I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
,
p
p
.
254
~
261
I
SS
N:
2
502
-
4
7
52
,
DOI
:
1
0
.
1
1
5
9
1
/ijee
cs
.v
3
8
.
i
1
.
pp
254
-
2
6
1
254
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ur
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l ho
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:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
Dev
elo
pment of
cl
ustering
wi
th
Ba
y
esia
n alg
o
rithm f
o
r optima
l
ro
ute
forma
tion
i
n so
ft
wa
re
-
defi
ne
d radio
under
wa
t
er WS
N
Ano
o
p Sre
er
a
j
,
Vij
a
y
a
l
a
k
s
hm
i P
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Vela
y
utha
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Ra
j
endra
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El
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s a
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d
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o
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c
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En
g
i
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i
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g
,
V
e
l
s I
n
st
i
t
u
t
e
o
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c
i
e
n
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e
,
T
e
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h
n
o
l
o
g
y
a
n
d
A
d
v
a
n
c
e
d
S
t
u
d
i
e
s,
C
h
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n
n
a
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ju
n
19
,
2
0
2
4
R
ev
is
ed
Oct
8
,
2
0
2
4
Acc
ep
ted
Oct
30
,
2
0
2
4
Un
d
e
rwa
ter
wire
les
s
se
n
so
r
n
e
two
rk
s
(UWS
Ns
)
h
a
v
e
re
c
e
n
tl
y
o
ffe
re
d
c
h
a
n
c
e
s
to
in
v
e
stig
a
te o
c
e
a
n
s
a
n
d
th
u
s
e
n
h
a
n
c
e
t
h
e
u
n
d
e
rwa
ter
w
o
r
ld
.
WS
Ns
a
re
imp
e
ra
ti
v
e
fo
r
d
isc
o
v
e
ri
n
g
th
e
o
c
e
a
n
re
g
i
o
n
.
S
o
ftwa
r
e
-
d
e
fin
e
d
n
e
two
rk
i
n
g
(S
DN
)
imp
ro
v
e
s
fle
x
ib
il
it
y
a
n
d
u
se
s
th
e
c
lu
ste
rin
g
m
e
th
o
d
to
imp
ro
v
e
li
fe
sp
a
n
.
Th
is
a
rti
c
le
in
tro
d
u
c
e
s
th
e
De
v
e
lo
p
m
e
n
t
o
f
a
c
lu
ste
rin
g
p
ro
c
e
ss
with
a
Ba
y
e
sia
n
a
l
g
o
r
it
h
m
(CP
BA)
fo
r
o
p
ti
m
a
l
r
o
u
te
f
o
r
m
a
ti
o
n
i
n
so
ftwa
re
-
d
e
fin
e
d
ra
d
i
o
UWS
N
.
Th
e
c
lu
ste
rin
g
c
o
n
c
e
p
t
imp
r
o
v
e
s
e
n
e
rg
y
e
fficie
n
c
y
;
h
o
we
v
e
r,
c
lu
ste
r
h
e
a
d
(CH)
se
lec
ti
o
n
is
c
h
a
l
len
g
i
n
g
.
T
h
e
p
re
se
n
t
c
lu
ste
rin
g
m
e
c
h
a
n
ism
s
c
o
u
l
d
b
e
m
o
re
su
c
c
e
ss
fu
l
in
su
it
a
b
l
y
a
ss
ig
n
i
n
g
t
h
e
n
o
d
e
'
s
e
n
e
rg
y
.
Th
is
m
e
c
h
a
n
ism
u
ti
li
z
e
s
a
sla
p
sw
a
rm
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
to
p
ic
k
o
u
t
t
h
e
o
p
ti
m
a
l
CH
b
y
n
o
d
e
e
n
e
rg
y
a
n
d
d
ista
n
c
e
a
m
o
n
g
in
t
e
r
-
c
lu
ste
r
a
s
we
ll
a
s
in
tra
-
c
lu
ste
r.
In
a
d
d
it
i
o
n
,
th
e
Ba
y
e
sia
n
a
l
g
o
ri
th
m
se
lec
ts
th
e
b
e
st
fo
rwa
rd
e
r
fr
o
m
se
n
d
e
r
to
b
a
se
sta
ti
o
n
.
Th
u
s,
e
n
h
a
n
c
e
s
e
ffici
e
n
c
y
.
Th
e
sim
u
latio
n
re
su
lt
s
d
e
m
o
n
stra
te
th
a
t
th
e
UWS
N
imp
ro
v
e
s
b
o
t
h
th
e
2
3
%
p
a
c
k
e
t
fo
rwa
rd
r
a
ti
o
a
n
d
0
.
0
1
4
j
o
u
le
e
n
e
r
g
y
.
F
u
rth
e
rm
o
re
,
it
m
in
imiz
e
s
th
e
30
%
n
e
two
r
k
d
e
lay
.
K
ey
w
o
r
d
s
:
B
ay
esian
alg
o
r
ith
m
B
ay
esian
alg
o
r
ith
m
C
lu
s
ter
in
g
Slap
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
So
f
twar
e
-
d
ef
in
e
d
n
etwo
r
k
in
g
U
n
d
er
wate
r
wir
eless
s
en
s
o
r
n
etwo
r
k
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Vijay
alak
s
h
m
i P
Dep
ar
tm
en
t o
f
E
lectr
o
n
ics an
d
C
o
m
m
u
n
icatio
n
E
n
g
in
ee
r
i
n
g
,
Vels I
n
s
titu
te
o
f
Scien
ce
T
ec
h
n
o
lo
g
y
an
d
Ad
v
a
n
ce
d
St
u
d
ies
C
h
en
n
ai,
T
am
il Na
d
u
,
I
n
d
ia
E
m
ail:
v
iji.s
e@
v
is
tas.ac
.
in
1.
I
NT
RO
D
UCT
I
O
N
Un
d
er
wate
r
wir
eless
s
en
s
o
r
n
etwo
r
k
s
(
UW
SN
s
)
p
lay
s
a
s
ig
n
if
ican
t
r
o
le
in
h
elp
in
g
h
u
m
a
n
s
in
v
esti
g
ate
d
ata
u
n
d
er
th
e
o
ce
an
.
UW
SNs
h
av
e
b
ee
n
f
o
r
m
u
lated
as
h
elp
f
u
l
m
eth
o
d
s
f
o
r
a
v
a
r
iety
o
f
ap
p
licatio
n
s
th
at
tak
e
p
lace
u
n
d
er
wate
r
,
s
u
ch
as
a
d
m
itti
n
g
en
o
l
o
g
y
o
b
s
er
v
atio
n
,
s
u
r
r
o
u
n
d
in
g
o
b
s
er
v
atio
n
,
ts
u
n
am
i
m
o
n
ito
r
in
g
[
1
]
,
an
d
u
n
d
er
wate
r
o
b
s
er
v
atio
n
[
2
]
.
U
W
SN
s
co
n
tain
s
ev
er
al
s
en
s
o
r
n
o
d
es
d
is
tr
ib
u
ted
in
th
e
u
n
d
e
r
wate
r
s
u
r
r
o
u
n
d
in
g
s
to
co
llect
d
ata
a
n
d
f
o
r
war
d
it
to
th
e
b
ase
s
tatio
n
(
BS
)
v
ia
th
e
c
o
n
tr
o
ller
.
Ho
wev
er
,
UW
SNs
f
ac
e
m
an
y
d
is
p
u
tes,
s
u
ch
as r
o
u
g
h
u
n
d
e
r
wa
ter
s
u
r
r
o
u
n
d
i
n
g
s
,
r
estricte
d
tr
an
s
m
is
s
io
n
r
an
g
e,
an
d
lo
wer
d
ata
r
ates
[
3
]
.
A
m
etah
eu
r
is
tic
o
p
tim
izatio
n
tech
n
iq
u
e
is
d
ev
elo
p
e
d
in
r
esp
o
n
s
e
to
th
e
co
llectiv
e
b
eh
av
io
r
o
f
s
alp
s
,
a
s
ea
cr
ea
tu
r
e.
T
h
e
ch
o
ice
o
f
clu
s
ter
h
ea
d
s
(
C
H
s
)
in
U
W
SNs
is
ess
en
tia
l
f
o
r
ef
f
e
ctiv
e
d
ata
r
o
u
tin
g
a
n
d
co
llectin
g
[
4
]
.
T
h
e
s
lap
s
war
m
o
p
tim
izatio
n
m
ig
h
t
b
e
m
o
d
if
ied
as
f
o
llo
ws
f
o
r
C
H
s
elec
tio
n
in
UW
SN
s
:
-
I
d
en
tify
th
e
o
b
jectiv
e
f
u
n
ctio
n
: a
s
ce
r
tain
wh
ich
o
b
jectiv
e
f
u
n
ctio
n
r
eq
u
ir
es o
p
tim
izatio
n
.
I
n
th
e
co
n
te
x
t o
f
C
H
s
e
lectio
n
,
th
is
m
ig
h
t
in
clu
d
e
in
cr
ea
s
in
g
n
etwo
r
k
lo
n
g
ev
i
ty
,
r
ed
u
cin
g
en
er
g
y
u
s
ag
e,
o
r
im
p
r
o
v
in
g
d
ata
tr
an
s
m
is
s
io
n
ef
f
icien
cy
[
5
]
.
E
ac
h
p
o
s
s
ib
le
C
H
s
elec
tio
n
s
o
lu
tio
n
s
h
o
u
ld
b
e
r
ep
r
esen
te
d
u
s
in
g
th
e
en
c
o
d
in
g
s
o
lu
tio
n
r
e
p
r
esen
tatio
n
.
T
h
is
m
ig
h
t
b
e
a
b
in
ar
y
v
ec
t
o
r
,
wit
h
ea
ch
en
t
r
y
d
e
n
o
tin
g
wh
eth
e
r
o
r
n
o
t
a
s
en
s
o
r
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
Dev
elo
p
men
t o
f c
lu
s
teri
n
g
w
ith
B
a
ye
s
ia
n
a
lg
o
r
ith
m
fo
r
o
p
ti
ma
l ro
u
te
fo
r
ma
tio
n
in
…
(
A
n
o
o
p
S
r
ee
r
a
j
)
255
n
o
d
e
h
as
b
ee
n
ch
o
s
en
to
b
e
a
C
H.
E
s
tab
li
s
h
a
s
war
m
o
f
s
alp
ag
en
ts
at
r
an
d
o
m
l
o
ca
tio
n
s
in
th
e
s
o
lu
tio
n
s
p
ac
e
as th
e
in
itializatio
n
s
tep
.
-
Ass
es
s
f
itn
ess
:
u
s
in
g
th
e
o
b
je
ctiv
e
f
u
n
ctio
n
as
a
g
u
id
e,
ass
ess
ea
ch
s
alp
'
s
f
itn
ess
with
in
th
e
s
war
m
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
r
elev
an
t
C
H
s
elec
tio
n
is
s
h
o
wn
b
y
t
h
i
s
f
itn
ess
v
alu
e
[
6
]
.
Up
d
ate
lo
ca
tio
n
:
u
s
in
g
its
cu
r
r
en
t
lo
ca
tio
n
,
v
elo
city
,
a
n
d
th
e
p
o
s
itio
n
s
o
f
o
th
er
s
alp
s
,
ea
ch
s
alp
in
th
e
s
war
m
s
h
o
u
ld
h
av
e
its
p
o
s
itio
n
u
p
d
ated
.
T
h
e
b
est s
o
lu
tio
n
d
ev
elo
p
ed
s
o
f
ar
h
as a
weig
h
ted
ef
f
ec
t th
at
d
ir
ec
ts
th
is
u
p
d
ate.
I
t
er
ate:
c
o
n
tin
u
e
u
n
til
a
s
to
p
p
in
g
r
e
q
u
ir
em
e
n
t
(
s
u
ch
as
c
o
n
v
e
r
g
en
ce
o
r
a
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
)
is
s
atis
f
ied
b
y
co
n
tin
u
in
g
th
e
p
r
o
ce
s
s
o
f
u
p
d
atin
g
lo
ca
tio
n
s
,
ass
ess
in
g
f
itn
ess
,
an
d
lo
o
k
in
g
f
o
r
b
etter
s
o
lu
tio
n
s
.
C
h
o
o
s
e
C
Hs:
B
a
s
ed
o
n
t
h
e
lo
ca
tio
n
s
o
f
s
alp
s
with
th
e
h
ig
h
est
f
itn
ess
v
alu
es,
ch
o
o
s
e
C
Hs
wh
en
th
e
alg
o
r
it
h
m
r
ea
ch
es th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
[
7
]
.
Op
tim
al
r
o
u
te
o
p
tio
n
s
a
n
d
e
v
a
d
in
g
th
e
r
o
u
te
d
if
f
er
en
ce
ar
e
n
ec
ess
ar
y
f
o
r
tr
a
n
s
m
is
s
io
n
to
a
ch
iev
e
th
e
r
ec
eiv
er
s
u
itab
ly
an
d
ac
cu
m
u
late
en
er
g
y
.
An
o
p
tim
al
r
o
u
t
in
g
m
eth
o
d
is
d
e
m
an
d
e
d
to
tr
an
s
m
i
t
d
ata
f
r
o
m
s
en
s
o
r
s
in
th
e
clu
s
ter
s
an
d
to
th
e
b
ase
s
tatio
n
v
ia
th
e
co
n
tr
o
ller
.
T
h
e
m
ajo
r
f
o
cu
s
is
d
ec
r
ea
s
in
g
en
er
g
y
u
tili
za
tio
n
an
d
in
cr
ea
s
in
g
life
tim
e.
C
h
o
o
s
in
g
t
h
e
b
est
f
o
r
war
d
er
is
ess
en
tial
to
ef
f
ec
ti
v
e
d
ata
tr
a
n
s
f
er
in
UW
SN
s
,
wh
er
e
co
m
m
u
n
icatio
n
is
d
if
f
icu
lt
b
ec
au
s
e
o
f
th
e
u
n
d
er
wate
r
en
v
ir
o
n
m
en
t.
Pro
b
ab
ilis
tic
r
ea
s
o
n
in
g
m
ay
b
e
u
s
ed
to
in
f
o
r
m
ju
d
g
m
en
ts
v
ia
B
ay
esian
d
ec
is
io
n
m
o
d
els.
Her
e,
it
r
ef
er
s
to
c
h
o
o
s
in
g
th
e
o
p
tim
al
f
o
r
war
d
er
n
o
d
e
f
r
o
m
a
r
an
g
e
o
f
ch
o
ices
in
a
UW
SN
clu
s
te
r
in
g
s
itu
atio
n
.
I
d
en
tify
th
e
f
ac
to
r
s
th
at
af
f
ec
t
th
e
b
est
f
o
r
war
d
er
ch
o
ice
[
8
]
.
T
h
ese
in
clu
d
e
th
e
d
is
tan
ce
to
th
e
s
in
k
n
o
d
e,
th
e
a
m
o
u
n
t
o
f
e
n
er
g
y
le
f
t,
th
e
d
ata
tr
an
s
f
er
'
s
d
ep
en
d
ab
ilit
y
,
a
n
d
th
e
co
n
n
ec
tio
n
'
s
q
u
ality
.
Mo
d
el
th
e
d
is
tr
ib
u
tio
n
s
o
f
th
ese
p
ar
am
eter
s
u
s
in
g
B
ay
esian
in
f
er
en
ce
b
ased
o
n
ex
is
tin
g
d
ata
o
r
p
ast
k
n
o
wled
g
e.
Fo
r
ex
am
p
le,
th
e
u
tili
ty
f
u
n
ctio
n
m
ay
p
r
io
r
itize
en
er
g
y
ef
f
icien
cy
an
d
d
ep
e
n
d
ab
le
d
ata
tr
an
s
m
is
s
io
n
.
Select
th
e
f
o
r
war
d
in
g
n
o
d
e
th
at
o
p
tim
izes
th
e
an
ticip
ated
u
tili
ty
[
9
]
.
T
h
e
n
o
d
e
with
th
e
g
r
ea
test
p
r
ed
ict
ed
u
t
ilit
y
v
al
u
e
wo
u
l
d
b
e
th
i
s
o
n
e.
SDN
is
an
ass
u
r
in
g
m
eth
o
d
th
at
ch
an
g
es
f
lex
ib
le
d
ir
ec
tio
n
b
y
d
is
s
o
ciatin
g
th
e
d
ata
p
lan
e
f
r
o
m
t
h
e
co
n
tr
o
l p
lan
e.
A
f
u
zz
y
lo
g
ic
-
b
ased
SDN
m
ec
h
an
is
m
(
FP
O
-
MST
)
u
s
in
g
a
f
u
zz
y
p
ath
o
p
tim
izatio
n
an
d
a
f
u
zz
y
cu
t
-
s
et
o
p
tim
izatio
n
al
g
o
r
ith
m
to
im
p
r
o
v
e
th
e
r
o
u
tin
g
ef
f
icien
cy
.
Fu
r
t
h
er
m
o
r
e,
u
s
i
n
g
a
s
p
an
n
i
n
g
tr
ee
alg
o
r
ith
m
m
in
im
izes
th
e
c
o
m
p
lex
ity
an
d
r
aises
th
e
ac
ce
s
s
ib
ilit
y
o
f
u
n
d
er
wate
r
n
o
d
es.
T
o
s
o
lv
e
t
h
ese
is
s
u
es,
t
h
e
d
ev
el
o
p
m
en
t
o
f
a
clu
s
ter
in
g
p
r
o
ce
s
s
with
a
B
ay
esian
alg
o
r
ith
m
f
o
r
o
p
tim
al
r
o
u
te
f
o
r
m
atio
n
i
n
SDN
u
n
d
er
wate
r
W
SN is p
r
o
p
o
s
ed
.
T
h
e
C
PB
A
m
ec
h
an
is
m
is
s
u
m
m
ar
ized
as f
o
llo
ws.
C
lu
s
ter
in
g
p
r
im
ar
ily
g
r
o
u
p
s
th
e
s
en
s
o
r
n
o
d
es
to
g
eth
e
r
.
Fo
llo
win
g
th
e
clu
s
ter
g
r
o
wth
,
t
h
e
C
H
is
ch
o
s
en
b
y
th
e
s
lap
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
.
T
h
e
n
o
d
e
d
is
tan
ce
b
etwe
en
in
ter
an
d
in
tr
a
clu
s
ter
,
en
er
g
y
,
an
d
d
ela
y
ar
e
also
co
n
s
id
er
ed
.
T
h
e
r
esp
o
n
s
e
o
f
th
is
ar
ticle
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
ti
o
n
2
d
escr
ib
es
th
e
p
r
o
p
o
s
ed
m
ec
h
an
is
m
.
Sectio
n
3
s
h
o
ws
th
e
s
im
u
latio
n
o
u
tco
m
es.
Sectio
n
4
p
r
esen
ts
th
e
co
n
clu
s
io
n
as
well
as
th
e
f
u
tu
r
e
s
co
p
e.
An
SDN
is
p
r
o
p
o
s
ed
to
m
ak
e
n
etwo
r
k
s
m
o
r
e
q
u
ick
a
n
d
f
lex
ib
le.
SDW
SN
is
r
ea
lized
b
y
in
f
u
s
in
g
an
SDN
m
o
d
el
in
a
W
SN
[
1
0
]
.
So
f
twar
e
-
d
ef
in
e
d
en
e
r
g
y
h
ar
v
esti
n
g
W
SN
to
m
in
im
ize
th
e
s
en
s
o
r
n
o
d
es
o
v
er
h
ea
d
.
A
r
ein
f
o
r
ce
m
e
n
t
l
ea
r
n
in
g
alg
o
r
ith
m
to
m
a
x
im
ize
th
e
lo
n
g
-
ter
m
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SNR
)
p
er
f
o
r
m
an
ce
an
d
p
r
o
v
e
th
e
al
g
o
r
ith
m
'
s
co
n
v
er
g
e
n
ce
[
1
1
]
.
S
DN
d
if
f
er
en
tiates
th
e
d
ata
p
la
n
e
f
r
o
m
t
h
e
co
n
tr
o
l
p
lan
e
to
m
ak
e
a
n
etwo
r
k
s
tr
u
c
tu
r
e.
I
n
itially
,
lau
n
ch
th
e
SDN
an
d
th
en
in
q
u
i
r
e
ab
o
u
t
th
e
u
n
d
er
wate
r
n
o
d
e
an
d
th
e
s
u
r
f
ac
e
co
n
tr
o
ller
.
Nex
t,
t
h
e
clu
s
ter
in
g
is
b
u
ilt,
an
d
th
e
d
ata
is
f
o
r
war
d
ed
[
1
2
]
.
T
h
e
s
elec
tio
n
o
f
C
H
wa
s
estab
lis
h
ed
u
s
in
g
th
e
s
p
id
er
m
o
n
k
e
y
o
p
tim
izatio
n
(
SMO)
alg
o
r
ith
m
to
s
o
lv
e
th
e
is
s
u
es
o
f
clu
s
ter
r
o
u
tin
g
.
I
t
en
h
an
ce
s
th
e
s
y
s
tem
'
s
en
er
g
y
u
tili
za
tio
n
,
life
tim
e,
s
tab
ilit
y
,
an
d
q
u
ality
[
1
3
]
.
Sev
er
al
clu
s
ter
in
g
alg
o
r
it
h
m
s
p
ick
ed
o
u
t
th
e
n
ea
r
est
s
en
s
o
r
n
o
d
es
to
th
e
b
est
p
o
s
itio
n
,
w
h
ich
wer
e
d
eter
m
in
ed
as
C
Hs
d
u
r
in
g
s
elec
tio
n
.
As
a
r
esu
lt,
th
e
clu
s
ter
in
g
p
r
o
ce
s
s
m
ay
in
itiate
o
p
tim
al
C
H
p
o
s
itio
n
s
th
at
d
if
f
er
f
r
o
m
th
e
d
ef
in
ite
n
o
d
e
lo
ca
tio
n
s
.
I
n
a
d
d
itio
n
,
th
e
n
etwo
r
k
lo
a
d
r
aises
th
e
n
ea
r
est
n
o
d
es
af
ter
d
eter
m
in
in
g
th
e
C
Hs,
in
cr
ea
s
in
g
en
e
r
g
y
co
n
s
u
m
p
tio
n
an
d
s
h
o
r
ten
in
g
th
e
n
etwo
r
k
'
s
life
tim
e.
SMO
an
d
a
n
en
er
g
y
-
ef
f
icien
t CH selectio
n
m
ec
h
an
i
s
m
ar
e
p
ic
k
ed
o
u
t to
i
m
p
r
o
v
e
s
tab
ilit
y
an
d
life
tim
e
[
1
4
]
.
T
h
e
p
ar
ticle
s
war
m
o
p
tim
izati
o
n
(
PS
O)
m
ec
h
an
is
m
em
p
lo
y
s
th
e
p
o
s
itio
n
s
o
f
th
e
o
p
tim
al
C
Hs,
an
d
th
e
n
o
d
es
n
ea
r
est
to
th
ese
p
o
s
itio
n
s
ar
e
d
eter
m
in
ed
as
C
Hs
[
1
5
]
.
T
h
e
PS
O
alg
o
r
ith
m
r
aise
s
en
er
g
y
ef
f
icie
n
cy
an
d
life
s
p
an
.
Ho
wev
er
,
ch
o
o
s
in
g
th
e
C
H
is
lack
in
g
r
eg
ar
d
i
n
g
d
is
tan
ce
,
wh
ich
m
in
im
izes
en
er
g
y
ef
f
icien
c
y
.
A
PS
O
-
b
ased
e
n
er
g
y
ef
f
icien
t
C
H
s
elec
tio
n
th
at
co
v
er
s
th
e
life
tim
e
b
y
v
ar
y
i
n
g
th
e
way
e
ac
h
n
o
d
e
s
elec
ts
a
C
H,
wh
ich
allo
ws
co
n
tr
o
l
o
f
th
e
n
u
m
b
er
o
f
n
o
d
es
g
o
in
g
to
ev
er
y
clu
s
ter
[
1
6
]
.
T
h
u
s
,
en
er
g
y
co
n
s
u
m
p
tio
n
ca
n
b
e
co
n
tr
o
lled
d
u
r
i
n
g
d
ata
r
esp
o
n
s
e
f
r
o
m
e
v
er
y
C
H.
C
h
o
o
s
in
g
a
C
H
n
ec
ess
itate
s
g
r
ea
t
co
m
p
u
tatio
n
al
en
er
g
y
an
d
r
aises
en
er
g
y
u
tili
za
tio
n
[
1
7
]
.
PS
O
alg
o
r
ith
m
in
clu
s
t
er
in
g
with
r
o
u
tin
g
in
to
d
if
f
e
r
en
t
s
eg
m
en
ts
alo
n
g
with
th
e
v
er
tical
d
is
tan
ce
s
.
Se
p
ar
atin
g
th
ese
lay
er
s
ass
is
t
s
in
d
escr
ib
in
g
th
e
en
er
g
y
u
tili
za
tio
n
in
s
ev
er
al
lay
er
s
to
ev
alu
ate
th
e
en
e
r
g
y
-
u
tili
zin
g
b
alan
ce
an
d
in
itialize
th
e
p
ar
ticle
h
ier
ar
ch
ically
; th
u
s
,
it in
c
r
ea
s
es th
e
s
p
ee
d
o
f
th
e
p
ar
ticle
co
n
v
er
g
e
n
ce
a
n
d
h
an
d
lin
g
th
e
lo
n
g
life
s
p
a
n
.
A
n
o
p
tim
al
C
H
s
elec
tio
n
m
ec
h
an
is
m
is
estab
lis
h
ed
b
ased
o
n
m
u
ltip
le
co
n
s
tr
ain
t
s
s
u
ch
as
d
elay
,
d
is
tan
ce
,
s
ec
u
r
ity
,
a
n
d
r
is
k
.
Fo
r
C
H
s
elec
tio
n
,
th
e
ch
im
p
in
teg
r
ated
o
s
p
r
ey
o
p
tim
izatio
n
p
r
o
ce
s
s
m
in
im
izes
th
e
n
etwo
r
k
d
elay
[
1
8
]
.
A
s
alp
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
(
SS
OA)
s
elec
ts
th
e
C
H
an
im
ated
ly
f
o
r
th
e
clu
s
ter
in
g
tech
n
iq
u
e.
T
h
e
q
u
ality
o
f
th
e
s
er
v
ice
is
m
ea
s
u
r
ed
r
eg
a
r
d
in
g
life
s
p
a
n
,
en
er
g
y
u
tili
za
tio
n
,
an
d
t
h
r
o
u
g
h
p
u
t
[
1
9
]
.
T
h
e
ad
a
p
tiv
e
n
o
d
e
clu
s
ter
in
g
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.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
2
5
4
-
2
6
1
256
m
ec
h
an
is
m
u
tili
ze
s
a
d
r
ag
o
n
f
ly
o
p
tim
izatio
n
alg
o
r
ith
m
(
D
FO)
to
d
eter
m
in
e
th
e
p
er
f
ec
t
clu
s
ter
.
I
t
im
p
r
o
v
es
th
e
n
etwo
r
k
p
er
f
o
r
m
an
ce
[
2
0
]
.
C
lu
s
ter
in
g
-
b
ased
r
o
u
tin
g
m
e
ch
an
is
m
[
2
1
]
u
tili
zin
g
UW
SNs
estab
lis
h
ed
p
ar
am
eter
s
lik
e
r
esid
u
al
en
er
g
y
,
lo
ca
lity
a
n
d
d
is
tan
ce
b
etwe
en
C
H
an
d
B
S.
Ho
wev
er
,
a
r
e
-
clu
s
ter
in
g
m
eth
o
d
t
o
h
a
n
d
le
tr
af
f
ic
l
o
ad
a
n
d
en
er
g
y
r
aises
th
e
d
elay
.
A
v
ec
to
r
-
b
ased
f
o
r
war
d
in
g
alg
o
r
ith
m
is
u
tili
ze
d
to
en
h
an
c
e
th
e
r
o
u
tin
g
.
T
h
is
p
o
s
itio
n
-
b
ased
a
p
p
r
o
ac
h
is
esp
ec
ially
ca
lcu
lated
to
en
h
a
n
ce
t
h
e
d
elay
an
d
r
ate
o
f
d
ata
tr
an
s
m
is
s
io
n
.
Ho
wev
er
,
th
e
d
is
ad
v
an
tag
e
o
f
th
is
m
ec
h
an
is
m
is
th
at
d
elay
is
v
er
y
h
ig
h
[
2
2
]
.
T
h
e
r
o
u
te
d
e
v
iatio
n
is
p
o
llar
d
v
ia
th
e
B
ay
esian
alg
o
r
ith
m
th
at
u
tili
ze
s
th
e
s
u
b
s
eq
u
en
t
d
is
tr
ib
u
tio
n
in
cr
em
en
tally
wh
ile
v
er
if
icati
o
n
o
cc
u
r
s
[
2
3
]
.
T
h
e
B
ay
esian
alg
o
r
ith
m
co
m
p
u
tes
th
e
p
r
o
b
ab
ilit
y
o
f
co
n
d
itio
n
a
l
u
tili
zin
g
th
e
ea
r
lier
in
f
o
r
m
a
tio
n
to
d
ec
id
e
th
e
r
o
u
te
d
i
f
f
er
en
ce
an
d
o
p
tim
al
r
o
u
te
[
2
4
]
.
A
p
r
o
b
a
b
ilis
tic
m
eth
o
d
was
estab
lis
h
ed
o
n
B
ay
esian
in
f
er
en
c
e
to
f
ac
ilit
ate
p
r
o
f
icien
t
r
o
u
tin
g
[
2
5
]
.
T
h
e
f
r
ac
tio
n
al
b
o
r
d
er
co
lli
e
o
p
tim
izatio
n
(
FB
C
O)
m
eth
o
d
b
u
ild
s
th
e
r
o
u
tin
g
p
r
o
ce
d
u
r
e
m
o
r
e
p
r
o
f
icien
t;
it
is
im
p
o
r
tan
t
to
cr
ea
te
th
e
clu
s
ter
b
y
ap
p
ly
in
g
th
e
B
ay
esian
f
u
zz
y
cl
u
s
ter
in
g
m
eth
o
d
.
I
t
co
m
p
u
tes
th
e
f
itn
ess
f
u
n
ctio
n
b
y
Dela
y
,
lin
k
l
if
etim
e,
d
is
tan
ce
,
as
well
as
en
er
g
y
.
T
h
e
FB
C
O
m
ec
h
an
is
m
im
p
r
o
v
es
en
e
r
g
y
u
tili
za
tio
n
an
d
life
s
p
a
n
[
2
6
]
.
I
n
ter
n
et
o
f
t
h
in
g
s
(
I
o
T
)
-
d
r
i
v
en
im
ag
e
r
ec
o
g
n
itio
n
s
y
s
tem
u
s
e
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
to
n
o
tice
an
d
q
u
an
tify
m
icr
o
p
last
ics
in
wate
r
s
am
p
les
[
2
7
]
.
T
h
is
m
ec
h
an
is
m
is
m
o
r
e
s
ca
lab
le
an
d
r
ec
ep
tiv
e
o
win
g
to
I
o
T
d
ata
ca
p
tu
r
e
an
d
r
em
o
te
o
b
s
er
v
in
g
o
f
wate
r
in
f
r
astru
ctu
r
e
[
2
8
]
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
Su
p
p
o
s
e
we
in
s
tall
a
U
W
SN
in
a
p
r
ep
ar
ed
u
n
d
e
r
wate
r
ar
ea
,
an
d
all
s
en
s
o
r
n
o
d
es
ar
e
in
e
x
p
en
s
iv
e,
s
m
all,
an
d
lig
h
t
ar
b
itra
r
ily
d
is
t
r
ib
u
ted
v
ia
s
h
ip
o
r
u
n
m
a
n
n
ed
air
cr
af
t.
Af
ter
war
d
,
a
n
o
d
e
d
i
p
s
in
to
th
e
wate
r
;
it
s
ca
les
th
e
an
ch
o
r
ch
ain
to
a
c
er
tain
len
g
th
ac
co
r
d
in
g
t
o
th
e
d
ep
th
g
u
ess
.
Sen
s
o
r
s
b
in
d
th
e
b
u
o
y
t
o
b
u
ild
a
2
D
s
en
s
in
g
s
p
ac
e.
T
h
e
s
in
k
n
o
d
e
th
at
tr
a
n
s
m
its
with
th
e
s
ea
s
i
d
e
B
S
an
d
u
n
d
er
wate
r
n
o
d
es
ar
e
p
r
e
-
s
et
v
ia
s
ail
wh
ee
l,
u
n
m
an
n
ed
u
n
d
er
wate
r
v
eh
icle
(
UUV
)
.
I
n
SD
-
UW
SN,
th
e
s
in
k
n
o
d
es
ar
e
s
et
as
co
n
tr
o
ller
s
as
well
as
u
n
d
er
wate
r
n
o
d
es
v
ir
tu
al
s
witch
es.
T
h
u
s
,
a
s
o
f
twar
e
-
d
e
f
in
e
d
UW
SN
is
b
u
ilt.
Fig
u
r
e
1
ex
p
lain
s
th
e
s
tr
u
ctu
r
e
o
f
clu
s
ter
in
g
UW
SN.
T
h
e
UW
SN
co
n
tain
s
th
r
ee
m
a
in
elem
en
ts
:
u
n
d
er
wate
r
s
en
s
o
r
n
o
d
es,
s
in
k
n
o
d
es
th
at
p
lay
co
n
tr
o
ller
,
an
d
th
e
B
S.
T
h
e
s
en
s
o
r
n
o
d
e
i
s
clim
b
ed
o
n
th
e
b
u
o
y
,
an
d
t
h
e
an
ch
o
r
s
er
ies
lin
k
s
to
th
e
b
u
o
y
.
T
o
c
o
n
s
tr
u
ct
th
e
d
ata
p
lan
e,
u
n
d
er
wate
r
s
en
s
o
r
n
o
d
es
ar
e
p
lan
n
ed
as
Op
en
Flo
w
s
witch
es
ar
e
ex
ec
u
ted
b
y
ap
p
ly
in
g
SDN.
T
h
e
co
n
tr
o
ller
is
lo
ca
ted
o
n
th
e
w
ater
o
u
ts
id
e,
an
d
th
e
Op
en
Da
y
L
ig
h
t
is
estab
lis
h
ed
to
m
ak
e
th
e
co
n
tr
o
l
p
lan
e
.
T
h
e
co
n
tr
o
ller
h
an
d
les
all
tr
an
s
f
er
r
in
g
r
o
u
tes,
r
ec
o
g
n
izes
ce
n
tr
alize
d
m
an
ag
e
m
en
t,
a
n
d
m
er
g
es
r
eso
u
r
ce
s
ch
ed
u
lin
g
.
T
h
e
co
n
tr
o
ller
tr
a
n
s
m
its
a
r
ad
io
s
ig
n
al,
w
h
ile
th
e
s
in
k
s
u
n
d
er
n
ea
t
h
th
e
wate
r
a
r
e
s
en
s
o
r
n
o
d
es
th
at
ca
n
id
en
tify
th
e
s
o
u
n
d
.
B
ased
o
n
s
u
ch
f
ea
tu
r
es,
a
r
ad
io
s
ig
n
al
is
ap
p
lied
to
ass
o
ciate
th
e
d
ata
-
co
llectin
g
h
u
b
an
d
s
in
k
n
o
d
es;
also
,
an
au
d
io
s
ig
n
al
is
ap
p
lied
to
li
n
k
t
h
e
s
en
s
o
r
n
o
d
es.
C
o
n
tr
o
ller
s
ar
e
wea
p
o
n
s
with
air
b
ag
s
,
b
u
o
y
s
,
GPS,
an
d
R
F.
T
h
e
lin
k
ed
a
p
p
licatio
n
s
r
u
n
o
n
th
e
B
S in
an
ap
p
licatio
n
p
lan
e
.
Fig
u
r
e
1
.
Stru
ctu
r
e
o
f
clu
s
ter
in
g
UW
SN
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
Dev
elo
p
men
t o
f c
lu
s
teri
n
g
w
ith
B
a
ye
s
ia
n
a
lg
o
r
ith
m
fo
r
o
p
ti
ma
l ro
u
te
fo
r
ma
tio
n
in
…
(
A
n
o
o
p
S
r
ee
r
a
j
)
257
Sin
g
le
-
h
o
p
tr
an
s
m
is
s
io
n
is
ap
p
lied
f
o
r
co
n
tr
o
l
tr
an
s
m
is
s
io
n
,
an
d
m
u
lti
-
h
o
p
tr
a
n
s
m
is
s
io
n
is
u
tili
ze
d
f
o
r
d
ata
tr
a
n
s
m
is
s
io
n
.
T
h
e
R
F
s
ig
n
al
is
em
p
lo
y
ed
to
co
m
m
u
n
icate
am
o
n
g
co
n
tr
o
ller
s
an
d
B
Ss
.
T
h
e
co
n
tr
o
ller
ac
tiv
ates
all
s
en
s
o
r
n
o
d
es
in
t
h
e
r
eg
io
n
d
u
r
in
g
co
n
tr
o
l
tr
an
s
m
is
s
io
n
.
At
a
s
p
ec
if
ied
tim
e,
t
h
e
s
en
s
o
r
n
o
d
es
ar
e
lin
k
ed
to
th
e
co
n
tr
o
ller
th
r
o
u
g
h
o
n
e
-
h
o
p
co
n
tr
o
l
tr
a
n
s
m
is
s
io
n
an
d
n
ex
t
in
f
o
r
m
atio
n
o
n
th
e
ir
s
tatu
s
d
etails,
f
o
r
ex
am
p
le,
th
e
p
o
s
itio
n
an
d
s
tatu
s
o
f
th
e
r
eso
u
r
ce
.
W
h
en
th
e
u
n
d
er
wate
r
n
o
d
e
f
o
r
war
d
s
a
r
o
u
te
r
eq
u
est,
th
e
co
n
tr
o
ller
p
er
f
o
r
m
s
th
e
r
o
u
tin
g
alg
o
r
ith
m
,
allo
win
g
th
e
r
o
u
tin
g
co
n
clu
s
io
n
s
to
th
e
r
e
p
r
esen
tin
g
n
o
d
e.
2
.
1
.
Clus
t
er
ing
o
pera
t
io
n
Sin
g
le
-
h
o
p
tr
a
n
s
m
is
s
io
n
is
ap
p
lied
b
ased
o
n
th
e
co
n
tr
o
l
m
e
s
s
ag
e
d
u
r
in
g
th
e
clu
s
ter
in
g
p
r
o
ce
s
s
.
T
h
is
m
ess
ag
e
co
n
s
is
ts
o
f
n
o
d
e
I
D,
n
o
d
e
lo
ca
tio
n
,
tim
e
to
liv
e,
r
em
ain
in
g
en
e
r
g
y
,
a
n
d
r
eso
u
r
ce
r
esid
en
ce
.
T
h
e
willin
g
s
en
s
o
r
n
o
d
es
s
en
d
a
r
eq
u
est
m
ess
ag
e
to
th
e
co
n
tr
o
l
ler
.
T
h
en
,
th
e
c
o
n
tr
o
ller
d
ec
id
es
th
e
C
H
b
ased
o
n
th
e
s
lap
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
.
Salp
s
h
av
e
p
o
u
r
ed
i
n
to
th
e
ar
ea
o
f
d
ee
p
wate
r
s
ca
lled
th
e
Salp
s
er
ies
,
h
elp
in
g
to
im
p
r
o
v
e
f
o
r
ag
in
g
m
o
b
ilit
y
.
T
h
e
s
tatis
tical
f
o
r
m
u
la
f
o
r
th
e
W
SS
A
is
a
s
s
ig
n
ed
.
=
{
+
1
[
(
−
)
2
+
]
3
≥
0
+
1
[
(
−
)
2
+
]
3
<
0
(
1
)
W
h
er
e
r
ep
r
esen
ts
th
e
p
r
elim
i
n
ar
y
s
alp
s
p
o
s
itio
n
in
th
e
m
d
im
en
s
io
n
,
in
d
icate
s
th
e
Fo
o
d
Or
ig
in
p
o
s
itio
n
,
,
in
d
icate
s
t
h
e
u
p
p
er
as
well
as
lo
wer
b
o
u
n
d
1
,
2
an
d
3
d
en
o
tes
th
e
r
an
d
o
m
in
ter
v
al
estab
lis
h
ed
o
n
th
e
in
t
er
v
al
am
o
n
g
0
a
n
d
1
.
T
h
e
im
p
o
r
tan
t
p
r
o
p
er
ty
is
a
co
e
f
f
icie
n
t
1
ap
p
lied
to
th
e
eq
u
ilib
r
iu
m
o
f
th
e
ex
p
lo
r
atio
n
an
d
u
tili
za
tio
n
o
f
f
o
o
d
as
i
n
th
e
eq
u
atio
n
.
W
h
er
e
1
in
d
icate
s
a
s
ig
n
if
ican
t
co
ef
f
icien
t,
PC
r
ef
er
s
to
th
e
cu
r
r
en
t h
o
p
,
d
en
o
tes th
e
ex
p
o
n
e
n
tial f
u
n
ctio
n
,
a
n
d
T
C
r
ep
r
esen
ts
th
e
o
v
er
all
h
o
p
co
u
n
t
:
1
=
2
−
(
4
)
(
2
)
Af
ter
C
H
s
e
lectio
n
,
th
e
s
elec
ted
C
H
d
is
tr
ib
u
tes
a
jo
in
-
r
eq
u
est
m
ess
ag
e
t
o
co
m
m
u
n
icatio
n
r
an
g
e
s
en
s
o
r
n
o
d
es.
W
ith
in
th
e
co
m
m
u
n
icatio
n
r
a
n
g
e,
n
o
d
es
f
o
r
war
d
th
e
jo
in
-
r
e
p
ly
m
ess
ag
e
s
to
C
H.
T
h
en
,
C
H
g
r
o
u
p
s
th
e
n
ea
r
-
s
en
s
o
r
n
o
d
es.
I
n
th
is
m
eth
o
d
,
ea
ch
an
d
e
v
er
y
C
H
r
eg
is
tr
y
with
th
e
c
o
n
tr
o
ller
is
s
en
s
ib
le.
Af
ter
ce
r
tain
r
o
u
n
d
s
,
C
Hs
wi
ll
in
f
o
r
m
t
h
e
co
n
tr
o
ller
o
f
th
e
r
e
g
is
tr
atio
n
d
etails.
T
h
u
s
,
th
e
co
n
tr
o
ller
c
o
n
s
tan
tly
m
ain
tain
s
th
e
u
p
d
atio
n
.
So
m
e
tim
es,
s
ev
er
al
C
H
s
m
ay
n
o
t
wo
r
k
s
u
itab
ly
;
th
er
ef
o
r
e,
th
e
r
e
g
is
tr
atio
n
th
r
esh
o
ld
f
o
r
C
H
r
ests
o
n
th
e
c
o
n
tr
o
ller
.
I
f
th
e
r
eg
is
tr
atio
n
ex
p
ir
es,
t
h
e
co
n
tr
o
ller
d
ec
id
es
wh
ich
r
ep
r
esen
tin
g
C
H
f
ails
.
C
u
r
r
en
tly
,
t
h
e
c
o
n
tr
o
ller
d
is
tr
ib
u
tes
a
C
H
Failu
r
e
m
ess
ag
e
th
at
a
d
m
its
th
e
I
D
o
f
th
e
m
is
tak
e
C
H.
T
h
en
,
im
m
ed
iately
,
th
e
co
n
t
r
o
ller
ch
o
o
s
es
a
b
etter
C
H
f
r
o
m
th
e
s
u
r
r
o
u
n
d
in
g
r
eg
i
o
n
.
L
ater
,
th
e
c
o
n
tr
o
ller
d
is
s
em
in
ates a
C
lu
s
ter
-
R
eb
u
ild
m
ess
ag
e
th
at
ad
m
its
th
e
I
D
o
f
th
e
u
n
iq
u
e
C
H
an
d
th
e
in
n
o
v
ativ
e
C
H.
2
.
2
.
O
pti
m
a
l pa
t
h select
io
n
a
nd
UWSN
co
m
m
un
ica
t
io
n
T
h
e
C
Hs
m
ain
tain
th
eir
r
ad
i
o
s
tu
r
n
ed
o
n
to
o
b
tain
d
ata
f
r
o
m
C
Ms,
an
d
th
ey
ex
ec
u
te
a
f
ilter
in
g
p
r
o
ce
s
s
to
m
in
im
ize
d
u
p
licate
an
d
u
n
n
ec
ess
ar
y
d
ata.
On
ce
a
C
H
o
b
tain
s
d
ata
f
r
o
m
its
C
Ms,
it
ex
ec
u
tes
d
ata
ag
g
r
eg
atio
n
an
d
f
o
r
war
d
s
it
to
th
e
co
n
tr
o
ller
.
As
th
e
B
S
i
s
c
h
ar
ac
ter
is
tically
p
o
s
itio
n
ed
a
lo
n
g
d
is
tan
ce
awa
y
,
ap
p
ly
in
g
th
ese
ap
p
r
o
ac
h
es
will
en
s
u
r
e
a
m
an
ag
ea
b
le
a
m
o
u
n
t
o
f
en
er
g
y
tr
a
n
s
m
is
s
io
n
s
in
ce
f
o
r
war
d
in
g
to
th
e
B
S
o
n
th
e
g
r
o
u
n
d
wo
u
ld
r
esu
l
t
in
h
ig
h
e
n
er
g
y
u
tili
za
tio
n
a
n
d
h
ea
v
y
d
ata
lo
s
s
.
T
h
is
m
eth
o
d
f
o
r
war
d
s
th
e
d
ata
v
ia
th
e
co
n
tr
o
ller
t
o
th
e
B
S
to
p
r
eser
v
e
en
er
g
y
a
n
d
t
r
a
n
s
m
is
s
io
n
co
s
ts
.
T
h
e
Naiv
e
B
ay
es
tech
n
iq
u
e
is
estab
lis
h
ed
o
n
th
e
B
ay
esian
th
eo
r
em
.
Acc
ep
tin
g
tr
ain
in
g
d
ata
R
,
th
e
later
p
o
s
s
ib
ilit
y
o
f
a
th
eo
r
y
S,
P(S|
R
)
,
tr
ac
es th
e
B
ay
esian
th
eo
r
em
.
I
t a
p
p
lies
th
e
k
n
o
wled
g
e
o
f
p
r
i
o
r
ev
en
ts
to
f
o
r
ec
ast f
u
t
u
r
e
ev
en
ts
.
(
|
)
=
(
|
)
.
(
)
/
(
)
(
3
)
T
h
e
n
aiv
e
B
ay
esian
alg
o
r
ith
m
ch
o
o
s
es
th
e
n
ex
t
h
o
p
as
a
C
H
f
r
o
m
with
in
th
e
c
o
m
m
u
n
icatio
n
r
an
g
e
n
o
d
e,
an
d
th
ese
tr
an
s
f
er
th
e
d
ata
to
th
e
co
n
tr
o
ller
.
Fin
ally
,
t
h
e
co
n
tr
o
ller
ef
f
ec
tiv
ely
tr
an
s
f
er
s
d
ata
to
th
e
B
S.
T
h
r
o
u
g
h
o
u
t
th
is
r
o
u
te,
th
e
d
at
a
f
r
o
m
th
e
s
en
s
o
r
n
o
d
es
m
ay
b
e
s
ec
u
r
ely
a
n
d
r
ap
id
ly
f
o
r
wa
r
d
ed
to
th
e
B
S.
T
h
e
o
p
tim
al
p
ath
s
elec
tio
n
f
o
r
d
ata
tr
an
s
m
is
s
io
n
in
an
ef
f
icien
t
r
o
u
tin
g
co
n
s
eq
u
en
ce
in
less
er
en
er
g
y
u
tili
za
tio
n
an
d
im
p
r
o
v
es th
e
clu
s
ter
'
s
life
tim
e.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
W
e
u
tili
ze
d
th
e
Netwo
r
k
Sim
u
lat
or
-
3
to
e
x
ec
u
te
th
e
s
im
u
la
tio
n
o
f
th
e
clu
s
ter
in
g
m
et
h
o
d
[
2
9
]
.
T
h
e
ex
p
er
im
en
t
is
ca
r
r
ied
o
u
t
in
a
co
n
s
is
ten
t
ar
r
an
g
em
e
n
t,
wh
er
e
th
e
p
r
elim
in
a
r
y
en
er
g
y
o
f
all
n
o
d
es
is
th
e
s
am
e
.
T
h
e
in
tr
o
d
u
ce
d
s
ce
n
ar
io
im
it
ates
u
n
d
er
wate
r
s
en
s
o
r
n
o
d
es
f
u
n
ctio
n
in
g
in
a
th
r
ee
-
d
im
e
n
s
io
n
al
s
p
ac
e.
T
h
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.
3
8
,
No
.
1
,
Ap
r
il
20
2
5
:
2
5
4
-
2
6
1
258
n
o
d
es
wer
e
d
is
tr
ib
u
ted
ar
b
itra
r
ily
in
a
4
5
0
×
5
5
0
×
5
0
0
m
3
with
an
i
d
en
tical
allo
ca
tio
n
,
a
n
d
t
h
e
tr
an
s
m
is
s
io
n
r
ate
is
1
0
Kb
p
s
[
3
0
]
.
T
h
e
tr
a
n
s
m
is
s
io
n
r
an
g
e
o
f
e
v
er
y
s
en
s
o
r
n
o
d
e
is
p
r
e
-
s
et
to
5
0
m
eter
s
,
an
d
th
e
to
p
o
g
r
a
p
h
y
o
f
th
e
n
etwo
r
k
ch
a
n
g
es
h
ab
itu
a
lly
[
3
1
]
.
T
o
p
r
o
v
e
th
e
e
x
ec
u
tio
n
o
f
th
e
C
PB
A,
f
o
u
r
s
im
u
latio
n
m
etr
ics
ar
e
ev
alu
ated
with
FP
O
-
MST
.
T
h
ese
p
er
f
o
r
m
an
ce
m
etr
ics
ar
e
th
e
r
atio
o
f
p
ac
k
et
f
o
r
war
d
to
B
S,
life
tim
e,
r
em
ain
in
g
e
n
er
g
y
,
an
d
Dela
y
[
3
2
]
.
T
h
e
r
esu
lt
d
em
o
n
s
tr
ates
th
at
th
e
life
tim
e
o
f
th
e
C
lu
s
ter
r
o
u
n
d
is
en
h
a
n
ce
d
s
in
ce
em
p
lo
y
in
g
th
e
ca
t
s
war
m
o
p
tim
izatio
n
alg
o
r
ith
m
.
T
h
e
co
m
m
u
n
icatio
n
r
an
g
e
al
s
o
in
v
o
lv
es
clu
s
ter
s
tab
il
ity
as
if
ex
tr
a
m
em
b
er
s
ar
e
in
th
e
clu
s
ter
.
T
h
is
,
in
tu
r
n
,
m
in
im
izes
th
e
p
r
o
b
ab
ilit
y
o
f
th
e
clu
s
ter
b
ein
g
ig
n
o
r
ed
.
Fig
u
r
e
2
ex
p
lai
n
s
th
e
clu
s
ter
r
o
u
n
d
b
ased
o
n
th
e
n
et
wo
r
k
s
ize.
Fig
u
r
e
2
.
C
lu
s
ter
R
o
u
n
d
v
er
s
u
s
s
ize
o
f
UW
SN
Fro
m
Fig
u
r
e
2
,
wh
en
r
aisi
n
g
th
e
UW
SN
s
en
s
o
r
n
o
d
e
co
u
n
t
,
th
e
C
PB
A
m
ec
h
an
is
m
h
as
t
h
e
h
ig
h
est
clu
s
ter
r
o
u
n
d
s
in
ce
th
e
B
ay
e
s
ian
alg
o
r
ith
m
ch
o
o
s
es
th
e
b
est
f
o
r
war
d
er
.
Fu
r
th
er
m
o
r
e,
SDN
ca
n
im
p
r
o
v
e
r
o
u
tin
g
ef
f
icien
c
y
.
H
o
wev
er
,
t
h
e
co
n
v
en
tio
n
al
FP
O
-
MST
m
ec
h
an
is
m
h
as
a
2
1
5
s
m
aller
cl
u
s
ter
r
o
u
n
d
b
ec
au
s
e
it
in
cr
ea
s
es
n
etwo
r
k
co
m
p
le
x
i
ty
.
T
h
e
ca
p
ac
ity
o
f
en
er
g
y
t
h
a
t
r
em
ain
s
in
th
e
n
o
d
e
is
r
elate
d
to
t
h
e
r
e
m
ain
in
g
en
er
g
y
.
T
h
e
in
itial
en
er
g
y
o
f
e
v
er
y
u
n
d
er
wate
r
s
en
s
o
r
n
o
d
e
is
0
.
5
jo
u
le.
Af
ter
d
ata
f
o
r
war
d
in
g
,
r
ec
eiv
in
g
,
an
d
s
en
s
in
g
th
e
d
ata,
th
e
s
en
s
o
r
n
o
d
e
en
er
g
y
is
m
in
im
ized
[
3
3
]
.
Fig
u
r
e
3
d
em
o
n
s
tr
ates
th
e
R
em
ain
in
g
E
n
er
g
y
v
er
s
u
s
th
e
s
ize
o
f
UW
SN.
Fig
u
r
e
3
.
R
em
ain
i
n
g
e
n
er
g
y
v
er
s
u
s
th
e
s
ize
o
f
UW
SN
Fig
u
r
e
3
illu
s
tr
ates
th
at
th
e
p
r
o
p
o
s
ed
C
PB
A
an
d
FP
O
-
MST
m
ec
h
an
is
m
s
m
in
i
m
ize
th
e
r
em
ain
i
ng
en
er
g
y
wh
en
th
e
s
ize
o
f
UW
SN
is
in
cr
ea
s
ed
.
T
h
e
C
PB
A
u
tili
ze
s
less
en
er
g
y
th
a
n
a
n
FP
O
-
MST
s
in
ce
it
r
ea
ch
es
th
e
d
ata
f
u
s
io
n
an
d
th
e
least
en
er
g
y
c
o
n
v
e
y
ed
to
s
p
r
ea
d
d
ata
f
r
o
m
th
e
C
Hs
to
th
e
B
S
v
ia
th
e
co
n
tr
o
ller
with
th
e
b
est
r
o
u
te.
I
n
ad
d
itio
n
,
th
e
B
ay
esi
an
alg
o
r
ith
m
s
elec
ts
th
e
b
est
f
o
r
war
d
er
f
r
o
m
s
en
d
e
r
to
b
ase
s
tatio
n
.
T
h
u
s
,
th
e
C
PB
A
m
ec
h
an
is
m
en
h
a
n
ce
s
0
.
0
1
4
J
o
u
le
en
er
g
y
ef
f
icien
cy
.
Fig
u
r
e
4
ex
p
lain
s
a
Pack
et
Fo
r
war
d
R
atio
v
er
s
u
s
th
e
s
ize
o
f
UW
SN.
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
Dev
elo
p
men
t o
f c
lu
s
teri
n
g
w
ith
B
a
ye
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ith
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259
Fig
u
r
e
4
.
Pack
et
Fo
r
wa
r
d
R
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v
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s
th
e
s
ize
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f
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N
Fig
u
r
e
4
s
h
o
ws
th
e
r
atio
o
f
p
ac
k
ets
f
o
r
war
d
ed
t
o
th
e
B
S,
d
em
o
n
s
tr
atin
g
th
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C
PB
A
ca
n
f
o
r
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d
m
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p
ac
k
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m
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ar
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with
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-
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it
ex
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tes
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im
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lify
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ely
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l lin
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tio
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ch
ass
u
r
es th
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d
ev
elo
p
m
en
t o
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d
ata
p
ac
k
et
f
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r
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d
in
g
.
T
h
is
m
ec
h
an
is
m
u
tili
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s
a
s
lap
s
war
m
o
p
tim
izatio
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ith
m
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t
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is
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3
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atio
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ig
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5
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ates
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ize
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ata
t
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an
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io
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elay
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ata
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itted
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p
s
[
3
4
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izatio
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ase
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is
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elay
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u
r
e
5
.
Dela
y
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er
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s
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ize
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f
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4.
CO
NCLU
SI
O
N
I
n
UW
SN,
C
H
s
elec
tio
n
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s
u
r
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n
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ter
d
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s
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ily
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d
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C
H
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u
r
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en
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n
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k
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o
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ith
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iz
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est
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d
m
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im
ize
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er
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ad
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im
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ated
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ith
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ip
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r
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RP
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B
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AUTH
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Mr.
Ano
o
p
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r
e
e
r
a
j
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lea
d
in
g
a
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Ed
Tec
h
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m
p
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y
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ll
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d
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d
d
iz
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rn
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s
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d
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h
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o
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p
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s
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sta
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in
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d
ia
a
n
d
th
e
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g
o
t
g
o
o
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x
p
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rie
n
c
e
in
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c
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d
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m
ics
a
s
we
ll
a
s
Ed
u
c
a
ti
o
n
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d
u
stry
fo
r
m
o
re
t
h
a
n
1
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y
e
a
rs.
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is
a
lso
e
x
p
e
rien
c
e
d
a
s
El
e
c
tro
n
ics
a
n
d
I
n
st
ru
m
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n
tati
o
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g
in
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r
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n
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l
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n
d
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a
s
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d
u
stry
.
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is
a
re
se
a
rc
h
e
r
in
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ste
d
i
n
Art
ifi
c
ial
I
n
telli
g
e
n
c
e
,
M
a
c
h
i
n
e
Lea
rn
i
n
g
,
De
e
p
Lea
rn
in
g
,
S
DR,
Un
d
e
rwa
ter
Co
m
m
u
n
ica
ti
o
n
.
He
c
a
n
b
e
c
o
n
ta
c
ted
a
t
e
m
a
il
:
a
n
o
o
p
sre
e
ra
j@g
m
a
il
.
c
o
m
.
Dr
.
Vija
y
a
l
a
k
sh
m
i
P
is
p
re
se
n
tl
y
wo
rk
i
n
g
a
s
a
n
As
so
c
iate
P
ro
fe
ss
o
r
in
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
,
Ve
ls
In
stit
u
te
o
f
S
c
ien
c
e
,
Tec
h
n
o
l
o
g
y
&
Ad
v
a
n
c
e
d
S
t
u
d
ies
(VIST
AS)
Ch
e
n
n
a
i
.
S
h
e
c
o
m
p
let
e
d
B.
Tec
h
In
stru
m
e
n
tati
o
n
fro
m
M
a
d
ra
s
In
stit
u
te
o
f
Tec
h
n
o
lo
g
y
,
A
n
n
a
Un
i
v
e
rsity
a
n
d
M
.
E
Ap
p
li
e
d
El
e
c
tro
n
ics
fro
m
An
n
a
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i
v
e
rsity
,
Ch
e
n
n
a
i,
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d
i
a
.
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h
e
wa
s
a
wa
rd
e
d
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h
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d
e
g
re
e
in
t
h
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y
e
a
r
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r
h
e
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tstan
d
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g
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se
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rc
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c
o
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tr
ib
u
ti
o
n
in
th
e
fiel
d
o
f
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d
e
rwa
ter
C
o
m
m
u
n
ica
ti
o
n
&
M
a
c
h
i
n
e
lea
rn
in
g
a
p
p
li
c
a
ti
o
n
s.
S
h
e
h
a
s
1
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y
e
a
rs
o
f
rich
e
x
p
e
rien
c
e
in
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c
h
in
g
a
n
d
re
se
a
rc
h
.
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r
a
re
a
o
f
in
tere
st
in
c
lu
d
e
s
Un
d
e
rwa
ter
Co
m
m
u
n
ica
ti
o
n
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e
n
s
o
r
Ne
two
r
k
s
a
n
d
S
y
ste
m
d
e
sig
n
with
M
a
c
h
in
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Lea
rn
i
n
g
&
Da
ta
S
c
ien
c
e
.
S
h
e
h
a
s
su
p
e
r
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n
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n
d
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lars
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d
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h
.
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c
h
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lars
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h
e
h
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s
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th
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re
d
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m
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a
p
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c
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s
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th
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d
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e
d
jo
u
rn
a
ls.
S
h
e
h
a
s
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lso
p
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li
s
h
e
d
p
a
ten
ts
a
n
d
re
c
e
iv
e
d
g
ra
n
ts
.
Dr.
Vijay
a
lak
sh
m
i
P
is
fe
ll
o
w
i
n
I
ET
E
wi
th
Li
fe
t
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m
e
m
e
m
b
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d
h
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s
p
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l
m
e
m
b
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ip
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n
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n
Re
s
a
n
d
re
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d
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Re
s
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d
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x
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ll
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n
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rd
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n
d
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c
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lt
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ll
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n
c
e
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wa
rd
s
fro
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h
e
a
ls
o
c
o
n
tri
b
u
ted
in
e
x
te
n
sio
n
a
c
ti
v
it
ies
a
n
d
s
e
rv
ice
th
r
o
u
g
h
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a
s
a
P
ro
g
ra
m
m
e
Offic
e
r.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
v
ij
i.
se
@v
e
lsu
n
i
v
.
a
c
.
in
.
Dr
.
Ve
la
y
u
th
a
m
R
a
je
n
d
r
a
n
c
o
m
p
lete
d
h
is
M
.
Tec
h
d
e
g
re
e
in
P
h
y
sic
a
l
e
n
g
in
e
e
rin
g
fr
o
m
th
e
In
d
ian
I
n
s
ti
tu
te
o
f
S
c
ien
c
e
,
Ba
n
g
a
l
o
re
,
In
d
ia,
in
1
9
7
9
.
In
1
9
9
3
,
h
e
re
c
e
iv
e
d
P
h
.
D.
d
e
g
re
e
in
E
lec
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
e
n
g
i
n
e
e
rin
g
fro
m
Ch
ib
a
Un
i
v
e
rsity
,
JA
P
AN
.
He
h
a
s
m
o
re
t
h
a
n
3
5
y
e
a
rs’
e
x
p
e
rien
c
e
in
Ac
a
d
e
m
ic
a
n
d
Re
se
a
rc
h
Ex
p
e
rien
c
e
.
He
h
a
s
b
e
e
n
with
Na
ti
o
n
a
l
I
n
stit
u
te
o
f
Oc
e
a
n
Tec
h
n
o
lo
g
y
,
Ch
e
n
n
a
i,
In
d
ia,
a
s
a
P
ro
jec
t
Dire
c
to
r
a
n
d
He
a
d
(M
a
ri
n
e
In
stru
m
e
n
tati
o
n
&
Oc
e
a
n
Ac
o
u
stics
a
n
d
Oc
e
a
n
Ob
se
rv
a
ti
o
n
S
y
ste
m
s).
He
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
P
r
o
fe
ss
o
r
&
Dire
c
to
r,
in
De
p
t
o
f
ECE
.
H
is
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
Un
d
e
rwa
ter
Wi
re
les
s
S
e
n
so
r
n
e
two
rk
,
C
o
g
n
it
i
v
e
Ra
d
i
o
&
S
o
ftwa
re
De
fin
e
d
Ra
d
i
o
Co
m
m
u
n
ica
ti
o
n
,
HF
ra
d
a
r,
Tsu
n
a
m
i
wa
rn
in
g
sy
ste
m
a
n
d
u
n
d
e
rwa
ter
n
o
ise
m
e
a
su
re
m
e
n
t
sy
ste
m
s.
He
is
a
Li
fe
fe
ll
o
w
o
f
Ultras
o
n
ic
S
o
c
iety
o
f
I
n
d
ia,
I
n
d
ia
(USI)
,
Ja
n
u
a
r
y
2
0
0
1
,
As
so
c
iate
M
e
m
b
e
r
o
f
Ac
o
u
stica
l
S
o
c
iety
o
f
Am
e
rica
,
USA,
Ja
n
u
a
ry
2
0
1
0
,
M
e
m
b
e
r
o
f
IEE
E
,
USA,
Ja
n
u
a
ry
2
0
1
0
,
Li
fe
fe
ll
o
w
o
f
In
stit
u
ti
o
n
o
f
El
e
c
tro
n
i
c
s
a
n
d
Tele
c
o
m
m
u
n
ica
ti
o
n
En
g
i
n
e
e
r
in
g
(IE
TE
),
I
n
d
ia,
Ja
n
u
a
ry
2
0
1
2
.
He
wa
s
e
lec
ted
twic
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a
s
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a
irma
n
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ia
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e
c
u
ti
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n
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o
a
rd
o
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ta
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o
y
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o
p
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ra
ti
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n
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n
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l
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CP
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o
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n
terg
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rn
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tal
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ra
p
h
ic
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m
m
issio
n
(IO
C)
/
Wo
rl
d
M
e
teo
r
o
lo
g
ica
l
Or
g
a
n
iza
ti
o
n
(W
M
O)
o
f
UN
S
CO,
in
Oc
to
b
e
r
2
0
0
8
.
a
n
d
S
e
p
tem
b
e
r
2
0
0
9
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
c
e
@v
e
lsu
n
i
v
.
a
c
.
in
.
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