T
E
L
KO
M
NIK
A
, V
ol
.
17
,
No.
4,
A
ug
us
t
20
1
9,
p
p.1
60
4
~
1
61
4
IS
S
N: 1
69
3
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6
93
0
,
accr
ed
ited
F
irst
Gr
ad
e b
y K
em
en
r
istekdikti,
Decr
ee
No: 2
1/E/
K
P
T
/20
18
DOI:
10.12928/TE
LK
OM
N
IK
A
.v
1
7
i
4
.
11379
◼
16
04
Rec
ei
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ed
Nov
e
mb
er
5
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8
;
Rev
i
s
e
d
Dec
e
mb
er 20
,
2
01
8
;
A
c
c
ep
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J
an
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y
31
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20
1
9
Clust
ering
and
dat
a a
gg
re
gat
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me i
n u
n
der
w
ater
w
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reles
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ic sens
or ne
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w
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ani K
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ishn
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u
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anv
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REVA Uni
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c
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W
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a
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ti
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wi
d
e
a
re
a
o
f
a
p
p
l
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c
a
ti
o
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.
T
o
e
x
tr
a
c
t
th
e
d
a
t
a
fro
m
u
n
d
e
rwat
e
r
a
n
d
tra
n
s
m
i
t
to
wat
e
rs
u
r
fa
c
e
,
n
u
m
e
ro
u
s
c
l
u
s
t
e
ri
n
g
a
n
d
d
a
ta
a
g
g
re
g
a
t
i
o
n
s
c
h
e
m
e
s
a
re
e
m
p
l
o
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e
d
.
Th
e
m
a
i
n
o
b
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e
c
ti
v
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s
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f
c
l
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s
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ri
n
g
a
n
d
d
a
t
a
a
g
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g
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o
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h
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m
e
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to
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e
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s
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e
c
o
n
s
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m
p
t
i
o
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f
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e
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y
a
n
d
p
ro
l
o
n
g
th
e
l
i
fe
t
i
m
e
o
f
th
e
n
e
two
rk
.
In
th
i
s
p
a
p
e
r
,
we
fo
c
u
s
o
n
i
n
i
ti
a
l
c
l
u
s
te
ri
n
g
o
f
s
e
n
s
o
r
n
o
d
e
s
b
a
s
e
d
o
n
t
h
e
i
r
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e
o
g
ra
p
h
i
c
a
l
l
o
c
a
ti
o
n
s
u
s
i
n
g
f
u
z
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y
l
o
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i
c
.
Th
e
p
r
o
b
a
b
i
l
i
ty
o
f
d
e
g
re
e
o
f
b
e
l
o
n
g
i
n
g
n
e
s
s
o
f
a
s
e
n
s
o
r
n
o
d
e
to
i
t
s
c
l
u
s
te
r,
a
l
o
n
g
wit
h
n
u
m
b
e
r
o
f
c
l
u
s
te
r
s
i
s
a
n
a
l
y
s
e
d
a
n
d
d
i
s
c
u
s
s
e
d
.
B
a
s
e
d
o
n
t
h
e
e
n
e
r
g
y
a
n
d
d
i
s
t
a
n
c
e
t
h
e
c
l
u
s
te
r h
e
a
d
n
o
d
e
s
a
r
e
d
e
te
rm
i
n
e
d
.
F
i
n
a
l
l
y
u
s
i
n
g
u
s
i
n
g
s
i
m
i
l
a
ri
ty
f
u
n
c
ti
o
n
d
a
ta
a
g
g
r
e
g
a
t
i
o
n
i
s
a
n
a
l
y
s
e
d
a
n
d
d
i
s
c
u
s
s
e
d
.
T
h
e
p
ro
p
o
s
e
d
s
c
h
e
m
e
i
s
s
i
m
u
l
a
te
d
i
n
M
ATL
AB
a
n
d
c
o
m
p
a
re
d
wit
h
L
E
ACH
a
l
g
o
r
i
th
m
.
Th
e
s
i
m
u
l
a
t
i
o
n
re
s
u
l
t
s
i
n
d
i
c
a
t
e
th
a
t
th
e
p
ro
p
o
s
e
d
s
c
h
e
m
e
p
e
rfo
rm
s
b
e
tt
e
r
i
n
m
a
x
i
m
i
z
i
n
g
n
e
two
r
k
l
i
fe
t
i
m
e
a
n
d
m
i
n
i
m
i
z
i
n
g
e
n
e
rg
y
c
o
n
s
u
m
p
ti
o
n
.
Key
w
ords
:
b
e
l
o
n
g
i
n
g
n
e
s
s
,
c
l
u
s
te
ri
n
g
,
e
u
c
l
i
d
e
a
n
d
i
s
ta
n
c
e
,
S
SE,
UW
ASN
Copy
righ
t
©
2
0
1
9
Uni
v
e
rsi
t
a
s
Ahm
a
d
D
a
hl
a
n.
All
rig
ht
s
r
e
s
e
rve
d
.
1.
Int
r
o
d
u
ctio
n
T
he
r
ap
i
d
gro
wth
of
r
es
ea
r
c
h
i
n
un
d
er
w
at
er
en
v
i
r
on
m
en
t
i
s
du
e
to
n
um
erous
un
de
r
wate
r
c
o
m
m
u
n
i
c
a
ti
o
n
ap
pl
i
c
ati
on
s
s
uc
h
as
oc
ea
no
grap
hi
c
da
t
a
c
o
l
l
e
c
ti
on
,
d
i
s
as
ter
prev
e
nti
on
,
un
d
ers
ea
ex
p
l
o
r
ati
on
,
s
urv
e
i
l
l
a
nc
e
a
pp
l
i
c
at
i
on
s
[1
-
3].
T
he
un
i
q
ue
c
ha
l
l
en
ge
s
f
ac
ed
b
y
U
W
A
S
Ns
i
nc
l
ud
e
l
arg
e
propa
ga
t
i
on
de
l
a
y
(
1.
5x
1
0
m
/
s
)
,
l
ow
ba
n
d
w
i
dth
(
K
H
z
)
,
hi
g
h
b
i
t
err
or
r
ate
s
,
h
i
gh
m
ob
i
l
i
t
y
an
d
di
f
f
i
c
ul
t
y
i
n
r
ec
ha
r
g
i
ng
th
e
ba
t
ter
y
wh
en
c
om
pa
r
ed
to
wi
r
e
l
es
s
c
o
m
m
un
i
c
ati
on
i
n
th
e
te
r
r
e
s
tr
i
al
n
et
wor
k
.
T
he
k
e
y
i
s
s
ue
s
i
n
ne
t
wor
k
top
ol
og
y
are
t
o
i
m
prov
e
t
he
n
et
w
ork
l
i
f
eti
m
e
an
d
s
us
tai
na
bi
l
i
t
y
.
Cl
us
teri
ng
an
d
d
ata
ag
grega
ti
o
n
s
c
he
m
es
as
s
i
s
t
to
ac
c
om
pl
i
s
h
th
i
s
b
y
m
ak
i
ng
the
ne
t
w
ork
s
m
al
l
er
an
d
s
tab
l
e
[4
,
5].
T
he
f
un
da
m
en
ta
l
no
tc
h
be
h
i
nd
c
l
us
ter
i
ng
i
s
to
di
v
i
de
the
ne
t
w
ork
i
nt
o
s
m
al
l
er
u
ni
ts
a
nd
l
o
gi
c
a
l
l
y
organ
i
z
e
the
un
i
ts
to
m
an
ag
e
th
em
ea
s
i
l
y
.
C
l
us
ter
i
ng
he
l
ps
i
n
r
ed
uc
i
ng
t
he
c
om
m
un
i
c
ati
on
o
v
erh
ea
d,
en
er
g
y
ef
f
i
c
i
en
c
y
,
o
n
the
who
l
e
c
on
s
um
pti
on
of
po
w
er,
an
d
i
nc
r
ea
s
i
ng
t
he
l
i
f
eti
m
e
of
the
ne
t
wor
k
[6]
.
Res
ea
r
c
he
r
s
are
v
i
go
r
o
u
s
l
y
w
ork
i
ng
on
v
ari
ou
s
n
et
w
ork
c
l
us
teri
n
g
i
s
s
ue
s
s
uc
h a
s
s
ev
eral
w
a
y
s
of
c
l
us
teri
ng
[7
-
9
] o
pti
m
i
z
i
ng
t
he
nu
m
be
r
of
c
l
us
ters
,
s
el
ec
ti
on
of
c
l
us
ter
he
ad
[
10
,
11
],
c
om
m
un
i
c
ati
on
am
on
g
c
l
us
ters
[12
],
an
d
da
ta
ag
gre
ga
t
i
on
i
n c
l
us
ters
[1
3
-
15].
Data
a
gg
r
eg
ati
on
i
s
d
ef
i
ne
d
as
the
p
r
oc
es
s
tha
t
ac
c
um
ul
ate
s
the
da
t
a
to
m
i
ni
m
i
z
e
th
e
tr
an
s
m
i
s
s
i
on
of
r
ed
un
da
nt
da
ta
an
d
tr
an
s
m
i
t
the
ag
gre
ga
te
d
da
ta
t
o
th
e
s
i
nk
or
th
e
B
as
e
S
t
ati
on
(
B
S
)
.
T
he
m
ai
n
ai
m
of
the
da
ta
ag
gr
eg
at
i
o
n
proc
es
s
i
s
to
c
on
gre
ga
t
e
th
e
da
ta
f
r
om
the
s
en
s
ors
an
d
tr
an
s
m
i
t
i
t
t
o
the
B
S
wi
th
l
ea
s
t
l
at
en
c
y
[1
3
-
15
]
.
H
e
nc
e
i
t
m
i
ni
m
i
z
es
th
e
en
erg
y
an
d
i
nc
r
ea
s
e
the
l
i
f
eti
m
e o
f
th
e n
et
w
ork
.
W
e
propos
e
a
s
c
he
m
e
to
pe
r
f
or
m
c
l
us
ter
ba
s
ed
d
ata
a
gg
r
e
ga
t
i
on
c
o
ns
i
d
eri
ng
th
e
pa
r
am
ete
r
s
l
i
k
e e
ne
r
g
y
a
nd
di
s
ta
nc
e
w
i
th
t
he
f
ol
l
o
wi
ng
s
tep
s
:
(
1)
Ini
t
i
a
l
c
l
us
teri
ng
i
s
p
erf
or
m
ed
b
y
us
i
n
g
f
u
z
z
y
l
og
i
c
.
A
t
the
s
am
e
ti
m
e,
nu
m
be
r
of
c
l
us
ters
i
n
the
n
et
w
ork
are de
t
erm
i
ne
d
us
i
ng
S
um
of
S
qu
are
d
E
r
r
or (
S
S
E
)
pa
r
am
ete
r
.
(
2)
B
as
ed
on
t
he
di
s
ta
nc
e a
nd
en
erg
y
l
e
v
el
th
e
Cl
us
ter He
ad
n
od
es
ar
e s
el
ec
ted
.
(
3)
Cl
us
ter h
ea
d n
o
de
s
ac
t
as
ag
gre
ga
tors
.
(
4)
Us
i
ng
the
c
on
c
e
pt
of
s
i
m
i
l
arit
y
f
un
c
t
i
on
wi
th
E
uc
l
i
de
a
n
di
s
tan
c
e
,
t
he
s
e
ag
gr
eg
at
ors
tr
an
s
m
i
t
the
a
gg
r
e
ga
te
d d
ata
t
o B
as
e S
t
ati
on
(
B
S
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NIK
A
IS
S
N: 1
69
3
-
6
93
0
◼
Cl
us
teri
ng
an
d
da
t
a a
gg
r
eg
ati
o
n s
c
he
m
e
i
n u
nd
erw
ate
r
wi
r
el
es
s
...
(
V
a
ni
K
r
i
s
h
na
s
wamy
)
1605
O
ur
c
on
tr
i
b
uti
on
s
i
n t
h
i
s
pa
pe
r
i
n
c
om
pa
r
i
s
on
to
t
he
ex
i
s
ti
n
g
w
ork
s
i
nc
l
ud
e t
h
e f
ol
l
o
w
i
n
g
:
−
Dev
el
o
pm
en
t
of
a
m
ath
em
ati
c
al
m
od
el
to
e
v
al
ua
t
e
the
prob
ab
i
l
i
t
y
of
s
en
s
or
no
d
e
be
l
on
g
i
ng
ne
s
s
to
f
orm
the
i
ni
ti
a
l
c
l
us
t
ers
.
T
he
s
en
s
or
no
de
s
are
de
p
l
o
y
ed
s
t
oc
ha
s
ti
c
a
l
l
y
an
d t
he
no
d
es
po
s
i
t
i
o
ns
ar
e s
tat
i
on
ar
y
ac
c
ord
i
ng
to
t
h
ei
r
c
om
m
un
i
c
ati
on
r
a
ng
e.
−
Des
i
gn
i
n
g f
u
z
z
y
c
l
us
teri
ng
s
c
he
m
e b
as
ed
on
t
he
d
ev
el
op
ed
m
ath
em
ati
c
al
m
od
el
.
−
Dete
r
m
i
ni
ng
the
nu
m
be
r
of
c
l
us
ters
us
i
n
g S
S
E
pa
r
am
ete
r
.
−
Des
i
gn
i
n
g a
n
en
erg
y
l
e
v
el
s
c
he
m
e f
or c
l
us
ter
he
ad
s
el
ec
ti
on
i
n
th
e
hi
erar
c
hi
c
a
l
to
po
l
og
y
.
−
Us
i
ng
s
i
m
i
l
arit
y
f
u
nc
ti
o
n,
d
es
i
gn
da
t
a
a
gg
r
e
ga
t
i
on
s
c
he
m
e
f
or
tr
an
s
m
i
tti
ng
t
he
ag
gregat
e
d
da
ta
to
t
he
B
S
.
−
Com
pa
r
ati
v
e
an
a
l
y
s
i
s
of
propos
ed
s
c
he
m
e
wi
th
LE
A
C
H
al
g
orit
hm
[16
]
i
n
term
s
o
f
ne
twork
l
i
f
es
pa
n
an
d
de
ath
r
a
te
of
n
od
es
.
T
he
r
es
t
of
the
pa
pe
r
i
s
or
ga
n
i
z
ed
as
f
ol
l
o
w
s
.
S
ec
ti
o
n
2
bre
i
f
s
ab
ou
t
w
ork
s
r
el
ate
d
t
o
v
ari
ou
s
c
l
us
ter
i
ng
s
c
he
m
es
,
s
el
ec
ti
on
of
c
l
us
ter
he
ad
an
d
s
e
v
era
l
c
l
us
t
e
r
ba
s
ed
da
t
a
ag
gre
ga
t
i
on
s
c
h
em
es
i
n
ne
twork
s
.
S
ec
ti
on
3
ex
p
l
a
i
ns
the
n
et
wor
k
m
od
el
an
d
e
n
erg
y
m
od
el
f
or
c
l
us
ter
f
or
m
ati
on
r
es
pe
c
ti
v
el
y
.
It
ex
p
l
ai
ns
the
pro
po
s
ed
f
u
z
z
y
s
c
he
m
e
i
n
c
l
us
ter
i
ng
,
t
he
c
l
us
t
er
he
ad
s
el
ec
t
i
on
c
on
s
i
d
erin
g
pa
r
am
ete
r
s
l
i
k
e
en
erg
y
l
ev
e
l
a
nd
di
s
ta
nc
e
an
d
d
at
a
ag
gregat
i
o
n
s
c
he
m
e
us
i
ng
s
i
m
i
l
arit
y
f
un
c
ti
on
.
S
ec
ti
on
4
de
al
s
wi
th
s
i
m
ul
ati
on
an
d
i
ts
pa
r
am
ete
r
s
.
S
ec
ti
on
5
de
a
l
s
w
i
t
h
r
es
u
l
t
a
na
l
y
s
i
s
.
S
um
m
ar
y
of
the
prop
os
ed
wor
k
an
d
f
utu
r
e
wor
k
s
ar
e
pres
en
ted
i
n
S
ec
ti
on
6
.
2.
Rel
ated
W
o
r
k
s
In
[17
],
th
e
au
t
ho
r
s
ha
v
e
prop
os
ed
a
n
ag
e
nt
ba
s
ed
r
ou
t
i
ng
prot
oc
ol
w
h
i
c
h
i
s
en
er
g
y
ef
f
i
c
i
en
t.
T
he
pro
c
es
s
of
d
y
na
m
i
c
c
l
us
teri
n
g
i
s
i
ni
t
i
at
i
at
ed
an
d
the
c
l
us
t
er
he
a
d
a
l
o
ng
wi
th
th
e
a
ge
n
ts
i
s
r
es
po
ns
i
bl
e
f
or
da
t
a
a
gg
r
e
ga
t
i
on
at
the
af
f
ec
ted
area.
T
he
y
ha
v
e
pro
po
s
ed
a
n
al
g
orit
hm
to
i
nc
r
ea
s
e
th
e
c
on
ne
c
ti
v
i
t
y
an
d
r
e
l
i
ab
i
l
i
t
y
i
n
th
e
ne
t
wor
k
.
In
[18
],
the
au
tho
r
s
ha
v
e
us
ed
F
u
z
z
y
C
l
us
ter
i
ng
M
e
an
s
(
F
CM)
to
s
el
ec
t
th
e
c
l
us
ter
h
ea
ds
f
r
om
an
o
pti
m
al
nu
m
be
r
of
c
l
us
ters
an
d
s
e
tup
th
e
Un
de
r
w
ate
r
Is
om
orphi
c
S
en
s
or
Net
wor
k
(
U
W
IS
N)
.
In
a
dd
i
t
i
o
n
t
o
t
hi
s
,
a
s
c
he
m
e
f
or
de
ter
m
i
ni
n
g
t
he
r
ea
l
c
l
us
ter
h
ea
ds
an
d
s
el
ec
ti
ng
th
em
ha
v
e
al
s
o
b
ee
n
pro
po
s
ed
.
T
he
au
tho
r
s
i
n
[
19
]
h
av
e
p
r
op
os
ed
a
r
o
uti
ng
pr
oto
c
o
l
ba
s
ed
o
n
gri
d
w
i
t
h
f
u
z
z
y
l
o
gi
c
w
he
r
e
the
en
ti
r
e
n
et
wor
k
i
s
s
ep
ara
t
e
d
i
nto
v
ari
ou
s
v
i
r
t
ua
l
grid
s
.
E
v
er
y
grid
i
n
the
n
et
w
ork
ha
s
on
l
y
on
e
ac
ti
v
e n
od
e
whi
c
h
i
s
s
e
l
ec
t
ed
us
i
ng
t
he
f
u
z
z
y
l
og
i
c
s
y
s
tem
.
In
[2
0],
t
he
au
t
ho
r
s
ha
v
e
propos
e
d
a
ne
w
G
P
S
-
f
r
ee
r
ou
ti
ng
protoc
o
l
wi
th
Di
s
tr
i
bu
t
ed
Unde
r
wate
r
Cl
us
teri
ng
S
c
he
m
e
(
DUCS)
w
h
i
c
h
ut
i
l
i
z
es
da
ta
ag
gre
ga
t
i
on
t
o
r
em
ov
e
the
r
ed
un
da
nt
i
nf
or
m
ati
on
an
d
r
ed
uc
e
the
d
ata
l
os
s
i
n
U
W
A
S
N
.
I
n
[
21
],
th
e
au
t
ho
r
s
h
av
e
pro
po
s
ed
a
s
c
he
m
e
f
or
an
op
t
i
m
al
s
el
ec
t
i
on
of
c
l
us
ter
he
a
d
a
n
d
c
l
us
ter
s
i
z
e
us
i
ng
f
u
z
z
y
l
og
i
c
al
o
ng
w
i
t
h
i
nte
r
an
d
i
ntr
a c
l
us
ter c
om
m
un
i
c
ati
on
c
o
ns
i
de
r
i
ng
th
e
en
erg
y
a
nd
t
he
m
ul
ti
p
l
e
pa
t
hs
f
or U
W
A
S
N.
T
he
au
tho
r
s
i
n
[
22
-
24
]
h
a
v
e
r
ec
om
m
en
de
d
c
l
us
teri
n
g
an
d
a
gg
r
eg
ati
on
t
ec
hn
i
q
ue
s
i
n
U
W
A
S
Ns
whi
c
h
are
b
as
ed
on
f
u
z
z
y
l
og
i
c
s
y
s
tem
tha
t
c
ap
ti
v
at
es
th
e
r
es
i
d
ua
l
e
n
erg
y
,
the
no
de
de
ns
i
t
y
,
the
l
i
nk
qu
a
l
i
t
y
,
the
l
oa
d
an
d
the
d
i
s
tan
c
e
to
the
s
i
nk
/B
as
e
S
t
ati
o
n
(
B
S
)
n
od
e.
In
[25
]
,
the
a
uth
ors
ha
v
e
f
oc
us
ed
on
de
s
i
g
ni
ng
a
n
e
ne
r
g
y
ef
f
i
c
i
en
t
r
ou
t
i
ng
pr
ot
oc
ol
to
tr
a
ns
f
e
r
the
d
ata
be
t
wee
n
s
en
s
or
n
od
es
ut
i
l
i
z
i
ng
t
he
f
i
x
ed
c
o
u
r
i
er
no
de
s
i
no
r
d
er
to
en
h
an
c
e
the
l
i
f
et
i
m
e
an
d
de
c
r
ea
s
e
t
he
en
d
t
o
en
d
de
l
a
y
i
n
th
e
ne
t
wor
k
.
T
he
a
uth
ors
i
n
[2
6
]
ha
v
e
an
al
y
z
ed
th
e
c
on
s
um
pti
on
of
en
erg
y
i
n
U
W
A
S
Ns
f
or
v
ario
us
t
r
an
s
m
i
s
s
i
on
m
ec
ha
ni
s
m
s
wi
th
ef
f
ec
t
of
c
ha
ng
i
ng
am
bi
en
t c
on
di
t
i
o
n
s
.
3.
Clu
ste
r
ing
T
hi
s
s
ec
ti
on
pres
en
ts
n
e
twork
m
od
el
,
c
l
us
teri
ng
t
erm
i
no
l
og
y
an
d
e
ne
r
g
y
m
od
el
em
pl
o
y
ed
f
or
de
s
i
g
ni
ng
th
e
prop
os
ed
c
l
us
teri
ng
s
c
h
e
m
e.
It
i
nc
l
u
de
s
pro
po
s
ed
f
u
z
z
y
c
l
us
ter
i
ng
s
c
he
m
e wi
th
s
e
l
ec
t
i
on
of
c
l
us
ter hea
d a
nd
da
ta
ag
gre
ga
ti
on
s
c
he
m
e.
3
.1.
N
etw
o
r
k
M
o
d
el
T
he
ne
t
wor
k
m
od
el
ex
p
l
a
i
ne
d
he
r
e
i
s
s
i
m
i
l
ar
t
o
t
ha
t
pres
e
nte
d
i
n
[16
,
2
1]
wi
th
t
he
s
ub
s
eq
ue
nt
f
ea
tures
.
T
he
s
en
s
or
no
de
s
are
pl
ac
e
d
r
an
do
m
l
y
i
n
an
u
nd
erw
ate
r
en
v
i
r
on
m
en
t
to
f
or
m
a
3
-
D
s
tat
i
c
ne
t
w
ork
where
th
e
c
om
m
un
i
c
ati
on
s
be
t
w
e
en
t
he
s
en
s
or
n
od
es
are
f
ul
l
d
up
l
ex
.
T
he
3
-
D
p
os
i
t
i
on
i
nf
or
m
ati
on
of
ea
c
h
s
en
s
or
no
d
e
i
s
ac
hi
ev
ed
b
y
po
s
i
t
i
o
ni
n
g
al
go
r
i
thm
s
or
b
y
the
us
e
of
ha
r
d
war
e u
n
i
ts
,
whi
c
h
are d
ete
c
te
d b
y
ac
o
u
s
ti
c
wav
es
.
Evaluation Warning : The document was created with Spire.PDF for Python.
◼
IS
S
N: 16
93
-
6
93
0
T
E
L
KO
M
NIK
A
V
ol
.
17
,
No
.
4
,
A
ug
us
t
20
19
:
1
6
04
-
1
6
14
1606
T
he
s
en
s
or
no
de
s
i
n
th
e
ne
t
wor
k
are
ho
m
og
en
eo
us
an
d
tr
an
s
m
i
t
the
r
eq
u
i
r
ed
i
nf
orm
ati
on
wi
th
d
i
f
f
erent rang
es
of
c
om
m
un
i
c
ati
on
r
ad
i
us
. T
he
p
os
i
t
i
on
of
th
e s
i
nk
no
de
or
B
S
i
s
us
ua
l
l
y
o
n
the
s
urf
ac
e
of
t
he
s
ea
.
T
h
e
en
erg
y
po
s
s
e
s
s
ed
b
y
the
B
S
i
s
un
l
i
m
i
t
ed
an
d
i
t
c
an
c
o
m
m
un
i
c
ate
us
i
ng
u
nd
e
r
w
at
er
ac
o
us
ti
c
wav
es
a
nd
r
a
di
o
wav
es
.
T
he
pr
oc
es
s
i
ng
an
d
ag
gre
ga
t
i
on
of
da
t
a
at
th
e
B
S
i
s
c
arr
i
ed
ou
t
b
y
e
ac
h
s
en
s
or
n
od
e.
D
urin
g
th
i
s
proc
es
s
,
the
en
erg
y
of
the
s
en
s
or
n
od
e
i
s
r
ed
uc
ed
grad
ua
l
l
y
;
th
i
s
i
n
turn
r
es
ul
ts
i
n
a
d
ea
d
n
od
e.
T
he
ne
twor
k
i
s
c
on
s
i
d
ered
t
o
b
e
de
ad
when
the
de
a
d
n
od
es
nu
m
be
r
ex
c
ee
ds
be
y
o
nd
t
he
thres
ho
l
d
l
i
m
i
t.
T
he
r
ef
ore,
the
ai
m
of
the
hi
erar
c
hi
c
al
t
op
ol
o
g
y
s
c
h
em
e
i
s
to
i
nc
r
ea
s
e
th
e
n
et
w
ork
l
i
f
eti
m
e
to
the
m
a
x
i
m
u
m
f
ea
s
i
bl
e e
x
te
nt.
3
.
2
.
E
n
er
g
y
M
o
d
el
T
he
m
aj
or
l
i
m
i
tat
i
o
n
of
U
W
A
S
N
i
s
i
ts
e
ne
r
g
y
c
ap
ab
i
l
i
t
y
to
r
ec
h
arge
t
he
ba
tte
r
y
whi
c
h
c
an
no
t
be
do
n
e
f
r
eq
ue
nt
l
y
.
In
un
de
r
w
at
er
en
v
i
r
on
m
en
t
the
c
on
s
um
pti
o
n
of
en
erg
y
b
y
t
he
n
od
es
de
pe
nd
s
o
n
th
e
f
ol
l
o
w
i
ng
f
a
c
tors
.
1)
T
o
s
en
s
e,
r
ec
ei
v
e
an
d
proc
es
s
th
e
da
ta.
2)
T
o
c
on
v
e
y
th
e
c
ol
l
ec
t
i
v
e
da
t
a
to
th
e
s
i
nk
.
T
he
f
i
r
s
t
f
ac
tor
i
s
c
on
s
i
de
r
ed
,
as
the
e
ne
r
g
y
c
on
s
u
m
pti
on
i
s
l
es
s
when
c
om
pa
r
ed
t
o
t
he
s
ec
on
d.
T
hi
s
m
od
el
of
en
erg
y
c
on
s
um
pti
on
f
or
tr
an
s
m
i
tti
n
g
da
t
a
b
y
t
he
nod
es
i
s
pres
e
nte
d
i
n [
2
4].
T
he
Uni
t
en
erg
y
c
o
ns
um
ed
to
proc
es
s
o
ne
bi
t
of
m
es
s
ag
e
i
s
de
no
t
ed
as
(
)
an
d
i
s
e
v
a
l
ua
ted
ac
c
ord
i
ng
to
t
he
(
1
):
(
)
=
X
X
(
)
(
1)
w
he
r
e
P
r
r
ep
r
es
e
nts
th
e
thr
es
ho
l
d
po
wer
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n
od
e
f
or
r
ec
ei
v
i
n
g
th
e
da
ta
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ac
k
ag
e,
d
r
ep
r
es
e
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di
s
ta
nc
e
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tr
a
ns
m
i
tti
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ta
pa
c
k
ag
e,
a
nd
T
p
r
e
pres
en
ts
t
he
t
i
m
e
of
the
tr
an
s
m
i
tti
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da
ta
pa
c
k
ag
e wh
i
c
h
i
s
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ep
r
es
e
nt
ed
as
f
ol
l
o
w
s
:
=
w
he
r
e
M
b
a
nd
S
v
r
e
pres
en
ts
the
s
i
z
e
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d
th
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tr
a
ns
m
i
s
s
i
on
s
pe
ed
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t
a
pa
c
k
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e
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ec
ti
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e
l
y
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T
he
en
erg
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att
e
nu
a
ti
o
n
A
(
d
)
w
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th
th
e
tr
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s
m
i
tti
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g
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s
tan
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c
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s
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s
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al
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ul
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ted
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ol
l
o
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s
:
A
(
d
)
=
d
λ
X
β
d
w
he
r
e
λ
r
ep
r
es
e
nts
th
e
e
ne
r
g
y
s
pre
ad
i
ng
f
ac
tor
w
h
i
c
h
i
s
1
f
or
c
y
l
i
nd
r
i
c
al
,
1.
5
f
or
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r
ac
ti
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al
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d
2
f
or s
ph
eric
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pre
ad
i
ng
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pe
c
ti
v
e
l
y
.
T
he
pa
r
am
ete
r
=
10
(
)
10
a
bs
orpti
on
c
o
ef
f
i
c
i
en
t
α
(
f
)
, whi
c
h c
a
n b
e
c
al
c
u
l
at
ed
us
i
ng
th
e f
ol
l
o
w
i
ng
eq
u
ati
on
:
(
)
=
0
.
11
10
−
3
(
2
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1
+
2
+
44
(
10
−
3
)
2
(
4100
+
2
)
+
2
.
75
10
−
7
2
+
3
10
−
6
w
he
r
e
‘
f
’
i
s
t
he
f
r
eq
u
en
c
y
o
f
the
c
arr
i
er
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o
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ti
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g
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l
i
n
K
H
z
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an
d
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f
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i
s
i
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dB
/
m
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Cons
i
de
r
i
n
g
the
un
de
r
w
a
ter
en
v
i
r
on
m
en
t,
the
am
ou
nt
of
en
erg
y
c
on
s
um
ed
to
tr
an
s
m
i
t
‘
l
’
b
i
t
of
da
ta
o
v
er
a
di
s
tan
c
e
d
b
y
t
he
no
d
e i
s
g
i
v
en
b
y
E
t
x
(
d,
l
)
as
s
ho
w
n
i
n
th
e
(
2
):
(
,
1
)
=
1
+
1
X
X
(
2)
w
he
r
e H r
ep
r
es
en
ts
th
e
de
pth
of
th
e n
o
de
i
n
mtrs
.
C
=
(
2
π
(
0:
67
)
10
9:5
)
T
he
am
ou
nt
of
e
ne
r
g
y
c
o
n
s
u
m
ed
to
r
ec
e
i
v
e
’
l
’
bi
t
of
da
ta
b
y
the
r
ec
e
i
v
er
i
s
g
i
v
en
b
y
E
r
x
(
d,
l
)
.
T
o
be
s
pe
c
i
f
i
c
the
thres
ho
l
d
v
a
l
ue
‘
d
0’
i
s
s
et,
whi
c
h
i
s
r
el
ate
d
to
the
tr
a
ns
f
er
di
s
tan
c
e.
If
the
tr
a
ns
f
er
di
s
tan
c
e
i
s
l
es
s
t
ha
n
‘
d0
’
,
t
he
n
th
e
en
erg
y
c
o
ns
um
pti
on
prop
orti
on
al
to
th
e
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ata
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at
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m
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c
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e
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n
od
e
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c
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us
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ga
t
he
r
s
a
n
d
tr
an
s
m
i
ts
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-
bi
t
to
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c
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turn th
e c
l
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he
a
d c
on
de
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ov
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r
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ei
v
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n
f
or
m
ati
on
to
l
-
bi
t.
3
.
3
.
P
r
o
p
o
s
ed
F
u
z
z
y
Clu
s
t
er
ing
S
cheme
T
he
c
l
us
ter
f
or
m
ati
on
i
s
pe
r
f
or
m
ed
b
y
tak
i
ng
i
nto
ac
c
ou
nt
t
he
gi
v
e
n
3
-
D
n
et
w
or
k
en
v
i
r
on
m
en
t.
C
l
us
ter
s
tr
uc
t
ure
i
s
c
ha
r
ac
ter
i
z
ed
b
y
t
w
o
t
y
p
es
of
no
d
es
c
al
l
ed
M
em
be
r
c
l
us
ter
no
de
s
a
nd
C
l
us
terH
ea
d
n
od
es
w
h
i
c
h
are
c
o
ns
i
d
ered
as
th
e
ba
c
k
bo
ne
of
the
n
et
w
ork
.
T
he
M
em
be
r
c
l
us
t
ernod
es
t
ha
t
are
c
o
nn
ec
ted
to
i
ts
o
wn
C
l
us
terH
ea
d
no
d
e
l
i
e
d
orm
an
t
to
s
a
v
e
the
e
ne
r
g
y
c
on
s
um
pti
on
of
the
n
et
w
ork
.
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he
r
ea
s
the
Cl
us
terH
ea
d
no
d
es
tha
t
ar
e
c
on
ne
c
t
ed
t
o
the
c
l
os
es
t
n
ei
gh
b
or
n
o
de
of
oth
er
c
l
us
ters
i
s
us
ua
l
l
y
i
n
a
s
h
al
l
o
wer
l
oc
ati
on
.
T
he
pr
oc
es
s
o
f
s
el
ec
ti
ng
th
e
Cl
us
terHe
ad
no
de
s
a
nd
t
he
r
ec
on
s
tr
uc
t
i
on
of
the
ne
t
wor
k
i
s
c
al
l
e
d
as
a
r
ou
n
d
.
T
hi
s
proc
es
s
i
s
ac
hi
ev
e
d
pe
r
i
o
di
c
a
l
l
y
to
d
ec
r
ea
s
e
th
e
c
on
s
um
pti
on
of
en
erg
y
i
n
th
e
ne
t
wor
k
whi
c
h
i
n t
urn
i
nc
r
ea
s
es
th
e
ne
t
w
ork
's
l
i
f
eti
m
e
.
B
ef
ore
th
e
c
om
m
un
i
c
ati
o
n
be
g
i
ns
be
t
ween
th
e
s
e
ns
or
no
de
s
,
i
n
i
ti
al
l
y
the
n
od
es
are
di
v
i
d
ed
ac
c
ordi
n
g
to
l
oc
at
i
on
s
of
the
no
de
s
i
n
to
a
nu
m
be
r
of
f
uz
z
y
s
ub
s
et
s
us
i
ng
f
u
z
z
y
c
l
us
teri
n
g
m
od
el
.
D
urin
g
t
he
f
orm
ati
on
of
c
l
us
ters
,
t
he
n
od
es
whi
c
h
are
c
l
os
er
to
th
e
l
oc
at
i
on
an
d
whi
c
h
r
eq
u
i
r
e
l
es
s
en
erg
y
f
or
c
om
m
un
i
c
ati
on
ar
e
as
s
i
gn
ed
to
t
he
s
am
e
c
l
us
ter.
In
i
ti
al
l
y
at
the
c
om
m
en
c
e
m
en
t
of
ea
c
h
r
ou
n
d,
whi
c
h
i
s
b
as
ed
o
n
as
s
ured
proba
bi
l
i
t
i
es
,
ev
er
y
n
od
e
i
n
th
e
ne
t
w
ork
be
l
o
n
gs
to
t
he
i
ni
t
i
al
s
u
bs
ets
of
the
c
l
us
ters
.
T
he
proc
es
s
of
s
el
ec
ti
o
n
of
the
c
l
us
ter
he
ad
s
wi
l
l
be
pe
r
f
orm
ed
by
e
ac
h
s
ub
s
e
t
i
n
p
aral
l
e
l
an
d
the
m
es
s
ag
es
are
tr
a
ns
m
i
tte
d
t
o
t
he
ba
s
e
s
tat
i
on
.
T
he
s
i
z
e
of
the
ne
t
wor
k
an
d
ti
m
e
r
eq
u
i
r
ed
f
or
the
c
al
c
ul
ati
o
n
are
r
e
du
c
ed
i
n t
hi
s
s
c
he
m
e.
A
m
eth
od
i
s
pro
po
s
e
d
to
s
eg
r
eg
ate
t
he
u
nd
er
wate
r
a
c
ou
s
ti
c
w
i
r
e
l
es
s
s
en
s
or
n
o
de
s
i
nt
o
m
pr
i
m
ar
y
f
u
z
z
y
c
l
us
ters
.
F
urther,
the
o
pti
m
al
nu
m
be
r
s
of
c
l
us
ters
ar
e
ob
ta
i
n
ed
u
s
i
ng
an
e
l
bo
w
m
eth
od
. T
he
n
ex
t s
tep
,
de
t
erm
i
ne
s
th
e m
atri
x
wi
j
;
1≤
i
<
n
;
1≤
j
<
m
b
y
m
ea
ns
of
f
u
z
z
y
c
l
us
t
erin
g
m
eth
od
. P
r
i
or to
ev
er
y
r
o
un
d,
ea
c
h
no
de
i
s
r
el
ate
d t
o
a
s
ui
ta
bl
e
c
l
us
ter
de
pe
n
di
n
g
on
th
e d
eg
r
e
e
of
be
l
o
ng
i
ng
ne
s
s
of
ea
c
h
no
de
.
If
the
no
de
y
i
i
s
a
p
pl
i
ed
to
B
j
,
the
n
i
t
s
at
i
s
f
i
es
the
c
o
nd
i
ti
o
n
gi
v
en
i
n
(
5
)
.
∑
1
−
1
1
=
1
≤
<
∑
2
2
=
1
(
5)
T
he
r
an
do
m
nu
m
be
r
w
i
t
h
un
i
f
orm
di
s
tr
i
bu
ti
o
n
i
s
be
twee
n
0
an
d
1
i
s
de
no
t
e
d
b
y
r.
T
he
ba
s
i
c
de
f
i
n
i
t
i
on
of
tr
ad
i
ti
on
a
l
c
l
us
teri
n
g
i
s
t
he
di
v
i
s
i
on
of
b
as
i
c
s
et
of
i
de
nt
i
c
al
o
bj
ec
ts
i
nt
o
nu
m
erous
s
ub
s
et
s
.
I
n
r
e
al
i
t
y
,
the
r
ea
l
c
l
u
s
ters
whi
c
h
ar
e
f
orm
ed
are
t
y
p
i
c
al
l
y
m
uc
h
c
om
pl
ex
a
nd
the
d
ep
e
nd
e
nc
y
of
ob
j
ec
t
s
to
the
m
i
s
m
ore
f
uz
z
y
.
In
s
ho
r
t,
s
uc
h
c
l
us
ters
are
c
al
l
ed
f
u
z
z
y
s
ub
s
ets
of
th
e
ba
s
i
c
s
et
wh
i
c
h
are
c
om
pris
ed
of
de
l
i
c
at
e
p
arts
.
T
hi
s
i
s
h
o
w
,
the
c
o
nc
ep
t
of
f
u
z
z
y
c
l
us
teri
n
g
w
as
pro
po
s
ed
.
I
n
thi
s
p
ap
er,
t
he
proc
es
s
of
i
ni
ti
al
i
z
ati
on
i
s
pr
op
os
e
d
c
on
s
i
d
erin
g
the
f
uz
z
y
c
l
us
teri
ng
m
od
el
.
B
a
s
i
c
al
l
y
i
n
th
i
s
c
l
us
teri
n
g
m
eth
od
,
t
he
s
et
of
n
ob
j
ec
ts
are
pa
r
ti
ti
o
ne
d
A=
a
1
, a
2
, a
3
…
a
n
i
nt
o m
f
uz
z
y
c
l
us
t
ers
B
1,
B
2
.
...
B
m
, t
he
c
l
us
teri
n
g
i
s
de
n
ote
d a
s
nm
m
atri
x
:
=
[
]
(
1
<
,
1
)
w
he
r
e
w
ij
=
th
e
de
gr
ee
of
be
l
o
ng
i
ng
ne
s
s
of
the
i
t
h
ob
j
ec
t
to
the
j
th
c
l
us
ter.
T
he
m
atri
x
=
[
]
m
us
t
c
on
v
i
nc
e
th
e
s
ub
s
eq
ue
nt
c
on
di
t
i
o
ns
.
F
or
ea
c
h
ob
j
ec
t
a
i
a
nd
c
l
us
t
er
B
j
,
0≤
w
i
j
≤
1
Evaluation Warning : The document was created with Spire.PDF for Python.
◼
IS
S
N: 16
93
-
6
93
0
T
E
L
KO
M
NIK
A
V
ol
.
17
,
No
.
4
,
A
ug
us
t
20
19
:
1
6
04
-
1
6
14
1608
f
or
ea
c
h
o
bj
ec
t
a
i
,
∑
j
=
1
m
w
i
j
=
1
.
F
or
ea
c
h
c
l
us
te
r
B
j
,
0
≤
∑
j
=
1
n
w
ij
<
n
.
T
o
c
al
c
u
l
at
e
t
he
c
en
ter
of
the
c
l
us
ter
B
j
;
1
jm
i
s
b
j
.
T
he
de
gree
of
be
l
on
g
i
ng
ne
s
s
of
ob
j
ec
t
of
a
i
to
c
l
us
ter
b
j
i
s
ex
pres
s
ed
i
n
t
erm
s
of
the
di
s
tan
c
e
be
t
ween
t
he
a
i
an
d
th
e
c
en
t
er
of
the
c
l
us
ter
b
j
whi
c
h
i
s
r
ep
r
es
en
te
d
as
f
ol
l
o
w
s
d
i
s
t
(
a
i
,
b
j
).
T
he
po
s
s
i
bi
l
i
t
y
of
an
y
ob
j
ec
t
be
l
o
ng
i
ng
t
o
th
e
c
l
us
ter
c
an
b
e
de
term
i
ne
d
b
y
the
di
s
t
an
c
e
be
t
wee
n
th
e
o
bj
ec
t
an
d
t
he
c
en
t
er
of
c
l
us
ter.
F
or
e
x
a
m
pl
e
s
ho
r
t
er
the
d
i
s
tan
c
e
b
et
ween
t
he
c
l
us
ter
c
en
ter
b
j
an
d
th
e
ob
j
e
c
t
x
j
,
greate
r
are
the
op
p
ort
un
i
t
i
es
f
or
the
ob
j
ec
t
x
i
be
l
on
gi
ng
to
t
he
c
orr
es
po
nd
i
ng
c
l
us
ter
B
j
.
T
he
am
ou
nt
of
be
l
on
gi
ng
n
es
s
of
an
ob
j
ec
t
a
i
to
c
l
us
ter
B
j
i
s
ex
pres
s
e
d u
s
i
ng
t
he
(
6
)
.
=
1
(
,
)
2
(
6)
T
he
de
f
i
ni
t
i
on
of
de
gre
e
of
be
l
on
g
i
n
gn
es
s
w
ij
i
s
ob
t
ai
ne
d
b
y
no
r
m
al
i
z
i
n
g
th
e
(
6
)
whi
c
h
s
at
i
s
f
i
es
the
c
on
di
t
i
o
ns
of
m
atri
x
=
[
]
.
=
1
d
ist
(
x
i
,
b
j
)
2
∑
1
d
ist
(
x
i
,
b
l
)
2
m
l
=
1
(
7)
Cl
us
teri
ng
al
g
orit
hm
s
are
broad
l
y
c
l
as
s
i
f
i
ed
i
nt
o
ha
r
d
an
d
s
of
t
c
l
us
teri
n
g
a
l
go
r
i
thm
s
.
F
u
z
z
y
c
l
us
ter
i
ng
b
el
o
ng
s
t
o
s
of
t
c
l
us
ter
i
ng
b
ec
au
s
e
i
n
th
i
s
a
l
g
orit
hm
ev
er
y
ob
j
ec
t
be
l
o
ng
s
t
o
m
ul
ti
pl
e
c
l
us
ters
.
T
he
f
ol
l
o
wi
ng
are
t
he
ad
v
a
nta
g
es
of
s
of
t
c
l
us
ter
i
ng
.
1)
E
v
er
y
ob
j
ec
t
be
l
on
gs
to
m
ul
ti
pl
e
c
l
us
ters
;
h
en
c
e
th
e
us
er
c
an
ob
s
er
v
e
m
ul
ti
pl
e
the
m
es
f
or
a
c
l
us
ter.
2)
V
ari
ou
s
c
l
us
t
ers
ge
t
f
orm
ed
f
or
v
ari
ou
s
the
m
e
s
.
3)
In
ord
er
to
c
a
l
c
ul
ate
the
ord
er
of
t
he
ob
j
ec
t
ap
propr
i
ate
l
y
,
the
m
ea
s
ure
r
el
ate
d
b
et
ween
c
l
us
ters
an
d
ob
j
ec
ts
c
an
be
us
ed
as
a
r
el
ev
a
nc
e
m
ea
s
ure.
T
he
i
ni
t
i
a
l
i
z
at
i
o
n
of
f
u
z
z
y
c
l
us
teri
ng
i
s
ba
s
e
d
o
n
ex
pe
c
tat
i
on
m
ax
i
m
i
z
a
ti
o
n
al
g
orit
hm
[23
].
T
hi
s
ex
pe
c
tat
i
on
m
ax
i
m
i
z
ati
o
n
al
g
orit
hm
us
ed
f
or
f
uz
z
y
c
l
us
teri
n
g
ge
n
erate
s
‘
m’
di
f
f
erent
c
l
us
ters
are br
i
ef
ed
i
n t
h
e s
tep
s
w
h
i
c
h i
s
gi
v
en
i
n
P
r
oc
es
s
1:
1)
Cl
as
s
i
f
y
th
e s
et
of
‘
n
’
ob
j
ec
t
s
ba
s
ed
o
n i
ts
f
ea
tures
i
nto
‘
m’
c
l
us
ters
.
2)
T
he
c
l
us
ter c
en
ters
B
j
are s
el
ec
te
d b
y
u
ni
f
orm
di
s
tr
i
bu
ti
on
of
r
an
d
om
v
ec
tors
.
3)
Com
pu
te
the
de
gr
ee
of
b
el
o
ng
i
ng
n
es
s
(w
ij
)
us
i
n
g
th
e
di
s
t
an
c
e
be
t
ween
the
n
od
es
wi
th
i
n
the
c
l
us
ter.
4)
Nor
m
al
i
z
e
(w
ij
)
un
t
i
l
we g
et
‘
m
’
c
l
us
ters
3
.
4
.
D
etermin
atio
n
of
t
h
e
Nu
mb
er
of
Clust
er
s
T
o
de
c
i
de
th
e
s
u
i
tab
l
e
c
l
us
ter
nu
m
be
r
i
s
a
c
um
be
r
s
om
e
tas
k
es
pe
c
i
al
l
y
i
n
f
u
z
z
y
c
l
us
teri
n
g.
T
he
granu
l
arit
y
of
the
c
l
us
teri
ng
a
nd
th
e
di
s
c
ov
er
y
of
an
ap
pr
op
r
i
ate
b
al
a
nc
e
be
t
w
e
en
prec
i
s
i
on
a
nd
c
om
pres
s
i
bi
l
i
t
y
ha
v
e
t
o b
e m
an
ag
ed
. T
he
s
um
of
s
qu
are
d
err
or (
S
S
E
)
f
or
ea
c
h c
l
us
t
er i
s
d
ef
i
ne
d
i
n
(
8
)
.
(
)
=
∑
=
1
(
,
)
2
(
8)
(
)
=
∑
∑
(
,
)
2
=
1
=
1
(
9)
T
he
S
S
E
i
ns
i
de
e
ac
h
c
l
us
t
er
c
an
be
de
c
r
ea
s
ed
b
y
i
n
c
r
ea
s
i
ng
t
he
n
um
be
r
of
c
l
u
s
ters
.
T
he
ab
ov
e
c
on
c
e
pt
r
es
ul
ts
i
n
b
ett
er
c
h
arac
ters
of
the
da
ta
ob
j
ec
ts
w
h
i
c
h
are
r
e
tai
n
ed
f
r
om
a
nu
m
be
r
of
c
l
us
ters
,
i
n
a
m
an
ne
r
s
uc
h
tha
t
the
ob
j
ec
ts
i
n
the
c
l
us
ter
are
m
ore
an
al
og
ou
s
to
ea
c
h
oth
er.
T
he
r
e
wi
l
l
be
a
tr
i
v
i
a
l
r
ed
uc
ti
o
n
i
n
S
S
E
i
n
e
ac
h
c
l
us
ter
du
e
to
th
e
s
pl
i
tt
i
ng
of
c
l
us
ter
i
nt
o
s
ub
c
l
us
ters
.
T
o
de
c
i
de
on
the
nu
m
be
r
of
c
l
us
ters
,
E
l
b
o
w
m
eth
od
whi
c
h
i
s
a
n
ef
f
i
c
i
en
t
a
l
go
r
i
thm
i
s
em
pl
o
y
e
d.
W
he
n t
he
n
u
m
be
r
of
c
l
us
ters
m
>
0
; th
e f
ol
l
o
w
i
ng
s
te
ps
are f
ol
l
o
wed:
1)
Dete
r
m
i
ne
th
e
S
S
E
(
m
)
;
2)
S
k
etc
h t
he
c
urv
e b
et
w
e
en
t
he
d
ete
r
m
i
ne
d
S
S
E
(
m)
a
n
d t
he
v
aria
b
l
e
‘
m
’
;
3)
T
he
ac
c
urate
nu
m
be
r
o
f
c
l
us
ter
wi
l
l
be
i
m
pl
i
ed
f
r
o
m
t
he
m
os
t
s
i
gn
i
f
i
c
an
t
i
nf
l
ec
ti
on
po
i
nt
o
n
the
c
urv
e;
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NIK
A
IS
S
N: 1
69
3
-
6
93
0
◼
Cl
us
teri
ng
an
d
da
t
a a
gg
r
eg
ati
o
n s
c
he
m
e
i
n u
nd
erw
ate
r
wi
r
el
es
s
...
(
V
a
ni
K
r
i
s
h
na
s
wamy
)
1609
w
he
r
e
p
(
p
≥
0)
r
ep
r
es
e
nts
a
pa
r
am
ete
r
to
de
term
i
ne
the
pr
i
orit
y
w
e
i
g
hti
ng
of
the
de
gree
of
be
l
on
g
i
ng
ne
s
s
w
ij
.
T
he
am
ou
nt
of
f
i
tne
s
s
of
the
da
t
a
c
an
be
ev
al
ua
te
d
us
i
ng
t
he
S
S
E
f
or
f
u
z
z
y
c
l
us
teri
n
g
wi
th
‘
m
’
c
l
us
ters
as
de
f
i
ne
d i
n
(
9
)
.
3
.
5
.
S
el
ec
t
ion
of
Clust
e
r
Head
A
c
c
ordi
n
g
to
th
e
an
al
y
s
i
s
o
f
en
erg
y
c
on
s
um
pti
on
,
t
he
r
e
are
f
ew
d
i
s
ad
v
a
nta
ge
s
of
us
i
ng
LE
A
CH pr
oto
c
o
l
i
n
un
de
r
wate
r
en
v
i
r
on
m
en
t.
−
T
he
en
erg
y
of
th
e
no
d
es
whi
c
h a
r
e
d
i
s
tan
t f
r
om
th
e s
i
nk
/B
S
i
s
ex
h
au
s
ted
ea
r
l
y
.
−
T
he
c
l
us
ter
he
ad
no
d
es
a
r
e
r
an
d
om
l
y
s
e
l
ec
te
d
a
nd
ge
ts
c
on
c
e
ntrat
ed
d
ue
to
whi
c
h
the
en
erg
y
ef
f
i
c
i
en
c
y
of
th
e
no
d
e
w
i
l
l
de
c
r
ea
s
e.
T
o
av
o
i
d
th
e
di
s
ad
v
a
nta
ge
s
of
the
L
E
A
CH
protoc
o
l
[
16
]
an
d
t
o
s
e
l
ec
t
the
c
l
us
ter
he
ad
no
de
wi
th
h
i
gh
en
erg
y
a
nd
to
en
ha
nc
e
th
e
l
i
f
e
t
i
m
e
of
ne
t
wor
k
,
a
n
e
w
s
c
he
m
e
of
s
el
ec
t
i
ng
th
e
c
l
us
ter
he
a
d
no
de
i
s
prop
o
s
ed
b
y
tak
i
ng
t
he
i
de
a
f
r
om
the
w
ork
gi
v
en
i
n
[8]
.
T
he
s
i
nk
/B
S
wi
l
l
broadc
as
t
t
he
i
nf
orm
ati
on
;
t
he
n
od
es
are
c
l
as
s
i
f
i
e
d
i
nto
di
f
f
erent
l
e
v
el
s
b
as
ed
on
t
he
s
tr
en
gth
of
the
i
nf
orm
ati
on
an
d
t
he
di
s
tan
c
e
f
r
om
the
s
i
nk
as
l
e
v
e
l
1,
l
e
v
e
l
2
as
s
ho
wn
i
n
the
F
i
gu
r
e
1.
T
he
no
de
ne
arer
t
o
th
e
ba
s
e
s
tat
i
o
n
h
as
th
e
l
es
s
er
l
e
v
e
l
n
um
be
r
an
d
m
ore
c
ha
nc
e
of
s
el
ec
ti
ng
as
C
H (
Cl
us
ter H
ea
d).
F
i
gu
r
e
1.
E
ne
r
g
y
l
e
v
e
l
c
l
as
s
i
f
i
c
ati
on
T
he
w
a
i
t t
i
m
e o
f
th
e n
od
e i
s
r
ep
r
es
en
t
ed
us
i
ng
the
f
orm
ul
a
:
=
[
(
1
−
)
]
+
(
−
1
)
+
/
(
10
)
w
he
r
e
E
r
=
r
es
i
d
ua
l
en
er
g
y
of
no
de
i
;
E
i
=
i
ni
t
i
a
l
en
erg
y
of
no
de
i
;
N=
nu
m
be
r
of
n
od
es
;
L
i
=
l
ev
el
nu
m
be
r
of
no
d
e
i
;
I
D
i
=
Id
en
t
i
f
i
c
ati
on
nu
m
be
r
of
n
od
e
i
.
F
r
om
the
(
9
)
,
i
t
i
s
t
o
i
nf
er
t
ha
t
th
e
m
e
m
be
r
no
de
wi
t
h
m
ore
en
erg
y
wi
l
l
broa
dc
as
t
the
m
es
s
ag
e
m
ore
qu
i
c
k
l
y
whe
n
c
om
p
ared
to
o
the
r
no
de
s
.
If
t
w
o
or
m
ore
n
od
es
ha
v
e
s
am
e
en
erg
y
i
n
d
i
ff
erent
en
erg
y
l
e
v
e
l
s
,
th
en
the
no
d
e
wi
t
h
hi
g
he
r
e
ne
r
g
y
l
e
v
el
i
s
c
ho
s
en
pr
i
m
aril
y
t
o
tr
an
s
m
i
t
the
m
es
s
ag
e.
P
r
oc
es
s
2
ex
p
l
ai
ns
a
bo
ut
th
e
s
tep
s
i
n
v
o
l
v
ed
i
n s
e
l
ec
t
i
ng
t
he
c
l
us
t
er he
ad
n
od
e
.
Pr
oc
es
s
2
:
T
he
f
ol
l
o
w
i
ng
are th
e s
tep
s
f
ol
l
o
w
e
d t
o
s
el
ec
t
the
Cl
us
ter Head
(
CH)
no
de
.
1)
Ini
t
i
a
l
l
y
d
urin
g
s
et
ti
n
g
up
of
ne
t
wor
k
,
ba
s
e
s
tat
i
on
bro
a
dc
as
ts
the
m
es
s
ag
es
to
a
l
l
the
no
d
es
.
E
v
er
y
no
d
e
wi
l
l
k
no
w
th
ei
r
en
erg
y
L
e
v
el
nu
m
be
r
L
i
us
i
ng
t
he
s
tr
en
gth
of
the
r
ec
ei
v
ed
po
wer
.
T
he
n,
c
al
c
ul
ate
T
i
us
i
ng
L
i
an
d
en
erg
y
l
e
v
e
l
.
2)
Node
s
(
m
f
or
ev
er
y
r
ou
n
d)
w
i
t
h
h
i
g
he
r
T
i
are
s
el
ec
te
d
as
c
l
us
ter
h
ea
d
no
de
s
.
A
c
c
ordi
n
gl
y
us
i
ng
CS
MA
(
C
arr
i
er
S
en
s
e M
u
l
ti
pl
e A
c
c
es
s
)
MA
C (
M
ed
i
um
A
c
c
es
s
Con
tr
ol
)
prot
oc
o
l
CH
w
i
l
l
broadc
as
t a
d
v
erti
s
em
en
t m
es
s
ag
e (AD
V
)
i
n t
i
m
e w
i
t
h
T
i
.
CH
CH
CH
S
I
N
K
/
BS
C
L
U
S
T
E
R
H
E
A
D
N
O
D
E
M
E
M
B
E
R
N
O
D
E
LEV
EL
1
LEV
EL
2
LEV
EL
3
Evaluation Warning : The document was created with Spire.PDF for Python.
◼
IS
S
N: 16
93
-
6
93
0
T
E
L
KO
M
NIK
A
V
ol
.
17
,
No
.
4
,
A
ug
us
t
20
19
:
1
6
04
-
1
6
14
1610
3)
Depe
nd
i
ng
on
th
e
s
tr
en
g
th
of
the
r
ec
ei
v
ed
s
i
gn
al
,
ev
er
y
m
e
m
be
r
no
de
ot
he
r
th
an
CH
wi
l
l
de
term
i
ne
i
t's
C
H f
or the
n
e
x
t round
.
4)
O
nc
e
ag
ai
n
us
i
ng
CS
MA
M
A
C
protoc
o
l
e
v
er
y
no
n
CH
no
de
wi
l
l
tr
a
ns
m
i
t
a
j
oi
n
-
r
e
qu
es
t
ba
c
k
to
i
ts
c
h
os
en
CH
.
5)
Us
i
ng
T
DMA
(
T
i
m
e
Di
v
i
s
i
o
n
Mu
l
ti
pl
e
A
c
c
es
s
)
CH
no
de
w
i
l
l
s
c
he
du
l
e
f
or
da
ta
tr
an
s
m
i
s
s
i
on
wi
th
i
n t
he
c
l
us
ter.
T
he
un
i
f
orm
di
s
tr
i
bu
t
i
on
of
CH
f
or
th
e
e
nt
i
r
e
n
et
w
or
k
i
s
en
s
ured,
whe
n
a
n
od
e
r
ec
ei
v
es
the
s
tr
on
g
s
i
g
na
l
of
A
D
V
m
es
s
ag
e,
i
t
wi
l
l
s
urr
en
d
er
th
e
op
p
ortun
i
t
y
to
turn
ou
t
t
o
be
CH
w
h
i
c
h
av
o
i
ds
t
he
CH
to
ge
t c
l
os
e.
3
.
5
.
D
ata
A
g
g
r
egat
ion
S
c
h
eme
T
he
c
l
us
ter
he
ad
pe
r
i
od
i
c
a
l
l
y
r
ec
ei
v
es
t
he
i
nf
or
m
ati
on
f
r
o
m
the
m
e
m
be
r
no
de
s
i
n
the
ne
t
w
ork
.
T
he
c
ol
l
ec
ti
v
e
da
t
a
r
ec
ei
v
e
d
b
y
t
h
e
CH
wi
l
l
be
tr
a
ns
m
i
tte
d
to
th
e
s
i
nk
.
S
eq
ue
nt
i
a
l
l
y
to
av
o
i
d
t
he
d
ata
r
e
du
n
da
n
c
y
whi
c
h
r
es
u
l
ts
i
n
du
pl
i
c
ati
on
of
da
ta
a
nd
r
e
du
c
e
the
en
erg
y
c
on
s
um
pti
on
i
n
tr
a
ns
m
i
s
s
i
on
s
,
a
da
ta
ag
gre
ga
t
i
o
n
s
c
he
m
e
i
s
propos
ed
tak
i
ng
the
i
de
a
f
r
om
the
wor
k
gi
v
en
i
n
[1
4].
A
d
ata
ag
gre
ga
t
i
on
s
c
he
m
e
ha
s
b
ee
n
i
m
pl
em
en
ted
am
on
g
CHs
us
i
ng
th
e
c
on
c
ep
t
of
s
i
m
i
l
arit
y
f
un
c
ti
on
.
E
uc
l
i
de
a
n
di
s
t
an
c
e
f
orm
ul
a
i
s
us
ed
wi
th
s
i
m
i
l
ari
t
y
f
un
c
t
i
on
.
CH
c
ol
l
ec
ts
al
l
t
he
da
t
a
tr
a
ns
m
i
tte
d
f
r
om
i
t’
s
m
e
m
be
r
no
de
s
an
d
ac
c
um
ul
ate
s
as
a
s
et
of
da
t
a
c
a
l
l
e
d
v
ec
tor
.
T
he
c
o
m
pa
r
i
s
on
s
of
tw
o
v
ec
t
ors
are
pe
r
f
orm
ed
us
i
ng
s
i
m
i
l
arit
y
f
un
c
ti
on
an
d
i
f
t
w
o
v
ec
tors
are
f
ou
nd
to
be
al
i
k
e
the
n
CH
wi
l
l
tr
an
s
m
i
t
on
l
y
o
ne
d
ata
i
n
p
l
ac
e
of
bo
th
t
o
th
e
s
i
nk
.
T
hi
s
proc
es
s
av
o
i
ds
t
he
da
ta
r
ed
u
nd
a
nc
y
wh
i
c
h
i
n
turn
r
e
du
c
es
t
he
en
e
r
g
y
c
on
s
um
pti
on
i
n t
he
n
et
w
ork
4
.
S
i
mu
latio
n
T
hi
s
s
ec
ti
on
pres
en
ts
s
i
m
u
l
at
i
on
m
od
el
,
s
i
m
ul
ati
on
p
a
r
am
ete
r
i
np
uts
an
d
p
erf
orm
an
c
e
pa
r
am
ete
r
s
.
4
.
1
.
S
imu
latio
n
M
o
d
el
A
n
od
e
i
s
c
on
s
i
d
ered
to
be
de
ad
or
al
i
v
e
de
p
en
d
i
n
g
o
n
the
a
v
a
i
l
ab
l
e
en
erg
y
.
If
the
no
de
en
erg
y
r
ed
uc
es
to
0,
t
he
n
i
t
i
s
c
on
s
i
de
r
ed
as
a
de
ad
n
od
e.
S
i
m
ul
tan
e
ou
s
l
y
i
n
the
ne
t
w
ork
,
i
f
the
c
ou
nt
of
de
ad
n
od
es
ex
c
e
ed
s
a
c
ut
o
ff
v
al
u
e,
the
n
the
en
t
i
r
e
ne
t
wor
k
i
s
s
ai
d
to
be
de
c
ea
s
ed
.
Net
w
ork
en
v
i
r
o
nm
en
t
di
s
c
us
s
ed
i
n
S
ec
ti
on
3
i
s
s
i
m
ul
at
ed
f
or
a
na
l
y
z
i
ng
th
e
p
erf
or
m
an
c
e
o
f
c
l
us
teri
n
g s
c
he
m
e.
T
he
s
i
m
ul
ati
on
s
w
er
e
c
arr
i
ed
o
ut
us
i
ng
MA
T
LA
B
a
nd
the
pe
r
f
or
m
an
c
es
of
LE
A
CH
an
d
the
propos
e
d
F
B
C
al
go
r
i
t
h
m
w
ere
a
na
l
y
z
e
d
i
n
term
s
of
the
nu
m
be
r
of
de
ad
an
d
a
l
i
v
e
no
de
s
,
Num
be
r
of
c
l
us
ter
us
i
ng
S
S
E
an
d
th
e
tot
a
l
en
erg
y
.
T
he
s
en
s
or
no
d
es
w
ere
r
an
do
m
l
y
de
pl
o
y
e
d
i
n
th
e
r
eg
i
on
S
an
d
are
ab
l
e
to
c
om
m
un
i
c
ate
wi
th
e
ac
h
oth
er.
W
e
as
s
u
m
e
tha
t
th
e
ba
s
e
s
tat
i
on
i
s
bu
i
l
t
at
t
wo
d
i
f
f
erent
po
s
i
ti
o
ns
(
25
25
50
)
an
d
(
50
50
100
)
.
F
or
s
i
m
ul
ati
o
ns
,
the
nu
m
be
r
of
s
en
s
or
no
de
s
N
s
et
w
er
e
10
0
an
d
wi
th
da
t
a
pa
c
k
et
s
i
z
e
of
40
0
bi
ts
f
or
ev
er
y
tr
an
s
m
i
s
s
i
on
ti
m
e.
T
he
i
ni
t
i
al
e
ne
r
g
y
of
ea
c
h
n
od
e
a
nd
el
ec
tr
on
i
c
en
erg
y
s
et
a
r
e
0.
5
J
an
d
50
nJ
/b
i
t
r
es
pe
c
t
i
v
el
y
.
In
ad
d
i
ti
on
to
thi
s
,
th
e
e
ne
r
g
y
of
t
he
ba
s
e
s
tat
i
on
i
s
a
s
s
u
m
ed
to
be
un
l
i
m
i
ted
as
i
t i
s
s
o
l
ar p
o
w
e
r
ed
.
4
.
2
.
S
imu
latio
n
P
a
r
amet
er
s
T
he
s
i
m
ul
ati
on
i
np
uts
are
s
ho
w
n
i
n
T
ab
l
e
1
.
T
he
pe
r
f
o
r
m
an
c
e
pa
r
am
ete
r
s
are
ex
pl
a
i
ne
d
as
f
ol
l
o
w
s
.
−
Num
be
r
of
c
l
us
ters
:
E
l
bo
w
m
eth
od
whi
c
h
i
s
an
ef
f
i
c
i
e
nt
a
l
g
orit
hm
i
s
em
pl
o
y
e
d
t
o
f
i
nd
o
ut
the
n
um
be
r
of
c
l
us
ter us
i
ng
S
S
E
p
aram
ete
r
.
−
Li
f
e
c
y
c
l
e:
T
he
s
tud
y
of
l
i
f
e
c
y
c
l
e
of
the
no
de
s
w
i
t
h
r
es
pe
c
t
to
t
he
i
r
i
n
i
ti
al
e
ne
r
g
y
an
d
po
s
i
t
i
on
of
th
e b
as
e s
tat
i
on
s
are c
on
du
c
t
ed
.
−
E
ne
r
g
y
c
on
s
um
pti
on
:
In
da
t
a
a
gg
r
eg
ati
on
ph
as
e
t
he
C
H
ag
greg
ate
s
t
he
da
t
a
an
d
tr
an
s
m
i
t
s
to
th
e
s
i
nk
.
T
he
c
om
pa
r
ati
v
e
a
na
l
y
s
i
s
of
c
on
s
um
pti
on
of
en
erg
y
wi
t
h
a
nd
w
i
t
ho
ut
da
ta
ag
gre
ga
t
i
on
i
s
pe
r
f
orm
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NIK
A
IS
S
N: 1
69
3
-
6
93
0
◼
Cl
us
teri
ng
an
d
da
t
a a
gg
r
eg
ati
o
n s
c
he
m
e
i
n u
nd
erw
ate
r
wi
r
el
es
s
...
(
V
a
ni
K
r
i
s
h
na
s
wamy
)
1611
T
ab
l
e 1
. T
he
P
aram
ete
r
s
f
o
r
S
i
m
ul
ati
on
V
a
r
iab
le
P
a
r
a
m
e
t
e
r
V
a
lue
S
D
is
t
r
ibu
t
ion
a
r
e
a
1
0
0
x
1
0
0
x
100m
3
n
N
u
m
b
e
r
o
f
n
o
d
e
s
100
l
len
g
t
h
o
f
e
v
e
r
y
d
a
t
a
4
0
0
b
it
p
a
c
k
a
g
e
E
e
l
e
c
E
n
e
r
g
y
c
o
s
t
o
f
d
a
t
a
a
g
g
r
e
g
a
t
ion
1
0
mJ
/
b
it
E
DA
Fac
t
o
r
s
in
o
b
jec
t
iv
e
f
u
n
c
t
ion
5
mJ
/
b
it
α
,
β
,
γ
0
.
2
,
0
.
3
,
0
.
5
5.
Re
sult
A
n
al
ys
is
T
hi
s
s
ec
ti
on
pres
en
ts
the
c
om
pa
r
ati
v
e
an
al
y
s
i
s
of
pro
po
s
ed
s
c
he
m
e
(
de
no
t
ed
as
F
B
C)
an
d
LE
A
CH
al
g
orit
hm
[1
6].
5
.
1
.
D
etermin
atio
n
of
Clu
ster
A
mo
u
n
t
E
x
pe
r
i
m
en
ts
wer
e
c
arr
i
e
d
ou
t
f
or
50
i
terat
i
o
ns
us
i
n
g
proc
es
s
(
1).
T
he
v
al
ue
o
f
S
S
E
wi
th
i
n
e
ac
h
c
l
us
ter
was
de
t
erm
i
ne
d
b
y
th
e
v
aria
bl
e
m
.
F
i
g
ure
2
de
pi
c
ts
the
r
e
l
at
i
o
ns
hi
p
be
t
ween
S
S
E
a
n
d
the
nu
m
be
r
of
c
l
us
ters
.
T
he
gra
ph
s
h
o
w
s
tha
t
w
h
en
the
nu
m
be
r
of
c
l
us
ter
am
ou
nts
to
2,
S
S
E
ha
s
h
i
g
he
r
v
a
l
u
e.
Cons
e
qu
e
ntl
y
,
n
um
be
r
of
c
l
us
ters
s
et
to
2
for
c
on
du
c
ti
ng
al
l
th
e
ex
pe
r
i
m
en
ts
.
F
i
gu
r
e
2
.
R
el
a
ti
o
n b
et
w
e
en
nu
m
be
r
s
of
c
l
us
ters
v
s
S
S
E
5
.
2
.
S
t
u
d
y
of
Lif
e
C
yc
le
T
he
l
i
f
e
c
y
c
l
e
of
the
U
W
A
S
N
i
s
gre
atl
y
i
nf
l
ue
nc
ed
b
y
t
wo
f
ac
tors
.
(
1)
T
he
i
n
i
ti
al
e
ne
r
g
y
of
the
n
od
es
.
(
2)
T
he
po
s
i
ti
on
of
the
B
as
e
S
tat
i
on
(
B
S
)
.
In
t
he
ex
pe
r
i
m
en
t
c
o
nd
uc
te
d
i
t
w
as
de
c
i
d
ed
to
ha
v
e
t
w
o
di
f
f
erent
s
i
t
ua
t
i
on
s
:
f
i
r
s
t
c
on
s
i
d
eri
ng
t
he
f
i
r
s
t
f
ac
tor
where
ev
er
y
no
d
e
i
n
the
ne
t
w
ork
ha
s
the
s
am
e
i
ni
ti
al
e
ne
r
g
y
as
0.5
J
an
d
s
e
c
on
dl
y
un
i
f
orm
l
y
di
s
tr
i
bu
t
i
n
g
the
en
erg
y
am
on
g t
he
n
od
es
be
t
ween
0.3
t
o 0
.
6
J
.
T
he
po
s
i
ti
o
ns
of
the
B
S
w
a
s
v
arie
d
at
(
25
,
25
,
5
0)
whi
c
h
w
as
n
ea
r
er
to
t
he
n
et
w
o
r
k
area
an
d
at
(
50
,
5
0,1
0
0)
whi
c
h
was
c
on
s
i
de
r
ab
l
y
f
ar
f
r
om
the
ne
t
wor
k
area.
T
he
en
t
i
r
e
ex
pe
r
i
m
en
t
was
r
ep
ea
t
ed
f
or
LE
A
CH
a
l
go
r
i
thm
an
d
F
B
C.
It
was
fou
nd
t
ha
t
t
he
d
ea
th
of
a
f
e
w
n
od
es
i
n
th
e
ne
t
w
ork
area
d
i
d
n
ot
ha
v
e
an
i
m
m
en
s
e
i
m
pa
c
t
on
ne
t
w
ork
l
i
f
eti
m
e,
es
pe
c
i
a
l
l
y
w
h
en
th
e
r
ed
un
da
nc
y
of
th
e
ne
t
wor
k
c
ov
erage
was
m
ore.
A
f
ter
tha
t
we
c
on
s
i
de
r
e
d
de
ad
no
d
es
w
i
th
ne
t
w
ork
l
i
f
eti
m
e.
F
i
gu
r
es
3
an
d
4
de
p
i
c
ts
the
r
el
a
ti
on
s
hi
p
be
t
w
ee
n
t
he
p
erc
en
tag
e
of
de
a
d
no
de
s
an
d
the
r
ou
n
ds
.
F
i
gu
r
es
3
a
nd
4
s
ho
w
s
th
at
the
po
s
i
ti
o
ns
of
the
B
S
are
di
f
f
erent,
i
r
r
es
pe
c
ti
v
e
of
th
e
f
ac
t
tha
t
the
n
et
w
ork
no
d
es
c
o
nta
i
ne
d
a
n
e
qu
al
i
n
i
t
i
al
e
n
erg
y
.
In
bo
th
the
s
i
t
ua
t
i
on
s
the
pro
po
s
ed
al
g
orit
hm
F
B
C
ac
hi
e
v
ed
b
et
ter
r
es
ul
ts
w
h
en
c
om
pa
r
ed
to
the
L
E
A
CH
a
l
go
r
i
th
m
r
es
ul
ti
ng
i
n
prol
o
ng
i
ng
th
e
ex
pi
r
y
of
th
e
n
od
es
.
T
he
m
os
t
i
m
po
r
tan
t
r
e
as
on
f
or
th
i
s
i
s
tha
t,
i
n
ou
r
pro
po
s
ed
al
g
orit
hm
the
s
el
ec
ti
o
n
of
c
l
us
ter
h
ea
ds
w
ere
ac
h
i
e
v
ed
ef
f
ec
ti
v
el
y
.
T
hi
s
i
n
t
urn
de
c
r
ea
s
ed
th
e
c
on
s
um
pti
on
of
the
en
erg
y
f
or
t
he
c
om
m
un
i
c
ati
on
be
t
w
e
en
t
he
no
d
es
.
S
i
m
ul
tan
eo
us
l
y
the
Evaluation Warning : The document was created with Spire.PDF for Python.
◼
IS
S
N: 16
93
-
6
93
0
T
E
L
KO
M
NIK
A
V
ol
.
17
,
No
.
4
,
A
ug
us
t
20
19
:
1
6
04
-
1
6
14
1612
no
de
s
wi
th
hi
gh
ou
ts
ta
nd
i
n
g
en
erg
y
ha
d
th
e
pref
erenc
e
of
be
i
ng
c
l
us
ter
h
ea
ds
,
w
hi
c
h
b
al
an
c
ed
the
c
o
ns
um
pti
on
of
th
e
e
ne
r
g
y
i
n
ea
c
h
n
et
w
ork
no
d
e
a
nd
t
he
r
e
b
y
a
v
o
i
di
ng
th
e e
ar
l
y
de
ath
of
th
e
no
de
s
.
He
nc
e
t
he
l
i
f
es
pa
n
of
the
ne
t
wor
k
was
ex
te
nd
ed
.
It
was
al
s
o
ob
s
er
v
e
d
f
r
om
the
gra
ph
tha
t
the
B
S
ne
arer
t
o
t
he
n
et
w
ork
area
pe
r
f
or
m
ed
be
tt
er
w
h
en
c
om
pa
r
ed
to
the
B
S
l
oc
ate
d
at
a
l
arger
d
i
s
tan
c
e
i
n t
he
ne
t
w
o
r
k
area.
In
bo
th
th
e
s
i
t
ua
t
i
o
ns
th
e
pr
op
os
e
d
al
g
orit
hm
pe
r
f
or
m
ed
w
el
l
when
c
om
pa
r
ed
t
o L
E
A
CH.
F
i
gu
r
e
3
.
D
ea
d
no
d
es
v
s
r
o
un
ds
wi
th
s
am
e
en
erg
y
at
th
e
B
S
(
25
, 2
5
, 5
0)
F
i
gu
r
e
4
.
D
ea
d
no
d
es
v
s
r
o
un
ds
wi
th
s
am
e
en
erg
y
at
th
e
B
S
(
50
, 5
0
,10
0)
P
r
ac
ti
c
a
l
l
y
w
h
en
w
e
c
on
s
i
de
r
the
op
t
i
m
al
c
os
t
po
i
nt
,
s
m
al
l
er
r
an
ge
c
on
tr
ol
s
the
r
ed
un
da
nc
y
of
th
e
n
et
w
o
r
k
.
Dur
i
ng
s
uc
h
s
i
tua
t
i
o
n,
to
as
s
ure
th
at
t
he
en
t
i
r
e
ne
t
w
ork
i
s
c
on
ne
c
te
d,
on
a
c
on
d
i
t
i
o
n
tha
t
al
l
no
de
s
i
n
the
n
et
w
ork
s
ta
y
al
i
v
e
f
or
a
l
o
ng
er
du
r
ati
on
.
E
v
en
when
on
e
n
od
e
de
m
i
s
e,
the
qu
al
i
t
y
of
s
erv
i
c
e
a
c
r
os
s
the
ne
t
wor
k
i
s
m
o
m
en
tari
l
y
r
ed
uc
e
d
r
es
ul
ti
ng
i
n f
oc
us
i
n
g m
ore i
nte
r
es
t o
n t
h
e l
i
v
i
n
g rate
of
U
W
A
S
N.
F
i
gu
r
es
5
a
nd
6
d
ep
i
c
t
th
e
r
el
at
i
o
n
be
t
wee
n
r
ate
of
s
urv
i
v
ab
i
l
i
t
y
of
no
de
s
an
d
the
ne
t
w
ork
l
i
f
e
c
y
c
l
e.
T
he
graphs
s
ho
w
th
at
w
h
en
c
o
m
pa
r
ed
to
LE
A
C
H
al
go
r
i
t
hm
,
F
B
C
ha
s
a
s
m
al
l
er
c
urv
ed
s
l
op
e
whi
c
h
i
nd
i
c
a
tes
th
at
t
he
proc
es
s
of
no
de
s
d
y
i
n
g
w
as
r
e
as
on
ab
l
y
p
l
ac
i
d.
T
hi
s
i
s
be
c
au
s
e
i
n
F
B
C,
bo
th
t
he
d
i
s
tan
c
e
an
d
en
erg
y
ar
e
c
on
s
i
d
ered
t
o
s
h
are
the
en
erg
y
c
on
s
u
m
pti
on
be
t
ween
e
ac
h
no
de
.
T
hu
s
as
s
urin
g
tha
t
no
ne
of
the
no
d
es
i
n
the
ne
t
wor
k
di
m
i
ni
s
he
d
th
e
i
r
e
ne
r
g
y
, u
l
ti
m
ate
l
y
th
e
l
i
f
es
pa
n o
f
th
e n
od
e
was
ex
ten
de
d
.
F
i
gu
r
e
5
.
N
od
es
a
l
i
v
e
v
s
r
o
un
ds
wi
th
s
am
e
en
erg
y
at
th
e
B
S
(
25
, 2
5
, 5
0)
F
i
gu
r
e
6
.
N
od
es
a
l
i
v
e
v
s
r
o
un
ds
wi
th
s
am
e
en
erg
y
at
th
e
B
S
(
50
, 5
0
,10
0)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NIK
A
IS
S
N: 1
69
3
-
6
93
0
◼
Cl
us
teri
ng
an
d
da
t
a a
gg
r
eg
ati
o
n s
c
he
m
e
i
n u
nd
erw
ate
r
wi
r
el
es
s
...
(
V
a
ni
K
r
i
s
h
na
s
wamy
)
1613
F
i
gu
r
e
7
de
pi
c
ts
t
he
c
on
s
um
pti
on
of
t
he
en
erg
y
i
n
t
he
ne
t
wor
k
,
c
on
s
i
de
r
i
ng
b
oth
the
s
c
he
m
es
of
wi
th
an
d
wi
t
ho
ut
d
ata
ag
greg
ati
on
.
T
he
o
v
era
l
l
c
on
s
um
pti
on
of
th
e
en
erg
y
i
n
th
e
ne
t
w
ork
wi
th
an
d
w
i
t
ho
ut
da
ta
ag
gr
eg
at
i
o
n
i
s
i
nd
i
c
at
ed
b
y
r
e
d
l
i
ne
an
d
b
l
ue
l
i
n
e
r
es
pe
c
ti
v
e
l
y
.
In
th
e
pro
po
s
ed
s
c
he
m
e
of
c
l
us
teri
ng
an
d
c
l
us
ter
he
a
d
s
el
ec
ti
o
n,
t
he
en
erg
y
i
s
s
av
ed
at
ea
c
h
ph
as
e.
T
he
da
ta
ag
grega
ti
on
a
nd
tr
a
ns
m
i
s
s
i
on
of
ag
gregat
ed
da
ta
to
t
he
B
S
i
s
pe
r
f
or
m
ed
b
y
c
l
us
ter
he
ad
n
od
es
.
A
l
s
o,
the
e
ne
r
g
y
i
s
s
a
v
ed
i
n
t
he
da
ta
a
gg
r
eg
ati
on
s
c
he
m
e
as
i
t
us
es
s
i
m
i
l
arit
y
f
un
c
ti
on
thr
ou
g
h
whi
c
h
i
t
r
ed
uc
es
the
n
um
be
r
of
du
p
l
i
c
at
e
da
t
a
tr
an
s
m
i
s
s
i
on
s
f
r
o
m
c
l
us
ter
-
he
a
ds
to
the
B
S
/s
i
nk
.
A
s
a
r
es
ul
t,
n
et
w
ork
s
us
i
n
g
c
l
us
t
erin
g
wi
th
da
t
a
ag
greg
ati
on
s
c
he
m
e d
ev
ou
r
l
es
s
en
erg
y
c
om
pa
r
ed
to
a
n
et
w
ork
wi
t
ho
ut
c
l
us
teri
ng
an
d
da
t
a a
g
gregat
i
o
n.
F
i
gu
r
e
7
.
E
ne
r
g
y
c
o
ns
um
pti
on
v
s
of
f
ered l
oa
d
6
.
Co
n
clus
ion
In
thi
s
s
tud
y
,
we
propos
e
a
ne
w
c
l
us
teri
ng
s
c
he
m
e
us
i
ng
f
u
z
z
y
l
og
i
c
c
on
s
i
de
r
i
ng
en
erg
y
an
d
the
prob
ab
i
l
i
t
y
of
be
l
on
g
i
ng
ne
s
s
of
the
s
en
s
or
no
d
es
.
T
he
c
l
us
t
er
he
a
d
s
el
ec
ti
o
n
i
s
pe
r
f
or
m
ed
ba
s
ed
on
t
he
f
ac
tors
s
uc
h
as
the
en
erg
y
an
d
di
s
t
an
c
e.
F
urth
er,
us
i
ng
s
i
m
i
l
ari
t
y
f
un
c
ti
on
th
e
ag
grega
ted
da
ta
at
CH
i
s
tr
an
s
m
i
tte
d
to
t
he
B
S
.
A
s
i
m
ul
ati
o
n
r
es
ul
t
s
ho
w
s
tha
t
t
he
propos
e
d
s
c
he
m
e
pe
r
f
or
m
s
be
tte
r
i
n
pr
ol
o
ng
i
ng
t
he
l
i
f
es
pa
n
of
the
ne
t
wor
k
.
A
s
a
f
utu
r
e
en
ha
nc
em
en
t,
v
ario
us
P
S
O
(
P
arti
c
l
e
S
war
m
O
pti
m
i
z
ati
o
n)
a
l
tern
ati
v
es
c
ou
l
d
be
us
ed
to
r
es
ol
v
e
the
pro
bl
em
of
c
l
us
ter
h
ea
d
s
e
l
ec
ti
on
a
nd
an
a
l
y
s
i
s
c
ou
l
d
b
e
c
arr
i
e
d
ou
t
b
as
ed
on
the
i
r
pe
r
f
or
m
an
c
es
.
In
ad
d
i
t
i
on
to
th
i
s
v
ar
i
ou
s
i
n
i
ti
al
c
l
u
s
teri
ng
al
g
orit
hm
s
c
ou
l
d
b
e
de
s
i
gn
e
d
to
de
c
r
ea
s
e
th
e
r
ed
un
d
an
t
d
ata
.
T
hi
s
w
o
ul
d
r
es
ul
t
i
n
t
he
e
ne
r
g
y
be
i
n
g
c
on
s
er
v
e
d
an
d
i
n
turn
i
nc
r
ea
s
i
ng
th
e
l
i
f
es
pa
n
of
the
ne
t
w
ork
.
W
e
are
f
oc
u
s
i
ng
to
wor
k
on
v
ario
us
d
ata
ag
greg
ati
n
s
c
he
m
es
to
ac
hi
ev
e b
e
tte
r
ac
c
urac
y
of
da
ta
.
Ref
er
en
ce
s
[1
]
Ak
y
i
l
d
i
z
T
F,
Po
m
p
i
l
i
D
,
M
e
l
o
d
i
a
T
.
Und
e
rw
a
te
r
a
c
o
u
s
ti
c
s
e
n
s
o
r
n
e
tw
o
rk
s
:
re
s
e
a
rc
h
c
h
a
l
l
e
n
g
e
s
.
Ad
Ho
c
Ne
tw
o
rk
s
.
2
0
0
5
;
3
(
3
):
257
-
2
7
9
.
[2
]
P
V
Am
o
l
i
.
An
O
v
e
rv
i
e
w
o
n
Curre
n
t
Re
s
e
a
r
c
h
e
s
o
n
Und
e
r
w
a
te
r
Se
n
s
o
r
Net
w
o
rk
s
:
Ap
p
l
i
c
a
l
ti
o
n
s
.
Cha
l
l
e
n
g
e
s
a
n
d
Fu
tu
r
e
T
re
n
d
s
.
In
te
r
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tr
i
c
a
l
a
n
d
Com
p
u
te
r
En
g
i
n
e
e
r
i
n
g
.
2
0
1
6
;
6
(
3
)
:
9
5
5
.
[3
]
I
F
Ak
y
i
l
d
i
z
,
D
Po
m
p
i
l
i
,
T
M
e
l
o
d
i
a
.
Sta
te
-
of
-
th
e
-
a
rt
i
n
p
r
o
to
c
o
l
re
s
e
a
rc
h
f
o
r
u
n
d
e
rw
a
te
r
a
c
o
u
s
ti
c
s
e
n
s
o
r
n
e
tw
o
rk
s
.
W
U
W
N
e
t
0
6
.
2
0
0
6
:
7
-
16.
[4
]
Rak
e
s
h
Ku
m
a
r
a
n
d
Nav
d
e
e
p
Si
n
g
h
.
A
Su
rv
e
y
o
n
Dat
a
A
g
g
re
g
a
ti
o
n
a
n
d
Clu
s
te
r
i
n
g
S
c
h
e
m
e
s
i
n
Und
e
rw
a
te
r
Se
n
s
o
r
Net
w
o
rk
s
.
In
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
G
ri
d
a
n
d
Di
s
tri
b
u
te
d
C
o
m
p
u
ti
n
g
.
2
0
1
4
;
7
:
29
-
52
.
[5
]
Ku
m
a
r
R
,
Si
n
g
h
N
,
A
s
u
rv
e
y
o
n
d
a
t
a
a
g
g
re
g
a
ti
o
n
a
n
d
c
l
u
s
te
r
i
n
g
s
c
h
e
m
e
s
i
n
u
n
d
e
rw
a
te
r
s
e
n
s
o
r
n
e
tw
o
rk
s
.
I
n
t.
J
.
G
ri
d
D
i
s
tr
i
b
.
C
o
m
p
u
t
.
2
0
1
4
;
7
(
6
):
29
-
52
.
[6
]
Y
u
J
Y
,
Cho
n
g
PHJ
.
A
s
u
rv
e
y
o
f
c
l
u
s
t
e
ri
n
g
s
c
h
e
m
e
s
fo
r
m
o
b
i
l
e
a
d
h
o
c
n
e
tw
o
rk
s
.
IEEE
Com
m
u
n
i
c
a
t
i
o
n
s
Su
r
v
e
y
s
a
n
d
T
u
t
o
ri
a
l
s
.
2
0
0
5
;
7
(
1
)
:
32
-
48.
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