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.
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,
Octo
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er
20
25
,
p
p
.
4
9
8
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9
9
2
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
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1
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v
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i
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.
pp
4
9
8
3
-
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2
4983
J
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’s en
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ih
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Dep
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-
Hu
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in
T
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Un
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ity
Mo
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am
m
ad
Alk
h
attab
St.,
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’
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d
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m
ail: sam
ih
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m
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.
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u
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1.
I
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RO
D
UCT
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O
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T
h
e
in
ter
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o
f
th
in
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s
(
I
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es
as
a
p
r
o
m
in
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t
tech
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lo
g
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esh
ap
in
g
th
e
d
ig
ital
lan
d
s
ca
p
e,
f
ac
ilit
atin
g
s
ea
m
less
co
m
m
u
n
icatio
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am
o
n
g
m
y
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iad
i
n
ter
co
n
n
ec
ted
n
o
d
es
v
ia
a
s
h
ar
e
d
n
etwo
r
k
[
1
]
,
[
2
]
.
W
ith
its
v
er
s
atility
,
I
o
T
f
in
d
s
ap
p
licatio
n
s
ac
r
o
s
s
v
ar
io
u
s
d
o
m
ain
s
,
n
o
tab
ly
in
e
n
h
an
ci
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g
tar
g
et
d
etec
tio
n
with
in
m
ilit
ar
y
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d
s
ec
u
r
ity
d
o
m
ain
s
[
3
]
.
Sp
ec
if
ically
,
wi
r
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s
en
s
o
r
n
etwo
r
k
s
(
W
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Ns)
ar
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ep
lo
y
ed
to
m
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ito
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k
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d
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llect
d
ata
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n
tar
g
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ac
tiv
ities
[
4
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–
[
6
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.
T
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g
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d
d
ata
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tr
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lized
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m
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r
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ata
[
7
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is
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T
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r
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atter
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h
o
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ch
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g
in
g
an
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h
ar
g
in
g
th
e
m
m
ay
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e
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tly
an
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ted
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s
[
8
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.
T
h
u
s
,
p
o
we
r
co
n
s
u
m
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tio
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a
cr
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cial
f
ac
to
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th
at
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ts
I
o
T
s
y
s
tem
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er
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o
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m
an
ce
[
9
]
,
[
1
0
]
.
T
h
e
p
r
esen
ce
o
f
a
m
alicio
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s
e
n
tity
m
ay
im
p
ac
t
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o
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h
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o
r
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ce
o
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th
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I
o
T
n
etwo
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k
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th
e
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o
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p
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o
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ar
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g
n
o
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I
d
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m
alicio
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s
n
o
d
es
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cr
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o
r
s
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tem
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n
d
n
etwo
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k
o
p
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atio
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[
1
1
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;
th
u
s
,
it
is
v
ital
to
d
is
co
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n
u
s
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al
b
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av
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I
o
T
d
ev
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d
d
ev
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ad
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cr
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o
m
aly
d
etec
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ith
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tin
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u
s
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o
d
es
[
1
2
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.
Ou
r
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ch
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v
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tim
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u
ltip
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s
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T
DM
A)
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ch
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lin
g
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T
h
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ce
n
tr
al
a
u
th
o
r
ity
co
llects
th
ese
d
ec
is
io
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s
an
d
ap
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K
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of
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le
to
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eter
m
in
e
t
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e
g
lo
b
al
s
tate
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f
th
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tar
g
et
[
1
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
9
8
3
-
4
9
9
2
4984
A
s
o
p
h
is
ticated
f
o
r
m
o
f
m
alic
io
u
s
n
o
d
es,
ter
m
e
d
d
e
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e
n
t
m
alicio
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n
o
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es
(
DM
Ns),
h
a
s
g
ar
n
er
ed
r
ec
en
t
atten
tio
n
[
7
]
,
[
1
4
]
–
[
1
6
]
.
DM
Ns
ad
ju
s
t
th
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k
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ased
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d
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lis
ten
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s
en
s
in
g
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lt
s
f
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m
o
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e
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o
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wn
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ata
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ce
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lo
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al
d
ec
is
io
n
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y
wh
en
n
ec
ess
ar
y
[
1
7
]
,
[
1
8
]
.
A
s
tu
d
y
in
[
1
9
]
em
p
h
asizes
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e
im
p
o
r
tan
ce
o
f
th
e
n
u
m
b
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o
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es
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DM
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n
h
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r
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T
o
d
etec
t
DM
Ns
in
T
D
-
W
SNs
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ti
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g
a
T
DM
A
r
ep
o
r
tin
g
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ty
le,
a
s
ch
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e
is
p
r
o
p
o
s
ed
to
c
h
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g
e
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ep
o
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g
o
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er
s
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d
m
o
n
it
o
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o
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e
p
er
f
o
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ce
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No
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es
s
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o
win
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en
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o
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ce
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er
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d
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Ns.
W
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ile
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e
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h
o
ws
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is
e,
it
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ily
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d
d
r
ess
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ar
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et
s
tates,
wh
er
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p
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tates.
An
o
th
er
s
tu
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y
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ten
d
s
th
is
s
ch
em
e
to
m
u
ltis
tate
T
D
-
W
S
Ns
[
1
3
]
.
B
a
lb
u
d
h
e
et
a
l.
[
2
0
]
p
r
o
p
o
s
e
d
a
p
r
o
jec
t
th
at
in
ten
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o
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ar
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ter
m
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s
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tem
s
.
T
h
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s
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tem
in
c
lu
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a
m
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cr
o
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g
lo
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l p
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it
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tem
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G
P
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n
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l
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tem
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m
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b
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m
m
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s
(
G
SM)
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s
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ar
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m
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ter
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in
t
er
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t
o
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t
h
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s
d
ev
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ce
,
an
d
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r
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en
t
t
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f
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r
m
er
(
C
T
)
.
T
h
e
p
r
o
je
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s
p
u
r
p
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to
r
em
o
v
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ex
c
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h
u
m
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lab
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ir
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l
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er
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y
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o
n
i
to
r
in
g
s
o
lu
t
i
o
n
.
T
h
e
s
tu
d
y
[
2
1
]
aim
s
t
o
d
ev
e
lo
p
an
in
d
u
s
t
r
i
al
in
te
r
n
e
t
o
f
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in
g
s
(
I
I
o
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)
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d
ed
g
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co
m
p
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t
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b
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ito
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g
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s
e
in
a
m
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f
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r
in
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f
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in
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w
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d
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ed
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er
g
y
m
et
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s
.
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h
e
s
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te
m
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th
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m
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ag
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q
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eu
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el
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n
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p
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t
(
MQ
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)
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l
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el
iv
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d
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m
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ter
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ata
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ed
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to
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t
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ct
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aly
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ica
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m
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ic
s
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o
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k
ilo
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t
t
-
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r
(
k
W
h
)
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o
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p
ar
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o
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an
al
y
s
is
.
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h
e
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f
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d
th
at
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at
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g
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g
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ed
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c
ed
c
en
tr
al
p
r
o
ce
s
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g
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n
i
t
(
C
PU)
u
ti
l
iz
at
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b
u
t
m
ain
ta
in
ed
co
n
s
t
a
n
t
m
em
o
r
y
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s
ag
e
,
s
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g
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t
in
g
th
e
s
y
s
t
em
co
u
ld
im
p
r
o
v
e
co
r
p
o
r
a
te
ed
g
e
–
f
o
g
co
m
p
u
t
in
g
te
ch
n
o
lo
g
i
es
f
o
r
r
em
o
t
e
ap
p
l
ica
t
io
n
s
.
T
h
e
s
tu
d
y
in
[
2
2
]
u
s
ed
en
er
g
y
m
o
n
it
o
r
in
g
(
E
n
er
Mo
n
)
,
a
n
in
t
er
n
et
o
f
th
in
g
s
lo
n
g
r
an
g
e
(
L
o
R
a
)
s
y
s
tem
,
to
tr
a
ck
p
o
we
r
u
s
e
an
d
wa
s
t
e
in
m
u
lt
ip
le
p
l
ac
e
s
.
T
h
e
in
v
e
s
tig
at
io
n
d
i
s
co
v
er
ed
w
as
tef
u
l
p
o
wer
u
s
e
in
au
d
i
to
r
i
u
m
s
,
p
o
o
l
h
e
at
er
s
,
wa
ter
p
u
m
p
s
,
a
n
d
e
le
ctr
ic
b
o
il
er
s
,
s
t
r
e
s
s
in
g
th
e
n
e
ed
f
o
r
m
o
r
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e
f
f
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ct
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e
an
d
ef
f
i
ci
en
t
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er
g
y
m
an
ag
em
en
t
s
y
s
t
em
s
.
A
b
b
a
e
t
a
l
.
[
2
3
]
h
av
e
d
ev
e
l
o
p
e
d
a
l
o
w
-
c
o
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t
a
u
to
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o
m
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m
ar
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o
T
-
b
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s
ed
i
r
r
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g
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t
i
o
n
m
o
n
i
t
o
r
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n
g
a
n
d
co
n
t
r
o
l
s
y
s
t
e
m
.
T
h
e
s
y
s
t
em
s
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n
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ct
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t
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l
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am
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m
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ag
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t
w
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in
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.
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m
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f
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s
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tu
r
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f
f
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t
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w
a
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p
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.
T
h
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d
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ta
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t
t
o
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w
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.
M
a
in
t
a
i
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in
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m
o
i
s
t
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r
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ev
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s
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n
1
0
0
%
a
n
d
4
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%
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s
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r
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g
a
t
io
n
.
K
a
n
a
k
ar
i
s
e
t
a
l
.
[
2
4
]
p
r
e
s
e
n
ts
a
n
I
o
T
s
y
s
t
e
m
th
a
t
m
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p
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M
QT
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.
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t
an
a
l
y
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p
o
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m
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W
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m
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a
f
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w
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p
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,
a
n
d
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to
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No
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m
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R
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s
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P
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.
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h
e
s
y
s
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d
i
s
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ay
s
r
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a
l
-
t
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m
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d
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t
a
,
in
c
l
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d
in
g
p
o
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u
s
a
g
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a
n
d
m
i
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d
p
ac
k
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s
,
an
d
p
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v
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tr
a
n
s
m
i
s
s
i
o
n
s
t
o
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t
e
n
d
b
a
t
t
e
r
y
l
i
f
e.
T
h
e
s
y
s
t
e
m
d
e
m
o
n
s
t
r
a
t
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d
c
o
m
p
a
r
ab
l
e
p
o
w
er
c
o
n
s
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m
p
ti
o
n
p
e
r
f
o
r
m
a
n
c
e
o
v
e
r
2
1
i
t
e
r
a
t
i
o
n
s
.
T
h
e
p
l
a
t
f
o
r
m
d
e
s
ig
n
ed
i
n
[
2
5
]
m
a
n
ag
e
s
s
o
l
a
r
-
p
o
w
er
e
d
w
i
r
e
l
e
s
s
s
e
n
s
o
r
n
o
d
e
s
i
n
i
n
d
u
s
tr
i
a
l
I
o
T
ap
p
l
i
c
a
t
i
o
n
s
,
f
o
c
u
s
i
n
g
o
n
l
o
w
-
c
o
s
t
v
o
l
t
ag
e
s
en
s
o
r
a
c
c
u
r
a
c
y
.
I
t
c
h
e
ck
s
a
n
d
an
a
ly
z
e
s
Ar
d
u
i
n
o
p
r
o
t
o
ty
p
e
s
.
T
h
e
ex
p
er
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m
en
t
f
o
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s
e
s
o
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s
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n
s
o
r
a
c
c
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cy
a
n
d
v
o
l
t
ag
e
-
r
e
l
a
t
ed
m
e
t
r
ic
s
.
T
h
e
s
o
l
u
t
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n
i
m
p
r
o
v
e
s
t
h
e
ef
f
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c
ie
n
c
y
o
f
s
o
la
r
p
o
w
e
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g
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n
e
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a
t
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a
n
d
o
f
f
er
s
o
p
t
im
a
l
I
I
o
T
o
p
e
r
a
t
io
n
s
e
t
t
in
g
s
.
T
h
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r
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s
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l
t
s
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d
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ca
t
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t
h
a
t
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p
r
o
p
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ly
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t
i
m
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t
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r
p
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p
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c
t
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n
.
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h
i
s
p
a
p
e
r
e
l
a
b
o
r
a
t
e
s
o
n
th
e
p
r
ev
i
o
u
s
s
t
u
d
i
e
s
[
1
9
]
,
[
1
3
]
b
y
c
o
n
d
u
c
t
in
g
a
p
r
a
c
t
i
c
a
l
te
s
t
b
ed
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s
i
n
g
A
r
d
u
in
o
n
o
d
e
s
an
d
r
a
d
io
f
r
e
q
u
e
n
c
y
(
R
F
)
m
o
d
u
le
s
.
T
h
e
m
a
in
o
b
j
e
c
t
iv
e
s
o
f
th
e
ex
p
er
i
m
en
t
a
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o
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s
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v
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im
p
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-
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le
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w
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k
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f
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D
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p
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s
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n
t
h
e
wo
r
k
c
o
n
d
u
c
t
e
d
in
[
1
3
]
.
Ab
e
d
in
e
t
a
l.
[
2
6
]
o
f
f
e
r
a
n
en
e
r
g
y
-
e
f
f
i
ci
e
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t
t
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ch
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iq
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in
g
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en
i
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t
e
r
n
e
t
o
f
th
i
n
g
s
(
Gr
e
e
n
-
I
o
T
)
s
y
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t
e
m
s
.
T
h
e
a
l
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m
o
p
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a
t
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s
in
th
r
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e
p
h
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s
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u
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2
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Similar
ly
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th
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d
y
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ic
m
alici
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ay
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s
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d
o
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co
d
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to
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s
h
o
w
th
e
f
u
n
ctio
n
of
th
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m
alicio
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s
ty
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r
esp
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tiv
ely
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e
Alg
o
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ith
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s
2,
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em
o
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ate
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ased
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e
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ec
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en
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e
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ze
r
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ly
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n
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ly
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im
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u
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e.
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y
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t
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io
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e
n
o
d
e
ac
ts
m
alicio
u
s
ly
s
elec
tiv
ely
to
av
o
id
d
etec
tio
n
.
In
o
u
r
ex
p
e
r
im
en
tal
s
ce
n
ar
io
s
,
v
ar
io
u
s
n
u
m
b
er
s
an
d
t
y
p
es
of
m
alicio
u
s
n
o
d
es
h
av
e
b
ee
n
u
s
e
d
.
Fo
r
th
e
p
r
ev
io
u
s
alg
o
r
ith
m
s
,
in
itializatio
n
s
h
o
u
ld
b
e
s
tar
ted
b
y
d
ef
in
i
n
g
:
th
e
n
u
m
b
er
o
f
n
o
d
es
,
,
,
,
,
,
an
d
.
Alg
o
r
ith
m
2
.
DAO
m
alicio
u
s
n
o
d
e
ty
p
es
1:
if
M
type
==
DAO
then
2:
if
M
order
≤
2
then
3:
Decision ← 1
4:
else
5:
s
←
0
6:
for
i
=
1
to
M
o
d
r
d
e
r
−
1
do
7:
if
Rx
-
Decision[i
−
1]
==
TRUE
-
Decision
t
h
e
n
8:
s
←
s +
1
9:
end
if
10:
end
for
11:
if True
-
Decision == 0 and s >
2
then
12:
Decision
←
True
-
d
e
c
i
si
o
n
13:
else
14:
Decision ←
1
15:
end
if
16:
end
if
17:
end
if
2
.
3
.
Co
o
rdina
t
o
r
T
h
e
co
o
r
d
in
ato
r
n
o
d
e
c
o
n
s
is
ts
o
f
a
n
Ar
d
u
in
o
Me
g
a
an
d
a
n
R
F
tr
an
s
ce
iv
er
.
Ar
d
u
i
n
o
Me
g
a
is
u
s
ed
as
th
e
m
ain
m
icr
o
c
o
n
tr
o
ller
o
f
th
e
co
o
r
d
i
n
ato
r
n
o
d
e.
T
h
e
c
o
o
r
d
in
ato
r
n
o
d
e’
s
m
ain
task
is
to
r
ec
eiv
e
th
e
n
o
d
es’
lo
ca
l
d
ec
is
io
n
s
to
m
ak
e
g
lo
b
a
l
d
ec
is
io
n
s
.
I
n
ad
d
itio
n
,
th
e
c
o
o
r
d
in
at
o
r
n
o
d
e
is
r
esp
o
n
s
ib
l
e
f
o
r
d
etec
tin
g
an
d
id
en
tify
in
g
m
alicio
u
s
n
o
d
es.
As
s
h
o
wn
in
Alg
o
r
ith
m
5,
a
f
ter
in
itializatio
n
,
th
e
co
o
r
d
i
n
ato
r
s
en
d
s
a
b
ea
c
o
n
m
ess
ag
e
to
th
e
s
e
lecte
d
n
o
d
e,
i
n
clu
d
in
g
th
e
node
id
en
tifie
r
.
H
en
ce
,
all
n
o
d
es
id
en
tify
th
e
o
r
d
er
an
d
r
etu
r
n
th
eir
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
n
erg
y
ev
a
lu
a
tio
n
o
f
d
ep
en
d
e
n
t m
a
licio
u
s
n
o
d
es d
etec
tio
n
i
n
…
(
Mo
a
th
A
ls
a
fa
s
feh
)
4987
d
ec
is
io
n
to
th
e
co
o
r
d
in
ato
r
.
A
d
ec
is
io
n
b
u
f
f
e
r
is
u
p
d
ated
with
f
r
esh
d
ec
is
io
n
v
alu
es.
T
h
e
co
o
r
d
in
ato
r
th
e
n
ch
ec
k
s
if
all
n
o
d
es
h
a
v
e
b
ee
n
co
n
tacte
d
b
e
f
o
r
e
p
r
o
ce
e
d
in
g
to
th
e
n
ex
t
r
o
u
n
d
.
Af
ter
co
m
p
letin
g
th
e
m
a
x
im
u
m
n
u
m
b
er
o
f
r
o
u
n
d
s
,
th
e
m
alicio
u
s
n
o
d
e
d
etec
tio
n
m
ec
h
an
is
m
is
en
ab
led
,
wh
er
e
t
h
er
e
is
a
s
h
u
f
f
le
in
th
e
n
o
d
es’
o
r
d
er
f
o
r
th
e
n
ex
t t
u
r
n
.
Fin
ally
,
r
ep
o
r
ti
n
g
is
r
esu
m
ed
.
Alg
o
r
ith
m
3
.
DAZ
m
alicio
u
s
n
o
d
e
ty
p
es
1:
if
M
type
== DAZ
then
2:
if
M
order
≤
2
then
3:
Decision ← 0
4:
else
5:
s
←
0
6:
for
i
=
1
to
M
o
r
d
e
r
−
1
do
7:
if
Rx
-
Decision[i
−
1]
==
TRUE
-
Decision
t
h
e
n
8:
s
←
s +
1
9:
end
if
10:
end
for
11:
if
True
-
Decision
==
1
and
s
>
2
then
12:
Decision
←
True
-
d
e
c
i
si
o
n
13:
else
14:
Decision ←
0
15:
end
if
16:
end
if
17:
end
if
Alg
o
r
ith
m
4
.
DAF
m
alicio
u
s
n
o
d
e
ty
p
es
1:
if
M
t
y
p
e
==
DAF
t
h
e
n
2:
if
M
order
≤
2
then
3:
Decision
←
False
-
D
e
c
i
s
i
on
4:
else
5:
s
←
0
6:
for
i
=
1
to
M
o
r
d
e
r
−
1
do
7:
if
Rx
-
Decision[i
−
1]
==
TRUE
-
Decision
t
h
e
n
8:
s
←
s +
1
9:
end
if
10:
end
for
11:
if
True
-
Decision
==
0
and
s
>
2
then
12:
Decision
←
True
-
D
e
c
i
si
o
n
13:
else
if
True
-
Decision
==
1
and
s
>
2
then
14:
Decision
←
True
-
D
e
c
i
si
o
n
15:
else
16:
Decision
←
False
-
D
e
c
i
s
i
on
17:
end
if
18:
end
if
19:
end
if
Alg
o
r
ith
m
5
.
C
o
o
r
d
in
at
o
r
1:
Initialization:
Define
Number
of
Nodes
(
N
),
Max
Iteration
(
T
max
),
Initial
Iteration
(
T
n
=
0
),
index
2:
Start
Reporting
3:
while
T
n
<
T
m
a
x
do
4:
for
index
=
0
to
N
−
1
do
5:
Send
message
to
node
with
data.ID
6:
Receive
decision
from
the
node
7:
end
for
8:
if
index
>
(N
-
1)
t
h
e
n
9:
Proceed
to
the
next
round
10:
Reset
i
n
d
e
x
11:
T
n
←
T
n
+
1
12:
end
if
13:
if
T
n
≥
T
max
t
h
e
n
14:
Perform
malicious
node
detection
15:
Shuffle
the
order
of
no
de
s
for
the
next
t
u
r
n
16:
T
n
←
0
17:
Resume
Reporting
18:
end
if
19:
end
while
In
A
l
g
o
r
i
t
h
m
6,
t
h
e
m
a
l
i
c
i
o
u
s
node
t
y
p
e
is
i
d
e
n
t
i
f
i
e
d
by
c
a
l
c
u
l
a
t
i
n
g
t
h
e
p
r
o
b
a
b
i
l
i
t
y
of
one
f
o
r
each
n
o
d
e
.
T
h
er
e
a
r
e
th
r
ee
m
ain
c
ateg
o
r
ies:
in
d
ep
en
d
en
t
m
alici
o
u
s
,
d
ep
e
n
d
en
t
m
alicio
u
s
,
an
d
n
o
r
m
al
n
o
d
e.
T
h
e
n
o
d
e
is
class
if
ied
as
a
d
ep
en
d
en
t
m
alicio
u
s
node
if
th
e
cu
r
r
en
t
p
r
o
b
ab
ilit
y
v
alu
e
is
g
r
ea
te
r
th
an
th
e
p
r
ev
io
u
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
9
8
3
-
4
9
9
2
4988
v
alu
e
p
lu
s
a
t
h
r
esh
o
ld
v
alu
e
or
less
th
an
th
e
p
r
e
v
io
u
s
v
alu
e
m
in
u
s
th
e
t
h
r
esh
o
ld
.
I
f
th
is
co
n
d
itio
n
is
m
et,
t
h
en
th
e
p
r
o
b
a
b
ilit
y
m
ag
n
itu
d
e
d
ete
r
m
in
es
th
e
d
ep
e
n
d
en
t
m
alicio
u
s
,
wh
eth
er
it’s
d
ep
en
d
en
t
alwa
y
s
o
n
e,
d
ep
en
d
en
t
alwa
y
s
ze
r
o
,
o
r
d
e
p
en
d
e
n
t
a
lway
s
f
alse.
I
f
th
e
co
n
d
itio
n
is
n
o
t
m
et,
th
en
th
e
n
o
d
e
i
s
class
if
ied
as
an
in
d
ep
en
d
en
t
m
alicio
u
s
n
o
d
e
.
Als
o
,
th
e
m
ag
n
itu
d
e
of
th
e
li
k
elih
o
o
d
d
ef
i
n
es
th
e
I
n
d
ep
en
d
en
t
m
alicio
u
s
node
ty
p
e,
wh
ich
is
th
e
s
am
e
as
th
e
d
ep
e
n
d
en
t
m
alicio
u
s
.
T
h
e
n
o
d
e
is
co
n
s
id
er
ed
a
n
o
r
m
al
n
o
d
e
if
th
e
b
e
h
av
io
r
is
with
in
an
ac
ce
p
tab
le
r
an
g
e
a
r
o
u
n
d
th
e
tar
g
et
p
r
o
b
a
b
ilit
y
H0
.
Alg
o
r
ith
m
6
.
Ma
licio
u
s
d
etec
tio
n
1:
Initialization:
Define:
σ=0,
Threshold
(∆),
Max
Iterations
(T
max
),
Target
Probability
(
H0
),
Independent
One
(IO),
Independent
-
Zero
(Iz),
Independent
-
False
(If),
Dependent
One
(Do),
Dependent
Zero (Dz), Dependent False (Df), Number of Nodes (Nm)
2:
for
each
node
i
in
N
do
3:
for
each
round
j
up
to
T
max
do
4:
Calculate
new
value
of
σ
5:
σ
←
σ
+
D
buff[j][i]
6:
Estimate
P
i,n
7:
if
P
i,n
>
(P
i,n,temp
+
∆)
or
P
i,n
<
(P
i,n,temp
−
∆)
then
8:
if
P
i,n
==
0
t
h
e
n
9:
Dz
←
Dz
+
Sn
10:
else
if
P
i,n
==
1
t
h
e
n
11:
Do
←
Do
+
Sn
12:
else
13:
Df
←
Df
+
Sn
14:
end
if
15:
else
16:
if
P
i,n
==
0
t
h
e
n
17:
lz
←
lz
+
Sn
18:
else
if
P
i,n
==
1
t
h
e
n
19:
I0 ← I0
+
Sn
20:
else
if
(H0
−
∆)
≤
P
i,n
≤
(H0
+
∆)
t
h
e
n
21:
If
←
If
+
Sn
22:
else
23:
Nm
←
Nm
+
Sn
24:
end
if
25:
end
if
26:
end
for
27:
end
for
3.
R
E
S
UL
T
S
A
ND
D
I
S
CU
SS
I
O
N
In
t
h
is
s
ec
tio
n
,
th
e
p
er
f
o
r
m
a
n
ce
of
th
e
p
r
o
p
o
s
ed
m
alicio
u
s
d
etec
tio
n
alg
o
r
ith
m
is
ass
ess
ed
.
T
h
e
ev
alu
atio
n
p
r
o
ce
s
s
co
n
s
id
er
s
th
e
ac
cu
r
ac
y
of
d
e
p
en
d
e
n
t
m
alicio
u
s
node
d
etec
tio
n
an
d
th
e
e
n
er
g
y
c
o
s
t
ass
o
ciate
d
with
th
e
d
etec
tio
n
p
r
o
ce
s
s
.
T
h
e
r
eq
u
ir
ed
en
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
s
u
cc
ess
f
u
l d
etec
tio
n
is
m
ea
s
u
r
ed
to
ev
alu
ate
th
e
en
er
g
y
ef
f
icien
cy
(
E
E
)
of
th
e
s
y
s
tem
.
T
h
e
ev
alu
atio
n
in
clu
d
ed
ex
p
er
im
en
ts
co
n
d
u
cted
with
d
if
f
er
e
n
t
n
u
m
b
er
s
o
f
s
en
s
o
r
s
an
d
d
if
f
er
en
t m
alicio
u
s
n
o
d
es.
3
.
1
.
P
er
f
o
r
m
a
nce
of
t
he
det
ec
t
io
n
a
l
g
o
r
i
t
h
m
T
h
e
p
e
r
f
o
r
m
a
n
c
e
of
t
h
e
d
e
t
e
c
tio
n
a
l
g
o
r
i
t
h
m
6
is
e
v
a
l
u
a
t
e
d
f
o
r
each
node
i
n
d
i
v
i
d
u
a
ll
y
.
T
h
e
p
er
f
o
r
m
a
n
c
e
is
m
ea
s
u
r
ed
in
ter
m
s
o
f
th
e
P1
v
alu
e,
wh
ich
ac
co
r
d
i
n
g
to
[
1
9
]
ca
lcu
lated
u
s
in
g
1.
1
=
∑
=
1
(
1
)
wh
er
e
l
d
en
o
tes
th
e
lo
ca
l
d
ec
is
io
n
of
a
n
o
d
e
at
r
o
u
n
d
i
,
a
n
d
T
is
th
e
m
ax
im
u
m
n
u
m
b
er
of
r
o
u
n
d
s
.
T
wo
s
ce
n
ar
io
s
ar
e
co
n
s
id
er
e
d
.
I
n
th
e
f
i
r
s
t
s
ce
n
ar
io
,
we
c
o
n
s
id
er
t
h
r
ee
n
o
r
m
al
s
en
s
o
r
n
o
d
es
an
d
two
m
alic
io
u
s
n
o
d
es
with
o
n
e
I
AF
an
d
I
AZ
.
No
te
th
at
th
e
tar
g
et
s
tatu
s
is
s
et
to
0,
f
alse
-
alar
m
p
r
o
b
ab
ilit
y
of
th
e
tar
g
et
is
0
.
2
.
T
h
e
p
er
f
o
r
m
a
n
ce
of
each
node
is
p
l
o
tted
in
Fig
u
r
e
3.
Sin
ce
th
e
tar
g
et
is
ass
u
m
ed
ab
s
en
t
an
d
th
e
f
alse
-
alar
m
is
0
.
2
,
it
is
ex
p
ec
te
d
th
at
th
e
v
alu
e
of
P1
of
n
o
r
m
a
l
n
o
d
es
will
be
n
ea
r
ly
0
.
2
;
o
th
er
wis
e,
th
e
node
will
be
id
e
n
tifie
d
as
m
alicio
u
s
.
I
t is cle
ar
f
r
o
m
th
is
f
ig
u
r
e
th
at
No
d
e
1
,
2
,
an
d
3
ar
e
b
eh
av
i
n
g
n
o
r
m
ally
,
h
o
wev
er
,
No
d
es 4
a
n
d
5
d
em
o
n
s
tr
ate
d
m
alicio
u
s
b
eh
av
io
r
.
Sp
ec
if
ical
ly
,
th
e
P1
v
al
u
e
due
to
No
d
e
5
is
0,
in
d
icatin
g
th
at
th
e
node
is
I
AZ
,
wh
ile
th
e
P1
v
alu
e
d
u
e
to
No
d
e
4
is
n
ea
r
ly
0
.
8
,
in
d
icatin
g
th
at
th
is
n
o
d
e
i
s
m
alicio
u
s
o
r
I
AF.
I
n
th
e
s
ec
o
n
d
s
ce
n
a
r
io
,
n
o
d
es 3
an
d
4
ac
t
as
DAZ
an
d
DAF;
r
esp
ec
tiv
ely
,
wh
ile
o
th
er
n
o
d
es
ar
e
k
ep
t
n
o
r
m
al.
T
h
en
th
e
p
er
f
o
r
m
an
c
e
o
f
all
n
o
d
es
is
p
lo
tted
in
Fig
u
r
e
4.
I
t
is
clea
r
f
r
o
m
th
is
f
ig
u
r
e
th
at
No
d
es
1
,
2
,
an
d
5
h
av
e
alm
o
s
t id
en
tical
p
e
r
f
o
r
m
a
n
ce
.
On
th
e
o
th
er
h
an
d
,
No
d
es 3
an
d
4
h
av
e
ab
n
o
r
m
al
b
eh
av
io
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
n
erg
y
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a
lu
a
tio
n
o
f
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ep
en
d
e
n
t m
a
licio
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s
n
o
d
es d
etec
tio
n
i
n
…
(
Mo
a
th
A
ls
a
fa
s
feh
)
4989
Fig
u
r
e
3
.
Per
f
o
r
m
an
c
e
o
f
n
o
d
e
s
in
ter
m
s
o
f
P1
at
ev
e
r
y
1
0
0
r
o
u
n
d
s
(
3
n
o
r
m
al
n
o
d
es a
n
d
tw
o
m
alicio
u
s
with
I
AZ
an
d
I
AF)
Fig
u
r
e
4
.
Per
f
o
r
m
an
c
e
o
f
n
o
d
e
s
r
eg
ar
d
in
g
P1
at
ev
er
y
1
0
0
r
o
u
n
d
s
(
3
n
o
r
m
al
n
o
d
es a
n
d
two
m
alicio
u
s
with
DAZ
an
d
DAF)
3
.
2
.
I
m
pa
c
t
of
t
he
nu
m
ber
of
no
des
on
EE
I
n
th
e
f
ir
s
t
m
ea
s
u
r
em
en
t
s
et,
we
s
tu
d
y
t
h
e
im
p
ac
t
o
f
i
n
cr
ea
s
in
g
th
e
n
u
m
b
er
o
f
n
o
d
es
o
n
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
th
e
co
o
r
d
in
a
to
r
.
As
s
h
o
wn
in
Fig
u
r
e
5,
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
in
c
r
ea
s
ed
f
r
o
m
ab
o
u
t
0
.
7
5
W
h
at
th
r
ee
n
o
d
es
to
2
W
h
at
f
iv
e
n
o
d
es.
On
th
e
o
th
er
h
an
d
,
it
s
ee
m
s
th
at
th
e
n
u
m
b
er
o
f
m
alicio
u
s
n
o
d
es h
as n
o
s
ig
n
if
ican
t im
p
a
ct
o
n
en
er
g
y
c
o
n
s
u
m
p
tio
n
.
T
h
i
s
is
clea
r
f
o
r
b
o
th
th
r
ee
a
n
d
f
iv
e
-
n
o
d
e
s
ce
n
a
r
io
s
.
Fig
u
r
e
5
.
Nu
m
b
er
o
f
m
alicio
u
s
n
o
d
es v
er
s
u
s
en
er
g
y
co
n
s
u
m
p
tio
n
at
th
r
ee
a
n
d
f
iv
e
n
o
d
es scen
ar
io
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
9
8
3
-
4
9
9
2
4990
3
.
3
.
I
m
pa
c
t
of
us
ing
m
a
licio
us
det
ec
t
io
n
on
EE
Nex
t,
we
s
tu
d
y
th
e
im
p
ac
t
o
f
u
s
in
g
a
m
alicio
u
s
d
etec
tio
n
al
g
o
r
ith
m
o
n
th
e
E
E
.
Fig
u
r
e
6
s
h
o
ws
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
th
e
th
r
ee
an
d
f
iv
e
-
n
o
d
e
s
ce
n
ar
io
s
:
o
n
e
with
th
e
m
alicio
u
s
d
etec
tio
n
alg
o
r
ith
m
an
d
th
e
o
th
er
with
o
u
t.
I
t
is
clea
r
th
at
wh
en
th
e
r
e
is
a
s
m
all
n
u
m
b
e
r
o
f
n
o
d
es
(
e.
g
.
,
th
r
ee
n
o
d
es
s
ce
n
ar
io
)
th
er
e
is
a
n
eg
lig
ib
le
d
if
f
er
e
n
ce
in
en
er
g
y
co
n
s
u
m
p
tio
n
with
an
d
with
o
u
t
th
e
p
r
esen
ce
of
a
m
alicio
u
s
d
etec
tio
n
alg
o
r
ith
m
.
Ho
wev
er
,
wh
e
n
th
e
n
u
m
b
er
o
f
n
o
d
es
in
c
r
ea
s
ed
to
f
iv
e
n
o
d
es,
th
e
m
alicio
u
s
d
etec
tio
n
alg
o
r
ith
m
i
n
cr
ea
s
ed
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
f
r
o
m
1
to
1
.
3
W
h
,
wh
ich
is
ab
o
u
t
3
0
%.
Fig
u
r
e
6
.
Nu
m
b
er
o
f
s
en
s
o
r
n
o
d
es v
er
s
u
s
en
er
g
y
co
n
s
u
m
p
tio
n
with
an
d
with
o
u
t m
alicio
u
s
d
etec
tio
n
alg
o
r
ith
m
s
4.
C
O
N
CL
US
I
O
N
In
th
is
p
ap
er
,
an
ex
p
e
r
im
en
tal
s
tu
d
y
was
co
n
d
u
cted
to
ev
alu
a
te
th
e
p
e
r
f
o
r
m
an
ce
of
th
e
m
alicio
u
s
n
o
d
e
d
etec
tio
n
an
d
id
e
n
tific
atio
n
alg
o
r
ith
m
s
p
r
o
p
o
s
ed
in
p
r
e
v
io
u
s
r
esear
ch
.
T
h
e
p
r
im
a
r
y
o
b
jectiv
e
was
to
ass
e
s
s
b
o
th
th
e
ac
cu
r
ac
y
a
n
d
e
n
er
g
y
ef
f
i
cien
cy
of
th
ese
alg
o
r
ith
m
s
with
in
an
I
o
T
Netwo
r
k
.
T
h
e
e
x
p
er
im
en
tal
s
etu
p
in
clu
d
ed
one
co
o
r
d
in
ato
r
an
d
f
iv
e
s
en
s
o
r
n
o
d
es,
with
v
ar
io
u
s
n
u
m
b
er
s
an
d
ty
p
es
of
m
alicio
u
s
n
o
d
es
in
tr
o
d
u
ce
d
to
s
im
u
late
r
ea
lis
tic
attac
k
s
c
en
ar
io
s
.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ated
th
at
th
e
d
etec
tio
n
alg
o
r
it
h
m
was
ef
f
ec
tiv
e
in
d
is
tin
g
u
is
h
in
g
b
etwe
en
n
o
r
m
a
l
an
d
m
alicio
u
s
n
o
d
es,
s
u
cc
ess
f
u
lly
id
en
tify
in
g
b
o
th
t
h
e
p
r
esen
ce
an
d
t
y
p
e
o
f
m
alicio
u
s
b
eh
av
i
o
r
.
Ad
d
itio
n
ally
,
th
e
e
n
er
g
y
ef
f
icien
cy
o
f
th
e
alg
o
r
ith
m
was
ev
alu
ated
,
with
th
e
an
aly
s
is
r
ev
ea
lin
g
h
o
w
its
en
er
g
y
co
n
s
u
m
p
tio
n
v
a
r
ied
with
ch
an
g
es
in
n
etwo
r
k
co
n
f
i
g
u
r
atio
n
s
s
p
ec
if
ically
wh
en
in
cr
ea
s
in
g
th
e
n
u
m
b
e
r
of
m
ali
cio
u
s
n
o
d
es
o
r
th
e
to
tal
n
u
m
b
er
o
f
s
en
s
o
r
n
o
d
es.
T
h
ese
f
in
d
in
g
s
o
f
f
er
v
alu
ab
le
in
s
ig
h
ts
in
to
th
e
s
ca
lab
ili
ty
a
n
d
p
r
ac
tical
d
ep
lo
y
m
en
t
o
f
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
in
lar
g
e
r
o
r
m
o
r
e
d
y
n
am
ic
W
SN
en
v
ir
o
n
m
en
ts
.
T
h
e
r
esu
lts
v
alid
ate
th
e
alg
o
r
ith
m
’
s
ef
f
ec
tiv
en
ess
in
ter
m
s
o
f
b
o
th
ac
cu
r
ac
y
an
d
en
er
g
y
ef
f
icien
cy
.
Fu
tu
r
e
en
h
an
ce
m
e
n
ts
co
u
ld
f
o
cu
s
o
n
im
p
r
o
v
in
g
d
etec
tio
n
s
p
ee
d
an
d
test
in
g
th
e
alg
o
r
ith
m
u
n
d
er
a
b
r
o
ad
e
r
r
an
g
e
o
f
o
p
er
atio
n
al
a
n
d
en
v
i
r
o
n
m
e
n
tal
co
n
d
itio
n
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
th
o
r
s
wo
u
ld
lik
e
to
t
h
a
n
k
th
e
NAT
O/Scien
ce
f
o
r
Pea
ce
an
d
Secu
r
ity
Pro
g
r
am
f
o
r
c
o
-
f
u
n
d
in
g
th
e
p
r
o
ject,
Dev
elo
p
in
g
Ph
y
s
ical
L
ay
er
Secu
r
ity
Sch
em
es
f
o
r
I
n
ter
n
et
of
T
h
in
g
s
Netwo
r
k
s
,
u
n
d
er
g
r
an
t
n
u
m
b
er
SPS
G
5
9
7
9
.
R
E
F
E
RE
NC
E
S
[
1
]
F
.
Li
a
n
d
P
.
X
i
o
n
g
,
“
P
r
a
c
t
i
c
a
l
se
c
u
r
e
c
o
m
mu
n
i
c
a
t
i
o
n
f
o
r
i
n
t
e
g
r
a
t
i
n
g
w
i
r
e
l
e
ss
se
n
s
o
r
n
e
t
w
o
r
k
s
i
n
t
o
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s,”
I
EEE
S
e
n
so
rs J
o
u
rn
a
l
,
v
o
l
.
1
3
,
n
o
.
1
0
,
p
p
.
3
6
7
7
–
3
6
8
4
,
O
c
t
.
2
0
1
3
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
1
3
.
2
2
6
2
2
7
1
.
[
2
]
D
.
P
r
i
n
c
y
,
D
.
K
a
l
a
i
v
a
n
i
,
a
n
d
T.
V
i
j
a
y
a
r
a
g
h
a
v
a
n
,
“
A
d
e
si
g
n
t
h
i
n
k
i
n
g
a
p
p
r
o
a
c
h
o
f
met
a
h
e
u
r
i
st
i
c
e
m
p
o
w
e
r
me
n
t
f
o
r
e
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
a
n
d
o
p
t
i
mi
z
e
d
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
i
n
I
o
T
-
e
n
a
b
l
e
d
w
i
r
e
l
e
ss
s
e
n
so
r
n
e
t
w
o
r
k
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d
e
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re
e
i
n
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m
p
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ter
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n
g
i
n
e
e
rin
g
fro
m
M
u
t
a
h
Un
i
v
e
rsity
in
2
0
0
9
.
Alsa
fa
sfe
h
’s
re
se
a
rc
h
in
tere
sts
a
re
i
n
p
a
ra
ll
e
l
p
r
o
c
e
ss
in
g
,
c
o
m
p
u
ter
v
i
sio
n
,
ima
g
e
p
r
o
c
e
ss
in
g
,
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
fo
r
wire
les
s
se
n
so
r
n
e
two
r
k
s
(W
S
Ns
)
a
n
d
in
tern
e
t
o
f
th
i
n
g
s
(Io
T)
n
e
two
r
k
a
p
p
li
c
a
ti
o
n
s.
Alsa
fa
sfe
h
h
a
s
p
u
b
li
sh
e
d
m
o
re
t
h
a
n
2
4
a
rti
c
les
in
p
re
stig
io
u
s
S
c
o
p
u
s
-
in
d
e
x
e
d
j
o
u
rn
a
ls.
Dr.
Alsa
fa
sfe
h
wa
s
a
wa
rd
e
d
se
v
e
ra
l
n
a
ti
o
n
a
l
p
rize
s
a
n
d
sc
h
o
lars
h
i
p
s
to
e
a
rn
a
P
h
.
D
.
a
n
d
a
b
a
c
h
e
lo
r’s
d
e
g
re
e
.
He
h
a
s
stro
n
g
re
latio
n
sh
i
p
s
wi
th
d
iffere
n
t
l
o
c
a
l,
re
g
i
o
n
a
l,
a
n
d
in
tern
a
ti
o
n
a
l
re
se
a
rc
h
e
rs.
Alsa
fa
sfe
h
h
a
s
b
e
e
n
wo
rk
in
g
a
s
a
c
o
-
P
I
fo
r
two
in
tern
a
ti
o
n
a
ll
y
f
u
n
d
e
d
re
se
a
rc
h
p
ro
jec
ts
a
n
d
o
n
e
p
ro
jec
t
f
o
r
c
a
p
a
c
it
y
b
u
il
d
i
n
g
i
n
h
ig
h
e
r
e
d
u
c
a
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
a
lsa
fa
sf
e
h
@tu
sk
e
g
e
e
.
e
d
u
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
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n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
9
8
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-
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9
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4992
Abd
u
ll
a
h
Alh
a
sa
n
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t
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s
b
o
r
n
in
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rd
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n
in
1
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8
1
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re
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e
iv
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d
a
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S
c
.
d
e
g
re
e
in
c
o
m
p
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ter
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n
g
in
e
e
rin
g
fro
m
t
h
e
Un
iv
e
rsity
o
f
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m
e
n
in
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0
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,
h
is
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c
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d
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re
e
in
c
o
m
p
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ter
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rin
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fro
m
Jo
rd
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n
Un
iv
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rsit
y
o
f
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c
ien
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e
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n
d
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h
n
o
lo
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y
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n
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rd
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0
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,
a
n
d
h
is
P
h
.
D.
d
e
g
re
e
i
n
wire
les
s
n
e
two
r
k
s
fro
m
th
e
U
n
iv
e
rsit
y
o
f
Ne
wc
a
stle
in
th
e
UK
in
2
0
1
2
.
In
2
0
1
2
,
h
e
j
o
in
e
d
t
h
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
E
n
g
in
e
e
ri
n
g
a
t
Al
-
Hu
ss
e
in
Bin
Tala
l
Un
iv
e
rsity
,
Jo
rd
a
n
.
Cu
rre
n
tl
y
,
Dr.
Alh
a
sa
n
a
t
is
a
fu
ll
p
r
o
fe
ss
o
r
in
wire
les
s
n
e
two
rk
in
g
.
He
h
a
s
p
u
b
li
sh
e
d
m
a
n
y
a
rti
c
les
in
h
i
g
h
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l
e
v
e
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sc
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ti
fic
jo
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rn
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ls
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n
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t
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rn
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ti
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l
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fe
re
n
c
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s.
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m
a
in
re
se
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rc
h
in
tere
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c
lu
d
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r
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ti
n
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l
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c
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li
z
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ti
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n
i
n
a
d
h
o
c
n
e
two
rk
s,
wire
les
s
se
n
so
r
n
e
two
rk
s,
m
o
b
i
le
a
d
h
o
c
n
e
two
r
k
s
(M
AN
ET
),
v
e
h
icu
lar
a
d
h
o
c
n
e
two
rk
s
(VA
NET)
,
d
e
la
y
-
to
lera
n
t
n
e
two
r
k
s,
p
a
ra
ll
e
l
c
o
m
p
u
ti
n
g
,
a
n
d
sig
n
a
l
a
n
d
ima
g
e
p
r
o
c
e
ss
in
g
.
He
c
a
n
b
e
c
o
n
tac
te
d
a
t
e
m
a
il
:
a
b
a
d
@a
h
u
.
e
d
u
.
j
o
.
S
a
m
i
h
a
Alf
a
l
a
h
a
t
h
o
ld
s
a
n
M
.
S
c
.
d
e
g
re
e
in
c
o
m
p
u
ter
e
n
g
i
n
e
e
rin
g
fr
o
m
Al
-
Ya
rm
o
u
k
Un
i
v
e
rsity
,
Ir
b
id
,
J
o
rd
a
n
,
in
2
0
1
5
,
a
n
d
a
B.
S
c
.
d
e
g
re
e
in
c
o
m
p
u
ter
e
n
g
i
n
e
e
rin
g
fro
m
Jo
rd
a
n
U
n
iv
e
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y
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
,
Irb
i
d
,
J
o
rd
a
n
,
i
n
2
0
0
6
.
S
h
e
c
u
rre
n
tl
y
se
rv
e
s
a
s
a
n
in
stru
c
t
o
r
a
n
d
re
se
a
rc
h
a
ss
ist
a
n
t
a
t
Al
-
Hu
ss
e
in
Bin
Tala
l
Un
i
v
e
rsity
,
Jo
r
d
a
n
,
wh
e
re
sh
e
tea
c
h
e
s
v
a
rio
u
s
c
o
u
rse
s
in
c
o
m
p
u
ter
sy
ste
m
s,
n
e
two
rk
in
g
,
a
n
d
in
tern
e
t
o
f
th
in
g
s
(Io
T)
,
in
c
lu
d
in
g
d
istri
b
u
te
d
sy
ste
m
s,
n
e
two
rk
p
r
o
g
ra
m
m
in
g
,
a
n
d
e
m
b
e
d
d
e
d
sy
ste
m
s.
He
r
re
se
a
r
c
h
in
tere
sts
in
c
l
u
d
e
Io
T
sy
ste
m
s,
m
a
c
h
in
e
lea
rn
in
g
,
n
e
tw
o
rk
a
u
t
o
m
a
ti
o
n
,
a
n
d
c
y
b
e
rse
c
u
rit
y
.
S
a
m
ih
a
h
a
s
c
o
n
tri
b
u
ted
to
Eu
r
o
p
e
a
n
-
fu
n
d
e
d
p
r
o
jec
ts
su
c
h
a
s
th
e
NA
TO
P
HY
S
EC
in
it
iativ
e
,
fo
c
u
sin
g
o
n
p
h
y
sic
a
l
lay
e
r
se
c
u
rit
y
f
o
r
I
o
T,
a
n
d
th
e
IRE
EDER
p
ro
jec
t,
a
ime
d
a
t
a
li
g
n
i
n
g
Jo
rd
a
n
ian
u
n
d
e
rg
ra
d
u
a
te
c
u
rricu
l
a
with
EU
sta
n
d
a
rd
s.
S
h
e
is
a
n
I
E
EE
m
e
m
b
e
r
a
n
d
a
p
r
o
a
c
ti
v
e
c
o
n
tri
b
u
t
o
r
to
c
u
rricu
l
u
m
d
e
v
e
lo
p
m
e
n
t
fo
r
e
n
g
in
e
e
ri
n
g
d
e
p
a
rtme
n
ts.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
m
ih
a
_
m
o
sa
@a
h
u
.
e
d
u
.
jo
.
Evaluation Warning : The document was created with Spire.PDF for Python.