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12
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Octo
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8
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2
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207
202
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n
co
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ast,
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cit
y
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eq
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cit
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v
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ed
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d
r
iv
in
g
m
o
v
e
m
en
ts
.
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r
af
f
ic
o
p
tim
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tio
n
is
o
n
e
ap
p
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ac
h
to
k
ee
p
u
p
th
e
cu
r
r
en
t
co
n
g
esti
o
n
co
n
tr
o
l
p
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to
c
o
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d
o
th
er
tr
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ic
p
ar
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s
f
o
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e
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en
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a
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ce
d
co
n
g
esti
o
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co
n
tr
o
l
p
r
o
to
co
ls
[
9
]
.
T
h
e
d
ata
tr
an
s
m
is
s
io
n
b
et
w
ee
n
v
e
h
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s
a
n
d
R
SU
s
m
u
s
t
b
e
r
eliab
le
i
n
tr
i
g
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er
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n
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ch
an
g
es
in
V
A
NE
T
d
r
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in
g
e
n
v
ir
o
n
m
e
n
t
s
.
A
to
o
l
ca
lled
Mu
lt
i
-
Ob
j
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tiv
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Net
wo
r
k
Op
ti
m
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d
An
al
y
s
is
T
o
o
l
(
MO
NOP
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T
I
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cr
ea
te
d
b
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[
1
0
]
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tili
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Sn
ea
li
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A
l
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r
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m
(
S
A
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t
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s
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t
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t
d
ep
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d
en
tire
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ir
r
eg
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lar
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ties
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h
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it
o
n
a
n
y
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a
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g
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ter
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,
th
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ap
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o
w
s
a
n
o
p
ti
m
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o
f
V
A
NE
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in
d
ela
y
s
e
n
s
it
i
v
e
ap
p
licatio
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s
f
o
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b
o
th
cit
y
an
d
h
ig
h
w
a
y
tr
af
f
ic
s
ce
n
ar
io
s
u
s
in
g
T
ag
u
c
h
i
m
eth
o
d
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
I
n
1
9
6
0
,
Dr
.
Gen
ich
i
T
ag
u
c
h
i
in
titi
a
ted
th
e
T
ag
u
ch
i
m
e
th
o
d
[
1
1
]
f
o
cu
s
in
g
o
n
lo
w
co
s
t
o
f
p
r
o
d
u
cin
g
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ig
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q
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ali
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y
p
r
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d
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ct
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ile
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ed
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cin
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th
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p
r
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ctio
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p
r
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s
s
ap
p
l
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in
g
a
r
o
b
u
s
t
ex
p
er
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m
en
tal
d
esig
n
[
1
2
]
.
T
h
e
T
ag
u
ch
i
m
e
th
o
d
is
an
ex
p
er
im
e
n
tal
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esi
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n
to
ev
al
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ate
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ar
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s
f
ac
to
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s
in
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l
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ci
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t
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ch
ar
ac
ter
is
tic
s
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r
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ctio
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o
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s
.
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ag
u
ch
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's
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et
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r
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ased
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l
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OA
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in
g
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tin
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n
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al
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n
g
s
[
1
3
]
.
OA
w
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ich
is
an
i
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e
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ar
a
m
eter
i
n
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atica
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p
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tr
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d
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co
m
m
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n
ic
atio
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s
[
1
4
]
.
T
h
e
T
ag
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ch
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m
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d
co
n
tain
s
th
r
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p
h
ases
w
h
ic
h
ar
e
c
lar
if
ied
in
F
ig
u
r
e
1
[
9
]
.
Fig
u
r
e
1
.
T
ag
u
ch
i
m
et
h
o
d
p
h
ase
[
9
]
2
.
1
.
B
a
s
ic
Desig
n Specif
ica
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p
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en
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(
DOE
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in
T
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m
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cted
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n
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tr
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s
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e
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ce
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n
Fig
u
r
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2
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ll
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2
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2
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M
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(
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I
n
d
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N:
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8
0
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a
s
s
h
o
w
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in
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ab
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.
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ex
p
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m
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.
P
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Me
tr
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me
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[
2
0
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.
3.
RE
SU
L
T
S
A
ND
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AL
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u
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[1
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o
c
n
e
tw
o
rk
s:
A
re
v
ie
w
,
”
J.
N
e
t
w
.
Co
m
p
u
t.
A
p
p
l.
,
p
p
.
1
–
1
7
,
2
0
1
3
.
[3
]
N.
L
u
,
Y.
Ji,
F
.
L
iu
,
a
n
d
X.
W
a
n
g
,
“
A
D
e
d
ica
ted
M
u
lt
i
-
c
h
a
n
n
e
l
M
A
C
P
ro
to
c
o
l
De
sig
n
f
o
r
V
A
NE
T
w
it
h
A
d
a
p
ti
v
e
Bro
a
d
c
a
stin
g
,
”
n
o
.
2
0
0
8
,
2
0
1
0
.
[4
]
A.
S
e
n
a
rt,
M
.
Bo
u
r
o
c
h
e
,
V.
Ca
h
il
l,
a
n
d
S
tef
a
n
W
e
b
e
r,
“
V
e
h
icu
lar
Ne
tw
o
rk
s
a
n
d
A
p
p
li
c
a
ti
o
n
s,”
in
M
id
d
lew
a
re
f
o
r
Ne
tw
o
rk
Ec
c
e
n
tri
c
a
n
d
M
o
b
il
e
A
p
p
li
c
a
ti
o
n
s,
S
p
ri
n
g
e
r
Be
rli
n
He
id
e
lb
e
rg
,
2
0
0
9
,
p
p
.
3
6
9
–
3
8
1
.
[5
]
G.
-
J.
v
a
n
Ro
o
y
e
n
,
S
.
Zea
d
a
ll
y
,
a
n
d
M
.
J.
B
o
o
y
se
n
,
“
S
u
rv
e
y
o
f
m
e
d
ia
a
c
c
e
ss
c
o
n
tro
l
p
r
o
to
c
o
ls
f
o
r
v
e
h
icu
lar
a
d
h
o
c
n
e
tw
o
rk
s,” IE
T
Co
m
m
u
n
.
,
v
o
l.
5
,
n
o
.
1
1
,
p
p
.
1
6
1
9
–
1
6
3
1
,
Ju
l.
2
0
1
1
.
[6
]
A
s
a
d
M
a
q
so
o
d
a
n
d
Re
h
a
n
u
ll
a
h
K
h
a
n
,
“
V
e
h
icu
lar A
d
-
Ho
c
Ne
tw
o
rk
s,” In
t.
J.
Co
m
p
u
t.
S
c
i.
,
v
o
l
.
9
,
1
,
n
o
.
3
,
p
p
.
4
0
1
–
4
0
8
.
[7
]
H.
T
ri
v
e
d
i,
P
.
V
e
e
ra
ra
g
h
a
v
a
n
,
S
.
L
o
k
e
,
A
.
De
s
a
i,
a
n
d
J.
S
in
g
h
,
“
S
m
a
rtV
A
NE
T
:
T
h
e
c
a
se
f
o
r
a
c
ro
ss
-
la
y
e
r
v
e
h
icu
lar
n
e
tw
o
rk
a
rc
h
it
e
c
tu
re
,
”
in
P
ro
c
e
e
d
in
g
s
-
2
5
t
h
IEE
E
In
ter
n
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
A
d
v
a
n
c
e
d
In
f
o
r
m
a
t
io
n
Ne
tw
o
rk
in
g
a
n
d
A
p
p
li
c
a
ti
o
n
s W
o
rk
sh
o
p
s,
W
A
IN
A
2
0
1
1
,
2
0
1
1
,
p
p
.
3
6
2
–
3
6
8
.
[8
]
S
.
A
l
-
S
u
lt
a
n
,
M
.
M
.
A
l
-
Do
o
ri,
A
.
H.
A
l
-
Ba
y
a
tt
i,
a
n
d
H.
Zed
a
n
,
“
A
c
o
m
p
re
h
e
n
siv
e
su
rv
e
y
o
n
v
e
h
icu
lar
A
d
Ho
c
n
e
tw
o
rk
,
”
Jo
u
rn
a
l
o
f
Ne
tw
o
rk
a
n
d
Co
m
p
u
ter A
p
p
l
ica
ti
o
n
s,
v
o
l
.
3
7
.
p
p
.
3
8
0
–
3
9
2
,
Ja
n
-
2
0
1
4
.
[9
]
M
o
h
a
m
e
d
El
sh
a
ik
h
,
M
o
h
d
Na
z
ri
M
o
h
d
W
a
rip
,
On
g
Bi
Ly
n
n
,
R.
Ba
d
li
sh
a
h
A
h
m
a
d
,
P
h
a
k
len
Eh
k
a
n
,
F
a
z
ru
l
F
a
iz
Zak
a
ria,
a
n
d
F
a
iru
l
A
f
z
a
l
A
h
m
a
d
F
u
a
d
,
“
E
n
e
rg
y
Co
n
su
m
p
ti
o
n
O
p
ti
m
iz
a
ti
o
n
w
it
h
Ic
h
i
T
a
g
u
c
h
i
M
e
th
o
d
f
o
r
W
irele
ss
S
e
n
s
o
r
Ne
tw
o
rk
s,” in
2
0
1
4
2
n
d
I
n
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
El
e
c
tr
o
n
ic
De
sig
n
,
2
0
1
4
,
p
p
.
5
5
4
–
5
5
9
.
[1
0
]
A.
M
c
A
u
le
y
a
n
d
K.
M
a
n
o
u
sa
k
is,
“
M
o
n
o
p
a
ti
:
A
M
u
l
ti
-
Ob
jec
ti
v
e
Ne
tw
o
rk
Op
ti
m
iza
ti
o
n
a
n
d
A
n
a
ly
si
s
T
o
o
l
A
p
p
li
e
d
to
Hie
ra
rc
h
ica
l
Ne
tw
o
rk
S
tru
c
tu
re
s,” M
IL
COM
2
0
0
6
-
2
0
0
6
IE
EE
M
il
.
C
o
m
m
u
n
.
Co
n
f
.
,
2
0
0
6
.
[1
1
]
T
.
F
e
a
rn
,
“
T
a
g
u
c
h
i
m
e
th
o
d
s,” NI
R
Ne
ws
,
v
o
l.
1
2
,
2
0
0
1
.
[1
2
]
H.
M
o
h
a
m
e
d
,
M
.
H.
L
e
e
,
S
.
S
a
ll
e
h
,
B.
S
a
n
u
g
i,
a
n
d
M
.
S
a
ra
h
in
t
u
,
“
A
d
-
Ho
c
Ne
t
w
o
rk
De
sig
n
w
it
h
M
u
lt
i
p
le
M
e
tri
c
s
Us
in
g
T
a
g
u
c
h
i
’
s
L
o
ss
F
u
n
c
ti
o
n
,
”
in
2
0
1
1
I
n
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
I
n
f
o
rm
a
ti
c
s,
2
0
1
1
,
n
o
.
Ju
ly
,
p
p
.
7
–
1
1
.
[1
3
]
A
.
A
wa
d
a
,
B.
Weg
m
a
n
n
,
I.
V
ieri
n
g
,
a
n
d
A
.
Kle
in
,
“
A
m
a
th
e
m
a
ti
c
a
l
m
o
d
e
l
f
o
r
u
se
r
tra
ff
ic
in
c
o
v
e
ra
g
e
a
n
d
c
a
p
a
c
it
y
o
p
ti
m
iza
ti
o
n
o
f
a
c
e
ll
u
lar n
e
tw
o
rk
,
”
in
IE
EE
V
e
h
icu
lar T
e
c
h
n
o
lo
g
y
Co
n
f
e
re
n
c
e
,
2
0
1
1
.
[1
4
]
A
.
A
wa
d
a
,
B.
Weg
m
a
n
n
,
I.
V
ie
rin
g
,
a
n
d
A
.
Kle
in
,
“
A
Jo
in
t
Op
ti
m
iza
ti
o
n
o
f
A
n
ten
n
a
P
a
ra
m
e
ter
s
in
a
Ce
ll
u
lar
Ne
tw
o
rk
Us
in
g
T
a
g
u
c
h
i’s
M
e
th
o
d
,
”
in
2
0
1
1
IEE
E
7
3
r
d
V
e
h
icu
lar T
e
c
h
n
o
lo
g
y
Co
n
fe
re
n
c
e
(V
T
C
S
p
rin
g
),
2
0
1
1
,
p
p
.
1
–
5.
[1
5
]
K.
P
a
w
li
k
o
w
s
k
i,
H.
D.
J.
Je
o
n
g
,
a
n
d
J.
S
.
R.
L
e
e
,
“
On
c
re
d
ib
il
it
y
o
f
si
m
u
latio
n
stu
d
ies
o
f
tele
c
o
m
m
u
n
ica
ti
o
n
n
e
tw
o
rk
s,” IE
EE
Co
m
m
u
n
.
M
a
g
.
,
v
o
l.
4
0
,
n
o
.
1
,
p
p
.
1
3
2
–
1
3
9
,
2
0
0
2
.
[1
6
]
A.
V
a
rg
a
a
n
d
R.
Ho
rn
ig
,
“
A
n
Ov
e
rv
ie
w
o
f
th
e
OMNe
T++
S
i
m
u
latio
n
E
n
v
iro
n
m
e
n
t,
”
in
P
r
o
c
e
e
d
in
g
s
o
f
th
e
1
st
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
S
i
m
u
latio
n
T
o
o
ls
a
n
d
T
e
c
h
n
iq
u
e
s
f
o
r
Co
m
m
u
n
ica
ti
o
n
s,
Ne
tw
o
rk
s
a
n
d
S
y
ste
m
s
&
W
o
rk
sh
o
p
s,
2
0
0
8
,
p
p
.
6
0
:1
–
6
0
:
1
0
.
[1
7
]
R.
Na
g
e
l
a
n
d
S
.
Ei
c
h
ler,
“
Ef
f
ic
ien
t
a
n
d
re
a
li
stic
m
o
b
il
it
y
a
n
d
c
h
a
n
n
e
l
m
o
d
e
li
n
g
f
o
r
V
A
NE
T
sc
e
n
a
rio
s
u
sin
g
OMNe
T
+
+
a
n
d
INET
-
f
ra
m
e
w
o
r
k
,
”
in
P
ro
c
e
e
d
in
g
s
o
f
th
e
1
st
in
tern
a
ti
o
n
a
l
c
o
n
f
e
re
n
c
e
o
n
S
im
u
latio
n
t
o
o
ls
a
n
d
tec
h
n
iq
u
e
s f
o
r
c
o
m
m
u
n
ica
ti
o
n
s,
n
e
tw
o
rk
s
a
n
d
sy
ste
m
s &
w
o
rk
sh
o
p
s,
2
0
0
8
,
p
p
.
8
9
:
1
–
8
9
:8
.
[1
8
]
D.
Kle
in
a
n
d
M
.
Ja
rsc
h
e
l,
“
A
n
O
p
e
n
F
l
o
w
Ex
ten
sio
n
f
o
r
th
e
OMNe
T
+
+
INET
F
ra
m
e
w
o
rk
,
”
in
P
ro
c
e
e
d
in
g
s
o
f
th
e
S
ix
th
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
S
im
u
latio
n
T
o
o
ls
a
n
d
T
e
c
h
n
iq
u
e
s,
2
0
1
3
,
p
p
.
3
2
2
–
3
2
9
.
[1
9
]
N.
Ku
m
a
r,
P
.
A
.
A
lv
i,
A
.
S
in
g
h
,
a
n
d
A
.
S
w
a
m
i,
“
A
S
tu
d
y
o
f
Ro
u
ti
n
g
P
r
o
to
c
o
ls
f
o
r
A
d
-
h
o
c
Ne
t
w
o
rk
,
”
In
t.
J.
A
p
p
l.
o
r
In
n
o
v
.
E
n
g
.
M
a
n
a
g
.
,
v
o
l.
2
,
n
o
.
6
,
p
p
.
1
5
4
–
1
5
9
,
2
0
1
3
.
[2
0
]
A
.
A
l
-
m
a
a
sh
ri
a
n
d
M
.
Ou
ld
-
Kh
a
o
u
a
,
“
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
o
f
M
A
NE
T
Ro
u
ti
n
g
P
r
o
to
c
o
ls
i
n
t
h
e
P
re
se
n
c
e
o
f
S
e
l
f
-
S
im
il
a
r
T
ra
ff
ic,”
in
3
1
st
IE
EE
Co
n
f
e
re
n
c
e
o
n
L
o
c
a
l
Co
m
p
u
ter
Ne
tw
o
rk
s,
P
ro
c
e
e
d
in
g
s
2
0
0
6
,
2
0
0
6
,
p
p
.
8
0
1
–
8
0
7
.
[
2
1
]
.
[2
1
]
D.
O.
Jö
rg
a
n
d
M
.
H
e
isse
n
b
ü
tt
e
l,
“
P
e
rf
o
rm
a
n
c
e
Co
m
p
a
riso
n
Of
M
A
NE
T
Ro
u
ti
n
g
P
ro
to
c
o
ls
In
Diff
e
r
e
n
t
Ne
t
w
o
rk
S
ize
s Co
m
p
u
ter S
c
ien
c
e
P
ro
jec
t,
”
2
0
0
3
.
[2
2
]
S
.
A
.
Be
n
M
u
ss
a
,
M
.
M
a
n
a
f
,
K.
Z.
G
h
a
f
o
o
r
&
Z.
D
o
u
k
h
a
,
“
S
im
u
la
ti
o
n
t
o
o
ls
fo
r
v
e
h
icu
la
r
a
d
h
o
c
n
e
tw
o
rk
s:
A
c
o
mp
a
riso
n
stu
d
y
a
n
d
fu
t
u
r
e
p
e
rs
p
e
c
ti
v
e
s
”
In
P
ro
c
e
e
d
in
g
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
W
irele
ss
Ne
t
w
o
rk
s
a
n
d
M
o
b
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