(data
extractio
n
)
can b
e
defi
n
e
d
by the total
numb
e
r
of succe
ssful
pa
cket
s
transmiss
ions to the total n
u
mb
e
r
of packets tran
smission
s.
(
1
)
Cluster head sends t
he
packet to the base stati
on.
It has two possibiliti
e
s either
su
cc
es
sf
ul t
r
an
smi
ssi
on
or f
a
ilu
re t
r
a
n
smi
ssi
on,
it
mean
s t
hat
b
a
se
st
at
ion
may
su
cc
es
s
f
ully
receives the
data p
a
cket
called
as info
rmation
extr
ac
tion
or it may fail to
extrac
t the information
from the clu
s
t
e
r hea
d’s
call
ed as pa
cket
failure.
Relia
bility in
the tra
n
smi
s
sion
of p
a
cket
ca
n b
e
e
n
han
ce
d by
the ho
p by
hop
erro
r
recovery
sch
e
me. In thi
s
scheme
me
ssag
e i
s
d
e
co
ded
by the
i
n
terme
d
iate
CH,
bit e
rro
r is
corre
c
ted
by
the CH
and
then
bro
a
d
c
a
s
t it ag
ain. If
after de
co
din
g
word
e
rro
r
come
s th
en
CH
requ
est to se
nd data ba
ck by the previo
us CH.
Whe
n
⁄
chan
ges with
and when its value rea
c
he
s the lowe
st level, then this
is given by
whi
c
h is
be
st value or g
o
o
d
numb
e
r of
clu
s
ters, whi
c
h minimizes t
he overall
energy con
s
u
m
ption.
3.3. Multi-Ob
ject Ad
aptiv
e
S
w
a
r
m Opti
mization Te
chniques
for
Cluste
r For
maton
Traditio
nal a
ppro
a
ch for cluster form
ation i
s
not
suit
able fo
r la
rge
or
noi
sy net
work; i
t
may get
stu
c
k in
findin
g
th
e optim
al
sol
u
tion.
Multi-o
b
ject ada
ptive
swa
r
m opti
m
ization
(MASO)
is a
obj
ect
b
a
se
d al
gorith
m
. This te
ch
nique
is u
s
ed
in
swarm
op
timization f
o
r finding
the
b
e
st
feasibl
e
positi
on solutio
n
. It use
s
the pre
v
ious
po
sition
details and h
a
s expe
rien
ce for finding th
e
new glob
al fit po
sition
solu
tion. In this
method
ea
ch
obje
c
t is initi
a
lize
d
for find
ing the
glob
al
fit
solutio
n
for a target proble
m
. Some fixe
d threshold v
a
lue is p
r
ovid
ed for a parti
cular solution
and
obje
c
t rep
eat
edly iterate til
l
that parti
cul
a
r threshol
d i
s
not fou
nd.
Obje
ct
is initi
a
lized with few
para
m
eters i
n
itially and tri
e
s to find th
e
fitness
val
u
e
for the mat
c
hed threshol
d. Obje
ct ke
e
p
s
track the record of each node’
s indivi
dual best fitness location
given by
and global
fitness and lo
cation given by
found at the time of ite
r
ation.
Obje
ct uses these value
s
for
finding the
b
e
st optio
ns t
o
move to
wa
rds a b
e
tte
r
positio
n. Wh
en a o
b
je
ct found
an in
di
vidual
solutio
n
bette
r than p
r
evio
us solution
(fitness),
then i
t
repla
c
e
s
the old value b
y
new on
e a
n
d
update thei
r data table. Let
and
are the location
and velo
city vectors of ea
ch obj
ect
at instan
ce
. There a
r
e two self-le
a
rning f
a
ctor
and
.
1
(
2
)
1
1
(
3
)
From th
e ab
ove discu
s
se
d network m
odel ,w
here n
u
mbe
r
of sen
s
or no
de
s is
given b
y
then total n
ode
s in a net
work
can b
e
given as
,
,
…
.,
an
d portio
ned i
n
to
clu
s
t
e
r
s
,
,….
. Location o
r
positio
n of each o
b
ject sh
ows the coo
r
dinate
s
of
cluster h
e
ad
s i
n
WSN.
,
,
…
.
(
4
)
is the position
of
th object at time
and
r
e
p
r
es
en
t th
e
c
l
us
te
r
h
e
a
d
’
s
c
o
or
d
i
na
te
points. Ene
r
g
y
consumptio
n is directly prop
orti
o
nal to the distan
ce of the com
m
unication, so
distan
ce bet
wee
n
clu
s
ter
head an
d its membe
r
s
sh
ould be re
du
ced. Sen
s
or
node
belongs
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1368 – 137
5
1372
to,
,
when
is minimum. If fitness value is
more it mean
s distan
ce between cluste
r
head a
nd cl
u
s
ter me
mbe
r
s is less.
3.4. Energ
y
Cons
umptio
n in the Ne
tw
o
r
k
In a sen
s
o
r
n
e
twork, con
s
umption of e
ner
gy by the
node
s in tra
n
s
missio
n or
e
n
co
ding
the data
pa
cket or at t
he time
of receiv
ing
or deo
codi
ng
the pa
cket i
n
hop
by h
o
p
comm
uni
cati
on. Now h
e
re con
s
id
erin
g
information
bits en
cod
ed
as symb
ol bits of
of linear
block code
a
s
,
,
. Encoded
sysmb
o
l is d
e
noted a
s
a p
a
cket from thi
s
no
w si
ze of
packet is
bits
.
Energy
Con
s
umption in t
he pa
cket transmi
ssion i
n
one h
op
communi
catio
n
wh
ere
numbe
r of in
termedi
ate n
ode is
and distan
ce b
e
twee
n nod
es
is co
nsi
dered
as
in local
clu
s
ter. BW is the bandwi
d
th utilized,
,
is the energ
y
consum
ptio
n in the
transmitter and
r
e
cc
iever
,
+
(
5
)
No
w findin
g
t
he
con
s
um
ption of
ene
rgy
in tra
n
smissi
on the
pa
cke
t
in bet
ween
clu
s
ter
numbe
rof sy
mbol in blo
ck
cod
e
is defin
ed as
now for blo
ck
size one
intermed
iate hop with:
,
,
,
(
6
)
In the above
equatio
n
is the inter clu
s
ter distan
ce of
hop.
,
is the powe
r
of antenna g
a
in,
is the er
ror proba
bility in per hop trans
mi
ssion. Total Energy utilized in per
packet tran
smissi
on in the
hop comm
u
n
icatio
n .
,
,
,
(
7
)
is the energy utilized by the base ban
d sign
al in en
co
ding an
d de
coding the
sig
nal.
Energy utilized
in overall
communi
cation
or transmi
ssion
of packet, average
transmissio
n in per pa
cket tran
smi
ssi
on time in
a singa
l hop. For this purpo
se p
r
o
bability of error
in block code
,
,
compute
d
as:
∑
1
(
8
)
Time for tran
smissio
n
tha packet in on
e
hop com
m
un
ication
can b
e
given as:
(
9
)
No
w we
can
give the overall energy co
ns
um
ption in
per pa
cket tra
n
smi
ssi
on.
,
∑
,
,
(
1
0
)
Whe
r
e
is the numbe
r of ho
p,
impat on both tran
smission time and
energy con
s
u
m
ption.
Once clu
s
ter
is forme
d
, the clu
s
ter he
a
d
is nea
r to the ce
nter of clu
s
ter a
r
ea
and
,
2
⁄
. Here we h
a
ve taken, that each
cl
ust
e
r ha
s avera
ge numb
e
r of
sensor
node
s and de
fined as
∑
. Now
is maximum number of clu
s
ters for minimizi
n
g
the total energy con
s
um
p
t
ion. Overall energy
co
n
s
umptio
n is function of the numb
e
r
of
clu
s
t
e
r
s
.
by setting the
derivatives
of
(with res
pec
t to
H
) ,we
obtained th
e
minimum or
maximum number of
as
∗
. If
∗
is the mini
mum, it has optimal numb
e
r of
clu
s
ters.With
resp
ect to
minimizing
the to
tal
energy con
s
umption. Successful pa
cket
transmissio
n rate
will get saturated as the number of
increa
ses without sho
w
ing the loca
l
extremism.
denote
s
the su
ccessful pa
cket transmi
ssi
ons
with re
sp
ect to
c
l
us
ters
.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Energ
y
Efficient Erro
r Rat
e
Optim
i
zation Tran
sm
issi
on in Wirele
ss Sensor… (Shara
da K. A)
1373
Total ene
rgy
con
s
umptio
n and b
e
st n
u
mbe
r
or o
p
timum num
be
r of clu
s
ters
is
∗
.it varies
wit
h
num
ber
of
node
s in
net
work. If
increa
se
s tha
n
energy con
s
u
m
ption a
n
d
numbe
r of no
des in
some
clu
s
ter al
so in
cre
a
ses.
Increases
with
.
4. Simulation and Result Anal
y
s
is
The
system
requireme
nt is win
d
o
w
s 8.1
ent
e
r
pri
s
e
s
64-bit
ope
rati
ng
system
wi
th 4G
B
RAM. We ha
ve use
d
Sen
s
ori
a
simulat
o
r
whi
c
h i
s
b
a
se
d on
C#
prog
ram
m
ing
and
used d
o
t
net
frame
w
ork 4.
0 visual stu
d
i
o
s 20
10. We
have
con
d
u
c
ted si
mulati
on study ba
sed on follo
wi
ng
para
m
eters b
y
consi
deri
n
g
network lifetime,
increa
si
ng numb
e
r o
f
nodes (i
ncreasi
ng net
wo
rk
size) a
nd co
mmuni
cation
overhe
ad. We have Com
pare
d
with e
x
isting “Ene
rgy Aware S
w
arm
Optimiz
a
tion
with Inter c
l
us
ter S
earc
h
f
o
r
Wirele
ss S
ensor
Net
w
ork”
syste
m
s
a
nd foun
d that
ou
r
prop
osed
system is more
efficient. We
have varie
d
netwo
rk n
o
d
e
s si
ze li
ke
600, 120
0 an
d
1800 in o
u
r si
mulation an
d Simulation pa
ramete
rs
con
s
ide
r
ed a
r
e
shown in Tabl
e 1.
Table 1. Net
w
ork no
de
s si
mulation
Net
w
ork Parameter
Value
No. of Nodes
600,1200,18
00
Netw
or
k Size
30
3
0
Base station loca
tion
1m*1m
Size of Data Packet
2000 bits
Energ
y
of senso
r
node initially
0.1 J
Energ
y
dissipatio
n
50
nj/bits
Data packet proc
essing delay
0.1 ms
Amplification ene
rg
y
100 pJ/bit/m2
Ideal energ
y
con
s
umption
50 nj/bit
Band
w
i
dth
5000
bit/s
Rate of T
r
ansmission
100 bit/s
Random num
ber
bet
w
een 0
and 1
M
SNR
Random num
ber
bet
w
een 0
and 1
2000
250
1000
4
15dB
Distri
bution
of sen
s
o
r
no
des i
n
the a
r
ea of
30
30
and
base statio
n locatio
n
con
s
id
ere
d
a
t
1m*1m. After no
de
s distribution
clust
e
r form
ation
is take
n pla
c
e, than overall
energy co
nsu
m
ption is
co
mputed, we compa
r
ed n
e
tw
ork lifetime
with differe
nt netwo
rk
si
ze
and
throug
hput.
From
Figu
re
2 we h
a
ve
compa
r
ed
ou
r propo
sed
system
(PS)
si
mulated val
u
es
with
existing
syste
m
(ES) value
s
. We fo
und
that in
PS as netwo
rk
si
ze
incr
ea
se
s, n
e
twork lifetim
e
also in
crea
se
s but in ES as network
si
z
e
incr
ea
se
s it
s lif
et
ime dec
rea
s
e
s
.
In the followin
g
gra
p
h
s
sho
w
n in Fi
gure
3, 4 and 5;
we are
analyze
d netwo
rk life
time for
600, 120
0 an
d 1800 n
ode
s re
spe
c
tivel
y
. Here auth
o
rs
com
pute
d
the netwo
rk life time, when it
rea
c
he
s 30%
for both existing and prop
ose
d
wo
rk
s. In Figure 3 n
e
twork lifetim
e rea
c
he
d 30
%
after 53
ro
un
ds
of iteratio
n for
existing
while
for
propo
sed
wo
rk numb
e
r
of round
s i
s
9
6
.
For
1200
node
s n
e
twork lifetim
e for existin
g
system fo
r 30
% node
s de
ath after 58
rou
nds
of iteratio
n
while for p
r
op
ose
d
system
numbe
r of ro
und
s is 70 wh
ich is sho
w
n i
n
Figure 4 a
n
d
when n
e
twork
is co
nsi
d
e
r
ed
with 180
0 n
ode
s lifetime of existing m
odel rea
c
hed
30% after 6
8
rou
n
d
s
whil
e in
ca
se of prop
ose
d
syste
m
it goes up to
111 round
s.
So our p
r
op
o
s
ed
wo
rk i
s
more
stable
and
efficient in terms of network lifetime as
compa
r
e to existing sy
stem.
In the
Figu
re
6 auth
o
rs
ana
lyzed th
e
nod
e’s
de
cay
rat
e
for 60
0, 1
2
00 a
n
d
180
0
node
s.In
Propo
se
d wo
rk no
de’
s de
cay rate is al
ways lo
we
r than existing
one
s for all network si
ze
s.
In
existing
syste
m
node’
s d
e
c
ay rate i
s
fa
st wh
en
the
numbe
r of n
ode
s in
cre
a
ses in the
se
n
s
or
netwo
rk.
In Figure
7 n
e
twork throug
hput is
comp
are
b
e
twe
en
existing an
d prop
osed
systems an
d
we have an
al
yzed an
d pro
v
ed that prop
ose
d
system
’
s
throu
ghp
ut is much bette
r than existin
g
one.
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ISSN: 16
93-6
930
TELKOM
NIKA
Vol. 14, No. 4, Dece
mb
er 201
6 : 1368 – 137
5
1374
Figure 2. Net
w
ork Lifetime
Figure 3.
Net
w
ork Lifetime
analysi
s
for 600
node
s
Figure 4. Net
w
ork Lifetime
analysi
s
for 1200
node
s
Figure 5. Net
w
ork Lifetime
analysi
s
for 1800
node
s
Figure 6. Nod
e
decay rate for differe
nt network
Siz
e
Figure 7. Through
put Com
pari
s
on fo
r Existing
and propo
se
d
work
5. Conclusio
n
In our propo
sed n
e
two
r
k ,We have a
c
hieved bette
r ene
rgy mod
e
l for WS
N b
y
usin
g
MASO techni
que by red
u
c
ing the ove
r
all ene
rgy consumption i
n
a netwo
rk.
Enhanced the
netwo
rk lifetime for different in
crea
se
d net
work si
ze
s
.
N
o
de’s dec
a
y
r
a
te is also less
in
the
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
1693-6
930
Energ
y
Efficient Erro
r Rat
e
Optim
i
zation Tran
sm
issi
on in Wirele
ss Sensor… (Shara
da K. A)
1375
prop
osed wo
rk
a
s
comp
ared
to existin
g
works.
Ou
r pro
p
o
s
ed
work i
s
enha
n
c
ed
the
network
quality by
se
lecting
the m
a
ximum o
r
o
p
timum n
u
mb
er
of clu
s
ters
ba
sed on ne
twork
effici
en
cy
and in future
we want to verify with the real mute syst
em.
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