I
AE
S
I
n
t
e
r
n
at
ion
al
Jou
r
n
al
of
Ar
t
if
icial
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
Vol.
14
,
No.
4
,
Augus
t
2025
,
pp.
2788
~
2796
I
S
S
N:
2252
-
8938
,
DO
I
:
10
.
11591/i
jai
.
v
14
.i
4
.
pp
27
88
-
2796
2788
Jou
r
n
al
h
omepage
:
ht
tp:
//
ij
ai
.
iaes
c
or
e
.
c
om
A
n
op
t
imal p
h
e
r
om
on
e
-
b
ase
d
r
ou
t
e
d
is
c
ov
e
r
y s
t
age
f
or
5G
c
om
m
u
n
ic
a
t
io
n
p
r
o
c
e
ss i
n
w
i
r
e
le
ss s
e
n
sor
n
e
t
w
or
k
s
S
in
d
u
j
a
M
ys
or
e
S
id
d
ar
am
u
1
,
Kan
a
t
h
u
r
Ra
m
as
wam
y
Re
k
h
a
2
1
D
e
pa
r
tm
e
nt
of
E
le
c
tr
oni
c
s
a
nd C
omm
uni
c
a
ti
on E
ngi
ne
e
r
in
g, V
is
ve
s
va
r
a
y
a
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
it
y, B
e
l
a
ga
vi
, I
ndi
a
2
D
e
pa
r
tm
e
nt
of
E
le
c
tr
oni
c
s
a
nd C
omm
uni
c
a
ti
on E
ngi
ne
e
r
in
g, S
J
B
I
ns
ti
tu
te
of
T
e
c
hnol
ogy,
B
e
nga
lu
r
u, I
ndi
a
Ar
t
icle
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
ived
M
a
r
29,
2024
R
e
vis
e
d
M
a
r
19,
2025
Ac
c
e
pted
J
un
8,
2025
T
h
e
rap
i
d
ad
v
an
c
emen
t
o
f
5
G
co
mmu
n
i
ca
t
i
o
n
u
n
d
er
s
co
re
s
t
h
e
n
ee
d
fo
r
h
ei
g
h
t
en
e
d
effi
ci
en
c
y
w
i
t
h
i
n
w
i
rel
e
s
s
s
en
s
o
r
n
e
t
w
o
r
k
s
(W
SN
s
),
w
h
ere
ch
al
l
en
g
es
s
u
ch
a
s
d
at
a
l
o
s
s
,
i
n
effi
c
i
en
c
y
,
an
d
j
i
t
t
er
are
ex
acerb
at
e
d
b
y
co
mp
l
ex
o
p
era
t
i
o
n
s
.
T
h
i
s
p
a
p
er
p
re
s
en
t
s
t
h
e
o
p
t
i
ma
l
p
h
ero
mo
n
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-
b
a
s
ed
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i
s
co
v
ery
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ag
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g
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t
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m,
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n
s
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red
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at
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ral
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s
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s
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n
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e
d
y
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am
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c
an
d
d
ema
n
d
i
n
g
5
G
en
v
i
r
o
n
me
n
t
s
.
T
h
e
s
t
u
d
y
co
n
d
u
ct
s
a
co
mp
ara
t
i
v
e
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al
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s
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s
o
f
O
p
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D
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ai
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s
t
t
rad
i
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h
e
A
d
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-
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e
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t
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SR),
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RP),
fo
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e
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rm
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ce
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erg
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m
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t
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C)
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t
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fes
p
an
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ro
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t
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d
i
s
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d
s
cal
a
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i
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u
r
res
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l
t
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ea
l
t
h
a
t
O
p
RD
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s
i
g
n
i
f
i
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l
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t
p
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t
h
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n
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en
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n
a
l
p
ro
t
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l
s
,
ev
i
d
e
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ci
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g
a
2
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creas
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n
P
D
R,
a
5
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l
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cy
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6
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ro
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8
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1
1
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s
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d
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s
s
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n
g
t
h
e
r
o
b
u
s
t
d
eman
d
s
o
f
5
G
n
et
w
o
r
k
s
.
K
e
y
w
o
r
d
s
:
5G
c
omm
unica
ti
on
A
d
h
o
c
o
n
-
d
e
m
a
n
d
d
i
s
t
a
n
c
e
v
e
c
t
o
r
De
s
ti
na
ti
on
-
s
e
que
nc
e
d
dis
tanc
e
-
v
e
c
tor
Dyna
mi
c
s
our
c
e
r
outi
ng
W
ir
e
les
s
s
e
n
s
or
ne
twor
k
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
S
induj
a
M
ys
or
e
S
iddar
a
mu
De
pa
r
tm
e
nt
of
E
lec
tr
onics
a
nd
C
omm
unica
ti
on
E
n
ginee
r
ing,
Vis
ve
s
va
r
a
ya
T
e
c
hnologi
c
a
l
Unive
r
s
it
y
B
e
laga
vi
,
I
ndia
E
mail:
s
induj
a
k29@gmail.
c
om
1.
I
NT
RODU
C
T
I
ON
T
he
a
dve
nt
of
5G
tec
hnology
ha
s
us
he
r
e
d
in
a
ne
w
e
r
a
of
wir
e
les
s
c
omm
unica
ti
on,
c
ha
r
a
c
ter
ize
d
by
unpr
e
c
e
de
nted
da
ta
s
pe
e
ds
,
lowe
r
late
nc
y,
a
nd
the
a
bil
it
y
to
c
onne
c
t
a
va
s
t
numbe
r
o
f
de
vice
s
s
im
ult
a
ne
ous
ly
[
1]
.
T
his
lea
p
f
o
r
wa
r
d
pr
e
s
e
nts
both
op
por
tuni
ti
e
s
a
nd
c
ha
ll
e
nge
s
f
o
r
wir
e
les
s
s
e
ns
or
ne
twor
ks
(
W
S
Ns
)
,
whic
h
a
r
e
pivot
a
l
in
va
r
ious
a
ppli
c
a
ti
ons
r
a
nging
f
r
om
s
mar
t
c
it
ies
a
nd
indus
tr
ial
a
utom
a
ti
on
to
h
e
a
lt
hc
a
r
e
moni
tor
ing
a
nd
e
nvir
onmenta
l
s
e
ns
ing.
W
hil
e
5G
pr
omi
s
e
s
to
e
nha
nc
e
the
c
a
pa
bil
it
ies
of
W
S
Ns
,
tr
a
dit
ional
r
outi
ng
p
r
otocols
s
tr
uggle
to
mee
t
the
de
mands
of
thi
s
ne
w
lands
c
a
pe
.
T
he
s
e
pr
otocols
o
f
ten
f
a
ll
s
hor
t
in
dyna
mi
c
a
ll
y
a
da
pti
ng
to
the
high
mobi
li
ty
,
va
r
i
a
ble
tr
a
f
f
ic
pa
tt
e
r
ns
,
a
nd
the
s
tr
ingent
e
ne
r
gy
c
ons
tr
a
int
s
inher
e
nt
in
W
S
Ns
,
ther
e
by
c
r
e
a
ti
ng
a
ga
p
in
th
e
e
f
f
icie
nt
de
ploym
e
nt
of
5G
tec
hnologi
e
s
with
in
thes
e
ne
twor
ks
.
T
he
ne
e
d
f
or
r
out
ing
mec
ha
nis
ms
th
a
t
c
a
n
s
e
a
ml
e
s
s
ly
int
e
gr
a
te
with
5G's
a
r
c
hit
e
c
tu
r
e
while
opti
mi
z
ing
e
ne
r
gy
c
ons
umpt
ion
(
E
C
)
a
nd
e
ns
ur
ing
r
e
li
a
ble
da
ta
t
r
a
ns
mi
s
s
ion
i
s
mor
e
c
r
it
ica
l
than
e
ve
r
[
2
]
.
Addr
e
s
s
ing
thi
s
ga
p,
the
c
onc
e
pt
of
a
phe
r
omo
ne
-
ba
s
e
d
r
oute
dis
c
ove
r
y
s
tage
p
r
e
s
e
nts
a
nove
l
a
ppr
oa
c
h
by
bor
r
owing
s
tr
a
tegie
s
f
r
om
the
na
t
ur
a
l
wor
ld
,
s
pe
c
if
ica
ll
y
the
f
or
a
g
ing
be
ha
vior
of
a
nts
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
Ar
ti
f
I
ntell
I
S
S
N:
2252
-
8938
A
n
op
ti
mal
phe
r
omone
-
bas
e
d
r
oute
dis
c
ov
e
r
y
s
tag
e
for
5G
c
omm
unication
…
(
S
induj
a
M
y
s
or
e
Siddar
amu)
2789
to
im
pr
ove
r
outi
ng
in
W
S
Ns
unde
r
5G
c
omm
unic
a
ti
on
s
ys
tems
.
T
his
bio
-
ins
pir
e
d
method
of
f
e
r
s
a
dyna
mi
c
a
nd
a
da
pti
ve
s
olut
ion
c
a
pa
ble
of
s
e
lf
-
or
ga
nizing
i
n
r
e
s
pons
e
to
c
ha
nging
ne
twor
k
c
ondit
ions
,
thus
p
r
omi
s
ing
s
igni
f
ica
nt
im
pr
ove
ments
in
ne
twor
k
e
f
f
icie
nc
y
a
nd
r
e
s
il
ienc
e
[
3]
.
T
he
a
ppli
c
a
ti
on
o
f
s
uc
h
a
ph
e
r
omone
-
ba
s
e
d
s
tr
a
tegy
a
im
s
not
only
to
br
idge
the
c
ur
r
e
nt
r
e
s
e
a
r
c
h
ga
ps
by
pr
ov
idi
ng
a
mor
e
r
obus
t
a
nd
e
ne
r
gy
-
e
f
f
icie
nt
r
outi
ng
mec
ha
nis
m
but
a
ls
o
to
unlock
th
e
f
ul
l
potential
of
W
S
Ns
in
the
5G
e
r
a
.
B
y
e
nha
nc
ing
the
pe
r
f
or
manc
e
of
W
S
Ns
,
thi
s
a
pp
r
oa
c
h
c
ould
gr
e
a
tl
y
be
ne
f
it
a
wide
r
a
nge
of
a
ppli
c
a
ti
ons
,
f
r
om
r
e
a
l
-
ti
me
moni
tor
ing
a
nd
c
ontr
ol
in
indus
tr
ial
s
e
tt
ings
to
c
r
i
ti
c
a
l
da
ta
c
oll
e
c
ti
on
in
r
e
mot
e
or
ha
z
a
r
dous
e
nvir
onments
,
ther
e
by
f
a
c
il
it
a
ti
ng
the
s
e
a
ml
e
s
s
int
e
gr
a
ti
on
of
W
S
Ns
int
o
the
5G
inf
r
a
s
tr
uc
tur
e
.
F
igur
e
1
s
hows
the
be
ha
vior
of
a
ne
twor
k
node
ope
r
a
ti
ng
withi
n
a
phe
r
omone
-
ba
s
e
d
r
outi
ng
pr
otocol,
typi
c
a
ll
y
us
e
d
in
s
c
e
na
r
ios
s
uc
h
a
s
W
S
Ns
or
a
nt
c
olony
opti
mi
z
a
ti
o
n
(
AC
O)
a
lgor
it
hms
.
T
he
node
c
yc
les
thr
ough
a
s
e
r
ies
of
s
tate
s
to
mana
ge
da
ta
pa
c
ke
t
r
outi
ng
e
f
f
icie
ntl
y
[
4]
.
I
n
it
s
de
f
a
ult
s
tate
,
the
node
r
e
mains
'I
dle,
'
c
ons
e
r
ving
r
e
s
our
c
e
s
while
moni
tor
ing
f
or
incoming
da
ta
or
a
wa
it
ing
ins
tr
uc
ti
on
s
.
Upon
r
e
c
e
ivi
ng
da
ta
f
o
r
t
r
a
ns
mi
s
s
ion,
the
node
tr
a
ns
it
ions
to
the
'P
he
r
omone
e
mi
s
s
ion'
s
tate
,
whe
r
e
it
meta
phor
ica
ll
y
e
mi
ts
phe
r
omones
to
ma
r
k
the
da
ta's
pa
th,
much
li
ke
a
nts
lea
ve
tr
a
il
s
f
o
r
other
s
to
f
oll
ow.
T
his
phe
r
omone
s
e
r
ve
s
a
s
a
na
vigational
guide
f
or
other
node
s
,
indi
c
a
ti
ng
a
viable
r
oute.
F
igur
e
1.
F
unda
menta
l
f
low
c
ha
r
t
of
a
phe
r
omone
-
ba
s
e
d
r
outi
ng
pr
otocol
S
im
ult
a
ne
ous
ly,
the
node
e
nga
ge
s
in
'f
or
wa
r
ding
a
tt
it
ude
e
s
ti
mation
,
'
e
va
luating
it
s
c
a
pa
c
it
y
a
nd
will
ingnes
s
to
f
or
wa
r
d
pa
c
ke
ts
,
whic
h
c
ould
de
pe
nd
on
the
node
's
c
ur
r
e
nt
load
,
e
ne
r
gy
r
e
s
e
r
ve
s
,
or
the
s
tr
e
ngth
a
nd
pe
r
s
is
tenc
e
of
the
phe
r
omone
tr
a
il
[
5]
.
A
c
r
it
ica
l
c
omponent
o
f
thi
s
p
r
oc
e
s
s
is
the
'ph
e
r
omone
e
va
por
a
ti
on
,
'
r
e
f
lec
ti
ng
the
tempor
a
l
na
tur
e
of
r
outi
ng
pa
ths
.
P
he
r
omones
g
r
a
dua
ll
y
dis
s
ipate
ov
e
r
ti
me
,
mi
r
r
or
ing
the
de
c
r
e
a
s
ing
de
s
ir
a
bil
it
y
o
f
pa
ths
that
a
r
e
le
s
s
f
r
e
que
nted
or
outdate
d
due
to
ne
twor
k
c
ha
nge
s
.
T
his
na
tur
a
l
de
c
a
y
pr
e
ve
nts
the
ove
r
-
r
e
li
a
nc
e
on
o
lder
r
outes
a
nd
p
r
omot
e
s
the
dis
c
ove
r
y
of
ne
w,
po
tentially
mor
e
e
f
f
icie
nt
pa
ths
[
6]
,
[
7]
.
Additi
ona
l
ly,
the
node
is
r
e
s
pons
ibl
e
f
or
'upda
ti
n
g
r
outi
ng
table
,
'
whic
h
incor
po
r
a
tes
the
dyna
mi
c
phe
r
omone
inf
or
mat
ion
to
a
djus
t
the
r
ou
ti
ng
de
c
is
ions
.
T
his
e
ns
ur
e
s
that
the
mos
t
e
f
f
icie
nt
r
outes
,
i
ndica
ted
by
s
tr
onge
r
phe
r
omone
leve
ls
,
a
r
e
pr
e
f
e
r
r
e
d
f
o
r
f
utur
e
pa
c
ke
t
f
or
wa
r
d
ing
[
8]
,
[
9
]
.
L
a
s
tl
y,
the
s
ys
tem
is
gove
r
ne
d
by
'ti
me
r
t
r
igger
e
d
e
ve
nt
s
,
'
whic
h
li
ke
l
y
include
the
r
outi
ne
de
c
r
e
ment
of
phe
r
omone
l
e
ve
ls
to
s
im
ulate
e
va
por
a
ti
on
a
nd
the
pe
r
iodi
c
r
e
a
s
s
e
s
s
men
t
of
r
outi
ng
s
tr
a
tegie
s
.
T
his
ti
me
-
ba
s
e
d
mec
ha
nis
m
e
ns
ur
e
s
the
ne
twor
k
a
da
pts
to
e
volvi
ng
c
ondit
ions
,
maintai
ning
the
r
e
leva
nc
e
a
nd
e
f
f
ic
ienc
y
of
the
r
outi
ng
pa
t
hs
.
2.
M
E
T
HO
DOL
OG
Y
F
igur
e
2
s
hows
the
p
r
opos
e
d
methodology
,
a
c
om
pr
e
he
ns
ive
methodology
f
o
r
the
de
ve
lopm
e
nt
of
a
phe
r
omone
-
ba
s
e
d
r
outi
ng
a
lgor
it
hm
,
s
pe
c
if
ica
ll
y
d
e
s
igned
f
or
5G
W
S
Ns
.
T
he
methodology
is
or
ga
ni
z
e
d
int
o
f
ive
dis
ti
nc
t
s
tage
s
:
‒
T
he
f
i
r
s
t
s
tage
,
"
a
lgor
it
hm
de
s
ign
,
"
invol
ve
s
the
c
r
e
a
ti
on
of
the
r
ou
ti
ng
a
l
go
r
it
hm.
T
his
de
s
ign
is
ins
pir
e
d
by
the
e
f
f
icie
nt
f
o
r
a
ging
be
ha
vior
o
f
a
nts
,
uti
li
z
i
ng
phe
r
omone
tr
a
il
s
f
o
r
dyna
mi
c
r
oute
s
e
lec
ti
on.
T
he
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
Ar
ti
f
I
ntell
,
Vol.
14
,
No.
4
,
Augus
t
20
25
:
278
8
-
2796
2790
a
lgor
it
hm
incor
po
r
a
tes
mec
ha
nis
ms
f
or
both
de
po
s
it
ing
a
nd
e
va
por
a
ti
ng
phe
r
o
mones
,
a
im
ing
to
f
in
d
the
mos
t
e
f
f
icie
nt
pa
t
h
f
or
da
ta
t
r
a
ns
mi
s
s
ion.
‒
I
n
the
"
S
im
ulation
e
nvir
onment
s
e
tup
"
s
tage
,
tool
s
li
ke
NS3
or
OM
Ne
T
+
+
a
r
e
us
e
d
to
s
im
ulate
va
r
ious
W
S
N
s
c
e
na
r
ios
unde
r
5G
ne
twor
k
c
ondit
ions
.
T
his
s
tep
is
c
r
it
ica
l
f
or
tes
ti
ng
the
a
lgor
it
hm
a
c
r
os
s
a
r
a
nge
of
ne
twor
k
dyna
mi
c
s
,
incl
uding
node
mob
il
it
y
a
nd
tr
a
f
f
ic
va
r
iations
,
e
ns
ur
ing
the
a
lgor
it
h
m
is
r
obus
t
a
nd
ve
r
s
a
ti
le
[
10]
–
[
12
]
.
‒
Th
e
thi
r
d
s
tage
is
"
pe
r
f
or
manc
e
be
nc
hmar
king
,
"
w
he
r
e
the
ne
wly
de
ve
loped
a
lgor
it
hm
is
r
igor
ous
ly
t
e
s
ted
a
ga
ins
t
tr
a
dit
ional
r
outi
ng
p
r
otocols
,
s
uc
h
a
s
A
d
hoc
on
-
de
mand
dis
tanc
e
ve
c
tor
(
AODV
)
a
nd
de
s
ti
na
ti
on
-
s
e
que
nc
e
d
dis
tanc
e
-
ve
c
tor
(
DSDV)
.
T
he
e
va
luation
f
oc
us
e
s
on
ke
y
pe
r
f
o
r
manc
e
indi
c
a
tor
s
,
including
late
nc
y,
pa
c
ke
t
de
li
ve
r
y
r
a
ti
o
(
P
DR
)
,
a
nd
EC
,
to
va
li
da
te
the
a
lgor
it
hm's
e
f
f
icie
nc
y
a
nd
e
f
f
e
c
ti
ve
ne
s
s
[
13]
.
‒
"
Optim
iza
ti
on
a
nd
t
uning"
invol
ve
it
e
r
a
ti
ve
r
e
f
ine
ment
of
the
a
lgor
it
hm's
pa
r
a
mete
r
s
.
T
his
s
tage
may
a
ls
o
int
e
gr
a
te
mac
hine
lea
r
ning
tec
hniques
to
a
da
pti
ve
ly
e
nha
nc
e
the
a
lgor
it
hm
ba
s
e
d
on
the
c
oll
e
c
ted
pe
r
f
or
manc
e
da
ta,
e
ns
ur
ing
that
the
r
outi
ng
de
c
i
s
ions
c
onti
nuous
ly
im
pr
ove
in
r
e
s
pons
e
to
c
ha
nging
ne
twor
k
e
nvir
onments
.
‒
F
inally,
the
"
r
e
a
l
-
wor
ld
tes
ti
ng
a
nd
va
li
da
ti
on
"
ph
a
s
e
moves
the
a
lgor
i
thm
f
r
om
theor
y
to
p
r
a
c
ti
c
e
.
He
r
e
,
pil
ot
tes
ts
a
r
e
c
onduc
ted
withi
n
a
ppli
c
a
ti
on
-
s
pe
c
if
ic
s
c
e
na
r
ios
to
ve
r
if
y
the
pr
otocol's
p
r
a
c
ti
c
a
li
ty
a
nd
e
f
f
e
c
ti
ve
ne
s
s
.
Adjus
tm
e
nts
a
r
e
made
ba
s
e
d
on
thes
e
r
e
a
l
-
wor
ld
tes
ts
be
f
or
e
the
a
lgor
it
hm
is
r
e
c
omm
e
nde
d
f
or
br
oa
de
r
de
ploym
e
nt.
T
his
e
ns
ur
e
s
that
whe
n
the
a
lgor
it
hm
is
f
inally
de
ployed,
it
is
not
only
theor
e
ti
c
a
ll
y
s
ound
but
a
ls
o
p
r
ove
n
in
p
r
a
c
ti
c
a
l
a
pp
li
c
a
ti
ons
.
F
igur
e
2
.
P
r
opos
e
d
methodology
f
o
r
the
opti
mal
p
he
r
omone
-
ba
s
e
d
r
oute
dis
c
ove
r
y
s
tage
(
OpR
DS)
a
lgor
it
hm
3.
P
ROP
OS
E
D
M
E
T
HO
D
T
he
pr
opos
e
d
phe
r
omone
-
ba
s
e
d
r
outi
ng
a
lgor
i
thm
f
o
r
5G
c
omm
unica
ti
on
in
W
S
Ns
dr
a
ws
ins
pir
a
ti
on
f
r
om
the
f
o
r
a
ging
be
ha
vior
of
a
nts
,
w
he
r
e
they
f
ind
the
s
hor
tes
t
pa
th
be
twe
e
n
their
c
ol
ony
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
Ar
ti
f
I
ntell
I
S
S
N:
2252
-
8938
A
n
op
ti
mal
phe
r
omone
-
bas
e
d
r
oute
dis
c
ov
e
r
y
s
tag
e
for
5G
c
omm
unication
…
(
S
induj
a
M
y
s
or
e
Siddar
amu)
2791
f
ood
s
our
c
e
s
us
ing
phe
r
omone
tr
a
il
s
.
T
he
a
lgo
r
it
h
m's
c
or
e
is
to
dyna
mi
c
a
ll
y
a
da
pt
r
outi
ng
pa
ths
ba
s
e
d
on
the
"
s
tr
e
ngth"
of
phe
r
omone
tr
a
il
s
,
whic
h
r
e
pr
e
s
e
nt
the
r
oute's
qua
li
ty
or
e
f
f
icie
nc
y.
F
igu
r
e
3
s
hows
a
p
r
opos
e
d
a
lgor
it
hm
f
o
r
a
s
ophis
ti
c
a
ted
W
S
N
int
e
gr
a
ted
with
5G
tec
hnology
f
or
e
f
f
icie
nt
da
ta
tr
a
ns
mi
s
s
ion
a
nd
a
dva
nc
e
d
a
na
lyt
ics
.
S
e
ns
or
s
1
to
s
e
r
ve
a
s
the
da
ta
c
oll
e
c
ti
on
e
nd
point
s
,
c
onti
nuous
ly
moni
to
r
ing
a
nd
ga
ther
ing
e
nvir
onmenta
l
inpu
ts
[
14]
–
[
16]
.
F
igur
e
3
.
P
r
opos
e
d
f
unc
ti
ona
l
block
diagr
a
m
o
f
Op
R
DS
a
lgor
it
hm
L
e
ve
r
a
ging
the
high
-
s
pe
e
d
a
nd
low
-
late
nc
y
c
a
pa
bil
it
ies
of
5G
ne
twor
ks
,
thes
e
s
e
ns
or
s
r
e
lay
their
da
ta
to
a
n
e
mbedde
d
ha
r
dwa
r
e
model,
r
e
f
e
r
r
e
d
to
a
s
the
doc
ument
c
ontaine
r
with
a
n
OpR
DS.
T
hi
s
c
e
ntr
a
l
unit
is
tas
ke
d
with
the
ini
ti
a
l
da
ta
pr
oc
e
s
s
ing,
whi
c
h
in
c
ludes
c
oll
e
c
ti
on
a
nd
pa
tt
e
r
n
r
e
c
ognit
ion,
pos
s
ibl
y
to
s
tr
e
a
ml
ine
the
da
ta
f
or
s
ubs
e
que
nt
a
na
lys
is
.
P
os
t
i
nit
ial
pr
oc
e
s
s
ing,
the
da
ta
a
r
e
tr
a
ns
mi
tt
e
d
onc
e
mo
r
e
via
the
5G
ne
twor
k
to
a
c
loud
-
ba
s
e
d
s
tor
a
ge
s
ys
t
e
m,
indi
c
a
ti
ng
a
two
-
ti
e
r
da
ta
tr
a
ns
mi
s
s
ion
a
ppr
oa
c
h
to
e
ns
ur
e
r
obus
tnes
s
a
nd
s
c
a
labili
ty.
I
n
the
c
loud
,
a
big
da
t
a
da
taba
s
e
hous
e
s
the
incoming
s
e
ns
or
da
ta,
e
quipped
to
mana
ge
the
e
xtens
ive
volum
e
a
nd
va
r
iety
c
ha
r
a
c
te
r
is
ti
c
of
W
S
Ns
.
T
his
da
taba
s
e
s
e
r
ve
s
a
s
the
f
ound
a
ti
on
f
or
the
s
ubs
e
que
nt
big
da
ta
c
h
a
r
t
a
na
lys
is
pha
s
e
,
w
he
r
e
s
ophis
ti
c
a
ted
a
lgor
it
hms
a
na
lyze
the
d
a
ta
t
o
unve
il
tr
e
nds
,
pa
tt
e
r
ns
,
a
nd
ins
ight
s
[
17]
–
[
19]
.
F
inally,
the
a
na
lyze
d
da
ta
a
r
e
dis
s
e
mi
na
ted
f
or
pr
a
c
ti
c
a
l
us
e
,
potentially
a
c
r
os
s
mul
t
ipl
e
platf
o
r
ms
.
T
his
c
ould
include
vis
ua
li
z
a
ti
on
on
a
c
omput
e
r
f
or
human
a
na
lys
ts
or
dir
e
c
t
r
e
lay
to
mobi
le
de
vice
s
f
or
r
e
a
l
-
ti
me
moni
tor
ing.
T
he
s
ys
tem's
de
s
ign
r
e
f
lec
ts
a
c
ompr
e
he
ns
ive
a
ppr
oa
c
h
to
da
ta
-
dr
iven
de
c
is
ion
-
making,
ha
r
ne
s
s
ing
the
powe
r
of
5G
to
e
na
ble
a
s
e
a
ml
e
s
s
f
low
f
r
om
da
ta
c
oll
e
c
ti
on
th
r
ough
to
a
c
ti
ona
ble
ins
ig
hts
.
3.
1.
P
r
op
os
e
d
m
at
h
e
m
at
ical
m
od
e
l
f
or
Op
RD
S
f
or
5G
c
om
m
u
n
icat
ion
p
r
oc
e
s
s
in
WS
N
T
he
p
r
opos
e
d
mathe
matica
l
model
f
or
the
OpR
DS
a
lgor
it
h
m
in
5G
W
S
Ns
is
ins
pir
e
d
by
AC
O
tec
hniq
ue
s
.
I
t
us
e
s
vir
tual
phe
r
omones
to
mar
k
e
f
f
icie
nt
da
ta
tr
a
ns
mi
s
s
ion
pa
ths
,
dyna
mi
c
a
ll
y
a
djus
ti
ng
thes
e
mar
ke
r
s
ba
s
e
d
on
the
s
uc
c
e
s
s
of
pa
c
ke
t
de
li
ve
r
ie
s
.
T
he
model
opti
mi
z
e
s
r
ou
te
dis
c
ove
r
y
a
nd
s
e
lec
ti
on
by
r
e
inf
or
c
ing
pa
ths
with
s
uc
c
e
s
s
f
ul
de
li
ve
r
ies
,
the
r
e
by
e
nc
our
a
ging
their
r
e
us
e
.
T
his
a
ppr
oa
c
h
a
ll
o
ws
f
or
a
n
a
da
pti
ve
ne
twor
k
that
e
f
f
icie
ntl
y
mana
ge
s
the
dyna
mi
c
c
ondit
ions
of
5G
c
omm
unica
ti
on
,
s
ign
if
ica
ntl
y
e
nha
nc
ing
da
ta
thr
oughput,
r
e
duc
ing
late
nc
y,
a
nd
im
pr
oving
the
ove
r
a
ll
r
e
li
a
bil
it
y
a
nd
e
ne
r
gy
e
f
f
i
c
ienc
y
of
the
W
S
N
[
20
]
–
[
22]
.
3.
1.
1.
P
h
e
r
om
on
e
u
p
d
at
e
r
u
le
W
he
r
e
(
)
is
the
phe
r
omone
leve
l
on
the
li
nk
f
r
om
n
ode
i
to
node
j
a
t
ti
me
t,
is
the
e
va
por
a
ti
on
r
a
te
(
0<
<
1
)
,
a
nd
∆
(
)
is
the
a
mount
o
f
phe
r
omone
a
dde
d
ba
s
e
d
on
the
r
e
c
e
nt
pa
c
ke
t
tr
a
ns
mi
s
s
ion,
whic
h
c
ould
de
pe
nd
on
f
a
c
tor
s
li
ke
late
nc
y
a
nd
e
ne
r
gy
e
f
f
icie
nc
y.
T
he
A
lgor
it
hm
1
s
hows
the
s
tep
-
by
-
s
tep
pr
oc
e
s
s
of
(
1)
.
(
+
1
)
=
(
1
−
)
.
(
)
+
∆
(
)
(
1)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
Ar
ti
f
I
ntell
,
Vol.
14
,
No.
4
,
Augus
t
20
25
:
278
8
-
2796
2792
Algor
it
hm_1
:
S
teps
f
or
phe
r
omone
upda
te
r
ule
us
i
ng
tr
a
ns
mi
s
s
ion
f
e
e
dba
c
k
a
nd
e
va
por
a
ti
on
Step_1:
monitor packet transmission
Upon
successful
transmission
of
a
packet
from
node
i
to
node
j,
trigger
the
ph
eromone
update process.
Step_2:
calculate pheromone evaporation
Com
pute
the
pheromone
decay
for
the
link
from
node
i
to
node
j
by
multiplying
the
current
pheromone level
(
)
by the evaporation rate
(
1
−
)
.
Step_3:
determine pheromone increment
Calculate
the
increment
∆
(
)
based
on
the
quality
of
the
recent
packet
transmission,
whi
ch
could incorporate factors like latency and energy efficiency.
Step_4:
update pheromone level
Add
the
pheromone
increment
from
Step_3
t
o
the
decayed
pheromone
level
from
Step_2
to
get
the updated pheromone level
(
+
1
)
Step_5:
store updated pheromone
Save the new pheromone level
(
+
1
)
in the system for the link from node i to node j.
Step_6:
adapt to network conditions
Continuously
repeat
this
process
for
all
links
after
each
packet
transmission
to
ensure
the
pheromone lev
els accurately reflect current network conditions and transmission quality.
3.
1.
2.
Rout
e
s
e
lec
t
ion
p
r
ob
ab
i
li
t
y
T
he
r
oute
s
e
lec
ti
on
pr
oba
bil
it
y
in
a
phe
r
omone
-
ba
s
e
d
s
ys
tem
de
ter
mi
ne
s
the
li
ke
li
hood
of
a
node
c
hoos
ing
a
pa
r
ti
c
ular
pa
th
f
o
r
pa
c
ke
t
f
or
wa
r
ding
.
T
his
p
r
oba
bil
it
y
is
c
a
lcula
ted
us
ing
the
phe
r
omo
ne
leve
l
a
nd
the
de
s
ir
a
bil
it
y
of
the
li
nk
a
s
given
in
(
1
)
,
wh
ich
a
r
e
in
f
luenc
e
d
by
f
a
c
tor
s
s
uc
h
a
s
the
r
e
c
e
nt
s
u
c
c
e
s
s
of
tr
a
ns
mi
s
s
ions
(
phe
r
omone
s
tr
e
ngth)
a
nd
li
nk
qua
li
ty
(
li
ke
late
nc
y)
.
Highe
r
p
r
oba
bil
it
ies
a
r
e
a
s
s
igned
t
o
r
outes
with
s
tr
onge
r
phe
r
o
mone
leve
ls
a
nd
be
tt
e
r
li
nk
qua
li
ty,
guid
ing
node
s
to
f
a
vor
thes
e
pa
ths
[
23]
–
[
25
]
.
=
[
(
)
]
.
[
ƞ
]
∑
[
(
)
]
.
[
ƞ
]
(
2)
W
he
r
e
is
the
p
r
oba
bil
it
y
of
s
e
lec
ti
ng
the
li
nk
f
r
om
node
i
to
node
j,
ƞ
is
the
de
s
ir
a
bil
it
y
of
the
li
nk
(
e
.
g
.
,
i
nv
e
r
s
e
o
f
la
te
nc
y)
,
a
n
d
a
r
e
pa
r
a
m
e
te
r
s
t
ha
t
c
on
t
r
o
l
the
r
e
l
a
t
iv
e
i
n
f
l
ue
nc
e
o
f
p
he
r
o
mo
ne
s
t
r
e
ng
th
a
nd
l
i
nk
de
s
i
r
a
b
i
li
ty
,
a
n
d
is
th
e
s
e
t
o
f
n
e
i
gh
bo
r
no
de
s
of
i
.
T
he
A
l
go
r
it
h
m
2
s
how
s
t
he
s
tep
-
by
-
s
te
p
p
r
o
c
e
s
s
o
f
(
2
)
.
Algor
it
hm_2
:
S
tep
f
o
r
c
omput
ing
r
oute
s
e
lec
ti
on
p
r
oba
bil
it
y
ba
s
e
d
on
phe
r
o
mone
a
nd
li
nk
de
s
ir
a
bil
it
y
Step_1:
f
or each link from node i to neighbor node j, calculate
(
)
ƞ
.
Step_2:
s
um
the
calculated
values
for
all
links
from
node
i
to
all
its
neighbors
k
to
form
the denominator.
Step_3:
d
ivide
the
value
from
Step_1
for
the
link
to
node
j
by
the
sum
from
Step_2
to
get
, the probability for selecting the link to node j.
Step_4:
u
se
to probabilistically select the next hop for routing.
3.
1.
3.
P
h
e
r
o
m
on
e
e
vap
or
at
io
n
T
his
e
qua
ti
on
a
ppli
e
s
whe
n
no
ne
w
phe
r
omone
is
a
dde
d,
r
e
f
lec
ti
ng
the
na
tur
a
l
de
c
a
y
of
phe
r
o
mone
ove
r
ti
me
due
to
e
va
por
a
ti
on
.
T
he
s
e
e
qua
ti
ons
pr
ovide
a
f
r
a
mew
or
k
f
or
im
pleme
nti
ng
the
phe
r
omo
ne
-
ba
s
e
d
r
outi
ng
a
lgo
r
it
hm,
a
ll
owing
f
or
dyna
mi
c
a
nd
a
d
a
pti
ve
r
oute
op
ti
mi
z
a
ti
on
in
W
S
Ns
tailo
r
e
d
f
or
the
5G
c
omm
unica
ti
on
c
ontext.
T
he
ba
lanc
e
be
twe
e
n
e
xplor
a
ti
on
(
f
indi
ng
ne
w
r
outes
)
a
nd
e
x
p
loi
tation
(
us
ing
known
e
f
f
icie
nt
r
outes
)
is
ke
y
to
the
a
lg
or
it
hm's
e
f
f
e
c
ti
ve
ne
s
s
,
e
na
bli
ng
it
to
r
e
s
pond
f
le
xibl
y
to
c
ha
nging
ne
twor
k
c
ondit
ions
.
T
he
A
lgo
r
it
hm
3
s
ho
ws
the
s
tep
-
by
-
s
tep
pr
oc
e
s
s
of
(
3
).
(
+
1
)
=
(
1
−
)
.
(
)
(
3)
Algor
it
hm_3:
S
tep
f
o
r
p
he
r
omone
e
va
por
a
ti
on
f
or
a
da
pti
ve
r
outi
ng
in
W
S
Ns
Step_1:
i
dentify
the
pheromone
level
(
)
on
the
link
from
node
i
to
node
j
at
the
cur
rent
time t.
Step_2:
c
alculate
the
reduced
pheromone
level
due
to
evaporation
by
multiplying
(
)
by
(
1
−
)
, where
is the evaporation rate.
Step_3:
u
pdate the pheromone level for the link to
(
+
1
)
with the result from Step_2.
Step_4:
s
tore the updated pheromone level
(
+
1
)
fo
r future use in route selection.
4.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
F
or
a
s
im
ulation
invol
ving
a
phe
r
omone
-
ba
s
e
d
r
outi
ng
a
lgor
it
hm
in
a
5G
W
S
N,
a
pp
r
opr
iate
va
lues
f
or
the
r
a
nge
would
be
de
ter
mi
ne
d
by
the
s
pe
c
if
ic
r
e
quir
e
ments
of
the
s
im
ulation
a
nd
the
e
xpe
c
ted
r
e
a
l
-
wor
ld
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
Ar
ti
f
I
ntell
I
S
S
N:
2252
-
8938
A
n
op
ti
mal
phe
r
omone
-
bas
e
d
r
oute
dis
c
ov
e
r
y
s
tag
e
for
5G
c
omm
unication
…
(
S
induj
a
M
y
s
or
e
Siddar
amu)
2793
c
ondit
ions
.
T
a
ble
1
s
hows
the
s
im
ulation
pa
r
a
met
e
r
s
f
or
the
phe
r
omone
-
ba
s
e
d
r
oute
dis
c
ove
r
y
s
tage
f
or
the
5G
c
omm
unica
ti
on
pr
oc
e
s
s
in
W
S
N
.
T
he
s
e
va
lue
s
would
c
r
e
a
te
a
s
im
ulation
e
nvir
onment
that
is
c
ompl
e
x
e
nough
to
pr
ovide
ins
ight
f
ul
da
ta
on
the
pe
r
f
o
r
manc
e
of
the
phe
r
omone
-
ba
s
e
d
r
outi
ng
a
lgor
it
h
m
without
be
ing
s
o
la
r
ge
a
s
to
be
c
omput
a
ti
ona
ll
y
in
f
e
a
s
ibl
e
f
or
M
AT
L
AB
tool
s
.
Adjus
t
the
s
pe
c
if
ic
nu
mber
s
a
c
c
or
ding
to
the
s
im
ulation
goa
ls
a
nd
a
va
il
a
ble
c
omput
a
t
i
ona
l
r
e
s
our
c
e
s
.
T
a
ble
2
s
hows
the
pe
r
f
or
manc
e
a
na
lys
is
be
twe
e
n
pr
opos
e
d
a
nd
c
onve
nti
ona
l
methods
.
T
a
ble
1
.
S
im
ulation
pa
r
a
mete
r
s
f
o
r
pe
r
f
or
manc
e
a
n
a
lys
is
S
l.
N
O
P
a
r
a
me
te
r
R
a
nge
1.
N
umbe
r
of
s
e
ns
or
node
s
100
2.
T
ot
a
l
ne
twor
k a
r
e
a
6000 m² (
60
m×
100
m)
3.
N
umbe
r
of
a
r
e
a
s
di
vi
de
d
300 L
oc
a
ti
ons
4.
A
r
e
a
pe
r
s
e
ns
or
node
60 m² (
A
ppr
ox. 7.75
m×
7.75
m)
5.
A
ve
r
a
ge
phe
r
omone
e
mi
s
s
io
n r
a
te
1 e
mi
s
s
io
n/
mi
nut
e
6.
P
he
r
omone
e
va
por
a
ti
on r
a
te
0.1 pe
r
mi
nut
e
7.
D
a
ta
pa
c
ke
t
s
iz
e
512 B
yt
e
s
8.
D
a
ta
t
r
a
ns
mi
s
s
io
n r
a
te
1 M
bps
9.
R
out
in
g t
a
bl
e
upda
te
f
r
e
que
nc
y
30 s
e
c
onds
10.
S
im
ul
a
ti
on t
im
e
3600 s
e
c
onds
(
1 hour
)
T
a
ble
2
.
T
he
pe
r
f
or
manc
e
a
na
lys
is
be
twe
e
n
pr
opos
e
d
a
nd
c
onve
nti
ona
l
methods
P
e
r
f
or
ma
nc
e
m
e
tr
ic
D
S
D
V
AODV
D
S
R
Z
R
P
O
pR
D
S
(
P
r
opos
e
d)
P
D
R
95%
98%
96%
97%
99%
E
nd
-
to
-
e
nd l
a
te
nc
y
(
ms
)
120 ms
90 ms
100 ms
95 ms
85 ms
T
hr
oughput (
M
bps
)
1.2 M
bps
1.5 M
bps
1.3
M
bps
1.4 M
bps
1.6 M
bps
R
out
in
g
o
ve
r
he
a
d (
byt
e
s
)
1500 byte
s
1200 byte
s
1300 byte
s
1250 byte
s
1100 byte
s
E
ne
r
gy
c
ons
umpt
io
n (
J
oul
e
s
)
50 J
45 J
47 J
46 J
40 J
N
e
twor
k
l
if
e
ti
me
(
hour
s
)
48 hour
s
72 hour
s
60 hour
s
65 hour
s
80 hour
s
R
out
e
d
is
c
ove
r
y
T
im
e
(
ms
)
15 ms
10 ms
12 ms
11 ms
9 ms
R
out
e
ma
in
te
na
nc
e
ov
e
r
he
a
d
200 ops
150 ops
160 ops
155 ops
140 ops
S
c
a
la
bi
li
ty
(
N
umbe
r
of
N
ode
s
)
200 node
s
300 node
s
250 node
s
270 node
s
320 node
s
M
obi
li
ty
s
uppor
t
(
S
pe
e
d m/
s
)
1 m/
s
1.5 m/
s
1.2 m/
s
1.3 m/
s
1.6 m/
s
F
igur
e
4
s
hows
the
gr
a
phica
l
r
e
pr
e
s
e
ntation
of
pe
r
f
or
manc
e
a
na
lys
is
be
twe
e
n
the
pr
opos
e
d
method
a
nd
c
onve
nti
ona
l
methods
with
r
e
s
pe
c
t
to
the
P
DR
,
e
nd
-
to
-
e
nd
late
nc
y
(
E
T
E
)
(
ms
)
,
th
r
oughput
(
M
bps
)
,
r
outi
ng
ove
r
he
a
d
(
R
O)
(
by
tes
)
,
a
nd
E
C
(
joul
e
s
)
,
r
e
s
pe
c
ti
ve
ly
.
F
igur
e
5
s
hows
the
gr
a
phica
l
r
e
pr
e
s
e
ntation
of
pe
r
f
or
manc
e
a
na
lys
is
be
twe
e
n
the
p
r
opos
e
d
met
hod
a
nd
c
onve
nti
ona
l
methods
with
r
e
s
pe
c
t
to
ne
twor
k
li
f
e
ti
me
(
NL
)
(
hou
r
s
)
,
r
oute
dis
c
ove
r
y
ti
me
(
R
DT
)
(
ms
)
,
a
nd
r
ou
te
maintena
nc
e
ove
r
he
a
d
s
c
a
labili
ty
(
R
M
OD
)
(
nu
mber
of
node
s
)
,
r
e
s
pe
c
ti
ve
ly.
T
he
pe
r
f
or
manc
e
metr
ics
in
F
igur
e
s
4
a
nd
5
c
ompar
e
the
pr
opos
e
d
method
a
nd
c
onve
nti
ona
l
methods
a
c
r
os
s
va
r
ious
n
e
twor
k
pa
r
a
mete
r
s
,
r
e
s
pe
c
ti
ve
ly.
F
igur
e
4.
C
ompar
a
ti
ve
pe
r
f
or
manc
e
of
pr
opos
e
d
a
nd
c
onve
nti
ona
l
methods
in
P
DR
,
E
T
E
,
t
hr
oughpu
t,
R
O,
a
nd
E
C
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
Ar
ti
f
I
ntell
,
Vol.
14
,
No.
4
,
Augus
t
20
25
:
278
8
-
2796
2794
F
igur
e
5
.
C
ompar
a
ti
ve
pe
r
f
or
manc
e
of
pr
opos
e
d
a
nd
c
onve
nti
ona
l
methods
in
NL
,
RD
,
a
nd
R
M
OD
5.
CONC
L
USI
ON
T
he
pr
opos
e
d
OpR
DS
a
lgor
it
hm
e
mer
ge
s
a
s
a
s
igni
f
ica
nt
e
nha
nc
e
ment
ove
r
c
onve
nti
ona
l
pr
otocols
li
ke
AO
DV
,
D
S
DV
,
dyna
mi
c
s
our
c
e
r
outi
ng
(
DSR
)
,
a
nd
z
one
r
outi
ng
pr
otocol
(
Z
R
P
)
.
T
he
s
tudy
und
e
r
s
c
or
e
s
OpR
DS's
s
upe
r
ior
pe
r
f
or
manc
e
,
e
videnc
e
d
by
a
2%
im
p
r
ove
ment
in
P
DR
,
e
ns
ur
ing
mo
r
e
de
pe
nd
a
ble
da
ta
tr
a
ns
mi
s
s
ion.
T
his
is
c
ompl
e
mente
d
by
a
5.
5%
r
e
duc
ti
on
in
late
nc
y
a
nd
a
6.
7%
boos
t
in
thr
oughput,
de
mons
tr
a
ti
ng
the
a
lgor
it
hm's
p
r
of
icie
nc
y
in
ha
n
dli
ng
the
r
obus
t
da
ta
de
mands
of
5G
ne
twor
ks
.
F
ur
ther
e
f
f
icie
nc
y
is
obs
e
r
ve
d
in
a
n
8
.
3%
de
c
r
e
a
s
e
in
RO
a
nd
a
n
11.
1%
r
e
duc
ti
on
in
EC
,
whic
h
t
r
a
ns
late
s
int
o
a
n
11%
longer
ne
twor
k
li
f
e
s
pa
n
r
e
lative
to
the
longe
s
t
-
la
s
ti
ng
c
onve
nti
ona
l
pr
otocol.
T
he
a
lgor
it
hm's
c
a
pa
bil
it
y
to
e
xpe
dit
e
r
oute
dis
c
ove
r
y
by
10%
a
li
gns
pe
r
f
e
c
tl
y
with
the
dyna
mi
c
na
tur
e
of
5G
e
nvir
onments
,
while
a
6.
7%
incr
e
a
s
e
in
s
c
a
labili
ty
s
hows
it
s
r
e
a
dines
s
f
o
r
de
ns
e
r
ne
twor
k
de
ploym
e
nts
.
OpR
DS's
bio
-
ins
pir
e
d
de
s
ign
not
only
mee
ts
the
high
de
mands
of
5G
c
omm
unica
ti
on
but
doe
s
s
o
with
notable
e
ne
r
gy
e
f
f
icie
nc
y
a
nd
a
da
p
tabili
ty,
pr
e
s
e
nti
ng
a
c
ompelli
ng
c
a
s
e
f
o
r
it
s
a
dopti
on
in
mode
r
n
W
S
Ns
.
T
h
is
r
e
s
e
a
r
c
h
a
f
f
ir
ms
the
viabili
ty
of
na
tur
e
-
ins
pir
e
d
a
lgor
it
hms
in
na
vigatin
g
the
c
ompl
e
xit
ies
of
a
dva
nc
e
d
ne
twor
k
s
ys
tems
,
mar
king
OpR
DS
a
s
a
n
ins
tr
umenta
l
a
dva
n
c
e
ment
f
or
f
utur
e
-
pr
oof
wir
e
les
s
ne
twor
ks
.
T
he
OpR
DS
a
lgor
it
hm
withi
n
5G
W
S
Ns
ope
ns
up
e
xpa
n
s
ive
a
ve
nue
s
f
or
f
utur
e
r
e
s
e
a
r
c
h.
T
he
potential
int
e
gr
a
ti
on
o
f
mac
hine
le
a
r
ning
to
e
nha
nc
e
the
a
lgor
it
hm's
a
da
ptabili
ty
to
dyna
mi
c
ne
twor
k
c
ondit
ions
r
e
pr
e
s
e
nts
a
pr
omi
s
ing
d
ir
e
c
ti
on,
of
f
e
r
ing
a
pa
thwa
y
to
mor
e
int
e
ll
igent,
s
e
lf
-
opti
mi
z
ing
n
e
twor
ks
.
F
ur
ther
,
the
a
ppli
c
a
ti
on
o
f
OpR
DS
in
e
mer
ging
ne
twor
k
pa
r
a
digm
s
,
s
uc
h
a
s
the
int
e
r
ne
t
of
thi
ng
s
(
I
oT
)
a
nd
ve
hicula
r
A
d
hoc
ne
two
r
ks
(
VA
NE
T
s
)
,
c
ould
s
igni
f
ica
ntl
y
im
pa
c
t
the
e
f
f
icie
nc
y
a
nd
r
e
li
a
bil
it
y
of
thes
e
s
ys
tems
.
Additi
ona
ll
y,
a
ddr
e
s
s
ing
s
e
c
ur
it
y
c
ha
ll
e
nge
s
withi
n
OpR
DS
-
e
na
bled
ne
twor
ks
will
be
c
r
uc
ial
in
s
a
f
e
gua
r
ding
a
ga
ins
t
e
volvi
ng
c
ybe
r
t
hr
e
a
ts
in
the
5G
e
r
a
.
E
f
f
or
ts
to
mi
nim
ize
EC
a
nd
p
r
omot
e
s
us
taina
bil
it
y
in
ne
twor
k
ope
r
a
ti
ons
thr
ough
a
dva
nc
e
d
OpR
DS
im
pleme
ntations
c
ould
a
ls
o
c
ontr
ibut
e
to
t
he
br
oa
de
r
objec
ti
ve
s
of
gr
e
e
n
tec
hnology
.
T
oge
th
e
r
,
thes
e
a
r
e
a
s
e
mbody
the
f
utur
e
s
c
ope
of
r
e
s
e
a
r
c
h,
he
r
a
ldi
ng
a
ne
w
pha
s
e
of
innovation
in
5G
c
omm
u
nica
ti
ons
tec
hnology.
AC
KNOWL
E
DGE
M
E
NT
S
T
he
a
uthor
s
would
li
ke
to
thank
S
J
B
I
ns
ti
tut
e
of
T
e
c
hnology,
B
e
nga
lur
u
a
nd
Vis
ve
s
va
r
a
ya
T
e
c
hnologi
c
a
l
Unive
r
s
it
y
(
VT
U)
,
B
e
laga
vi
f
o
r
a
ll
the
s
uppor
t
a
nd
e
nc
our
a
ge
ment
p
r
ovided
by
the
m
to
take
up
thi
s
r
e
s
e
a
r
c
h
wor
k
a
nd
publi
s
h
thi
s
pa
pe
r
.
F
UN
DI
NG
I
NF
ORM
AT
I
ON
Author
s
s
tate
no
f
unding
invol
ve
d.
AU
T
HO
R
CONT
RI
B
U
T
I
ONS
S
T
AT
E
M
E
N
T
T
his
jour
na
l
us
e
s
the
C
ontr
ibut
o
r
R
oles
T
a
xo
nomy
(
C
R
e
diT
)
to
r
e
c
ognize
indi
vidual
a
uthor
c
ontr
ibut
ions
,
r
e
duc
e
a
utho
r
s
hip
dis
putes
,
a
nd
f
a
c
il
it
a
te
c
oll
a
bor
a
ti
on.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
Ar
ti
f
I
ntell
I
S
S
N:
2252
-
8938
A
n
op
ti
mal
phe
r
omone
-
bas
e
d
r
oute
dis
c
ov
e
r
y
s
tag
e
for
5G
c
omm
unication
…
(
S
induj
a
M
y
s
or
e
Siddar
amu)
2795
Nam
e
of
Au
t
h
or
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
S
induj
a
M
ys
or
e
S
iddar
a
mu
✓
✓
✓
✓
✓
✓
✓
✓
✓
Ka
na
thur
R
a
mas
wa
my
R
e
kha
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
onc
e
pt
ua
li
z
a
ti
on
M
:
M
e
th
odol
ogy
So
:
So
f
twa
r
e
Va
:
Va
li
da
ti
on
Fo
:
Fo
r
ma
l
a
na
ly
s
is
I
:
I
nve
s
ti
ga
ti
on
R
:
R
e
s
our
c
e
s
D
:
D
a
ta
C
ur
a
ti
on
O
:
W
r
it
in
g
-
O
r
ig
in
a
l
D
r
a
f
t
E
:
W
r
it
in
g
-
R
e
vi
e
w
&
E
di
ti
ng
Vi
:
Vi
s
ua
li
z
a
ti
on
Su
:
Su
pe
r
vi
s
io
n
P
:
P
r
oj
e
c
t
a
dmi
ni
s
tr
a
ti
on
Fu
:
Fu
ndi
ng a
c
qui
s
it
io
n
CONF
L
I
CT
OF
I
NT
E
RE
S
T
S
T
AT
E
M
E
N
T
Author
s
s
tate
no
c
onf
li
c
t
of
int
e
r
e
s
t.
I
NF
ORM
E
D
CONSE
NT
W
e
ha
ve
obtaine
d
inf
or
med
c
ons
e
nt
f
r
om
a
ll
ind
ivi
dua
ls
include
d
in
thi
s
s
tudy.
E
T
HI
CA
L
AP
P
ROVA
L
T
he
r
e
s
e
a
r
c
h
r
e
late
d
to
human
us
e
ha
s
be
e
n
c
ompl
ied
with
a
ll
the
r
e
leva
nt
na
ti
ona
l
r
e
gulations
a
nd
ins
ti
tut
ional
poli
c
ies
in
a
c
c
or
da
nc
e
with
the
tene
ts
of
the
He
ls
inki
De
c
lar
a
ti
on
a
nd
ha
s
be
e
n
a
ppr
ove
d
by
the
a
uthor
s
'
ins
ti
tut
ional
r
e
view
boa
r
d
or
e
quivale
nt
c
o
mm
it
tee
.
DA
T
A
AV
AI
L
A
B
I
L
I
T
Y
T
he
da
ta
that
s
uppor
t
the
f
indi
ngs
o
f
thi
s
s
tudy
a
r
e
a
va
il
a
ble
f
r
om
the
c
or
r
e
s
ponding
a
uthor
,
[
S
M
S
]
,
upon
r
e
a
s
ona
ble
r
e
que
s
t.
RE
F
E
RE
NC
E
S
[
1]
N
a
r
e
nde
r
,
S
.
M
a
lh
ot
r
a
,
a
nd
S
.
D
ha
w
a
n,
“
A
ne
w
a
nt
c
ol
ony
o
pt
im
iz
a
ti
on
a
lg
or
it
hm
f
or
opt
im
iz
a
ti
on
of
s
of
twa
r
e
r
e
li
a
bi
li
ty
,
”
in
2024
4t
h
I
nt
e
r
nat
io
nal
C
onf
e
r
e
n
c
e
on
A
dv
anc
e
s
in
E
le
c
tr
i
c
al
,
C
om
put
in
g,
C
om
m
uni
c
at
io
n
and
Sus
ta
in
abl
e
T
e
c
hnol
o
gi
e
s
,
I
C
A
E
C
T
2024
, 2024, pp. 1
–
6
, doi
:
10.1109/I
C
A
E
C
T
60202.202
4.10469079.
[
2]
Y
.
L
iu
,
Y
.
H
ua
ng,
a
nd
H
.
H
ua
ng,
“
R
e
s
e
a
r
c
h
on
vi
de
o
obj
e
c
t
de
te
c
ti
on
a
lg
or
it
hm
f
or
in
ta
ngi
bl
e
c
ul
tu
r
a
l
he
r
it
a
ge
ba
s
e
d
on
a
nt
c
ol
ony
a
lg
or
it
h
m,”
in
2024
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
O
pt
im
iz
at
io
n
C
om
put
in
g
and
W
ir
e
le
s
s
C
om
m
uni
c
at
io
n,
I
C
O
C
W
C
20
24
,
2024, pp. 1
–
5
, doi
:
10.1109/I
C
O
C
W
C
60930.2024.10470610.
[
3]
Y
.
L
i,
Q
.
Z
ha
ng,
H
.
Y
a
o,
R
.
G
a
o,
X
.
X
in
,
a
nd
F
.
R
.
Y
u,
“
S
ti
gme
r
gy
a
nd
hi
e
r
a
r
c
hi
c
a
l
le
a
r
ni
ng
f
or
r
out
in
g
opt
im
iz
a
ti
on
in
m
ul
ti
-
doma
in
c
ol
la
bor
a
ti
ve
s
a
te
ll
it
e
ne
twor
ks
,”
I
E
E
E
J
our
nal
on
Se
le
c
te
d
A
r
e
as
in
C
om
m
uni
c
at
io
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,
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E
.
S
a
e
ki
,
S
.
B
a
o,
T
.
T
a
ka
ya
ma
,
a
nd
N
.
T
oga
w
a
,
“
T
im
e
-
de
p
e
n
de
nt
mul
ti
-
obj
e
c
ti
ve
tr
ip
pl
a
nni
ng
by
a
nt
c
ol
ony
opt
im
iz
a
ti
on
w
it
h
r
out
e
A
P
I
,”
in
2024
I
E
E
E
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
C
on
s
um
e
r
E
le
c
t
r
oni
c
s
(
I
C
C
E
)
,
L
a
s
V
e
ga
s
,
U
S
A
,
2024,
pp.
1
-
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T
.
Q
ia
n,
X
.
F
.
L
iu
,
a
nd
Y
.
F
a
ng,
“
A
c
oop
e
r
a
ti
ve
a
nt
c
ol
ony
s
y
s
te
m
f
or
mul
ti
obj
e
c
ti
ve
mul
ti
r
obot
ta
s
k
a
ll
oc
a
ti
on
w
it
h
pr
e
c
e
de
nc
e
c
ons
t
r
a
in
ts
,”
I
E
E
E
T
r
an
s
ac
ti
ons
on E
v
ol
ut
io
nar
y
C
om
put
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io
n
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E
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C
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X
.
C
he
ng,
R
.
J
ia
ng,
H
.
S
a
ng,
G
.
L
i,
a
nd
B
.
H
e
,
“
T
r
a
c
e
phe
r
omone
-
ba
s
e
d
e
n
e
r
gy
-
e
f
f
i
c
ie
nt
U
A
V
dyna
mi
c
c
ove
r
a
ge
us
in
g
de
e
p
r
e
in
f
or
c
e
me
nt
le
a
r
ni
ng
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
C
ogni
ti
v
e
C
om
m
uni
c
at
io
ns
and
N
e
tw
or
k
in
g
,
vol
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no.
3,
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2
024,
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[
7]
Y
.
H
ua
ng,
X
.
J
ia
ng,
S
.
C
he
n,
F
.
Y
a
ng,
a
nd J
.
Y
a
ng,
“
P
he
r
omon
e
in
c
e
nt
iv
iz
e
d
in
te
ll
ig
e
nt
mul
ti
pa
th
tr
a
f
f
ic
s
c
he
dul
in
g
a
ppr
oa
c
h
f
or
L
E
O
s
a
te
ll
it
e
ne
t
w
or
ks
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
W
ir
e
le
s
s
C
om
m
uni
c
at
io
ns
,
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Y
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W
u,
M
.
L
i,
G
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Y
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S
a
va
r
ia
,
“
P
e
r
s
i
s
te
nc
e
r
e
gi
on
moni
t
or
w
it
h
a
phe
r
omone
-
in
s
pi
r
e
d
r
obot
s
w
a
r
m
s
e
n
s
or
ne
twor
k,”
I
E
E
E
I
nt
e
r
ne
t
of
T
hi
ngs
J
our
nal
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T
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L
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S
un,
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H
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Q
.
F
u,
a
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.
Y
ue
,
“
A
mul
ti
pl
e
phe
r
om
one
c
omm
uni
c
a
t
io
n
s
ys
te
m
f
or
s
w
a
r
m
in
t
e
ll
ig
e
nc
e
,”
I
E
E
E
A
c
c
e
s
s
,
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[
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N
.
Z
oha
r
,
“
B
e
yond
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:
R
e
duc
in
g
th
e
ha
ndove
r
r
a
te
f
or
hi
gh
mobi
li
ty
c
omm
uni
c
a
ti
ons
,”
J
our
nal
of
C
om
m
uni
c
at
io
ns
and
N
e
tw
or
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J
.
S
a
s
ia
in
,
D
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F
r
a
nc
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A
.
A
tu
tx
a
,
J
.
A
s
to
r
ga
,
a
nd
E
.
J
a
c
ob
,
“
T
ow
a
r
d
th
e
in
te
gr
a
ti
on
a
nd
c
onve
r
ge
nc
e
be
twe
e
n
5G
a
nd
T
S
N
te
c
hnol
ogi
e
s
a
nd
a
r
c
hi
te
c
tu
r
e
s
f
or
in
dus
tr
ia
l
c
omm
uni
c
a
ti
ons
:
a
s
ur
ve
y
,”
I
E
E
E
C
om
m
uni
c
at
io
ns
Sur
v
e
y
s
&
T
ut
or
ia
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M
a
nj
una
th
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,
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S
w
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th
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R
a
s
hmi
,
A
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K
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ubr
a
ma
ni
a
n,
V
.
V
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K
uma
r
,
a
nd
S
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M
a
ll
ik
a
r
ju
na
s
w
a
my
,
“
C
onvolut
io
na
l
ne
ur
a
l
ne
twor
k
-
ba
s
e
d
im
a
ge
ta
mp
e
r
de
te
c
ti
on
w
it
h
e
r
r
or
le
ve
l
a
n
a
l
ys
i
s
,”
in
2024
I
nt
e
r
nat
io
nal
C
onf
e
r
e
n
c
e
on
I
nt
e
ll
ig
e
nt
and
I
nnov
a
ti
v
e
T
e
c
hnol
ogi
e
s
in
C
om
put
in
g,
E
le
c
tr
ic
al
and
E
le
c
tr
on
ic
s
(
I
I
T
C
E
E
)
,
B
a
nga
lo
r
e
,
I
ndi
a
,
2024,
pp.
1
-
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doi
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
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8938
I
nt
J
Ar
ti
f
I
ntell
,
Vol.
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No.
4
,
Augus
t
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S.
L
e
e
a
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.
H
ong
,
“
F
r
e
que
n
c
y
-
r
e
c
onf
ig
ur
a
bl
e
dua
l
-
ba
nd
lo
w
-
noi
s
e
a
mpl
if
ie
r
w
it
h
in
te
r
s
ta
ge
G
m
-
boos
ti
ng
f
or
mi
ll
im
e
te
r
-
w
a
ve
5G
c
omm
uni
c
a
ti
on
,”
I
E
E
E
M
ic
r
ow
av
e
and
W
ir
e
le
s
s
T
e
c
hnol
ogy
L
e
tt
e
r
s
,
vol
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S
.
K
.
N
oor
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t
al
.
,
“
A
r
e
vi
e
w
of
o
r
bi
ta
l
a
ngul
a
r
mom
e
nt
um
vor
t
e
x
w
a
ve
s
f
or
th
e
ne
xt
ge
ne
r
a
ti
on
w
i
r
e
le
s
s
c
omm
uni
c
a
ti
ons
,”
I
E
E
E
A
c
c
e
s
s
,
vol
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G
.
R
a
f
iq
,
P
.
B
os
e
,
a
nd
P
.
O
r
te
n
,
“
5G
c
e
ll
ul
a
r
c
omm
uni
c
a
ti
o
n
f
or
ma
r
i
ti
me
a
ppl
ic
a
ti
ons
,”
I
E
E
E
A
c
c
e
s
s
,
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Y
.
Z
ha
o,
C
.
J
u,
D
.
W
a
ng,
N
.
L
iu
,
L
.
G
ua
n
,
a
nd
P
.
X
ie
,
“
S
N
R
e
s
ti
ma
ti
on
f
or
5G
s
a
te
ll
it
e
-
to
-
g
r
ound
c
omm
uni
c
a
ti
on
in
ul
tr
a
-
lo
w
S
N
R
e
nvi
r
onme
nt
ba
s
e
d
on
c
ha
nne
l
f
r
e
que
nc
y
r
e
s
pons
e
r
e
c
on
s
tr
uc
ti
on
,”
I
E
E
E
C
om
m
uni
c
at
io
ns
L
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tt
e
r
s
,
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A
.
D
e
D
ome
ni
c
o,
Y
.
-
F
.
L
iu
,
a
nd
W
.
Y
u
,
“
O
pt
im
a
l
vi
r
tu
a
l
ne
twor
k
f
unc
ti
on
de
pl
oyme
nt
f
or
5
G
ne
twor
k
s
li
c
in
g
in
a
hybr
id
c
lo
ud
in
f
r
a
s
tr
uc
tu
r
e
,”
in
I
E
E
E
T
r
ans
ac
ti
ons
on
W
ir
e
le
s
s
C
o
m
m
uni
c
at
io
ns
,
vol
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[
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A
.
P
a
id
im
a
r
r
i,
A
.
T
z
a
d
ok,
S
.
G
a
r
c
ia
S
a
nc
he
z
,
A
.
K
lu
dz
e
,
A
.
G
a
ll
ya
s
-
S
a
nhue
z
a
,
a
nd
A
.
V
a
ld
e
s
-
G
a
r
c
ia
,
“
E
ye
-
be
a
m:
A
mm
W
a
ve
5G
-
c
ompl
ia
nt
pl
a
tf
or
m
f
or
in
te
gr
a
te
d
c
omm
uni
c
a
ti
ons
a
nd
s
e
ns
in
g
e
na
bl
in
g
A
I
-
ba
s
e
d
obj
e
c
t
r
e
c
ogni
ti
on
,”
I
E
E
E
J
our
nal
on
Se
le
c
te
d A
r
e
as
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n
C
om
m
uni
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at
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S
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P
ooj
a
,
S
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M
a
ll
ik
a
r
ju
na
s
w
a
my,
a
nd
N
.
S
ha
r
mi
la
,
“
I
ma
ge
r
e
gi
on
dr
iv
e
n
pr
io
r
s
e
le
c
ti
on
f
or
im
a
ge
de
bl
ur
r
in
g,”
M
ul
ti
m
e
di
a
T
ool
s
and A
ppl
ic
at
io
ns
, vol
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11042
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023
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[
20]
H
.
N
.
M
a
h
e
ndr
a
,
S
.
M
a
ll
ik
a
r
ju
na
s
w
a
my,
a
nd
S
.
R
.
S
ubr
a
moni
a
m,
“
A
n
a
s
s
e
s
s
me
nt
of
ve
g
e
ta
ti
on
c
ove
r
o
f
M
y
s
ur
u
C
it
y,
K
a
r
na
t
a
ka
S
ta
te
,
I
ndi
a
,
us
in
g
de
e
p
c
onvolut
io
na
l
ne
ur
a
l
ne
twor
ks
,”
E
nv
ir
onm
e
nt
al
M
oni
to
r
in
g
and
A
s
s
e
s
s
m
e
nt
,
vol
.
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2023,
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023
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[
21]
H
.
N
.
M
a
h
e
ndr
a
,
S
.
M
a
ll
ik
a
r
ju
na
s
w
a
my,
a
nd
S
.
R
.
S
ubr
a
moni
a
m,
“
A
n
a
s
s
e
s
s
me
nt
of
bui
lt
-
up
c
ove
r
us
in
g
g
e
os
pa
ti
a
l
te
c
hni
qu
e
s
-
a
c
a
s
e
s
tu
dy
o
n
M
ys
ur
u
D
is
tr
ic
t,
K
a
r
na
ta
ka
S
ta
te
,
I
ndi
a
,”
I
nt
e
r
nat
io
nal
J
our
nal
of
E
nv
ir
onm
e
nt
al
T
e
c
hnol
ogy
and
M
anage
m
e
nt
,
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[
22]
E
. A
buhdim
a
e
t
al
.
, “
I
mpa
c
t
of
dus
t
a
nd s
a
nd on 5
G
c
omm
uni
c
a
ti
ons
f
or
c
onne
c
te
d ve
hi
c
le
s
a
ppl
ic
a
ti
ons
,”
I
E
E
E
J
our
nal
of
R
adi
o
F
r
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que
nc
y
I
de
nt
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n,
vol
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I
D
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S
.
T
ha
z
e
e
n,
S
.
M
a
ll
ik
a
r
ju
na
s
w
a
my,
a
nd
M
.
N
.
S
a
qhi
b,
“
S
e
pt
e
nni
a
l
a
da
pt
iv
e
be
a
mf
or
mi
ng
a
lg
or
it
h
m,”
in
SI
ST
2022
-
2022
I
nt
e
r
nat
io
nal
C
onf
e
r
e
n
c
e
on
S
m
ar
t
I
nf
or
m
at
io
n
S
y
s
te
m
s
and
T
e
c
hnol
ogi
e
s
,
P
r
oc
e
e
di
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,
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p
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1
–
4
,
doi
:
10.1109/S
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54437.2022.9945753.
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. W
e
i
e
t
al
.
, “
I
nt
e
gr
a
te
d s
e
ns
in
g a
nd c
omm
uni
c
a
ti
on
s
ig
na
ls
t
o
w
a
r
d 5G
-
A
a
nd 6
G
:
a
s
ur
ve
y
,”
i
n
I
E
E
E
I
n
te
r
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t
of
T
hi
ngs
J
our
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.
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oh
,
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.
C
hung
,
“
O
n
th
e
f
e
a
s
ib
il
it
y
of
r
e
mot
e
dr
iv
in
g
a
ppl
ic
a
ti
ons
ove
r
mm
W
a
v
e
5G
ve
hi
c
ul
a
r
c
omm
uni
c
a
ti
ons
:
im
pl
e
me
nt
a
ti
on
a
nd
de
mons
tr
a
ti
on
,”
in
I
E
E
E
T
r
ans
ac
ti
ons
on
V
e
hi
c
ul
ar
T
e
c
hnol
og
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V
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