I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
. 14, No. 5, O
c
to
be
r
2025
, pp.
4151
~
4161
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
5
.pp
4151
-
4161
4151
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
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or
e
.c
om
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gy
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a C
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r
ap
p
a
1,
2
, P
oor
n
im
a G
ovi
n
d
as
w
am
y
1
1
D
e
pa
r
t
m
e
nt
of
E
l
e
c
t
r
oni
c
s
a
nd C
om
m
uni
c
a
t
i
on E
ngi
ne
e
r
i
ng, B
.
M
.
S
.
C
ol
l
e
ge
o
f
E
ngi
ne
e
r
i
ng, V
i
s
ve
s
va
r
a
ya
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
i
t
y
,
B
e
l
a
ga
vi
, I
ndi
a
2
D
e
pa
r
t
m
e
nt
o
f
E
l
e
c
t
r
on
i
c
s
a
nd
C
o
m
m
u
ni
c
a
t
i
o
n
E
n
gi
ne
e
r
i
ng,
G
o
ve
r
n
m
e
n
t
E
n
gi
n
e
e
r
i
ng
C
o
l
l
e
ge
, V
i
s
ve
s
va
r
a
ya
T
e
c
h
no
l
o
gi
c
a
l
U
n
i
ve
r
s
i
t
y
,
B
e
l
a
ga
vi
, I
ndi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
O
c
t
7, 2024
R
e
vi
s
e
d
J
ul
24, 2025
A
c
c
e
pt
e
d
A
ug 6, 2025
In
wireless
sensor
networks
(WSNs),
achieving
energy
efficiency,
se
curity,
and
minimizing
route
change
propagation
time
is
essential
for
maint
aining
optimal
performance.
This
paper
introduces
a
new
approach
that
combines
Bray Ja
ccar
d
Curtis
-
based Calinski Harabasz
k
-
m
eans (BJC
-
CHKMea
ns) for
clusteri
ng
and
Karl
Pearson
correlatio
n
-
based
egret
swarm
optimization
algorit
hm
(KPC
-
ESOA)
for
selecting
the
best
cluster
head
(CH)
an
d
path,
along
with
c
lassifying
long
short
-
term
memory
with
gated
recurren
t
units
(
CLE
-
GRU
)
for
detecting
harmful
nodes.
The
methodology
aims
to
e
nhance
energy
usage,
improve
routing
efficiency,
and
strengthen
secur
ity
by
identifying
malicious
nodes.
Additionally,
it
integrates
a
secure
routin
g
table
using
elbow
de
-
swinging
k
-
anonymi
ty
(EDS
-
KA)
and
employs
digital
signature
algorit
hm
-
based
Zeta
Bernoulli
Merkle
tree
(DSA
-
ZB
MT)
to
ensure
secure
communication
with
sink
nodes.
The
WSN
-
DS
dataset
was
used
for
training
and
testing,
with
rigorous
preprocessing,
feature
extr
action,
and
selectio
n
to
maintain
data
integrity.
Experimental
results
reveal
ed
that
the
proposed
BJC
-
CHKMean
s
and
CLE
-
GRU
models
outperform
trad
itional
methods
in
power
consumption,
latency,
and
accura
cy.
The
system
ac
hieved
a
power
consumption
of
2.1
mW
for
clustering
and
1.9
m
W
for
classifi
cation,
while
also
providi
ng
near
-
perfect
accuracy
in
de
tecting
harmful
nodes.
These
findings
demonstrate
that
the
framework
signif
icantly
enhances
the
energy
efficiency
and
security
of
WSNs,
making
it
a
highly
effective s
olutio
n for larg
e, dynami
c sensor n
etworks.
K
e
y
w
o
r
d
s
:
D
ig
it
a
l
s
ig
na
tu
r
e
E
ne
r
gy e
f
f
ic
ie
nc
y
H
a
r
m
f
ul
node
de
te
c
ti
on
S
e
c
ur
e
r
out
in
g
W
ir
e
le
s
s
s
e
ns
or
ne
twor
ks
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
M
a
r
ut
hi
H
a
num
a
nt
ha
ppa
C
h
a
ndr
a
ppa
D
e
pa
r
tm
e
nt
of
E
le
c
tr
oni
c
s
a
nd C
om
m
uni
c
a
ti
on E
ngi
ne
e
r
in
g
,
B
.M
.S
. C
ol
le
ge
of
E
ngi
ne
e
r
in
g
V
is
ve
s
va
r
a
y
a
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
it
y
B
e
nga
lu
r
u
-
560019, Ka
r
na
ta
ka
, I
ndi
a
E
m
a
il
:
m
a
r
ut
hi
be
la
ge
r
e
@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
N
W
ir
e
le
s
s
s
e
n
s
or
ne
twor
ks
(
W
S
N
s
)
a
r
e
w
id
e
ly
us
e
d
in
a
ppl
ic
a
ti
ons
s
uc
h
a
s
e
nvi
r
onm
e
nt
a
l
m
oni
to
r
in
g, he
a
lt
hc
a
r
e
, i
ndus
tr
ia
l
a
ut
om
a
ti
on, a
nd s
ur
ve
il
la
nc
e
due
t
o t
he
ir
s
c
a
la
bi
li
ty
, c
os
t
-
e
f
f
e
c
ti
ve
ne
s
s
, a
nd
f
le
xi
bi
li
ty
[
1
]
. T
he
s
e
ne
twor
ks
c
ons
i
s
t
of
nume
r
ous
s
e
ns
or
node
s
t
ha
t
ga
th
e
r
a
nd t
r
a
ns
m
it
da
ta
t
o a
c
e
nt
r
a
li
z
e
d
ba
s
e
s
ta
ti
on.
D
e
s
pi
te
th
e
ir
a
dva
nt
a
ge
s
,
W
S
N
s
f
a
c
e
s
ig
ni
f
ic
a
nt
c
ha
ll
e
nge
s
,
p
a
r
ti
c
ul
a
r
ly
in
th
e
a
r
e
a
s
of
e
ne
r
gy
e
f
f
ic
ie
nc
y
[
2]
,
s
e
c
ur
it
y
[
3]
,
a
nd
ti
m
e
ly
pr
opa
ga
ti
on
of
r
out
e
c
ha
nge
s
[
4]
,
e
s
pe
c
i
a
ll
y
in
ti
m
e
-
s
e
n
s
it
iv
e
a
nd
c
r
it
ic
a
l
e
nvi
r
onm
e
nt
s
.
E
ne
r
gy
e
f
f
ic
ie
nc
y
r
e
m
a
in
s
a
pr
im
a
r
y
c
onc
e
r
n
a
s
s
e
ns
or
node
s
ty
pi
c
a
ll
y
ope
r
a
te
on
li
m
it
e
d
ba
tt
e
r
y
pow
e
r
.
C
om
m
uni
c
a
ti
on
ta
s
k
s
a
nd
f
r
e
que
nt
r
out
in
g
upda
te
s
c
a
n
qui
c
kl
y
dr
a
in
e
ne
r
gy,
le
a
di
ng
to
e
a
r
ly
node
f
a
il
ur
e
s
a
nd
r
e
duc
e
d
ne
twor
k
li
f
e
s
p
a
n
[
5]
.
I
n
a
d
di
ti
on,
W
S
N
s
a
r
e
of
te
n
de
pl
oye
d
in
un
s
e
c
ur
e
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
4151
-
4161
4152
lo
c
a
ti
ons
,
m
a
ki
ng
th
e
m
vul
ne
r
a
bl
e
to
s
e
c
ur
it
y
th
r
e
a
ts
s
u
c
h
a
s
e
a
v
e
s
dr
oppi
ng,
da
t
a
ta
m
pe
r
in
g,
or
phy
s
ic
a
l
c
a
pt
ur
e
of
node
s
[
6]
,
[
7]
.
S
uc
h
th
r
e
a
ts
c
a
n
c
om
pr
om
is
e
da
ta
in
te
gr
it
y
a
nd
di
s
r
upt
c
om
m
uni
c
a
ti
on,
e
s
pe
c
ia
ll
y
if
m
a
li
c
io
us
node
s
a
r
e
not
pr
om
p
tl
y
id
e
nt
if
ie
d
a
nd
is
ol
a
te
d.
A
not
he
r
c
r
it
ic
a
l
is
s
ue
is
th
e
e
f
f
ic
ie
nt
pr
opa
ga
ti
o
n
of
r
out
e
c
ha
nge
s
.
N
e
twor
ks
m
us
t
dyna
m
ic
a
ll
y
a
da
pt
to
node
f
a
il
ur
e
s
or
e
nvi
r
onm
e
nt
a
l
obs
ta
c
le
s
to
m
a
in
ta
in
c
om
m
uni
c
a
ti
on.
H
ow
e
ve
r
,
if
r
out
e
upda
te
s
a
r
e
de
la
ye
d
or
po
or
ly
c
oor
di
na
te
d,
th
e
y
c
a
n
r
e
s
ul
t
in
in
c
r
e
a
s
e
d
e
ne
r
gy c
ons
um
pt
io
n, da
ta
l
os
s
, a
nd l
a
te
nc
y, ul
ti
m
a
te
ly
de
gr
a
di
n
g ne
twor
k pe
r
f
or
m
a
nc
e
[
8]
.
T
o
a
ddr
e
s
s
th
e
s
e
in
te
r
c
onne
c
te
d
c
ha
ll
e
nge
s
,
th
is
pa
pe
r
in
tr
oduc
e
s
a
c
om
pr
e
he
n
s
iv
e
f
r
a
m
e
w
or
k
th
a
t
e
ns
ur
e
s
s
e
c
ur
e
,
e
n
e
r
gy
-
e
f
f
ic
ie
nt
,
a
nd
r
e
s
pons
iv
e
r
out
in
g
in
W
S
N
s
.
I
t
in
te
gr
a
te
s
B
r
a
y
J
a
c
c
a
r
d
C
ur
ti
s
-
ba
s
e
d
C
a
li
ns
ki
H
a
r
a
ba
s
z
k
-
m
e
a
n
s
(
B
J
C
-
C
H
K
M
e
a
ns
)
f
or
e
f
f
e
c
ti
ve
c
lu
s
te
r
in
g,
K
a
r
l
P
e
a
r
s
on
c
or
r
e
la
ti
on
-
ba
s
e
d
e
gr
e
t
s
w
a
r
m
opt
im
iz
a
ti
on
a
lg
or
it
hm
(
K
P
C
-
E
S
O
A
)
f
o
r
opt
im
iz
e
d
c
lu
s
te
r
he
a
d
(
C
H
)
s
e
le
c
ti
on
a
nd
r
out
in
g,
a
nd
Z
e
ta
B
e
r
noul
li
M
e
r
kl
e
tr
e
e
(
Z
B
M
T
)
f
or
s
e
c
ur
e
a
nd
a
da
pt
a
bl
e
c
om
m
uni
c
a
ti
on
pa
th
s
.
U
nl
ik
e
c
onv
e
nt
io
na
l
a
ppr
oa
c
he
s
th
a
t
of
te
n
pr
io
r
it
iz
e
one
a
s
pe
c
t
ove
r
ot
he
r
s
,
th
is
in
te
gr
a
te
d
m
e
th
od
ba
la
nc
e
s
e
n
e
r
gy
m
a
na
ge
m
e
nt
,
s
e
c
ur
it
y,
a
nd
r
out
e
a
da
pt
a
bi
li
ty
.
I
t
e
na
bl
e
s
r
e
a
l
-
ti
m
e
m
a
li
c
io
us
node
de
te
c
ti
on
[
9]
,
m
in
im
iz
e
s
la
te
n
c
y,
a
nd
e
xt
e
nds
t
he
ope
r
a
ti
ona
l
li
f
e
s
pa
n of
t
he
ne
twor
k unde
r
dyna
m
ic
c
ondi
ti
ons
[
10]
, [
11]
.
T
h
is
pa
pe
r
pr
o
po
s
e
s
a
un
if
i
e
d
f
r
a
m
e
w
or
k
t
o
e
nh
a
n
c
e
W
S
N
p
e
r
f
o
r
m
a
n
c
e
b
y
im
pr
ov
in
g
e
n
e
r
gy
e
f
f
i
c
i
e
n
c
y
,
s
e
c
ur
e
c
om
m
un
ic
a
t
io
n
,
a
n
d
r
ou
te
a
d
a
pt
a
b
il
i
ty
.
I
t
i
nt
e
gr
a
te
s
m
ul
ti
pl
e
te
c
h
ni
q
ue
s
t
o
a
d
dr
e
s
s
k
e
y
c
h
a
ll
e
n
ge
s
s
u
c
h
a
s
ha
r
m
f
ul
n
od
e
d
e
t
e
c
t
io
n
a
n
d
dy
na
m
i
c
r
out
i
ng.
B
J
C
-
C
H
K
M
e
a
ns
c
lu
s
te
r
s
n
od
e
s
ba
s
e
d
o
n
lo
c
a
t
io
n
a
n
d
e
ne
r
gy
t
o
m
i
ni
m
i
z
e
in
tr
a
-
c
l
u
s
te
r
c
o
m
m
u
ni
c
a
ti
o
n
a
nd
r
e
du
c
e
po
w
e
r
u
s
a
ge
.
K
P
C
-
E
S
O
A
o
pt
i
m
i
z
e
s
CH
s
e
l
e
c
t
io
n
a
nd
a
id
s
i
n
id
e
n
ti
f
y
in
g
m
a
li
c
io
us
no
de
s
t
hr
ou
gh
f
e
a
tu
r
e
s
e
l
e
c
ti
on.
Z
B
M
T
e
n
s
ur
e
s
s
e
c
ur
e
a
nd
r
e
li
a
b
le
da
ta
t
r
a
ns
m
i
s
s
io
n b
e
t
w
e
e
n
nod
e
s
a
nd
th
e
b
a
s
e
s
t
a
ti
on
. A
ddi
t
io
n
a
l
ly
,
c
l
a
s
s
if
y
in
g l
o
ng
s
h
or
t
-
t
e
r
m
m
e
m
or
y
w
it
h
g
a
te
d
r
e
c
ur
r
e
nt
uni
ts
(
C
L
E
-
G
R
U
)
,
a
m
a
c
hi
n
e
le
a
r
ni
n
g
m
o
de
l
,
d
e
t
e
c
t
s
m
a
li
c
io
u
s
no
de
s
by
a
n
a
l
yz
i
ng
th
e
ir
be
h
a
v
io
r
.
T
o
ge
th
e
r
,
t
h
e
s
e
m
e
t
hod
s
i
m
pr
ov
e
s
e
c
ur
i
ty
,
c
on
s
e
r
v
e
e
n
e
r
g
y,
a
n
d
e
x
te
nd
ne
tw
or
k
li
f
e
ti
m
e
.
2.
B
A
C
K
G
R
O
U
N
D
W
S
N
s
a
r
e
s
e
lf
-
or
g
a
ni
z
in
g
s
ys
t
e
m
s
us
e
d
to
m
oni
to
r
ph
ys
i
c
a
l
or
e
nvi
r
onm
e
nt
a
l
c
on
di
ti
on
s
.
I
ni
ti
a
ll
y
de
ve
l
ope
d
f
or
m
il
i
ta
r
y a
ppl
i
c
a
ti
on
s
,
th
e
y a
r
e
now
w
id
e
ly
a
ppl
i
e
d
in
h
e
a
lt
hc
a
r
e
, a
ut
om
a
ti
on
,
a
nd e
nvi
r
onm
e
nt
a
l
m
oni
to
r
in
g
[
12]
.
T
h
e
s
e
n
e
twor
k
s
u
s
e
w
ir
e
le
s
s
c
om
m
uni
c
a
ti
on
t
o
tr
a
n
s
m
it
s
e
ns
e
d
d
a
ta
to
a
c
e
nt
r
a
l
ba
s
e
s
ta
ti
o
n
[
13]
.
K
e
y
c
h
a
ll
e
n
ge
s
in
W
S
N
s
i
nc
lu
de
e
ne
r
gy
e
f
f
ic
i
e
nc
y,
s
e
c
ur
it
y,
a
nd
r
e
li
a
bl
e
d
a
ta
tr
a
n
s
m
i
s
s
io
n,
p
a
r
ti
c
ul
a
r
ly
in
c
r
it
ic
a
l
a
ppl
ic
a
ti
on
s
li
k
e
s
m
a
r
t
tr
a
ns
por
t
a
n
d
de
f
e
n
s
e
[
14]
.
R
out
in
g
r
e
m
a
in
s
c
o
m
pl
e
x
due
to
li
m
it
e
d
e
ne
r
gy,
dyna
m
i
c
to
pol
o
gi
e
s
,
a
n
d
de
c
e
nt
r
a
l
iz
e
d
a
r
c
hi
te
c
tu
r
e
,
pr
om
pt
in
g
th
e
ne
e
d
f
or
e
f
f
ic
ie
nt
pr
ot
oc
ol
s
t
a
il
or
e
d
to
s
pe
c
if
ic
a
ppl
i
c
a
ti
ons
[
15]
,
[
16]
.
S
e
ve
r
a
l
r
e
s
e
a
r
c
h
e
r
s
h
a
ve
pr
opos
e
d
s
ol
ut
i
ons
t
o
a
d
dr
e
s
s
th
e
s
e
i
s
s
ue
s
.
G
opa
la
n
e
t
al
.
[
17]
in
t
e
gr
a
t
e
d
b
a
c
t
e
r
ia
l
f
or
a
gi
n
g
opt
im
iz
a
ti
on
w
it
h
ha
r
m
o
ny
s
e
a
r
c
h
a
lg
or
it
h
m
(
B
F
O
-
HS
A
)
f
or
c
lu
s
t
e
r
in
g
a
nd
c
r
o
s
s
-
l
a
ye
r
-
b
a
s
e
d
op
por
tu
ni
s
ti
c
r
out
in
g
pr
ot
o
c
ol
(
C
O
R
P
)
f
or
r
out
in
g,
a
c
hi
e
vi
ng
i
m
pr
ove
d
pa
c
k
e
t
de
li
v
e
r
y,
r
e
d
uc
e
d
d
e
la
y,
a
nd
lo
n
ge
r
ne
t
w
or
k
li
f
e
.
A
d
a
pt
i
ve
e
ne
r
g
y
-
e
f
f
ic
i
e
nt
ba
l
a
n
c
e
d
un
e
ve
n
c
lu
s
te
r
in
g
(
A
E
B
U
C
)
pr
o
to
c
ol
a
dj
u
s
t
s
c
lu
s
t
e
r
in
g
dy
na
m
i
c
a
ll
y
ba
s
e
d
on
no
de
de
n
s
it
y
to
e
n
ha
n
c
e
e
ne
r
gy
ba
l
a
nc
e
a
n
d
CH
s
e
l
e
c
ti
o
n
[
18]
.
T
a
bb
a
s
s
um
a
nd
P
a
th
a
k
[
19]
c
om
bi
ne
d
lo
w
e
n
e
r
g
y
a
d
a
pt
iv
e
c
l
us
t
e
r
in
g hi
e
r
a
r
c
hy
(
L
E
A
C
H
)
w
i
th
f
uz
z
y
l
ogi
c
a
nd
a
r
ti
f
i
c
ia
l
ne
ur
a
l
n
e
twor
k
(
ANN
)
t
o
bui
l
d
a
n
i
nt
r
us
io
n
de
t
e
c
t
io
n
s
y
s
te
m
w
it
h
97%
a
c
c
ur
a
c
y
.
K
a
vi
a
r
a
s
a
n
a
nd
S
r
in
iv
a
s
a
n
[
2
0]
u
s
e
d
a
da
pt
i
ve
r
e
m
or
a
opt
i
m
iz
a
ti
on
a
lg
or
it
hm
(
A
R
O
A
)
f
or
e
n
e
r
gy
-
a
w
a
r
e
CH
s
e
l
e
c
ti
o
n,
s
ig
ni
f
ic
a
nt
ly
e
xt
e
ndi
ng
ne
tw
or
k
li
f
e
ti
m
e
.
K
a
po
or
a
nd
S
ha
r
m
a
[
21]
a
p
pl
ie
d
gl
ow
w
or
m
s
w
a
r
m
opt
im
iz
a
ti
on
(
G
S
O
)
t
o
im
pr
ov
e
e
n
e
r
gy
us
e
, c
onne
c
ti
vi
t
y,
a
nd
c
o
ve
r
a
g
e
w
it
h f
a
s
te
r
c
onv
e
r
ge
n
c
e
.
S
r
iv
id
ya
a
nd D
e
vi
[
22]
p
r
opos
e
d a
hybr
id
m
e
th
od c
om
bi
ni
ng
b
i
o
-
in
s
pi
r
e
d hi
e
r
a
r
c
hi
c
a
l
or
de
r
c
hi
c
ke
n
s
w
a
r
m
opt
im
iz
a
ti
on
(
B
I
H
O
-
C
S
O
)
,
e
ne
r
gy
c
om
pe
te
nt
pa
r
ti
c
le
s
w
a
r
m
opt
im
iz
a
ti
on
(
E
C
P
S
O
)
,
a
nd
r
e
c
ur
s
iv
e
bi
na
r
y
pa
r
ti
ti
oni
ng
de
c
is
io
n
tr
e
e
(
RBP
-
DT
)
f
or
im
pr
ove
d
CH
s
e
le
c
ti
on
a
nd
r
out
in
g
in
W
S
N
s
,
a
c
hi
e
vi
ng
hi
ghe
r
th
r
oughput
but
la
c
ki
ng
in
tr
us
io
n
de
te
c
ti
on.
A
not
he
r
s
tu
dy
us
e
d
s
a
f
e
w
e
ig
ht
e
d
c
lu
s
t
e
r
in
g
w
it
h
a
de
c
is
io
n
tr
e
e
(
D
T
)
c
la
s
s
if
ie
r
,
a
tt
a
in
in
g
78%
a
c
c
ur
a
c
y
a
nd
lo
nge
r
ne
twor
k
li
f
e
,
th
ough
w
it
h
li
m
it
e
d
f
e
a
tu
r
e
us
e
[
23]
.
X
ue
e
t
al
.
[
24]
a
da
pt
e
d
c
r
os
s
-
la
ye
r
-
ba
s
e
d
H
a
r
r
is
-
ha
w
k
s
-
opt
im
iz
a
ti
on
(
CL
-
HHO
)
a
c
hi
e
ve
d
lo
w
e
ne
r
gy
c
ons
um
pt
io
n (
0.1
m
J
)
but
f
a
c
e
d l
in
k f
a
il
ur
e
i
s
s
ue
s
. C
h
e
r
a
ppa
e
t
al
.
[
25]
a
da
pt
e
d
a
da
pt
iv
e
s
a
il
f
is
h opti
m
iz
a
ti
on
(
A
S
F
O
)
-
ba
s
e
d
pr
ot
oc
ol
w
it
h
k
-
m
e
doi
ds
of
f
e
r
e
d
lo
w
pow
e
r
us
e
a
nd
hi
gh
th
r
oughput
but
s
tr
uggl
e
d
w
it
h
node
id
e
nt
if
ic
a
ti
on.
S
ur
e
s
h
a
nd
P
r
a
s
a
d
[
26]
a
da
pt
e
d
L
E
A
C
H
f
or
I
o
T
,
im
pr
ovi
ng
pa
c
ke
t
-
de
li
ve
r
y
r
a
ti
o
(
P
D
R
)
but
f
a
c
in
g
r
out
in
g
ta
bl
e
in
s
ta
bi
li
ty
.
T
he
s
e
s
tu
di
e
s
hi
ghl
ig
ht
th
e
r
ol
e
of
opt
im
iz
a
ti
on
a
nd
m
a
c
hi
ne
le
a
r
ni
ng
i
n
e
nha
nc
in
g W
S
N
s
, t
hough c
h
a
ll
e
nge
s
r
e
m
a
in
unr
e
s
ol
ve
d.
3.
P
R
O
P
O
S
E
D
M
E
T
H
O
D
O
L
O
G
Y
T
he
a
ppr
oa
c
h
e
nt
a
il
s
de
v
e
lo
pi
ng
a
s
a
f
e
,
e
ne
r
gy
-
e
f
f
ic
ie
nt
W
S
N
s
ys
te
m
th
a
t
s
e
e
k
s
to
m
in
im
iz
e
th
e
pr
opa
ga
ti
on of
r
out
e
c
ha
nge
a
nd opti
m
iz
e
de
te
c
ti
ng ma
li
c
io
us
n
ode
s
. I
t
c
om
bi
ne
s
B
J
C
-
C
H
K
M
e
a
ns
t
o c
lu
s
te
r
,
K
P
C
-
E
S
O
A
to
s
e
le
c
t
f
e
a
tu
r
e
s
a
nd
CH
opt
im
iz
a
ti
on,
Z
B
M
T
to
of
f
e
r
a
s
e
c
ur
e
c
om
m
uni
c
a
ti
on
to
s
in
k,
C
L
E
-
G
R
U
to
de
te
c
t
m
a
li
c
io
us
node
s
.
T
he
pr
oc
e
s
s
e
n
c
om
pa
s
s
e
s
da
ta
pr
e
pr
oc
e
s
s
in
g,
a
nd
tr
a
ns
m
is
s
io
n
w
it
h
di
s
c
r
e
te
da
ta
s
e
c
ur
it
y, a
s
w
e
ll
a
s
e
f
f
ic
ie
nt
e
ne
r
gy c
ons
um
pt
io
n,
s
a
f
e
r
out
in
g a
nd e
nf
or
c
e
d s
e
c
ur
it
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
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:
2252
-
8938
A
n e
ne
r
gy
-
e
ff
ic
ie
nt
and s
e
c
ur
e
f
r
am
e
w
o
r
k
f
or
w
ir
e
le
s
s
s
e
ns
o
r
…
(
M
ar
ut
hi
H
anumanthappa C
handr
appa
)
4153
3.1.
D
at
as
e
t
u
s
e
d
T
he
e
xpe
r
im
e
nt
m
a
ke
s
u
s
e
of
th
e
W
S
N
-
D
S
da
ta
s
e
t,
a
s
im
ul
a
t
e
d
W
S
N
de
te
c
ti
on
s
ys
te
m
c
r
e
a
t
e
d
by
A
lm
om
a
ni
e
t
al
.
[
27
]
.
T
hi
s
da
ta
s
e
t
is
s
pe
c
if
ic
a
ll
y
de
s
ig
ne
d
to
a
s
s
e
s
s
how
w
e
ll
m
a
c
hi
ne
le
a
r
ni
ng
a
lg
or
it
hm
s
c
a
n
de
te
c
t
a
nd
c
ount
e
r
a
c
t
m
a
li
c
io
us
a
c
ti
vi
ti
e
s
in
W
S
N
s
.
I
t
in
c
lu
de
s
bot
h
nor
m
a
l
tr
a
f
f
ic
a
nd
a
tt
a
c
k
tr
a
f
f
ic
,
c
ove
r
in
g
c
om
m
on
W
S
N
th
r
e
a
ts
li
ke
bl
a
c
khol
e
,
gr
a
yhol
e
,
a
nd
f
lo
odi
ng
a
tt
a
c
ks
.
K
e
y
f
e
a
tu
r
e
s
of
th
e
da
ta
s
e
t,
s
uc
h
a
s
pa
c
ke
t
s
iz
e
,
s
ig
na
l
s
tr
e
ngt
h,
e
ne
r
gy
c
ons
um
pt
io
n,
a
nd
node
m
obi
li
ty
,
a
r
e
c
r
it
ic
a
l
f
o
r
e
va
lu
a
ti
ng
W
S
N
pe
r
f
or
m
a
nc
e
i
n t
e
r
m
s
of
s
e
c
ur
it
y, e
ne
r
gy e
f
f
ic
ie
nc
y, a
nd r
out
in
g e
f
f
e
c
ti
ve
ne
s
s
.
3.2.
P
r
op
os
e
d
m
e
t
h
od
ol
ogy
T
hi
s
pa
pe
r
pr
opos
e
s
a
c
om
pr
e
he
n
s
iv
e
m
e
th
odol
ogy
a
im
e
d
a
t
i
m
pr
ovi
ng
e
ne
r
gy
e
f
f
ic
ie
nc
y,
s
e
c
ur
it
y,
a
nd
r
out
e
opt
im
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[
27]
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Evaluation Warning : The document was created with Spire.PDF for Python.
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to
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2025
:
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4154
ne
twor
k
w
hi
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m
a
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li
a
bl
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da
ta
de
li
ve
r
y.
T
hi
s
is
do
ne
by
s
ol
vi
ng
a
m
ul
ti
-
obj
e
c
ti
ve
opt
im
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a
ti
on
pr
obl
e
m
.
W
he
r
e
is
th
e
to
ta
l
e
n
e
r
gy
c
ons
um
pt
io
n
of
th
e
ne
two
r
k,
is
th
e
e
ne
r
gy
c
on
s
um
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d
dur
in
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ns
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e
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(
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+
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2)
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m
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th
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pl
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L
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R
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B
J
C
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nd r
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ti
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3.3.
P
e
r
f
or
m
an
c
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e
va
lu
at
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T
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pr
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s
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f
f
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m
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m
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lu
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.
E
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it
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gr
it
y a
nd r
e
li
a
bi
li
ty
w
it
hi
n t
he
W
S
N
.
=
×
100
(
4)
R
out
e
c
ha
nge
pr
opa
ga
ti
on t
im
e
r
e
f
e
r
s
t
o how f
a
s
t
th
e
ne
twor
k a
dj
us
ts
t
o c
ha
nge
s
i
n i
ts
s
tr
uc
tu
r
e
, l
ik
e
node
f
a
il
ur
e
s
or
m
obi
li
ty
,
a
nd
upda
te
s
it
s
r
out
in
g
pa
th
s
.
T
hi
s
m
e
a
s
ur
e
m
e
nt
is
vi
ta
l
in
dyna
m
ic
n
e
twor
ks
w
he
r
e
node
a
va
il
a
bi
li
ty
f
lu
c
tu
a
te
s
.
K
e
e
pi
ng
th
is
ti
m
e
to
a
m
in
im
um
is
e
s
s
e
nt
ia
l
f
or
m
a
in
ta
in
in
g
ne
twor
k
s
ta
bi
li
ty
a
nd e
ns
ur
in
g s
m
oot
h, unint
e
r
r
upt
e
d da
ta
t
r
a
ns
m
is
s
io
n.
=
−
(
5)
D
e
te
c
ti
on
a
c
c
ur
a
c
y
ga
uge
s
th
e
a
c
c
ur
a
c
y
of
a
s
ys
t
e
m
to
not
ic
e
m
a
li
c
io
us
node
s
w
it
hi
n
th
e
W
S
N
.
I
t
is
e
s
ti
m
a
te
d
on
th
e
ba
s
is
of
tr
ue
pos
it
iv
e
s
(
T
P
)
,
f
a
ls
e
pos
it
iv
e
s
(
F
P
)
,
tr
ue
ne
ga
ti
ve
s
(
T
N
)
,
a
nd
f
a
ls
e
ne
ga
ti
ve
s
(
F
N
)
.
H
ig
h
a
c
c
ur
a
c
y
im
pl
ie
s
th
a
t
th
e
s
ys
t
e
m
is
s
e
c
ur
e
a
nd
ha
s
a
c
or
r
e
c
t
r
a
te
of
id
e
nt
if
yi
ng
s
e
c
ur
it
y
-
r
e
la
te
d
a
nd r
e
gul
a
r
node
s
.
=
+
+
+
+
×
100
(
6)
B
y
a
na
ly
z
in
g
th
e
s
e
m
e
tr
ic
s
,
it
is
pos
s
ib
le
to
ha
ve
a
c
le
a
r
pi
c
tu
r
e
of
how
th
e
pr
opos
e
d
a
ppr
oa
c
h
c
a
n
be
e
f
f
e
c
ti
ve
in
e
nha
nc
in
g
e
ne
r
gy
e
f
f
ic
ie
nc
y,
s
e
c
ur
it
y
a
nd
c
om
m
uni
c
a
ti
on
in
W
S
N
s
.
T
he
f
in
di
ngs
a
f
f
ir
m
it
s
e
f
f
e
c
ti
ve
ne
s
s
in
m
in
im
iz
e
d
e
ne
r
gy
c
ons
um
pt
io
n,
e
ns
ur
e
d
r
e
li
a
bi
li
ty
in
tr
a
ns
m
is
s
io
n,
a
dj
us
tm
e
nt
to
pa
th
c
ha
nge
s
a
nd d
e
te
c
ti
on of
m
a
li
c
io
us
node
s
.
3.4. Ve
r
if
ic
at
io
n
of
e
n
c
r
yp
t
e
d
m
e
s
s
age
s
T
he
e
nc
r
ypt
-
th
e
n
-
s
ig
n
m
e
th
ods
e
m
pl
oye
d
in
our
r
e
s
e
a
r
c
h
e
nd
e
a
vor
to
gua
r
a
nt
e
e
th
e
c
onf
id
e
nt
ia
li
ty
,
in
te
gr
it
y,
a
nd
non
-
r
e
pudi
a
ti
on
of
c
om
m
uni
c
a
te
d
m
e
s
s
a
ge
s
a
m
ong
e
nt
it
ie
s
.
T
he
s
ugge
s
t
e
d
m
e
th
odol
ogy
gua
r
a
nt
e
e
s
pr
iv
a
c
y
by
im
pl
e
m
e
nt
in
g
a
s
e
r
ie
s
of
pr
e
li
m
in
a
r
y
m
e
a
s
ur
e
s
. T
he
da
ta
be
in
g
tr
a
ns
f
e
r
r
e
d
in
c
lu
de
s
a
n
e
nc
r
ypt
e
d
c
oor
di
na
te
,
th
e
c
ur
r
e
nt
ti
m
e
(
)
,
a
nd
a
s
ig
na
tu
r
e
in
te
ge
r
c
r
e
a
te
d
a
t
r
a
ndom.
B
y
us
in
g
th
e
publ
ic
ke
y of
t
he
s
e
nde
r
, t
he
r
e
c
ip
ie
nt
c
a
n v
e
r
if
y t
he
ge
nui
ne
ne
s
s
of
a
s
ig
ne
d c
om
m
uni
c
a
ti
on.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
n e
ne
r
gy
-
e
ff
ic
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nt
and s
e
c
ur
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f
r
am
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w
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k
f
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w
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s
s
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…
(
M
ar
ut
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H
anumanthappa C
handr
appa
)
4155
4.
P
E
R
F
O
R
M
A
N
C
E
E
V
A
L
U
A
T
I
O
N
T
he
r
e
s
ul
ts
a
nd
a
n
a
ly
s
is
s
e
c
ti
on
e
va
lu
a
te
s
th
e
pr
opos
e
d
m
e
t
hod'
s
pe
r
f
or
m
a
nc
e
ba
s
e
d
on
e
ne
r
gy
c
ons
um
pt
io
n,
P
D
R
,
la
te
nc
y,
ne
twor
k
li
f
e
ti
m
e
,
a
nd
de
te
c
ti
on
a
c
c
ur
a
c
y.
S
im
ul
a
ti
on
r
e
s
ul
ts
s
how
th
a
t
th
e
in
te
gr
a
ti
on
of
B
J
C
-
C
H
K
M
e
a
n
s
,
K
P
C
-
E
S
O
A
,
a
nd
C
L
E
-
G
R
U
e
nha
nc
e
s
W
S
N
e
f
f
ic
ie
nc
y
a
nd
s
e
c
ur
it
y.
C
om
pa
r
e
d
to
e
xi
s
ti
ng
m
e
th
ods
,
th
e
a
ppr
oa
c
h
a
c
hi
e
ve
s
be
tt
e
r
e
ne
r
gy
s
a
vi
ng
s
,
r
e
li
a
bl
e
tr
a
ns
m
is
s
io
n,
a
nd
a
c
c
ur
a
te
m
a
li
c
io
us
nod
e
de
te
c
ti
on, de
m
ons
tr
a
ti
ng s
tr
ong s
c
a
la
bi
li
ty
a
nd r
obus
tn
e
s
s
unde
r
di
ve
r
s
e
c
ondi
ti
ons
.
4.1.
Q
u
an
t
it
a
t
iv
e
an
al
ys
is
F
ig
ur
e
2
hi
ghl
ig
ht
s
th
e
la
te
nc
y
pe
r
f
or
m
a
nc
e
of
th
e
pr
opos
e
d
K
P
C
-
E
S
O
a
lg
or
it
hm
in
c
om
pa
r
is
on
to
ot
he
r
a
lg
or
it
hm
s
,
s
uc
h
a
s
e
gr
e
t
s
w
a
r
m
opt
im
iz
a
ti
on
(
E
S
O
)
,
b
a
c
te
r
ia
l
f
or
a
gi
ng
opt
im
iz
a
ti
on
(
B
F
O
)
,
s
pot
te
d
hye
na
opt
im
iz
e
r
(
S
H
O
)
,
a
nd
a
r
ti
f
ic
ia
l
be
e
c
ol
ony
opt
im
iz
a
ti
o
n
(
A
B
C
O
)
,
a
s
th
e
num
be
r
of
node
s
in
c
r
e
a
s
e
s
.
L
a
te
nc
y
,
m
e
a
s
ur
e
d
in
m
il
li
s
e
c
onds
(
m
s
)
,
in
c
r
e
a
s
e
s
f
or
a
ll
a
lg
or
it
hm
s
a
s
th
e
num
be
r
of
node
s
gr
ow
s
,
w
hi
c
h
is
e
xpe
c
te
d
du
e
to
th
e
a
ddi
ti
ona
l
c
om
m
uni
c
a
ti
on
a
nd
da
ta
ha
ndl
in
g
ne
e
de
d
in
la
r
ge
r
ne
twor
ks
.
H
ow
e
v
e
r
,
K
P
C
-
E
S
O
c
ons
is
te
nt
ly
s
how
s
lo
w
e
r
la
te
nc
y
a
c
r
os
s
a
ll
node
c
ount
s
c
om
pa
r
e
d
to
th
e
ot
he
r
s
.
W
hi
le
E
S
O
a
nd
B
F
O
pe
r
f
or
m
r
e
la
ti
ve
ly
w
e
ll
,
th
e
y
s
ti
ll
la
g
be
hi
nd
K
P
C
-
E
S
O
,
pa
r
ti
c
ul
a
r
ly
a
s
th
e
num
be
r
o
f
node
s
in
c
r
e
a
s
e
s
.
T
he
c
ons
i
s
te
nt
ly
lo
w
e
r
la
te
nc
y
of
K
P
C
-
E
S
O
s
ugge
s
ts
it
s
r
out
e
opt
im
iz
a
ti
on
is
m
or
e
e
f
f
ic
ie
nt
,
m
in
im
iz
in
g
th
e
ti
m
e
f
or
da
ta
pa
c
ke
ts
to
tr
a
ve
l
a
c
r
os
s
th
e
n
e
twor
k,
e
ve
n
a
s
th
e
ne
twor
k
gr
ow
s
.
T
hi
s
m
a
ke
s
K
P
C
-
E
S
O
pa
r
ti
c
ul
a
r
ly
w
e
ll
-
s
ui
te
d f
or
l
a
r
ge
-
s
c
a
le
W
S
N
s
, w
he
r
e
r
e
duc
in
g l
a
te
nc
y i
s
c
r
uc
ia
l
f
or
r
e
a
l
-
ti
m
e
a
ppl
ic
a
ti
ons
.
F
ig
ur
e
2. C
om
pa
r
is
on of
la
te
nc
y of
pr
opos
e
d m
ode
l
w
it
h t
he
pe
e
r
m
e
th
ods
F
ig
ur
e
3
il
lu
s
tr
a
te
s
th
e
r
e
s
pons
e
ti
m
e
of
th
e
pr
opos
e
d
K
P
C
-
E
S
O
a
lg
or
it
hm
ve
r
s
us
E
S
O
,
B
F
O
,
S
H
O
,
a
nd
A
B
C
O
a
s
nod
e
c
ount
in
c
r
e
a
s
e
s
.
W
hi
le
a
ll
m
e
th
od
s
s
h
ow
r
is
in
g
r
e
s
pons
e
ti
m
e
s
w
it
h
ne
twor
k
s
iz
e
,
K
P
C
-
E
S
O
c
ons
is
te
nt
ly
a
c
hi
e
ve
s
t
he
l
ow
e
s
t
va
lu
e
s
. T
hi
s
e
f
f
ic
ie
nc
y s
te
m
s
f
r
om
i
ts
opt
im
iz
e
d
CH
s
e
le
c
ti
on a
nd
r
out
in
g,
m
in
im
iz
in
g
de
la
ys
.
T
he
r
e
s
ul
ts
c
onf
ir
m
K
P
C
-
E
S
O
’
s
s
c
a
la
bi
li
ty
a
nd
s
ui
ta
bi
li
ty
f
or
la
r
ge
W
S
N
s
r
e
qui
r
in
g f
a
s
t,
r
e
a
l
-
ti
m
e
c
om
m
uni
c
a
ti
on.
F
ig
ur
e
3. C
om
pa
r
is
on of
r
e
s
pons
e
t
im
e
of
pr
opos
e
d m
ode
l
w
it
h
t
he
pe
e
r
m
e
th
ods
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
4151
-
4161
4156
F
ig
u
r
e
4
s
h
ow
s
th
e
f
i
tn
e
s
s
va
lu
e
s
ove
r
it
e
r
a
ti
o
ns
f
o
r
CH
s
e
le
c
ti
on
us
in
g
K
P
C
-
E
S
O
A
,
c
om
pa
r
e
d
t
o
E
S
O
A
,
B
F
O
,
S
H
O
,
a
nd
A
B
C
O
.
W
hi
le
a
l
l
m
e
th
o
ds
i
m
pr
o
ve
w
it
h
m
o
r
e
it
e
r
a
ti
ons
,
K
P
C
-
E
S
O
A
c
ons
is
te
n
tl
y
a
c
hi
e
ve
s
th
e
hi
ghe
s
t
f
i
tn
e
s
s
,
r
e
f
le
c
t
in
g
be
tt
e
r
e
ne
r
gy
e
f
f
ic
ie
nc
y,
c
onne
c
ti
vi
ty
,
a
nd
d
is
ta
nc
e
op
ti
m
iz
a
ti
o
n.
I
ts
a
d
va
nc
e
d
C
H
s
e
le
c
ti
on
a
ll
ow
s
qui
c
ke
r
c
onve
r
ge
nc
e
to
opt
i
m
a
l
s
ol
ut
io
ns
,
e
n
ha
nc
in
g
e
ne
r
gy
ba
la
nc
e
a
nd
e
xt
e
ndi
n
g
ne
two
r
k
l
if
e
.
T
h
is
de
m
ons
t
r
a
te
s
K
P
C
-
E
S
O
A
’
s
s
upe
r
io
r
e
f
f
ic
ie
nc
y
a
nd
pe
r
f
o
r
m
a
nc
e
ove
r
tr
a
di
ti
ona
l
a
pp
r
oa
c
he
s
.
F
ig
ur
e
4. C
om
pa
r
is
on of
f
it
ne
s
s
v
s
.
it
e
r
a
ti
ons
of
pr
opos
e
d m
ode
l
w
it
h t
he
pe
e
r
m
e
th
ods
F
ig
ur
e
5
s
how
s
th
e
e
xe
c
ut
io
n
ti
m
e
of
va
r
io
us
c
lu
s
te
r
in
g
m
e
th
ods
,
w
he
r
e
B
J
C
-
C
H
K
M
e
a
n
s
out
pe
r
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doi
d,
f
or
w
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d
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a
p
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c
it
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m
a
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ke
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C
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a
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ir
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h
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it
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lo
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t
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x
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c
ut
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e
.
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hi
s
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f
f
ic
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m
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it
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ti
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ppl
ic
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s
s
in
g
a
nd
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tt
e
r
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ne
r
gy
m
a
na
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m
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nt
.
I
n
c
ont
r
a
s
t,
hi
ghe
r
e
xe
c
ut
io
n
ti
m
e
s
in
m
e
th
ods
li
ke
B
ir
c
h
m
a
ke
th
e
m
le
s
s
s
ui
ta
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f
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ti
m
e
-
c
r
it
ic
a
l
or
l
a
r
ge
-
s
c
a
le
ne
twor
ks
.
F
ig
ur
e
5. C
lu
s
te
r
in
g
te
c
hni
que
e
xe
c
ut
io
n t
im
e
c
om
pa
r
is
on
F
ig
ur
e
6
pr
e
s
e
nt
s
a
c
om
pa
r
is
on
of
th
e
a
c
c
ur
a
c
y
r
a
te
s
f
or
va
r
io
us
ha
r
m
f
ul
node
c
la
s
s
if
ic
a
ti
on
m
e
th
ods
,
in
c
lu
di
ng
th
e
pr
opos
e
d
C
L
E
-
G
R
U
m
ode
l
a
nd
th
e
e
xi
s
ti
ng
G
R
U
,
bi
di
r
e
c
ti
ona
l
lo
ng
s
hor
t
-
te
r
m
m
e
m
or
y
(
Bi
-
L
S
T
M
)
,
lo
ng
s
hor
t
-
te
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m
m
e
m
or
y
(
L
S
T
M
)
,
a
n
d
r
e
c
ur
r
e
nt
ne
ur
a
l
ne
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k
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R
N
N
)
m
ode
ls
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A
c
c
ur
a
c
y,
s
how
n
a
s
a
pe
r
c
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nt
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ge
,
r
e
f
le
c
ts
e
a
c
h
m
ode
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s
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bi
li
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or
r
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tl
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te
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t
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r
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f
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it
hi
n
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N
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T
he
pr
opos
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d
C
L
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m
ode
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nds
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,
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c
hi
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ng
c
l
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to
98.6%
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ur
a
c
y,
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h
is
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ig
ni
f
ic
a
nt
ly
hi
ghe
r
th
a
n
th
e
ot
he
r
m
ode
ls
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T
hi
s
c
om
pa
r
is
on
e
m
pha
s
iz
e
s
t
ha
t
th
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C
L
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-
G
R
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ode
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dva
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tu
r
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xt
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on
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l
a
s
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if
ic
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ti
on
t
e
c
hni
que
s
e
na
bl
e
it
to
m
or
e
e
f
f
e
c
ti
ve
ly
di
f
f
e
r
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nt
ia
te
be
twe
e
n
nor
m
a
l
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nd
ha
r
m
f
ul
node
s
. T
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ne
a
r
ly
pe
r
f
e
c
t
a
c
c
ur
a
c
y of
C
L
E
-
G
R
U
pos
it
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ns
i
t
a
s
a
l
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a
di
ng opti
on f
o
r
i
m
p
r
ovi
ng W
S
N
s
e
c
ur
it
y,
pa
r
ti
c
ul
a
r
ly
in
e
nvi
r
onm
e
nt
s
w
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r
e
pr
e
c
is
e
id
e
nt
if
ic
a
ti
on
of
ha
r
m
f
ul
node
s
is
e
s
s
e
nt
ia
l
f
or
m
a
in
ta
in
in
g ne
twor
k pe
r
f
or
m
a
nc
e
a
nd da
ta
i
nt
e
gr
it
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
n e
ne
r
gy
-
e
ff
ic
ie
nt
and s
e
c
ur
e
f
r
am
e
w
o
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k
f
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le
s
s
s
e
ns
o
r
…
(
M
ar
ut
hi
H
anumanthappa C
handr
appa
)
4157
F
ig
ur
e
6. H
a
r
m
f
ul
node
c
la
s
s
if
ic
a
ti
on a
c
c
ur
a
c
y
F
ig
ur
e
7
pr
e
s
e
nt
s
a
pow
e
r
c
on
s
um
pt
io
n
a
na
ly
s
is
f
or
bot
h
c
lu
s
te
r
in
g
a
nd
c
la
s
s
if
ic
a
ti
on
te
c
hni
que
s
.
F
ig
ur
e
7
(
a
)
c
om
pa
r
e
s
pow
e
r
c
ons
um
pt
io
n
a
c
r
os
s
c
lu
s
te
r
in
g
m
e
th
ods
,
s
how
in
g
B
J
C
-
C
H
K
M
e
a
n
s
a
s
th
e
m
os
t
e
ne
r
gy
-
e
f
f
ic
ie
nt
a
t
2.1 m
W
.
I
ts
opt
im
iz
e
d
c
lu
s
te
r
in
g
r
e
duc
e
s
in
tr
a
-
c
lu
s
te
r
c
om
m
uni
c
a
ti
on,
m
a
ki
ng
it
id
e
a
l
f
or
e
ne
r
gy
-
c
ons
tr
a
in
e
d W
S
N
s
. O
th
e
r
m
e
th
ods
, w
hi
le
e
f
f
e
c
ti
ve
, c
on
s
um
e
m
or
e
pow
e
r
, l
im
it
in
g t
he
ir
s
ui
ta
bi
li
ty
f
o
r
s
uc
h
e
nvi
r
onm
e
nt
s
.
F
ig
ur
e
7
(
b
)
s
how
s
ha
r
m
f
ul
node
de
te
c
ti
o
n
m
e
th
ods
,
w
he
r
e
C
L
E
-
G
R
U
ha
s
th
e
lo
w
e
s
t
pow
e
r
us
a
ge
a
t
1.9
m
W
.
I
ts
e
f
f
ic
ie
nt
de
s
ig
n
ba
la
n
c
e
s
c
om
p
ut
a
ti
on
a
nd
m
e
m
or
y
w
hi
le
m
a
in
ta
in
in
g
hi
gh
a
c
c
ur
a
c
y.
T
hi
s
m
a
ke
s
C
L
E
-
G
R
U
w
e
ll
-
s
ui
te
d
f
or
W
S
N
s
,
w
he
r
e
lo
w
pow
e
r
c
ons
um
pt
io
n
is
c
r
uc
ia
l
f
or
pr
ol
onge
d ne
twor
k ope
r
a
ti
on.
(
a
)
(
b)
F
ig
ur
e
7. P
ow
e
r
c
ons
um
pt
io
n a
na
ly
s
is
of
(
a
)
c
lu
s
te
r
in
g a
nd
(
b)
c
la
s
s
if
ic
a
ti
on t
e
c
hni
que
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 14, No. 5, O
c
to
be
r
2025
:
4151
-
4161
4158
4.2.
Q
u
al
it
at
iv
e
an
al
ys
is
T
he
us
e
r
in
te
r
f
a
c
e
(
U
I
)
de
ve
lo
pe
d
f
or
th
is
pr
oj
e
c
t
is
de
s
ig
n
e
d
to
pr
ovi
de
s
m
oot
h
a
nd
e
f
f
ic
ie
nt
in
te
r
a
c
ti
on
w
it
h
th
e
W
S
N
s
ys
te
m
a
s
s
how
n
in
F
ig
ur
e
8.
I
t
is
o
r
ga
ni
z
e
d
in
to
m
ul
ti
pl
e
f
unc
ti
ona
l
c
om
pone
nt
s
,
m
a
ki
ng
it
us
e
r
-
f
r
ie
ndl
y
f
or
bot
h
th
e
te
s
ti
ng
a
nd
tr
a
in
in
g
p
ha
s
e
s
.
E
s
s
e
nt
ia
l
f
unc
ti
ons
li
ke
h
a
r
m
f
ul
node
de
te
c
ti
on,
c
lu
s
t
e
r
in
g,
w
e
ig
ht
e
d
gr
a
ph
c
r
e
a
ti
on,
a
nd
O
P
s
e
le
c
ti
on
a
r
e
pr
e
s
e
nt
e
d
a
s
but
to
ns
,
a
ll
ow
in
g
us
e
r
s
to
a
c
ti
va
te
e
a
c
h pr
oc
e
s
s
s
te
p by s
te
p. F
ig
ur
e
9 de
pi
c
ts
a
w
e
ig
ht
e
d
ne
twor
k gr
a
ph f
or
t
he
W
S
N
i
n t
hi
s
w
or
k.
E
a
c
h
node
r
e
pr
e
s
e
nt
s
a
s
e
ns
or
w
it
hi
n
th
e
ne
twor
k,
a
nd
th
e
c
onn
e
c
ti
ng
e
dge
s
s
how
th
e
c
om
m
uni
c
a
ti
on
li
nks
be
twe
e
n
th
e
m
.
T
h
e
num
be
r
s
on
th
e
s
e
e
dge
s
r
e
f
le
c
t
th
e
w
e
ig
ht
s
of
th
e
c
onne
c
ti
ons
,
w
hi
c
h
c
oul
d
r
e
la
te
to
f
a
c
to
r
s
l
ik
e
e
ne
r
gy c
ons
um
pt
io
n, node
di
s
ta
nc
e
, or
s
ig
na
l
s
tr
e
ng
th
. T
he
s
e
w
e
ig
ht
s
a
r
e
e
s
s
e
nt
ia
l
in
de
te
r
m
in
in
g
th
e
m
os
t
e
f
f
ic
ie
nt
pa
th
s
f
or
da
ta
tr
a
ns
m
is
s
io
n.
N
ode
s
a
r
e
c
onn
e
c
te
d
ba
s
e
d
on
pr
oxi
m
it
y
a
nd
c
om
m
uni
c
a
ti
on
c
a
pa
bi
li
ty
,
c
r
e
a
ti
ng
a
w
e
b
th
r
ough
w
hi
c
h
d
a
ta
f
lo
w
s
a
c
r
os
s
m
ul
ti
pl
e
r
out
e
s
.
L
ow
e
r
w
e
ig
ht
s
in
di
c
a
t
e
m
or
e
e
ne
r
gy
-
e
f
f
ic
ie
nt
or
f
a
s
te
r
li
nks
,
w
hi
le
hi
ghe
r
w
e
ig
ht
s
s
ugge
s
t
g
r
e
a
te
r
e
ne
r
gy
us
e
or
le
s
s
e
f
f
ic
ie
nt
pa
th
s
.
T
hi
s
gr
a
ph
li
ke
ly
s
uppor
ts
node
c
lu
s
te
r
in
g,
CH
s
e
le
c
ti
on,
a
nd
r
out
in
g
ta
bl
e
f
or
m
a
ti
on,
a
im
e
d
a
t
opt
im
iz
in
g
e
ne
r
gy
e
f
f
ic
ie
nc
y a
nd s
e
c
ur
it
y i
n t
he
W
S
N
.
F
ig
ur
e
8. F
r
ont
-
e
nd
f
or
t
he
pr
opos
e
d m
e
th
od
F
ig
ur
e
9. W
e
ig
ht
e
d
gr
a
ph c
ons
tr
uc
ti
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
A
n e
ne
r
gy
-
e
ff
ic
ie
nt
and s
e
c
ur
e
f
r
am
e
w
o
r
k
f
or
w
ir
e
le
s
s
s
e
ns
o
r
…
(
M
ar
ut
hi
H
anumanthappa C
handr
appa
)
4159
F
ig
ur
e
10
de
m
ons
tr
a
te
s
in
te
r
f
a
c
e
s
u
c
c
e
s
s
f
ul
ly
c
om
pl
e
te
s
ke
y
ta
s
ks
in
W
S
N
ope
r
a
ti
on
s
,
s
uc
h a
s
node
in
it
ia
li
z
a
ti
on,
ha
r
m
f
ul
node
de
te
c
ti
on,
w
e
ig
ht
e
d
gr
a
ph
c
ons
tr
uc
ti
on,
a
nd
c
lu
s
te
r
in
g.
I
t
s
e
ts
up
50
node
s
,
pos
it
io
ni
ng
th
e
m
a
nd
a
s
s
ig
ni
ng
e
ne
r
gy
va
lu
e
s
.
H
a
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m
f
ul
node
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c
ti
on
a
nd
w
e
ig
ht
e
d
gr
a
ph
c
ons
tr
uc
ti
on
e
ns
ur
e
e
f
f
ic
ie
nt
c
om
m
uni
c
a
ti
on
a
nd
r
out
in
g.
T
he
c
lu
s
te
r
in
g
pr
oc
e
s
s
c
om
pa
r
e
s
a
lg
or
it
hm
s
li
ke
B
J
C
-
C
H
K
M
e
a
n
s
,
K
M
e
a
ns
,
a
nd
K
M
e
doi
d,
f
oc
us
in
g
on
c
la
s
s
if
ic
a
ti
on
r
e
s
ul
ts
.
T
he
pr
opos
e
d
C
L
E
-
G
R
U
m
ode
l
s
ta
nds
out
w
it
h
m
or
e
a
c
c
ur
a
te
a
nd
r
e
li
a
bl
e
c
la
s
s
if
ic
a
ti
on,
m
a
ki
ng
it
a
be
tt
e
r
s
ol
ut
io
n
f
or
s
e
c
ur
in
g
a
nd
opt
im
iz
in
g
W
S
N
ope
r
a
ti
ons
.
T
he
in
te
r
f
a
c
e
s
im
pl
if
ie
s
te
s
ti
ng
a
nd
pe
r
f
or
m
a
nc
e
e
va
lu
a
ti
on,
of
f
e
r
in
g
r
e
a
l
-
ti
m
e
f
e
e
dba
c
k a
nd s
y
s
te
m
m
oni
to
r
in
g.
F
ig
ur
e
10. I
nt
e
r
f
a
c
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C
E
S
[
1]
D
.
K
a
ndr
i
s
,
C
.
N
a
ka
s
,
D
.
V
om
va
s
,
a
nd
G
.
K
oul
our
a
s
,
“
A
ppl
i
c
a
t
i
ons
of
w
i
r
e
l
e
s
s
s
e
n
s
or
ne
t
w
or
ks
:
a
n
up
-
to
-
da
t
e
s
ur
ve
y,”
A
ppl
i
e
d
Sy
s
t
e
m
I
nnov
at
i
on
, vol
. 3, no. 1, pp. 1
–
24, 2020, doi
:
10.3390/
a
s
i
3010014.
[
2]
J
.
A
m
ut
ha
,
S
.
S
ha
r
m
a
,
a
nd
J
.
N
a
ga
r
,
“
W
S
N
s
t
r
a
t
e
gi
e
s
ba
s
e
d
on
s
e
n
s
or
s
,
d
e
pl
oym
e
nt
,
s
e
ns
i
ng
m
ode
l
s
,
c
ove
r
a
ge
a
nd
e
ne
r
gy
e
f
f
i
c
i
e
nc
y:
r
e
vi
e
w
,
a
ppr
oa
c
he
s
a
nd
ope
n
i
s
s
ue
s
,
”
W
i
r
e
l
e
s
s
P
e
r
s
onal
C
om
m
u
ni
c
at
i
ons
,
vol
.
111,
no.
2,
pp.
1089
–
1115,
2020,
doi
:
10.1007/
s
11277
-
019
-
06903
-
z.
[
3]
D
.
E
.
B
oubi
c
he
,
S
.
A
t
hm
a
ni
,
S
.
B
oubi
c
he
,
a
nd
H
.
T
.
-
C
r
uz
,
“
C
ybe
r
s
e
c
ur
i
t
y
i
s
s
ue
s
i
n
w
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
ks
:
c
ur
r
e
nt
c
ha
l
l
e
nge
s
a
nd s
ol
ut
i
ons
,”
W
i
r
e
l
e
s
s
P
e
r
s
onal
C
om
m
uni
c
at
i
on
s
, vol
. 117, no. 1, pp. 177
–
21
3, 2021, doi
:
10.1007/
s
11277
-
020
-
07213
-
5.
[
4]
R
.
Z
a
gr
ouba
a
nd
A
.
K
a
r
di
,
“
C
om
pa
r
a
t
i
ve
s
t
udy
of
e
ne
r
gy
e
f
f
i
c
i
e
nt
r
out
i
ng
t
e
c
hni
que
s
i
n
w
i
r
e
l
e
s
s
s
e
n
s
or
ne
t
w
or
ks
,
”
I
nf
or
m
at
i
on
,
vol
. 12, no. 1, pp. 1
–
28, 2021, doi
:
10.3390/
i
nf
o12010042.
[
5]
M
.
S
.
B
e
ns
a
l
e
h,
R
.
S
a
i
da
,
Y
.
H
.
K
a
c
e
m
,
a
nd
M
.
A
bi
d,
“
W
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
k
de
s
i
gn
m
e
t
hodol
ogi
e
s
:
a
s
ur
ve
y,”
J
our
nal
of
Se
ns
or
s
, vol
. 2020, 2020, doi
:
10.1155/
2020/
9592836.
[
6]
M
.
A
be
di
ni
a
nd
I
.
A
.
-
A
nba
gi
,
“
A
c
t
i
ve
e
a
ve
s
dr
oppe
r
’
s
de
t
e
c
t
i
on
s
y
s
t
e
m
i
n
m
ul
t
i
-
hop
w
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
ks
,”
i
n
I
E
E
E
Sy
m
pos
i
um
on C
om
put
e
r
s
and
C
om
m
uni
c
at
i
ons
, 2022, vol
. 2022
-
J
une
, doi
:
10.1109/
I
S
C
C
55528.2022.9912466.
[
7]
M
.
F
a
r
i
s
,
M
.
N
.
M
a
hm
ud,
M
.
F
.
M
.
S
a
l
l
e
h,
a
nd
A
.
A
l
noor
,
“
W
i
r
e
l
e
s
s
s
e
ns
or
n
e
t
w
or
k
s
e
c
ur
i
t
y:
a
r
e
c
e
nt
r
e
vi
e
w
ba
s
e
d
on
s
t
a
t
e
-
of
-
t
he
-
a
r
t
w
or
ks
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
E
ngi
ne
e
r
i
ng
B
us
i
ne
s
s
M
anage
m
e
nt
,
vol
.
15,
pp.
1
-
29,
F
e
b.
2023,
doi
:
10.1177/
18479790231157220.
[
8]
Z
. L
i
u, Y
.
L
i
u, a
nd X
.
W
a
ng, “
I
nt
e
l
l
i
ge
nt
r
out
i
ng a
l
gor
i
t
hm
f
or
w
i
r
e
l
e
s
s
s
e
n
s
or
ne
t
w
or
ks
dyna
m
i
c
a
l
l
y gui
de
d by
di
s
t
r
i
but
e
d ne
ur
a
l
ne
t
w
or
ks
,”
C
om
put
e
r
C
om
m
uni
c
at
i
ons
, vol
. 207, pp. 100
–
112, 2023, doi
:
10.10
16/
j
.c
om
c
om
.2023.05.018.
[
9]
W
.
B
.
N
e
dha
m
a
nd
A
.
K
.
M
.
A
.
-
Q
ur
a
ba
t
,
“
A
c
om
pr
e
he
n
s
i
ve
r
e
vi
e
w
of
c
l
u
s
t
e
r
i
ng
a
ppr
oa
c
he
s
f
or
e
ne
r
gy
e
f
f
i
c
i
e
nc
y
i
n
w
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
ks
,”
I
nt
e
r
nat
i
onal
J
our
nal
of
C
om
put
e
r
A
ppl
i
c
at
i
ons
i
n
T
e
c
hnol
ogy
,
vol
.
72,
no.
2,
pp.
139
–
160,
2023,
doi
:
10.1504/
I
J
C
A
T
.2023.133035.
[
10]
R
.
A
hm
a
d,
R
.
W
a
z
i
r
a
l
i
,
a
nd
T
.
A
.
-
A
i
n,
“
M
a
c
hi
n
e
l
e
a
r
ni
ng
f
or
w
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
ks
s
e
c
ur
i
t
y:
a
n
ove
r
vi
e
w
of
c
h
a
l
l
e
nge
s
a
nd
i
s
s
ue
s
,
”
Se
ns
o
r
s
, vol
. 22, no. 13, 2022, doi
:
10.3390/
s
22134730.
[
11]
Z
.
A
l
A
ghba
r
i
,
A
.
M
.
K
h
e
dr
,
W
.
O
s
a
m
y,
I
.
A
r
i
f
,
a
nd
D
.
P
.
A
gr
a
w
a
l
,
“
R
out
i
ng
i
n
w
i
r
e
l
e
s
s
s
e
ns
or
n
e
t
w
or
ks
u
s
i
ng
opt
i
m
i
z
a
t
i
on
t
e
c
hni
que
s
:
a
s
ur
ve
y,”
W
i
r
e
l
e
s
s
P
e
r
s
onal
C
om
m
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ne
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l
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s
s
Se
n
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ns
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e
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ks
:
a
ppl
i
c
a
t
i
ons
,
c
ha
l
l
e
nge
s
a
nd
r
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e
a
r
c
h
t
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B
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P
a
nt
,
S
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.
B
hoj
n
e
,
a
nd
C
.
R
.
P
r
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s
a
d,
“
W
i
r
e
l
e
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s
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e
n
s
or
ne
t
w
or
ks
f
a
c
e
c
h
a
l
l
e
nge
s
a
n
d
i
s
s
ue
s
r
e
l
a
t
e
d
t
o
s
e
c
ur
i
t
y,”
i
n
2023
3r
d
I
n
t
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
A
dv
anc
e
C
om
put
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ng
and
I
nnov
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i
v
e
T
e
c
hnol
ogi
e
s
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n
E
ngi
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l
i
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a
t
i
on
i
n
w
i
r
e
l
e
s
s
s
e
ns
or
ne
t
w
or
k:
t
e
c
hni
que
s
,
a
l
gor
i
t
hm
s
a
na
l
ys
i
s
a
nd
c
h
a
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e
nge
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,”
2021
9t
h
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
R
e
l
i
abi
l
i
t
y
,
I
nf
oc
om
T
e
c
hnol
ogi
e
s
and
O
pt
i
m
i
z
at
i
on
(
T
r
e
nds
and
F
ut
ur
e
D
i
r
e
c
t
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)
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a
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A
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e
ne
r
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f
i
c
i
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nt
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out
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ng
pr
ot
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ol
w
i
t
h
f
uz
z
y
ne
ur
a
l
ne
t
w
or
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i
n
w
i
r
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l
e
s
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s
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ns
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n
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da
pt
i
ve
e
ne
r
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-
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f
f
i
c
i
e
nt
une
ve
n
c
l
us
t
e
r
i
ng
r
out
i
ng
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ot
oc
ol
f
or
W
S
N
s
,”
I
E
I
C
E
T
r
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E
f
f
e
c
t
i
ve
da
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a
t
r
a
ns
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i
s
s
i
on
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hr
ough
e
ne
r
gy
-
e
f
f
i
c
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e
nt
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l
us
-
t
e
r
i
ng
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nd
f
uz
z
y
-
ba
s
e
d
I
D
S
r
out
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ng
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ppr
oa
c
h i
n W
S
N
s
,”
V
i
r
t
ual
R
e
al
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t
y
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nt
e
l
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ge
nt
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a
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e
ve
l
opi
ng
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nove
l
e
n
e
r
gy
e
f
f
i
c
i
e
nt
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out
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ng
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ot
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ol
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n
W
S
N
us
i
ng
a
da
pt
i
ve
r
e
m
or
a
opt
i
m
i
z
a
t
i
on a
l
gor
i
t
hm
,”
E
x
pe
r
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Sy
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