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42
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81
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CC B
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Un
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id
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RO
D
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m
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allen
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in
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ata
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
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52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
2
6
:
81
-
92
82
ev
alu
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m
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On
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h
as
g
ain
ed
s
ig
n
if
ican
t
atten
tio
n
[
3
]
.
W
SN
f
ac
ilit
ates
th
e
o
b
s
er
v
atio
n
o
f
ev
en
ts
an
d
p
h
e
n
o
m
e
n
a
ac
r
o
s
s
v
ast
ar
ea
s
th
r
o
u
g
h
c
o
m
p
ac
t
s
en
s
o
r
n
o
d
e
co
m
p
u
ter
d
ev
ices
an
d
wir
ele
s
s
co
m
m
u
n
icatio
n
m
eth
o
d
s
[
4
]
.
T
h
e
d
e
v
elo
p
m
e
n
t
o
f
d
ata
tr
an
s
m
is
s
io
n
m
ed
ia
y
ea
r
ly
h
as
led
to
im
p
r
o
v
em
e
n
t
s
in
v
ar
io
u
s
f
u
n
ctio
n
s
,
in
clu
d
i
n
g
tr
an
s
m
is
s
io
n
s
p
ee
d
an
d
p
er
f
o
r
m
an
ce
o
n
W
SN
[
5
]
.
W
SN
is
o
n
e
o
f
th
e
r
ec
en
t
d
ata
tr
an
s
m
is
s
io
n
tech
n
o
lo
g
y
th
at
allo
ws
f
o
r
th
e
tr
an
s
m
is
s
io
n
o
f
s
en
s
o
r
d
ata
with
m
in
im
al
p
o
wer
c
o
n
s
u
m
p
tio
n
[
6
]
.
M
u
ltip
le
n
o
d
es
u
tili
ze
W
SN
to
co
llect
in
f
o
r
m
atio
n
o
n
v
ar
i
o
u
s
p
h
en
o
m
en
a,
s
u
c
h
as
ch
a
n
g
es
in
tem
p
er
atu
r
e,
lig
h
t
in
ten
s
ity
,
an
d
h
ea
t.
T
h
e
d
ata
c
o
llected
b
y
s
en
s
o
r
n
o
d
es
is
tr
an
s
m
itted
to
s
in
k
s
th
r
o
u
g
h
q
u
er
y
r
esp
o
n
s
es
u
s
in
g
s
p
ec
if
ic
r
o
u
tin
g
p
r
o
t
o
co
ls
[
7
]
.
On
ce
th
e
d
ata
ar
r
iv
es
at
th
e
s
in
k
,
it
ca
n
b
e
p
r
o
ce
s
s
ed
o
r
s
to
r
ed
in
t
h
e
d
ata
ce
n
ter
[
8
]
.
W
ith
in
a
W
SN,
s
e
n
s
o
r
n
o
d
es
co
m
m
u
n
icate
with
n
ea
r
b
y
n
o
d
es
f
o
r
r
o
u
tin
g
p
u
r
p
o
s
es
an
d
d
ata
tr
a
n
s
m
is
s
io
n
,
em
p
lo
y
in
g
s
h
o
r
t
-
r
a
n
g
e
r
ad
i
o
wav
es
[
9
]
.
W
SN
tech
n
o
lo
g
y
is
co
m
m
o
n
l
y
u
s
ed
to
m
o
n
ito
r
en
v
i
r
o
n
m
e
n
tal
co
n
d
itio
n
s
an
d
o
t
h
er
a
p
p
licatio
n
s
in
th
e
c
u
r
r
e
n
t
er
a
[
1
0
]
.
I
n
a
d
d
itio
n
t
o
e
n
v
ir
o
n
m
en
tal
m
o
n
ito
r
i
n
g
,
W
SN
tech
n
o
lo
g
y
is
wid
ely
a
p
p
lied
in
th
e
ag
r
icu
ltu
r
al
s
ec
to
r
an
d
s
m
ar
t
cities
[
1
1
]
.
Go
o
d
W
SN
p
er
f
o
r
m
an
ce
is
ess
en
tia
l
to
o
p
tim
ize
p
er
f
o
r
m
a
n
ce
in
s
m
ar
t
ag
r
icu
ltu
r
e
an
d
cities
an
d
ac
h
iev
e
d
esira
b
le
r
esu
lts
[
1
2
]
.
T
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
a
W
S
N
d
e
p
e
n
d
s
o
n
f
a
c
t
o
r
s
s
u
c
h
a
s
t
r
a
n
s
a
c
ti
o
n
s
p
e
e
d
b
e
t
w
e
e
n
n
o
d
e
s
[
1
3
]
.
O
n
e
a
p
p
r
o
a
c
h
t
o
e
n
h
a
n
c
i
n
g
W
S
N
p
e
r
f
o
r
m
a
n
c
e
i
s
t
h
r
o
u
g
h
r
o
u
t
i
n
g
a
l
g
o
r
i
t
h
m
s
[
1
4
]
.
A
n
e
x
a
m
p
l
e
o
f
s
u
c
h
a
n
a
l
g
o
r
it
h
m
i
s
t
h
e
l
o
w
-
e
n
e
r
g
y
a
d
a
p
t
i
v
e
c
l
u
s
t
e
r
i
n
g
h
i
e
r
a
r
c
h
y
(
L
E
AC
H
)
,
w
h
i
c
h
i
n
v
o
l
v
e
s
c
r
e
a
t
i
n
g
C
H
t
o
c
o
ll
e
c
t
d
a
ta
f
r
o
m
C
M
a
n
d
f
o
l
l
o
ws
a
w
el
l
-
d
e
f
i
n
e
d
p
r
o
t
o
c
o
l
[
1
5
]
.
H
o
w
e
v
e
r
,
o
n
e
l
i
m
i
t
a
ti
o
n
o
f
L
E
A
C
H
is
t
h
e
p
o
t
e
n
ti
a
l
f
o
r
m
a
ti
o
n
o
f
p
o
o
r
l
y
s
t
r
u
c
t
u
r
e
d
c
l
u
s
t
e
r
s
,
w
h
e
r
e
C
H
a
n
d
C
M
p
o
s
i
t
i
o
n
ed
f
a
r
a
p
a
r
t
c
a
n
l
e
a
d
t
o
s
u
b
o
p
t
i
m
a
l
p
e
r
f
o
r
m
a
n
c
e
[
1
6
]
.
T
o
ad
d
r
ess
th
is
lim
itatio
n
,
p
r
ev
io
u
s
s
tu
d
ies
h
av
e
im
p
r
o
v
ed
th
e
L
E
AC
H
m
eth
o
d
b
y
in
co
r
p
o
r
atin
g
a
d
is
tan
ce
p
ar
am
eter
,
w
h
ich
all
o
ws f
o
r
s
elec
tin
g
th
e
clo
s
est C
H
to
th
e
s
in
k
in
its
m
u
ltip
le
s
ce
n
ar
io
s
.
T
o
a
d
d
r
ess
th
e
s
tr
ay
n
o
d
e
is
s
u
e
in
th
is
s
tu
d
y
,
a
m
u
lti
-
h
o
p
p
in
g
co
m
m
u
n
icatio
n
m
o
d
el
with
th
e
s
h
o
r
test
p
ath
r
o
u
tin
g
(
SGR
)
was
im
p
lem
en
ted
[
1
7
]
.
Ho
wev
er
,
en
e
r
g
y
e
f
f
icien
cy
was
n
o
t
a
p
r
im
ar
y
co
n
ce
r
n
in
ad
d
r
ess
in
g
m
u
ltip
l
e
C
H
o
r
s
tr
ay
n
o
d
e
p
r
o
b
lem
s
[
1
8
]
.
An
o
th
e
r
m
et
h
o
d
u
tili
ze
d
to
im
p
r
o
v
e
W
SN
p
er
f
o
r
m
an
ce
is
th
e
m
u
lti
-
ch
an
n
e
l
m
o
d
el
,
wh
ic
h
aim
s
to
r
e
d
u
ce
tr
af
f
ic
d
e
n
s
ity
b
y
u
tili
zin
g
m
u
ltip
le
p
r
o
to
c
o
l
ch
an
n
els
[
1
9
]
.
I
n
th
is
m
o
d
el,
C
H
s
er
v
es
as
a
r
ef
er
en
ce
c
h
an
n
e
l
f
o
r
C
M
[
2
0
]
.
Alth
o
u
g
h
wid
ely
em
p
lo
y
ed
,
th
e
Mu
lti
-
C
h
an
n
el
m
o
d
el
f
ac
es
ch
allen
g
es
s
u
ch
as
th
e
f
o
r
m
a
tio
n
o
f
d
is
tan
t
C
H
an
d
C
M,
r
esu
ltin
g
in
in
cr
ea
s
ed
e
n
er
g
y
co
n
s
u
m
p
ti
o
n
f
o
r
tr
an
s
ac
tio
n
s
[
2
1
]
.
T
h
e
m
u
lti
-
c
h
an
n
el
m
o
d
el
r
e
d
u
ce
s
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
s
er
v
er
b
u
r
d
en
b
y
s
en
d
in
g
d
ata
o
n
ly
to
C
H,
wh
ich
th
en
f
o
r
wa
r
d
s
it
to
th
e
s
in
k
.
Ho
wev
er
,
t
h
e
m
o
d
el
also
f
ac
es
ch
allen
g
e
s
wh
en
f
o
r
m
in
g
C
H
an
d
C
M,
esp
ec
ially
wh
en
th
ey
ar
e
lo
ca
ted
f
ar
f
r
o
m
ea
ch
o
th
e
r
,
wh
ich
ca
n
r
esu
lt in
h
am
p
er
e
d
d
ata
tr
an
s
ac
tio
n
s
r
eq
u
ir
in
g
s
u
b
s
tan
tial
en
er
g
y
co
n
s
u
m
p
tio
n
.
Desp
ite
ex
ten
s
iv
e
s
tu
d
ies
o
n
clu
s
ter
in
g
-
b
ase
d
r
o
u
tin
g
p
r
o
t
o
co
ls
s
u
ch
as
L
E
AC
H
an
d
its
v
ar
ia
n
ts
,
s
ev
er
al
f
u
n
d
am
en
tal
ch
all
en
g
es
r
em
ain
u
n
r
eso
lv
e
d
,
p
ar
ticu
lar
ly
r
elate
d
to
lo
w
th
r
o
u
g
h
p
u
t,
in
ef
f
icien
t
clu
s
ter
f
o
r
m
atio
n
,
an
d
s
u
b
o
p
tim
al
d
ata
f
o
r
war
d
in
g
p
ath
s
.
E
x
is
tin
g
ap
p
r
o
ac
h
es
o
f
ten
s
u
f
f
er
f
r
o
m
la
r
g
e
in
tr
a
-
c
lu
s
ter
d
is
tan
ce
b
etwe
en
C
Hs
an
d
C
Ms,
wh
ich
le
ad
s
to
in
cr
ea
s
ed
tr
an
s
m
is
s
io
n
d
elay
an
d
d
ata
lo
s
s
.
Mo
r
e
o
v
e
r
,
co
n
v
en
tio
n
al
m
u
lti
-
h
o
p
an
d
m
u
lti
-
ch
an
n
el
r
o
u
tin
g
s
ch
em
es
ten
d
to
f
o
c
u
s
o
n
en
er
g
y
e
f
f
icien
cy
o
r
ch
an
n
el
u
tili
za
tio
n
in
d
ep
en
d
en
tly
,
wit
h
o
u
t
ex
p
licitly
m
in
im
izin
g
d
a
ta
v
ar
iatio
n
with
in
clu
s
ter
s
.
T
h
i
s
r
esear
ch
ad
d
r
e
s
s
es
th
ese
lim
itatio
n
s
b
y
p
r
o
p
o
s
in
g
a
MH
C
P
p
r
o
to
co
l
t
h
at
s
y
s
tem
atica
lly
in
teg
r
ates c
lu
s
ter
p
ar
titi
o
n
in
g
an
d
m
u
lti
-
h
o
p
r
o
u
tin
g
to
en
h
a
n
ce
th
r
o
u
g
h
p
u
t p
e
r
f
o
r
m
an
ce
i
n
W
SN
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
Sev
er
al
s
t
u
d
ies
h
av
e
d
e
v
el
o
p
e
d
a
m
u
lti
-
c
h
an
n
e
l
m
o
d
e
l
b
y
i
n
teg
r
ati
n
g
t
h
e
c
lu
s
te
r
i
n
g
p
r
o
c
es
s
,
r
ef
er
r
ed
to
as
m
u
lti
-
c
h
an
n
e
l
cl
u
s
te
r
i
n
g
h
i
er
ar
ch
y
(
MCC
H)
.
T
h
is
m
o
d
el
co
n
s
is
ts
o
f
f
o
u
r
s
t
ag
es
.
Fi
r
s
tl
y
,
t
h
e
d
at
a
is
d
et
e
r
m
i
n
ed
i
n
te
r
m
s
o
f
te
m
p
e
r
atu
r
e
an
d
h
u
m
i
d
it
y
.
S
ec
o
n
d
l
y
,
C
H
is
f
o
r
m
e
d
,
t
h
i
r
d
ly
,
t
h
e
p
r
o
x
i
m
it
y
b
etw
ee
n
C
H
an
d
C
M
is
esta
b
l
is
h
ed
,
a
n
d
f
i
n
al
ly
,
t
h
e
cl
u
s
t
er
in
g
p
r
o
c
ess
is
im
p
l
em
e
n
te
d
u
s
in
g
a
s
i
n
g
le
li
n
k
a
g
e
[
2
2
]
.
MCC
H
m
o
d
el
h
as
s
h
o
wn
t
h
e
p
o
t
en
tia
l
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
c
e
o
f
W
SN
[
2
3
]
.
A
n
o
t
h
e
r
s
tr
ate
g
y
t
o
o
p
t
im
i
ze
W
SN
ef
f
ic
ie
n
c
y
i
n
v
o
lv
es
a
m
u
lti
-
h
o
p
s
c
h
e
m
e
,
w
h
e
r
e
b
y
i
n
d
iv
i
d
u
al
s
e
n
s
o
r
n
o
d
es
o
p
e
r
a
te
as
f
o
r
w
ar
d
i
n
g
in
t
er
m
e
d
i
ar
ies
,
r
el
ay
in
g
d
at
a
i
n
c
r
e
m
e
n
t
all
y
t
o
t
h
e
b
ase
s
t
ati
o
n
a
n
d
s
u
b
s
eq
u
e
n
t
ly
to
th
e
s
i
n
k
[
2
4
]
,
[
2
5
]
.
U
n
li
k
e
th
e
m
u
lti
-
c
h
a
n
n
e
l
m
o
d
el
th
at
d
iv
i
d
es
c
h
an
n
e
ls
,
t
h
is
m
o
d
el
i
n
v
o
l
v
es
e
ac
h
n
o
d
e
c
o
m
m
u
n
i
ca
ti
n
g
w
it
h
o
n
e
a
n
o
t
h
e
r
to
s
e
n
d
d
a
ta
[
2
6
]
.
I
n
t
h
is
s
t
u
d
y
,
t
h
e
m
o
d
el
u
til
ize
d
t
o
im
p
r
o
v
e
t
h
e
p
e
r
f
o
r
m
a
n
c
e
o
f
W
SN
is
m
u
lti
-
h
o
p
p
i
n
g
[
2
7
]
.
T
o
d
e
v
el
o
p
t
h
is
m
o
d
el
,
a
cl
u
s
t
er
i
n
g
p
r
o
ce
s
s
t
h
at
g
r
o
u
p
ed
W
SN
n
o
d
es
b
y
p
ar
titi
o
n
i
n
g
a
n
d
co
n
n
e
cti
n
g
t
h
e
m
to
C
H
was
i
n
c
o
r
p
o
r
a
te
d
.
T
h
is
s
t
u
d
y
is
a
th
e
o
r
eti
ca
l d
ev
el
o
p
m
en
t
in
th
e
f
ie
ld
o
f
W
SN
,
a
n
d
it is
th
e
f
ir
s
t
o
f
i
ts
k
in
d
[
2
8
]
.
I
ts
n
o
v
el
ty
c
o
n
t
r
i
b
u
ti
o
n
is
i
n
t
h
e
f
o
r
m
o
f
m
i
n
i
m
i
zi
n
g
d
a
ta
v
a
r
i
ati
o
n
s
wit
h
i
n
a
cl
u
s
t
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
r
o
vin
g
th
e
p
erfo
r
ma
n
ce
o
f
w
ir
ele
s
s
s
en
s
o
r
n
etw
o
r
k
u
s
in
g
mu
lti
-
h
o
p
p
in
g
…
(
R
o
b
b
y
R
iz
ky
)
83
Fig
u
r
e
1
s
h
o
ws
th
e
to
p
o
lo
g
ica
l
d
esig
n
wh
er
e
ea
ch
b
lu
e
s
en
s
o
r
n
o
d
e
ca
n
ac
t
as
a
r
elay
s
tatio
n
in
th
e
ch
ain
th
at
f
o
r
war
d
s
d
ata
p
ac
k
ets
to
th
e
b
ase
s
tatio
n
[
2
9
]
.
T
h
e
b
ase
s
tatio
n
t
h
en
co
llec
ts
an
d
a
n
aly
ze
s
th
e
ac
tiv
it
ies
th
at
h
av
e
b
ee
n
p
er
f
o
r
m
ed
.
Af
ter
th
at,
t
h
e
co
n
tr
o
l
s
tatio
n
r
ea
d
s
an
d
an
aly
ze
s
t
h
e
d
ata
f
o
r
s
p
ec
if
ic
p
u
r
p
o
s
es [
3
0
]
,
[
3
1
]
.
Fig
u
r
e
2
illu
s
tr
ates
th
e
clu
s
ter
in
g
p
ar
titi
o
n
p
r
o
ce
s
s
,
wh
ich
in
clu
d
es
th
e
in
itial
clu
s
ter
in
g
,
iter
ativ
e
p
r
o
ce
s
s
,
an
d
f
in
al
clu
s
ter
in
g
s
tag
es
[
3
2
]
.
Fig
u
r
e
2
(
a)
s
h
o
ws
th
e
in
itial
g
r
o
u
p
in
g
o
f
s
en
s
o
r
n
o
d
es
in
to
p
r
elim
in
ar
y
clu
s
ter
s
.
Fig
u
r
e
2
(
b
)
p
r
esen
ts
th
e
iter
ativ
e
p
r
o
ce
s
s
wh
er
e
CM
an
d
ce
n
tr
o
id
p
o
s
itio
n
s
ar
e
u
p
d
ated
.
Fig
u
r
e
2
(
c)
s
h
o
ws
th
e
f
in
al
c
lu
s
ter
in
g
r
esu
lt
af
ter
th
e
cl
u
s
ter
s
b
ec
o
m
e
s
tab
le.
I
n
th
e
p
a
r
titi
o
n
al
clu
s
ter
in
g
tech
n
iq
u
e,
a
clu
s
ter
ce
n
ter
p
o
i
n
t
(
ce
n
tr
o
id
)
is
d
eter
m
in
ed
.
I
n
th
is
s
tu
d
y
,
th
e
ce
n
tr
o
id
is
r
ep
r
esen
ted
b
y
th
e
C
H,
wh
ich
co
llects
d
ata
f
r
o
m
C
M
[
3
3
]
-
[
3
4
]
.
T
h
is
s
tu
d
y
aim
s
to
d
ev
elo
p
a
W
SN
p
r
o
t
o
co
l
th
at
im
p
r
o
v
es
n
etwo
r
k
p
er
f
o
r
m
an
ce
b
y
in
teg
r
atin
g
m
u
lti
-
h
o
p
p
i
n
g
an
d
clu
s
ter
in
g
p
a
r
titi
o
n
tech
n
iq
u
es.
Fig
u
r
e
1
.
Mu
lti
-
h
o
p
p
in
g
r
o
u
ti
n
g
n
etwo
r
k
to
p
o
lo
g
y
d
esig
n
(
a)
(
b
)
(
c)
Fig
u
r
e
2
.
Par
titi
o
n
cl
u
s
ter
in
g
p
r
o
ce
s
s
; (
a)
f
ir
s
t c
lu
s
ter
,
(
b
)
iter
atio
n
p
r
o
ce
s
s
,
an
d
(
c)
f
in
al
clu
s
ter
T
h
e
m
ai
n
co
n
t
r
i
b
u
ti
o
n
o
f
t
h
is
s
tu
d
y
li
es
i
n
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
an
MH
C
P
p
r
o
t
o
co
l
t
h
at
ex
p
l
ici
tl
y
m
i
n
im
iz
es
d
a
ta
v
a
r
ia
ti
o
n
wi
th
i
n
cl
u
s
t
er
s
w
h
i
le
im
p
r
o
v
i
n
g
t
h
r
o
u
g
h
p
u
t
p
e
r
f
o
r
m
a
n
c
e
i
n
W
SN
s
.
U
n
li
k
e
co
n
v
e
n
ti
o
n
a
l
L
E
AC
H
-
b
as
e
d
r
o
u
ti
n
g
,
w
h
i
ch
r
el
ies
o
n
p
r
o
b
ab
i
lis
ti
c
CH
s
el
ec
t
io
n
a
n
d
m
ay
r
es
u
lt
i
n
p
o
o
r
l
y
s
tr
u
ct
u
r
e
d
cl
u
s
t
er
s
,
t
h
e
p
r
o
p
o
s
ed
MH
C
P
i
n
t
eg
r
ates
t
h
r
e
e
k
e
y
m
ec
h
a
n
is
m
s
:
(
i
)
ad
a
p
ti
v
e
CH
f
o
r
m
ati
o
n
i
n
s
p
ir
ed
b
y
L
E
AC
H,
(
ii
)
E
u
cli
d
ea
n
d
i
s
tan
ce
-
b
as
ed
p
r
o
x
i
m
i
ty
o
p
ti
m
i
za
ti
o
n
b
et
wee
n
C
H
an
d
C
M
,
an
d
(
iii
)
c
lu
s
te
r
i
n
g
p
a
r
ti
ti
o
n
u
s
i
n
g
a
K
-
m
ea
n
s
-
b
as
ed
a
p
p
r
o
a
c
h
t
o
r
ed
u
ce
i
n
tr
a
-
cl
u
s
te
r
v
ar
iat
io
n
.
I
n
co
n
t
r
as
t
t
o
ex
is
ti
n
g
MCC
H
a
n
d
tr
a
d
i
ti
o
n
al
m
u
l
ti
-
h
o
p
r
o
u
ti
n
g
s
ch
em
es,
MH
C
P
f
o
c
u
s
es
o
n
s
p
atia
l
o
p
ti
m
i
za
t
io
n
a
n
d
cl
u
s
t
er
co
m
p
ac
tn
ess
r
at
h
e
r
th
a
n
c
h
a
n
n
el
d
i
v
is
i
o
n
al
o
n
e.
T
h
is
i
n
te
g
r
at
io
n
p
r
o
v
id
es
a
n
ew
r
o
u
t
in
g
p
er
s
p
ec
ti
v
e
t
h
at
b
al
a
n
ce
s
d
at
a
tr
a
n
s
m
is
s
i
o
n
e
f
f
ici
en
c
y
a
n
d
n
etw
o
r
k
p
e
r
f
o
r
m
a
n
ce
,
t
h
e
r
e
b
y
o
f
f
er
i
n
g
a
d
is
t
in
ct
m
et
h
o
d
o
l
o
g
ic
al
c
o
n
t
r
i
b
u
ti
o
n
to
W
SN r
o
u
ti
n
g
p
r
o
t
o
c
o
l
d
esi
g
n
.
3.
M
E
T
H
O
D
3
.
1
.
M
ulti
-
ho
pp
ing
clus
t
er
in
g
pa
rt
it
io
n
T
h
e
MH
C
P
is
a
p
r
o
to
c
o
l
d
e
v
e
lo
p
ed
f
o
r
W
SN
th
at
ca
n
ad
d
r
e
s
s
th
e
lo
w
-
p
er
f
o
r
m
an
ce
is
s
u
es
r
eg
ar
d
in
g
d
ata
lo
s
s
an
d
s
ig
n
if
ican
t
d
ela
y
s
o
n
W
SN
.
T
h
e
MH
C
P
m
eth
o
d
im
p
r
o
v
es
W
SN
p
er
f
o
r
m
a
n
ce
in
th
r
e
e
s
tag
es
.
T
h
ese
in
clu
d
e
f
o
r
m
in
g
CH
as
a
r
ef
er
en
ce
f
o
r
C
M
to
s
en
d
d
ata,
th
e
d
eter
m
in
atio
n
o
f
th
e
p
r
o
x
im
ity
o
f
n
o
d
es
to
C
H,
an
d
g
r
o
u
p
in
g
n
o
d
es u
s
in
g
th
e
p
ar
titi
o
n
in
g
tech
n
i
q
u
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
2
6
:
81
-
92
84
3
.
2
.
Sim
ula
t
i
o
n
env
iro
nm
en
t
a
nd
da
t
a
s
et
des
cr
iptio
n
T
h
e
p
er
f
o
r
m
an
ce
e
v
alu
atio
n
o
f
th
e
p
r
o
p
o
s
ed
MH
C
P
p
r
o
t
o
co
l
was
co
n
d
u
cted
u
s
in
g
a
s
im
u
latio
n
en
v
ir
o
n
m
en
t d
ev
elo
p
e
d
in
M
AT
L
AB
.
T
h
e
s
im
u
lated
W
SN
co
n
s
is
ts
o
f
1
0
0
s
en
s
o
r
n
o
d
es
r
an
d
o
m
ly
d
is
tr
ib
u
ted
with
in
a
two
-
d
im
e
n
s
io
n
al
ar
e
a
o
f
3
0
0
×
3
0
0
u
n
its
.
E
ac
h
n
o
d
e
is
in
itialized
with
an
en
e
r
g
y
lev
el
o
f
1
0
0
u
n
its
,
an
d
th
e
n
etwo
r
k
o
p
er
ates
with
a
p
r
e
d
ef
in
ed
tr
an
s
m
is
s
io
n
v
elo
city
o
f
1
0
,
0
0
0
u
n
its
.
T
h
e
d
ataset
u
s
e
d
in
th
is
s
tu
d
y
is
s
y
n
th
etica
lly
g
en
er
at
ed
th
r
o
u
g
h
s
im
u
latio
n
an
d
r
e
p
r
esen
ts
s
p
atial
n
o
d
e
d
is
tr
ib
u
t
io
n
an
d
s
en
s
o
r
d
ata
tr
an
s
m
is
s
io
n
b
eh
av
io
r
co
m
m
o
n
ly
ad
o
p
ted
in
W
SN
p
er
f
o
r
m
an
ce
ev
alu
atio
n
.
T
h
is
s
im
u
latio
n
-
b
ased
d
ataset
allo
ws
co
n
tr
o
lled
an
aly
s
is
o
f
th
r
o
u
g
h
p
u
t,
d
elay
,
a
n
d
p
ac
k
et
lo
s
s
u
n
d
er
id
e
n
tical
n
etwo
r
k
co
n
d
itio
n
s
f
o
r
f
ai
r
co
m
p
ar
is
o
n
with
L
E
AC
H
an
d
MCC
H
p
r
o
to
co
ls
.
T
o
en
s
u
r
e
a
f
air
a
n
d
co
n
tr
o
ll
ed
p
er
f
o
r
m
an
ce
e
v
alu
atio
n
,
t
h
e
s
im
u
latio
n
en
v
ir
o
n
m
en
t
a
n
d
n
etwo
r
k
co
n
f
ig
u
r
atio
n
p
ar
am
eter
s
ar
e
p
r
ed
ef
in
e
d
an
d
s
u
m
m
a
r
ized
i
n
T
ab
le
1
.
T
h
ese
p
ar
am
ete
r
s
d
escr
ib
e
th
e
n
u
m
b
e
r
o
f
s
en
s
o
r
n
o
d
es,
in
itial
en
er
g
y
,
n
etwo
r
k
ar
ea
d
im
en
s
io
n
s
,
an
d
tr
an
s
m
is
s
io
n
v
elo
city
u
s
ed
th
r
o
u
g
h
o
u
t
t
h
e
ex
p
er
im
en
ts
.
T
h
e
MH
C
P
m
eth
o
d
,
as
s
h
o
w
n
in
Fig
u
r
e
3
,
co
n
s
is
ts
o
f
th
r
ee
s
tag
es
.
T
h
e
f
ir
s
t
s
tag
e
i
n
v
o
lv
es
th
e
f
o
r
m
atio
n
o
f
a
CH
ad
th
at
s
e
r
v
es
as
a
r
e
f
er
en
ce
f
o
r
CM
.
T
h
e
s
ec
o
n
d
s
tag
e
is
th
e
f
o
r
m
atio
n
o
f
p
r
o
x
im
ity
b
etwe
en
CH
an
d
m
em
b
e
r
s
.
T
h
e
th
ir
d
s
tag
e
is
th
e
g
r
o
u
p
i
n
g
p
r
o
ce
s
s
with
th
e
p
ar
titi
o
n
in
g
tec
h
n
iq
u
e.
T
ab
le
1
.
Simu
latio
n
p
ar
am
eter
s
P
a
r
a
me
t
e
r
V
a
l
u
e
N
u
mb
e
r
o
f
n
o
d
e
s
1
0
0
En
e
r
g
y
1
0
0
X
max
3
0
0
Y
max
3
0
0
V
e
l
o
c
i
t
y
1
0
,
0
0
0
Fig
u
r
e
3
.
T
h
e
MH
C
P
m
eth
o
d
3
.
3
.
F
o
rma
t
io
n O
F
CH
I
n
th
is
s
tag
e,
C
H
f
o
r
m
atio
n
is
p
er
f
o
r
m
ed
u
s
in
g
a
r
an
d
o
m
p
r
o
b
a
b
ilit
y
m
eth
o
d
th
at
is
d
iv
id
ed
in
to
m
u
ltip
le
s
ess
io
n
s
b
ased
o
n
th
e
d
esire
d
n
u
m
b
er
o
f
C
H
an
d
o
b
s
er
v
atio
n
p
er
io
d
.
E
ac
h
n
o
d
e
is
m
ad
e
C
H
f
o
r
a
s
ess
io
n
to
en
s
u
r
e
eq
u
al
d
is
tr
ib
u
tio
n
.
T
h
e
p
o
s
itio
n
o
f
C
H
is
m
ad
e
u
n
s
tab
le
o
r
alter
n
atin
g
to
cr
ea
te
d
y
n
a
m
ic
f
o
r
m
atio
n
o
r
c
h
an
g
es in
ea
c
h
s
ess
io
n
.
(
)
=
{
1
0
−
×
(
1
)
(
1
)
T
(
n
)
,
wh
ich
is
th
e
T
h
r
esh
o
ld
p
er
r
o
u
n
d
n
o
d
e,
b
ec
o
m
e
s
C
H
wh
en
m
>T
(
n
)
.
P
,
R
,
N,
an
d
G
d
en
o
te
th
e
ap
p
lied
CH
p
r
o
b
ab
ilit
y
,
r
o
u
n
d
,
n
o
d
e
in
th
e
f
o
r
m
o
f
a
g
r
o
u
p
,
an
d
n
o
d
es
th
at
h
a
v
e
n
o
t
y
et
b
e
co
m
e
CH
.
Fo
r
C
H
s
elec
tio
n
in
Alg
o
r
ith
m
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
r
o
vin
g
th
e
p
erfo
r
ma
n
ce
o
f
w
ir
ele
s
s
s
en
s
o
r
n
etw
o
r
k
u
s
in
g
mu
lti
-
h
o
p
p
in
g
…
(
R
o
b
b
y
R
iz
ky
)
85
A
lg
o
r
ith
m
1.
C
H
s
elec
tio
n
alg
o
r
ith
m
Input
: N
total node
CH
total CH
X
the length of the map
Y
the width of the map
array [nodes]
group of all node
Output
: array [nodes]
, a collection of filtered nodes
nodes
∈
N/CH
for i in CH:
X0 = generate random value f
rom 0 to X
Y0 = generate random value from 0 to Y
For j in range(len(nodes)):
find min distance of node(x,y) to X0,Y0
if found:
Append to CH
break
3
.
4
.
Dis
t
a
nce
ca
lcula
t
io
n
o
f
ea
ch
no
de
a
nd
ch
T
h
e
s
ec
o
n
d
s
tag
e
in
v
o
lv
es
f
i
n
d
in
g
th
e
n
o
d
es
clo
s
est
to
C
H
.
T
h
e
s
p
atial
g
a
p
b
etwe
en
in
d
iv
id
u
al
n
o
d
es
an
d
th
e
CH
is
o
b
tain
ed
th
r
o
u
g
h
th
e
E
u
clid
ea
n
d
i
s
tan
ce
co
m
p
u
tatio
n
.
T
h
is
ap
p
r
o
ac
h
s
er
v
es
as
a
g
eo
m
etr
ic
m
ea
s
u
r
e
t
o
e
v
alu
at
e
p
o
s
itio
n
al
d
is
p
ar
ity
o
r
clo
s
e
n
ess
b
etwe
en
two
co
o
r
d
in
ate
p
o
in
ts
.
Du
r
i
n
g
th
is
p
h
ase,
th
e
p
r
im
ar
y
o
u
tp
u
t
is
th
e
ca
lcu
lated
E
u
clid
ea
n
v
alu
e
lin
k
in
g
ea
ch
n
o
d
e
to
its
ad
jace
n
t
n
o
d
es.
I
n
a
two
-
d
im
en
s
io
n
al
co
o
r
d
in
ate
s
y
s
tem
,
th
e
d
is
tan
ce
b
etwe
en
two
n
o
d
es
d
ef
in
ed
b
y
(
x
1
,
x
2
)
an
d
(
y
1
,
y
2
)
f
o
llo
ws
th
e
co
n
v
en
tio
n
al
E
u
clid
ea
n
ex
p
r
ess
io
n
(
Alg
o
r
ith
m
2
)
.
(
2
,
1
)
=
√
∑
(
2
,
1
)
2
=
1
(
2
)
D
,
X1
,
X2
,
Xij,
an
d
X2
J
d
en
o
t
e
th
e
n
o
d
e
p
o
in
t,
X
ax
is
(
C
H)
,
Y
ax
is
,
X
ax
is
n
u
m
b
er
(
C
H)
,
an
d
Y
ax
is
n
u
m
b
er
.
Alg
o
r
ith
m
2
.
Alg
o
r
ith
m
c
alcu
l
atio
n
o
f
th
e
d
is
tan
ce
o
f
ea
ch
n
o
d
e
an
d
C
H
Input
: n1(x,y)
x dan y at
node1
n2(x,y)
x dan y at node 2
X
the length of the map
Y
the width of the map
Output
: distance
distance from node1 and node2
3
.
5
.
T
he
pro
ce
s
s
o
f
g
ro
up
ing
wit
h pa
rt
it
io
n t
ec
hn
iqu
es
T
h
e
th
ir
d
s
tag
e
i
n
v
o
lv
es
an
iter
ativ
e
g
r
o
u
p
in
g
p
r
o
ce
s
s
th
at
p
ar
titi
o
n
s
th
e
d
ataset
in
to
K
clu
s
ter
s
p
r
ed
eter
m
in
e
d
f
r
o
m
th
e
s
tar
t
.
E
ac
h
n
o
d
e
is
ass
ig
n
ed
a
clu
s
te
r
I
D,
an
d
th
e
d
is
s
im
ilar
ity
b
etwe
en
ea
ch
ce
n
tr
o
id
an
d
d
ata
p
o
in
t
is
ca
lcu
lated
.
T
h
e
clu
s
ter
with
th
e
s
m
allest
d
is
s
im
ilar
ity
is
s
elec
ted
f
o
r
d
ata
r
elo
ca
tio
n
in
a
n
iter
atio
n
,
an
d
th
e
r
elo
ca
tio
n
o
f
d
ata
in
th
e
cl
u
s
ter
is
ex
p
r
ess
ed
b
y
a
m
em
b
e
r
s
h
ip
v
alu
e
o
f
0
o
r
1
,
i
n
d
icatin
g
t
h
e
m
em
b
er
s
h
ip
s
tatu
s
o
f
ea
c
h
o
b
s
er
v
atio
n
with
in
a
g
iv
en
cl
u
s
ter
.
T
h
e
clu
s
ter
in
g
p
r
o
ce
d
u
r
e
d
ef
in
es
K
as
th
e
p
r
ed
ef
in
e
d
clu
s
ter
co
u
n
t
an
d
e
m
p
lo
y
s
a
s
elec
ted
d
is
tan
ce
m
e
tr
ic
to
m
ap
o
b
s
er
v
atio
n
s
to
t
h
e
ir
n
ea
r
est
ce
n
tr
o
id
.
Af
ter
war
d
,
ce
n
tr
o
id
co
o
r
d
in
at
es
ar
e
r
ec
alcu
lated
b
ased
o
n
th
e
cu
r
r
e
n
t
clu
s
ter
co
m
p
o
s
itio
n
,
an
d
th
e
iter
ativ
e
cy
cle
co
n
tin
u
es
u
n
til
s
tab
ilit
y
o
r
co
n
v
er
g
e
n
ce
is
ac
h
iev
e
d
.
T
h
e
ce
n
tr
o
i
d
is
th
en
r
ec
alc
u
lated
b
ased
o
n
th
e
d
ata
th
at
f
o
llo
ws
ea
ch
clu
s
ter
,
an
d
th
e
p
r
o
ce
s
s
is
r
ep
ea
ted
u
n
til
c
o
n
v
er
g
en
t
co
n
d
itio
n
s
ar
e
m
et
.
T
h
ese
in
clu
d
e
(
a)
a
ch
an
g
e
in
t
h
e
o
b
jectiv
e
f
u
n
cti
o
n
is
b
elo
w
th
e
d
esire
d
th
r
esh
o
ld
o
r
(
b
)
t
h
er
e
ar
e
n
o
d
ata
m
o
v
in
g
clu
s
ter
s
,
o
r
(
c)
a
ch
an
g
e
in
ce
n
t
r
o
id
p
o
s
itio
n
i
s
b
elo
w
th
e
s
et
th
r
esh
o
l
d
.
On
l
y
o
n
e
clu
s
ter
h
as
a
m
em
b
er
s
h
i
p
v
alu
e
o
f
1
,
wh
ile
th
e
o
th
er
s
h
a
v
e
a
v
alu
e
o
f
0
f
o
r
ea
ch
d
ata
p
o
in
t
.
T
h
is
p
r
o
ce
s
s
is
ca
lcu
lated
u
s
in
g
(
3
)
.
a
ij
=
{
1
arg
m
i
n
{
(
,
)
}
0
ℎ
(
3
)
d
(
x
i
,c
j
)
e
x
p
r
ess
es
th
e
d
is
s
im
ilar
ity
(
d
is
tan
ce
)
o
f
th
e
d
ata
i
to
clu
s
ter
c
j
I
n
d
eter
m
in
i
n
g
th
e
ce
n
tr
o
id
,
p
o
in
t
C
is
o
b
tain
ed
b
y
ca
lcu
lati
n
g
th
e
av
er
a
g
e
o
f
ea
c
h
f
ea
tu
r
e
f
r
o
m
all
d
ata
b
elo
n
g
in
g
to
ea
ch
clu
s
ter
.
T
h
e
av
er
a
g
e
f
ea
tu
r
e
o
f
all
d
at
a
in
a
clu
s
ter
is
ex
p
r
ess
ed
in
(
4
)
.
c
j
=
1
∑
=
1
(
4
)
NK
is
th
e
am
o
u
n
t
o
f
d
ata
th
at
is
jo
in
ed
in
a
cl
u
s
ter
.
T
h
is
p
r
o
ce
s
s
is
ca
r
r
ied
o
u
t
b
y
c
h
o
o
s
in
g
t
h
e
clo
s
est clu
s
ter
an
d
m
in
im
izin
g
th
e
o
b
jectiv
e/n
o
n
-
n
e
g
ativ
e
co
s
t f
u
n
ctio
n
,
as sh
o
wn
i
n
(
5
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
2
6
:
81
-
92
86
=
∑
∑
(
X
i
,
C
j
)
=
1
=
1
(
5
)
T
h
e
p
r
o
ce
s
s
o
f
m
in
im
izi
n
g
t
h
e
to
tal
s
q
u
ar
ed
d
is
tan
ce
b
et
wee
n
ea
ch
p
o
in
t
Xi
an
d
th
e
n
ea
r
est
C
j
clu
s
ter
r
ep
r
esen
tatio
n
.
Fro
m
a
p
h
y
s
ical
p
er
s
p
ec
tiv
e,
m
in
im
izin
g
t
h
e
E
u
clid
ea
n
d
is
tan
ce
b
etwe
en
C
H
an
d
C
M
r
ed
u
ce
s
tr
an
s
m
is
s
io
n
p
o
wer
r
eq
u
ir
e
m
en
ts
an
d
p
r
o
p
ag
atio
n
d
elay
,
wh
ich
d
ir
ec
tly
im
p
ac
ts
th
r
o
u
g
h
p
u
t
an
d
p
ac
k
et
d
eliv
er
y
r
eliab
ilit
y
.
T
h
e
clu
s
ter
in
g
p
ar
titi
o
n
m
ec
h
an
is
m
en
s
u
r
es
th
at
n
o
d
es
with
in
th
e
s
am
e
clu
s
ter
ex
h
ib
i
t
lo
wer
s
p
atial
d
is
p
er
s
io
n
,
th
er
eb
y
r
ed
u
cin
g
d
ata
v
ar
iatio
n
an
d
co
n
g
esti
o
n
d
u
r
in
g
m
u
l
ti
-
h
o
p
f
o
r
war
d
in
g
.
C
o
n
s
eq
u
en
tly
,
th
e
MH
C
P
p
r
o
to
co
l
i
m
p
r
o
v
es
n
etwo
r
k
p
er
f
o
r
m
a
n
ce
b
y
alig
n
i
n
g
r
o
u
t
in
g
d
ec
is
io
n
s
with
p
h
y
s
ical
d
is
tan
ce
co
n
s
tr
ain
ts
in
h
er
en
t in
wir
eless
co
m
m
u
n
ic
atio
n
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
NS
Fig
u
r
e
4
s
h
o
ws
th
e
MH
C
P
p
r
o
to
co
l
m
o
d
el
t
h
at
ad
d
r
ess
es
th
e
is
s
u
e
o
f
lo
w
W
SN
p
er
f
o
r
m
a
n
ce
th
r
o
u
g
h
th
r
ee
s
tag
es
.
T
h
e
f
ir
s
t
s
tag
e
in
v
o
lv
es
th
e
f
o
r
m
atio
n
o
f
CH
,
wh
er
e
C
H
s
er
v
es
as
a
r
ef
er
en
ce
f
o
r
CM
.
T
h
e
s
ec
o
n
d
s
tag
e
f
o
c
u
s
es
o
n
d
eter
m
in
in
g
th
e
p
r
o
x
im
ity
o
f
n
o
d
es
to
th
eir
r
esp
ec
tiv
e
C
H.
Fin
ally
,
th
e
th
i
r
d
s
tag
e
p
ar
titi
o
n
th
e
n
o
d
es
in
to
K
clu
s
ter
s
an
d
r
elo
ca
tes
th
em
iter
ativ
ely
b
ased
o
n
th
e
ir
d
is
s
im
ilar
ity
an
d
m
em
b
er
s
h
ip
v
alu
e
.
I
n
t
h
e
d
is
cu
s
s
io
n
o
f
th
e
f
ir
s
t
s
tep
,
th
e
n
u
m
b
er
o
f
K
is
d
eter
m
in
ed
as
a
co
n
s
tan
t
v
a
r
iab
le,
an
d
th
e
ce
n
tr
o
id
o
f
K
is
ch
o
s
en
r
an
d
o
m
ly
.
T
h
e
s
ec
o
n
d
s
tep
i
n
v
o
lv
es
f
in
d
in
g
t
h
e
clo
s
est
ce
n
tr
o
id
to
ea
ch
n
o
d
e
in
th
e
clu
s
ter
u
s
in
g
d
is
tan
ce
ca
lcu
latio
n
s
.
T
h
e
s
h
o
r
test
d
is
tan
ce
to
t
h
e
ce
n
tr
o
id
is
ca
lcu
l
ated
f
o
r
ea
ch
n
o
d
e,
an
d
th
e
to
tal
d
is
tan
ce
f
r
o
m
al
l
n
o
d
es
to
th
e
ce
n
tr
o
i
d
is
d
eter
m
in
ed
.
T
h
is
s
tep
is
r
ep
ea
ted
iter
ativ
ely
,
an
d
th
e
f
itn
ess
v
alu
e
is
ca
lcu
lated
.
T
h
e
f
itn
ess
v
alu
e
is
d
ef
in
ed
as 1
d
iv
id
ed
b
y
th
e
m
ea
n
d
is
tan
ce
f
r
o
m
th
e
n
o
d
e
to
th
e
ce
n
tr
o
id
p
lu
s
1
.
W
h
en
th
e
f
it
n
ess
v
alu
e
in
c
r
ea
s
es,
th
e
lates
t
s
o
lu
tio
n
is
ac
ce
p
ted
,
an
d
th
e
n
ewe
s
t
ce
n
tr
o
id
is
u
s
ed
as
a
r
ef
er
en
ce
f
o
r
th
e
n
ex
t
r
an
d
o
m
g
en
er
atio
n
.
T
h
e
K
-
m
ea
n
s
clu
s
ter
in
g
alg
o
r
ith
m
(
Alg
o
r
ith
m
3
)
is
lik
ely
to
s
to
p
wh
en
th
e
v
alu
e
o
f
d
n
d
o
es n
o
t c
h
an
g
e
f
o
r
5
iter
atio
n
s
,
th
er
eb
y
in
d
icatin
g
t
h
e
o
p
tim
a
l p
o
in
t
.
Fig
u
r
e
4
.
T
h
e
MH
C
P p
r
o
to
co
l
m
o
d
els
MA
T
L
AB
was
u
s
ed
to
im
p
lem
en
t
th
e
MH
C
P
p
r
o
to
co
l,
as
s
h
o
wn
in
Fig
u
r
e
5
.
I
n
th
is
p
r
o
to
co
l,
ea
ch
C
M
is
lin
k
ed
to
C
H,
an
d
th
e
clu
s
ter
s
ar
e
in
ter
co
n
n
ec
ted
u
s
in
g
m
u
lti
-
h
o
p
p
i
n
g
tech
n
i
q
u
e
s
.
T
o
h
ig
h
lig
h
t
th
e
co
n
tr
ib
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
MH
C
P
p
r
o
to
co
l,
a
co
m
p
ar
a
tiv
e
an
aly
s
is
with
ex
is
tin
g
r
o
u
tin
g
p
r
o
to
co
ls
is
p
r
esen
ted
in
T
ab
le
2
.
T
h
e
co
m
p
ar
is
o
n
f
o
cu
s
es
o
n
clu
s
ter
in
g
s
tr
ateg
y
,
m
u
lti
-
h
o
p
ca
p
a
b
ilit
y
,
ch
an
n
el
m
o
d
el,
th
r
o
u
g
h
p
u
t
p
er
f
o
r
m
an
ce
,
an
d
in
h
er
en
t lim
itatio
n
s
o
f
ea
ch
m
e
th
o
d
.
Al
g
o
r
ith
m
3
.
MH
C
P
a
lg
o
r
ith
m
Input
: n (x, y)
the positions of the nodes in x and y form
sensor_value
the value of the sensor reading carried by the node
X
the length of the map
Y
the width of the map
F (x, y)
function of measuring the distance be
tween nodes
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
r
o
vin
g
th
e
p
erfo
r
ma
n
ce
o
f
w
ir
ele
s
s
s
en
s
o
r
n
etw
o
r
k
u
s
in
g
mu
lti
-
h
o
p
p
in
g
…
(
R
o
b
b
y
R
iz
ky
)
87
Tot_node
total node
N
total CH
Output
: clusted_head
Node_hop nodes that are connected between clusters
Generate random centroid C(x,y) = r[x1
-
x2]
nodes
∈
[]
I = 0
While True:
Find distance of all nodes
Find minimum distance and
node
Append to array nodes
While true:
Initiate x & y as max map
Initiate random position of CH
While true:
Measure the minimum distance of the node and CH
Generate new CH
I += 1
If I == tot_node:
Break
Fig
u
r
e
5
.
T
h
e
MH
C
P
p
r
o
to
co
l
T
ab
le
2
.
C
o
m
p
a
r
is
o
n
o
f
MH
C
P with
ex
is
tin
g
r
o
u
tin
g
p
r
o
t
o
c
o
ls
M
e
t
h
o
d
C
l
u
st
e
r
i
n
g
M
u
l
t
i
-
hop
C
h
a
n
n
e
l
m
o
d
e
l
Th
r
o
u
g
h
p
u
t
p
e
r
f
o
r
ma
n
c
e
M
a
i
n
l
i
mi
t
a
t
i
o
n
LEA
C
H
Y
e
s
No
S
i
n
g
l
e
Lo
w
–
mo
d
e
r
a
t
e
P
o
o
r
c
l
u
st
e
r
s
t
r
u
c
t
u
r
e
M
C
C
H
Y
e
s
Y
e
s
M
u
l
t
i
-
c
h
a
n
n
e
l
H
i
g
h
H
i
g
h
e
n
e
r
g
y
c
o
n
su
mp
t
i
o
n
M
u
l
t
i
-
h
o
p
LEA
C
H
Y
e
s
Y
e
s
S
i
n
g
l
e
M
o
d
e
r
a
t
e
N
o
p
a
r
t
i
t
i
o
n
o
p
t
i
mi
z
a
t
i
o
n
M
H
C
P
(
P
r
o
p
o
se
d
)
Y
e
s
Y
e
s
S
i
n
g
l
e
I
mp
r
o
v
e
d
S
i
mu
l
a
t
i
o
n
-
b
a
s
e
d
4
.
1
.
T
hro
ug
hp
ut
T
h
r
o
u
g
h
p
u
t
r
e
f
er
s
to
th
e
a
m
o
u
n
t
o
f
d
ata
th
at
a
n
o
d
e
ca
n
tr
a
n
s
m
it
o
r
r
ec
eiv
e
with
in
a
s
p
ec
if
ied
tim
e
in
ter
v
al
.
I
t
p
r
o
v
i
d
es
in
s
ig
h
t
in
to
th
e
d
ata
r
ate
o
f
a
n
etwo
r
k
an
d
ca
n
b
e
u
s
ed
as
a
p
e
r
f
o
r
m
an
ce
m
et
r
ic
f
o
r
r
o
u
tin
g
p
r
o
t
o
co
ls
.
A
h
ig
h
e
r
th
r
o
u
g
h
p
u
t v
al
u
e
ty
p
ically
in
d
ica
tes b
etter
p
r
o
to
co
l
p
er
f
o
r
m
an
c
e.
ℎ
=
n
um
b
e
r
of
p
a
c
k
e
t
s
se
n
t
p
a
c
k
a
g
e
d
e
l
iv
e
r
y
t
im
e
4
.
2
.
P
a
ck
e
t
l
o
s
s
Pack
et
lo
s
s
i
s
d
eter
m
in
ed
b
y
co
m
p
ar
in
g
th
e
n
u
m
b
e
r
o
f
u
n
s
u
cc
ess
f
u
lly
d
eliv
er
ed
p
ac
k
ets
to
th
e
o
v
er
all
q
u
an
tity
o
f
p
ac
k
ets
tr
an
s
m
itted
f
r
o
m
th
e
s
o
u
r
ce
n
o
d
e
to
t
h
e
d
esti
n
atio
n
with
in
a
n
etwo
r
k
,
u
s
u
all
y
ex
p
r
ess
ed
as
a
r
atio
o
r
p
er
ce
n
tag
e.
I
t
o
cc
u
r
s
wh
e
n
o
n
e
o
r
m
o
r
e
tr
an
s
m
itted
d
ata
p
ac
k
ets
d
o
n
o
t
s
u
cc
ess
f
u
lly
ar
r
iv
e
at
th
eir
d
esig
n
ated
d
esti
n
atio
n
.
=
p
a
c
k
e
t
s
t
ha
t
e
xp
e
rie
n
c
e
l
o
ss
p
a
c
k
a
g
e
se
n
t
4
.
3
.
Dela
y
Dela
y
is
th
e
tim
e
r
eq
u
ir
ed
f
o
r
d
ata
to
tr
av
el
f
r
o
m
th
e
s
o
u
r
ce
n
o
d
e
to
th
e
d
esti
n
atio
n
n
o
d
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
2
6
:
81
-
92
88
Dela
y
=
d
eliv
er
y
tim
e
–
r
ec
e
p
tio
n
tim
e
Fig
u
r
e
6
illu
s
tr
ates
th
e
d
elay
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
MH
C
P
p
r
o
to
co
l
ac
r
o
s
s
d
if
f
er
en
t
co
m
m
u
n
icatio
n
ch
an
n
els.
T
h
e
h
o
r
izo
n
tal
ax
is
r
ep
r
esen
ts
th
e
ch
an
n
el
in
d
ex
u
s
ed
in
th
e
m
u
lti
-
h
o
p
tr
an
s
m
is
s
io
n
p
r
o
ce
s
s
,
wh
ile
th
e
v
er
tical
ax
i
s
in
d
icate
s
th
e
av
er
ag
e
d
el
a
y
m
ea
s
u
r
ed
in
s
ec
o
n
d
s
.
As
s
h
o
wn
in
Fig
u
r
e
6
,
th
e
d
elay
p
e
r
f
o
r
m
an
ce
v
ar
ies
ac
r
o
s
s
ch
an
n
els
1
to
4
.
C
h
an
n
el
2
ex
h
ib
its
th
e
lo
west
d
ela
y
v
alu
e
,
in
d
icatin
g
a
m
o
r
e
ef
f
icien
t
d
ata
f
o
r
war
d
in
g
p
ath
,
wh
ile
C
h
an
n
el
3
s
h
o
ws
th
e
h
ig
h
est
d
elay
d
u
e
to
lo
n
g
er
tr
an
s
m
is
s
i
o
n
p
ath
s
o
r
h
ig
h
er
r
ela
y
lo
ad
.
T
h
is
v
ar
iatio
n
co
n
f
ir
m
s
th
at
ch
an
n
el
s
elec
tio
n
an
d
m
u
lti
-
h
o
p
r
o
u
tin
g
s
tr
u
ctu
r
e
s
ig
n
if
ican
tly
in
f
lu
en
ce
en
d
-
to
-
e
n
d
d
elay
in
t
h
e
MH
C
P p
r
o
to
co
l.
Fig
u
r
e
7
p
r
esen
ts
t
h
e
p
ac
k
et
lo
s
s
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
MH
C
P
p
r
o
t
o
co
l
ac
r
o
s
s
d
if
f
er
en
t
co
m
m
u
n
icatio
n
ch
a
n
n
els.
T
h
e
h
o
r
iz
o
n
tal
ax
is
r
ep
r
esen
ts
th
e
ch
an
n
el
in
d
e
x
,
w
h
ile
th
e
v
er
t
ical
ax
is
in
d
icate
s
th
e
p
ac
k
et
lo
s
s
r
ate
o
b
s
er
v
ed
d
u
r
in
g
d
ata
tr
an
s
m
is
s
io
n
.
As
i
llu
s
tr
ated
in
Fig
u
r
e
7
,
C
h
an
n
e
l
2
ex
p
er
ien
ce
s
th
e
h
ig
h
est
p
ac
k
et
lo
s
s
r
ate,
in
d
ic
atin
g
a
h
ig
h
e
r
p
r
o
b
ab
ilit
y
o
f
p
ac
k
et
d
r
o
p
s
d
u
e
to
c
o
n
g
esti
o
n
o
r
lo
n
g
er
m
u
lti
-
h
o
p
p
ath
s
.
I
n
co
n
tr
ast,
C
h
a
n
n
el
3
ex
h
ib
its
th
e
lo
west
p
ac
k
et
lo
s
s
,
s
u
g
g
esti
n
g
a
m
o
r
e
s
tab
le
c
o
m
m
u
n
icatio
n
p
at
h
.
T
h
ese
r
esu
lts
in
d
icate
th
at
th
e
d
is
tr
ib
u
tio
n
o
f
tr
a
f
f
ic
ac
r
o
s
s
c
h
an
n
els
s
ig
n
if
ican
tly
af
f
ec
ts
p
ac
k
et
d
eliv
er
y
r
eliab
ilit
y
in
th
e
MH
C
P
p
r
o
to
co
l.
Fig
u
r
e
8
illu
s
tr
ates
th
e
th
r
o
u
g
h
p
u
t
p
er
f
o
r
m
a
n
ce
o
f
th
e
co
n
v
en
tio
n
al
L
E
AC
H
r
o
u
tin
g
p
r
o
to
co
l
ac
r
o
s
s
d
if
f
er
en
t
co
m
m
u
n
icatio
n
ch
an
n
els.
T
h
e
h
o
r
iz
o
n
tal
ax
is
r
ep
r
esen
ts
th
e
ch
an
n
el
i
n
d
ex
,
wh
ile
th
e
v
er
tical
ax
is
in
d
icate
s
th
e
ac
h
iev
ed
th
r
o
u
g
h
p
u
t
m
ea
s
u
r
e
d
in
k
ilo
b
its
p
er
s
ec
o
n
d
(
k
b
p
s
)
.
Fig
u
r
e
6
.
Dela
y
s
Fig
u
r
e
7
.
Data
l
o
s
s
As
s
h
o
wn
in
Fig
u
r
e
8
,
th
e
th
r
o
u
g
h
p
u
t
ac
h
iev
e
d
b
y
th
e
L
E
AC
H
p
r
o
to
co
l
v
ar
ies
s
ig
n
if
ican
tly
ac
r
o
s
s
ch
an
n
els.
C
h
an
n
el
4
ex
h
ib
its
th
e
h
ig
h
est
th
r
o
u
g
h
p
u
t,
wh
ile
C
h
an
n
els
1
an
d
3
s
h
o
w
r
elativ
ely
lo
wer
v
alu
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
I
mp
r
o
vin
g
th
e
p
erfo
r
ma
n
ce
o
f
w
ir
ele
s
s
s
en
s
o
r
n
etw
o
r
k
u
s
in
g
mu
lti
-
h
o
p
p
in
g
…
(
R
o
b
b
y
R
iz
ky
)
89
T
h
is
v
ar
iatio
n
in
d
icate
s
th
at
L
E
AC
H
p
er
f
o
r
m
an
ce
is
h
ig
h
ly
d
ep
en
d
e
n
t
o
n
cl
u
s
ter
f
o
r
m
atio
n
an
d
tr
a
n
s
m
is
s
io
n
p
ath
s
,
wh
ich
m
ay
r
esu
lt in
u
n
b
alan
ce
d
d
ata
f
o
r
war
d
in
g
an
d
r
ed
u
ce
d
th
r
o
u
g
h
p
u
t e
f
f
icien
cy
i
n
ce
r
tain
ch
an
n
els.
Fig
u
r
e
9
illu
s
tr
ates
th
e
th
r
o
u
g
h
p
u
t
p
e
r
f
o
r
m
an
ce
o
f
th
e
M
C
C
H
p
r
o
to
co
l
ac
r
o
s
s
d
if
f
er
e
n
t
co
m
m
u
n
ica
tio
n
ch
an
n
els.
T
h
e
h
o
r
izo
n
tal
ax
is
r
ep
r
esen
ts
th
e
ch
an
n
el
in
d
e
x
,
wh
ile
th
e
v
er
tical
ax
is
in
d
icate
s
th
e
ac
h
iev
ed
th
r
o
u
g
h
p
u
t
m
ea
s
u
r
ed
in
k
b
p
s
.
As
s
h
o
wn
in
Fig
u
r
e
9
,
th
e
MCC
H
p
r
o
to
co
l
ac
h
ie
v
es
s
ig
n
if
ican
tly
h
ig
h
e
r
th
r
o
u
g
h
p
u
t
c
o
m
p
ar
ed
to
th
e
co
n
v
en
tio
n
al
L
E
AC
H
p
r
o
to
co
l
ac
r
o
s
s
all
ch
an
n
els.
C
h
an
n
el
4
r
ec
o
r
d
s
th
e
h
ig
h
est
th
r
o
u
g
h
p
u
t,
wh
ile
C
h
an
n
el
2
ex
h
i
b
its
th
e
lo
west
v
al
u
e.
T
h
is
im
p
r
o
v
em
en
t
is
m
ain
ly
attr
ib
u
ted
to
th
e
u
tili
za
tio
n
o
f
m
u
ltip
le
c
h
an
n
els,
wh
ich
r
ed
u
ce
s
tr
af
f
ic
co
n
g
esti
o
n
an
d
im
p
r
o
v
es
p
ar
allel
d
at
a
t
r
an
s
m
is
s
io
n
.
Ho
wev
er
,
th
e
h
ig
h
th
r
o
u
g
h
p
u
t
in
MCC
H
is
ac
h
iev
ed
at
th
e
ex
p
en
s
e
o
f
in
cr
ea
s
ed
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
ch
an
n
el
m
a
n
a
g
em
en
t
co
m
p
le
x
ity
.
Fig
u
r
e
1
0
illu
s
tr
ates
th
e
th
r
o
u
g
h
p
u
t
p
er
f
o
r
m
a
n
ce
o
f
th
e
MH
C
P
p
r
o
to
co
l
ac
r
o
s
s
v
ar
io
u
s
co
m
m
u
n
icatio
n
ch
an
n
els.
T
h
e
h
o
r
izo
n
tal
ax
is
r
ep
r
esen
ts
th
e
ch
an
n
el
in
d
e
x
,
wh
ile
th
e
v
er
tical
ax
is
in
d
icate
s
th
e
ac
h
iev
ed
th
r
o
u
g
h
p
u
t
m
ea
s
u
r
ed
in
k
b
p
s
.
Fig
u
r
e
8
.
T
h
e
L
E
AC
H
th
r
o
u
g
h
p
u
t
Fig
u
r
e
9
.
MCC
H
t
h
r
o
u
g
h
p
u
t
As
s
h
o
wn
in
Fig
u
r
e
1
0
,
th
e
p
r
o
p
o
s
ed
MH
C
P
p
r
o
to
co
l
ac
h
iev
es
s
tab
le
an
d
im
p
r
o
v
ed
t
h
r
o
u
g
h
p
u
t
ac
r
o
s
s
all
ch
an
n
els.
C
h
an
n
el
4
r
ec
o
r
d
s
th
e
h
ig
h
est
th
r
o
u
g
h
p
u
t,
wh
ile
C
h
an
n
el
3
ex
h
ib
its
a
r
elativ
ely
lo
wer
v
alu
e
d
u
e
to
lo
n
g
er
m
u
lti
-
h
o
p
tr
an
s
m
is
s
io
n
p
ath
s
.
C
o
m
p
ar
ed
to
th
e
co
n
v
en
tio
n
al
L
E
AC
H
p
r
o
to
co
l,
MH
C
P
d
em
o
n
s
tr
ates
a
s
ig
n
if
ican
t
th
r
o
u
g
h
p
u
t
im
p
r
o
v
em
en
t
b
y
o
p
t
im
izin
g
clu
s
ter
p
ar
titi
o
n
in
g
a
n
d
r
ed
u
ci
n
g
in
tr
a
-
clu
s
ter
d
is
tan
ce
.
Alth
o
u
g
h
MCC
H
ac
h
iev
es
h
ig
h
er
ab
s
o
lu
te
th
r
o
u
g
h
p
u
t,
MH
C
P
o
f
f
e
r
s
a
m
o
r
e
b
alan
ce
d
p
er
f
o
r
m
an
ce
b
y
av
o
id
in
g
ex
c
ess
iv
e
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
co
m
p
le
x
ch
a
n
n
el
m
a
n
ag
em
en
t,
m
ak
in
g
it
m
o
r
e
s
u
itab
le
f
o
r
en
e
r
g
y
-
c
o
n
s
tr
ain
e
d
W
SN
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
42
,
No
.
1
,
Ap
r
il
20
2
6
:
81
-
92
90
Fig
u
r
e
1
0
.
T
h
e
MH
C
P
t
h
r
o
u
g
h
p
u
t
5.
DIS
CU
SS
S
I
O
N
T
h
is
r
esear
ch
s
ee
k
s
to
e
n
h
a
n
ce
o
f
W
SNs
b
y
d
ev
elo
p
in
g
p
r
o
to
c
o
ls
u
s
in
g
t
h
e
MH
C
P
m
eth
o
d
,
a
clu
s
ter
in
g
tech
n
i
q
u
e
d
esig
n
e
d
to
im
p
r
o
v
e
W
SN
p
er
f
o
r
m
an
ce
f
o
r
o
p
tim
al
r
esu
lts
.
T
h
e
MH
C
P
m
eth
o
d
co
m
p
r
is
es
th
r
ee
s
tag
es:
(
i
)
f
o
r
m
in
g
C
H
as
a
r
ef
er
en
ce
f
o
r
C
M
to
tr
an
s
m
it
d
ata,
(
ii
)
d
eter
m
in
in
g
th
e
p
r
o
x
im
ity
o
f
n
o
d
es
to
th
e
C
H,
an
d
(
iii
)
g
r
o
u
p
in
g
n
o
d
es
u
s
in
g
a
p
a
r
titi
o
n
in
g
tech
n
iq
u
e
to
m
itig
ate
tr
an
s
m
is
s
io
n
laten
cy
an
d
p
ac
k
et
lo
s
s
.
T
h
e
p
r
im
ar
y
co
n
tr
ib
u
tio
n
o
f
th
is
s
tu
d
y
lies
in
th
e
d
ev
elo
p
m
en
t
o
f
a
clu
s
ter
in
g
m
eth
o
d
th
at
b
r
in
g
s
C
H
an
d
C
M
n
o
d
es
clo
s
er
to
g
eth
er
,
o
p
tim
izin
g
t
h
e
g
r
o
u
p
in
g
p
r
o
ce
s
s
,
an
d
m
i
n
im
iz
in
g
d
ata
v
ar
iatio
n
with
in
clu
s
ter
s
.
T
o
e
v
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
MH
C
P
m
eth
o
d
,
T
h
e
te
s
t
d
ata
was
s
im
u
lated
u
s
in
g
MA
T
L
AB
,
with
1
0
0
n
o
d
es,
en
er
g
y
o
f
1
0
0
,
Xm
ax
o
f
3
0
0
,
Ym
ax
o
f
3
0
0
,
an
d
v
elo
city
o
f
1
0
,
0
0
0
.
T
h
e
MH
C
P
m
eth
o
d
was
co
m
p
ar
ed
to
th
e
L
E
AC
H
m
eth
o
d
,
an
d
th
e
r
esu
lts
s
h
o
wed
th
at
th
e
MH
C
P
m
eth
o
d
o
u
tp
er
f
o
r
m
ed
th
e
L
E
AC
H
m
eth
o
d
in
ter
m
s
o
f
th
r
o
u
g
h
p
u
t
v
alu
es
.
T
h
e
L
E
AC
H
a
lg
o
r
ith
m
h
ad
th
r
o
u
g
h
p
u
t
v
alu
es
o
f
5
1
.
2
2
2
9
,
1
3
4
.
0
5
7
0
,
5
5
.
1
9
3
7
,
an
d
2
9
2
.
4
2
7
3
f
o
r
c
lu
s
ter
s
1
,
2,
3
,
a
n
d
4
,
r
esp
e
ctiv
ely
.
Me
an
wh
ile,
th
e
MH
C
P
m
eth
o
d
h
ad
a
th
r
o
u
g
h
p
u
t
o
f
1
4
2
.
0
0
3
3
,
2
4
4
.
1
3
1
8
,
1
1
9
.
0
8
0
4
,
a
n
d
3
0
5
.
6
1
5
9
f
o
r
cl
u
s
ter
s
1
,
2
,
3
,
an
d
4
.
T
h
ese
r
esu
lts
d
em
o
n
s
tr
ate
th
at
th
e
MH
C
P
m
eth
o
d
h
as
a
h
ig
h
er
t
h
r
o
u
g
h
p
u
t
v
alu
e
c
o
m
p
ar
e
d
to
th
e
L
E
AC
H
alg
o
r
ith
m
,
in
d
icatin
g
b
etter
p
e
r
f
o
r
m
an
ce
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
MH
C
P
m
eth
o
d
was
ev
alu
ated
b
y
co
m
p
ar
in
g
it
with
o
th
er
s
im
ilar
s
tu
d
ies
th
at
u
s
ed
th
e
MCC
H
m
eth
o
d
t
o
a
n
aly
z
e
p
er
f
o
r
m
a
n
ce
.
T
h
e
s
im
u
latio
n
s
wer
e
p
er
f
o
r
m
ed
u
s
in
g
th
e
s
am
e
p
ar
am
eter
s
,
b
u
t th
e
r
esu
lts
d
if
f
er
e
d
.
T
h
e
th
r
o
u
g
h
p
u
t v
alu
es f
o
r
MCC
H
m
eth
o
d
wer
e
5
0
8
.
5
1
6
5
,
2
5
5
.
5
6
6
1
,
4
7
9
.
8
2
8
9
,
a
n
d
6
4
6
.
5
6
1
8
f
o
r
ch
an
n
el
s
1
,
2
,
3
,
an
d
4
.
On
th
e
o
th
e
r
h
an
d
,
th
e
th
r
o
u
g
h
p
u
t
v
alu
es
f
o
r
th
e
MH
C
P m
eth
o
d
in
clu
s
ter
s
1
,
2
,
3
,
an
d
4
wer
e
1
4
2
.
0
0
3
3
,
2
4
4
.
1
3
1
8
,
1
1
9
.
0
8
0
4
,
an
d
3
0
5
.
6
1
5
9
.
B
ased
o
n
th
is
an
aly
s
is
,
th
e
MCC
H
m
eth
o
d
o
u
tp
er
f
o
r
m
s
th
e
MH
C
P
.
6.
CO
NCLU
SI
O
NS
I
n
co
n
cl
u
s
io
n
,
th
e
MH
C
P m
eth
o
d
was e
m
p
lo
y
ed
in
th
e
d
ev
e
lo
p
m
en
t o
f
a
W
SN p
r
o
to
co
l,
s
p
ec
if
ically
b
y
m
o
d
i
f
y
in
g
t
h
e
r
o
u
tin
g
p
r
o
t
o
co
l
to
d
iv
id
e
th
e
n
etwo
r
k
i
n
to
clu
s
ter
s
u
s
in
g
a
p
ar
titi
o
n
in
g
tech
n
iq
u
e
with
C
H
as
th
e
r
ef
er
en
ce
ch
a
n
n
el.
T
h
e
s
elec
tio
n
o
f
C
H
in
v
o
lv
ed
r
an
d
o
m
ly
ch
o
o
s
in
g
1
0
0
n
o
d
es
b
as
ed
o
n
a
p
r
o
b
ab
ilit
y
f
o
r
m
u
la
an
d
d
esig
n
in
g
th
eir
d
is
tan
ce
s
f
r
o
m
C
M
u
s
in
g
th
e
E
u
clid
ea
n
ap
p
r
o
ac
h
.
T
h
is
d
esig
n
en
s
u
r
ed
ef
f
icien
t
d
ata
tr
an
s
ac
tio
n
s
b
etwe
en
C
H
an
d
C
M
wh
ile
co
n
s
er
v
in
g
en
er
g
y
.
T
h
e
g
r
o
u
p
in
g
p
r
o
ce
s
s
was
ca
r
r
ied
o
u
t
b
y
s
elec
tin
g
ce
r
tain
p
o
p
u
latio
n
co
m
p
o
n
en
ts
as
in
itial
clu
s
ter
ce
n
ter
s
an
d
class
if
y
in
g
ea
ch
co
m
p
o
n
en
t
to
th
e
n
ea
r
est
clu
s
ter
ce
n
ter
b
a
s
ed
o
n
th
e
m
in
im
u
m
d
is
tan
ce
.
T
h
e
p
o
s
itio
n
o
f
th
e
clu
s
ter
ce
n
te
r
was
r
ec
alcu
lated
iter
ativ
ely
u
n
til
all
d
ata
co
m
p
o
n
en
ts
wer
e
ap
p
r
o
p
r
iately
c
lass
if
ied
at
ea
ch
c
lu
s
ter
ce
n
t
er
,
r
esu
ltin
g
in
th
e
f
o
r
m
atio
n
o
f
n
ew
clu
s
ter
ce
n
ter
p
o
s
itio
n
s
.
T
h
is
s
tu
d
y
c
o
m
b
in
ed
th
r
ee
alg
o
r
ith
m
s
,
n
am
ely
th
e
L
E
AC
H
alg
o
r
ith
m
f
o
r
C
H
f
o
r
m
atio
n
,
E
u
clid
ea
n
d
is
tan
ce
ca
lcu
latio
n
f
o
r
p
o
s
itio
n
in
g
C
H
an
d
C
M
clo
s
er
to
g
eth
er
,
an
d
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
f
o
r
t
h
e
clu
s
ter
in
g
p
r
o
ce
s
s
.
T
h
e
e
v
alu
atio
n
o
f
th
e
MH
C
P
m
eth
o
d
y
ield
ed
n
o
tab
le
r
esu
lts
.
C
lu
s
ter
1
ac
h
iev
ed
a
th
r
o
u
g
h
p
u
t
v
al
u
e
o
f
1
4
2
.
0
0
3
3
,
C
lu
s
ter
2
h
ad
2
4
4
.
1
3
1
8
,
C
lu
s
ter
3
s
h
o
wed
1
1
9
.
0
8
0
4
,
a
n
d
C
lu
s
ter
4
ex
h
i
b
ited
th
e
h
ig
h
est
t
h
r
o
u
g
h
p
u
t
v
alu
e
o
f
3
0
5
.
6
1
5
9
.
T
h
ese
r
es
u
lts
d
em
o
n
s
tr
ate
t
h
e
ef
f
ec
tiv
en
ess
o
f
th
e
MH
C
P m
eth
o
d
in
o
p
tim
izin
g
t
h
e
th
r
o
u
g
h
p
u
t v
al
u
e
s
with
in
th
e
f
o
r
m
ed
clu
s
ter
s
.
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