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k
et
lo
s
s
,
attain
s
h
i
g
h
t
h
r
o
u
g
h
p
u
t
an
d
p
r
ev
en
t
s
g
l
o
b
al
s
y
n
c
h
r
o
n
izatio
n
a
s
d
is
cu
s
s
ed
in
T
ab
le
1
.
T
h
e
R
E
D
g
ate
w
a
y
d
r
o
p
s
th
e
p
ac
k
ets
w
h
e
n
t
h
e
a
v
er
ag
e
q
u
e
u
e
s
ize
i
s
g
r
ea
ter
th
a
n
m
a
x
i
m
u
m
t
h
r
es
h
o
ld
v
al
u
e
Fen
g
et
al.
[
9
]
p
r
esen
t
s
t
h
e
o
r
ig
in
a
l
A
R
E
D.
A
r
ev
i
s
ed
v
er
s
io
n
o
f
A
R
E
D
(
A
d
ap
tiv
e
R
E
D)
is
p
r
esen
ted
b
y
Flo
y
d
e
t
al.
[
1
0
]
,
w
h
ic
h
is
a
l
s
o
n
a
m
ed
a
s
A
d
ap
ti
v
e
R
E
D.
An
i
m
p
r
o
v
ed
AR
E
D
tech
n
iq
u
e
d
escr
ib
ed
i
n
[
6
]
o
p
tim
izes
th
e
b
o
u
n
d
s
o
n
th
e
m
ax
i
m
u
m
d
r
o
p
p
r
o
b
ab
ilit
y
a
n
d
ad
j
u
s
ts
t
h
e
lo
w
er
t
h
r
es
h
o
ld
o
f
t
h
e
e
x
p
o
n
en
t
ial
av
er
ag
i
n
g
w
ei
g
h
t
o
n
lin
ea
r
s
tab
ilit
y
co
n
d
it
io
n
s
.
T
h
e
tech
n
i
q
u
es,
m
e
n
tio
n
ed
ab
o
v
e,
h
av
e
b
ee
n
ap
p
lied
s
u
cc
e
s
s
f
u
ll
y
i
n
w
ir
ed
n
et
w
o
r
k
s
,
to
i
m
p
r
o
v
e
t
h
e
T
C
P
p
e
r
f
o
r
m
a
n
ce
in
ad
h
o
c
n
e
t
w
o
r
k
s
.
Sev
er
al
tech
n
iq
u
e
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
em
p
h
asiz
i
n
g
o
n
ad
d
r
ess
in
g
li
n
k
b
r
ea
k
a
g
es,
r
o
u
tin
g
alg
o
r
it
h
m
f
ai
lu
r
es
an
d
m
o
b
ilit
y
[
1
1
]
.
1
.
2
.
T
he
pro
ble
m
T
h
e
co
n
g
es
tio
n
co
n
tr
o
l
p
r
o
b
le
m
i
n
ad
-
h
o
c
w
ir
ele
s
s
n
et
w
o
r
k
s
,
d
e
s
cr
ib
ed
in
A
n
to
n
o
p
o
u
lo
et
al
[
1
2
]
id
en
ti
f
ies
t
h
at
t
h
e
m
ai
n
ca
u
s
e
f
o
r
p
er
f
o
r
m
an
ce
d
e
g
r
ad
atio
n
in
w
ir
eles
s
n
et
w
o
r
k
i
s
ex
ce
s
s
i
v
e
co
n
g
esti
o
n
.
Fo
r
s
u
c
h
n
et
w
o
r
k
s
t
h
e
u
til
izatio
n
o
f
t
h
e
cr
o
s
s
-
la
y
er
d
es
ig
n
ap
p
r
o
ac
h
is
ad
v
o
ca
ted
.
T
h
e
y
al
s
o
ar
g
u
ed
th
a
t
t
h
e
la
y
er
ed
ap
p
r
o
ac
h
o
f
th
e
OSI
/I
SO
m
o
d
el
is
n
o
t
s
u
f
f
ic
ie
n
t
en
o
u
g
h
to
p
r
o
v
id
e
s
u
b
s
t
an
tial
p
er
f
o
r
m
a
n
ce
en
h
a
n
ce
m
en
t
i
n
w
ir
eles
s
n
et
w
o
r
k
s
w
it
h
d
y
n
a
m
ic
n
at
u
r
e.
T
o
p
r
o
v
id
e
a
p
r
o
m
is
i
n
g
s
o
l
u
t
io
n
Xu
K.
et
al
[
1
1
]
p
r
o
p
o
s
ed
NR
E
D
(
Neig
h
b
o
r
h
o
o
d
R
E
D)
tech
n
iq
u
e,
w
h
ich
i
s
an
ex
te
n
s
io
n
o
f
o
r
i
g
in
a
l
R
E
D
[
8
]
d
ev
elo
p
ed
f
o
r
w
ir
ed
n
et
w
o
r
k
s
.
An
N
R
E
D
b
r
in
g
s
t
h
e
co
n
ce
p
t
o
f
d
is
tr
ib
u
t
ed
n
ei
g
h
b
o
r
h
o
o
d
q
u
eu
e.
I
t
i
s
g
i
v
e
n
i
n
t
h
e
tab
le
g
iv
e
n
h
er
e
w
i
th
d
eg
r
ee
alg
o
r
it
h
m
,
W
C
A
(
W
ei
g
h
ted
C
l
u
s
te
r
in
g
A
l
g
o
r
ith
m
)
[
1
3
]
,
[
1
4
]
et
c.
T
h
u
s
,
C
o
n
g
e
s
tio
n
co
n
tr
o
l
is
th
e
m
ain
p
r
o
b
le
m
ar
ea
an
d
ad
d
in
g
l
y
q
u
eu
in
g
to
o
,
w
h
ic
h
h
as
n
o
t
b
ee
n
wo
r
k
ed
to
g
eth
er
f
o
r
MA
NE
T
S e
ar
lier
,
w
h
ich
r
ai
s
e
s
a
r
eq
u
ir
e
m
e
n
t o
f
a
tech
n
iq
u
e
f
o
r
MA
NE
T
S .
1
.
3
.
T
he
pro
po
s
e
d so
lutio
n
T
o
o
v
er
co
m
e
t
h
e
p
r
o
b
lem
a
h
y
b
r
id
tech
n
iq
u
e
i
s
i
n
tr
o
d
u
ce
d
in
th
i
s
r
esear
ch
.
W
e
r
ef
er
to
th
is
tech
n
iq
u
e
a
s
Mo
b
ile
R
E
D
(
M
o
b
ile
R
an
d
o
m
E
ar
l
y
Dete
ct
io
n
)
an
d
ab
b
r
ev
iated
a
s
M
R
E
D.
I
n
t
h
i
s
tec
h
n
iq
u
e
th
e
o
r
ig
in
al
AR
E
D
(
A
d
ap
ti
v
e
R
E
D)
,
[
1
0
]
is
ap
p
lie
d
in
s
tead
o
f
R
E
D
[
8
]
at
th
e
clu
s
ter
h
ea
d
n
o
d
es
in
a
clu
s
ter
ed
n
et
w
o
r
k
.
T
h
e
d
if
f
er
e
n
ce
in
,
R
E
D,
AR
E
D,
NR
E
D
&
MR
E
D,
is
th
e
w
a
y
t
h
eir
d
r
o
p
p
r
o
b
ab
ilit
ies.
T
h
e
R
E
D
g
ate
w
a
y
s
tar
ts
d
r
o
p
p
in
g
th
e
p
ac
k
ets
w
h
e
n
th
e
av
er
a
g
e
q
u
e
u
e
s
ize
r
ea
ch
e
s
t
h
e
m
a
x
i
m
u
m
th
r
esh
o
ld
v
alu
e
w
h
il
e
in
AR
E
D
(
A
d
ap
tiv
e
R
E
D)
,
w
h
ic
h
d
y
n
a
m
icall
y
ch
a
n
g
es
th
e
r
an
g
e
o
f
m
a
x
i
m
u
m
d
r
o
p
p
r
o
b
ab
ilit
y
P
m
a
x
ac
co
r
d
in
g
to
d
if
f
er
en
t
n
et
w
o
r
k
s
ce
n
ar
io
s
an
d
ad
j
u
s
ts
P
m
ax
t
o
li
m
it
a
v
er
ag
e
q
u
e
u
e
s
ize
Q
a
v
e
i
n
a
s
tead
y
r
an
g
e,
th
u
s
,
it
is
m
o
r
e
s
u
i
tab
le
f
o
r
ad
h
o
c
n
et
w
o
r
k
s
(
d
y
n
a
m
ic
to
p
o
lo
g
y
)
in
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
.
T
h
e
s
ce
n
ar
io
is
s
u
p
p
o
s
ed
to
b
e
s
im
u
lated
o
n
MA
NE
T
t
y
p
e
o
f
n
et
w
o
r
k
s
in
w
h
ic
h
n
o
t
o
n
l
y
c
lu
s
ter
n
o
d
es
b
u
t
also
clu
s
ter
h
ea
d
r
eg
u
lar
l
y
c
h
an
g
e
th
eir
lo
ca
tio
n
.
M
R
E
D
also
w
o
r
k
s
s
a
m
e
as
AR
E
D
[
1
5
]
,
[
1
6
]
.
B
u
t,
th
e
e
v
er
ch
an
g
i
n
g
p
o
s
itio
n
o
f
C
l
u
s
ter
h
ea
d
ch
a
n
g
es
t
h
e
v
alu
e
s
w
h
ich
i
s
th
e
m
ai
n
ch
a
llen
g
e,
its
d
elt
in
t
h
is
r
esear
c
h
.
As
f
ar
as
c
u
r
r
en
t
r
esear
ch
i
n
co
n
ce
r
n
ed
.
I
n
A
b
i
n
as
h
a
Mo
h
a
n
et.
al.
[
1
7
]
a
n
o
v
al
w
o
r
k
o
n
q
u
e
u
e
m
an
a
g
e
m
en
t
w
a
s
d
o
n
e
u
s
i
n
g
b
asic
R
E
D
tech
n
iq
u
e.
T
h
e
y
h
av
e
g
iv
e
n
a
j
o
in
ed
ea
r
l
y
co
n
g
esti
o
n
b
ased
s
o
lu
t
io
n
o
n
cr
o
s
s
la
y
er
d
e
s
ig
n
ed
to
o
p
tim
ize
co
n
g
e
s
tio
n
co
n
tr
o
l.
S.
Su
b
h
ar
m
an
a
m
i
n
[
1
8
]
h
as
s
u
g
g
e
s
ted
p
r
ed
ictiv
e
co
n
g
e
s
ti
o
n
co
n
tr
o
l
u
s
i
n
g
a
p
r
ed
ictiv
e
co
n
g
es
tio
n
i
n
d
ex
o
f
a
n
o
d
e
as
r
atio
o
f
cu
r
r
en
t
q
u
eu
e
o
cc
u
p
a
n
c
y
o
v
er
th
e
a
v
ailab
le
to
tal
q
u
eu
e
s
ize
&
th
at
n
o
d
e.
I
t
co
m
p
leted
u
s
in
g
A
ODV
p
r
o
to
co
l
an
d
p
r
o
ac
tiv
el
y
d
e
f
in
ed
&
f
i
n
d
s
c
o
n
g
es
tio
n
.
Se
v
er
al
r
esear
ch
w
o
r
k
s
ar
e
g
o
in
g
o
n
,
in
th
e
cu
r
r
ec
n
t
s
ce
n
ar
io
k
ee
p
in
g
p
o
w
er
an
d
en
er
g
y
a
s
m
a
i
n
p
ar
a
m
eter
s
.
W
o
r
k
d
o
n
e
in
[
1
9
]
is
d
o
n
e
u
s
i
n
g
in
te
g
r
atio
n
in
o
p
tical
&
wir
eless
n
et
w
o
r
k
s
w
h
ich
also
p
r
o
p
o
s
es
a
p
o
w
er
co
n
s
u
m
p
tio
n
m
o
d
el
f
o
r
s
u
ch
t
y
p
e
o
f
n
et
w
o
r
k
s
[
2
0
]
.
T
h
e
p
ap
er
is
co
n
s
is
tin
g
o
f
6
s
ec
tio
n
s
in
w
h
ic
h
Sectio
n
1
co
n
tain
s
i
n
tr
o
d
u
ctio
n
ab
o
u
t
t
h
e
p
r
o
b
lem
Sectio
n
2
g
iv
e
s
th
e
R
ese
ar
ch
m
e
th
o
d
w
h
ic
h
g
i
v
es
m
o
r
e
clar
it
y
ab
o
u
t
th
e
r
elev
a
n
ce
o
f
th
e
p
r
o
b
lem
w
it
h
s
ev
er
al
s
c
h
e
m
es,
m
o
d
u
le
s
an
d
tech
n
iq
u
es
w
h
ic
h
ar
e
ev
o
l
v
ed
ea
r
lier
to
r
em
o
v
e
co
n
g
e
s
tio
n
to
g
iv
e
b
etter
Qo
S
(
Qu
alit
y
o
f
Ser
v
ice)
.
Sectio
n
2
also
g
i
v
es
clea
r
id
ea
ab
o
u
t
t
h
e
p
r
o
to
co
ls
d
is
co
v
er
ed
in
cu
r
r
en
t
s
ce
n
ar
io
f
o
r
t
h
e
co
n
g
es
tio
n
co
n
tr
o
l b
y
g
iv
in
g
s
i
m
u
lta
n
eo
u
s
e
x
p
lan
a
tio
n
o
f
R
E
D,
AR
E
D,
NR
E
D
&
MRED.
Sectio
n
3
g
i
v
es t
h
e
r
esu
lt
an
al
y
s
is
f
o
r
ef
f
icie
n
t
clu
s
ter
i
n
g
tech
n
iq
u
e
an
d
th
e
p
r
o
p
o
s
ed
clu
s
ter
in
g
an
d
q
u
eu
in
g
co
m
b
i
n
atio
n
,
m
ak
in
g
it
a
s
a
h
y
b
r
id
tech
n
iq
u
e
MRED.
A
ll
ex
p
er
i
m
en
ts
,
s
c
en
ar
io
s
i
m
u
latio
n
an
d
its
an
al
y
s
i
s
d
o
n
e
ex
p
lain
ed
in
Sectio
n
3
an
d
4
s
i
m
u
lta
n
eo
u
s
l
y
.
Sectio
n
5
g
iv
e
s
co
n
cl
u
s
i
o
n
f
o
llo
w
ed
b
y
th
e
r
e
f
er
en
ce
s
.
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:
2
0
8
8
-
8708
A
n
a
lysi
n
g
Mo
b
ile
R
a
n
d
o
m
E
a
r
ly
Dete
ctio
n
fo
r
C
o
n
g
esti
o
n
C
o
n
tr
o
l in
Mo
b
ile
A
d
-
h
o
c
..
.
(
S
a
u
r
a
b
h
S
h
a
r
ma
)
1307
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
M
o
bil
e
ra
nd
o
m
ea
rly
det
ec
t
io
n
(
O
R
M
RE
D)
T
h
e
co
n
g
esti
o
n
i
n
an
ad
h
o
c
n
et
w
o
r
k
ca
n
b
e
tr
ac
ed
to
th
e
en
tire
s
p
ac
e
ar
o
u
n
d
th
e
n
o
d
e
b
ec
au
s
e
i
n
ad
h
o
c
n
et
w
o
r
k
n
o
d
e
h
as
to
co
m
p
ete
f
o
r
th
e
ch
a
n
n
el
r
eq
u
ir
e
m
e
n
t
s
w
it
h
th
e
n
o
d
es
th
at
lie
in
th
e
s
a
m
e.
“
Nei
g
h
b
o
u
r
h
o
o
d
”
is
th
e
n
a
m
e
g
i
v
e
n
f
o
r
th
is
“sp
ac
e”
i
n
[
1
1
]
to
n
a
m
e
i
t
as
N
R
E
D.
N
R
E
D
is
co
m
p
ar
ed
w
it
h
MRED
i
n
t
h
is
r
esear
c
h
,
i
n
wh
ich
MRED
tech
n
iq
u
e
is
ap
p
l
ied
o
n
th
e
cl
u
s
ter
h
ea
d
i
n
t
h
e
clu
s
ter
ed
n
et
w
o
r
k
.
C
lu
s
ter
-
h
ea
d
co
n
tain
s
t
h
e
i
n
f
o
r
m
atio
n
o
f
it
s
m
e
m
b
er
n
o
d
e
as
w
e
ll
as
o
f
o
t
h
er
cl
u
s
ter
-
h
ea
d
s
,
is
t
h
e
r
ea
s
o
n
w
h
y
w
e
ap
p
l
y
M
R
E
D
o
n
c
lu
s
ter
-
h
ea
d
.
I
t
w
ill
a
ls
o
r
ed
u
ce
t
h
e
lo
ad
f
r
o
m
t
h
e
m
e
m
b
er
n
o
d
es
i
n
a
cl
u
s
ter
b
y
ca
lcu
lati
n
g
th
e
av
er
a
g
e
q
u
e
u
e
s
ize
o
r
w
e
ca
n
s
a
y
c
h
a
n
n
el
u
tili
za
t
io
n
.
T
h
e
q
u
e
u
e
s
ize
o
n
th
e
cl
u
s
ter
-
h
ea
d
n
o
d
es
d
eter
m
in
e
s
th
e
d
eg
r
ee
o
f
co
n
g
e
s
tio
n
in
n
et
w
o
r
k
.
Fo
r
th
is
f
ir
s
t
w
e
h
av
e
to
ch
o
o
s
e
th
e
clu
s
ter
-
h
ea
d
f
ir
s
t,
as sh
o
w
n
i
n
Fi
g
u
r
e
1
[
2
]
.
T
h
e
o
b
j
ec
ts
in
o
n
e
clu
s
ter
ar
e
s
i
m
ilar
in
ter
m
s
o
f
s
y
n
c
h
r
o
n
i
s
atio
n
th
a
n
th
e
o
b
j
ec
ts
th
at
lies
in
o
th
er
clu
s
ter
.
E
v
er
y
clu
s
ter
s
elec
t
s
a
clu
s
ter
h
ea
d
a
n
d
all
th
e
o
th
er
n
o
d
es
w
h
ic
h
lie
i
n
th
e
tr
a
n
s
m
i
s
s
io
n
r
a
n
g
e
o
f
th
at
clu
s
ter
-
h
ea
d
ar
e
ca
lled
th
e
m
e
m
b
er
n
o
d
es
o
f
t
h
at
cl
u
s
ter
,
as
s
h
o
w
n
i
n
Fi
g
u
r
e
1
[
1
]
.
Sev
er
al
alg
o
r
it
h
m
s
ar
e
p
r
o
p
o
s
ed
f
o
r
th
e
s
elec
tio
n
o
f
clu
s
ter
-
h
ea
d
,
b
u
t
w
e
ar
e
u
s
i
n
g
th
e
h
i
g
h
est
d
eg
r
ee
al
g
o
r
ith
m
to
f
in
d
th
e
cl
u
s
t
er
-
h
ea
d
.
Fig
u
r
e
1
.
Selectin
g
c
l
u
s
ter
h
ea
d
2
.
2
.
Clus
t
er
-
hea
d c
o
ng
estio
n det
ec
t
io
n
Af
ter
t
h
e
cl
u
s
ter
-
h
ea
d
s
elec
t
io
n
t
h
ese
c
lu
s
ter
-
h
ea
d
s
h
a
v
e
to
d
etec
t th
e
co
n
g
esti
o
n
i
n
t
h
e
n
et
w
o
r
k
.
I
t
is
s
i
m
ilar
to
th
e
co
n
g
es
tio
n
d
ete
ctio
n
in
N
R
E
D
ex
ce
p
t
th
at
t
h
e
co
n
g
esti
o
n
is
d
etec
ted
at
th
e
clu
s
ter
g
ate
w
a
y
o
r
clu
s
ter
h
ea
d
n
o
d
es.
A
b
r
ie
f
o
v
er
v
ie
w
is
p
r
o
v
id
ed
h
er
e
f
o
r
co
n
g
es
tio
n
d
etec
t
io
n
i
n
ad
h
o
c
n
et
w
o
r
k
.
A
s
it
i
s
d
if
f
ic
u
lt
to
g
e
t
th
e
ac
t
u
al
q
u
e
u
e
s
ize
o
f
n
o
d
e
in
ad
h
o
c
n
etw
o
r
k
d
u
e
to
ch
a
n
g
e
in
tr
a
f
f
ic
p
atter
n
an
d
n
et
w
o
r
k
to
p
o
lo
g
y
,
s
o
,
ch
a
n
n
el
u
t
ilis
at
i
o
n
is
u
s
ed
to
m
ea
s
u
r
e
th
e
q
u
e
u
e
s
ize
i
n
ad
h
o
c
n
et
w
o
r
k
a
n
d
th
er
e
is
also
a
d
ir
ec
t
r
elatio
n
s
h
ip
b
et
w
ee
n
c
h
an
n
el
u
tili
za
t
io
n
an
d
i
n
p
u
t
-
o
u
tp
u
t q
u
eu
e
s
ize
an
d
t
h
er
e
ar
e
5
d
if
f
e
r
en
t r
ad
io
s
tates th
a
t
ar
e
m
o
n
i
to
r
ed
b
y
t
h
e
n
o
d
es.
T
h
ese
r
ad
io
s
tate
s
ar
e:
a)
T
r
an
s
m
it,
b
)
R
ec
eiv
e,
c)
C
a
r
r
ier
s
en
s
in
g
b
u
s
y
,
d
)
Vir
tu
al
ca
r
r
ier
s
en
s
in
g
b
u
s
y
(
e
.
g
.
d
ef
er
r
al
to
R
T
S,
C
T
S
etc.
)
,
an
d
e)
I
d
le.
Fig
u
r
e
2
s
h
o
w
th
e
f
lo
w
d
ia
g
r
a
m
f
o
r
C
lu
s
ter
h
ea
d
s
elec
t
io
n
.
Fig
u
r
e
2
.
Flo
w
d
iag
r
a
m
f
o
r
c
lu
s
ter
h
ea
d
s
elec
tio
n
S
tart
G
et
o
n
e
-
h
o
p
n
ei
g
h
b
o
r
l
i
st
Hi
g
h
d
eg
r
ee
S
t
ar
t
A
dm
i
t
t
i
m
e
r
W
ai
t
f
o
r
c
l
us
t
e
r
jo
i
n
r
e
qu
e
s
t
B
e
c
am
e
C
l
us
t
e
r
H
e
ad
C
l
us
t
e
r
wi
t
h
ne
i
g
hb
o
r
i
ng
no
de
s
A
dm
i
t
t
i
m
e
r
e
xpi
r
e
s
R
e
c
e
i
v
e
d
jo
i
n
r
e
qu
e
st
J
o
i
n
C
l
us
t
e
r
Yes
Yes
No
No
No
Yes
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
3
0
5
–
1314
1308
T
h
ese
r
ad
io
s
tates
ar
e
d
iv
id
ed
in
to
3
ca
teg
o
r
ies
i
n
,
w
h
er
e
s
t
ates
1
)
a
n
d
2
)
co
n
tr
ib
u
te
o
f
n
o
d
e
to
th
e
to
tal
ch
an
n
el
u
til
izatio
n
b
y
th
e
n
o
d
es.
States
3
)
a
n
d
4
)
ar
e
th
e
co
n
tr
ib
u
tio
n
o
f
t
h
e
n
o
d
e
’
s
n
ei
g
h
b
o
r
s
to
t
h
e
ch
an
n
el
u
tili
za
tio
n
,
an
d
s
tate
5
)
is
ass
u
m
ed
as
e
m
p
t
y
q
u
e
u
e.
I
n
o
r
ig
in
al
NR
E
D,
a
n
o
d
e
esti
m
ates
3
ch
a
n
n
el
u
tili
za
t
io
n
r
atio
s
,
i.e
.
to
tal
c
h
an
n
el
u
t
ilizatio
n
r
atio
(
U
busy
)
,
tr
an
s
m
itt
in
g
r
atio
(
U
tx
)
a
n
d
r
ec
eiv
i
n
g
r
atio
(
U
rx
)
an
d
m
ain
ta
in
s
t
h
e
lo
g
s
f
o
r
ti
m
e
p
er
io
d
in
ea
ch
5
r
ad
io
s
tate
s
as.
T
tx
,
T
rx
,
T
cs
,
T
v
cs
an
d
T
idle
.
L
et
T
int
r
ep
r
esen
ts
th
e
to
tal
ti
m
e
p
er
io
d
s
p
en
t a
t e
ac
h
s
tate.
T
h
en
u
tili
za
t
io
n
r
ati
o
s
b
ec
o
m
e:
(
1
)
(
2
)
(
3
)
W
h
er
e,
T
int
= T
tx
+
T
rx
+
T
cs
+
T
v
cs
+ T
idle
.
U
busy
=
clu
s
ter
-
h
ea
d
q
u
eu
e
s
iz
e.
U
tx
=
o
u
tg
o
in
g
q
u
e
u
e
ch
a
n
n
e
l b
an
d
w
id
t
h
u
s
ag
e,
a
n
d
U
rx
=in
co
m
in
g
q
u
e
u
e
ch
a
n
n
el
b
an
d
w
id
t
h
u
s
ag
e
T
h
e
n
et
w
o
r
k
i
s
s
aid
to
b
e
i
n
ea
r
l
y
co
n
g
est
io
n
s
tate
i
f
U
bu
sy
ex
ce
ed
s
it
s
t
h
r
es
h
o
ld
v
al
u
e
.
No
w
t
h
i
s
ch
a
n
n
el
u
tili
za
t
io
n
is
tr
a
n
s
lated
i
n
to
an
in
d
ex
o
f
t
h
e
q
u
e
u
e
s
ize
b
y
u
s
i
n
g
”
W
h
er
e,
W
is
ch
an
n
el
b
an
d
w
id
t
h
in
b
p
s
C
is
a
v
er
ag
e
p
ac
k
et
s
ize
in
b
it
s
(
C
o
n
s
tan
t)
T
h
e
v
ar
iab
le
q
is
n
o
t
d
im
en
s
i
o
n
all
y
co
r
r
ec
t,
an
d
it
is
ex
p
r
ess
ed
in
p
k
t
s
/s
ec
r
ath
er
t
h
an
p
a
ck
ets.
I
t
is
o
n
l
y
a
s
ca
l
in
g
f
ac
to
r
t
h
at
a
f
f
e
cts
t
h
e
c
h
o
ice
o
f
th
e
v
al
u
e
s
f
o
r
m
in
i
m
u
m
a
n
d
m
ax
i
m
u
m
t
h
r
es
h
o
ld
(
T
h
m
in
a
n
d
Th
m
ax
)
.
Si
m
i
lar
l
y
,
q
tx
an
d
q
rx
ca
n
b
e
ca
lcu
lated
u
s
i
n
g
U
tx
a
n
d
U
rx
.
No
w
,
t
h
e
av
er
a
g
e
q
u
eu
e
s
ize
is
I
n
itiall
y
a
v
g
is
0
an
d
w
q
is
w
ei
g
h
t
p
ar
a
m
eter
.
Si
m
ilar
l
y
,
w
e
c
an
also
g
et
av
g
tx
an
d
av
g
rx
u
s
i
n
g
q
tx
an
d
q
rx
.
av
g
tx
an
d
av
g
rx
ar
e
th
e
av
er
a
g
e
q
u
e
u
e
s
ize
o
f
th
e
i
n
co
m
in
g
an
d
o
u
tg
o
in
g
q
u
e
u
e.
2
.
3
.
Clus
t
er
-
hea
d
co
ng
estio
n no
t
i
f
ica
t
io
n
Un
d
er
MRED,
th
e
cl
u
s
ter
g
at
e
w
a
y
o
r
C
lu
s
ter
h
ea
d
n
o
d
e
ch
ec
k
s
t
h
e
e
s
ti
m
ated
a
v
er
ag
e
q
u
eu
e
s
ize
av
g
p
er
io
d
icall
y
a
n
d
co
m
p
ar
es
it
w
it
h
a
m
in
i
m
u
m
t
h
r
esh
o
l
d
T
h
m
in
.
I
f
q
u
e
u
e
is
lar
g
er
th
an
th
r
e
s
h
o
ld
,
ea
r
l
y
co
n
g
es
tio
n
i
s
d
etec
ted
.
T
h
en
t
h
e
n
o
d
e
ca
lcu
lates
a
d
r
o
p
p
r
o
b
ab
ilit
y
p
b
b
ased
o
n
th
e
a
v
er
a
g
e
q
u
e
u
e
s
ize
a
n
d
b
r
o
ad
ca
s
ts
it to
o
th
er
clu
s
ter
-
h
ea
d
.
T
h
is
p
ap
er
also
r
ep
lace
s
t
h
e
s
p
ec
if
ied
tar
g
et
r
an
g
e
o
f
a
v
er
ag
e
q
u
eu
e
s
ize
as
q
target
= [
Th
min
+ 0
.
4
(
Th
max
-
Th
min
)
,
Th
min
+0
.
6
(
Th
max
-
Th
min
)]
T
h
e
b
o
u
n
d
o
n
q
targ
et
an
d
p
m
ax
i
s
b
ased
o
n
AR
E
D
[
1
0
]
.
Her
e,
w
e
p
r
ese
n
t
th
e
al
g
o
r
it
h
m
f
o
r
ca
lcu
lati
n
g
p
b
u
s
i
n
g
p
s
eu
d
o
co
d
e.
”
“
Alg
o
rit
h
m
1
:
C
alc
u
lati
n
g
Dr
o
p
P
r
o
b
ab
ilit
y
p
b
Sa
v
ed
Va
ria
bles
:
a
vg
:
a
ve
r
a
g
e
q
u
eu
e
s
iz
e
F
ix
ed
P
a
ra
m
et
er
s
:
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:
2
0
8
8
-
8708
A
n
a
lysi
n
g
Mo
b
ile
R
a
n
d
o
m
E
a
r
ly
Dete
ctio
n
fo
r
C
o
n
g
esti
o
n
C
o
n
tr
o
l in
Mo
b
ile
A
d
-
h
o
c
..
.
(
S
a
u
r
a
b
h
S
h
a
r
ma
)
1309
Th
min
:
min
imu
m
th
r
esh
o
ld
fo
r
q
u
eu
e
Th
max
:
ma
ximu
m
th
r
esh
o
ld
fo
r
q
u
eu
e
up
max
:
u
p
p
er th
r
esh
o
ld
o
f
p
max
lp
max
:
lo
w
er th
r
esh
o
ld
o
f p
max
T
NCN
:
time
in
terva
l fo
r
p
erfo
r
min
g
th
is
a
ctio
n
Va
ria
ble P
a
ra
m
et
er
:
p
max
:
d
r
o
p
p
r
o
b
a
b
ilit
y
w
h
en
a
vg
is
eq
u
a
l to
Th
max
,
i
n
itia
lly
p
max
is
s
et
w
ith
lo
w
er th
r
e
s
h
o
ld
o
f p
max
.
f
o
r
ea
ch
T
NCN
a
vg
←
esti
ma
ted
Qu
eu
eS
iz
e
()
f
o
r
ea
ch
in
ter
v
al
s
ec
o
n
d
s
:
if
(
av
g
>
q
targ
et
&
&
p
m
ax
<
u
p
m
ax
)
p
m
ax
= p
m
ax
+
α
;
else
if
(
av
g
<q
targ
et
&
&
p
m
a
x
>lp
m
ax
)
p
m
ax
=p
m
ax
*
β
;
if
Th
min
≤
a
vg
< Th
max
p
b
←
p
max
*
(
a
vg
–
Th
min
)
/(
Th
max
–
Th
min
)
P
norm
←
p
b
/ a
vg
else if
Th
max
≤
a
vg
p
b
←
1
P
norm
W
h
e
r
e,
P
norm
i
s
n
o
r
ma
liz
ed
p
r
o
b
a
b
ilit
y
an
d
th
e
v
al
u
e
u
s
e
d
b
y
e
s
ti
m
ated
Q
u
eu
e
Size(
)
is
ca
lc
u
lated
f
r
o
m
ch
an
n
el
u
ti
lizatio
n
as
in
d
e
x
q
u
eu
e
s
ize.
T
h
r
ee
f
ield
s
,
p
ac
k
e
t
T
y
p
e,
P
norm
,
an
d
l
if
e
ti
m
e,
ar
e
u
s
ed
b
y
t
h
e
NC
N
p
ac
k
ets
as
in
[
1
1
]
.
T
h
e
f
ield
“p
ac
k
et
T
y
p
e”
r
ep
r
esen
ts
a
NC
N
p
ac
k
et.
C
lu
s
ter
-
h
ea
d
s
ca
lcu
late
th
eir
lo
ca
l
d
r
o
p
p
r
o
b
a
b
ilit
y
b
y
u
s
i
n
g
N
o
r
m
aliz
ed
P
r
o
b
a
b
ilit
y
i.e
.
P
nor
m
an
d
p
a
ck
et
d
r
o
p
p
in
g
is
s
to
p
p
ed
af
ter
lif
eti
m
e
p
er
io
d
.
I
n
ca
s
e
o
f
m
u
ltip
le
N
C
N
p
ac
k
et
s
ar
e
r
ec
eiv
ed
lar
g
est P
norm
i
s
s
t
o
r
ed
at
P
norm
f
ield
.
”
2
.
4
.
Clus
t
er
g
a
t
ew
a
y
/hea
d pa
ck
e
t
dro
p
Sin
ce
co
n
g
e
s
tio
n
i
s
d
etec
ted
an
d
n
o
ti
f
ied
to
o
th
er
clu
s
ter
-
h
e
ad
s
,
n
o
w
,
w
e
e
x
p
lai
n
h
o
w
t
h
e
s
e
clu
s
ter
-
h
ea
d
n
o
d
es c
o
o
p
er
ativ
el
y
d
r
o
p
p
ac
k
ets to
r
ea
lized
th
e
e
x
p
ec
ted
d
r
o
p
p
r
o
b
ab
ilit
y
p
b
o
v
er
t
h
e
d
is
tr
ib
u
ted
q
u
eu
e
.
Ov
er
all
d
r
o
p
p
r
o
b
a
b
ilit
y
lo
ca
l
s
h
ar
e
o
f
c
lu
s
ter
-
h
ea
d
s
i
s
ca
lc
u
lated
an
d
is
p
r
o
p
o
r
tio
n
al
t
o
its
q
u
eu
e
s
ize.
I
n
o
u
r
clu
s
ter
ed
m
o
d
el,
th
er
e
ar
e
t
wo
q
u
eu
es
th
a
t
ar
e
as
s
o
ciate
d
a
t
ea
ch
cl
u
s
ter
-
h
ea
d
n
o
d
e,
i.e
.
th
e
o
u
tg
o
i
n
g
q
u
e
u
e
an
d
in
co
m
i
n
g
q
u
eu
e.
B
o
th
th
e
q
u
eu
e
s
ca
lc
u
late
a
n
d
i
m
p
le
m
en
t
p
ac
k
et
d
r
o
p
p
r
o
b
ab
ilit
y
s
ep
ar
atel
y
.
Fo
r
t
h
i
s
we
ar
e
u
s
i
n
g
t
h
e
s
a
m
e
p
s
eu
d
o
co
d
e
as u
s
ed
in
[
8
]
.
”
Alg
o
rit
h
m
2
:
R
a
n
d
o
m
Dr
o
p
(
)
ac
tio
n
at
o
u
t
g
o
in
g
q
u
e
u
e
Sa
v
ed
Va
ria
bles
:
cn
t
tx
:
o
u
tg
o
in
g
p
a
ck
et
a
r
r
ived
s
in
ce
la
s
t d
r
o
p
a
vg
tx
:
a
ve
r
a
g
e
o
u
t
g
o
in
g
q
u
e
u
e
s
iz
e
O
t
her
P
a
ra
m
et
er
s
:
p
c
:
a
cc
u
m
u
la
tive
d
r
o
p
p
r
o
b
a
b
ilit
y.
f
o
r
ea
ch
p
a
ck
et
a
r
r
iva
l
cn
t
tx
←
cn
t
tx
+ 1
if
n
o
r
ma
liz
ed
P
b
<
1
p
b
←
n
o
r
ma
liz
ed
P
b
*
a
vg
tx
p
c
←
p
b
/
(
1
−
co
u
n
t
tx
*
p
b
)
else
p
c
←
1
if
p
c
>
0
a
R
a
n
d
o
mN
u
mb
er
←
r
a
n
([0
,
1
]
)
if
a
R
a
n
d
o
mN
u
mb
er
≤
p
c
d
r
o
p
th
e
a
r
r
ivin
g
p
a
ck
et
cn
t
tx
←
0
else
cn
t
tx
←−
1
”
R
an
d
o
m
n
u
m
b
er
b
et
w
ee
n
0
a
n
d
1
ar
e
g
e
n
er
ated
b
y
u
s
in
g
t
h
e
f
u
n
ctio
n
r
an
(
[
0
,
1
]
)
in
th
e
ab
o
v
e
p
s
eu
d
o
co
d
e.
Sa
m
e
ac
tio
n
is
p
er
f
o
r
m
ed
o
n
in
co
m
i
n
g
q
u
eu
e
b
y
u
s
in
g
av
g
rx
a
n
d
cn
t
rx
i
n
p
lace
o
f
av
g
tx
an
d
cn
t
tx
.
So
,
th
e
p
ar
am
eter
s
o
f
m
o
b
ilit
y
i
s
also
ch
ec
k
ed
an
d
u
s
ed
as t
h
e
p
ar
am
eter
s
to
ch
ec
k
d
r
o
p
in
g
o
f
p
a
ck
ets.
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
3
0
5
–
1314
1310
T
h
e
r
ea
s
o
n
w
h
y
w
e
ap
p
l
y
M
R
E
D
o
n
clu
s
ter
-
h
ea
d
n
o
d
es a
r
e:
a.
C
lu
s
ter
-
h
ea
d
co
n
tai
n
s
t
h
e
i
n
f
o
r
m
atio
n
o
f
i
ts
m
e
m
b
er
n
o
d
e
as
w
ell
a
s
o
f
o
th
er
cl
u
s
ter
-
h
ea
d
s
.
b.
I
t
w
i
ll
r
ed
u
ce
t
h
e
b
o
u
r
d
o
n
f
r
o
m
t
h
e
m
e
m
b
er
n
o
d
es,
in
a
clu
s
ter
,
o
f
ca
lc
u
lati
n
g
th
e
av
er
a
g
e
q
u
eu
e
s
ize
o
r
w
e
ca
n
s
a
y
ch
a
n
n
e
l
u
ti
lizati
o
n
.
T
h
e
q
u
eu
e
s
ize
o
n
t
h
e
clu
s
ter
-
h
ea
d
n
o
d
es
d
eter
m
i
n
e
s
th
e
d
eg
r
ee
o
f
co
n
g
es
tio
n
i
n
n
et
w
o
r
k
.
3.
E
XP
E
R
I
M
E
NT
A
L
SE
T
UP
AND
RE
SUL
T
S AN
AL
Y
SI
S
3
.
1
.
Scena
rio
T
h
e
s
ce
n
ar
io
o
f
t
h
is
m
o
d
el
co
n
s
i
s
ts
o
f
v
er
y
s
m
al
l e
x
p
er
i
m
en
tal
s
et
u
p
o
f
1
7
m
o
b
ile
n
o
d
es,
2
g
ate
w
a
y
s
ar
e
test
ed
h
er
e
o
n
th
e
Net
w
o
r
k
Si
m
u
lato
r
-
2
(
NS2
)
.
T
h
e
to
p
o
lo
g
y
is
a
r
ec
tan
g
u
lar
ar
ea
w
it
h
1
0
0
0
m
len
g
th
a
n
d
1
0
0
0
m
w
id
th
.
T
h
e
t
w
o
g
ate
w
a
y
s
ar
e
p
lace
d
o
n
ea
ch
s
id
e
o
f
th
e
ar
ea
;
t
h
eir
x
,
y
-
co
o
r
d
in
ates
i
n
g
r
id
ar
e
(
1
5
0
,
2
8
0
)
,
(
8
0
0
,
2
5
0
)
.
A
ll
s
i
m
u
latio
n
s
ar
e
r
u
n
f
o
r
1
5
0
s
ec
o
n
d
s
o
f
s
i
m
u
lated
ti
m
e.
Fo
u
r
o
f
t
h
e
1
7
m
o
b
ile
n
o
d
es
ar
e
co
n
s
ta
n
t
b
it
r
ate
tr
a
f
f
ic
s
o
u
r
ce
s
a
s
s
h
o
w
n
i
n
th
e
tab
le
i
n
Fi
g
u
r
e
3
.
T
h
e
y
ar
e
d
is
tr
ib
u
te
d
r
an
d
o
m
l
y
w
it
h
i
n
th
e
m
o
b
ile
ad
h
o
c
n
et
w
o
r
k
.
Af
ter
t
h
is
ti
m
e
th
e
s
o
u
r
ce
s
co
n
ti
n
u
e
s
en
d
i
n
g
d
ata
u
n
til
o
n
e
s
ec
o
n
d
b
ef
o
r
e
th
e
en
d
o
f
th
e
s
i
m
u
latio
n
.
P
A
R
A
M
E
T
E
R
VA
L
U
E
S
S
im
ula
t
i
o
n
t
im
e
150
s
e
c
T
opo
lo
gy
s
i
z
e
1000
X
1000
No.
of
n
ode
s
17
No.
of
c
lu
s
ter
s
2
Node
m
o
b
il
i
t
y
0
to
20
m
/s
e
c
R
ou
t
ing
P
r
otoco
l
DSDV
F
r
e
q
u
e
n
c
y
11
M
H
z
T
r
a
f
f
ic
t
y
pe
C
B
R
M
A
C
I
E
E
E
802.
11
M
obil
it
y
m
o
de
l
R
a
n
do
m
W
a
ypo
in
t
M
a
x
.
n
o.
o
f
pa
c
ke
ts
10000
P
a
u
s
e
ti
m
e
10s
e
c
Fig
u
r
e
3
.
P
ar
am
eter
s
u
s
ed
3
.
2
.
Clus
t
er
f
o
r
m
a
t
io
n & c
l
us
t
er
cha
ng
es
T
h
e
n
o
d
es
in
th
e
c
lu
s
ter
ar
e
m
o
b
ile
in
n
at
u
r
e
t
h
u
s
t
h
e
cl
u
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ter
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d
es
a
s
w
ell
a
s
t
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h
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d
s
ch
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o
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itio
n
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u
n
k
n
o
w
i
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g
l
y
.
T
h
e
clu
s
ter
c
h
a
n
g
e
v
ar
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d
clu
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ter
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d
ch
a
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v
ar
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tio
n
w
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h
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p
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t
to
n
o
d
e
m
o
b
ilit
y
ar
e
s
h
o
w
n
i
n
f
i
g
4
.
W
e
ca
n
o
b
s
er
v
e
th
a
t th
e
n
et
w
o
r
k
is
m
o
r
e
s
tab
le
in
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w
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m
o
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ilit
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s
ce
n
ar
io
s
.
T
h
e
s
i
m
u
l
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s
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o
r
t
h
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r
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u
lts
w
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e
ca
r
r
ied
w
it
h
No
.
o
f
n
o
d
e
=
1
7
an
d
T
o
p
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ize
=
1
0
0
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x
1
0
0
0
.
A
s
th
e
n
o
d
e
m
o
b
ilit
y
in
cr
ea
s
e
th
e
clu
s
ter
c
h
a
n
g
e
i
n
cr
ea
s
e
p
a
u
s
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ti
m
e
f
o
r
m
o
b
ile
n
o
d
es th
e
c
h
a
n
g
e
s
also
d
ec
r
ea
s
e,
th
u
s
,
cl
u
s
ter
h
ea
d
ch
a
n
g
es
an
d
clu
s
ter
h
ea
d
c
h
an
g
e
s
also
d
ec
r
ea
s
es
i
f
n
o
d
e
m
o
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il
it
y
d
ec
r
ea
s
es
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n
d
p
au
s
e
ti
m
e
in
cr
ea
s
es
f
r
o
m
1
to
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0
0
s
ec
s
w
h
ic
h
ca
n
b
e
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s
il
y
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ee
n
in
t
h
e
d
ata
s
et
k
ep
t
i
n
T
ab
le
2
[
2
]
,
g
r
ap
h
s
h
o
w
n
i
n
Fig
u
r
e
4
a
d
r
o
p
in
p
au
s
e
ti
m
e
as p
er
d
er
ea
s
ed
m
o
b
ilit
y
.
Fig
u
r
e
4
.
C
lu
s
ter
ch
a
n
g
e
s
v
s
n
o
d
e
m
o
b
ilit
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:
2
0
8
8
-
8708
A
n
a
lysi
n
g
Mo
b
ile
R
a
n
d
o
m
E
a
r
ly
Dete
ctio
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r
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g
esti
o
n
C
o
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o
l in
Mo
b
ile
A
d
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h
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.
(
S
a
u
r
a
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h
S
h
a
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ma
)
1311
T
ab
le
2
.
C
lu
s
ter
C
h
an
g
es V
s
No
d
e
Mo
b
ilit
y
S
.
N
o
C
l
u
st
e
r
H
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d
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h
a
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l
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st
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r
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h
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me
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l
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st
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e
a
d
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h
a
n
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P
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se
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i
me
(
se
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N
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me
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st
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.
3
.
Q
ue
uin
g
a
m
o
ng
c
lus
t
er
s
I
n
th
e
p
r
o
p
o
s
ed
MRED
s
c
h
e
m
e,
th
er
e
ar
e
s
ev
er
al
p
ar
a
m
eter
s
w
h
ic
h
w
ill
a
f
f
ec
t
t
h
e
p
er
f
o
r
m
an
ce
.
T
h
e
q
u
eu
i
n
g
is
d
o
n
e
i
n
b
et
w
ee
n
th
e
g
ate
w
a
y
n
o
d
es o
f
d
i
f
f
er
e
n
t c
lu
s
ter
s
w
h
ic
h
w
i
ll
g
et
u
p
d
ated
b
y
t
h
e
cl
u
s
ter
h
ea
d
as
s
h
o
w
n
in
p
r
ev
io
u
s
s
ec
tio
n
.
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n
th
is
s
ec
tio
n
,
w
e
w
ill
tr
y
t
o
d
eter
m
i
n
e
th
eir
o
p
ti
m
a
l
v
al
u
es.
Mo
r
eo
v
er
,
o
u
r
s
ch
e
m
e
f
o
r
esti
m
a
tin
g
t
h
e
av
er
ag
e
q
u
e
u
e
s
ize
o
f
t
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e
n
eig
h
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d
q
u
eu
e
is
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ea
lized
b
y
est
i
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ati
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h
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el
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VAL
an
d
Q
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SIZ
E
as
s
h
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F
ig
u
r
e
5
.
A
s
th
e
m
ai
n
g
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al
o
f
th
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s
c
h
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m
e
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to
ac
h
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v
e
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w
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er
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ela
y
an
d
h
i
g
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p
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o
r
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er
to
w
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h
MRED
g
ate
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a
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es s
ag
,
a
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d
d
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s
/m
ar
k
s
th
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r
iv
i
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P
ac
k
ets
w
it
h
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p
r
o
b
ab
ilit
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p
to
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o
tify
T
C
P
en
d
o
f
th
e
in
it
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l c
o
n
g
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tio
n
w
h
en
s
ag
>m
an
.
W
e
h
av
e
to
ca
lcu
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t
h
e
q
a
vg
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o
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in
d
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t t
h
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u
m
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er
o
f
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(
in
q
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e)
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a
n
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ed
p
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it ti
m
e.
q
avg
=(
1
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)
q
`
a
vg
+w
q
Fig
u
r
e
5
.
Qu
eu
e
s
ize
v
s
ti
m
e
T
ab
le
3
.
Qu
eu
e
s
ize
Vs T
i
m
e
T
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me
0
10
20
30
Est
i
m
a
t
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d
A
v
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r
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e
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0
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0
0
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20
R
e
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l
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v
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1
0
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35
32
I
n
g
r
ap
h
o
f
Fi
g
u
r
e
4
o
f
d
ata
r
ep
r
esen
ted
i
n
T
ab
le
3
in
cr
ea
s
ed
m
o
b
ilit
y
lo
w
s
ize
in
clu
s
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r
s
is
s
ee
n
a
n
d
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o
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ch
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g
es
ac
co
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g
l
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,
Qu
e
u
s
ize
d
ec
s
ea
s
es
as
p
er
p
au
s
e
ti
m
e
d
ec
r
ea
s
e.
T
h
u
s
,
f
r
o
m
Fi
g
.
5
it’
s
clea
r
t
h
at
in
itial
l
y
th
e
m
o
b
ili
t
y
is
th
er
e
p
ac
k
et
s
ize
i
n
cr
ea
s
e
w
i
th
ti
m
e
p
ass
es
an
d
in
cr
ea
s
e
s
Q
u
e
u
e
s
ize
d
ec
r
ea
s
es
d
r
o
p
p
r
o
b
a
b
ilit
y
also
in
cr
ea
s
es.
4.
RE
SU
L
T
S AN
D
T
H
RO
U
G
H
P
UT
ANA
L
YS
I
S
Af
ter
MRE
D
is
ap
p
lied
,
w
e
o
b
s
er
v
e
th
at
th
e
f
air
n
e
s
s
i
n
d
ice
s
u
n
d
er
th
e
b
o
th
s
ce
n
ar
io
s
ar
e
i
m
p
r
o
v
ed
q
u
ick
l
y
alo
n
g
w
it
h
th
e
in
cr
ea
s
e
o
f
p
m
ax
.
Fo
r
th
e
h
id
d
en
ter
m
in
al
s
ce
n
ar
io
,
th
e
f
air
n
e
s
s
i
n
d
ex
is
clo
s
e
to
1
(
th
e
h
ig
h
e
s
t
v
al
u
e)
af
ter
p
m
ax
is
la
r
g
er
th
an
0
.
1
.
Fo
r
th
e
ex
p
o
s
e
d
ter
m
i
n
al
s
ce
n
ar
io
,
f
air
n
ess
i
n
d
ex
is
al
s
o
ab
o
v
e
0
.
9
5
w
h
e
n
p
m
ax
is
lar
g
er
th
a
n
0
.
1
4
.
T
h
e
th
r
o
u
g
h
p
u
t
lo
s
s
co
m
es
f
r
o
m
t
w
o
r
ea
s
o
n
s
.
Fir
s
t,
b
ef
o
r
e
a
p
ac
k
et
is
0
50
10
0
15
0
20
0
25
0
0
10
20
30
40
50
60
Q
ue
ue
S
i
z
e
(N
o.
of
P
a
ck
e
ts
)
T
i
m
e
E
s
tim
at
e
d
Av
e
ra
ge
Qu
e
u
e
…
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.
8
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
3
0
5
–
1314
1312
d
r
o
p
p
ed
b
y
NR
E
D,
it
m
a
y
h
a
v
e
u
s
ed
th
e
ch
a
n
n
el.
Dr
o
p
p
in
g
s
u
c
h
p
ac
k
et
s
ce
r
tain
l
y
w
a
s
t
es
s
o
m
e
b
an
d
w
id
th
.
Seco
n
d
,
th
e
NR
E
D
s
c
h
e
m
e
te
n
d
s
to
k
ee
p
t
h
e
w
ir
ele
s
s
c
h
a
n
n
el
s
li
g
h
tl
y
u
n
d
er
u
t
ilized
.
T
h
u
s
,
a
s
m
all
f
r
ac
tio
n
o
f
b
an
d
w
id
t
h
i
s
also
s
ac
r
i
fi
ce
d
.
(
MRED)
an
d
(
w
it
h
NR
E
D)
s
h
o
w
th
e
d
y
n
a
m
ic
s
o
f
th
e
t
w
o
co
n
n
ec
tio
n
s
b
y
p
lo
ttin
g
t
h
e
i
n
s
tan
ta
n
eo
u
s
t
h
r
o
u
g
h
p
u
t
o
f
ea
ch
fl
o
w
a
s
i
t
h
a
s
b
ee
n
s
h
o
w
n
in
[
2
1
]
.
Fro
m
F
ig
u
r
e
6
,
w
e
o
b
s
er
v
e
th
at
w
h
en
n
o
d
e
5
m
o
v
es d
o
w
n
,
th
e
t
w
o
co
n
n
ec
tio
n
s
ar
e
o
u
t o
f
in
ter
f
er
en
ce
w
i
th
ea
c
h
o
th
er
.
`
Fig
u
r
e
6
.
Av
er
ag
e
t
h
r
o
u
g
h
p
u
t v
s
p
m
ax
T
ab
le
4
.
A
v
er
ag
e
T
h
r
o
u
g
h
p
u
t
Vs p
m
ax
P
max
0
0
.
0
5
0
.
1
1
.
1
5
0
.
2
0
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2
5
0
.
3
N
R
ED
2
0
.
4
8
0
0
8
0
0
8
0
0
8
0
1
8
0
3
8
0
4
M
R
ED
3
0
.
6
8
0
0
8
4
0
8
3
0
8
2
8
8
2
8
8
2
4
T
h
e
th
r
o
u
g
h
p
u
t
o
f
t
h
e
p
r
o
p
o
s
ed
MRED
i
s
c
h
ec
k
ed
in
co
m
p
ar
is
io
n
to
N
R
E
D
a
n
d
it
h
as
b
ee
n
f
o
u
n
d
th
at
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
M
R
E
D
is
i
m
p
r
o
v
ed
3
%.
A
s
i
n
c
ase,
o
f
MRED
t
h
e
clu
s
ter
in
g
an
d
q
u
eu
i
n
g
m
a
k
e
th
e
r
es
u
lts
s
o
i
m
p
r
o
v
ed
.
I
n
ce
r
tain
to
p
o
lo
g
y
s
e
v
er
al
b
o
ttlen
ec
k
n
ei
g
h
b
o
u
r
h
o
o
d
s
m
a
y
b
e
p
r
esen
t
at
t
h
e
s
a
m
e
ti
m
e.
T
h
e
o
v
er
all
th
r
o
u
g
h
p
u
t o
f
ea
c
h
fl
o
w
i
s
g
i
v
e
n
in
f
o
llo
w
i
n
g
Fig
u
r
e
7.
5.
CO
NCLU
SI
O
N
T
h
is
an
al
y
s
i
s
co
n
cl
u
d
es
th
a
t
t
h
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RE
F
E
R
E
NC
E
S
[1
]
R.
N.
De
v
ik
a
r,
e
t
a
l.
“
Iss
u
e
s
in
Ro
u
ti
n
g
M
e
c
h
a
n
ism
f
o
r
P
a
c
k
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ts
F
o
rw
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rd
in
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:
A
S
u
rv
e
y
”,
In
ter
n
a
t
io
n
a
l
J
o
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rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
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ter
En
g
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g
(
IJ
ECE
)
,
2
0
1
6
,
v
o
l
.
6
,
p
p
.
4
2
1
-
4
3
0
.
[2
]
C.
Ce
ti
n
k
a
y
a
,
“
M
u
lt
i
-
c
h
a
n
n
e
l
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M
A
C
p
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to
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o
l
f
o
r
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les
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LA
N
s
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,
Ad
Ho
c
N
e
two
rk
s
2
0
1
5
,
v
o
l.
2
8
,
pp.
17
-
3
7
.
[3
]
S
.
S
h
a
rm
a
,
Dr.
R.
A
g
a
r
w
a
l
,
“
A
n
a
ly
sin
g
Qo
s P
a
ra
m
e
ters
in
M
AN
ET
S
:
A
S
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rv
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y
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,
S
e
c
o
n
d
In
ter
n
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ti
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Co
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fer
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RT
S
T
M
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D
-
1
5
),
Rec
e
n
t
T
re
n
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s
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e
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e
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h
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.
[4
]
P
.
K.
S
u
ri,
Dr
.
M
.
K
.
S
o
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a
n
d
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Clu
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Ro
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P
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ter
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Co
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0
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[5
]
A
.
A
.
Ha
n
b
a
li
,
E.
A
lt
m
a
n
,
a
n
d
P
.
Na
in
,
“
A
S
u
rv
e
y
o
f
T
CP
o
v
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r
A
d
Ho
c
Ne
tw
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rk
s
”,
IEE
E
Co
mm
u
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ti
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s S
u
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n
d
T
u
to
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0
0
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,
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l.
7
,
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o
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3
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p
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2
2
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.
[6
]
K.
X
u
,
M
.
G
e
rla,
L
.
Qi,
a
n
d
Y.
S
h
u
,
“
En
h
a
n
c
i
n
g
T
CP
fa
irn
e
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d
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,
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c
.
ACM
M
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1
,
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1
6
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.
[7
]
V
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n
k
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ta
N.
P
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d
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m
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b
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a
n
a
n
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Ra
n
d
y
H.
Ka
tz
,
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T
CP
F
a
st
S
tart:
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T
e
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h
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e
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o
r
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p
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d
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up
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T
ra
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.
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f
IEE
E
Gl
o
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8
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ter
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M
in
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fer
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e
1
9
9
8
.
[8
]
M
.
Ch
a
tt
e
rjee
,
S
.
K.
Da
s
a
n
d
D.
T
u
rg
u
t
,
“
A
n
o
n
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d
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m
a
n
d
w
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h
ted
c
lu
ste
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g
a
lg
o
rit
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m
(W
CA
)
f
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r
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d
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o
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n
e
tw
o
rk
s
”
,
in
Pro
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IEE
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GLO
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n
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7
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0
1
.
[9
]
W
.
F
e
n
g
,
D.
Ka
n
d
lu
r,
D.
S
a
h
a
,
a
n
d
K.
G
.
S
h
in
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“
A
s
e
lf
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o
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ig
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r
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g
a
te
wa
y
”,
in
Pro
c
e
e
d
in
g
s
o
f
th
e
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Co
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fer
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c
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Co
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p
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ter
Co
m
m
u
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ica
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o
n
s (
INFOCOM
’9
9
)
1
9
9
9
,
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l.
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,
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p
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1
3
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0
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3
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0
]
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.
F
lo
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d
,
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G
u
m
m
a
d
i,
a
n
d
S
.
S
c
h
e
n
k
e
r,
“
A
d
a
p
ti
v
e
RED:
a
n
a
l
g
o
rit
h
m
f
o
r
in
c
re
a
sin
g
th
e
ro
b
u
s
tn
e
ss
o
f
RED’s
a
c
ti
v
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q
u
e
u
e
m
a
n
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g
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m
e
n
t,
”
T
e
c
h
n
ica
l
Re
p
o
rt
,
2
0
0
1
,
v
o
l
.
5
,
p
p
.
7
-
1
7
.
[1
1
]
J.
Ch
e
n
,
C.
Hu
,
a
n
d
Z.
Ji
,
“
A
n
i
m
p
ro
v
e
d
A
RED
a
lg
o
rit
h
m
f
o
r
c
o
n
g
e
stio
n
c
o
n
tr
o
l
o
f
n
e
tw
o
rk
tran
sm
issio
n
,
”
M
a
th
e
ma
ti
c
a
l
Pro
b
lem
s in
E
n
g
in
e
e
rin
g
2
0
1
0
(2
0
),
A
rti
c
le ID
3
2
9
0
3
5
,
p
p
.
14
-
2
8
.
[1
2
]
Ch
risto
s
A
n
to
n
o
p
o
u
lo
a
n
d
S
tav
r
o
s
Ko
u
b
ias
,
“
Co
n
g
e
stio
n
Co
n
tro
l
F
ra
m
e
w
o
r
k
f
o
r
A
d
-
Ho
c
W
irel
e
ss
Ne
t
w
o
rk
s,”
W
ire
l
e
ss
Per
so
n
a
l
C
o
mm
u
n
ica
ti
o
n
2
0
1
0
,
v
o
l
.
5
2
,
p
p
.
7
5
3
-
7
7
5
.
[1
3
]
M
.
Ch
a
tt
e
rjee
,
S
.
Da
s,
a
n
d
D.
T
u
rg
u
t,
“
W
CA:
a
we
ig
h
ted
c
lu
s
ter
in
g
a
lg
o
rit
h
m
fo
r
mo
b
il
e
A
d
‐
h
o
c
n
e
two
rk
s,”
J
o
u
rn
a
l
o
f
Clu
ste
r C
o
mp
u
ti
n
g
(S
p
e
c
ial
Iss
u
e
o
n
M
o
b
il
e
A
d
h
o
c
Ne
tw
o
rk
s),
2
0
0
2
,
v
o
l.
5
,
p
p
.
1
9
3
-
2
0
4
.
[1
4
]
A
.
P
a
re
k
h
,
“
S
e
lec
ti
n
g
ro
u
te
rs
in
a
d
h
o
c
w
irele
ss
n
e
t
w
o
rk
s
”
,
In
Pro
c
.
o
f
th
e
S
BT
/IE
EE
In
ter
n
a
ti
o
n
a
l
m
T
e
lec
o
mm
u
n
ica
ti
o
n
s
S
y
mp
o
siu
m
,
1
9
9
4
.
[1
5
]
A
.
Ep
h
re
m
id
e
s,
J.E
.
W
ies
e
lt
h
ier,
D.J.
Ba
k
e
r.
“
A
d
e
sig
n
c
o
n
c
e
p
t
f
o
r
re
li
a
b
le
m
o
b
il
e
ra
d
i
o
n
e
tw
o
rk
s
w
it
h
f
re
q
u
e
n
c
y
h
o
p
p
i
n
g
sig
n
a
ll
in
g
,
”
In
Pro
c
e
e
d
in
g
s o
f
th
e
IEE
E
1
9
8
7
,
v
o
l.
75
,
p
p
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