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R
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[
1
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–
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.
Ho
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k
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in
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m
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ts
p
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a
s
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n
if
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t
ch
allen
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C
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s
tan
tly
ch
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y
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ter
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v
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co
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co
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p
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b
u
s
t
an
d
s
ca
lab
le
r
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p
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to
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ticle
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tem
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ap
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I
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I
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2088
-
8
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Lew
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Ar
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l
u
tio
n
s
ar
e
ev
alu
ate
d
,
an
d
f
u
tu
r
e
r
esear
ch
ar
ea
s
ar
e
id
e
n
tifie
d
.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
ar
ticle
was
d
ev
elo
p
ed
t
h
r
o
u
g
h
a
s
y
s
tem
atic
r
ev
iew
o
f
th
e
s
cien
tific
liter
atu
r
e
to
an
aly
ze
th
e
s
tate
o
f
th
e
ar
t
in
VANE
T
r
o
u
tin
g
,
with
a
p
ar
ticu
lar
f
o
cu
s
o
n
th
e
in
teg
r
atio
n
o
f
AI
an
d
S
DN
tech
n
iq
u
es.
T
h
e
s
tep
s
in
th
e
m
eth
o
d
o
lo
g
ical
p
r
o
ce
s
s
f
o
llo
wed
to
d
e
v
elo
p
th
is
r
ev
iew
ar
e
d
escr
ib
e
d
b
elo
w.
2
.
1
.
Sea
rc
h str
a
t
eg
y
T
h
e
in
f
o
r
m
atio
n
co
llectio
n
w
as
co
n
d
u
cted
th
r
o
u
g
h
th
e
in
d
e
x
ed
d
atab
ases
C
lar
iv
ate
W
eb
o
f
Scien
ce
(
W
o
S)
an
d
Sco
p
u
s
,
wh
ich
wer
e
s
elec
ted
f
o
r
th
eir
b
r
o
a
d
co
v
er
ag
e
an
d
r
elev
a
n
ce
to
th
e
f
iel
d
o
f
co
m
p
u
tatio
n
al
s
cien
ce
an
d
en
g
in
ee
r
in
g
.
T
h
e
s
ea
r
ch
f
o
cu
s
ed
o
n
ar
ticles
p
u
b
lis
h
ed
b
etwe
en
J
an
u
a
r
y
2
0
1
9
a
n
d
J
u
l
y
2
0
2
4
,
u
s
in
g
co
m
b
in
atio
n
s
o
f
k
ey
ter
m
s
s
u
ch
as
VANE
T
,
v
eh
icu
la
r
ad
h
o
c
n
etwo
r
k
s
,
s
o
f
twar
e
-
d
ef
in
ed
n
etwo
r
k
in
g
,
m
ac
h
in
e
lear
n
in
g
,
ar
tific
ial
in
tellig
en
ce
an
d
r
o
u
tin
g
,
t
h
e
d
etailed
s
ea
r
ch
s
tr
ateg
y
f
o
r
ea
ch
d
atab
ase
is
p
r
esen
ted
in
T
ab
le
1
,
wh
ile
Fig
u
r
e
1
illu
s
tr
ates
th
e
m
eth
o
d
o
lo
g
y
f
o
llo
win
g
th
e
s
y
s
tem
atic
r
ev
iew
f
lo
w
ch
ar
t
ac
co
r
d
in
g
to
th
e
PR
I
SMA
g
u
i
d
elin
es.
T
ab
le
1
.
Sear
ch
s
tr
in
g
s
u
s
ed
in
th
e
s
y
s
tem
atic
liter
atu
r
e
r
ev
ie
w
D
a
t
a
b
a
s
e
Eq
u
a
t
i
o
n
S
c
o
p
u
s
TI
TLE
-
A
B
S
-
K
EY
(
“
V
a
n
e
t
*
”
O
R
“
V
e
h
i
c
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l
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r
a
d
h
o
c
n
e
t
w
o
r
k
*
”
)
A
N
D
TI
TLE
-
A
B
S
-
K
EY
(
“
ma
c
h
i
n
e
*
l
e
a
r
n
i
n
g
*
”
O
R
“
ML
”
O
R
“
S
D
N
*
”
O
R
“
s
o
f
t
w
a
r
e
d
e
f
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n
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n
e
t
w
o
r
k
i
n
g
”
O
R
“
S
o
f
t
w
a
r
e
-
D
e
f
i
n
e
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N
e
t
w
o
r
k
i
n
g
”
)
W
o
S
TS
=
(
“
V
a
n
e
t
*
”
O
R
“
V
e
h
i
c
u
l
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h
o
c
n
e
t
w
o
r
k
*
”
)
A
N
D
TS
=
(
“
mac
h
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n
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*
l
e
a
r
n
i
n
g
*
”
O
R
“
ML
”
O
R
“
S
D
N
*
”
O
R
“
so
f
t
w
a
r
e
d
e
f
i
n
e
d
n
e
t
w
o
r
k
i
n
g
”
O
R
“
S
o
f
t
w
a
r
e
-
D
e
f
i
n
e
d
N
e
t
w
o
r
k
i
n
g
”
)
Fig
u
r
e
1
.
Me
th
o
d
o
lo
g
y
wo
r
k
f
l
o
w
f
o
r
p
ap
er
s
cr
ee
n
in
g
a
n
d
in
clu
s
io
n
ac
co
r
d
i
n
g
to
PR
I
SM
A
[
5
]
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
.
6
,
Decem
b
e
r
20
25
:
5
3
8
8
-
5
4
0
0
5390
2
.
2
.
I
nclus
io
n a
nd
ex
clus
io
n
cr
it
er
ia
R
ev
iew
ar
ticles an
d
o
r
ig
in
al
s
tu
d
ies ad
d
r
ess
in
g
r
o
u
tin
g
s
o
lu
ti
o
n
s
in
VANE
T
u
s
in
g
AI
,
SD
N,
o
r
o
th
e
r
em
er
g
in
g
tech
n
o
l
o
g
ies
(
s
u
ch
a
s
UAVs,
f
o
g
co
m
p
u
tin
g
,
5
G)
wer
e
in
clu
d
ed
.
On
ly
a
r
ticles
in
E
n
g
lis
h
th
at
wer
e
av
ailab
le
in
f
u
ll
tex
t
a
n
d
with
v
er
if
iab
le
r
ef
er
en
ce
s
wer
e
co
n
s
id
er
e
d
.
Du
p
licate
ar
ticles,
co
n
f
e
r
en
ce
p
u
b
licatio
n
s
,
an
d
p
ap
e
r
s
wh
o
s
e
p
r
im
ar
y
f
o
cu
s
d
id
n
o
t f
it th
e
o
b
jectiv
es o
f
th
is
r
ev
iew
wer
e
ex
clu
d
ed
.
2
.
3
.
P
re
pro
ce
s
s
ing
a
nd
clea
nin
g
t
he
da
t
a
s
et
T
o
en
s
u
r
e
th
e
q
u
ality
o
f
th
e
an
aly
ze
d
s
et,
a
Py
th
o
n
s
cr
ip
t
ca
lled
Scien
to
Py
was
u
s
ed
to
clea
n
th
e
in
itial
s
et
o
f
d
o
cu
m
e
n
ts
.
T
h
is
to
o
l
e
n
ab
led
u
s
t
o
id
e
n
tify
a
n
d
elim
in
ate
d
u
p
licate
r
ec
o
r
d
s
b
etwe
en
th
e
two
d
atab
ases
,
wh
ile
p
r
io
r
itizin
g
th
e
v
er
s
io
n
in
d
e
x
ed
in
th
e
W
eb
o
f
Scien
ce
.
As
a
r
esu
lt
o
f
th
is
p
r
o
ce
s
s
,
a
f
in
al
s
et
o
f
3
8
3
u
n
iq
u
e
d
o
cu
m
e
n
ts
was
o
b
tain
ed
f
o
r
an
aly
s
is
.
Fro
m
th
is
r
ef
in
e
d
d
ataset,
7
4
s
tu
d
ies
wer
e
u
ltima
tel
y
s
elec
ted
th
r
o
u
g
h
r
ig
o
r
o
u
s
s
cr
ee
n
in
g
,
f
o
c
u
s
in
g
ex
clu
s
iv
ely
o
n
AI
,
SDN,
an
d
em
er
g
in
g
tec
h
n
o
lo
g
y
ap
p
r
o
ac
h
es
to
VANE
T
r
o
u
tin
g
,
in
s
tr
ict
ad
h
er
en
ce
to
m
eth
o
d
o
lo
g
ical
cr
it
er
ia.
2
.
4
.
Da
t
a
a
na
ly
s
is
T
h
e
an
aly
s
is
was stru
ctu
r
ed
in
two
co
m
p
lem
e
n
tar
y
p
h
ases
:
a.
T
h
e
VOSv
iewe
r
to
o
l
was
u
s
ed
to
id
e
n
tify
a
n
d
v
is
u
alize
s
em
an
tic
r
elatio
n
s
h
ip
s
b
etw
ee
n
k
ey
ter
m
s
,
g
en
er
atin
g
co
-
o
cc
u
r
r
en
ce
m
ap
s
th
at
allo
wed
u
s
to
id
e
n
tify
tr
en
d
s
,
th
em
atic
clu
s
ter
s
,
an
d
r
e
s
ea
r
ch
g
ap
s
in
th
e
f
ield
s
o
f
VANE
T
,
AI
,
a
n
d
SDN.
Fig
u
r
e
2
s
h
o
ws th
e
r
esu
l
tin
g
co
-
o
cc
u
r
r
e
n
ce
m
ap
.
b.
Qu
alitativ
e
th
em
atic
an
al
y
s
is
:
T
h
e
r
ev
iewe
d
ar
ticles
wer
e
c
lass
if
ied
ac
co
r
d
in
g
to
th
e
tech
n
o
lo
g
ies
u
s
ed
,
alg
o
r
ith
m
s
ap
p
lied
,
ty
p
e
o
f
ar
ch
itectu
r
e
p
r
o
p
o
s
ed
,
ad
v
a
n
t
ag
es,
lim
itatio
n
s
,
an
d
ch
alle
n
g
es
id
en
tifie
d
.
C
o
m
p
ar
ativ
e
tab
les
an
d
s
u
p
p
o
r
tin
g
f
i
g
u
r
es
wer
e
also
c
o
n
s
tr
u
cted
to
s
u
m
m
ar
ize
th
e
c
o
n
t
r
ib
u
tio
n
s
o
f
th
e
an
aly
ze
d
ar
ticles,
p
r
o
v
id
in
g
a
s
tr
u
ctu
r
ed
v
iew
o
f
th
e
s
tate
o
f
th
e
ar
t.
Fig
u
r
e
2
.
Ma
p
o
f
k
ey
c
o
n
ce
p
ts
an
d
th
eir
r
elatio
n
s
h
ip
s
in
th
e
f
ield
o
f
VANE
T
,
AI
an
d
SDN
3.
RE
VI
E
W
A
RT
I
CL
E
S O
N
V
ANE
T
VANE
T
h
av
e
u
n
d
er
g
o
n
e
s
ig
n
if
ican
t
ev
o
lu
tio
n
o
win
g
to
th
e
in
teg
r
atio
n
o
f
tech
n
o
lo
g
ies,
s
u
ch
as
ML
an
d
SDN.
B
y
ce
n
tr
alizin
g
n
etwo
r
k
co
n
tr
o
l
an
d
en
a
b
lin
g
p
r
o
g
r
am
m
a
b
ilit
y
,
SDN
o
f
f
er
s
a
s
o
lid
f
o
u
n
d
atio
n
f
o
r
ef
f
icien
t
an
d
f
lex
ib
le
VANE
T
m
an
ag
em
en
t
[
6
]
–
[
9
]
.
I
n
co
n
tr
ast,
ML
ca
n
an
aly
ze
lar
g
e
v
o
l
u
m
es
o
f
d
ata
in
r
ea
l
tim
e,
allo
win
g
f
o
r
o
p
tim
ized
r
o
u
tin
g
,
co
n
g
esti
o
n
p
r
ed
ictio
n
,
an
d
im
p
r
o
v
ed
s
ec
u
r
ity
i
n
VA
NE
T
[
1
]
,
[
4
]
,
[
1
0
]
,
[
1
1
]
.
ML
h
as
b
ee
n
p
r
o
v
e
n
to
b
e
a
p
o
wer
f
u
l
to
o
l
f
o
r
im
p
r
o
v
in
g
th
e
ef
f
icien
c
y
an
d
s
af
ety
o
f
VANE
T
.
Var
io
u
s
ML
tech
n
iq
u
es
s
u
ch
as
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
an
d
s
u
p
er
v
is
ed
lear
n
in
g
h
av
e
b
ee
n
ex
p
lo
r
ed
to
o
p
tim
ize
r
o
u
tin
g
d
ec
is
io
n
s
an
d
p
r
ed
ict
r
elev
an
t
ev
en
ts
[
3
]
,
[
1
2
]
,
[
1
3
]
.
Fo
r
ex
am
p
le,
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
en
ab
les
v
eh
icles
to
m
ak
e
o
p
tim
al
r
o
u
t
in
g
d
ec
is
io
n
s
in
r
ea
l
-
tim
e
b
y
a
d
ap
tin
g
to
ch
a
n
g
in
g
tr
a
f
f
ic
co
n
d
itio
n
s
[
1
2
]
,
[
1
3
]
.
Ho
wev
er
,
th
e
im
p
lem
e
n
tatio
n
o
f
ML
in
VANE
T
p
r
esen
ts
c
h
allen
g
es
s
u
ch
as
th
e
s
ca
r
city
o
f
lab
eled
d
ata
an
d
th
e
n
ee
d
to
d
ev
elo
p
co
m
p
u
tatio
n
ally
ef
f
icien
t
m
o
d
els
[
1
3
]
.
Fu
r
th
er
m
o
r
e,
d
ata
p
r
iv
ac
y
a
n
d
s
ec
u
r
ity
ar
e
k
e
y
co
n
ce
r
n
s
,
p
ar
ticu
lar
ly
in
en
v
ir
o
n
m
en
ts
wh
er
e
s
en
s
itiv
e
in
f
o
r
m
atio
n
is
s
h
ar
ed
b
etwe
en
v
eh
i
cles
[
1
4
]
.
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
Th
e
ev
o
lu
tio
n
o
f ro
u
tin
g
i
n
V
A
N
E
T:
a
n
a
n
a
lysi
s
o
f so
lu
tio
n
s
b
a
s
ed
o
n
…
(
Lew
ys C
o
r
r
ea
S
á
n
ch
ez
)
5391
S
D
N
o
f
f
e
r
s
a
p
r
o
m
i
s
i
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[
1
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1
7
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in
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r
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d
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o
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ith
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[
4
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[
1
0
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[
1
8
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1
4
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[
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
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4.
VANE
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1
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p
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v
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le
tr
ajec
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y
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r
ed
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wh
ic
h
o
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tim
izes
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af
ety
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ess
ag
e
d
is
s
em
in
atio
n
[
3
2
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a
n
d
f
ac
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ates
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tellig
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h
o
p
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ased
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n
t
r
af
f
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s
ity
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d
r
o
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te
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m
e
[
2
2
]
,
[
2
7
]
,
[
2
9
]
,
[
3
3
]
.
AI
h
as
also
b
ee
n
em
p
lo
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ed
to
m
itig
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b
r
o
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d
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m
s
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T
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y
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s
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ay
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class
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ier
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o
r
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o
r
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d
in
g
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is
io
n
s
[
2
3
]
.
Oth
er
m
eth
o
d
s
,
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u
ch
as
p
ar
ticle
s
war
m
o
p
tim
izatio
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(
PS
O
)
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p
led
with
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e
d
en
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ity
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k
clu
s
ter
in
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(
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)
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o
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ith
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,
a
r
e
em
p
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d
to
s
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t
th
e
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est
v
eh
icles
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cl
u
s
ter
lead
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s
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y
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g
r
o
u
tin
g
s
tab
ilit
y
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n
d
p
er
f
o
r
m
a
n
ce
[
3
4
]
.
T
h
ese
u
s
e
ca
s
es
d
em
o
n
s
tr
ate
th
e
p
o
ten
tial
o
f
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o
p
tim
ize
tr
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f
ic
m
an
a
g
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en
t,
im
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r
o
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r
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ety
,
an
d
f
ac
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ate
v
eh
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-
to
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v
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icle
co
m
m
u
n
icatio
n
.
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ca
n
p
r
ed
ict
v
eh
icle
m
o
v
em
e
n
t
[
3
4
]
,
[
3
5
]
,
o
p
tim
ize
r
o
u
tes
b
y
co
n
s
id
er
in
g
tr
af
f
ic
d
e
n
s
ity
an
d
lin
k
q
u
ality
[
3
5
]
,
[
3
6
]
,
a
n
d
m
itig
ate
c
o
n
g
esti
o
n
t
h
r
o
u
g
h
alter
n
ativ
e
r
o
u
tes
[
3
1
]
,
[
3
3
]
–
[
3
6
]
.
T
h
e
in
teg
r
atio
n
o
f
AI
in
to
VANE
T
h
as
s
ig
n
if
ican
t
p
o
te
n
tial
f
o
r
im
p
r
o
v
in
g
r
o
u
tin
g
ef
f
icien
cy
,
r
ed
u
cin
g
co
n
g
esti
o
n
,
in
c
r
ea
s
in
g
p
ac
k
et
d
eliv
er
y
r
ates,
a
n
d
m
in
im
izin
g
d
ata
tr
an
s
m
is
s
io
n
d
elay
s
[
2
6
]
–
[
3
0
]
,
[
3
2
]
.
At
th
e
s
ec
u
r
ity
lev
el,
AI
co
n
tr
ib
u
tes
to
d
etec
tin
g
a
n
d
p
r
ev
e
n
tin
g
attac
k
s
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th
e
n
ticatin
g
n
o
d
es,
an
d
p
r
o
tectin
g
u
s
er
p
r
iv
ac
y
[
2
3
]
,
[
3
7
]
–
[
3
9
]
.
Ho
wev
er
,
im
p
lem
en
tin
g
AI
in
VANE
T
p
o
s
es
ch
allen
g
es,
s
u
ch
as
in
ter
m
itten
t
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n
n
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tiv
ity
,
r
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r
ce
lim
itatio
n
s
,
an
d
d
ata
p
r
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v
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p
r
o
tectio
n
[
2
0
]
,
[
2
4
]
,
[
2
7
]
,
[
2
8
]
,
[
3
0
]
,
[
3
2
]
,
[
3
8
]
–
[
4
2
]
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
cu
s
to
war
d
s
d
ev
elo
p
in
g
m
o
r
e
ef
f
icien
t
AI
alg
o
r
ith
m
s
,
im
p
r
o
v
in
g
r
ea
l
-
tim
e
d
ata
co
llectio
n
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d
in
te
g
r
atin
g
AI
with
tech
n
o
lo
g
ies
s
u
ch
as
b
lo
ck
ch
ain
a
n
d
ed
g
e
c
o
m
p
u
tin
g
[
2
1
]
,
[
2
2
]
,
[
2
6
]
–
[
2
8
]
,
[
3
7
]
,
[
3
9
]
–
[
4
1
]
.
T
h
is
co
n
v
er
g
en
ce
p
r
o
m
is
es
to
r
ev
o
lu
tio
n
ize
m
o
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ilit
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g
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er
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m
ar
ter
,
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d
m
o
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e
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n
n
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ted
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s
p
o
r
tatio
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s
y
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s
.
Fig
u
r
e
3
,
wh
ich
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s
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ates
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e
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tio
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p
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.
F
i
g
u
r
e
3
.
D
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b
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A
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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
Th
e
ev
o
lu
tio
n
o
f ro
u
tin
g
i
n
V
A
N
E
T:
a
n
a
n
a
lysi
s
o
f so
lu
tio
n
s
b
a
s
ed
o
n
…
(
Lew
ys C
o
r
r
ea
S
á
n
ch
ez
)
5393
5.
VANE
T
AND
SO
F
T
WAR
E
-
DE
F
I
N
E
D
N
E
T
WO
RK
S
I
n
te
g
r
ati
n
g
s
o
f
tw
ar
e
-
d
ef
in
ed
n
etw
o
r
k
(
SD
N
)
i
n
t
o
V
ANE
T
h
as
e
m
e
r
g
e
d
as
a
p
r
o
m
is
i
n
g
s
t
r
ate
g
y
f
o
r
im
p
r
o
v
i
n
g
t
h
e
f
l
e
x
i
b
ili
ty
a
n
d
p
r
o
g
r
a
m
m
a
b
ili
ty
o
f
r
o
u
ti
n
g
p
r
o
t
o
c
o
ls
in
v
e
h
ic
u
l
a
r
e
n
v
i
r
o
n
m
e
n
ts
.
SD
N
i
n
t
r
o
d
u
ce
s
a
c
en
tr
ali
ze
d
co
n
t
r
o
ll
er
t
h
at
p
r
o
v
i
d
es
a
g
l
o
b
al
v
iew
o
f
th
e
n
etw
o
r
k
,
a
ll
o
wi
n
g
f
o
r
m
o
r
e
in
f
o
r
m
e
d
a
n
d
e
f
f
ic
ie
n
t
r
o
u
t
in
g
d
ec
is
io
n
s
co
m
p
a
r
e
d
t
o
t
r
a
d
i
ti
o
n
al
p
r
o
to
c
o
ls
.
T
h
is
ce
n
t
r
ali
za
ti
o
n
f
ac
i
lit
ates
d
y
n
a
m
ic
a
d
a
p
t
ati
o
n
t
o
v
a
r
y
i
n
g
co
n
d
i
ti
o
n
s
,
s
u
c
h
as
t
r
af
f
ic
d
en
s
it
y
,
v
e
h
i
cle
m
o
b
ili
t
y
,
an
d
l
in
k
av
ail
a
b
ili
ty
,
t
h
u
s
o
p
ti
m
iz
in
g
n
et
wo
r
k
p
e
r
f
o
r
m
a
n
c
e
i
n
VAN
E
T
[
4
3
]
–
[
4
9
]
.
Sev
er
al
u
s
e
ca
s
es
h
av
e
h
ig
h
lig
h
ted
th
e
r
o
le
o
f
SDN
in
o
p
tim
izin
g
tr
af
f
ic
an
d
m
a
n
ag
in
g
r
eso
u
r
ce
s
in
VANE
T
.
Fo
r
ex
am
p
le,
SDN
en
ab
les
tr
af
f
ic
to
b
e
r
e
d
ir
ec
ted
to
war
d
s
less
co
n
g
est
ed
r
o
u
tes,
th
er
eb
y
m
in
im
izin
g
d
elay
s
an
d
im
p
r
o
v
in
g
th
e
ef
f
icie
n
cy
o
f
v
e
h
ic
u
lar
f
lo
w
[
4
7
]
–
[
4
9
]
.
I
n
e
n
v
ir
o
n
m
en
ts
with
h
ig
h
v
eh
icle
d
e
n
s
ity
,
th
e
SDN
co
n
tr
o
ller
d
y
n
am
ically
allo
ca
te
s
r
eso
u
r
ce
s
s
u
ch
as
b
an
d
wid
th
an
d
p
r
o
ce
s
s
in
g
ca
p
ac
ity
to
v
e
h
icles
an
d
r
o
a
d
u
n
its
to
im
p
r
o
v
e
t
h
e
q
u
ality
o
f
s
er
v
ice
(
Qo
S)
[
4
8
]
,
[
5
0
]
–
[
5
4
]
.
T
h
ese
SDN
ca
p
ab
ilit
ies
en
ab
le
d
y
n
am
ic
n
etwo
r
k
r
ec
o
n
f
ig
u
r
atio
n
[
4
7
]
,
[
5
5
]
–
[
6
2
]
;
Qo
S
o
p
tim
izatio
n
f
o
r
d
if
f
er
e
n
t
ty
p
es
o
f
tr
af
f
ic
[
4
7
]
,
[
4
8
]
,
[
6
3
]
–
[
6
9
]
;
a
n
d
th
e
im
p
lem
en
tatio
n
o
f
ce
n
tr
alize
d
s
ec
u
r
ity
p
o
licies,
s
tr
en
g
th
en
in
g
an
o
m
aly
d
etec
tio
n
an
d
p
r
o
tectio
n
ag
ain
s
t m
alicio
u
s
attac
k
s
[
4
4
]
,
[
6
2
]
,
[
7
0
]
,
[
7
1
]
.
Ho
wev
er
,
th
e
im
p
lem
e
n
tatio
n
o
f
SDN
in
VANE
T
p
r
esen
ts
s
ev
er
al
ch
allen
g
es.
T
h
e
co
m
p
lex
ity
o
f
th
e
im
p
lem
en
tatio
n
is
co
n
s
id
er
ab
le
b
ec
au
s
e
it r
eq
u
ir
es th
e
in
teg
r
atio
n
o
f
s
p
ec
ialized
h
ar
d
w
ar
e
an
d
s
o
f
twar
e
in
v
eh
icles
an
d
r
o
ad
s
id
e
u
n
it
s
(
R
SU
s
)
[
4
9
]
,
[
5
5
]
,
[
6
0
]
,
[
6
2
]
,
[
6
9
]
,
[
7
2
]
.
Fu
r
th
er
m
o
r
e,
t
h
e
ce
n
tr
aliza
tio
n
o
f
n
etwo
r
k
m
an
ag
e
m
en
t
i
n
tr
o
d
u
c
es
a
s
in
g
le
p
o
in
t
o
f
f
ailu
r
e,
w
h
er
e
th
e
f
ailu
r
e
o
f
t
h
e
SDN
c
o
n
tr
o
ller
ca
n
im
p
ac
t
th
e
en
tire
n
etwo
r
k
'
s
o
p
er
atio
n
[
4
9
]
,
[
5
2
]
,
[
6
2
]
,
[
6
4
]
,
[
7
1
]
,
[
7
3
]
,
[
7
4
]
.
Scalab
ilit
y
is
also
a
s
i
g
n
if
ican
t
ch
allen
g
e
b
ec
au
s
e
th
e
co
n
tr
o
ller
'
s
ab
ilit
y
to
m
an
ag
e
th
e
n
etwo
r
k
e
f
f
icie
n
tly
m
ay
b
e
lim
ited
b
y
an
i
n
cr
ea
s
e
in
th
e
n
u
m
b
e
r
o
f
v
eh
icles a
n
d
n
etwo
r
k
d
y
n
a
m
ics
[
4
9
]
,
[
5
1
]
,
[
5
4
]
,
[
6
2
]
,
[
6
9
]
,
[
7
5
]
.
Desp
ite
th
ese
ch
allen
g
es,
S
DN
in
teg
r
atio
n
in
VANE
T
h
as
th
e
p
o
ten
tial
to
o
p
tim
ize
n
etwo
r
k
p
er
f
o
r
m
an
ce
an
d
e
n
ab
le
ad
v
a
n
ce
d
ap
p
licatio
n
s
in
au
to
n
o
m
o
u
s
d
r
iv
in
g
,
r
o
ad
s
af
ety
,
an
d
in
tellig
en
t
tr
af
f
ic
m
an
ag
em
en
t.
Fu
tu
r
e
r
esear
ch
will
f
o
cu
s
o
n
s
o
lv
in
g
s
ca
lab
ilit
y
,
s
ec
u
r
ity
,
an
d
d
ep
lo
y
m
en
t
c
o
m
p
lex
ity
is
s
u
es
to
m
ax
im
ize
th
e
b
en
ef
its
o
f
SDN
in
v
eh
icu
lar
en
v
ir
o
n
m
en
ts
[
2
1
]
,
[
2
2
]
,
[
2
6
]
,
[
2
7
]
,
[
2
8
]
,
[
3
7
]
,
[
3
9
]
–
[
4
1
]
.
Alth
o
u
g
h
SDN
in
teg
r
atio
n
in
VANE
T
h
as
ex
ce
llen
t
p
o
ten
tial
f
o
r
o
p
tim
izin
g
th
e
n
etwo
r
k
p
er
f
o
r
m
a
n
ce
,
its
im
p
lem
en
tatio
n
p
r
esen
ts
s
ig
n
if
ican
t
ch
allen
g
es.
T
ab
le
3
in
Ap
p
en
d
ix
s
u
m
m
ar
izes
th
e
c
u
r
r
en
t
r
esear
ch
o
n
SDN
in
VANE
T
,
r
ev
ea
lin
g
t
h
e
d
iv
e
r
s
ity
o
f
a
p
p
r
o
ac
h
es
an
d
tech
n
o
lo
g
ies
u
s
ed
to
im
p
r
o
v
e
asp
ec
ts
,
s
u
ch
as
q
u
ality
o
f
s
er
v
ice,
s
ec
u
r
ity
,
a
n
d
r
o
u
tin
g
.
6.
CH
AL
L
E
NG
E
S AN
D
F
U
T
URE R
E
S
E
ARCH
DI
RE
C
T
I
O
NS
T
h
e
in
teg
r
atio
n
o
f
AI
an
d
SDN
in
VANE
T
o
f
f
e
r
s
ex
ce
ll
en
t
p
o
ten
tial
f
o
r
r
o
u
tin
g
,
b
u
t
p
r
esen
ts
ch
allen
g
es
th
at
m
u
s
t
b
e
ad
d
r
ess
ed
to
r
ea
lize
its
b
en
ef
its
f
u
lly
.
Ad
d
r
ess
in
g
th
e
cu
r
r
e
n
t
ch
allen
g
es
an
d
ex
p
lo
r
in
g
n
ew
av
e
n
u
es o
f
r
esear
ch
ar
e
cr
itical.
Op
e
n
is
s
u
es a
n
d
ch
allen
g
es a
r
e
d
escr
ib
ed
:
a.
I
m
p
lem
en
tatio
n
c
o
m
p
lex
ity
:
I
n
teg
r
atin
g
SDN
in
to
VANE
T
is
ch
allen
g
in
g
d
u
e
t
o
tech
n
o
l
o
g
y
an
d
p
r
o
to
c
o
l
h
eter
o
g
en
eity
,
r
e
q
u
ir
in
g
s
ig
n
if
ican
t
in
v
estme
n
t
in
h
ar
d
w
ar
e
ad
a
p
tatio
n
,
s
o
f
twar
e
d
e
v
elo
p
m
en
t,
an
d
co
m
p
o
n
en
t
c
o
o
r
d
i
n
atio
n
.
Veh
icle
d
iv
er
s
ity
,
co
n
s
tan
t
m
o
b
ilit
y
,
an
d
th
e
n
ee
d
t
o
in
te
g
r
a
te
5
G
an
d
I
o
T
f
u
r
th
er
in
c
r
ea
s
e
th
is
co
m
p
lex
ity
.
Ad
o
p
tin
g
o
p
en
s
tan
d
ar
d
s
,
s
u
ch
as
Op
en
Flo
w,
an
d
en
s
u
r
in
g
s
ec
u
r
ity
b
y
d
esig
n
ar
e
cr
u
cial
f
o
r
e
f
f
ec
tiv
e
m
itig
atio
n
.
b.
Scalab
ilit
y
:
VANE
T
s
ca
lab
ilit
y
is
cr
itical
d
u
e
to
th
e
ex
p
o
n
e
n
tial
g
r
o
wth
o
f
co
n
n
ec
ted
v
eh
icles.
An
SDN
co
n
tr
o
ller
m
u
s
t
ef
f
icien
tly
m
an
ag
e
in
cr
ea
s
in
g
c
o
n
n
ec
tio
n
s
an
d
d
y
n
am
ic
to
p
o
lo
g
y
ch
an
g
es
am
id
s
t
h
ig
h
v
eh
icle
m
o
b
ilit
y
,
in
c
r
ea
s
ed
t
r
af
f
ic,
a
n
d
lim
ited
d
e
v
ice
r
e
s
o
u
r
ce
s
.
S
o
l
u
ti
o
n
s
s
u
ch
as
c
o
n
te
n
t
d
e
li
v
e
r
y
n
et
wo
r
k
s
(
C
DNs
)
a
n
d
m
ic
r
o
s
e
r
v
ic
e
a
r
c
h
it
ec
tu
r
es
ca
n
e
n
h
an
c
e
s
ca
la
b
il
it
y
,
b
u
t
r
e
q
u
i
r
e
ca
r
e
f
u
l
co
n
s
i
d
er
ati
o
n
o
f
l
ate
n
cy
a
n
d
d
ata
c
o
n
s
is
t
en
c
y
.
c.
Data
s
ca
r
city
:
Dev
elo
p
in
g
m
a
ch
in
e
lear
n
in
g
m
o
d
els
f
o
r
V
ANE
T
is
lim
ited
b
y
th
e
lack
o
f
h
i
g
h
-
q
u
ality
,
h
ig
h
-
v
o
lu
m
e
lab
ele
d
d
ata,
m
ak
in
g
m
an
u
al
co
llectio
n
an
d
lab
elin
g
co
s
tly
.
T
ec
h
n
iq
u
es
s
u
ch
as
ac
tiv
e
lear
n
in
g
an
d
f
ed
er
ate
d
lear
n
in
g
ca
n
m
itig
ate
th
is
b
y
allo
win
g
tr
ain
in
g
with
less
d
ata
an
d
d
is
tr
ib
u
tin
g
th
e
co
m
p
u
tatio
n
al
l
o
ad
,
b
u
t a
s
s
o
ciate
d
p
r
iv
ac
y
an
d
s
ec
u
r
ity
ch
all
en
g
es m
u
s
t b
e
ad
d
r
ess
ed
.
d.
C
o
m
p
u
tatio
n
ally
ef
f
icie
n
t
m
o
d
els
:
ML
m
o
d
els
in
VANE
T
m
u
s
t
b
e
co
m
p
u
tatio
n
ally
ef
f
ic
ien
t
f
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
d
ev
ices.
T
ec
h
n
iq
u
es
s
u
ch
as
q
u
an
tizatio
n
,
p
r
u
n
in
g
,
f
e
d
er
ated
lear
n
in
g
,
a
n
d
s
p
ec
ialized
h
ar
d
war
e
(
g
r
ap
h
ics
p
r
o
ce
s
s
in
g
u
n
it
s
(
GPUs
)
an
d
ten
s
o
r
p
r
o
ce
s
s
in
g
u
n
it
s
(
T
PUs
)
)
ca
n
r
ed
u
ce
m
o
d
el
s
ize,
co
m
p
lex
ity
,
an
d
im
p
r
o
v
e
p
er
f
o
r
m
an
ce
.
B
alan
cin
g
ac
c
u
r
ac
y
an
d
co
m
p
u
tatio
n
al
co
m
p
lex
ity
ar
e
cr
u
cial
f
o
r
lo
w
laten
cy
an
d
r
ed
u
ce
d
p
o
wer
co
n
s
u
m
p
ti
o
n
.
e.
D
a
t
a
s
e
c
u
r
i
t
y
a
n
d
p
r
i
v
a
c
y
:
S
e
c
u
r
i
t
y
a
n
d
p
r
i
v
a
c
y
a
r
e
k
ey
c
o
n
c
e
r
n
s
i
n
VA
N
E
T
,
as
c
y
b
e
r
a
t
t
a
c
k
s
c
a
n
c
o
m
p
r
o
m
i
s
e
d
a
t
a
i
n
t
e
g
r
i
t
y
,
c
o
n
f
i
d
e
n
t
i
a
li
t
y
,
a
n
d
u
s
e
r
s
a
f
e
t
y
.
R
o
b
u
s
t
a
u
t
h
e
n
t
i
ca
t
i
o
n
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a
u
t
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o
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i
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at
i
o
n
,
e
n
c
r
y
p
t
i
o
n
,
a
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a
t
i
o
n
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a
n
d
p
s
e
u
d
o
n
y
m
i
z
a
t
i
o
n
m
e
c
h
a
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i
s
m
s
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r
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t
i
a
l
t
o
p
r
o
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t
s
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s
it
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v
e
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f
o
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m
a
t
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a
n
d
m
a
i
n
t
a
i
n
u
s
e
r
p
r
i
v
a
c
y
.
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
.
6
,
Decem
b
e
r
20
25
:
5
3
8
8
-
5
4
0
0
5394
f.
Pre
d
ictio
n
ac
cu
r
ac
y
:
T
h
e
p
r
e
d
ictio
n
ac
cu
r
ac
y
o
f
VANE
T
i
s
in
f
lu
en
ce
d
b
y
h
ig
h
n
etwo
r
k
d
y
n
am
ics,
d
ata
u
n
ce
r
tain
ty
,
an
d
m
o
d
el
co
m
p
l
ex
ity
.
I
m
p
r
o
v
in
g
a
cc
u
r
ac
y
r
e
q
u
ir
es
d
ata
f
u
s
io
n
tec
h
n
iq
u
es,
h
y
b
r
id
m
o
d
els,
ad
ap
tiv
e
lear
n
in
g
alg
o
r
ith
m
s
,
a
n
d
co
n
s
id
er
in
g
e
n
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
an
d
v
e
h
icle
in
ter
ac
tio
n
s
in
p
r
ed
ictio
n
s
.
g.
C
o
m
p
u
tatio
n
al
c
o
m
p
lex
ity
o
f
AI
:
T
h
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
o
f
AI
alg
o
r
ith
m
s
,
c
o
m
p
ellin
g
d
ee
p
lear
n
in
g
m
o
d
els,
p
o
s
es
a
s
ig
n
if
ican
t
c
h
allen
g
e
f
o
r
r
es
o
u
r
ce
-
c
o
n
s
tr
ain
ed
VANE
T
d
ev
ices.
Mo
d
el
o
p
tim
izatio
n
tech
n
iq
u
es
(
q
u
a
n
tizatio
n
,
p
r
u
n
in
g
)
,
s
p
ec
ializ
ed
h
ar
d
war
e,
an
d
ex
p
lo
r
in
g
ef
f
icien
t
n
eu
r
al
n
etwo
r
k
ar
c
h
itectu
r
es lik
e
C
NNs a
n
d
R
NNs c
an
ad
d
r
ess
th
is
.
7.
DIS
CU
SS
I
O
N
VANE
T
ar
e
cr
u
cial
f
o
r
I
T
S,
im
p
r
o
v
in
g
tr
an
s
p
o
r
tatio
n
s
af
e
ty
an
d
ef
f
icien
cy
.
Ho
wev
er
,
ef
f
icien
tly
r
o
u
tin
g
d
ata
p
ac
k
ets
i
n
s
u
ch
d
y
n
am
ic
a
n
d
h
ig
h
l
y
m
o
b
ile
en
v
ir
o
n
m
e
n
ts
is
a
s
ig
n
if
ica
n
t
ch
allen
g
e
d
u
e
t
o
co
n
s
tan
t
to
p
o
lo
g
ical
v
a
r
iatio
n
s
,
in
ter
f
er
en
ce
,
an
d
u
n
s
tab
le
c
o
m
m
u
n
icatio
n
lin
k
s
.
T
h
e
co
n
v
er
g
en
ce
o
f
AI
an
d
SDN
is
em
er
g
in
g
as
a
p
r
o
m
is
in
g
s
o
lu
tio
n
to
th
ese
co
m
p
le
x
ities
,
o
f
f
er
in
g
tr
an
s
f
o
r
m
ativ
e
p
o
ten
tial
b
u
t
also
en
tailin
g
p
r
ac
tical
lim
itatio
n
s
th
at
r
eq
u
ir
e
d
etailed
an
aly
s
is
.
a.
AI
-
SDN
co
n
v
er
g
e
n
ce
: p
o
te
n
tial a
n
d
p
r
ac
tical
lim
itatio
n
s
T
h
e
in
teg
r
atio
n
o
f
AI
an
d
S
DN
h
as
s
ig
n
if
ican
tly
d
r
iv
e
n
t
h
e
ev
o
lu
tio
n
o
f
r
o
u
tin
g
i
n
V
ANE
T
.
AI
,
th
r
o
u
g
h
tech
n
iq
u
es
s
u
c
h
as
m
ac
h
in
e
lear
n
in
g
an
d
d
ee
p
l
ea
r
n
in
g
,
en
ab
les
VANE
T
to
p
r
ed
ict
tr
af
f
ic,
o
p
tim
ize
r
o
u
tes,
an
d
ad
ap
t
r
o
u
tin
g
p
r
o
to
co
ls
in
r
ea
l
-
tim
e,
th
er
eb
y
tr
an
s
f
o
r
m
in
g
n
etwo
r
k
m
an
ag
em
e
n
t
f
r
o
m
a
r
ea
ctiv
e
to
a
p
r
o
ac
tiv
e
ap
p
r
o
ac
h
.
SDN,
f
o
r
its
p
ar
t,
o
f
f
er
s
a
f
lex
ib
le
an
d
p
r
o
g
r
am
m
ab
le
p
latf
o
r
m
th
at
ce
n
tr
alize
s
n
etwo
r
k
co
n
tr
o
l,
p
r
o
v
id
in
g
a
co
m
p
r
eh
en
s
i
v
e
v
iew
o
f
th
e
t
o
p
o
lo
g
y
an
d
f
ac
ilit
atin
g
th
e
d
y
n
am
ic
co
n
f
ig
u
r
atio
n
o
f
r
o
u
tin
g
p
o
licies.
T
h
e
in
teg
r
atio
n
b
etwe
en
th
e
two
tech
n
o
lo
g
ies
cr
ea
tes
a
r
o
b
u
s
t
ar
ch
itectu
r
e
wh
er
e
AI
p
r
o
v
id
es
in
tellig
en
ce
f
o
r
d
ec
is
io
n
-
m
ak
in
g
an
d
SDN
f
ac
ilit
ates
its
im
p
lem
en
tatio
n
an
d
co
n
tr
o
l,
o
p
tim
izin
g
r
o
u
tin
g
an
d
d
em
o
cr
atizin
g
th
e
m
a
n
a
g
em
en
t o
f
co
m
p
le
x
n
etwo
r
k
s
.
b.
Secu
r
ity
an
d
p
r
iv
ac
y
in
em
er
g
in
g
s
o
lu
tio
n
s
Secu
r
ity
an
d
p
r
iv
ac
y
ar
e
f
u
n
d
am
e
n
tal
co
n
ce
r
n
s
in
V
ANE
T
,
g
iv
en
th
e
am
o
u
n
t
o
f
s
en
s
itiv
e
in
f
o
r
m
atio
n
s
h
ar
ed
an
d
th
eir
cr
itical
r
o
le
in
r
o
ad
s
af
ety
.
AI
p
lay
s
a
cr
u
cial
r
o
le
in
en
h
a
n
cin
g
s
ec
u
r
ity
b
y
d
etec
tin
g
an
d
p
r
e
v
en
tin
g
at
tack
s
,
au
th
en
ticatin
g
n
o
d
es,
an
d
s
af
eg
u
ar
d
in
g
p
r
iv
ac
y
b
y
id
en
tify
in
g
an
o
m
alo
u
s
b
eh
av
i
o
r
.
SDN
co
m
p
lem
en
ts
th
is
b
y
ce
n
tr
aliz
in
g
s
ec
u
r
ity
p
o
licies,
e
n
ab
lin
g
f
aster
th
r
ea
t
r
esp
o
n
s
es a
n
d
ef
f
ec
tiv
e
m
itig
a
tio
n
.
T
h
e
co
m
b
in
atio
n
o
f
AI
a
n
d
SDN
en
ab
les r
o
b
u
s
t tr
u
s
t m
ec
h
an
is
m
s
,
an
d
em
er
g
in
g
tech
n
o
lo
g
ies,
s
u
ch
as
b
lo
ck
ch
ain
,
ar
e
b
ein
g
ex
p
lo
r
ed
to
s
tr
en
g
th
en
au
th
en
ti
ca
tio
n
an
d
d
ata
in
teg
r
ity
f
u
r
th
er
,
d
r
iv
in
g
p
r
e
d
i
ctiv
e
an
d
p
r
o
ac
tiv
e
s
ec
u
r
ity
.
c.
I
n
teg
r
atio
n
with
co
m
p
lem
e
n
ta
r
y
tech
n
o
lo
g
ies:
5
G,
E
d
g
e
an
d
UAVs
T
h
e
f
u
t
u
r
e
o
f
VANE
T
is
cl
o
s
ely
tied
to
in
teg
r
atio
n
with
co
m
p
lem
en
tar
y
tech
n
o
lo
g
ies
th
a
t
en
h
an
ce
its
ca
p
ab
ilit
ies.
5
G
tech
n
o
lo
g
y
,
with
its
u
ltra
-
f
ast
s
p
ee
d
s
an
d
lo
w
laten
cy
,
is
ess
en
tial
f
o
r
d
em
a
n
d
in
g
v
eh
icu
lar
ap
p
licatio
n
s
s
u
c
h
as
au
to
n
o
m
o
u
s
d
r
i
v
in
g
.
E
d
g
e
c
o
m
p
u
tin
g
b
r
i
n
g
s
co
m
p
u
tin
g
r
es
o
u
r
ce
s
clo
s
er
to
v
eh
icles
an
d
R
SUs
,
r
ed
u
ci
n
g
laten
cy
b
y
p
r
o
ce
s
s
in
g
d
ata
l
o
ca
lly
.
Un
cr
ewe
d
ae
r
ial
v
e
h
icles
(
UAVs)
o
f
f
er
f
lex
ib
le,
o
n
-
d
em
a
n
d
n
etwo
r
k
co
v
er
ag
e,
ac
tin
g
as
m
o
b
ile
r
e
lay
s
.
T
h
is
in
teg
r
atio
n
,
co
m
b
i
n
ed
with
SDN,
en
ab
les
d
y
n
am
ic
r
eso
u
r
ce
allo
ca
tio
n
an
d
o
p
tim
ized
r
o
u
tin
g
,
f
o
s
ter
in
g
a
d
is
tr
ib
u
ted
in
tellig
en
ce
p
ar
ad
ig
m
an
d
im
p
r
o
v
in
g
n
etwo
r
k
r
esil
ien
ce
.
8.
CO
NCLU
SI
O
N
T
h
e
ev
o
lu
tio
n
o
f
VANE
T
h
as
b
ee
n
ex
p
l
o
r
ed
with
a
p
ar
ticu
lar
f
o
cu
s
o
n
th
e
c
h
al
len
g
es
an
d
o
p
p
o
r
tu
n
ities
p
r
esen
ted
b
y
r
o
u
tin
g
in
th
ese
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
.
A
th
o
r
o
u
g
h
an
al
y
s
is
o
f
th
e
s
cien
tific
liter
atu
r
e
r
ev
ea
le
d
th
at
in
teg
r
a
tin
g
AI
a
n
d
SDN
is
a
k
e
y
s
tr
a
teg
y
f
o
r
o
v
er
c
o
m
in
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
r
o
u
tin
g
p
r
o
to
c
o
ls
an
d
en
h
a
n
c
in
g
th
e
ef
f
icien
cy
,
s
ec
u
r
ity
,
an
d
r
eliab
ilit
y
o
f
VANE
T
.
T
h
e
ab
ilit
y
o
f
AI
to
an
aly
ze
lar
g
e
v
o
lu
m
es
o
f
d
at
a
in
r
ea
l
-
tim
e,
c
o
u
p
le
d
with
t
h
e
f
lex
ib
ilit
y
a
n
d
ce
n
tr
alize
d
co
n
tr
o
l
o
f
f
er
e
d
b
y
SDN,
h
as
g
iv
en
r
is
e
to
in
n
o
v
ativ
e
s
o
lu
tio
n
s
th
at
en
ab
le
m
o
r
e
in
tellig
en
t
an
d
ad
a
p
tiv
e
tr
af
f
ic
m
an
ag
em
e
n
t.
T
h
is
s
tu
d
y
aim
ed
to
s
y
s
tem
atize
th
e
ex
is
tin
g
k
n
o
wled
g
e
o
n
th
e
jo
in
t
u
s
e
o
f
AI
a
n
d
SDN
in
th
e
co
n
tex
t
o
f
VANE
T
,
p
r
o
v
i
d
in
g
an
u
p
d
ate
d
f
r
am
ew
o
r
k
f
o
r
th
e
cu
r
r
e
n
t
s
o
lu
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h
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p
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I
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5395
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im
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t
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o
te
th
at
th
e
in
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r
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o
f
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SDN
in
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ten
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s
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r
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o
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ti
m
izatio
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h
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tech
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o
l
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ies
ca
n
also
co
n
tr
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te
to
im
p
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a
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tr
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f
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alies,
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alicio
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s
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a
n
d
a
u
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ticatin
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o
d
es.
R
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e
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ies,
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h
e
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g
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,
SDN,
an
d
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th
er
in
n
o
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ativ
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tech
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lo
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way
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o
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a
f
u
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r
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VANE
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will
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o
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s
u
s
tain
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tr
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s
p
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tatio
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s
y
s
t
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s
.
C
o
n
tin
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r
esear
ch
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t
h
is
ar
ea
p
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o
m
is
es
to
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ev
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lu
tio
n
ize
m
o
b
ilit
y
a
n
d
tr
a
n
s
f
o
r
m
th
e
wa
y
we
in
ter
ac
t w
i
th
o
u
r
e
n
v
ir
o
n
m
en
ts
.
F
UNDING
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Au
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RE
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R
E
NC
E
S
[
1
]
T.
C
h
a
t
t
e
r
j
e
e
,
R
.
K
a
r
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a
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.
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d
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m
,
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.
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t
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y
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.
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.
[
2
]
J.
B
h
a
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,
R
.
D
a
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,
H
.
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.
[
3
]
G
.
D
e
La
To
r
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e
,
P
.
R
a
d
,
a
n
d
K
.
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.
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.
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h
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,
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Evaluation Warning : The document was created with Spire.PDF for Python.