I
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t
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l J
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I
J
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Vo
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14
,
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4
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Dec
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er
20
25
,
p
p
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4
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8
I
SS
N:
2722
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v
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4
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wire
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s
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WS
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e
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i
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ll
y
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a
n
In
tell
ig
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ATLAB,
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ters
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DR),
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e
two
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k
li
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ti
m
e
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ti
m
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lex
it
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t
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t,
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n
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n
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m
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9
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a
s
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.
K
ey
w
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r
d
s
:
Fire
f
ly
o
p
tim
izatio
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alg
o
r
ith
m
I
n
ter
n
et
o
f
th
in
g
s
Sy
n
ap
tic
in
tellig
en
t
co
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tio
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n
e
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al
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etwo
r
k
W
ir
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s
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o
r
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etwo
r
k
T
h
is i
s
a
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p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Sh
o
b
an
b
a
b
u
R
am
aswam
y
J
ag
an
ath
an
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
E
n
g
in
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An
n
am
alai
Un
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s
ity
C
h
id
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b
ar
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T
am
il Na
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u
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I
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d
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E
m
ail:
s
h
o
b
an
b
a
b
u
r
j@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
W
SN
s
ar
e
b
ec
o
m
in
g
a
c
r
u
cial
tech
n
o
lo
g
y
f
o
r
d
ata
co
llectio
n
an
d
tr
a
n
s
m
is
s
io
n
ac
r
o
s
s
a
wid
e
v
ar
iety
o
f
ap
p
licatio
n
d
o
m
ain
s
d
u
e
t
o
t
h
e
g
r
o
wth
o
f
I
n
ter
n
et
o
f
T
h
in
g
s
(
I
o
T
)
d
ev
ices
[
1
]
.
I
n
I
o
T
-
en
a
b
led
wir
eless
s
en
s
o
r
n
etwo
r
k
s
(
W
SNs
)
,
th
e
d
ata
m
u
s
t
b
e
ef
f
icien
tly
tr
an
s
p
o
r
te
d
ac
r
o
s
s
m
u
ltip
le
ac
ce
s
s
p
o
in
ts
wh
ile
m
ain
tain
in
g
ef
f
ec
tiv
e
co
m
m
u
n
icatio
n
u
s
in
g
th
e
least
am
o
u
n
t
o
f
en
er
g
y
[
2
]
,
[
3
]
.
An
en
er
g
y
-
awa
r
e
d
ata
co
llectio
n
an
d
co
m
m
u
n
icatio
n
m
eth
o
d
s
m
u
s
t
b
e
d
ev
elo
p
e
d
to
r
ed
u
ce
th
e
n
o
d
e
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
in
cr
ea
s
e
th
e
n
etwo
r
k
life
tim
e
[
4
]
,
[
5
]
.
T
h
e
lo
w
p
r
o
ce
s
s
in
g
p
o
wer
an
d
h
ig
h
en
er
g
y
a
v
ailab
ilit
y
o
f
W
SNs
n
ec
ess
itate
en
er
g
y
-
ef
f
icie
n
t r
o
u
tin
g
m
eth
o
d
s
to
in
cr
ea
s
e
n
etwo
r
k
lo
n
g
ev
ity
[
6
]
,
[
7
]
.
A
s
ca
lab
le
r
o
u
tin
g
s
o
lu
tio
n
is
r
eq
u
ir
ed
t
o
p
r
o
ce
s
s
th
e
v
ast
am
o
u
n
t
o
f
d
ata
g
en
er
ate
d
b
y
I
o
T
d
ev
ices
[
8
]
,
[
9
]
.
T
h
e
I
o
T
-
e
n
ab
led
W
SN
n
etwo
r
k
s
m
an
a
g
e
th
e
v
ar
io
u
s
ty
p
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2722
-
2
5
8
6
F
I
N
D
-
R
OUTE
:
F
o
u
r
ier s
e
r
ie
s
in
teg
r
a
ted
d
ee
p
lea
r
n
in
g
mo
d
el
…
(
S
h
o
b
a
n
b
a
b
u
R
a
ma
s
w
a
my
Ja
g
a
n
a
th
a
n
)
469
o
f
n
o
d
es
u
s
in
g
e
n
er
g
y
-
awa
r
e
r
o
u
tin
g
alg
o
r
it
h
m
s
wh
ic
h
m
in
im
ize
th
e
laten
cy
an
d
en
s
u
r
e
ef
f
ec
tiv
e
co
m
m
u
n
icatio
n
[
10
]
.
A
d
r
am
atic
in
cr
ea
s
e
i
n
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
tech
n
iq
u
es
aim
s
to
p
r
o
v
id
e
a
d
ep
e
n
d
ab
l
e
s
o
lu
tio
n
f
o
r
d
if
f
ic
u
lt
o
p
tim
izatio
n
is
s
u
es
in
teg
r
ated
in
I
o
T
en
a
b
led
W
SN
[
11
]
.
T
h
ese
alg
o
r
ith
m
s
d
eter
m
in
e
an
o
p
tim
ize
d
s
o
lu
tio
n
to
ac
h
iev
e
q
u
ality
o
f
s
er
v
ice
(
Qo
S)
o
b
jectiv
es,
in
c
r
ea
s
ed
th
r
o
u
g
h
p
u
t
,
en
er
g
y
co
n
s
er
v
atio
n
,
PDR
,
an
d
m
in
im
ized
d
ata
g
ath
e
r
in
g
d
el
ay
f
o
r
n
etwo
r
k
ef
f
icien
c
y
.
I
n
co
n
tr
ast
to
n
etwo
r
k
s
th
at
r
el
y
s
o
lely
o
n
d
ir
ec
t
co
n
n
ec
tio
n
s
,
W
SNs
u
s
u
ally
u
s
e
clu
s
ter
in
g
with
m
u
ltil
ay
er
to
p
o
lo
g
ies to
r
e
d
u
ce
en
e
r
g
y
u
s
ag
e
an
d
o
f
f
er
a
m
o
r
e
r
o
b
u
s
t n
etwo
r
k
[
1
2
]
,
[1
3
]
.
I
o
T
-
en
a
b
led
W
SNs
co
n
s
is
t
o
f
s
ev
er
al
ch
allen
g
es
f
o
r
en
er
g
y
-
ef
f
icien
t
d
ata
r
o
u
tin
g
b
ec
a
u
s
e
o
f
th
e
d
y
n
am
ic
n
at
u
r
e
o
f
s
en
s
o
r
n
o
d
es
[
14
]
.
D
u
e
to
th
ese
d
y
n
a
m
ics,
ad
ap
tiv
e
r
o
u
tin
g
s
tr
ateg
ies
ar
e
ess
en
tial
to
ef
f
ec
tiv
ely
m
an
a
g
e
th
ese
s
h
if
tin
g
n
etwo
r
k
to
p
o
lo
g
ies
[
1
5
]
,
[
16
]
.
I
n
a
d
d
itio
n
,
th
e
tr
a
d
itio
n
al
r
o
u
tin
g
s
y
s
tem
s
ar
e
in
ab
ilit
y
to
m
an
ag
e
lar
g
e
am
o
u
n
ts
o
f
Pack
et
lo
s
s
,
less
id
ea
l
n
o
d
e
s
elec
tio
n
,
n
o
d
es
with
less
n
etwo
r
k
life
tim
e,
an
d
in
cr
ea
s
ed
laten
cy
wh
ich
co
n
tr
ad
icts
th
e
r
eliab
le
co
m
m
u
n
icatio
n
o
f
th
e
n
etwo
r
k
s
[
1
7
]
,
[
18
]
.
T
o
ad
d
r
ess
th
ese
ch
allen
g
es,
a
n
o
v
el
FIN
D
-
R
OUT
E
f
r
am
ewo
r
k
h
as
b
ee
n
p
r
o
p
o
s
ed
f
o
r
s
u
s
tain
ab
le
d
ata
tr
an
s
m
is
s
io
n
in
I
o
T
b
ased
W
SN e
n
v
ir
o
n
m
en
ts
.
T
h
e
p
r
im
ar
y
g
o
als o
f
th
e
FIN
D
-
R
OUT
E
f
r
am
ewo
r
k
ar
e
g
iv
en
in
th
e
f
o
llo
win
g
,
a.
T
h
e
m
ajo
r
p
u
r
p
o
s
e
o
f
th
is
s
tr
ateg
y
is
to
co
n
s
tr
u
ct
an
en
e
r
g
y
-
o
p
tim
ized
d
ata
co
m
m
u
n
icatio
n
in
I
o
T
-
W
SNs
to
im
p
r
o
v
e
n
etwo
r
k
r
eliab
ilit
y
wh
ile
m
i
n
im
izin
g
late
n
cy
f
o
r
s
ea
m
less
co
m
m
u
n
icatio
n
a
m
o
n
g
d
y
n
am
ic
n
etwo
r
k
co
n
d
itio
n
s
.
b.
T
h
e
f
u
zz
y
C
m
ea
n
s
-
b
alan
ce
d
i
ter
ativ
e
r
ed
u
cin
g
an
d
clu
s
ter
in
g
u
s
in
g
h
ie
r
ar
ch
ies
(
Fu
zz
y
-
B
I
R
C
H)
clu
s
ter
in
g
alg
o
r
ith
m
en
h
an
ce
s
th
e
clu
s
ter
in
g
ef
f
icien
cy
b
y
g
r
o
u
p
in
g
th
e
o
p
tim
al
n
o
d
es
f
o
r
m
in
im
izin
g
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
in
I
o
T
-
W
SNs
.
c.
T
h
e
o
p
tim
al
en
e
r
g
y
-
awa
r
e
n
o
d
es
ar
e
d
eter
m
in
ed
as
clu
s
ter
h
ea
d
s
(
C
Hs)
u
s
in
g
th
e
Fo
u
r
i
er
s
er
ies
-
b
ased
f
ir
ef
ly
o
p
tim
izatio
n
alg
o
r
ith
m
(
FS
FOA)
wh
ich
en
h
an
ce
s
th
e
d
ata
ag
g
r
eg
atio
n
to
en
s
u
r
e
lo
w
en
er
g
y
co
n
s
u
m
p
tio
n
with
r
eliab
le
d
at
a
d
eliv
er
y
am
o
n
g
th
e
n
etwo
r
k
s
.
d.
A
s
y
n
ap
tic
in
tellig
en
t
co
n
v
o
lu
t
io
n
al
n
eu
r
al
n
etwo
r
k
(
SICNN)
d
eter
m
in
es
an
o
p
tim
al
r
o
u
tin
g
p
ath
f
o
r
r
o
b
u
s
t
d
ata
tr
an
s
m
is
s
io
n
with
m
in
im
u
m
p
ac
k
et
l
o
s
s
.
e.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
FIN
D
-
R
OUT
E
f
r
am
ewo
r
k
is
ev
alu
ated
th
r
o
u
g
h
th
e
n
etwo
r
k
p
a
r
a
m
eter
s
in
clu
d
in
g
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
PDR
,
n
etwo
r
k
life
tim
e,
tim
e
co
m
p
lex
i
ty
,
th
r
o
u
g
h
p
u
t,
n
u
m
b
er
o
f
aliv
e
n
o
d
es,
PLR,
an
d
s
p
ac
e
co
m
p
lex
ity
.
T
h
e
s
u
b
s
eq
u
e
n
t
s
ec
tio
n
s
o
f
th
e
r
esear
ch
a
r
e
o
r
g
an
ized
as:
s
ec
tio
n
2
h
o
l
d
s
th
e
liter
atu
r
e
r
ev
ie
w.
Sectio
n
3
h
o
l
d
s
a
co
m
p
r
e
h
en
s
iv
e
d
es
cr
ip
tio
n
o
f
th
e
p
r
o
p
o
s
ed
FIN
D
-
R
OUT
E
m
eth
o
d
o
lo
g
y
.
Sectio
n
4
p
r
o
v
id
es
th
e
s
im
u
latio
n
o
u
tco
m
es.
T
h
e
co
n
clu
s
io
n
an
d
th
e
f
u
tu
r
e
e
n
h
a
n
ce
m
en
t a
r
e
d
is
cu
s
s
ed
in
Sectio
n
5
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
T
h
e
liter
atu
r
e
r
ev
iew
is
p
r
im
a
r
ily
f
o
c
u
s
ed
o
n
m
ac
h
in
e
lear
n
in
g
(
ML
)
,
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
R
L
)
,
an
d
d
ee
p
lear
n
in
g
(
DL
)
b
ased
r
o
u
tin
g
alg
o
r
ith
m
s
in
I
o
T
-
W
SNs
.
Nu
m
er
o
u
s
ex
is
tin
g
ap
p
r
o
a
ch
es
th
at
h
av
e
b
ee
n
s
u
g
g
ested
f
o
r
en
er
g
y
-
o
p
tim
ize
d
r
o
u
tin
g
in
I
o
T
-
W
SNs
h
a
v
e
b
ee
n
s
tu
d
ied
i
n
th
e
liter
atu
r
e.
I
n
2
0
2
3
Sey
f
o
llah
i
et
a
l.
[
19
]
s
u
g
g
ested
an
en
er
g
y
-
o
p
tim
ized
r
o
u
tin
g
s
y
s
tem
o
f
th
e
I
o
T
en
h
a
n
ce
d
b
y
m
etah
eu
r
is
tics
an
d
ML
.
Ho
wev
er
,
th
e
o
p
tim
al
lo
ad
b
al
an
ce
am
o
n
g
s
en
s
in
g
d
e
v
ices
a
n
d
en
e
r
g
y
ef
f
icien
c
y
a
r
e
th
e
p
r
im
ar
y
co
n
ce
r
n
s
with
en
er
g
y
r
eso
u
r
ce
s
in
I
o
T
d
ev
i
ce
s
.
C
h
an
d
n
an
i
an
d
Kh
air
n
ar
[
20
]
s
u
g
g
ested
an
eth
ical
ML
-
b
ased
s
o
lu
tio
n
f
o
r
en
er
g
y
-
o
p
tim
ized
r
o
u
tin
g
.
T
h
e
ML
B
DARP
m
o
d
el
u
s
es
ML
m
o
d
els
wh
ich
in
clu
d
e
n
e
u
r
al
n
etwo
r
k
s
(
NN)
an
d
d
ec
is
io
n
tr
ee
s
(
DT
)
to
ass
ess
t
h
e
r
eliab
le
PDR
.
Su
r
esh
et
a
l.
[
21
]
s
u
g
g
ested
a
r
eso
u
r
ce
-
ef
f
icien
t
r
o
u
tin
g
u
s
in
g
f
e
d
er
ated
d
ee
p
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
FDR
L
)
f
o
r
W
SN
in
teg
r
ated
b
y
th
e
I
o
T
.
T
h
e
s
u
g
g
ested
FDR
L
m
eth
o
d
p
r
o
v
id
es
en
e
r
g
y
-
c
o
n
s
tr
ain
ed
r
o
u
tin
g
an
d
d
ec
is
io
n
-
m
ak
in
g
in
a
d
y
n
a
m
ic
en
v
ir
o
n
m
en
t.
Sh
ah
i
d
et
a
l.
[
22
]
s
u
g
g
ested
lin
k
-
q
u
ality
-
b
ased
en
er
g
y
c
o
n
s
tr
ain
ed
r
o
u
tin
g
(
L
QE
E
R
)
is
an
I
o
T
e
n
v
ir
o
n
m
en
t
f
o
r
W
SN.
T
h
e
s
u
g
g
ested
L
QE
E
R
ap
p
r
o
ac
h
r
ed
u
ce
s
p
ac
k
et
lo
s
s
b
y
in
teg
r
atin
g
en
er
g
y
an
d
n
etwo
r
k
in
f
o
r
m
atio
n
to
id
en
tify
r
o
u
te
s
an
d
a
co
s
t
to
d
eter
m
in
e
th
e
b
est
n
o
d
es
f
o
r
p
ac
k
et
tr
an
s
m
is
s
io
n
.
Satti
b
a
b
u
e
t
a
l.
[
23
]
s
u
g
g
est
e
d
an
e
n
h
an
ce
m
e
n
t
o
f
t
h
e
p
er
f
o
r
m
a
n
c
e
o
f
I
o
T
-
i
n
t
eg
r
a
te
d
W
SN
b
as
ed
o
n
f
e
d
e
r
a
te
d
r
e
in
f
o
r
ce
m
e
n
t
le
ar
n
i
n
g
(
FR
L
)
.
I
n
co
m
p
a
r
is
o
n
t
o
r
ein
f
o
r
ce
m
e
n
t
le
a
r
n
in
g
-
b
ase
d
r
o
u
ti
n
g
(
R
L
B
R
)
,
t
h
e
FR
L
te
ch
n
i
q
u
e
im
p
r
o
v
es
W
SN
p
e
r
f
o
r
m
a
n
c
e
b
y
3
0
%
,
i
n
c
r
e
ase
s
en
er
g
y
e
f
f
ic
ie
n
c
y
b
y
1
5
%
to
2
4
%
,
an
d
in
cr
ea
s
es
p
a
ck
et
d
e
li
v
e
r
y
b
y
1
3
%
r
es
p
e
cti
v
e
ly
.
P
awa
r
an
d
J
a
d
h
a
v
[
24
]
s
u
g
g
est
ed
I
o
T
d
at
a
m
i
n
i
m
iz
ati
o
n
u
s
i
n
g
a
co
m
b
i
n
ati
o
n
o
f
o
p
ti
m
i
za
t
io
n
-
b
ase
d
t
o
p
o
l
o
g
y
a
n
d
DR
N
N
-
b
ase
d
d
e
te
cti
o
n
.
T
h
e
e
n
er
g
y
o
f
0
.
3
6
7
J
,
p
r
e
d
i
cti
o
n
er
r
o
r
o
f
0
.
2
3
7
,
d
e
la
y
o
f
0
.
5
9
5
s
,
a
n
d
PDR
o
f
0
.
4
6
9
w
er
e
a
ll
att
ai
n
e
d
b
y
t
h
e
s
u
g
g
est
e
d
NB
SHO
-
DR
NN
a
p
p
r
o
ac
h
.
Si
n
g
h
et
a
l
.
[
25
]
s
u
g
g
est
e
d
a
I
o
T
-
W
SN
b
as
ed
Q
o
S
i
m
p
r
o
v
e
m
e
n
t
u
s
in
g
e
d
g
e
-
e
n
a
b
l
ed
m
u
lti
-
o
b
jec
t
iv
e
o
p
ti
m
i
za
ti
o
n
.
I
n
th
e
s
u
g
g
este
d
a
p
p
r
o
a
c
h
,
e
d
g
e
c
o
m
p
u
ti
n
g
in
c
r
ea
s
es
th
e
s
ca
l
a
b
i
lit
y
an
d
e
n
h
a
n
c
es Q
o
S
i
n
I
o
T
a
p
p
lic
ati
o
n
s
.
Acc
o
r
d
in
g
to
t
h
e
liter
atu
r
e
r
e
v
iews,
ex
is
tin
g
d
ata
r
o
u
tin
g
m
et
h
o
d
s
d
o
n
o
t
ac
c
o
u
n
t
f
o
r
ef
f
ec
tiv
e
en
er
g
y
ch
ar
ac
ter
is
tics
.
As
a
r
esu
lt,
th
e
s
en
s
o
r
an
d
d
ata
co
n
n
ec
tio
n
u
n
its
o
f
I
o
T
-
W
SNs
r
eq
u
ir
e
ad
d
itio
n
al
en
e
r
g
y
wh
ic
h
Evaluation Warning : The document was created with Spire.PDF for Python.
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14
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4
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20
25
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a
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ata
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s
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io
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o
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itatio
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FIN
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T
H
E
F
I
N
D
-
RO
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O
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n
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tio
n
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o
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D
-
R
OUT
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f
r
am
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r
k
h
as
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ee
n
p
r
o
p
o
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ed
f
o
r
r
eliab
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a
n
d
en
er
g
y
-
o
p
tim
ized
d
ata
d
eliv
er
y
am
o
n
g
th
e
in
tellig
en
t
wir
eless
s
en
s
o
r
s
y
s
tem
.
I
n
itially
,
th
e
I
o
T
n
o
d
es
ar
e
o
r
g
an
ize
d
in
to
clu
s
ter
s
u
s
in
g
a
f
u
zz
y
-
B
I
R
C
H
alg
o
r
ith
m
to
m
an
ag
e
th
e
n
etwo
r
k
co
n
g
esti
o
n
.
An
FS
FO
alg
o
r
ith
m
s
elec
ts
a
C
Hs
f
r
o
m
th
e
s
et
o
f
clu
s
ter
ed
n
o
d
es
b
ased
th
e
m
u
lti
-
o
b
ject
iv
e
f
itn
ess
f
u
n
ctio
n
wh
ich
r
ed
u
ce
s
th
e
d
elay
an
d
en
s
u
r
es
f
aster
d
ata
tr
a
n
s
m
is
s
io
n
.
Fin
ally
,
th
e
SICNN
r
o
u
tes
t
h
e
ag
g
r
eg
ated
d
ata
to
war
d
th
e
b
ase
s
tatio
n
wh
ich
o
p
tim
izes th
e
c
o
n
s
u
m
p
tio
n
o
f
en
er
g
y
an
d
p
r
o
v
i
d
es r
eliab
le
d
ata
tr
an
s
m
is
s
io
n
with
m
i
n
im
al
p
ac
k
et
lo
s
s
in
I
o
T
-
b
ased
W
SNs
.
T
h
e
o
v
er
all
wo
r
k
f
lo
w
o
f
t
h
e
FIN
D
-
R
OUT
E
f
r
am
ewo
r
k
is
p
r
esen
ted
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
FIN
D
-
r
o
u
t
e
m
eth
o
d
o
lo
g
y
3
.
1
.
Clus
t
er
ing
v
ia
f
uzzy
-
B
I
RCH
a
lg
o
rit
hm
I
n
itially
,
th
e
I
o
T
n
o
d
es
ar
e
o
r
g
an
ized
i
n
to
cl
u
s
ter
s
u
s
in
g
a
f
u
zz
y
-
B
I
R
C
H
alg
o
r
ith
m
to
m
an
ag
e
th
e
n
etwo
r
k
c
o
n
g
esti
o
n
.
I
n
B
I
R
C
H,
a
clu
s
ter
is
d
ef
in
ed
b
y
its
clu
s
ter
f
ea
tu
r
es (
C
Fs
)
,
an
d
th
e
h
ier
ar
ch
ical
s
tr
u
ctu
r
e
o
f
clu
s
ter
s
is
d
i
s
p
lay
ed
u
s
in
g
a
C
F tr
ee
.
T
o
f
in
d
th
e
clu
s
ter
ce
n
tr
o
id
,
r
ep
r
esen
ted
b
y
{
X
i
⃑
⃑
⃑
}
in
B
I
R
C
H
clu
s
ter
in
g
,
wh
er
e
=
1
,
2
,
…
,
,
e
q
u
atio
n
(
1
)
ap
p
lied
c
o
n
s
ec
u
tiv
ely
.
X
0
⃑
⃑
⃑
⃑
=
∑
X
i
⃑
⃑
⃑
⃑
N
i
N
(
1
)
B
y
ch
o
o
s
in
g
th
e
n
u
m
b
er
o
f
clu
s
ter
s
,
th
e
p
r
o
ce
s
s
ed
d
ata
is
s
ep
ar
ated
in
to
d
is
cr
ete
s
u
b
g
r
o
u
p
s
ac
co
r
d
in
g
to
s
p
ec
if
ic
C
Fs
.
C
lu
s
ter
tag
s
ar
e
th
en
a
p
p
lied
to
th
ese
s
u
b
s
ets
to
clu
s
ter
th
em
i
n
an
en
er
g
y
-
c
o
n
s
tr
ain
ed
m
a
n
n
er
.
T
h
e
f
u
zz
y
c
-
m
ea
n
s
(
FC
M)
d
iv
id
es
n
v
ec
to
r
s
in
to
k
g
r
o
u
p
s
an
d
in
itializes
th
e
af
f
iliati
o
n
m
atr
ix
(
U)
b
y
ca
lcu
latin
g
th
e
clu
s
ter
in
g
ce
n
t
er
o
f
ea
ch
g
r
o
u
p
th
r
o
u
g
h
f
u
zz
y
p
a
r
titi
o
n
in
g
to
m
in
im
ize
t
h
e
o
b
jectiv
e
f
u
n
ctio
n
.
T
h
e
class
ce
n
ter
v
ec
to
r
a
n
d
th
e
af
f
iliatio
n
m
atr
ix
a
r
e
r
ep
r
es
en
ted
in
(
2
)
-
(
3
)
.
C
j
=
∑
u
ij
m
.
x
i
N
i
=
1
∑
u
ij
m
N
i
=
1
(
2
)
u
ij
=
1
∑
(
‖
x
i
−
c
j
‖
x
i
−
c
k
)
2
m
−
1
c
k
=
1
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2722
-
2
5
8
6
F
I
N
D
-
R
OUTE
:
F
o
u
r
ier s
e
r
ie
s
in
teg
r
a
ted
d
ee
p
lea
r
n
in
g
mo
d
el
…
(
S
h
o
b
a
n
b
a
b
u
R
a
ma
s
w
a
my
Ja
g
a
n
a
th
a
n
)
471
wh
er
e
m
d
e
n
o
tes
th
e
n
u
m
b
er
o
f
clu
s
ter
s
f
o
r
clu
s
ter
in
g
,
,
an
d
d
en
o
te
th
e
af
f
iliatio
n
o
f
m
atr
ix
co
n
ce
r
n
in
g
class
clu
s
ter
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
is
r
ep
r
esen
ted
in
(
4
)
.
J
m
=
∑
∑
u
ij
m
‖
x
i
−
c
j
‖
2
c
j
=
1
N
i
=
1
(
4
)
H
er
e,
c
j
d
en
o
tes
th
e
ce
n
ter
o
f
cl
ass
clu
s
ter
,
‖
x
i
−
c
j
‖
2
is
th
e
E
u
clid
ea
n
d
is
tan
ce
am
o
n
g
t
h
e
th
s
elec
ted
n
o
d
e
an
d
th
e
th
n
o
d
e.
T
h
e
f
u
zz
y
p
ar
t
itio
n
co
ef
f
icien
t
(
FP
C
)
m
ea
s
u
r
es th
e
d
eg
r
ee
o
f
f
u
zz
y
o
v
e
r
lap
b
etwe
en
clu
s
ter
s
.
I
t
r
an
g
es
f
r
o
m
0
to
1
,
in
d
icat
in
g
en
er
g
y
-
ef
f
icien
t
clu
s
ter
s
with
h
ig
h
er
v
alu
es.
T
h
er
ef
o
r
e
,
th
e
Fu
zz
y
-
B
I
R
C
H
clu
s
ter
in
g
alg
o
r
it
h
m
en
h
an
ce
s
th
e
clu
s
ter
in
g
ef
f
icien
cy
b
y
g
r
o
u
p
in
g
th
e
o
p
tim
al
n
o
d
es
f
o
r
m
in
im
izi
n
g
co
m
m
u
n
icatio
n
o
v
e
r
h
ea
d
i
n
I
o
T
-
W
SNs
3
.
2
.
CH
s
elec
t
io
n v
i
a
F
o
urie
r
s
er
ies
-
ba
s
ed
f
iref
ly
a
lg
o
rit
hm
T
h
e
o
p
tim
al
en
e
r
g
y
-
awa
r
e
n
o
d
es
ar
e
d
eter
m
in
e
d
as
C
Hs
u
s
in
g
th
e
FS
FOA
b
ased
o
n
th
e
clu
s
ter
ed
n
o
d
es b
y
Fu
zz
y
-
B
I
R
C
H
wh
ich
en
h
a
n
ce
s
th
e
d
ata
a
g
g
r
e
g
atio
n
with
r
eliab
le
d
ata
d
eliv
er
y
a
m
o
n
g
t
h
e
n
etwo
r
k
s
.
T
h
e
b
r
i
g
h
tn
ess
o
f
t
h
e
f
ir
ef
l
y
is
in
f
lu
en
ce
d
b
y
th
e
o
b
jectiv
e
f
u
n
ctio
n
a
n
d
it
is
u
s
ed
to
in
d
ic
ate
attr
ac
tiv
en
ess
o
f
FA
im
p
lem
en
tatio
n
s
.
T
h
e
f
itn
ess
f
u
n
ctio
n
in
cl
u
d
in
g
en
er
g
y
co
n
s
u
m
p
tio
n
,
n
etwo
r
k
life
tim
e,
n
o
d
e
d
e
g
r
ee
,
an
d
d
elay
is
r
ep
r
esen
te
d
b
y
th
e
f
ir
ef
ly
'
s
attr
ac
tiv
en
ess
an
d
in
tellig
en
ce
in
th
e
FA m
etah
e
u
r
is
tics
.
T
h
e
m
in
im
izatio
n
o
f
ch
allen
g
es is
s
tated
in
(
5
)
,
(
)
=
{
1
(
)
,
(
)
>
0
1
+
|
(
)
|
,
ℎ
(
5
)
f(
)
r
ep
r
esen
ts
th
e
o
b
jectiv
e
f
u
n
ctio
n
v
alu
e
at
p
o
in
t
,
wh
ile
(
)
r
ep
r
esen
ts
attr
ac
tiv
en
ess
.
I
n
d
iv
id
u
al
attr
ac
tio
n
d
ec
r
ea
s
es th
e
d
is
tan
ce
f
r
o
m
th
e
illu
m
in
atio
n
s
o
u
r
ce
d
u
e
t
o
th
e
b
r
ig
h
tn
ess
.
(
)
=
0
1
+
2
(
6
)
0
in
d
icate
s
th
e
b
r
ig
h
tn
ess
at
t
h
e
s
o
u
r
ce
,
wh
ile
(
)
d
ef
in
es
th
e
b
r
i
g
h
tn
ess
at
d
is
tan
ce
.
Ad
d
itio
n
ally
,
as
d
em
o
n
s
tr
ated
b
y
(
7
)
ea
ch
f
i
r
ef
l
y
in
d
iv
id
u
al
u
s
es
attr
ac
tio
n
,
wh
ich
is
p
r
o
p
o
r
tio
n
al
to
th
e
f
ir
ef
ly
'
s
lig
h
t
in
ten
s
ity
an
d
d
e
p
en
d
s
o
n
d
is
tan
ce
.
(
)
=
0
1
+
2
(
7
)
A
r
an
d
o
m
in
d
iv
id
u
al
th
at
ad
v
an
ce
s
in
iter
atio
n
+
1
t
o
war
d
a
n
ew
p
o
s
itio
n
in
th
e
d
ir
e
ctio
n
o
f
in
d
iv
id
u
al
with
h
ig
h
er
f
itn
ess
ca
n
b
e
f
o
u
n
d
u
s
in
g
t
h
e
f
o
llo
win
g
s
im
p
le
FA
'
s
s
ea
r
ch
m
eth
o
d
wh
ich
is
b
ased
o
n
(
8
)
.
+
1
=
+
0
.
−
,
2
(
−
)
+
(
−
0
.
5
)
(
8
)
T
h
e
r
an
d
o
m
n
u
m
b
er
d
er
iv
ed
f
r
o
m
a
Gau
s
s
ian
o
r
u
n
if
o
r
m
d
is
tr
ib
u
tio
n
is
r
ep
r
esen
ted
b
y
an
d
,
wh
er
e
is
th
e
r
an
d
o
m
izatio
n
p
ar
am
eter
an
d
r
ep
r
esen
ts
th
e
d
is
p
lace
m
en
t
o
f
two
o
b
s
er
v
e
d
f
ir
ef
lies
an
d
.
T
h
e
C
H
s
elec
tio
n
u
s
in
g
FS
FOA
is
p
r
es
en
ted
in
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
1
: CH selectio
n
v
ia
FS
FOA
Input:
FA parameters and Fourier coefficients
Output:
Optimized Cluster Head (CH) nodes
1.
Initialize firefly population with positions, fitness, and parameters.
2.
Compute fitness of each firefly using fitness parameters.
3.
Move fireflies towards brighter ones using:
(
+
1
)
=
+
(
−
)
+
∑
[
(
2
)
+
(
2
)
]
4.
Evaluate new positions based on the fitness function.
5.
Apply selection criteria to identify CHs.
6.
Deploy CHs for efficient communication.
T
h
e
v
alu
es
f
o
r
0
an
d
y
ield
s
s
a
t
is
f
ac
to
r
y
r
esu
lts
f
o
r
th
e
m
ajo
r
ity
o
f
is
s
u
es
ar
e
1
an
d
[
0
,
1
]
.
E
q
u
atio
n
(
9
)
is
u
s
ed
to
co
m
p
u
te
th
e
C
ar
tesi
an
d
is
tan
ce
.
,
=
‖
−
‖
=
√
∑
(
,
−
,
)
2
=
1
(
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
2
5
8
6
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
,
Vo
l
.
14
,
No
.
4
,
Dec
em
b
er
20
25
:
4
6
8
-
4
7
8
472
T
h
e
n
u
m
b
er
o
f
p
r
o
b
lem
p
ar
am
eter
s
is
in
d
icate
d
b
y
.
A
p
er
io
d
ic
f
u
n
ctio
n
'
s
in
f
in
ite
ex
ten
s
io
n
in
te
r
m
s
o
f
s
in
es
an
d
co
s
in
es
is
k
n
o
w
n
a
s
Fo
u
r
ier
s
er
ies
an
aly
s
is
.
E
q
u
atio
n
(
1
0
)
is
th
en
u
s
ed
to
d
ec
o
m
p
o
s
e
th
e
co
n
tin
u
o
u
s
f
u
n
ctio
n
'
s
Fo
u
r
ier
s
er
ies to
d
er
iv
e
its
d
is
cr
ete
f
o
r
m
.
,
=
0
+
∑
(
2
+
2
)
=
1
(
1
0
)
T
h
e
s
u
m
o
f
s
q
u
ar
e
e
r
r
o
r
s
was c
alcu
lated
f
o
r
ea
ch
p
er
io
d
th
at
was
in
clu
d
ed
in
th
e
Fo
u
r
ie
r
s
er
ies.
T
h
e
id
ea
l
p
er
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(
)
f
o
r
th
e
Fo
u
r
ier
s
er
ies
f
u
n
ctio
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will
b
e
d
eter
m
i
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ed
u
s
in
g
th
e
s
u
m
o
f
s
q
u
ar
e
e
r
r
o
r
s
.
An
FS
FO
alg
o
r
ith
m
s
elec
ts
o
p
tim
al
C
H
s
b
ased
o
n
th
e
lo
w
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
d
ela
y
,
an
d
h
ig
h
n
etwo
r
k
life
tim
e,
an
d
n
o
d
e
d
eg
r
ee
wh
ic
h
en
s
u
r
es f
as
ter
d
ata
tr
an
s
m
is
s
io
n
.
3
.
3
.
Ro
uting
v
ia
s
y
na
ptic
int
ellig
ent
co
nv
o
lutio
na
l neura
l net
wo
rk
T
h
e
SICNN
m
o
d
el
d
eter
m
in
e
s
an
o
p
tim
al
r
o
u
tin
g
p
ath
f
o
r
r
o
b
u
s
t
d
ata
tr
an
s
m
is
s
io
n
in
I
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T
-
W
SN.
I
n
th
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SICNN
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r
am
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r
k
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th
e
ef
f
ec
t
o
f
n
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ar
am
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r
s
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en
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s
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r
eliab
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m
u
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t
h
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n
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ain
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(
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te
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T
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co
m
p
ar
is
o
n
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etwe
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q
u
a
tio
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s
(
1
4
)
an
d
(
1
5
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in
d
icate
s
th
at
th
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p
ath
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al
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th
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ay
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e
n
e
g
ativ
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th
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p
a
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b
ias.
T
h
is
is
r
eq
u
ir
ed
to
ap
p
r
o
x
im
ate
th
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p
r
ev
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o
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s
ly
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ested
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ig
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r
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1
−
1
(
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(
1
4
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=
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′
−
1
(
1
5
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T
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p
er
f
o
r
m
a
n
ce
lo
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s
o
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th
e
p
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io
r
d
ata
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u
b
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h
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f
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m
th
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SI
ap
p
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u
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in
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th
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ig
n
if
ican
ce
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g
e
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ated
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ti
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h
er
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p
ar
am
eter
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itio
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ter
th
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eg
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lar
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i
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wh
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ar
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im
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ten
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h
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∆
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n
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u
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task
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th
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d
eg
r
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ch
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m
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u
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u
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eq
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SNs
.
4.
RE
SU
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AND
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f
f
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D
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clu
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en
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s
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m
p
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PDR
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NL
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tim
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co
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p
lex
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,
t
h
r
o
u
g
h
p
u
t,
n
u
m
b
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o
f
aliv
e
n
o
d
es,
PLR,
an
d
s
p
ac
e
co
m
p
lex
ity
.
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Fig
u
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[
1
]
K
.
A
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2
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3
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D
.
M
y
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[
4
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.
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.
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[
6
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A
.
Jen
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,
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[
7
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K
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.
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