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Feb
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y
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,
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.
427
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I
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2
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8
8
-
8708
I
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C
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,
Vo
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10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
427
-
437
428
r
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o
ce
s
s
in
g
ca
p
ab
ilit
y
a
n
d
m
ed
i
u
m
-
r
a
n
g
e
tr
an
s
ce
iv
e
r
.
W
SNC
p
r
o
ce
s
s
es
th
e
d
ata
b
ased
o
n
th
e
p
atien
t
‟
s
h
ea
lt
h
h
i
s
to
r
y
;
it
ca
te
g
o
r
is
es
t
h
e
p
r
io
r
ity
le
v
el
o
f
i
n
f
o
r
m
at
io
n
f
o
r
f
a
s
ter
tr
an
s
m
is
s
io
n
u
s
in
g
m
ed
iu
m
-
r
an
g
e
a
n
d
lo
n
g
-
r
an
g
e
co
m
m
u
n
icatio
n
an
d
f
o
r
q
u
ick
d
ia
g
n
o
s
is
.
I
n
m
y
p
r
ev
io
u
s
r
esear
ch
w
o
r
k
,
w
e
ex
p
lai
n
ed
d
y
n
a
m
ic
b
a
n
d
w
id
th
a
llo
ca
tio
n
f
o
r
cr
itical
p
atie
n
ts
i
n
a
w
i
r
eless
i
n
ter
f
ac
e
f
o
r
m
ed
iu
m
r
an
g
e
co
m
m
u
n
icatio
n
(
T
ir
e
-
2
)
.
Fig
u
r
e
1
.
3
-
t
ir
e
h
ea
lt
h
ca
r
e
s
er
v
ice
ar
ch
itect
u
r
e
Fig
u
r
e
2
.
W
ir
eless
b
o
d
y
ar
ea
n
et
w
o
r
k
T
h
e
tire
-
3
to
p
o
lo
g
y
h
a
n
d
les
a
lar
g
e
a
m
o
u
n
t
o
f
d
ata
f
r
o
m
h
eter
o
g
e
n
eo
u
s
d
e
v
ices
co
m
p
ar
ed
w
it
h
s
h
o
r
t
-
r
a
n
g
e
an
d
m
ed
i
u
m
-
r
a
n
g
e
co
m
m
u
n
ica
tio
n
to
p
o
lo
g
y
.
T
h
is
n
et
w
o
r
k
i
n
f
o
r
m
atio
n
is
m
a
n
a
g
ed
an
d
p
er
io
d
ically
u
p
d
ated
to
e
n
ti
r
e
d
ev
ices
a
m
o
n
g
t
h
e
n
et
wo
r
k
b
y
ce
n
tr
ali
s
ed
[
7
]
o
r
d
is
tr
ib
u
ted
m
a
n
n
er
.
Du
r
in
g
t
h
e
d
ata
u
p
d
ate
,
th
e
la
r
g
e
a
m
o
u
n
t
o
f
p
ac
k
et
tr
an
s
m
it
ted
b
et
w
ee
n
n
et
w
o
r
k
co
m
p
o
n
en
ts
,
a
n
d
it
lead
s
to
th
e
p
o
o
r
q
u
ality
o
f
s
er
v
ices
(
Qo
S)
.
No
t
o
n
l
y
th
at,
i
f
th
e
u
p
d
ates
h
ap
p
en
i
n
a
d
is
tr
ib
u
ted
ap
p
r
o
ac
h
,
th
en
ea
ch
in
ter
m
ed
iate
d
ev
i
ce
s
h
a
n
d
le
t
h
e
p
ac
k
et
f
o
r
war
d
in
g
a
n
d
n
et
w
o
r
k
m
an
a
g
e
m
en
t,
it
lead
s
to
a
b
o
ttlen
ec
k
p
r
o
b
le
m
.
No
w
ad
a
y
s
m
o
s
t
o
f
th
e
n
et
w
o
r
k
s
ar
e
u
t
ilis
i
n
g
t
h
e
SDN
ab
s
tr
ac
t
in
a
d
i
f
f
er
e
n
t
n
et
w
o
r
k
e
n
v
ir
o
n
m
e
n
t.
SDN
s
ep
ar
ates
t
h
e
n
et
w
o
r
k
co
n
tr
o
l
an
d
d
ata
p
lan
e,
an
d
it
m
a
n
ag
e
s
all
t
h
e
n
et
w
o
r
k
co
n
tr
o
l
p
lan
es
i
n
a
ce
n
tr
alis
ed
lo
ca
tio
n
.
T
h
r
o
u
g
h
t
h
i
s
ap
p
r
o
ac
h
,
all
th
e
in
t
er
m
ed
iate
d
ev
ices
ar
e
o
n
l
y
h
an
d
lin
g
t
h
e
p
ac
k
et
f
o
r
w
ar
d
i
n
g
t
h
at
m
ea
n
s
,
it
m
er
el
y
r
ec
eiv
es
t
h
e
p
ac
k
et
f
r
o
m
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
tr
an
s
m
it
t
h
o
s
e
to
th
e
d
esti
n
atio
n
n
o
d
e
b
ased
o
n
r
u
les.
T
h
e
co
n
tr
o
ller
p
er
f
o
r
m
s
th
e
p
ac
k
e
t
p
r
ep
r
o
ce
s
s
in
g
tas
k
s
,
d
ec
is
io
n
-
m
ak
in
g
a
m
o
n
g
th
e
p
ac
k
ets;
t
h
e
n
th
e
in
s
tr
u
ctio
n
p
as
s
ed
to
a
p
ar
t
icu
lar
f
o
r
w
ar
d
in
g
d
ev
ice
to
in
itiate
t
h
e
p
ac
k
e
t
f
o
r
w
ar
d
i
n
g
.
T
h
r
o
u
g
h
t
h
is
ap
p
r
o
ac
h
,
we
ca
n
r
ed
u
ce
t
h
e
f
o
r
w
ar
d
in
g
n
o
d
e
b
o
ttlen
ec
k
p
r
o
b
le
m
,
co
m
p
u
tatio
n
a
l
co
s
t,
an
d
n
et
w
o
r
k
in
f
o
r
m
a
tio
n
u
p
d
ate
an
d
m
a
n
a
g
e
m
en
t
co
s
t.
I
n
SDN,
co
n
tr
o
ller
n
o
d
e
co
m
p
u
tes
th
e
o
p
ti
m
is
ed
o
r
ef
f
icie
n
t
f
lo
w
p
ath
b
et
w
ee
n
a
s
o
u
r
ce
a
n
d
d
esti
n
a
tio
n
n
o
d
es.
SDN
n
et
w
o
r
k
m
a
x
i
m
u
m
u
s
ed
i
n
d
ata
ce
n
ter
n
et
w
o
r
k
to
o
p
ti
m
ize
t
h
e
lo
a
d
b
alan
ce
b
et
w
ee
n
th
e
n
et
wo
r
k
[
8
]
.
T
o
ch
o
o
s
e
th
e
ef
f
ic
ien
t
p
ath
d
i
f
f
er
en
t
s
tr
ateg
ie
s
ar
e
h
a
n
d
led
b
y
co
n
t
r
o
ller
esp
ec
iall
y
n
o
d
e
w
ei
g
h
t
m
o
d
el,
in
t
h
is
m
o
d
el
co
n
tr
o
lle
r
ass
i
g
n
s
th
e
w
ei
ght
o
r
p
r
io
r
ity
v
al
u
es
f
o
r
ea
ch
co
m
m
u
n
icatio
n
li
n
k
.
Usi
n
g
th
i
s
w
ei
g
h
t
co
n
tr
o
ller
s
elec
ts
a
n
e
f
f
icie
n
t
f
lo
w
p
ath
f
o
r
p
ac
k
et
tr
an
s
m
i
s
s
io
n
,
b
u
t
t
h
er
e
is
a
p
r
o
b
lem
to
ch
o
o
s
e
th
e
r
o
u
te
u
s
in
g
a
s
in
g
le
w
ei
g
h
t
v
alu
e.
B
ec
au
s
e,
if
s
h
o
r
ter
d
is
tan
ce
n
e
t
w
o
r
k
p
ath
o
cc
u
p
ied
b
y
m
a
x
i
m
u
m
n
o
o
f
n
o
d
es
its
lead
to
h
i
g
h
p
ac
k
et
er
r
o
r
r
ate
o
r
h
ig
h
tr
a
f
f
ic.
T
o
r
eso
lv
e
th
i
s
is
s
u
e,
w
e
d
ev
e
lo
p
ed
th
e
o
p
t
i
m
al
p
ac
k
et
r
o
u
ti
n
g
u
s
in
g
m
u
ltip
le
p
ar
am
e
n
ts
to
h
an
d
le
t
h
e
m
ed
ical
e
m
er
g
e
n
c
y
p
ac
k
ets
w
i
th
t
h
e
s
u
p
p
o
r
t
o
f
S
DN
f
ea
t
u
r
es.
B
ased
o
n
t
h
e
re
s
ea
r
ch
o
b
j
ec
tiv
e
w
e
s
tu
d
ied
s
o
m
e
e
x
i
s
ti
n
g
r
esear
c
h
w
o
r
k
a
n
d
its
f
u
t
u
r
e
en
h
a
n
ce
m
en
t
in
f
o
r
m
a
tio
n
.
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tima
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w
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tw
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429
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
W
SNC
r
ec
ei
v
es
t
h
e
d
i
f
f
er
en
t
s
e
n
s
o
r
n
o
d
e
in
f
o
r
m
at
io
n
at
d
if
f
er
en
t
ti
m
i
n
g
in
ter
v
a
l,
an
d
t
h
i
s
in
f
o
r
m
atio
n
is
tr
an
s
m
i
tted
to
th
e
P
r
e
T
r
an
s
m
i
s
s
io
n
E
v
al
u
at
io
n
(
P
T
E
)
p
r
o
ce
s
s
.
Af
ter
P
T
E
,
W
SNC
id
e
n
ti
f
ies
th
e
cr
itical
i
n
f
o
r
m
atio
n
co
n
tr
o
ller
to
in
itiate
th
e
f
as
t f
o
r
w
ar
d
i
n
g
a
n
d
p
r
o
ce
s
s
co
n
tr
o
ller
f
o
r
w
ar
d
s
th
e
p
ac
k
et
s
i
n
th
e
q
u
ic
k
e
s
t
a
n
d
s
h
o
r
tes
t
r
o
u
t
e.
T
o
p
r
ed
ict
th
e
f
as
t
p
ac
k
et
t
r
an
s
m
is
s
io
n
p
at
h
,
s
e
v
er
al
r
ese
ar
ch
er
s
d
ev
elo
p
ed
v
ar
io
u
s
r
o
u
t
in
g
al
g
o
r
ith
m
s
[
9
-
1
1
].
A
g
r
ee
d
y
al
g
o
r
ith
m
i
s
a
p
r
o
b
lem
-
s
o
l
v
in
g
tec
h
n
iq
u
e,
i
n
ea
c
h
s
tag
e,
it
f
i
n
d
s
o
p
ti
m
al
ch
o
ice
b
ased
u
p
o
n
lo
ca
l
co
n
d
itio
n
,
an
d
f
i
n
all
y
,
it
lead
s
to
g
lo
b
al
o
p
tim
i
s
a
tio
n
.
Mo
s
t
n
et
w
o
r
k
in
g
al
g
o
r
ith
m
s
ar
e
u
ti
lis
i
n
g
th
e
g
r
ee
d
y
ap
p
r
o
ac
h
,
L
i
k
e
Min
i
m
u
m
Sp
a
n
n
i
n
g
T
r
ee
(
MS
T
)
[
1
2
]
,
T
r
av
ellin
g
Sales
m
a
n
P
r
o
b
lem
(
T
SP
)
an
d
f
e
w
m
o
r
e.
T
h
e
ed
g
e
v
a
lu
e
b
as
ed
ea
ch
n
et
w
o
r
k
v
er
t
ices
s
elec
ted
it‟s
ca
l
led
as
a
w
ei
g
h
t
o
f
t
h
e
ed
g
e
.
Us
in
g
th
i
s
ed
g
e
v
al
u
e,
t
h
e
Dij
k
s
tr
a
ap
p
lies
s
o
m
e
co
n
d
itio
n
to
ch
o
o
s
e
t
h
e
n
ex
t
v
er
tice
s
.
I
t
f
in
d
s
th
e
l
o
ca
l
s
o
lu
tio
n
i
n
ea
c
h
s
tag
e,
a
n
d
it
lead
s
to
t
h
e
g
lo
b
al
o
p
tim
i
s
ed
r
esu
lt
.
I
n
t
h
is
ap
p
r
o
ac
h
,
th
e
o
p
ti
m
al
p
ath
n
o
d
e
s
elec
ted
u
s
i
n
g
o
n
e
p
ar
a
m
eter
ed
g
e
w
ei
g
h
t.
Ho
w
e
v
er
,
n
et
w
o
r
k
p
ac
k
et
t
r
an
s
m
is
s
io
n
d
ep
e
n
d
s
u
p
o
n
m
u
ltip
le
p
ar
a
m
eter
s
,
lik
e
d
ela
y
[
1
3
]
,
en
er
g
y
co
n
s
u
m
p
tio
n
[
1
4
-
20
]
,
B
an
d
w
id
t
h
,
Data
R
ate
[
21
]
,
T
r
af
f
ic
[
22
-
24
]
,
T
r
av
ellin
g
co
s
t
an
d
L
i
n
k
Qu
al
it
y
[
2
5
]
.
Usi
n
g
o
n
e
p
ar
a
m
e
ter
ca
n
‟
t
s
a
y
w
e
h
a
v
e
ac
h
ie
v
ed
t
h
e
o
p
ti
m
al
p
at
h
b
et
w
ee
n
s
o
u
r
ce
a
n
d
d
esti
n
at
io
n
.
T
o
r
eso
lv
e
th
is
ab
o
v
e
p
r
o
b
le
m
,
in
[2
6
]
,
A
.
B
o
zy
i
ğ
it,
G.
A
la
n
k
u
ş
an
d
E
.
Nasib
o
ğ
l
u
,
p
r
o
p
o
s
ed
a
m
o
d
if
ied
Dij
k
s
tr
a
'
s
al
g
o
r
ith
m
a
n
d
it
i
s
i
m
p
le
m
e
n
ted
in
a
r
ea
l
-
w
o
r
ld
p
u
b
lic
tr
an
s
p
o
r
t
n
et
w
o
r
k
.
I
n
th
i
s
ap
p
r
o
ac
h
,
th
e
au
th
o
r
ass
i
g
n
ed
s
o
m
e
n
eg
ativ
e
v
alu
e
f
o
r
e
ac
h
p
ath
a
n
d
f
o
u
n
d
th
e
alter
n
ate
r
o
ad
to
ac
h
iev
e
a
g
o
o
d
r
esu
lt
.
T
o
im
p
r
o
v
e
t
h
e
o
p
ti
m
al
p
at
h
s
elec
tio
n
,
w
e
p
r
o
p
o
s
e
an
Op
ti
m
ized
P
ac
k
et
R
o
u
ti
n
g
ap
p
r
o
ac
h
in
th
is
p
ap
er
.
3.
O
P
T
I
M
I
Z
E
D
P
ACK
E
T
RO
UT
I
N
G
W
SNC
o
r
C
o
n
tr
o
ller
ch
o
o
s
e
a
n
o
p
ti
m
a
l
a
n
d
ef
f
icie
n
t
r
o
u
te
p
ath
b
et
w
ee
n
W
SN
C
a
n
d
s
er
v
er
o
r
s
in
k
.
T
o
s
elec
t
th
e
o
p
ti
m
al
w
a
y
,
t
h
e
co
n
tr
o
ller
f
o
llo
w
s
a
g
r
ee
d
y
s
tr
ateg
y
a
n
d
t
h
is
p
ath
s
elec
tio
n
al
g
o
r
ith
m
d
i
v
id
ed
in
to
th
r
ee
s
ta
g
e
s
,
w
h
ic
h
ar
e:
a.
T
h
r
esh
o
ld
class
i
f
icatio
n
b.
Stag
e
cla
s
s
i
f
icatio
n
c.
No
d
e
s
elec
tio
n
C
o
n
tr
o
ller
f
i
n
d
s
a
T
h
r
esh
o
ld
lev
el
f
o
r
th
e
w
eig
h
t
s
u
s
in
g
T
h
r
es
h
o
ld
C
lass
if
ier
.
Af
ter
th
e
T
h
r
esh
o
ld
lev
el
ca
lcu
la
tio
n
co
n
tr
o
ller
m
o
v
es
to
th
e
s
ta
g
e
class
i
f
icat
io
n
alg
o
r
ith
m
.
I
n
th
at,
d
ep
en
d
in
g
u
p
o
n
th
e
T
h
r
esh
o
ld
lev
el,
th
e
co
n
tr
o
ller
d
r
iv
es
th
e
s
tag
e
le
v
el.
Fin
a
ll
y
,
t
h
e
co
n
tr
o
ller
w
ill
d
esi
g
n
a
No
d
e
s
elec
ti
o
n
alg
o
r
ith
m
u
s
in
g
s
tag
e
cla
s
s
i
f
icatio
n
r
esu
lt.
3
.
1
.
O
pti
m
is
ed
pa
t
h select
io
n
T
h
e
co
n
tr
o
ller
u
s
e
s
t
h
e
Gr
e
ed
y
ap
p
r
o
ac
h
to
s
elec
t
th
e
o
p
ti
m
is
ed
p
at
h
s
b
et
w
ee
n
s
o
u
r
ce
an
d
d
esti
n
atio
n
b
ased
o
n
t
w
o
d
i
f
f
er
en
t
n
o
d
e
w
e
ig
h
ts
w
h
ic
h
ar
e
No
d
e
d
elay
(
W
1
)
an
d
L
in
k
B
an
d
w
id
t
h
(
W
2
).
No
d
e
p
ac
k
et
p
r
io
r
ity
b
ased
co
n
tr
o
ller
d
er
iv
es
t
h
e
p
o
lic
y
to
ch
o
o
s
e
t
h
e
f
lo
w
p
ath
b
et
w
ee
n
s
o
u
r
ce
s
t
o
d
esti
n
atio
n
u
s
in
g
n
o
d
e
w
ei
g
h
t
s
.
3
.
1
.
1
.
No
de
s
elec
t
io
n pro
ce
s
s
First,
th
e
co
n
tr
o
ller
an
al
y
s
e
s
th
e
n
u
m
b
er
o
f
av
ailab
le
n
o
d
es
in
th
e
n
et
w
o
r
k
an
d
its
d
ir
ec
t
co
m
m
u
n
icatio
n
li
n
k
to
an
o
t
h
er
n
o
d
e
;
th
e
n
,
it
ca
lcu
la
tes
t
h
e
to
tal
v
al
u
es
o
f
w
ei
g
h
t1
(
T
W
1
)
,
it'
s
a
v
er
ag
e
(
A
W
1
)
,
an
d
p
er
ce
n
tag
e
lev
el
o
f
T
W
1
(
P
W
1
)
.
Sim
ilar
l
y
,
i
t
f
i
n
d
s
t
h
e
to
tal
(
T
W
2
)
,
A
v
er
ag
e
(
A
W
2
)
,
P
er
ce
n
tag
e
(
P
W
2
)
o
f
w
e
ig
h
t2
an
d
s
o
o
n
f
o
r
all
n
o
d
es.
W
eig
h
t1
:
∑
(
1
)
(
)
∑
(
2
)
Usi
n
g
(
1
)
,
(
3
)
Si
m
i
lar
l
y
,
W
eig
h
t2
:
∑
(
4
)
(
)
∑
(
5
)
(
6
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
427
-
437
430
B
y
u
s
i
n
g
(
3
)
an
d
(
6
)
,
co
n
tr
o
ller
ch
ec
k
s
th
e
w
ei
g
h
t
o
f
v
is
it
in
g
n
o
d
e(
VW
1
)
,
a
w
ei
g
h
t
p
er
ce
n
tag
e
o
f
v
is
i
tin
g
n
o
d
e
(
VP
W
1
)
an
d
f
in
d
s
th
e
a
v
er
ag
e
o
f
v
is
i
tin
g
n
o
d
e
w
ei
g
h
t p
er
ce
n
ta
g
e
(
A
VP
W
1
)
s
i
m
ilar
l
y
VP
W
2
.
(
)
(
)
(
7
)
(
)
(
)
(
8
)
(
)
(
9
)
(
)
(
1
0
)
w
h
er
e,
v
i
s
iti
n
g
_
No
d
e_
L
e
v
el
i
s
th
e
n
u
m
b
er
o
f
h
o
p
co
u
n
t to
r
ea
ch
th
e
v
is
iti
n
g
n
o
d
e
f
r
o
m
t
h
e
s
o
u
r
ce
n
o
d
e.
Dep
en
d
in
g
u
p
o
n
th
e
p
atien
t
c
o
n
d
itio
n
:
eith
er
n
o
r
m
al
o
r
ab
n
o
r
m
al
,
t
h
e
n
o
d
e
s
elec
tio
n
co
n
d
itio
n
w
il
l
c
h
an
g
e
.
No
w
t
h
e
co
n
tr
o
ller
u
s
e
s
t
h
e
O
p
ti
m
ized
P
ac
k
et
R
o
u
tin
g
al
g
o
r
ith
m
u
s
i
n
g
s
elec
tio
n
p
o
lic
y
.
3
.
2
.
T
hres
ho
ld
cla
s
s
if
ica
t
io
n
T
h
e
co
n
tr
o
ller
d
r
iv
es
th
e
n
o
d
e
s
elec
tio
n
u
s
in
g
t
h
e
t
h
r
es
h
o
ld
v
alu
e
o
f
w
ei
g
h
t
b
y
u
s
in
g
t
h
e
T
h
r
esh
o
ld
C
las
s
i
f
icatio
n
al
g
o
r
ith
m
(
A
l
g
.
3
.
1
)
.
I
n
th
is
,
t
h
e
co
n
tr
o
ller
f
i
n
d
s
th
e
T
h
r
es
h
o
ld
v
al
u
e
b
y
u
s
in
g
Av
er
ag
e
No
d
e
w
ei
g
h
t v
al
u
e
a
n
d
n
o
d
e
p
r
io
r
ity
v
alu
e.
A
l
g
o
r
ith
m
3
.
1
: T
h
r
esh
o
ld
_
C
lass
i
f
icatio
n
(
)
I
n
p
u
t:
Av
er
ag
e_
W
eig
h
t (
A
W
)
,
T
o
tal_
Prio
r
ity
_
L
e
v
el(
N)
Ou
tp
u
t:
T
h
r
esh
o
ld
_
L
e
v
el
(N)
I
n
itializatio
n
: i
n
itit
al_
v
al
u
e=
1
;
L
o
o
p
:
C
h
ec
k
in
itial_
v
a
lu
e
i
s
l
ess
t
h
an
o
r
E
q
u
al
N
(
)
[
(
)
]
I
n
cr
em
en
t in
i
tial_
v
al
u
e
E
n
d
// lo
o
p
E
n
d
E
n
d
// a
lg
o
r
ith
m
E
n
d
3
.
3
.
Sta
g
e
cla
s
s
if
ica
t
io
n
I
n
t
h
is
s
ta
g
e
cla
s
s
i
f
icat
io
n
,
t
h
e
co
n
tr
o
ller
u
s
e
s
t
h
e
th
r
es
h
o
ld
v
al
u
e
a
n
d
v
is
i
tin
g
n
o
d
e‟
s
w
ei
g
h
t
p
er
ce
n
tag
e
v
a
lu
e
a
n
d
to
tal
p
r
io
r
ity
le
v
el.
A
l
g
o
r
ith
m
8
.
2
: Stag
e_
C
las
s
i
f
i
ca
tio
n
(
)
I
n
p
u
t: T
h
r
esh
o
ld
_
L
e
v
el(
T
h
W
N
)
,
Vis
itin
g
_
No
d
e_
W
eig
h
t_
P
er
ce
n
tag
e
(
VP
W
)
,
T
o
tal_
P
r
i
o
r
ity
_
L
ev
e
l(
N)
Ou
tp
u
t: (
N+
1
)
Stag
e
clas
s
i
f
ica
tio
n
r
esu
l
t (
SL
N+
1
)
I
n
itializatio
n
: I
n
i
tial_
v
al
u
e=
1
;
T
h
W
=
T
h
r
esh
o
ld
_
C
lass
if
ic
tio
n
(
)
;
L
o
o
p
: I
n
itial_
v
al
u
e
les
s
t
h
an
o
r
eq
u
al
N
I
f
I
n
itial_
v
al
u
e
=1
Set: SL
Initial_
v
alue
i
s
VP
W
≤
T
h
W
I
n
itial_
v
alue
;
E
ls
e
I
f
I
n
it
ial_
v
al
u
e
is
N
Set: S
L
I
nitial_
v
alu
e
is
VP
W
>T
h
W
N
-
1
&
&
VP
W
≤
T
h
W
N
;
E
ls
e
I
f
I
n
it
ial_
v
al
u
e
m
o
r
e
t
h
an
1
&
&
n
o
t N
Set S
L
I
nitial_
v
alue
i
s
VP
W
>T
h
W
I
nitial_
v
alue
-
1
&
&
VP
W
≤
T
h
W
I
nitial_v
alue
E
n
d
I
f
I
n
cr
e
m
e
n
t
I
n
it
ial_
v
al
u
e;
E
n
d
L
o
o
p
Set: S
L
I
nitial_
v
alu
e+
1
is
VP
W
>T
h
W
I
nitial_
v
alue
E
n
d
A
l
g
o
r
ith
m
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
-
8708
Op
tima
l p
a
ck
et
r
o
u
tin
g
f
o
r
w
ir
eless
b
o
d
y
a
r
ea
n
e
tw
o
r
k
u
s
in
g
s
o
ftw
a
r
e
d
efin
ed
...
(
B
.
Ma
n
i
ck
a
va
s
a
g
a
m)
431
3
.
4
.
No
de
s
elec
t
io
n a
lg
o
rit
h
m
No
d
e
s
elec
tio
n
alg
o
r
it
h
m
(
Alg
o
r
ith
m
3
.
3
)
is
a
f
i
n
al
s
tep
in
t
h
e
Op
ti
m
ized
P
ac
k
et
R
o
u
t
in
g
alg
o
r
ith
m
.
I
n
th
is
,
t
h
e
co
n
tr
o
ller
s
elec
ts
t
h
e
ef
f
icie
n
t
f
o
r
w
ar
d
in
g
n
o
d
es
b
et
w
ee
n
s
o
u
r
ce
an
d
d
esti
n
at
i
o
n
u
s
i
n
g
th
r
es
h
o
ld
an
d
s
tag
e
cla
s
s
i
f
icat
io
n
al
g
o
r
ith
m
.
T
h
e
n
o
d
e
Selectio
n
alg
o
r
i
th
m
is
g
iv
e
n
b
elo
w
.
A
l
g
o
r
ith
m
3
.
3
: N
o
d
e_
s
elec
tio
n
(
)
I
n
p
u
t:
Vi
s
iti
n
g
_
No
d
e_
W
eig
h
t_
P
er
ce
n
tag
e
(
VP
W
)
,
Stag
e_
C
las
s
i
f
ier
_
Valu
e
s
(
S
L
N+
1
)
,
P
R
I
OR
I
T
Y_
L
ev
el
(
P
L
i
)
,
Gr
ap
h
_
Ver
tices (
V)
,
Gr
ap
h
_
W
eig
h
t(
W
1
,
W
2
)
,
Ou
tp
u
t: select
t
h
e
o
p
ti
m
al
a
n
d
ef
f
ice
in
t
f
o
r
w
ar
d
in
g
n
o
d
e.
I
n
itializatio
n
: i=
1
,
j
=1
,
k
=1
;
SL
=
Stag
e_
C
las
s
i
f
icatio
n
(
)
;
I
f
V
i,
n
o
t
So
u
r
ce
an
d
d
esti
n
a
tio
n
n
o
d
e
I
f
P
L
is
“
No
r
m
al”
I
f
V
i
(
VP
W
1
)
is
SL
j
an
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j)
I
f
V
i
(
VP
W
2
)
is
m
o
r
e
th
a
n
S
L
j
Select
V
i
E
ls
e
Select
V
j
E
n
d
E
ls
e
if
V
i
(
VP
W
1
)
is
SL
j
a
n
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j+
1)
I
f
V
i+
1
(
VP
W
2
)
is
m
o
r
e
t
h
an
S
L
j
+
2
Select
V
i+
1
E
ls
e
Select
V
i
E
n
d
E
ls
e
if
V
i
(
VP
W
1
)
is
SL
j
a
n
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j+
2)
I
f
V
i+
1
(
VP
W
2
)
is
m
o
r
e
t
h
an
S
L
N
Select
V
i+
1
E
ls
e
Select
V
i
E
n
d
E
n
d
E
ls
e
if
i
s
“
ab
n
o
r
m
al.
”
I
f
V
i
(
VP
W
1
)
is
SL
j
an
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j)
I
f
V
i
(
VP
W
2
)
is
less
th
an
o
r
S
L
j
Select
V
i
E
ls
e
if
V
i
(
VP
W
2
)
is
Select
V
j
E
n
d
E
ls
e
if
V
i
(
VP
W
1
)
is
SL
j
a
n
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j+
1)
I
f
V
i+
1
(
VP
W
2
)
is
less
th
a
n
S
L
j+
2
Select
V
i+
1
E
ls
e
Select
V
i
E
n
d
E
ls
e
if
V
i
(
VP
W
1
)
is
SL
j
a
n
d
V
(i+
1)
(
VP
W
1
)
is
SL
(j+
2)
I
f
V
i+
1
(
VP
W
2
)
is
less
th
a
n
S
L
N
Select
V
i+
1
E
ls
e
Select
V
i
E
n
d
E
n
d
E
n
d
E
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
427
-
437
432
A
l
g
o
r
ith
m
3
.
4
:
Op
ti
m
ized
_
P
ac
k
et_
R
o
u
tin
g
_
A
l
g
o
r
ith
m
(
)
I
n
itiali
s
e_
s
o
u
r
ce
(
Gr
ap
h
g
r
,
N
o
d
e
s
,
No
d
e
d
)
f
o
r
ea
ch
v
er
tex
v
in
Ver
tice
s
(
g
r
)
g
r
.
d
is
[
v
]
:=
in
f
i
n
it
y
g
r
.
p
i[
v
]
:=
n
il
g
r
.
d
is
[
s
]
:=
0
;
T
W
:=
T
o
tal_
W
eig
h
t,
A
W
:=
Av
er
ag
e_
W
eig
h
t,
P
W
:=
Av
er
ag
e_
W
eig
h
t_
P
er
ce
n
tag
e;
Mo
d
if
ied
_
d
ij
k
s
tr
a(
Gr
ap
h
g
r
,
No
d
e
s
,
No
d
e
d
,
W
eig
h
t W
1
,
W
eig
h
t W
2
,
)
in
itialis
e_
s
o
u
r
ce
(
g
r
,
s
,
d
)
V
:=
{
0
}
/*
Ma
k
e
v
i
s
i
ted
n
o
d
e
em
p
t
y
*
/
Q
:=
Ver
tices(
g
r
)
/*
i
n
itia
l
l
y
p
u
t a
ll
n
o
n
-
v
is
ited
n
o
d
es in
Q
*
/
w
h
i
le
Q
is
n
o
t e
m
p
t
y
u
:=
E
x
tr
ac
t(
Q
)
;
A
d
d
No
d
e
(
V,
u
)
; /*
A
d
d
u
to
S
*
/
f
o
r
ea
ch
v
er
tex
v
i
n
A
d
j
a
ce
n
t(
u
)
No
d
e
_
s
elec
tio
n
(
V,
u
,
v
)
4.
I
M
P
L
E
M
E
NT
AT
I
O
N
C
o
n
tr
o
ller
an
a
l
y
s
es
t
h
e
T
r
u
s
t
Valu
e
a
n
d
d
ep
en
d
s
u
p
o
n
tr
u
s
t
v
a
lu
e
;
it
s
e
lects
th
e
f
o
r
w
ar
d
in
g
n
o
d
e
u
s
i
n
g
R
o
u
tin
g
_
A
l
g
o
r
ith
m
_
w
it
h
_
Mu
lt
ip
le_
P
ar
am
eter
s
(
d
ela
y
an
d
b
an
d
w
id
t
h
)
.
T
est ca
s
e
d
etails:
T
o
tal
n
o
o
f
w
ir
ele
s
s
s
w
itc
h
e
s
o
r
n
o
d
es
-
6
No
s
,
T
o
tal
n
o
o
f
li
n
k
s
b
et
w
ee
n
s
w
itc
h
es
-
9
No
s
,
w
ei
g
h
t1
(
W
1
)
is
n
o
d
e
d
elay
it
s
u
n
i
ts
ar
e
in
s
ec
o
n
d
s
,
w
ei
g
h
t2
(
W
2
)
is
av
ailab
le
lin
k
b
a
n
d
w
i
d
th
t
w
o
f
o
r
w
ar
d
in
g
n
o
d
es,
its
u
n
it
m
ea
s
u
r
e
m
e
n
t
is
Mb
p
s
,
C
o
m
m
u
n
icatio
n
m
e
d
iu
m
:
Fas
t
E
th
er
n
et
at
m
a
x
i
m
u
m
o
f
1
0
0
Mb
p
s
tr
an
s
f
er
r
ate.
Fig
u
r
e
2
,
d
escr
ib
es th
e
o
p
ti
m
i
s
ed
p
ac
k
et
r
o
u
ti
n
g
alg
o
r
it
h
m
in
ea
c
h
lev
el.
4
.
1
.
Sta
g
e
0
Fig
u
r
e
2
(
a
)
d
escr
ib
es
t
h
e
I
n
it
ial
Sta
g
e
s
et
a
l
l
th
e
n
o
d
e
v
al
u
es
ar
e
in
f
i
n
it
y
,
Select
t
h
e
s
o
u
r
ce
an
d
a
d
esti
n
atio
n
n
o
d
e,
id
en
ti
f
y
t
h
e
n
o
o
f
ed
g
es,
an
d
ca
lc
u
late
T
W
1
,
A
W
1
,
PW
1
,
s
im
ilar
l
y
w
e
ig
h
t2
,
a.
N
=
9
;
b.
T
W
1
=
5
8
&
A
W
1
=6
.
4
&
P
W
1
=0
.
5
8
c.
T
W
2
=8
5
&
A
W
2
=9
.
4
&
P
W
2
=0
.
8
5
d.
T
h
W
1
=
2
.
1
3
e.
T
h
W
2
=
3
.
1
3
f.
S1
to
s
6
ar
e
f
o
r
w
ar
d
i
n
g
d
ev
ice
s
g.
W
1
,
W
2
is
d
elay
a
n
d
b
an
d
w
id
th
h.
So
u
r
ce
S1
; D
esti
n
atio
n
S6
i.
So
u
r
ce
≠
Des
tin
a
tio
n
(
S1
≠
S6
)
4
.
2
.
Sta
g
e
1
Fig
u
r
e
2
(
b
)
ex
p
lai
n
t
h
e
s
ta
g
e
1
p
r
o
ce
s
s
.
Her
e,
th
e
co
n
tr
o
ll
er
s
et
s
o
u
r
ce
n
o
d
e
w
ei
g
h
t
is
z
er
o
,
an
d
th
e
r
e
m
ai
n
i
n
g
n
o
d
e
w
e
ig
h
t is i
n
f
in
i
t
y
.
B
elo
w
.
a.
Set S1
w
eig
h
t (
0
,
0
)
b.
Set
w
eig
h
t a
s
I
n
f
in
i
t
y
f
o
r
r
e
m
a
in
i
n
g
n
o
d
es
4
.
3
.
Sta
g
e
2
I
n
F
ig
u
r
e
2
(
c)
,
th
e
co
n
tr
o
ller
s
elec
ts
th
e
n
e
x
t h
o
p
f
o
r
w
ar
d
in
g
n
o
d
e
f
r
o
m
n
o
d
e
S1
,
an
d
it‟s d
etails ar
e,
A
p
p
l
y
n
o
d
e
s
elec
tio
n
co
n
d
it
io
n
Fro
m
S1
to
S2
:
VW
S2
=0
+5
=5
; 0
+1
0
=1
0
;(
5
,
1
0
)
VP
W
1
S2
=2
.
9
,
V
P
W
2
S2
=
8
.
5
(
2
.
9
,
8
.
5
)
S2
v
alu
e
s
ar
e
ad
d
ed
in
th
e
tab
le
b
u
t n
o
t selec
ted
f
o
r
f
o
r
w
ar
d
i
n
g
.
Fro
m
S1
to
S3
:
VW
S3
=0
+7
=7
; 0
+
1
8
=1
8
;(
7
,
1
8
)
VP
W
1
S3
=4
.
0
6
,
VP
W
2
S3
=1
5
.
3
(
4
.
0
6
,
1
5
.
3
0
)
Select
S3
(
4
.
0
6
,
1
5
.
3
0
)
,
b
ec
au
s
e
VP
W
1
S1
&
&
VP
W
1
S3
in
a
s
a
m
e
ca
teg
o
r
y
t
h
en
,
co
n
tr
o
ll
er
s
elec
t
th
e
„
S3
‟
as
a
f
o
r
w
ar
d
in
g
n
o
d
e
an
d
v
is
ited
n
o
d
e.
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
-
8708
Op
tima
l p
a
ck
et
r
o
u
tin
g
f
o
r
w
ir
eless
b
o
d
y
a
r
ea
n
e
tw
o
r
k
u
s
in
g
s
o
ftw
a
r
e
d
efin
ed
...
(
B
.
Ma
n
i
ck
a
va
s
a
g
a
m)
433
4
.
4
.
Sta
g
e
3
I
n
s
tag
e
co
n
tr
o
ller
s
elec
ts
th
e
n
ex
t
f
o
r
w
ar
d
in
g
n
o
d
e,
th
e
d
etails
ar
e
ex
p
la
in
ed
b
elo
w
a
n
d
s
elec
ted
n
o
d
e
s
h
o
w
n
i
n
F
i
g
u
r
e
2
(
d
)
.
So
u
r
ce
o
f
S2
: 2
.
9
,
8
.
5
(
as p
er
s
tag
e
2
)
Fro
m
S3
to
S2
:
So
u
r
ce
o
f
S2
: 2
.
9
,
8
.
5
(
as p
er
s
tag
e
2
)
No
w
,
VW
S2
=(
W
1
(S
3,
S
2)
,
W
2
(
S
3,
S
2)
)
+
(
W
1
(S
3)
,
W
2
(S
3)
)
VW
S2
=(
1
5
,
2
4
)
VP
W
1
S2
=4
.
3
5
,
V
P
W
2
S2
=1
0
.
2
(
4
.
3
5
,
1
0
.
2
)
Ne
w
VP
W
1
an
d
o
ld
VP
W
1
b
o
th
ar
e
in
th
e
s
a
m
e
co
n
d
it
io
n
.
Ne
w
VP
W
2
is
g
r
ea
ter
t
h
an
o
ld
VP
W
2
.
So
,
w
e
ar
e
s
elec
ted
S2
as
a
v
is
ited
n
o
d
e
an
d
s
elec
ted
f
o
r
f
o
r
w
ar
d
in
g
.
Fro
m
S3
to
S5
:
VW
S5
=(
W
1
(S
3,
S
5)
,
W
2
(
S
3,
S
5)
)
+
(
W
1
(S
3)
,
W
2
(S
3)
)
VW
S5
=(
1
1
,
2
0
)
VP
W
1
S5
=3
.
1
9
,
V
P
W
2
S3
=
8
.
5
(
3
.
1
9
,
8
.
5
)
VP
W
1
is
in
2
s
tag
e
also
VP
W
2
<
A
W
1
,
f
o
r
th
is
r
ea
s
o
n
,
it i
s
s
till
i
n
v
i
s
iti
n
g
n
o
d
e
an
d
its
v
a
lu
es a
d
d
ed
in
tab
le
4
.
5
.
Sta
g
e
4
Fig
u
r
e
2(
e
)
,
d
escr
ib
es th
e
n
o
d
e
s
elec
tio
n
p
r
o
ce
s
s
f
r
o
m
No
d
e
S3
an
d
its
p
r
o
ce
s
s
ar
e
ex
p
lain
ed
b
elo
w
,
Fro
m
S3
to
S4
:
VW
S4
=(
W
1
(S
2,
S
4)
,
W
2
(
S
2,
S
4)
)
+
(
W
1
(S
2)
,
W
2
(S
2)
)
VW
S4
=(
1
9
,
3
9
)
VP
W
1
S4
=3
.
6
7
,
V
P
W
2
S4
=1
1
.
0
5
(
3
.
6
7
,
1
1
.
0
5
)
C
o
m
p
ar
in
g
S4
an
d
S5
,
b
o
th
ar
e
in
th
e
s
a
m
e
s
ta
g
e
b
u
t,
S4
h
as
lo
w
VP
W
1
an
d
Hi
g
h
b
an
d
w
id
t
h
.
So
th
e
co
n
tr
o
ller
s
elec
t
s
th
e
S4
as
a
v
is
ited
n
o
d
e
an
d
f
o
r
w
ar
d
in
g
d
ev
ice.
Fro
m
S3
to
S5
:
So
u
r
ce
o
f
S5
: 3
.
1
9
,
8
.
5
(
as p
er
s
tag
e
3
)
VW
S5
=(
W
1
(S
2,
S
5)
,
W
2
(
S
2,
S
5)
)
+
(
W
1
(S
2)
,
W
2
(S
2)
)
VW
S5
=(
2
1
,
3
6
)
VP
W
1
S5
=4
.
0
6
,
V
P
W
2
S3
=
1
0
.
2
(
4
.
0
6
,
1
0
.
2
)
Ne
w
VP
W
1
an
d
Old
VP
W
1
b
o
th
ar
e
i
n
th
e
s
a
m
e
s
ta
g
e,
b
u
t
Ne
w
VP
W
2
is
g
r
ea
ter
t
h
a
n
o
l
d
VP
W
2
an
d
A
W
2
.
Fo
r
th
is
co
n
tr
o
ller
r
e
m
o
v
es t
h
e
ex
is
ti
n
g
in
f
o
f
r
o
m
tab
le
an
d
ad
d
ed
th
e
n
e
w
i
n
f
o
.
No
te:
s
till
S5
in
v
i
s
iti
n
g
n
o
d
e
4
.
6
.
Sta
g
e
5
T
h
e
c
o
n
tr
o
ller
s
elec
ts
th
e
n
e
x
t
f
o
r
w
ar
d
in
g
n
o
d
e
o
f
S4
an
d
d
et
ai
ls
ar
e
ex
p
lai
n
ed
in
F
ig
u
r
e
2
(
f)
.
Fro
m
S4
to
S6
:
VW
S6
=(
W
1
(
S4
,
S6
)
,
W
2
(
S4
,
S
6
)
)
+
(
W
1
(
S4
)
,
W
2
(
S4
)
)
VW
S6
=(
1
9
,
3
9
)
VP
W
1
S6
=4
.
2
0
,
VP
W
2
S6
=9
.
9
8
(
4
.
2
0
,
9
.
9
8
)
VP
W
1
o
f
s
6
s
tag
e
2
an
d
its
V
P
W
2
is
g
r
ea
ter
th
an
A
V.
So
co
n
tr
o
ller
s
et
th
e
S6
as a
v
is
ited
n
o
d
e.
No
te:
Her
e,
co
n
tr
o
ller
n
o
t
s
to
p
p
ed
th
e
n
o
d
e
s
elec
tio
n
p
r
o
ce
s
s
b
ec
au
s
e
f
r
o
m
S4
h
as
an
o
th
er
o
n
e
d
ir
ec
t
co
n
n
ec
tio
n
.
Fro
m
S4
to
S5
:
So
u
r
ce
o
f
S5
: 4
.
0
6
,
1
0
.
2
(
as p
e
r
s
tag
e
4
)
VW
S5
=(
W
1
(
S4
,
S5
)
,
W
2
(
S4
,
S
5
)
)
+
(
W
1
(
S4
)
,
W
2
(
S4
)
)
VW
S5
=(
2
8
,
4
7
)
VP
W
1
S5
=4
.
0
6
,
VP
W
2
S3
=
9
.
9
8
(
4
.
0
6
,
9
.
9
8
)
Ne
w
VP
W
1
eq
u
al
to
Old
VP
W
1
an
d
New
VP
W
2
is
less
th
a
n
t
h
e
Old
o
n
e.
So
t
h
e
co
n
tr
o
ller
s
elec
ted
th
e
ex
i
s
ti
n
g
v
al
u
e.
A
n
d
S6
ch
a
n
g
ed
i
n
to
th
e
v
is
i
ted
n
o
d
e.
4
.
7
.
Sta
g
e
6
T
o
S6
:
So
u
r
ce
o
f
S5
: 4
.
0
6
,
1
0
.
2
(
as p
e
r
s
tag
e
4
)
VW
S6
=(
W
1
(S
5,
S
6)
,
W
2
(
S
5,
S
6)
)
+
(
W
1
(S
5)
,
W
2
(S
5)
)
VW
S5
=(
2
6
,
4
2
)
VP
W
1
S5
=3
.
7
7
,
V
P
W
2
S3
=
7
.
6
5
(
3
.
7
7
,
7
.
6
5
)
Her
e
Ne
w
VP
W
1
is
less
th
a
n
Old
VP
W
2
an
d
s
am
e
s
tag
e
,
b
u
t
n
e
w
VP
W
2
is
less
th
a
n
Old
VP
W
2
,
also
its
lo
w
er
th
a
n
A
W
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
427
-
437
434
So
,
th
e
co
n
tr
o
ller
s
elec
t
s
th
e
E
x
is
t
in
g
v
alu
e
o
f
S6
(
Fig
u
r
e
2
(
f
)
an
d
(
g)
)
,
th
e
Fi
n
all
y
,
co
n
tr
o
ll
er
s
elec
ts
th
e
p
at
h
f
r
o
m
s
o
u
r
ce
n
o
d
e
S1
to
d
esti
n
at
io
n
n
o
d
e
S6
,
an
d
it
f
o
r
w
ar
d
s
th
e
i
n
f
o
r
m
atio
n
u
si
ng
S1
–
S3
–
S2
–
S4
–
S6
w
it
h
a
to
tal
d
ela
y
o
f
2
9
an
d
b
an
d
w
id
th
o
f
4
7
f
o
r
s
ta
n
d
ar
d
p
ac
k
et
tr
a
n
s
m
is
s
io
n
.
4
.
8
.
Sta
g
e
7
T
h
e
co
n
tr
o
ller
s
elec
ts
th
e
s
a
m
e
alg
o
r
ith
m
to
s
elec
t
th
e
r
o
u
te
b
et
w
ee
n
s
o
u
r
ce
a
n
d
d
esti
na
ti
o
n
d
u
r
in
g
cr
itical
p
ac
k
et
tr
an
s
m
is
s
io
n
as
s
h
w
o
n
i
n
Fig
u
r
e
2
(
h
)
.
Fin
all
y
,
T
ab
le
1
d
escr
ib
es
th
e
p
ac
k
et
f
lo
w
i
n
f
o
r
m
atio
n
an
d
T
ab
le
2
co
n
tain
s
t
h
e
f
i
n
al
f
lo
w
r
esu
lt v
al
u
e
o
f
C
r
itical
pa
ck
et
tr
an
s
m
is
s
io
n
.
(
a)
(
b
)
(
c)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
Fig
u
r
e
2
.
Op
ti
m
ized
r
o
u
tin
g
al
g
o
r
ith
m
s
ta
g
e
w
i
s
e
p
r
o
ce
d
u
r
e,
(
(
a
-
g
)
n
o
r
m
al
p
ac
k
et
tr
an
s
m
is
s
io
n
.
(
h
)
Op
ti
m
al
p
ath
f
o
r
e
m
er
g
en
c
y
s
tate
)
; (
a)
Stag
e
0
: N
o
d
es a
n
d
its
co
n
n
ec
tio
n
s
ar
e
r
ep
r
esen
t
ed
in
Gr
ap
h
f
o
r
m
,
(
b
)
Stag
e
1
: So
u
r
ce
n
o
d
e
s
elec
tio
n
,
(
c)
Stag
e
2
Op
ti
m
al
p
at
h
alg
o
r
ith
m
,
(
d
)
Stag
e
3
Op
ti
m
a
l p
ath
alg
o
r
it
h
m
,
(
e)
Stag
e
4
Op
ti
m
al
p
ath
al
g
o
r
ith
m
,
(
f
)
Sta
g
e
5
Op
ti
m
al
p
at
h
alg
o
r
ith
m
,
(
g
)
Sta
g
e
6
Op
ti
m
a
l p
ath
alg
o
r
it
h
m
,
(
h
)
Op
ti
m
al
p
ath
s
elec
tio
n
in
e
m
er
g
e
n
c
y
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
-
8708
Op
tima
l p
a
ck
et
r
o
u
tin
g
f
o
r
w
ir
eless
b
o
d
y
a
r
ea
n
e
tw
o
r
k
u
s
in
g
s
o
ftw
a
r
e
d
efin
ed
...
(
B
.
Ma
n
i
ck
a
va
s
a
g
a
m)
435
T
ab
le
1
.
Flo
w
tab
le
d
etails o
f
o
p
tim
a
l p
ac
k
et
r
o
u
ti
n
g
al
g
o
r
it
h
m
i
n
s
tag
e
w
i
s
e
-
S1
S2
S3
S4
S5
S6
S
t
a
g
e
1
S1
0
,
0
∞
,
∞
∞
,
∞
∞
,
∞
∞
,
∞
∞
,
∞
S
t
a
g
e
2
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S
t
a
g
e
3
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S
t
a
g
e
4
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
∞
,
∞
S
t
a
g
e
5
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
36
2
9
,
4
7
S
t
a
g
e
6
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
2
9
,
4
7
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
2
9
,
4
7
S
t
a
g
e
7
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞,
∞
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
∞
,
∞
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
2
9
,
4
7
S1
0
,
0
1
5
,
2
4
7
,
1
8
1
9
,
3
9
2
1
,
3
6
2
9
,
4
7
T
ab
le
2.
Fin
al
f
lo
w
r
esu
lt o
f
cr
itical
p
ac
k
et
tr
an
s
m
is
s
io
n
-
S1
S2
S3
S4
S5
S6
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
∞
,
∞
∞
,
∞
S1
0
,
0
5
,
1
0
7
,
1
8
∞
,
∞
1
1
,
2
0
∞
,
∞
S1
0
,
0
5
,
1
0
7
,
1
8
1
0
,
2
5
1
1
,
2
2
∞
,
∞
S1
0
,
0
5
,
1
0
7
,
1
8
1
0
,
2
5
1
1
,
2
2
2
0
,
3
3
5.
P
E
RF
O
RM
ANE ANA
L
Y
SI
S
I
n
th
i
s
o
p
ti
m
al
p
ac
k
et
r
o
u
ti
n
g
alg
o
r
it
h
m
co
m
p
ar
ed
w
i
th
m
o
d
i
f
ied
d
ij
k
s
ta
‟
s
al
g
o
r
ith
m
f
o
r
th
i
s
w
e
u
tili
ze
d
1
3
f
o
r
w
ar
d
in
g
n
o
d
es
a
n
d
ech
ti
m
e
w
e
m
o
d
if
ied
th
e
e
d
g
es
f
o
r
th
e
v
er
t
ices.
Fi
n
all
y
t
h
e
p
r
o
ce
s
s
in
g
ti
m
e
o
f
alg
o
r
ith
m
a
n
al
y
s
ed
,
th
e
p
r
o
ce
s
s
i
n
g
t
h
i
m
e
co
m
p
ar
is
s
o
n
d
et
ails
ar
e
s
h
o
w
n
in
F
ig
u
r
e
3
.
Fig
u
r
e
3
.
P
r
o
ce
s
s
in
g
ti
m
e
f
o
r
m
o
d
i
f
i
ed
an
d
o
p
ti
m
a
l p
ac
k
et
a
l
g
o
r
ith
m
6.
CO
NCLU
T
I
O
N
AN
D
F
UT
U
RE
E
NH
A
NCEM
E
NT
I
n
th
is
r
esear
ch
w
o
r
k
w
e
f
i
n
d
th
e
Op
ti
m
ized
p
ac
k
et
r
o
u
tin
g
alg
o
r
ith
m
t
h
r
o
u
g
h
t
h
is
SD
N
co
n
tr
o
ller
s
elec
ts
t
h
e
P
ath
b
et
w
ee
n
s
o
u
r
ce
an
d
d
esti
n
at
io
n
u
s
i
n
g
co
m
b
in
at
io
n
o
f
n
o
d
e
d
ela
y
a
n
d
av
ailab
le
m
ed
iu
m
b
an
d
w
id
t
h
.
An
d
test
ca
s
e
w
e
ex
p
lain
ed
th
e
b
o
th
cr
itical
an
d
n
o
r
m
al
p
ac
k
et
tr
an
s
m
is
s
io
n
p
r
o
ce
d
u
r
es.
I
n
th
is
w
e
d
id
n
‟
t
e
x
p
lain
t
h
e
u
tili
za
ti
o
n
o
f
av
er
a
g
e
b
an
d
w
id
th
r
eq
u
ir
e
m
en
t
o
f
cr
itical
p
ac
k
et
tr
a
n
s
m
i
s
s
io
n
an
d
p
ac
k
e
t
tr
an
s
m
is
s
io
n
r
ate.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
427
-
437
436
RE
F
E
R
E
NC
E
S
[1
]
Bo
u
k
e
rc
h
e
,
e
t
a
l
.
,
“
S
e
c
u
re
lo
c
a
li
z
a
ti
o
n
a
lg
o
rit
h
m
s
f
o
r
w
irele
s
s
se
n
s
o
r
n
e
tw
o
rk
s,
”
IEE
E
Co
mm
u
n
ica
ti
o
n
s
M
a
g
a
zi
n
e
,
v
o
l.
4
6
,
p
p
.
9
6
-
1
0
1
,
2
0
0
8
.
[2
]
D.
G
.
L
e
sta
,
e
t
a
l
.
,
“
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
W
it
h
P
e
r
p
e
tu
a
l
M
o
tes
f
o
r
T
e
rre
s
tri
a
l
S
n
a
il
A
c
ti
v
it
y
M
o
n
it
o
ri
n
g
,
”
IEE
E
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l.
1
7
,
p
p
.
5
0
0
8
-
5
0
1
5
,
2
0
1
7
.
[3
]
T
.
Ko
n
e
,
e
t
a
l
.
,
“
P
e
rf
o
r
m
a
n
c
e
M
a
n
a
g
e
m
e
n
t
o
f
IEE
E
8
0
2
.
1
5
.
4
W
ire
les
s
S
e
n
so
r
Ne
t
w
o
rk
f
o
r
P
re
c
isio
n
Ag
ricu
lt
u
re
,
”
IEE
E
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l.
1
5
,
p
p
.
5
7
3
4
-
5
7
4
7
,
2
0
1
5
.
[4
]
F
.
W
a
n
g
,
e
t
a
l
.
,
“
E
n
e
rg
y
-
e
ff
icie
n
t
m
e
d
iu
m
a
c
c
e
ss
a
p
p
ro
a
c
h
f
o
r
w
ir
e
les
s
b
o
d
y
a
re
a
n
e
t
w
o
rk
b
a
se
d
o
n
b
o
d
y
p
o
stu
r
e,
”
Ch
in
a
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l.
1
2
,
p
p
.
1
2
2
-
1
3
2
,
2
0
1
5
.
[5
]
P
u
v
a
n
e
sh
w
a
ri
S
.
a
n
d
Vijay
a
sh
a
a
ra
th
i
S
.
,
“
Ef
f
icie
n
t
M
o
n
it
o
ri
n
g
sy
ste
m
f
o
r
c
a
rd
iac
p
a
ti
e
n
ts
u
sin
g
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
(W
S
N),
”
2
0
1
6
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
W
ire
les
s
Co
mm
u
n
ica
t
io
n
s,
S
ig
n
a
l
P
ro
c
e
ss
i
n
g
a
n
d
Ne
two
rk
in
g
(
W
iS
PNE
T
)
,
Ch
e
n
n
a
i
,
p
p
.
1
5
5
8
-
1
5
6
1
,
2
0
1
6
.
[6
]
A
.
A
.
Ja
b
e
r
a
n
d
R
.
Bick
e
r,
“
De
si
g
n
o
f
a
W
irele
s
s
S
e
n
so
r
No
d
e
f
o
r
V
i
b
ra
ti
o
n
M
o
n
i
to
ri
n
g
o
f
In
d
u
stri
a
l
M
a
c
h
in
e
ry
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
6
,
n
o
.
2
,
pp
.
6
3
9
-
6
5
3
, 2
016
.
[7
]
S
.
Ch
e
lb
i
,
e
t
a
l
.
,
“
A
n
Un
e
q
u
a
l
Clu
ste
r
-
b
a
se
d
Ro
u
t
in
g
P
ro
t
o
c
o
l
Ba
se
d
o
n
Da
ta
Co
n
tro
ll
i
n
g
f
o
r
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
6
,
n
o
.
5
,
pp
.
2
4
0
3
-
2
4
1
4
,
2
0
1
6
.
[8
]
T
.
E
.
A
li
,
e
t
a
l
.,
“
L
o
a
d
Ba
lan
c
e
in
Da
ta
Ce
n
ter
S
DN
Ne
tw
o
rk
s
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
8
,
n
o
.
5
,
pp
.
3
0
8
4
-
3
0
9
1
,
2
0
1
8
.
[9
]
X
.
Yu
a
n
,
e
t
a
l
.
,
“
P
e
rf
o
r
m
a
n
c
e
A
n
a
l
y
si
s
o
f
IEE
E
8
0
2
.
1
5
.
6
-
Ba
se
d
Co
e
x
isti
n
g
M
o
b
i
le
W
B
A
Ns
w
ith
P
ri
o
rit
ize
d
T
ra
ff
ic an
d
D
y
n
a
m
ic In
terfe
re
n
c
e
,
”
IEE
E
T
r
a
n
sa
c
ti
o
n
s
o
n
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l.
1
7
,
p
p
.
5
6
3
7
-
5
6
5
2
,
2
0
1
8
.
[1
0
]
J.
Co
ta
-
Ru
iz,
e
t
a
l
.
,
“
A
Re
c
u
rsiv
e
S
h
o
rtes
t
P
a
th
R
o
u
ti
n
g
A
lg
o
rit
h
m
W
it
h
A
p
p
li
c
a
ti
o
n
f
o
r
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
L
o
c
a
li
z
a
ti
o
n
,
”
IEE
E
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l.
1
6
,
p
p
.
4
6
3
1
-
4
6
3
7
,
2
0
1
6
.
[1
1
]
S
.
M
isra
a
n
d
S
.
S
a
rk
a
r,
“
P
ri
o
rit
y
-
Ba
se
d
T
i
m
e
-
S
lo
t
A
ll
o
c
a
ti
o
n
in
W
irele
ss
Bo
d
y
Are
a
Ne
t
w
o
rk
s
Du
rin
g
M
e
d
ica
l
Em
e
r
g
e
n
c
y
S
it
u
a
ti
o
n
s:
A
n
Ev
o
l
u
ti
o
n
a
ry
G
a
m
e
-
T
h
e
o
re
ti
c
P
e
rsp
e
c
ti
v
e
,
”
IEE
E
J
o
u
rn
a
l
o
f
Bi
o
me
d
ica
l
a
n
d
He
a
lt
h
In
fo
rm
a
t
ics
,
v
o
l.
1
9
,
p
p
.
5
4
1
-
5
4
8
,
2
0
1
5
.
[1
2
]
J.
E.
T
it
o
,
e
t
a
l
.
,
“
S
o
l
u
ti
o
n
o
f
trav
e
ll
in
g
sa
les
m
a
n
p
ro
b
lem
a
p
p
li
e
d
to
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
(
W
S
N)
th
ro
u
g
h
th
e
M
S
T
a
n
d
B&
B
m
e
th
o
d
s
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
S
PIE
,
v
ol
.
1
0
8
0
8
,
2
0
1
8
.
[1
3
]
X
.
L
a
i,
e
t
a
l
.
,
“
E
n
e
rg
y
Eff
ici
e
n
t
L
in
k
-
De
la
y
Aw
a
re
Ro
u
ti
n
g
in
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s,
”
IEE
E
S
e
n
so
rs
J
o
u
rn
a
l
,
v
o
l.
1
8
,
p
p
.
8
3
7
-
8
4
8
,
2
0
1
8
.
[1
4
]
M
.
N
.
V
.
Krish
n
a
,
e
t
a
l
.
,
“
Op
ti
m
iz
a
ti
o
n
o
f
E
n
e
rg
y
Aw
a
re
P
a
th
Ro
u
ti
n
g
P
ro
to
c
o
l
i
n
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
7
,
n
o
.
3
,
pp
.
1
2
6
8
-
1
2
7
7
,
2
0
1
7
.
[1
5
]
P
a
ti
l
M
.
,
S
h
a
rm
a
C.
(2
0
1
8
)
E
n
e
r
g
y
-
E
ff
icie
n
t
P
a
c
k
e
t
Ro
u
ti
n
g
M
o
d
e
l
f
o
r
W
irel
e
ss
S
e
n
so
r
Ne
t
w
o
rk
.
In
:
Ka
lam
A
.
,
Da
s
S
.
,
S
h
a
rm
a
K.
(e
d
s)
A
d
v
a
n
c
e
s
in
El
e
c
tro
n
ics
,
Co
m
m
u
n
ica
ti
o
n
a
n
d
C
o
m
p
u
ti
n
g
.
L
e
c
tu
re
No
t
e
s
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
v
o
l
4
4
3
.
S
p
ri
n
g
e
r,
S
in
g
a
p
o
re
[1
6
]
R
.
Ha
v
in
a
l,
e
t
a
l
.
,
“
EA
S
R:
G
r
a
p
h
-
b
a
se
d
F
ra
m
e
w
o
rk
f
o
r
En
e
rg
y
E
ff
icie
n
t
S
m
a
rt
Ro
u
ti
n
g
in
M
A
NE
T
u
sin
g
Av
a
il
a
b
il
it
y
Zo
n
e
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
5
,
n
o
.
6
,
pp
.
1
3
8
1
-
1
3
9
5
,
2
0
1
5
.
[1
7
]
S
.
Um
a
r,
e
t
a
l
.
,
“
T
re
e
Ba
se
d
En
e
rg
y
Ba
lan
c
in
g
Ro
u
ti
n
g
P
ro
to
c
o
l
b
y
S
e
l
f
Org
a
n
izi
n
g
in
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
ol
.
5
,
n
o
.
6
,
pp
.
1
4
8
6
-
1
4
9
1
,
2
0
1
5
.
[1
8
]
M
.
Ro
y
,
C.
Ch
o
w
d
h
u
ry
a
n
d
N.
A
sla
m
,
"
De
si
g
n
in
g
a
n
e
n
e
rg
y
e
ff
icie
n
t
W
B
AN
ro
u
ti
n
g
p
ro
t
o
c
o
l,
"
2
0
1
7
9
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mm
u
n
ica
t
io
n
S
y
ste
ms
a
n
d
Ne
tw
o
rk
s (
CO
M
S
NET
S
)
,
Ba
n
g
a
lo
re
,
p
p
.
2
9
8
-
3
0
5
,
2
0
1
7
.
[1
9
]
K.
S
u
n
d
a
ra
n
,
V
.
G
a
n
a
p
a
th
y
a
n
d
P
.
S
u
d
h
a
k
a
ra
,
"
En
e
rg
y
e
ff
icie
n
t
m
u
lt
i
-
e
v
e
n
t
b
a
se
d
d
a
ta
tran
sm
is
sio
n
u
si
n
g
a
n
t
c
o
lo
n
y
o
p
ti
m
iza
ti
o
n
in
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,
"
2
0
1
7
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
In
telli
g
e
n
t
Co
mp
u
ti
n
g
,
In
stru
me
n
ta
ti
o
n
a
n
d
Co
n
tro
l
T
e
c
h
n
o
lo
g
ies
(
ICICICT
)
,
Ka
n
n
u
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H.
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a
l
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,
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2
]
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.
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a
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3
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4
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N.
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t
a
l
.
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a
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s
in
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2
0
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u
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m
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ro
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sa
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p
p
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5
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L
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.
a
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of
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fo
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.
[2
6
]
Bo
z
y
i
ğ
it
,
e
t
a
l
.
,
“
P
u
b
li
c
tran
sp
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rt
ro
u
te p
lan
n
in
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:
M
o
d
if
ied
Dijk
stra
'
s
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lg
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m
,
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2
0
1
7
In
ter
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a
l
Co
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fer
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Co
mp
u
ter
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c
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E
n
g
in
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g
(
UBM
K),
An
t
a
lya
,
p
p
.
5
0
2
-
5
0
5
,
2
0
1
7
.
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