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ith
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1.
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m
ilit
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,
d
ata
g
ath
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in
g
,
an
d
s
o
o
n
[
1
]
.
B
e
ca
u
s
e
o
f
th
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m
in
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m
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life
ti
m
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atter
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s
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m
p
tio
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b
ec
o
m
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m
o
r
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ch
allen
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[
2
]
.
T
h
e
en
e
r
g
y
ef
f
ec
tiv
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s
o
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s
en
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ce
s
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t
to
co
m
m
u
n
icatio
n
as
well
as
p
r
o
ce
s
s
in
g
[
3
]
.
T
h
u
s
,
it
is
im
p
o
r
tan
t
f
o
r
d
ev
elo
p
in
g
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e
f
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g
y
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s
u
m
p
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p
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h
t
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h
an
ce
a
NL
as
well
as
s
tab
ilit
y
o
f
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C
lu
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ter
in
g
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im
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o
r
tan
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o
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o
r
attain
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g
an
o
p
tim
al
en
e
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g
y
ef
f
icien
cy
i
n
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SN
[
4
]
,
[
5
]
.
I
n
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SN
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g
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ed
th
r
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u
g
h
clu
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ter
h
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s
(
C
Hs)
[
6
]
.
An
at
tach
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C
H
ar
e
th
e
f
ir
s
t
lev
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n
o
d
es,
an
d
r
est
ar
e
th
e
s
ec
o
n
d
l
ev
el
n
o
d
es
[
7
]
.
C
H
in
ev
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y
clu
s
ter
co
llects
d
ata
f
r
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m
th
e
n
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b
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r
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o
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e
an
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tr
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its
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to
th
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ase
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tatio
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u
p
p
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r
t
o
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n
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o
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es
[
8
]
.
I
n
clu
s
ter
in
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a
n
ar
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itra
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C
H
s
elec
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im
p
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ts
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r
s
t
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tiv
ity
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ailu
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o
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th
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n
o
d
es,
an
d
co
n
s
tr
ain
e
d
n
etwo
r
k
life
tim
e
[
9
]
.
Simu
lt
an
eo
u
s
ly
,
o
p
tim
al
C
H
s
elec
tio
n
im
p
r
o
v
es
an
ef
f
ec
tiv
en
ess
an
d
life
tim
e
in
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SN
s
.
An
o
p
tim
ized
r
o
u
tin
g
ap
p
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o
ac
h
with
o
p
tim
al
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H
s
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tio
n
is
im
p
o
r
tan
t
f
o
r
b
etter
s
ca
lab
ilit
y
W
SNs
[
1
0
]
,
[
1
1
]
.
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d
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e
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[
1
3
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o
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ly
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o
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1
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5
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ee
n
b
asically
co
n
ce
n
tr
ated
o
n
in
tr
o
d
u
cin
g
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
ap
p
r
o
ac
h
es
f
o
r
s
o
lv
in
g
en
e
r
g
y
ch
allen
g
es.
T
h
e
r
ec
en
tly
d
e
v
elo
p
e
d
o
p
tim
izatio
n
ap
p
r
o
ac
h
es
lik
e
g
en
etic
alg
o
r
ith
m
s
(
GAs),
s
u
n
f
lo
wer
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
SF
O)
,
an
d
an
t
co
lo
n
y
o
p
tim
i
za
tio
n
(
AC
O)
[
1
6
]
,
[
1
7
]
.
T
h
ese
ap
p
r
o
ac
h
es
ar
e
s
u
p
p
o
r
tiv
e
i
n
m
ain
tain
n
etwo
r
k
ef
f
ec
tiv
en
ess
o
f
ef
f
icien
t r
o
u
ti
n
g
.
I
n
p
r
ev
io
u
s
r
esear
ch
wo
r
k
s
,
th
e
m
o
d
el
im
p
ac
ts
co
n
s
tr
ain
ed
b
atter
y
p
o
wer
as
well
as m
in
im
u
m
r
eso
u
r
ce
s
at
en
d
-
to
-
e
n
d
tr
a
n
s
m
is
s
io
n
[
1
8
]
.
I
n
r
ec
e
n
t
y
ea
r
s
,
a
n
u
m
b
er
o
f
r
esear
ch
h
av
e
u
s
ed
a
v
ar
iety
o
f
m
eth
o
d
s
f
o
r
C
H
s
elec
tio
n
an
d
r
o
u
tin
g
i
n
W
SN.
A
n
u
m
b
er
o
f
th
e
co
n
te
m
p
o
r
ar
y
ev
alu
atio
n
tech
n
i
q
u
e
s
ar
e
d
is
cu
s
s
ed
in
th
e
p
ar
t
th
at
f
o
llo
ws,
alo
n
g
with
s
o
m
e
o
f
th
eir
d
r
awb
ac
k
s
:
I
n
2
0
2
3
,
C
h
er
a
p
p
a
et
a
l.
[
1
9
]
d
ev
elo
p
ed
an
a
d
ap
tiv
e
s
ailf
is
h
o
p
tim
izatio
n
(
ASFO)
ap
p
r
o
ac
h
with
K
-
m
ed
o
i
d
s
f
o
r
th
e
ef
f
ec
tiv
e
C
H
s
elec
tio
n
in
s
en
s
o
r
n
o
d
es.
T
h
e
E
E
c
r
o
s
s
-
lay
er
-
ass
is
ted
ex
p
en
d
i
n
g
r
o
u
tin
g
p
r
o
to
c
o
l
(E
-
C
E
R
P)
ap
p
r
o
ac
h
was
u
tili
ze
d
f
o
r
a
d
ete
r
m
in
atio
n
o
f
b
est
r
o
u
te,
d
y
n
am
ically
r
ed
u
ce
d
th
e
n
etwo
r
k
o
v
e
r
h
ea
d
.
Per
f
o
r
m
an
ce
f
in
d
in
g
s
f
o
r
Qo
S
cr
iter
ia
in
clu
d
e
PDR
(
1
0
0
%
)
,
laten
cy
(
0
.
0
5
s
)
,
th
r
o
u
g
h
p
u
t
(
0
.
9
9
Mb
p
s
)
,
E
C
(
1
.
9
7
m
J
)
,
NL
(
5
9
0
8
cy
cle
s
)
,
an
d
PLR
(
0
.
5
%)
f
o
r
1
0
0
n
o
d
es.
I
n
2
0
2
4
,
R
o
b
er
ts
et
a
l
.
[
2
0
]
s
u
g
g
ested
an
en
h
an
ce
d
two
-
p
h
ased
p
ar
a
d
ig
m
f
o
r
clu
s
ter
-
b
ased
,
E
E
r
o
u
tin
g
in
W
SNs
.
T
h
e
ad
v
an
ce
d
m
eta
-
h
e
u
r
is
tic
m
o
d
els
s
u
ch
as
s
p
o
tted
h
y
en
a
o
p
ti
m
izatio
n
(
SHO)
an
d
s
ailf
is
h
o
p
tim
izatio
n
(
SF
O)
wer
e
in
teg
r
ated
in
to
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
.
T
h
e
d
e
v
elo
p
ed
h
y
b
r
id
f
r
am
ewo
r
k
p
er
f
o
r
m
ed
h
ig
h
er
th
a
n
th
e
p
r
ev
io
u
s
s
in
g
le
-
alg
o
r
ith
m
a
p
p
r
o
ac
h
es
in
ter
m
s
o
f
in
c
r
ea
s
in
g
d
ata
tr
an
s
m
is
s
io
n
r
eliab
ilit
y
,
m
ax
im
izin
g
E
C
,
an
d
p
r
o
lo
n
g
in
g
NL
.
I
n
2
0
2
4
,
E
l
Kh
ed
ir
i
et
a
l
.
[
2
1
]
in
tr
o
d
u
ce
d
a
n
o
v
el
h
y
b
r
id
m
etah
e
u
r
i
s
tic
th
at
in
teg
r
ated
ar
tific
ial
b
ee
co
lo
n
y
(
AB
C
)
an
d
AC
O
p
r
in
cip
les
to
im
p
r
o
v
e
th
e
s
o
lu
tio
n
s
ea
r
ch
p
r
o
ce
s
s
an
d
r
ed
u
ce
th
e
to
tal
E
C
o
f
W
SNs
.
Acc
o
r
d
in
g
to
th
e
f
in
d
in
g
s
,
th
e
m
et
h
o
d
u
s
ed
a
lo
t
less
en
er
g
y
th
a
n
B
ee
Sen
s
o
r
,
L
E
AC
H,
iAB
C
,
an
d
B
ee
clu
s
ter
,
with
r
ed
u
ctio
n
s
o
f
2
2
.
2
0
%,
4
7
.
2
5
%,
2
7
.
3
8
%,
an
d
3
2
.
4
0
%,
r
esp
ec
tiv
ely
.
I
n
2
0
2
4
,
Ma
n
o
h
ar
a
n
et
a
l
.
[
2
2
]
o
f
f
e
r
ed
a
n
o
v
el
m
et
h
o
d
f
o
r
ac
h
iev
in
g
th
e
b
est
en
er
g
y
-
ef
f
icien
t
r
o
u
tin
g
in
W
SNs
b
y
c
o
m
b
i
n
in
g
d
e
n
s
ity
-
b
ased
a
d
ap
tiv
e
s
o
f
t
clu
s
ter
in
g
with
ad
a
p
tiv
e
en
tr
o
p
y
b
ald
ea
g
le
s
ea
r
ch
o
p
tim
izatio
n
.
W
h
en
c
o
m
p
ar
ed
t
o
th
e
s
ev
er
al
cu
r
r
e
n
t
m
eth
o
d
s
,
th
e
s
u
g
g
ested
m
eth
o
d
o
lo
g
y
ac
h
ie
v
ed
b
etter
p
er
f
o
r
m
an
ce
in
ter
m
s
o
f
en
er
g
y
(
1
.
9
2
j)
,
laten
c
y
(
6
.
5
m
s
)
,
th
r
o
u
g
h
p
u
t (
3
2
0
.
1
k
b
p
s
)
,
an
d
PDR
(
2
1
8
.
7
%)
.
I
n
2
0
2
4
,
Su
lth
an
a
a
n
d
Du
r
aip
an
d
ian
[
2
3
]
s
u
g
g
ested
an
E
E
l
if
etim
e
-
awa
r
e
clu
s
ter
-
b
ased
r
o
u
tin
g
(
E
E
L
C
R
)
f
o
r
W
SN.
T
h
e
E
E
L
C
R
ap
p
r
o
ac
h
in
tr
o
d
u
ce
d
t
h
e
m
o
d
if
ied
g
ian
t
tr
ev
ally
o
p
tim
izatio
n
(
MG
T
O)
m
o
d
el
f
o
r
ef
f
ec
tiv
e
b
alan
ce
d
clu
s
ter
in
g
t
h
at
r
ed
u
ce
d
E
C
.
T
h
e
s
u
g
g
este
d
E
E
L
C
R
s
tr
ateg
y
wo
r
k
s
n
o
ti
ce
ab
ly
b
etter
t
h
an
cu
r
r
en
t
r
o
u
tin
g
tec
h
n
iq
u
es,
s
h
o
win
g
an
a
v
er
ag
e
NL
g
ain
o
f
5
2
.
6
2
5
%
in
s
im
u
latio
n
r
o
u
n
d
s
an
d
5
1
.
8
8
%
in
n
o
d
e
d
en
s
ity
co
n
s
id
er
atio
n
s
.
I
n
2
0
2
4
,
R
ek
h
a
an
d
Gar
g
[
2
4
]
i
n
tr
o
d
u
ce
d
th
e
h
y
b
r
id
K
-
m
ea
n
s
a
n
d
l
io
n
o
p
tim
izatio
n
(
K
-
L
io
n
E
R
)
m
eth
o
d
f
o
r
E
E
clu
s
ter
in
g
-
b
ased
r
o
u
tin
g
m
o
d
el
f
o
r
W
SN
s
u
p
p
o
r
ted
b
y
th
e
I
o
T
.
T
h
e
o
b
jectiv
e
o
f
th
e
d
ev
elo
p
ed
K
-
L
io
n
E
R
is
to
in
cr
ea
s
e
E
C
a
n
d
n
etwo
r
k
lo
n
g
ev
ity
.
T
h
e
s
u
g
g
ested
K
-
L
io
n
E
R
r
o
u
tin
g
m
o
d
el
ex
ten
d
ed
th
e
N
L
b
y
1
0
%
to
4
8
%
as
co
m
p
ar
e
d
to
th
e
p
r
ev
io
u
s
m
eth
o
d
s
.
I
n
2
0
2
5
,
Ma
b
u
n
g
a
an
d
C
r
u
z
[
2
5
]
p
r
esen
ted
a
n
o
v
el
m
u
ltio
b
jectiv
e
C
H
s
elec
tio
n
an
d
r
o
u
tin
g
m
et
h
o
d
f
o
r
p
r
o
v
id
in
g
en
er
g
y
-
awa
r
e
d
at
a
tr
an
s
m
is
s
io
n
in
W
S
N.
Her
e,
C
H
s
elec
tio
n
was
ca
r
r
ied
o
u
t
u
s
in
g
th
e
d
ev
elo
p
ed
ch
r
o
n
o
l
o
g
ical
wild
g
ee
s
e
o
p
tim
izatio
n
(
C
W
GO)
tech
n
iq
u
e
b
ased
o
n
m
u
ltip
le
co
n
s
tr
ain
ts
.
T
h
e
p
r
o
p
o
s
ed
C
W
GO
was
ex
am
in
ed
co
n
s
id
er
in
g
m
etr
ics,
lik
e
en
er
g
y
,
tr
u
s
t,
d
is
tan
ce
,
an
d
d
elay
,
an
d
was
f
o
u
n
d
t
o
h
av
e
attain
e
d
s
u
p
er
io
r
v
alu
es
o
f
0
.
9
6
3
J
,
0
.
7
0
0
,
1
9
.
4
6
8
m
,
an
d
0
.
2
5
2
s
,
r
esp
ec
tiv
ely
.
T
o
tac
k
le
th
ese
is
s
u
es,
a
n
o
v
el
k
o
o
k
a
b
u
r
r
a
o
p
tim
izatio
n
alg
o
r
ith
m
b
ased
d
y
n
a
m
ic
ad
ju
s
tm
en
t
s
tr
ateg
y
(
KOA
-
DAS)
m
eth
o
d
h
as
b
ee
n
d
e
v
elo
p
ed
f
o
r
th
e
E
E
clu
s
ter
in
g
an
d
r
o
u
tin
g
in
W
SN.
T
h
e
k
ey
co
n
tr
ib
u
tio
n
s
o
f
th
e
d
ev
elo
p
ed
KOA
-
DAS
h
av
e
b
ee
n
g
iv
en
a
s
f
o
llo
ws.
-
T
h
e
k
e
y
g
o
al
o
f
th
e
d
ev
elo
p
ed
m
o
d
el
is
to
im
p
r
o
v
e
t
h
e
NL
an
d
r
ed
u
ce
E
C
b
y
in
tr
o
d
u
cin
g
n
o
v
el
o
p
tim
izatio
n
alg
o
r
ith
m
s
.
-
T
h
e
C
H
is
s
elec
ted
u
s
in
g
th
e
SB
O
alg
o
r
ith
m
,
wh
ich
c
o
n
s
id
er
s
f
itn
ess
p
ar
am
eter
s
in
v
o
l
v
in
g
d
is
tan
ce
f
r
o
m
C
H
to
B
S,
r
em
ain
in
g
en
er
g
y
a
n
d
in
tr
a
-
c
o
m
m
u
n
icatio
n
co
s
t.
-
Af
ter
C
H
s
elec
tio
n
,
a
n
o
v
el
K
OA
-
DAS
tech
n
iq
u
e
h
as b
ee
n
u
tili
ze
d
f
o
r
ef
f
icien
tly
s
elec
tin
g
th
e
o
p
tim
al
d
ata
tr
an
s
m
is
s
io
n
r
o
u
te.
-
T
h
e
ex
ec
u
tio
n
tim
e,
av
er
a
g
e
r
esid
u
al
en
er
g
y
,
laten
cy
,
PDR
,
NL
,
co
m
p
u
tatio
n
co
s
t,
E
C
,
a
n
d
aliv
e
n
o
d
es
o
f
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
h
av
e
b
ee
n
th
o
r
o
u
g
h
ly
ass
ess
ed
.
T
h
e
r
em
ai
n
in
g
s
ec
tio
n
s
o
f
th
e
p
r
o
p
o
s
ed
KOA
-
DAS
tech
n
iq
u
e
ar
e
ar
r
a
n
g
ed
in
th
e
f
o
llo
wi
n
g
o
r
d
er
.
Sectio
n
2
d
escr
ib
es
th
e
r
elate
d
wo
r
k
s
f
o
r
e
n
er
g
y
-
ef
f
icien
t
clu
s
ter
in
g
-
b
ased
r
o
u
tin
g
an
d
s
ec
tio
n
3
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
KOA
-
DAS
m
eth
o
d
o
lo
g
y
in
d
etail.
T
h
e
s
im
u
latio
n
f
in
d
in
g
s
an
d
d
is
cu
s
s
io
n
ar
e
co
v
er
ed
in
s
ec
tio
n
4
,
an
d
th
e
c
o
n
clu
s
io
n
is
p
r
esen
te
d
in
s
ec
tio
n
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
2
,
J
u
n
e
20
2
6
:
724
-
734
726
2.
P
RO
P
O
SE
D
K
O
A
-
DAS
SY
ST
E
M
I
n
th
is
s
ec
tio
n
,
a
n
o
v
el
KOA
-
DAS
m
eth
o
d
h
as
b
ee
n
d
e
v
e
lo
p
ed
f
o
r
E
E
clu
s
ter
in
g
an
d
r
o
u
tin
g
in
W
SN.
I
n
itially
,
th
e
S
Ns
ar
e
r
a
n
d
o
m
ly
d
ep
lo
y
e
d
in
th
e
W
SN
en
v
ir
o
n
m
e
n
t.
I
n
th
e
C
H
s
elec
tio
n
p
h
ase,
th
e
k
ey
p
ar
am
eter
s
lik
e
lo
ca
tio
n
an
d
en
er
g
y
ar
e
in
itiated
,
an
d
th
e
n
o
d
es
ar
e
ev
alu
ated
b
ased
o
n
th
e
f
itn
ess
m
etr
ic
s
in
clu
d
in
g
d
is
tan
ce
f
r
o
m
t
h
e
C
H
to
th
e
B
S,
r
e
m
ain
in
g
en
er
g
y
,
an
d
in
tr
a
-
co
m
m
u
n
icatio
n
c
o
s
t.
Her
e,
th
e
SB
O
alg
o
r
ith
m
is
u
tili
ze
d
to
s
elec
t
th
e
o
p
tim
al
C
Hs.
T
h
e
s
u
g
g
ested
KOA
-
DAS
is
th
en
u
s
ed
in
th
e
r
o
u
tin
g
p
h
ase
to
ca
lcu
late
th
e
b
est
r
o
u
tes.
Fin
a
lly
,
th
e
r
o
u
tin
g
p
h
ase
d
eter
m
i
n
es
th
e
m
o
s
t
ef
f
icien
t
p
ath
f
o
r
d
ata
tr
an
s
m
is
s
io
n
f
r
o
m
ea
c
h
C
H
to
th
e
B
S.
Fig
u
r
e
1
d
em
o
n
s
tr
ates th
e
wo
r
k
f
lo
w
o
f
th
e
s
u
g
g
ested
KOA
-
DAS
tech
n
iq
u
e.
Fig
u
r
e
1
.
Ov
e
r
all
wo
r
k
f
l
o
w
f
o
r
th
e
p
r
o
p
o
s
ed
KOA
-
DAS
m
et
h
o
d
2
.
1
.
Clus
t
er
hea
d select
io
n us
ing
SB
O
a
lg
o
rit
hm
I
n
th
is
wo
r
k
,
th
e
SB
O
is
em
p
lo
y
ed
to
s
elec
t
o
p
tim
al
C
Hs
b
ased
o
n
th
e
f
itn
ess
m
etr
ics
in
clu
d
in
g
d
is
tan
ce
f
r
o
m
C
H
to
B
S,
r
em
ain
in
g
en
er
g
y
,
a
n
d
in
tr
a
-
c
o
m
m
u
n
icatio
n
co
s
t.
T
h
e
i
n
itializatio
n
p
eo
p
le
o
f
th
e
SB
O
m
eth
o
d
ar
e
g
en
er
ated
at
r
an
d
o
m
as
a
co
llectio
n
o
f
lo
ca
tio
n
s
.
T
h
e
f
o
llo
win
g
r
elatio
n
s
h
ip
in
(
1
)
p
r
o
v
id
es
th
e
lo
ca
tio
n
s
o
f
t
h
e
in
d
iv
i
d
u
al
s
.
(
)
.
=
(
1
,
)
∗
(
−
)
+
∈
(
1
)
T
h
e
attr
ac
tio
n
p
r
o
b
a
b
ilit
ies
o
f
m
ales
(
o
r
f
em
ales)
to
t
h
e
o
th
e
r
b
o
wer
s
ar
e
t
h
en
ca
lcu
lated
(
2
)
an
d
(
3
)
,
ju
s
t
lik
e
in
th
e
AB
C
o
p
tim
izer
.
=
∑
∈
(
2
)
=
{
1
1
+
(
)
(
)
≥
0
1
+
|
(
)
|
(
)
<
0
(
3
)
,
=
,
+
(
,
+
,
2
−
,
)
(
4
)
T
h
e
SB
O
ap
p
r
o
ac
h
was
u
s
ed
in
th
is
wo
r
k
t
o
id
en
tif
y
th
e
m
o
r
e
lik
ely
b
o
wer
s
(
,
)
.
T
h
e
iter
atio
n
am
o
u
n
t f
o
r
s
elec
tin
g
th
e
ai
m
in
g
b
o
wer
is
in
d
icate
d
b
y
in
(
5
)
,
an
d
it m
ay
b
e
f
o
u
n
d
f
o
r
ea
ch
v
ar
iab
le
b
y
(
5
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
E
n
erg
y
-
a
w
a
r
e
d
yn
a
mic
a
d
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s
tmen
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teg
r
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ted
ko
o
k
a
b
u
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a
o
p
timiz
a
tio
n
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(
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h
o
b
a
n
b
a
b
u
R
.
Ja
g
a
n
a
th
a
n
)
727
=
1
+
(
5
)
T
h
en
,
u
s
in
g
a
n
o
r
m
al
d
is
tr
ib
u
tio
n
(
with
,
m
ea
n
a
n
d
v
a
r
ian
ce
)
,
,
will
b
e
r
an
d
o
m
ly
alter
ed
with
a
ce
r
tain
p
r
o
b
ab
ilit
y
b
y
(
6
)
an
d
(
7
)
.
,
~
,
+
∗
(
0
,
1
)
(
6
)
=
∗
(
−
)
(
7
)
At
th
e
co
n
clu
s
io
n
o
f
ea
ch
iter
atio
n
,
th
e
o
ld
p
er
s
o
n
s
ar
e
co
m
b
in
ed
with
th
e
ac
co
m
p
lis
h
ed
in
d
iv
id
u
als u
s
in
g
th
e
s
p
ec
if
ied
p
r
o
ce
s
s
to
cr
ea
te
th
e
n
ew
in
d
iv
id
u
als.
2
.
2
.
Ro
uting
us
ing
K
O
A
-
DA
S
I
n
th
is
wo
r
k
,
KOA
-
DAS
is
u
tili
ze
d
to
d
eter
m
in
e
th
e
o
p
ti
m
al
r
o
u
te
f
r
o
m
ea
c
h
C
H
to
th
e
B
S.
T
h
e
KOA
ap
p
r
o
ac
h
is
p
o
p
u
latio
n
-
ass
is
ted
ap
p
r
o
ac
h
wh
ich
is
ca
p
ab
le
to
o
f
f
er
a
p
p
r
o
p
r
iate
s
o
lu
tio
n
s
f
o
r
o
p
tim
izatio
n
is
s
u
es
in
an
iter
a
tiv
e
-
ass
is
ted
p
r
o
ce
d
u
r
es
ac
co
r
d
in
g
to
a
n
ar
b
itra
r
y
s
ea
r
c
h
in
a
p
r
o
b
le
m
-
s
o
lv
in
g
m
an
n
er
.
T
h
e
lo
ca
tio
n
o
f
K
o
o
k
ab
u
r
r
a
at
t
h
e
im
p
lem
en
tatio
n
o
f
KOA’
s
b
eg
in
n
in
g
is
f
o
r
m
u
lated
in
(
8
)
an
d
(
9
)
.
=
[
1
⋮
⋮
]
×
=
[
1
,
1
…
1
,
…
1
,
⋮
⋱
⋮
⋱
⋮
,
1
⋮
,
1
…
⋱
…
,
⋮
,
…
⋱
…
,
⋮
,
]
×
(
8
)
,
=
+
.
(
−
)
(
9
)
A
g
r
o
u
p
o
f
esti
m
ated
o
u
tco
m
e
s
f
o
r
a
f
u
n
ctio
n
is
d
em
o
n
s
tr
ate
d
in
(
1
0
)
.
=
[
1
⋮
⋮
]
×
1
=
[
(
1
)
⋮
(
)
⋮
(
)
]
×
1
(
1
0
)
W
h
er
e,
d
en
o
tes
a
v
ec
to
r
o
f
e
s
tim
ated
o
b
jectiv
e
f
u
n
ctio
n
a
n
d
s
p
ec
if
ies
an
esti
m
ated
o
b
jectiv
e
f
u
n
ctio
n
ac
co
r
d
in
g
to
th
k
o
o
k
ab
u
r
r
a.
T
h
e
f
o
llo
win
g
s
ec
tio
n
d
escr
i
b
es
h
o
w
th
e
KOA
p
o
p
u
latio
n
is
u
p
d
ated
in
to
a
s
o
lu
tio
n
s
p
ac
e.
2
.
2
.
1
.
E
x
plo
ra
t
io
n
-
h
un
t
ing
s
t
ra
t
eg
y
T
h
e
KOA
tech
n
iq
u
e
p
r
eser
v
es
th
e
p
o
s
itio
n
o
f
o
th
er
k
o
o
k
ab
u
r
r
as
with
h
ig
h
er
o
b
jecti
v
e
f
u
n
ctio
n
v
alu
es
as
th
e
p
r
ey
'
s
lo
ca
tio
n
f
o
r
ea
ch
in
d
i
v
id
u
al
k
o
o
k
ab
u
r
r
a
to
m
im
ic
th
eir
h
u
n
tin
g
s
ty
le.
T
h
u
s
,
ac
co
r
d
in
g
to
a
co
m
p
ar
is
o
n
o
f
an
o
b
jectiv
e
f
u
n
ctio
n
v
alu
es,
an
ac
ce
s
s
ib
le
p
r
ey
g
r
o
u
p
f
o
r
ev
er
y
b
i
r
d
is
id
en
tifie
d
th
r
o
u
g
h
(
1
1
)
-
(
1
3
)
.
=
{
:
<
≠
1
}
,
ℎ
=
1
,
2
,
…
,
∈
{
1
,
2
,
…
}
(
1
1
)
,
1
=
,
+
.
(
,
−
.
,
)
,
=
1
,
2
,
…
,
,
=
1
,
2
,
…
,
(
1
2
)
=
{
1
,
1
<
,
(
1
3
)
W
h
er
e,
1
d
em
o
n
s
tr
ates
a
n
ew
r
ec
o
m
m
en
d
e
d
p
o
s
itio
n
o
f
th
k
o
o
k
ab
u
r
r
a
ac
co
r
d
in
g
to
in
itial
p
h
ase
o
f
KOA
an
d
,
1
d
en
o
tes a
th
d
im
en
s
io
n
.
2
.
2
.
2
.
E
x
plo
it
a
t
io
n
-
ens
uring
t
ha
t
t
he
prey
is
k
illed
I
n
th
e
KOA
d
esig
n
,
to
m
im
ic
th
e
m
o
v
e
m
en
t
b
e
h
av
io
u
r
o
f
k
o
o
k
ab
u
r
r
as
n
ea
r
th
eir
h
u
n
tin
g
g
r
o
u
n
d
,
a
n
ar
b
itra
r
y
l
o
ca
tio
n
is
g
en
er
ate
d
u
tili
zin
g
(
1
4
)
.
A
n
ew
p
o
s
it
io
n
esti
m
ated
f
o
r
ev
e
r
y
k
o
o
k
ab
u
r
r
a
m
o
d
if
ies
its
p
r
io
r
lo
ca
tio
n
if
it e
n
h
an
ce
s
a
v
alu
e
o
f
a
n
o
b
jectiv
e
f
u
n
ctio
n
u
s
in
g
(
1
5
)
.
,
2
=
,
+
(
1
−
2
)
.
(
−
)
,
=
1
,
2
,
…
,
,
=
1
,
2
,
…
,
=
1
,
2
,
…
,
(
1
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
2
,
J
u
n
e
20
2
6
:
724
-
734
728
=
{
2
,
2
<
,
(
1
5
)
W
h
er
e,
2
d
en
o
tes
a
r
ec
o
m
m
en
d
ed
lo
ca
tio
n
o
f
th
k
o
o
k
a
b
u
r
r
a
ac
co
r
d
in
g
an
o
th
er
lev
el
o
f
KOA.
,
2
illu
s
tr
ates
th
e
th
d
im
en
s
io
n
o
f
KOA;
2
m
ea
n
s
an
o
b
jectiv
e
f
u
n
ctio
n
;
d
en
o
tes
an
iter
atio
n
n
u
m
b
er
o
f
KOA
;
an
d
d
en
o
tes a
m
a
x
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
o
f
KO
A.
2
.
2
.
3
.
Dy
na
m
ic
a
djustm
ent
s
t
ra
t
eg
y
B
ased
o
n
KOA,
a
g
lo
b
al
ex
p
lo
r
atio
n
le
v
el
f
o
r
d
ev
elo
p
m
en
t
o
f
n
ew
eg
g
s
is
ad
m
in
is
ter
e
d
th
r
o
u
g
h
L
ev
y
f
lig
h
t
(
L
F)
ac
c
o
r
d
in
g
to
ar
b
itra
r
y
walk
s
.
N
o
w,
th
e
DA
S
is
in
tr
o
d
u
ce
d
f
o
r
s
tep
s
ize
in
ac
tu
al
KOA
with
L
ev
y
f
lig
h
t.
I
n
t
h
is
m
an
n
er
,
th
e
s
tep
s
ize
is
f
o
r
m
u
lated
in
(
1
6
)
.
=
(
1
)
|
(
)
−
(
)
(
)
−
(
)
|
(
1
6
)
T
h
u
s
,
th
o
u
g
h
a
s
tep
s
ize
is
b
ig
at
b
eg
in
n
in
g
,
n
u
m
b
er
o
f
iter
atio
n
s
en
h
an
ce
s
,
s
tep
s
ize
m
in
im
izes.
Hen
ce
,
th
e
DAS
f
o
r
th
e
s
tep
s
ize
in
ac
tu
al
KOA
is
d
eter
m
in
ed
as
well
as
ad
v
an
ta
g
eo
u
s
to
o
p
tim
izatio
n
th
r
o
u
g
h
t
h
e
f
aster
r
ate
as we
ll a
s
g
r
ea
ter
q
u
ality
s
o
lu
tio
n
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
th
e
s
u
g
g
ested
KOA
-
DAS
f
r
am
ewo
r
k
is
im
p
lem
en
ted
in
a
MA
T
L
AB
2
0
2
0
R
with
th
e
s
y
s
tem
co
n
f
ig
u
r
atio
n
s
o
f
I
n
tel
i5
p
r
o
ce
s
s
o
r
,
1
6
GB
R
AM
,
an
d
W
in
d
o
ws
10
OS.
T
h
e
d
ata
tr
an
s
f
er
f
r
o
m
a
SN
to
th
e
C
H
is
s
h
o
wn
in
Fig
u
r
e
2
.
Fig
u
r
e
2
in
d
icate
s
th
at
a
clu
s
ter
'
s
s
ix
clu
s
ter
lead
er
s
o
v
er
s
ee
ea
ch
o
f
its
SNs
.
I
n
a
W
SN,
th
er
e
ar
e
s
ev
er
al
S
Ns
th
at
m
ay
b
e
ac
tiv
e
o
r
in
ac
t
iv
e.
T
h
e
s
u
g
g
ested
C
H
m
o
d
el
cr
ea
tes
g
r
o
u
p
in
g
s
b
ased
o
n
d
is
tan
ce
s
b
etwe
en
n
o
d
es
b
y
r
o
u
tin
el
y
m
o
n
ito
r
in
g
t
h
e
n
o
d
es.
Nea
r
b
y
n
o
d
es
ar
e
g
r
o
u
p
ed
in
to
clu
s
ter
s
u
s
in
g
th
e
SB
O
alg
o
r
ith
m
,
an
d
th
e
C
H
is
ch
o
s
en
b
ased
o
n
h
o
w
m
u
ch
e
n
er
g
y
th
e
SN
u
s
es.
T
h
e
E
E
SN
is
s
elec
ted
as C
H,
th
o
u
g
h
th
is
ca
n
b
e
alter
ed
at
a
n
y
m
o
m
en
t.
Fig
u
r
e
2
.
Per
f
o
r
m
an
c
e
an
aly
s
i
s
o
f
C
H
s
elec
tio
n
an
d
r
o
u
tin
g
Fig
u
r
e
3
s
h
o
ws
th
e
C
H
s
elec
tio
n
s
y
s
tem
'
s
ef
f
icac
y
o
v
er
tim
e.
T
h
e
p
r
o
p
o
s
ed
tech
n
i
q
u
e
d
e
m
o
n
s
tr
ates
s
u
p
er
io
r
p
e
r
f
o
r
m
an
ce
b
y
r
e
d
u
cin
g
e
x
ec
u
tio
n
tim
e
c
o
m
p
ar
ed
to
c
o
n
v
e
n
tio
n
al
C
H
s
elec
tio
n
m
eth
o
d
s
.
C
o
m
p
ar
ed
to
th
e
c
u
r
r
en
t
C
H
s
elec
tio
n
p
r
o
ce
s
s
es,
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
was
f
aster
to
i
m
p
lem
en
t.
T
h
e
tim
e
will
b
e
p
r
o
lo
n
g
e
d
if
th
er
e
ar
e
m
o
r
e
C
Hs.
As
s
h
o
wn
in
Fig
u
r
e
3
,
co
m
p
ar
in
g
th
e
p
r
o
p
o
s
e
d
tech
n
i
q
u
e
t
o
o
th
er
ex
is
tin
g
s
tr
ateg
ies,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
ac
h
iev
ed
a
n
ex
ec
u
ti
o
n
tim
e
o
f
ju
s
t 6
7
s
ec
o
n
d
s
f
o
r
4
C
Hs
.
Fig
u
r
e
4
(
a)
illu
s
tr
ates
th
e
s
u
g
g
ested
KOA
-
DAS
m
eth
o
d
'
s
C
H
s
elec
tio
n
is
co
m
p
ar
ed
to
th
r
ee
tr
ad
itio
n
al
ap
p
r
o
ac
h
es
in
clu
d
i
n
g
ASFO,
E
E
L
C
R
,
an
d
K
-
L
i
o
n
E
R
.
Acc
o
r
d
in
g
to
t
h
e
a
v
er
a
g
e
r
esid
u
al
en
er
g
y
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
E
n
erg
y
-
a
w
a
r
e
d
yn
a
mic
a
d
ju
s
tmen
t in
teg
r
a
ted
ko
o
k
a
b
u
r
r
a
o
p
timiz
a
tio
n
…
(
S
h
o
b
a
n
b
a
b
u
R
.
Ja
g
a
n
a
th
a
n
)
729
th
e
s
u
g
g
ested
m
eth
o
d
'
s
C
H
s
elec
tio
n
in
2
5
0
r
o
u
n
d
s
is
3
9
.
2
3
3
7
J
,
wh
ile
th
e
cu
r
r
en
t
ap
p
r
o
ac
h
es
lik
e
ASFO,
E
E
L
C
R
,
an
d
K
-
L
io
n
E
R
h
av
e
3
4
.
3
2
9
5
J
,
3
2
.
4
3
5
1
J
,
an
d
2
9
.
4
2
5
3
J
,
r
esp
ec
tiv
ely
.
T
h
is
d
e
m
o
n
s
tr
ates
th
at
th
e
s
u
g
g
ested
KOA
-
DAS
m
o
d
el
o
u
tp
er
f
o
r
m
s
th
e
tr
ad
itio
n
al
C
H
s
elec
tio
n
p
r
o
ce
s
s
.
As
a
r
e
s
u
lt,
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
s
elec
ts
th
e
C
H
f
o
r
th
e
i
d
en
tify
in
g
clu
s
ter
ef
f
e
ctiv
ely
.
On
e
p
r
ac
tical
ass
ess
m
en
t
cr
iter
io
n
f
o
r
d
eter
m
in
in
g
t
h
e
lo
n
g
ev
ity
a
n
d
o
p
er
atio
n
al
ef
f
ec
tiv
en
ess
o
f
n
etwo
r
k
n
o
d
es
in
W
SNs
is
th
e
n
u
m
b
e
r
o
f
liv
in
g
n
o
d
es.
Fig
u
r
e
4
(
b
)
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
th
e
n
u
m
b
e
r
o
f
r
o
u
n
d
s
an
d
th
e
o
v
er
all
ef
f
icac
y
ev
alu
atio
n
o
f
th
e
aliv
e
n
o
d
e
co
u
n
t.
I
t is ev
id
en
t f
r
o
m
th
e
o
v
e
r
all
co
m
p
ar
is
o
n
t
h
at
th
e
s
u
g
g
ested
KOA
-
DAS
alg
o
r
ith
m
s
u
s
tain
s
a
h
ig
h
er
n
u
m
b
er
o
f
liv
in
g
n
o
d
es
th
r
o
u
g
h
o
u
t
a
r
an
g
e
o
f
r
o
u
n
d
s
.
Ad
d
itio
n
ally
,
it
is
d
e
m
o
n
s
tr
ated
th
at
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
o
u
tp
er
f
o
r
m
s
o
th
er
alg
o
r
ith
m
s
b
y
m
ain
tain
in
g
th
e
n
u
m
b
e
r
o
f
aliv
e
n
o
d
es
f
o
r
a
v
ar
iab
le
n
u
m
b
er
o
f
r
o
u
n
d
s
,
im
p
r
o
v
in
g
b
y
a
b
o
u
t
3
%
to
2
0
%.
T
h
is
s
h
o
ws
th
at
th
e
s
u
g
g
e
s
ted
alg
o
r
ith
m
ca
n
s
u
s
tain
th
e
n
o
d
e'
s
en
er
g
y
,
h
en
ce
in
cr
ea
s
in
g
th
e
n
etwo
r
k
'
s
o
p
er
atio
n
al
life
tim
e.
Fig
u
r
e
3
.
T
im
e
tak
en
f
o
r
C
H
s
elec
tio
n
in
W
SN
(
a)
(
b
)
Fig
u
r
e
4.
Su
g
g
ested
KOA
-
DAS
m
eth
o
d
'
s
C
H
s
e
lectio
n
:
(
a)
c
o
m
p
ar
is
o
n
o
f
av
e
r
ag
e
r
esid
u
al
en
er
g
y
an
d
(
b
)
co
m
p
ar
is
o
n
o
f
a
liv
e
n
o
d
es
Fig
u
r
e
5
illu
s
tr
ates
a
co
m
p
ar
i
s
o
n
o
f
th
e
PDR
o
v
er
d
if
f
er
e
n
t
n
u
m
b
er
s
o
f
r
o
u
n
d
s
f
o
r
f
o
u
r
d
if
f
er
en
t
ap
p
r
o
ac
h
es,
s
u
ch
as
p
r
o
p
o
s
e
d
KOA
-
DAS,
AS
FO,
E
E
L
C
R
,
an
d
K
-
L
io
n
E
R
.
Fig
u
r
es
5
(
a)
-
5
(
d
)
r
e
p
r
esen
t
d
if
f
er
en
t
s
ce
n
ar
io
s
o
r
tim
ef
r
am
es
with
in
cr
ea
s
in
g
n
u
m
b
e
r
s
o
f
r
o
u
n
d
s
r
an
g
i
n
g
f
r
o
m
6
0
0
to
1
5
0
0
.
I
n
all
s
ce
n
ar
io
s
,
th
e
p
r
o
p
o
s
ed
KO
A
-
DAS
m
o
d
el
co
n
s
is
ten
tly
a
ch
iev
es
a
h
ig
h
er
PDR
co
m
p
ar
ed
to
th
e
ex
is
tin
g
s
y
s
tem
s
.
T
h
e
f
in
d
in
g
s
in
d
ic
ate
th
at
th
e
d
e
v
elo
p
e
d
KO
A
-
DAS
f
r
am
ewo
r
k
p
r
o
v
id
es
a
m
o
r
e
r
eliab
le
co
m
m
u
n
icatio
n
with
h
ig
h
er
P
DR
in
W
SN
s
ac
r
o
s
s
v
ar
io
u
s
s
ce
n
ar
io
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
2
,
J
u
n
e
20
2
6
:
724
-
734
730
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
5
.
Pack
et
d
eliv
er
y
r
atio
co
m
p
ar
is
o
n
:
(
a)
p
e
r
f
o
r
m
an
ce
o
f
PDR
at
6
0
0
s
im
u
latio
n
r
o
u
n
d
s
,
(
b
)
p
e
r
f
o
r
m
an
ce
o
f
PDR
at
8
0
0
s
im
u
latio
n
r
o
u
n
d
s
,
(
c)
p
e
r
f
o
r
m
an
ce
o
f
PDR
at
1
2
0
0
s
im
u
la
tio
n
r
o
u
n
d
s
,
an
d
(
d
)
p
e
r
f
o
r
m
an
ce
o
f
PDR
at
1
5
0
0
s
im
u
latio
n
r
o
u
n
d
s
Fig
u
r
e
6
d
em
o
n
s
tr
ates
th
e
p
e
r
f
o
r
m
a
n
ce
esti
m
atio
n
o
f
E
C
b
ased
o
n
n
u
m
b
e
r
o
f
r
o
u
n
d
s
.
An
E
C
is
esti
m
ated
th
e
to
tal
e
n
er
g
y
lev
er
ag
ed
t
h
r
o
u
g
h
a
w
h
o
le
n
etwo
r
k
ac
c
o
r
d
i
n
g
t
o
th
e
n
u
m
b
er
o
f
en
e
r
g
ies
all
th
e
n
o
d
e
u
tili
ze
s
f
o
r
th
e
tr
a
n
s
m
is
s
io
n
o
f
d
ata
p
ac
k
ets.
T
h
e
d
e
v
e
lo
p
ed
KOA
-
DAS
ap
p
r
o
ac
h
at
tain
s
th
e
m
in
im
u
m
en
er
g
y
c
o
n
s
u
m
p
tio
n
o
f
8
.
3
2
J
,
2
1
.
4
9
J
,
3
5
.
6
4
J
,
4
6
.
6
5
J
,
a
n
d
5
3
.
6
9
J
o
n
th
e
n
u
m
b
er
o
f
r
o
u
n
d
s
o
f
1
0
0
0
,
2
0
0
0
,
3
0
0
0
,
4
0
0
0
,
an
d
5
0
0
0
in
d
iv
id
u
ally
.
Als
o
,
th
e
p
r
o
p
o
s
ed
KO
A
-
DAS
m
eth
o
d
ac
h
iev
es
2
3
.
4
4
%
b
etter
en
er
g
y
ef
f
icien
cy
th
an
ASFO,
1
9
.
3
1
%
b
etter
th
an
E
E
L
C
R
,
an
d
1
4
.
4
4
%
b
etter
th
an
K
-
L
io
n
E
R
o
n
av
er
ag
e
ac
r
o
s
s
th
e
r
an
g
e
o
f
s
en
s
o
r
n
o
d
es.
T
h
is
s
u
g
g
ests
th
at
th
e
p
r
o
p
o
s
ed
K
OA
-
DAS
is
m
o
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e
f
f
ec
tiv
e
i
n
o
p
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izin
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e
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er
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s
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e,
m
ak
in
g
it a
b
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p
tio
n
f
o
r
la
r
g
er
W
SN d
ep
lo
y
m
en
t
s
.
Fig
u
r
e
7
co
m
p
ar
es
th
e
NL
o
f
th
e
p
r
o
p
o
s
ed
KOA
-
DAS
tech
n
iq
u
e
with
th
e
cu
r
r
en
t
m
o
d
els
in
clu
d
in
g
ASFO,
E
E
L
C
R
,
an
d
K
-
L
io
n
E
R
b
ased
o
n
th
e
n
u
m
b
er
o
f
n
o
d
es.
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h
e
d
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elo
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e
d
ap
p
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h
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o
n
s
is
ten
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o
r
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e
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o
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ith
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s
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with
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n
if
ican
tly
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ig
h
er
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tin
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ar
o
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n
d
1
3
0
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o
r
2
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o
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es
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d
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g
ap
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o
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ately
6
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o
d
es.
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I
B
OA
p
er
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o
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s
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o
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ately
,
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iev
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ab
o
u
t
4
7
0
0
at
1
0
0
n
o
d
es,
wh
ile
DM
PR
P
f
o
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with
ar
o
u
n
d
5
5
0
0
at
th
e
s
am
e
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o
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t.
T
h
is
d
em
o
n
s
tr
ates
th
e
s
u
p
er
io
r
ef
f
icien
c
y
o
f
KOA
-
DAS
in
p
r
o
lo
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g
in
g
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as th
e
n
u
m
b
er
o
f
n
o
d
es in
cr
ea
s
es.
Fig
u
r
e
8
illu
s
tr
ates
th
e
c
o
s
t
c
o
m
p
ar
is
o
n
g
r
a
p
h
f
o
r
C
H
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,
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e
m
o
n
s
tr
ates
th
e
ef
f
icien
c
y
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
in
m
in
im
izin
g
co
m
m
u
n
icatio
n
a
n
d
co
m
p
u
tatio
n
co
s
ts
ac
r
o
s
s
v
ar
y
in
g
n
u
m
b
e
r
s
o
f
clu
s
ter
s
.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
ac
h
iev
es
th
e
lo
west
co
s
t
v
alu
es
th
r
o
u
g
h
o
u
t,
with
a
s
h
a
r
p
d
ec
lin
e
o
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s
er
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e
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as
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e
n
u
m
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er
o
f
clu
s
ter
s
in
c
r
ea
s
es
f
r
o
m
5
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I
n
c
o
n
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ast,
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in
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r
s
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ig
n
if
ican
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ig
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er
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o
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ts
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r
o
ad
e
r
er
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o
r
m
ar
g
in
s
,
r
e
f
lectin
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ilit
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T
h
e
o
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er
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c
o
s
t
r
ed
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ctio
n
in
th
e
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o
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ed
m
eth
o
d
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H
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tio
n
m
ec
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is
m
,
wh
ich
m
in
im
izes
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n
d
an
t
tr
a
n
s
m
is
s
io
n
s
an
d
o
p
tim
izes
en
er
g
y
u
s
ag
e,
th
er
e
b
y
e
n
h
an
ci
n
g
th
e
o
v
er
all
n
etwo
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k
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i
ty
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d
p
er
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o
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.
Fig
u
r
e
9
illu
s
tr
ates
th
e
co
m
p
ar
is
o
n
o
f
laten
cy
v
er
s
u
s
th
e
n
u
m
b
er
o
f
s
en
s
o
r
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o
d
es
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o
r
f
o
u
r
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if
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er
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t
ap
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ch
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o
p
o
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ed
KOA
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DAS,
A
SF
O
[
1
9
]
,
E
E
L
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R
[
2
3
]
,
an
d
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io
n
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R
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2
4
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.
T
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e
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E
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e
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m
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e
r
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f
SNs
i
n
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r
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ases
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o
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e
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ig
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el
ay
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o
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ed
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y
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u
c
in
g
e
n
d
-
to
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e
n
d
d
e
la
y
in
W
SNs
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
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E
n
g
I
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N:
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8
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r
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Fig
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I
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2
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el
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AS
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eth
o
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h
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o
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ter
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tin
g
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ested
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o
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ate
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r
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s
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ig
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er
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ter
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e,
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v
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NL
,
la
ten
cy
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o
m
p
u
tatio
n
co
s
t,
E
C
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Ac
co
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g
to
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e
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o
m
p
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ativ
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aly
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is
,
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e
p
r
o
p
o
s
ed
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m
eth
o
d
ac
h
iev
es
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lo
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er
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ef
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icien
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o
f
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3
.
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4
%,
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d
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4
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th
e
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R
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h
e
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s
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etr
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r
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atin
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
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8
7
9
2
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n
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a
w
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)
733
DATA AV
AI
L
AB
I
L
I
T
Y
Data
s
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ar
in
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o
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ap
p
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le
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cu
r
r
en
t stu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
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