Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
6, N
o
. 2
,
A
p
r
il
201
6, p
p
.
59
6
~
60
1
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
2.8
246
5
96
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Big Bang-Big Crunch Algorith
m for Dynamic Deploym
ent of
Wireless Sens
or Network
R.S
.
Upp
a
l*, Shakti Kum
a
r
*
*
* Department of
Computer Scien
ce
a
nd
Engineering, BBSBEC Fatehgarh
Sahib
** Baddi Univer
sity
of
Emerging
Scien
ces
& Technolog
y
Badd
i
Article Info
A
B
STRAC
T
Article histo
r
y:
Received J
u
l
1, 2015
Rev
i
sed
No
v
25
, 20
15
Accepted Dec 16, 2015
This paper p
r
op
oses soft computi
ng techniqu
e
Big Bang-Big C
r
unch (BB-
BC) to address
the main issue
of depl
o
y
ment of
wireless
sensor networks.
De
pl
oy
me
nt
i
s
the
ma
i
n
fac
t
or
tha
t
signif
i
can
tly affects the perf
ormance of
the wireless sensor network. This appr
oach m
a
xi
m
i
zes
the cov
e
r
a
ge ar
ea of
the giv
e
n set of
sensors. We imple
mented our
approach in M
A
TLAB and
compared it with ABC approach and found
that the proposed
approach
is
m
u
ch bett
er
than
the
said
appro
a
c
h
.
Keyword:
Ab
c algo
rith
m
B
i
g ba
n
g
bi
g c
r
u
n
c
h
Dy
nam
i
c depl
o
y
m
e
nt
Sens
or
co
ve
rag
e
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
R S Upp
a
l,
Depa
rt
m
e
nt
of
C
o
m
put
er Sci
e
nce a
n
d
E
ngi
n
eeri
n
g,
B
a
ba B
a
n
d
a
Si
ng
h B
a
ha
du
r E
ngi
neeri
n
g
C
o
l
l
ege,
Fateh
g
a
rh
Sah
i
b
,
Punj
ab Ind
i
a 14
040
7.
Em
a
il:
rsup
pal
@
gm
ai
l
.
co
m
1.
INTRODUCTION
Em
ergence
of W
i
reless Sensor
Net
w
orks in
the
recent
era ha
s re
volutionize the m
onitori
ng a
nd
su
rv
eillan
ce activ
ities in
th
e field
o
f
co
mm
u
n
icatio
n
.
Th
e
stu
d
y
of
W
i
reless Sen
s
or Net
w
orks requ
ires v
a
st
brea
dt
h
of
kn
o
w
l
e
d
g
e f
r
om
vari
et
y
of di
sci
p
l
i
n
es [1]
.
W
i
re
l
e
ss Sens
or
Ne
t
w
o
r
k i
s
u
n
d
er
st
oo
d as a col
l
ect
i
o
n
of
no
des o
r
ga
ni
zed i
n
t
o
a coo
p
e
r
at
i
v
e net
w
o
r
k
[1
0 f
r
om
2]
. These i
n
e
xpe
nsi
v
e, l
o
w po
we
r com
m
u
n
i
cat
i
o
n
nodes ca
n
be
depl
oyed t
h
roughout a physical space,
pr
oviding de
nse s
e
nsing cl
os
e to
physical phe
nomena,
pr
ocessi
ng a
n
d com
m
uni
cat
i
ng t
h
e i
n
f
o
rm
at
i
on, a
n
d co
o
r
di
nat
i
ng
act
i
o
ns wi
t
h
ot
her no
des [3]
.
Wi
rel
e
ss
Sens
or
Network c
onsists
of t
h
ousa
nds se
ns
or
nodes
,
depl
oyed eithe
r
ra
ndom
ly
or according to som
e
pre
-
defi
ned st
at
i
s
ti
cal
di
st
ri
but
i
o
n, ove
r geo
g
r
aphi
c regi
on
o
f
i
n
ter
e
st
[5
]. W
i
r
e
less Sen
s
or
N
e
two
r
ks
f
i
nd
di
ve
rsi
f
i
e
d a
p
pl
i
cat
i
on ra
n
g
i
ng
fr
om
m
onit
o
ri
n
g
bi
ol
o
g
i
cal
sy
st
em
t
o
m
oni
t
o
ri
ng
fo
r
e
st
fi
res t
h
r
o
u
gh ai
r
dr
o
ppe
d sens
o
r
s. De
pe
ndi
ng
up
o
n
t
h
e area
of i
n
t
e
re
st
, t
h
e
pl
acem
e
nt
of t
h
e sens
ors m
a
y
be l
o
cat
ed at
pre-
determ
ined location while other placem
ent coul
d be op
timall
y
deter
m
i
n
ed
using com
putational intelligence
[4]
.
Sensi
n
g
a
n
d
com
m
uni
cat
i
on are
t
w
o
pr
im
ary
funct
i
o
n
s
o
f
W
i
rel
e
ss
Sens
or
Net
w
o
r
ks.
The e
ffe
ct
i
v
ene
ss
of
sen
s
i
n
g a
n
d
com
m
uni
cat
i
on i
s
det
e
rm
i
n
ed
by
c
ove
rage
an
d c
o
nnect
i
v
i
t
y
of t
h
e
net
w
o
r
k
.
C
ove
ra
g
e
a
n
d
co
nn
ectiv
ity issu
es larg
ely
d
e
p
e
nd
up
o
n
t
h
e
ef
fi
ci
ent
d
e
pl
oy
m
e
nt
of
th
e nod
es i
n
th
e in
terest
reg
i
on
.
C
o
n
v
er
sel
y
, d
e
pl
oy
m
e
nt
deci
si
ons
req
u
i
r
e opt
i
m
al
calcul
a
t
i
ons
o
f
t
h
e
net
w
or
k’s
cove
ra
ge rat
i
o
, w
h
i
l
e
main
tain
in
g
the d
e
sired
con
n
ectiv
ity [4
], [7]. On
e im
p
o
r
tan
t
criterion
fo
r b
e
ing
ab
le to
d
e
p
l
o
y
an
efficien
t
sens
or
network is to find optim
al node
placem
ent strate
gies. De
ploying nodes in la
rge se
nsing fields re
qui
re
s
effi
ci
ent
t
o
p
o
l
ogy
c
ont
rol
[
6
]
.
Due t
o
t
h
e
d
i
versi
t
y
of a
p
p
l
i
cat
i
ons, re
q
u
i
r
em
ent
s
and d
e
si
gn
g
o
al
s n
o
si
ngl
e
di
st
i
n
ct
i
v
e ap
p
r
oac
h
i
s
avai
l
a
bl
e t
o
t
h
e desi
g
n
an
d de
pl
oy
m
e
nt
of se
ns
ors
net
w
or
ks [
8
]
.
The m
a
jor i
ssu
e i
n
t
h
e
depl
oy
m
e
nt
i
s
t
o
fi
nd
t
h
e
o
p
t
i
m
al
pl
acem
e
nt
of
n
o
d
es, s
o
t
h
at
a m
i
nim
u
m
num
ber
of
t
h
e
m
are neede
d
[
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 2, A
p
ri
l
20
16
:
59
6 – 6
0
1
59
7
Optim
al placement of nodes
means fi
nding
the optim
al loc
a
tion
of sens
ors in t
h
e re
gi
on of intere
st
of de
pl
oy
m
e
nt
. Thi
s
i
s
a sol
u
t
i
on o
f
M
a
t
h
em
at
i
cal
Probl
em
havi
ng a
n
o
b
je
ct
i
v
e fu
nct
i
on t
o
be m
a
xim
i
zed o
r
min
i
mized
with
in
con
s
trai
n
t
s. In
th
e R
O
I
,
co
vera
ge rat
i
o
s o
f
di
ffe
re
nt
pl
acem
e
nt
s are com
put
ed and sel
ect
i
o
n
of
o
p
t
i
m
al
pl
acem
e
nt
s co
ul
d
b
e
d
one
by
va
ri
ous
o
p
t
i
m
i
z
at
ion
t
ech
ni
q
u
es
[
4
]
.
Locat
i
o
ns o
f
W
i
rel
e
ss Se
ns
or
No
des c
o
m
put
e
d
i
n
opt
i
m
al
sol
u
t
i
o
n o
f
t
h
e t
echni
que
wo
ul
d s
o
l
v
e
d
e
p
l
o
y
m
e
n
t
issu
e of
W
S
N. Th
e d
e
p
l
o
y
m
e
n
t
p
o
s
ition
o
f
n
o
d
e
is co
n
s
i
d
ered
b
y
its lo
catio
n
coo
r
d
i
n
a
te (X, Y).
To fi
nd
out the position of these location c
o
ordinates wh
i
l
e
m
a
intain connectivity an
d
sensing in the
target
regi
on
i
s
t
h
e
k
e
y
i
ssue
of
de
pl
oy
m
e
nt
. The
o
p
t
i
m
al
l
o
cat
i
o
n
c
o
o
r
di
nat
e
s
o
f
wi
rel
e
ss
se
nso
r
n
ode
s a
r
e
t
o
be
com
put
ed usi
n
g va
ri
o
u
s al
g
o
r
i
t
h
m
s
for t
h
e opt
i
m
al
depl
oym
ent
of wi
re
l
e
ss senso
r
net
w
o
r
k
.
Th
e reg
i
on
o
f
in
terest is a t
w
o-d
i
m
e
n
s
io
nal g
r
id
and
the in
itial
d
e
p
l
oy
m
e
n
t
o
f
n
o
d
e
s fo
r algorithm is
ch
o
s
en
ran
d
o
m
l
y
con
s
i
d
eri
n
g
ra
nd
om
l
o
cat
i
on
coo
r
di
nat
e
s.
There a
r
e vari
ous al
g
o
r
i
t
h
m
s
whi
c
h c
oul
d b
e
used t
o
com
put
e l
o
cat
i
ons o
f
no
des a
nd co
vera
ge. T
h
e
m
o
st recent a
l
gorithm
is Artificia
l Bee
Colony (ABC
) algorithm
whic
h provi
des
bene
fit of num
e
rical
o
p
tim
izat
io
n
an
d clu
s
teri
n
g
.
Th
e cov
e
rag
e
p
r
ob
lem
is also
op
ti
m
i
zed
u
s
i
n
g Particle Swan
Op
tim
iza
tio
n
(PSO)
to gi
ve the
best covera
ge
by c
o
m
puting
t
h
e l
o
cation of se
ns
or nodes
placement [10].
I
n
th
is stud
y,
w
e
h
a
v
e
pr
oposed
a n
e
w
appr
o
a
ch
of
Big
Ban
g
Big
Crun
ch
al
g
o
r
ith
m
f
o
r
d
ynam
i
c
depl
oy
m
e
nt
of senso
r
s.
We have al
s
o
co
m
p
ared t
h
i
s
al
go
ri
t
h
m
t
o
AB
C
al
gori
t
h
m
whi
c
h s
h
o
w
s
bet
t
e
r
depl
oy
m
e
nt
.
2.
BIG BA
NG
-B
IG C
R
U
N
C
H
ALGO
RITH
M
Thi
s
i
s
nat
u
re i
n
spi
r
e
d
o
p
t
i
m
i
zat
i
on t
echni
q
u
e base
d o
n
t
h
eory
o
f
B
i
g B
a
ng t
h
e
o
ry
of
uni
verse
.
I
n
B
i
g B
a
ng p
h
a
s
e senso
r
pl
ac
em
ent
i
s
sel
e
ct
ed ran
d
o
m
l
y
and t
h
e
n
i
n
B
i
g C
r
u
n
ch
pha
se
m
i
nim
i
zes
fi
t
n
ess
fu
nct
i
o
n t
h
e
r
e
b
y
gi
vi
n
g
o
p
t
i
m
al
depl
oy
m
e
nt
and
co
ve
rage
.
Beg
i
n
/* Big Ba
ng P
h
ase */
Gene
rat
e
a
ra
n
dom
set
of
NC
candi
dat
e
s (
p
o
pul
at
i
o
n);
/*
En
d of
Big
Ban
g
Ph
ase
*
/
Wh
ile
n
o
t
TC
/
*
TC is a term
in
atio
n
criterion
*
/
Co
m
p
u
t
e th
e
fitn
ess
v
a
lu
e
of
all th
e cand
i
d
a
t
e
so
l
u
tio
n
s
;
Sort
t
h
e
p
o
p
u
l
a
t
i
on
fr
om
best
t
o
wo
rst
base
d
on
fi
t
n
es
s (c
ost
)
val
u
e;
/*
Big
Cr
un
ch
P
h
a
s
e *
/
For
g
u
i
d
i
n
g t
h
e ne
w sea
r
ch
c
o
m
put
e t
h
e ce
n
t
er o
f
m
a
ss usi
n
g
f
o
l
l
o
wi
n
g
E
quat
i
o
n;
(
1
)
Whe
r
e x
c
= p
o
s
i
t
i
on o
f
t
h
e ce
nt
er
of m
a
ss;
x
i
= posi
t
i
on
o
f
candi
dat
e
i
;
f
i
= fitn
ess fun
c
tion
v
a
l
u
e of cand
id
ate
i;
B
e
st
fi
t
i
ndi
vi
d
u
al
can
be
c
hos
en as
t
h
e ce
nt
e
r
of m
a
ss i
n
st
e
a
d
of
usi
n
g
E
q
.
1
;
/* E
n
d of Big
Crunch P
h
ase
*/
C
a
l
c
ul
at
e new
candi
dat
e
s ar
o
u
n
d
t
h
e cent
e
r
of m
a
ss by
ad
di
n
g
or s
u
bt
rac
t
i
ng a
no
rm
al
rand
om
num
ber ho
se
v
a
lu
e
d
e
creases as th
e iteration
s
elap
se
u
s
ing Equ
a
tion
;
(2)
Whe
r
e x
c
stand
s
for cen
t
er
of m
a
ss,
is th
e
u
p
p
e
r limit o
f
th
e p
a
ram
e
ter, rand
is a no
rm
al rando
m
n
u
m
b
e
r
an
d k is th
e k
th
iteratio
n
o
f
th
e alg
o
rith
m
.
Th
en
n
e
w po
int x
new
i
s
u
ppe
r
and
l
o
wer
b
o
u
nde
d.
End
wh
ile
End
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISS
N
:
2088-8708
Bi
g
B
a
n
g
-
Bi
g
C
r
unc
h Al
g
o
ri
t
h
m
f
o
r Dy
n
a
mi
c
De
pl
oy
ment
of
Wi
rel
e
ss Se
nso
r
N
e
t
w
ork
(
R
S
U
p
pal
)
59
8
3.
SENSO
R
DE
TECTION
M
O
DEL
Sens
or
det
ect
i
on m
odel
s
ca
n
be cat
eg
ori
z
e
d
i
n
t
w
o
way
s
i
n
W
S
Ns t
o
fi
n
d
out
t
h
e ef
fect
i
v
e cov
e
ra
ge
area. O
n
e of t
h
e
m
odel
s
i
s
base
d o
n
bi
n
a
ry
det
ect
i
o
n whi
c
h
i
s
base
d on
ass
u
m
p
t
i
on
t
h
at
t
h
e
r
e i
s
n
o
u
n
c
ertain
ty and
th
e o
t
h
e
r m
o
d
e
l is b
a
sed
o
n
p
r
o
b
a
b
ilistic d
e
tectio
n
m
o
d
e
l [12
]
wh
ich
pro
v
i
d
e
s m
o
re realisti
c
resu
lts as co
mp
are to
first
one b
ecau
s
e it
u
s
es prob
ab
ilis
ti
c term
s fo
r d
e
cid
i
n
g
th
e effectiv
e cov
e
rag
e
of the
area [6].
The
bi
na
ry
det
ect
i
on m
odel
i
s
ad
opt
e
d
here
[
11]
.
Co
v
e
rag
e
ratio of th
e
WSN is calcu
lated
u
s
i
n
g th
e
Equ
a
tion
1
:
(
1
)
Whe
r
e,
C
i
denot
es t
h
e
cove
ra
ge area
of a sen
s
o
r
i
,
S den
o
t
e
s
th
e set o
f
th
e no
d
e
s, an
d
A is th
e to
tal area of
in
terest.
In t
h
is m
odel,
for a two-dim
e
nsional physic
al space,
eac
h
sens
or
node
ra
nge
is conside
r
ed as circle
and is
place
d a
t
the centre
say (x, y)
whe
r
e
x
and
y
are c
o
o
r
di
nat
e
s
of ce
nt
re. I
n
a
r
ea
A,
n
set
of m
obi
l
e
no
des
suc
h
as
S = {
S
1
, S
2
, ………
…,
S
n
} i
s
t
o
be
de
pl
oy
ed
at
n
di
ffe
re
nt
l
o
cat
i
o
n
ha
vi
n
g
c
o
or
di
nat
e
s
(x
i
, y
i
),
so as
to
ens
u
re
opt
i
m
al cove
ra
ge. T
h
e
radi
us r
o
f
t
h
e ci
rcl
e
i
s
t
h
e
sensi
n
g
ran
g
e
of se
ns
or
. T
h
u
s
, t
h
e se
nsi
n
g
area i
s
, whi
l
e
i
t
s
co
m
m
uni
cat
i
on r
a
nge i
s
e
qual
and
great
e
r
t
h
a
n
t
w
i
ce t
h
e se
nsi
n
g ra
n
g
e. S
o
, i
n
a
gi
ve
n t
w
o
-
dim
e
nsional physical space of area
A, the
n num
b
ers of
s
e
ns
ors are
random
ly depl
oyed. T
h
e coordinates of
centre
of circle
s are
de
note
d
a
s
(x
i,
y
i
),
whe
r
e
i = 1,
2,
3, …
……….,
n
The a
r
ea
of
o
v
e
rl
ap
pi
n
g
bet
w
een se
nsi
n
g
o
f
t
w
o s
e
ns
or
s i
a
n
d
j
i
s
re
p
r
esen
t
e
d by
A
ij
and i
s
calculated
by
(
2
)
Whe
r
e,
d
ij
i
s
t
h
e
di
st
an
ce bet
w
ee
n t
h
e
sens
or
i
a
n
d
se
nso
r
j a
n
d i
s
ca
l
c
ul
at
ed
by
(
3
)
Tot
a
l
O
v
erl
a
p
p
i
ng
o
f
Depl
oy
m
e
nt
=
(4
)
The are
a
o
f
o
v
erl
a
ppi
ng
o
f
every
se
ns
or
wi
t
h
t
h
e
ot
he
r
(n
-1
) se
ns
or i
s
rep
r
ese
n
t
e
d i
n
t
h
e
fo
rm
of squ
a
re
matrix
o
f
ord
e
r n
called th
e Area Matrix.
4.
SIM
U
LATI
O
N
RESULTS
AN
D A
NAL
Y
S
IS
We i
m
p
l
e
m
en
ted
th
e p
r
op
osed
d
e
p
l
o
y
m
e
n
t
o
f
BBBC alg
o
rith
m
u
s
in
g
MATLAB. In
an area o
f
10
0
X 100 s
qua
re meter, 80 m
obile sensors we
re depl
oyed
. Detection ra
di
us of each sensor
was 7 m
e
ter and
p
opu
latio
n
size
w
a
s 20
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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:
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.
6, No
. 2, A
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l
20
16
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6 – 6
0
1
59
9
Fi
gu
re
1.
C
o
ve
rage
A
r
ea
of
t
h
e de
pl
oy
m
e
nt
vs
50
0 i
t
e
rat
i
o
n
s
Fi
g
u
re
2
.
C
o
v
e
rage
A
r
ea
of
t
h
e
depl
oy
m
e
nt vs
1
0
0
0
i
t
e
rat
i
ons
Fi
gu
re
3.
C
o
ve
rage
A
r
ea
of
t
h
e de
pl
oy
m
e
nt
vs
10
0
0
0
i
t
e
rat
i
ons
BBBC alg
o
r
ith
m
w
a
s ru
n
w
ith
500
, 1000
and
10
,00
0
iteratio
n
s
.
We co
m
p
ared
th
e p
e
rform
a
n
ce o
f
o
u
r
pr
o
pose
d
de
pl
o
y
m
e
nt
app
r
oac
h
wi
t
h
AB
C
ap
pr
oac
h
i
n
Tabl
e 1.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Bi
g
B
a
n
g
-
Bi
g
C
r
unc
h Al
g
o
ri
t
h
m
f
o
r Dy
n
a
mi
c
De
pl
oy
ment
of
Wi
rel
e
ss Se
nso
r
N
e
t
w
ork
(
R
S
U
p
pal
)
60
0
Tab
l
e 1
.
C
o
m
p
arison
o
f
ABC an
d
BB-BC Ap
pro
ach
Appr
oach
N
o of Sensor
s
No.
of I
t
er
ations
500
1000
1000
0
ABC [27]
100
96.
66%
98.
33%
99.
34%
BB-
BC 80
96.
98%
98.
60%
99.
52%
5.
CO
NCL
USI
O
N
In t
h
i
s
pa
per
,
we ha
ve pr
o
p
o
s
ed B
B
-
B
C
A
p
p
r
oach f
o
r t
h
e depl
oy
m
e
nt
of m
obi
l
e
wi
rel
e
ss senso
r
s
.
Th
e app
r
o
ach
was im
p
l
e
m
en
ted
in
M
A
TLAB with an
initial p
o
p
u
l
ation
o
f
size 20
, detectio
n
rad
i
us 7
and
with
th
ree d
i
fferen
t sizes o
f
iteratio
n
s
,
v
i
z. 50
0, 10
00
and
10
000
. I
t
w
a
s ob
serv
ed
th
at in
all
th
e th
r
ee it
er
ati
on
p
r
o
c
ed
ur
es, the p
r
o
p
o
s
ed
app
r
o
a
ch
w
ith
80
sensor
s p
e
r
f
o
r
m
e
d
b
e
tter
th
an
t
h
e A
B
C
ap
pr
o
a
ch
w
ith
100
sens
ors
.
T
h
e c
ove
ra
ge area
was i
m
pro
v
ed
up
o
n
by
usi
n
g
t
h
e B
B
-
B
C
a
p
p
r
oach e
v
e
n
wi
t
h
l
e
sser
n
u
m
ber of
sens
ors
.
T
hus
,
i
t
i
s
concl
u
de
d
t
h
at
B
B
-
B
C
i
s
a bet
t
e
r
depl
o
y
m
e
nt
t
echni
q
u
e f
o
r m
obi
l
e
sens
ors t
h
an t
h
e
AB
C
approach.
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