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o
we
v
e
r
,
n
o
de
s
n
e
a
r
e
s
t
to
t
h
e
B
S
o
f
t
e
n
f
a
c
e
e
x
c
e
s
s
i
ve
e
n
e
r
g
y
c
o
ns
u
m
pt
i
o
n
due
to
un
e
v
e
n
t
r
a
f
f
i
c
l
o
a
ds
,
l
e
a
d
i
ng
to
pr
e
m
a
t
ur
e
f
a
il
ur
e
a
n
d
n
e
t
wo
r
k
d
i
s
r
upt
i
o
n
.
T
hi
s
c
r
e
a
t
e
s
a
h
o
l
e
i
n
t
h
e
n
e
t
wo
r
k
l
e
a
d
in
g
to
t
h
e
h
ot
s
pot
pr
o
bl
e
m
.
T
h
e
a
pp
l
i
c
a
t
i
o
n
s
o
f
W
S
N
s
h
a
v
e
b
e
c
o
m
e
i
nc
r
e
a
s
in
g
ly
w
i
de
s
pr
e
a
d,
i
n
c
l
ud
i
ng
a
r
e
a
s
s
uc
h
a
s
t
a
r
ge
t
t
r
a
c
ki
n
g
[
8]
,
[
9]
,
e
n
vi
r
o
nm
e
n
t
a
l
m
o
ni
t
o
r
i
n
g,
s
e
c
ur
it
y
,
d
i
s
a
s
t
e
r
r
e
s
po
n
s
e
,
a
n
d
h
e
a
l
t
h
m
o
ni
t
o
r
i
n
g
[
10]
,
[
11]
.
A
s
s
uc
h
,
t
h
e
r
e
i
s
a
s
i
g
ni
f
i
c
a
n
t
de
m
a
n
d
to
r
e
s
o
l
v
e
t
h
e
h
o
t
s
p
ot
i
s
s
u
e
c
a
us
e
d
by
c
l
us
t
e
r
i
n
g
i
n
W
S
Ns
.
On
e
a
ppr
o
a
c
h
to
m
i
t
i
ga
t
e
t
hi
s
i
s
s
ue
i
s
a
hy
br
i
d
m
e
t
h
o
d
t
h
a
t
i
n
c
o
r
po
r
a
t
e
s
a
l
o
a
d
-
b
a
l
a
nc
i
ng
m
e
c
h
a
ni
s
m
w
i
t
h
a
n
a
dv
a
n
c
e
d
a
n
t
c
o
l
o
ny
o
pt
i
mi
z
a
t
i
o
n
a
l
go
r
i
t
hm
.
T
hi
s
a
dv
a
n
c
e
d
a
n
t
c
o
l
o
ny
o
pt
i
mi
z
a
t
i
o
n
i
s
a
n
a
t
ur
e
-
i
ns
p
i
r
e
d
pa
r
a
d
i
g
m
w
h
e
r
e
m
o
s
t
s
to
c
h
a
s
t
i
c
a
l
go
r
i
t
hm
s
yi
e
l
d
d
i
f
f
e
r
e
n
t
po
s
s
i
bl
e
s
o
l
ut
i
o
n
s
a
t
e
a
c
h
i
t
e
r
a
t
i
o
n
,
t
h
e
r
e
by
i
nc
r
e
a
s
i
ng
t
h
e
c
h
a
nc
e
s
o
f
e
x
p
l
o
r
i
ng
t
h
e
e
n
t
i
r
e
s
e
a
r
c
h
s
pa
c
e
[
12]
.
T
o
e
nh
a
nc
e
t
h
e
i
ni
t
i
a
l
f
e
a
s
i
b
l
e
s
o
l
ut
i
o
n
,
v
a
r
i
o
us
m
e
c
h
a
ni
s
m
s
s
uc
h
a
s
m
o
v
e
m
e
n
t
,
m
ut
a
t
i
o
n
,
e
x
c
ha
n
ge
,
a
n
d
c
o
o
pe
r
a
t
i
ve
pe
r
c
e
pt
i
o
n
a
r
e
e
m
p
l
o
y
e
d.
T
hi
s
i
t
e
r
a
t
i
v
e
im
pr
o
v
e
m
e
n
t
pr
o
c
e
s
s
c
o
n
t
i
n
ue
s
un
t
i
l
t
h
e
b
e
s
t
po
s
s
i
bl
e
s
o
l
ut
i
o
n
i
s
i
de
n
t
i
f
i
e
d
[
13]
.
T
h
e
a
ppr
o
a
c
h
a
do
pt
s
a
m
e
t
a
h
e
ur
i
s
t
i
c
m
e
t
h
o
d
u
t
i
li
z
i
ng
a
f
i
t
ne
s
s
f
u
nc
t
i
o
n
[
14]
,
wi
t
h
th
e
s
t
o
c
h
a
s
t
i
c
a
l
go
r
i
t
hm
a
im
i
ng
to
f
i
nd
e
i
t
h
e
r
t
h
e
gl
o
ba
l
mi
n
im
u
m
o
r
m
a
xim
u
m
o
f
t
hi
s
f
u
n
c
t
i
o
n
,
de
l
i
ve
r
i
ng
hi
g
h
-
qua
li
t
y
s
o
l
ut
i
o
ns
w
i
t
hi
n
a
r
e
a
s
o
na
bl
e
t
i
m
e
f
r
a
m
e
[
15]
.
T
hi
s
pa
pe
r
i
s
o
r
ga
ni
z
e
d
a
s
f
o
l
l
o
w
s
.
S
e
c
t
i
o
n
2,
o
u
t
l
i
ne
s
t
h
e
m
e
t
h
o
do
l
o
g
y
f
o
r
c
l
u
s
t
e
r
i
n
g
i
n
W
S
Ns
a
n
d
t
h
e
a
l
go
r
i
t
hm
i
s
pr
o
po
s
e
d
f
o
r
l
o
a
d
b
a
l
a
nc
i
n
g
w
i
t
h
a
dv
a
nc
e
d
a
n
t
c
o
l
o
ny
o
pt
i
mi
z
a
t
i
o
n
.
S
e
c
t
i
o
n
3
d
i
s
c
u
s
s
e
s
t
h
e
r
e
s
u
l
t
s
.
S
e
c
t
i
o
n
4
dr
a
ws
t
h
e
c
o
n
c
l
ud
i
ng
r
e
m
a
r
ks
a
nd
f
ut
ur
e
w
o
r
k.
2.
M
E
T
HO
D
T
hi
s
s
e
c
t
i
o
n
e
x
p
l
a
i
ns
t
h
e
c
l
us
t
e
r
i
n
g
i
n
W
S
Ns
.
T
h
e
n
pr
o
vi
de
s
de
t
a
i
l
e
d
i
nf
o
r
m
a
t
i
o
n
a
b
o
u
t
t
h
e
pr
o
p
o
s
e
d
a
l
go
r
i
t
hm
.
2.
1.
C
l
u
s
t
e
r
in
g
in
WS
Ns
C
l
u
s
t
e
r
i
n
g
i
s
a
w
i
de
ly
u
s
e
d
t
e
c
hni
qu
e
i
n
W
S
N
t
opo
l
o
g
y
m
a
na
ge
m
e
n
t
,
wh
e
r
e
n
o
de
s
a
r
e
o
r
ga
ni
z
e
d
i
n
t
o
gr
o
ups
c
a
l
l
e
d
c
l
u
s
t
e
r
s
b
a
s
e
d
o
n
f
a
c
to
r
s
l
i
ke
e
n
e
r
g
y
l
e
ve
l
s
a
n
d
l
o
c
a
t
i
o
n
[
16]
.
E
a
c
h
c
l
us
t
e
r
ha
s
a
C
H,
s
e
l
e
c
t
e
d
e
i
t
h
e
r
t
h
r
o
ugh
di
s
t
r
i
b
ut
e
d
m
e
t
h
o
ds
,
wh
e
r
e
n
o
de
s
s
h
a
r
e
s
t
a
t
us
a
n
d
t
h
e
o
n
e
wi
t
h
t
h
e
hi
g
he
s
t
e
n
e
r
g
y
is
c
h
o
s
e
n
,
or
c
e
n
t
r
a
l
i
z
e
d
m
e
t
h
o
ds
,
wh
e
r
e
a
B
S
s
e
l
e
c
t
s
C
Hs
b
a
s
e
d
o
n
a
gl
o
ba
l
vi
e
w,
t
h
o
ugh
t
hi
s
r
e
qu
i
r
e
s
m
o
r
e
c
o
m
m
u
ni
c
a
t
i
o
n
.
W
S
N
s
m
a
y
f
o
r
m
n
e
s
t
e
d
c
l
us
t
e
r
s
w
i
t
hi
n
a
s
upe
r
c
l
u
s
t
e
r
,
e
n
a
bli
ng
m
u
l
t
i
-
h
o
p
c
o
m
muni
c
a
t
i
o
n
wh
e
r
e
da
t
a
i
s
r
e
l
a
y
e
d
t
h
r
o
ugh
s
m
a
ll
e
r
C
Hs
to
a
s
upe
r
C
H
a
n
d
t
h
e
n
t
o
t
h
e
B
S
.
C
l
us
t
e
r
i
n
g
h
e
l
p
s
o
r
ga
ni
z
e
s
e
n
s
o
r
n
o
de
s
hi
e
r
a
r
c
hi
c
a
ll
y
,
o
p
t
i
mi
z
i
ng
e
n
e
r
g
y
us
e
a
n
d
r
e
duc
i
n
g
t
h
e
n
e
e
d
f
o
r
f
r
e
que
n
t
r
e
c
o
nf
i
gur
a
t
i
o
n
,
whi
c
h
o
c
c
ur
s
m
a
i
n
ly
a
t
t
h
e
C
H
l
e
ve
l
.
T
hi
s
t
e
c
hni
q
ue
e
nh
a
n
c
e
s
r
e
s
o
ur
c
e
ut
i
l
i
z
a
t
i
o
n
,
m
i
nim
i
z
e
s
r
e
dun
da
n
t
t
r
a
n
s
m
i
s
s
i
o
n
s
,
i
m
pr
o
v
e
s
s
c
a
l
a
bil
i
t
y
,
a
n
d
e
x
t
e
n
ds
n
e
t
w
o
r
k
l
if
e
s
pa
n
by
u
ni
f
o
r
ml
y
d
i
s
t
r
i
b
ut
i
n
g
e
n
e
r
g
y
.
T
h
e
f
i
gur
e
o
f
c
l
u
s
t
e
r
i
n
g
i
n
W
S
N
i
s
r
e
pr
e
s
e
n
t
e
d
i
n
F
i
gur
e
1.
F
i
gur
e
1.
C
l
u
s
t
e
r
i
n
g
i
n
w
i
r
e
l
e
s
s
s
e
ns
o
r
n
e
t
w
o
r
ks
(
W
S
N
s
)
2.
2.
P
r
op
os
e
d
al
go
r
it
h
m
T
hi
s
r
e
s
e
a
r
c
h
i
n
t
r
o
duc
e
s
a
n
o
v
e
l
l
o
a
d
-
b
a
l
a
nc
i
ng
a
l
go
r
i
t
hm
w
i
t
h
a
d
v
a
n
c
e
d
a
n
t
c
o
l
o
ny
o
pt
i
m
i
z
a
t
i
o
n
(
L
AC
O)
.
T
h
e
a
l
go
r
i
t
hm
i
s
t
h
o
r
o
ughl
y
de
t
a
i
l
e
d
i
n
Al
go
r
i
t
hm
1.
L
A
C
O
c
o
n
s
i
s
t
s
o
f
t
w
o
ke
y
s
t
r
a
t
e
g
i
e
s
:
t
h
e
l
o
a
d
-
b
a
l
a
n
c
i
ng
m
e
c
h
a
ni
s
m
a
n
d
t
h
e
a
dv
a
nc
e
d
AC
O
o
pe
r
a
to
r
.
I
n
t
h
e
dy
n
a
mi
c
e
nvi
r
o
nm
e
n
t
o
f
W
S
Ns
l
o
a
d
b
a
l
a
n
c
i
ng
i
s
t
h
e
ge
ne
r
a
l
pr
o
bl
e
m
t
h
a
t
a
f
f
e
c
t
s
ne
two
r
k
pe
r
f
o
r
m
a
n
c
e
[
17]
.
T
h
e
L
A
C
O
m
e
c
ha
ni
s
m
m
a
n
a
ge
s
wo
r
kl
o
a
d
by
us
i
n
g
h
e
ur
i
s
t
i
c
a
n
d
f
i
t
n
e
s
s
f
u
nc
t
i
o
ns
to
e
n
s
ur
e
e
f
f
i
c
i
e
n
t
s
c
h
e
du
l
i
ng
a
n
d
m
i
n
i
mi
z
e
pr
o
c
e
s
s
i
ng
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A
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br
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(
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L
A
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i
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s
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o
r
to
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f
f
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ba
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m
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d
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gy
dur
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s
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h
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2.
2.
1.
L
oad
b
al
an
c
in
g
wit
h
a
d
van
c
e
d
an
t
c
ol
on
y
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t
im
iz
at
ion
E
f
f
e
c
t
i
v
e
l
o
a
d
b
a
l
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Al
go
r
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1
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Al
go
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1
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P
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Step 1
.
Initialization of the parameters;
Step 2
.
Initialization of system resource set R and a definite set of tasks J
Step 3
.
Load balancing tasks;
Step 4
.
While the task is left to be done do
Nearest Neighbour Operator: For each task, find the nearest available resource (like
finding the closest worker to do the job);
Optimal
path
storage:
keep
track
of
the
current
best
path
found
for
scheduling
tasks;
Upd
ating
routing
table:
Information
about
the
best
paths
found
is
updated
in
the
routing table;
Ant
Sorting,
Ants
(representing
potential
task
schedulers)
are
sorted
based
on
the
updated routing table to guide their search;
Construction
of
path:
Ants
then
con
struct
their
paths
based
on
the
sorted
information;
Updating
pheromone
table:
As
ants
move
along
paths,
the
pheromone
table
(which
represents the desirability of paths) is updated;
if every ant has finished exploring its path options, then
Pheromone
table
update:
Evaluating
the
up
dated
pheromone
table
to
find
the
most promising paths;
Best
path
selection:
Selecting
the
best
path
based
on
the
evaluated
pheromone table;
I
f
Best_solution_obtained then
Best
Path
Selection:
Selecting
the
best
p
ath
based
on
the
evaluated
pheromone table;
Task
Assignment
to
Optimal
Node:
Assignin
g
the
task
to
this
optimal
node;
else
Next
Optimal
Solution:
If
the
best
so
lution
is
not
yet
found,
continue the search for the next best solution.
end if
end if
end while
G
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Evaluation Warning : The document was created with Spire.PDF for Python.
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[
19]
.
2.
2.
2
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L
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[
20]
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v
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s
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o
n
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t
h
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pa
t
h
.
i
s
t
h
e
l
e
n
gt
h
o
f
t
h
e
pa
t
h
[
23]
.
3.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
T
h
e
pr
o
p
o
s
e
d
a
ppr
o
a
c
h
wa
s
c
r
e
a
t
e
d
a
n
d
put
i
n
t
o
a
c
t
i
o
n
us
i
ng
t
h
e
M
A
T
L
AB
e
nvi
r
o
nm
e
n
t
R
2023a
,
kn
o
wn
f
o
r
i
t
s
n
u
m
e
r
i
c
a
l
c
o
m
put
i
n
g
c
a
pa
bil
i
t
i
e
s
.
M
A
T
L
AB
i
s
a
hi
g
h
-
pe
r
f
o
r
m
a
n
c
e
l
a
n
gua
g
e
f
o
r
t
e
c
h
ni
c
a
l
c
o
m
put
i
n
g
[
24]
.
T
a
bl
e
1
s
h
o
ws
t
h
e
i
ni
t
i
a
l
va
l
u
e
s
of
v
a
r
i
o
us
pa
r
a
m
e
t
e
r
s
t
h
a
t
we
r
e
s
e
t
i
n
M
A
T
L
AB
dur
i
n
g
t
h
e
s
i
m
u
l
a
t
i
o
n
o
f
W
S
Ns
.
T
h
e
c
o
m
pa
r
a
t
i
v
e
a
n
a
ly
s
i
s
o
f
t
h
e
ge
n
e
t
i
c
a
l
go
r
i
t
hm
(
G
A
)
,
s
wa
r
m
o
p
t
i
mi
z
a
t
i
o
n
a
l
go
r
i
t
hm
(
S
A
)
w
i
t
h
o
ur
pr
o
p
o
s
e
d
a
l
go
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i
t
hm
,
l
o
a
d
b
a
l
a
n
c
i
ng
w
i
t
h
a
dv
a
n
c
e
d
a
n
t
c
o
l
o
ny
o
pt
i
mi
z
a
t
i
o
n
(
L
A
C
O)
i
s
do
n
e
.
T
hi
s
r
e
s
e
a
r
c
h
e
nh
a
nc
e
s
W
S
N
a
pp
l
i
c
a
t
i
o
ns
i
n
e
nvi
r
o
nm
e
n
t
a
l
s
ur
v
e
il
l
a
nc
e
,
m
il
i
t
a
r
y
o
pe
r
a
t
i
o
n
s
,
t
r
a
n
s
po
r
t
a
t
i
o
n
m
o
ni
t
o
r
i
n
g,
h
e
a
l
t
hc
a
r
e
,
s
m
a
r
t
a
gr
i
c
u
l
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ur
e
,
a
n
d
i
n
du
s
t
r
i
a
l
a
ut
o
m
a
t
i
o
n
[
25]
.
T
a
bl
e
1.
P
a
r
a
m
e
t
e
r
i
ni
t
i
a
li
z
a
t
i
o
n
P
a
r
a
me
t
e
r
s
V
a
lu
e
s
0.8
10
P
0.5
T
ot
a
l
numbe
r
of
n
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d
e
s
50
N
umbe
r
of
r
o
unds
(
o
r
i
t
e
r
a
ti
o
ns
)
2000
N
umbe
r
of
ta
s
ks
100
I
ni
ti
a
l
E
n
e
r
g
y
(
E
o
)
0.5 J
3.
1
.
Re
m
ain
in
g
e
n
e
r
gy
an
al
ys
is
F
i
gur
e
2
i
ll
u
s
t
r
a
t
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s
t
h
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t
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p
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m
a
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r
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t
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h
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ph
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g
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s
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3.
2
.
Num
b
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r
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ac
c
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s
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it
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s
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n
F
i
gur
e
3
t
h
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L
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C
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whi
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t
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F
i
gur
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2.
R
e
m
a
i
ni
ng
e
n
e
r
g
y
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n
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ly
s
i
s
F
i
gur
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3.
Num
b
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r
o
f
a
c
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s
s
t
o
f
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t
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s
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n
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-
4
7
52
A
hy
br
id
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f
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3.
3
.
B
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it
an
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ys
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gur
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4
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F
i
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e
4.
B
e
s
t
f
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a
ly
s
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s
4.
CONC
L
USI
ON
T
h
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pr
o
p
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d
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l
go
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i
t
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pr
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g
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f
f
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n
c
y
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e
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a
bil
i
t
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n
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f
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p
a
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o
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wo
r
ks
by
us
i
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e
a
l
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t
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m
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d
di
s
t
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a
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de
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r
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y
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v
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l
s
.
T
hi
s
d
y
na
m
i
c
a
ppr
o
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pa
s
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t
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ly
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ddr
e
s
s
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t
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p
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bl
e
m
ne
a
r
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b
a
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t
a
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(
B
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)
.
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h
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o
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t
r
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n
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d
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o
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m
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t
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n
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n
t
s
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t
s
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s
,
e
nh
a
n
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N
a
pp
l
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t
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v
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ds
.
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t
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dv
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t
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o
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f
o
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r
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h
.
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ut
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pe
r
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m
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ur
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pr
a
c
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de
p
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n
t
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a
da
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i
t
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ur
i
t
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s
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a
l
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bil
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n
d
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to
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.
AC
K
NOWL
E
DGM
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NT
T
h
e
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s
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d
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h
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Na
t
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E
n
g
i
ne
e
r
i
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M
y
s
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s
uppor
t
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g
t
hi
s
pr
o
j
e
c
t
.
RE
F
E
R
E
NC
E
S
[
1]
F
.
M
.
M
a
hmud,
M
.
N
.
S
a
ll
e
h,
a
nd
M
.
F
.
M
.
A
ln
oo
r
,
“
W
ir
e
le
s
s
s
e
ns
or
n
e
tw
o
r
k
s
e
c
u
r
it
y
:
A
r
e
c
e
nt
r
e
v
i
e
w
ba
s
e
d
o
n
s
ta
t
e
-
of
-
th
e
-
a
r
t
w
o
r
ks
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
E
ngi
ne
e
r
in
g B
us
in
e
s
s
M
anage
m
e
nt
, vo
l.
15, 2023, d
o
i:
10.1177/184797902
31157220.
[
2]
W
.
A
bi
di
a
nd
T
.
E
z
z
e
di
ne
,
“
E
f
f
e
c
ti
v
e
c
lu
s
te
r
in
g
pr
o
t
oc
o
l
ba
s
e
d
o
n
ne
tw
o
r
k
di
v
is
i
o
n
f
o
r
h
e
t
e
r
o
g
e
n
e
o
us
w
ir
e
l
e
s
s
s
e
ns
o
r
ne
tw
or
ks
,
”
C
om
put
in
g
, vo
l.
102, n
o
. 2, pp. 413
–
425, 2020, d
o
i:
10.1007/s
0
0607
-
019
-
00757
-
w.
[
3]
G
.
M
.
A
bdul
s
a
hi
b
a
nd
O
.
I
.
K
ha
la
f
,
“
A
c
c
ur
a
t
e
a
nd
e
f
f
e
c
ti
v
e
da
ta
c
o
ll
e
c
t
i
o
n
w
it
h
mi
ni
mum
e
n
e
r
g
y
pa
th
s
e
l
e
c
ti
o
n
in
w
ir
e
le
s
s
s
e
ns
o
r
n
e
tw
or
ks
us
in
g
mo
bi
le
s
in
ks
,”
J
our
nal
of
I
n
f
or
m
at
i
on
T
e
c
hnol
ogy
M
anage
m
e
nt
,
vo
l.
13,
pp.
139
–
153,
2021,
do
i:
10.22059/J
I
T
M
.2021.80359.
[
4]
I
.
K
.
O
s
a
ma
h,
M
.
A
.
G
ha
id
a
,
I
.
K
.
O
s
a
ma
h,
a
nd
S
.
M
ua
y
e
d,
“
A
mo
di
f
ie
d
a
lg
o
r
it
hm
f
o
r
im
pr
ov
in
g
li
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ti
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e
W
S
N
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our
nal
of
E
ngi
ne
e
r
in
g and A
ppl
ie
d Sc
ie
nc
e
s
, v
o
l.
13, pp. 9277
–
9282, 202
2, do
i:
10.1155/2022/
7909472.
[
5]
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.
M
.
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e
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,
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.
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,
U
.
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.
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.
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a
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.
H
.
G
a
ndo
m
i,
“
I
-
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:
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t
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g,”
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E
E
E
I
nt
e
r
ne
t
of
T
hi
ngs
J
our
na
l,
v
o
l.
7,
n
o
.
1,
pp.
710
–
717, 2020,
do
i:
10.1109/J
I
O
T
.2019.2940988.
[
6]
A
.
S
a
r
ka
r
a
nd
T
.
M
.
S
e
nt
hi
l,
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lu
s
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d
s
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in
g
in
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ir
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l
e
s
s
s
e
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o
r
n
e
tw
or
k,”
J
our
nal
of
W
ir
e
le
s
s
N
e
tw
or
k
s
, v
ol
. 25, n
o
. 1, pp. 303
–
320, 201
9, do
i:
10.26438/i
jc
s
e
/
v
7i
6.618622.
[
7]
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.
I
.
K
ha
la
f
a
nd
B
.
S
a
bba
r
,
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n
ove
r
v
i
e
w
o
n
w
ir
e
l
e
s
s
s
e
ns
or
ne
tw
or
ks
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nd
f
in
di
ng
o
pt
im
a
l
l
oc
a
ti
o
n
of
n
o
d
e
s
,”
P
e
r
io
di
c
al
s
o
f
E
ngi
ne
e
r
in
g and N
at
ur
al
Sc
ie
nc
e
s
, v
o
l.
7, n
o
. 3, pp. 1096
–
1101
, 2019, do
i:
10.1166/j
c
tn
.2019.8134.
[
8]
O
.
I
.
K
ha
la
f
,
G
.
M
.
A
bdul
s
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hi
b,
a
nd
B
.
M
.
S
a
bba
r
,
“
O
pt
im
iz
a
ti
o
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ir
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l
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o
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o
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k
c
ov
e
r
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ge
us
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g
th
e
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e
e
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our
nal
of
I
n
f
or
m
at
io
n Sc
ie
nc
e
and E
ngi
ne
e
r
in
g
, vo
l.
36, pp. 3
77
–
386, 2020, do
i:
10.6688/J
I
S
E
.202003_36(
2
)
.0015.
[
9]
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.
I
.
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.
A
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ma
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.
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.
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.
N
.
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e
,
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ks
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E
E
E
A
c
c
e
s
s
,
v
o
l.
8,
pp.
227962
–
227969,
2020,
do
i:
10.1109/AC
C
E
S
S
.2020.3045004.
[
10]
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.
K
a
v
ia
npo
u
r
,
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.
S
ha
n
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.
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z
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m,
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.
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a
ma
ni
,
G
.
N
.
S
a
m
y
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.
D
e
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oe
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s
y
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li
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e
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t
e
r
n
e
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of
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in
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f
or
he
te
r
o
ge
n
e
o
us
d
e
v
ic
e
s
,
”
J
our
nal
o
f
C
om
put
e
r
N
e
tw
or
k
s
and
C
om
m
uni
c
at
io
ns
,
v
o
l.
2
019,
14 pa
ge
s
, 2019, do
i:
10.1155/2019
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747136
.
[
11]
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.
D
.
S
a
lm
a
n,
O
.
I
.
K
ha
la
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,
a
nd
G
.
M
.
A
bdul
s
a
hi
b,
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n
a
da
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iv
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in
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e
ll
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la
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m
s
y
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m
f
or
w
ir
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l
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s
s
s
e
ns
o
r
n
e
tw
o
r
k,”
Sc
ie
nc
e
,
vo
l.
15, n
o
. 1, pp. 142
–
147, 2019, d
o
i:
10.1155/2022
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186275.
[
12]
A
.
T
z
a
ne
t
o
s
a
nd
G
.
D
o
uni
a
s
,
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a
tu
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s
pi
r
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o
pt
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r
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ic
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l
I
nt
e
ll
ig
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nc
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R
e
v
ie
w
, vo
l.
54, pp. 1841
–
1862, 2021, d
oi
:
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7/
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10462
-
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-
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11, p. 10807, 2021, d
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[
14]
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our
nal
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I
ns
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r
e
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C
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put
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vo
l.
15,
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207
–
223,
2020,
do
i:
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J
B
I
C
.2020.10030553.
[
15]
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.
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.
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.
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.
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e
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de
of
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e
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e
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h
(
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–
2019)
,”
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E
E
E
A
c
c
e
s
s
, v
ol
. 9, pp. 766
–
26791, 2021, do
i:
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4044
-
8.
[
16]
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.
S
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or
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s
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o
l.
180, 202
0, p.
107376, do
i:
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.
c
o
mn
e
t.
2020.107376.
[
17]
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.
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.
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l.
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019, do
i:
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1176.
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619.
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18]
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.
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te
m
s
,
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o
l.
75
,
pp.
142
–
147, 2017, do
i:
10.1016/j
.
f
ut
ur
e
.2017.01.011.
[
19]
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.
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.
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le
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ngi
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g J
our
nal
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l.
80, pp. 397
–
407, 2023, d
o
i:
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/j
.a
e
j.
2023.08.058.
[
20]
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.
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.
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N
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s
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J
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S)
,
vo
l.
14,
n
o
.
4,
pp.
433
–
442,
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pr
.
2023,
do
i:
10.32985/i
je
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.14.4.7.
[
21]
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.
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l
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.
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ma
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io
nal
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our
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ne
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gy
,
I
n
f
or
m
at
io
n
and
C
om
m
uni
c
at
io
ns
,
v
o
l.
6,
no
.
2,
pp.
57
–
63,
2015,
do
i:
10.14257/i
je
i
c
.2015.6.2.03.
[
22]
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.
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i,
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.
Y
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l.
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o
. 6, p. 925, 20
22, do
i:
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10060925.
[
23]
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.
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, v
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l.
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o
. 4, pp. 123
–
134, 201
6, do
i:
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jh
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.2016.9.4.2.
[
24]
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