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.
23
,
No
.
2
,
A
u
g
u
s
t
20
21
,
p
p
.
7
1
7
~
7
2
4
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
:
1
0
.
1
1
5
9
1
/ijeecs.v
23
.i
2
.
pp
717
-
7
2
4
717
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
Ev
a
lua
tion o
f
a
w
ireless
low
-
e
nerg
y
mo
te
wi
th
fuzzy
a
lg
o
rithms
a
nd neural ne
two
rks for r
emo
te
e
n
v
iro
nmenta
l mo
ni
toring
Rica
rdo
Ya
uri,
J
inm
i Leza
ma
,
M
ilto
n Rio
s
Na
ti
o
n
a
l
In
st
it
u
te
o
f
Tele
c
o
m
m
u
n
ica
ti
o
n
s Re
se
a
rc
h
a
n
d
Trai
n
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g
-
N
a
ti
o
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a
l
Un
iv
e
rsity
o
f
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g
in
e
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rin
g
,
P
e
rú
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ma
r
2
9
,
2
0
2
1
R
ev
is
ed
May
18
,
2
0
2
1
Acc
ep
ted
J
u
n
2
,
2
0
2
1
Th
e
d
e
v
ice
s
d
e
v
e
lo
p
e
d
f
o
r
a
p
p
li
c
a
ti
o
n
s
i
n
t
h
e
in
ter
n
e
t
o
f
th
i
n
g
s
h
a
v
e
e
v
o
l
v
e
d
tec
h
n
o
l
o
g
ica
ll
y
in
t
h
e
imp
ro
v
e
m
e
n
t
o
f
h
a
r
d
wa
re
a
n
d
so
ftwa
re
c
o
m
p
o
n
e
n
ts,
in
th
e
a
re
a
o
f
o
p
ti
m
iza
ti
o
n
o
f
th
e
li
fe
ti
m
e
a
n
d
t
o
in
c
re
a
se
th
e
c
a
p
a
c
it
y
to
sa
v
e
e
n
e
rg
y
.
Th
is
p
a
p
e
r
sh
o
ws
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
f
u
z
z
y
l
o
g
ic
a
l
g
o
r
it
h
m
a
n
d
a
p
o
we
r
p
ro
p
a
g
a
ti
o
n
n
e
u
ra
l
n
e
t
wo
rk
a
lg
o
rit
h
m
in
a
wire
les
s
m
o
te
(Io
T
e
n
d
d
e
v
ice
).
Th
e
fu
z
z
y
a
lg
o
rit
h
m
c
h
a
n
g
e
s
th
e
tran
sm
issio
n
fre
q
u
e
n
c
y
a
c
c
o
rd
in
g
to
th
e
b
a
tt
e
ry
v
o
lt
a
g
e
a
n
d
so
lar ce
ll
v
o
lt
a
g
e
.
M
o
re
o
v
e
r,
th
e
imp
lem
e
n
tatio
n
o
f
a
lg
o
rit
h
m
s
b
a
se
d
o
n
n
e
u
ra
l
n
e
two
rk
s,
imp
l
ied
a
c
h
a
ll
e
n
g
e
in
t
h
e
e
v
a
lu
a
ti
o
n
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n
d
stu
d
y
o
f
th
e
e
n
e
rg
y
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o
m
m
it
m
e
n
t
fo
r
t
h
e
imp
lem
e
n
tatio
n
o
f
t
h
e
a
lg
o
rit
h
m
,
m
e
m
o
ry
sp
a
c
e
o
p
ti
m
iza
ti
o
n
a
n
d
l
o
w en
e
rg
y
c
o
n
s
u
m
p
ti
o
n
.
K
ey
w
o
r
d
s
:
E
m
b
ed
d
e
d
s
y
s
tem
Fu
zz
y
lo
g
ic
I
n
ter
n
et
o
f
th
in
g
s
Neu
r
al
n
etwo
r
k
W
ir
eles
s
s
en
s
o
r
T
h
is i
s
a
n
o
p
e
n
a
c
c
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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
:
R
icar
d
o
Yau
r
i
Natio
n
al
I
n
s
titu
te
o
f
T
elec
o
m
m
u
n
icatio
n
s
R
esear
ch
an
d
T
r
a
in
in
g
(
I
NI
C
T
E
L
-
UNI
)
Natio
n
al
Un
iv
er
s
ity
o
f
E
n
g
in
e
er
in
g
(
UNI
)
Natio
n
al
Un
iv
er
s
ity
o
f
San
M
ar
co
s
(
UNM
SM)
1
7
7
1
San
L
u
is
Av
en
u
e
,
L
im
a,
Per
ú
E
m
ail: r
y
au
r
i@
in
ictel
-
u
n
i.e
d
u
.
p
e,
r
y
a
u
r
ir
@
u
n
m
s
m
.
ed
u
.
p
e
1.
I
NT
RO
D
UCT
I
O
N
Sm
ar
t
I
o
T
d
e
v
ices
ar
e
cu
r
r
e
n
tly
g
en
er
atin
g
n
ew
ap
p
licatio
n
s
in
th
e
ar
ea
o
f
th
e
in
te
r
n
et
o
f
th
in
g
s
(
I
o
T
)
as p
ar
t o
f
a
Sy
s
tem
th
at
will p
r
o
v
id
e
u
s
er
s
with
an
in
f
o
r
m
atio
n
m
o
n
it
o
r
in
g
s
er
v
ice
[
1
]
.
Dif
f
er
en
t a
u
th
o
r
s
d
escr
ib
e
h
ar
d
war
e
a
n
d
s
o
f
tw
ar
e
tech
n
iq
u
es
to
im
p
r
o
v
e
n
o
d
e
u
p
tim
e
[
2
]
,
[
3
]
.
T
h
e
s
i
m
u
latio
n
o
f
f
u
zz
y
alg
o
r
ith
m
s
in
p
ar
allel
allo
ws
to
im
p
r
o
v
e
th
e
co
n
tr
o
l
an
d
u
s
e
o
f
th
e
en
er
g
y
s
o
t
h
at
th
e
I
o
T
d
ev
ices
ca
n
o
p
er
ate
f
o
r
lo
n
g
e
r
,
m
o
d
i
f
y
in
g
th
e
id
le
tim
e
an
d
th
e
tr
an
s
m
is
s
io
n
p
o
wer
.
T
h
is
is
d
escr
ib
ed
in
[
4
]
wh
er
e
it
is
s
h
o
wn
th
at
2
5
%
ef
f
icien
cy
ca
n
b
e
ac
h
iev
e
d
with
r
esp
ec
t
to
a
co
m
m
o
n
i
m
p
lem
en
tatio
n
.
T
h
e
wo
r
k
d
ev
elo
p
ed
in
[
5
]
s
h
o
ws
a
p
r
o
p
o
s
al
f
o
r
a
s
y
s
tem
b
ased
o
n
f
u
zz
y
lo
g
ic
alg
o
r
ith
m
s
u
s
in
g
th
e
MA
T
L
AB
p
latf
o
r
m
,
a
lway
s
f
o
cu
s
in
g
o
n
th
e
p
r
o
g
r
am
m
in
g
an
d
o
p
tim
iz
atio
n
o
f
th
ese
m
o
d
els
in
d
ev
ic
es
with
lo
w
h
ar
d
war
e
an
d
en
e
r
g
y
r
eso
u
r
ce
s
.
T
h
e
u
s
e
o
f
n
eu
r
al
n
etwo
r
k
s
an
d
th
eir
ap
p
licatio
n
in
em
b
e
d
d
ed
s
y
s
tem
s
with
lo
w
h
a
r
d
war
e
r
e
s
o
u
r
ce
s
an
d
p
o
wer
lim
itatio
n
s
(
also
co
n
s
id
er
in
g
u
b
iq
u
ity
an
d
wir
eless
tr
an
s
m
i
s
s
io
n
)
is
n
o
t
in
co
n
v
en
ien
t
f
o
r
im
p
lem
en
tin
g
th
is
ty
p
e
o
f
alg
o
r
ith
m
s
in
d
ev
ices
clo
s
er
to
th
e
d
ata
s
o
u
r
ce
[
6
]
-
[
8
]
.
As
a
r
esu
lt,
th
e
m
ac
h
in
e
lear
n
in
g
(
ML
)
m
o
d
els
an
d
ass
o
ciate
d
ML
in
f
er
e
n
ce
f
r
am
ewo
r
k
m
u
s
t
n
o
t
o
n
ly
r
u
n
ef
f
icien
tly
,
b
u
t
m
u
s
t
also
o
p
er
ate
in
a
f
ew
k
ilo
b
y
tes o
f
m
em
o
r
y
[
9]
-
[
1
1
]
.
W
i
t
h
t
h
e
a
b
o
v
e
,
i
t
c
a
n
b
e
u
n
d
e
r
s
t
o
o
d
t
h
a
t
a
d
d
i
n
g
a
b
l
o
c
k
o
f
i
n
te
l
l
i
g
e
n
c
e
u
s
i
n
g
i
n
f
e
r
e
n
c
e
a
l
g
o
r
i
t
h
m
s
,
t
h
e
I
o
T
n
o
d
e
s
c
a
n
r
e
d
u
c
e
t
h
ei
r
en
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
[
1
2
]
.
F
u
r
th
e
r
m
o
r
e
,
i
t
i
s
u
n
d
e
r
s
t
o
o
d
t
h
a
t
t
h
e
p
r
o
b
l
e
m
s
o
f
a
n
a
d
e
q
u
a
t
e
i
m
p
l
e
m
e
n
t
a
ti
o
n
o
f
al
g
o
r
i
t
h
m
s
i
n
a
s
e
n
s
o
r
n
o
d
e
[
1
3
]
(
n
e
u
r
a
l
n
e
t
w
o
r
k
s
o
r
f
u
z
z
y
a
l
g
o
r
i
t
h
m
s
)
c
a
n
b
e
r
e
d
u
c
e
d
b
y
c
o
m
p
a
r
i
n
g
t
h
e
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
d
u
r
i
n
g
c
o
m
m
u
n
i
c
a
t
i
o
n
a
n
d
t
h
e
e
n
e
r
g
y
u
s
e
d
t
o
e
x
e
c
u
t
e
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
23
,
No
.
2
,
Au
g
u
s
t
20
21
:
717
-
7
2
4
718
a
l
g
o
r
i
t
h
m
s
.
T
h
is
a
s
p
e
ct
t
a
k
e
s
o
n
a
n
i
m
p
o
r
t
a
n
t
r
e
l
e
v
a
n
c
e
w
h
e
n
w
i
r
e
l
es
s
s
e
n
s
o
r
n
o
d
e
s
a
r
e
u
s
e
d
i
n
m
o
n
i
t
o
r
i
n
g
e
n
v
i
r
o
n
m
e
n
t
s
w
h
e
r
e
t
h
e
y
n
e
e
d
t
o
f
u
n
c
t
i
o
n
c
o
n
t
i
n
u
o
u
s
l
y
,
u
b
i
q
u
i
t
o
u
s
l
y
a
n
d
u
n
a
t
te
n
d
e
d
f
o
r
l
o
n
g
p
e
r
i
o
d
s
o
f
t
i
m
e
.
W
ith
r
ev
is
io
n
s
m
ad
e
in
th
is
p
ap
er
,
alg
o
r
ith
m
ic
tech
n
iq
u
es
s
h
o
wn
to
r
ed
u
ce
p
o
wer
c
o
n
s
u
m
p
tio
n
h
ar
d
war
e
s
y
s
tem
s
th
at
n
ee
d
to
s
en
d
in
f
o
r
m
atio
n
with
v
ar
ia
b
le
f
r
eq
u
e
n
cies
with
o
u
t
af
f
ec
tin
g
th
e
b
eh
av
i
o
r
o
f
th
e
I
o
T
n
o
d
e
[
1
4
]
.
T
h
is
p
ap
e
r
co
n
tr
ib
u
tes
to
th
e
s
tu
d
y
o
f
th
e
f
ee
d
p
r
o
p
ag
atio
n
n
eu
r
al
n
et
wo
r
k
alg
o
r
ith
m
(
an
d
th
e
ca
lcu
latio
n
o
f
th
e
ce
n
tr
o
id
(
f
u
zz
y
alg
o
r
ith
m
)
f
o
r
its
im
p
lem
en
tatio
n
in
a
s
en
s
o
r
n
o
d
e
with
lim
ite
d
h
ar
d
war
e
r
eso
u
r
ce
s
[
1
5
]
,
b
ei
n
g
a
n
o
v
el
asp
ec
t
its
co
m
p
ar
ativ
e
ev
alu
atio
n
f
o
r
its
u
s
e
in
en
v
ir
o
n
m
e
n
tal
m
o
n
ito
r
in
g
ap
p
licatio
n
s
[
1
6
]
,
[
1
7
]
.
I
n
th
is
way
,
we
s
h
o
w
th
e
ev
alu
atio
n
o
f
a
wir
eless
s
en
s
o
r
n
o
d
e
with
f
u
zz
y
an
d
n
e
u
r
al
n
etwo
r
k
alg
o
r
ith
m
f
o
r
r
em
o
te
en
v
i
r
o
n
m
e
n
tal
m
o
n
ito
r
in
g
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
s
h
o
ws
th
e
d
esig
n
an
d
im
p
lem
e
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[
1
8
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,
[
1
9
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.
T
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p
u
t
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ata
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ased
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2
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1
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s
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us
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Fu
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el
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e
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atter
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u
r
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t
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ar
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s
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u
r
e
1.
Stru
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r
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2
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1
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Acc
o
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ased
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ased
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s
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e,
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u
les
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ated
with
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tece
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en
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s
eq
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en
t v
al
u
es c
alcu
lated
in
ea
ch
r
u
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as sh
o
wn
in
T
a
b
le
5
.
T
ab
le
5.
Fu
zz
y
ass
o
c
iatio
n
m
at
r
ix
V
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t
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in
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6
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ith
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e
n
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k
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2
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,
[
2
5
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.
T
h
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u
r
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3
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u
r
e
4
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ar
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n
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weig
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le
6.
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r
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n
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o
ciatio
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atr
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3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
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o
r
no
de
a
nd
t
ra
ns
m
is
s
io
n per
io
d c
o
ntr
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l
T
h
r
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r
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le
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n
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e
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s
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F
i
g
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r
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5
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i
n
o
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d
e
r
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v
a
lu
a
t
e
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ir
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.
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h
e
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,
c
a
l
l
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d
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M
1
"
,
w
as
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m
m
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F
i
g
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r
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6
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e
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M
2
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w
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v
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l
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t
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r
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t
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h
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.
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a
b
l
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r
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i
t
h
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s
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l
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it
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.
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u
r
e
5.
Ass
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(
lef
t)
a
n
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t)
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I
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er
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o
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ith
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r
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r
e
6.
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atter
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lar
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r
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u
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ab
le
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ce
s
s
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u
r
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f
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n
P
r
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e
ss
e
s
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1
[
ms]
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2
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3
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ms]
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11
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u
r
e
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atter
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l v
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r
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8.
Day
s
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f
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e
r
atio
n
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f
n
o
d
es M
1
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2
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d
M3
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a
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mb
e
r
Ti
me
[
s]
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u
r
a
t
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n
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s]
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me
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s]
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r
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me
[
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r
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t
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o
n
[
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s]
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a
y
1
36
71
20
47
30
63
N
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g
h
t
37
71
20
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30
63
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63
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47
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t
28
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47
30
63
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t
26
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63
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T
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9.
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y
tes s
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t in
th
e
p
e
r
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o
f
6
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a
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1
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r
s
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s
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6
7
3
7
5
8
4
0
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5
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6
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48
3
3
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60
4
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96
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0
Fig
u
r
e
8
s
h
o
ws
th
at
n
o
d
e
"M
1
"
ex
ce
ed
s
th
e
am
o
u
n
t
o
f
in
f
o
r
m
atio
n
s
en
t b
y
n
o
d
e
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2
"
s
in
ce
th
e
f
i
f
th
d
ay
.
Als
o
,
s
im
ilar
ly
,
d
u
r
in
g
th
e
4
d
ay
s
"M
1
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ex
ce
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s
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3
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i
n
th
e
am
o
u
n
t o
f
in
f
o
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atio
n
s
en
t.
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h
is
is
b
ec
au
s
e
th
e
s
o
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p
an
el
a
n
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atter
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o
f
th
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n
o
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1
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h
a
v
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a
s
u
itab
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o
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to
r
ed
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ce
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e
tr
an
s
m
is
s
io
n
p
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d
at
n
ig
h
t.
(
a)
(
b
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Fig
u
r
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8.
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(
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Featu
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F
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M
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3
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c
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3
.
8
8
F
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ad
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ata
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h
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d
e
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p
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r
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t
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d
e
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2
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t
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f
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r
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t
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o
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e
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n
t
h
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c
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s
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e
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3
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r
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r
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t
d
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e
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1
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t
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e
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r
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t
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3
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m
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m
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o
f
M
1
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2
2
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n
d
t
h
e
M2
b
y
1
2
%
.
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h
e
e
v
al
u
a
t
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n
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r
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t
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t
a
s
a
b
e
n
e
f
it
t
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e
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m
p
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t
a
n
c
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f
f
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s
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p
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ti
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t
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o
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t
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m
s
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h
e
r
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t
h
e
n
e
u
r
a
l
n
e
tw
o
r
k
m
o
d
e
l
is
a
b
o
u
t
5
0
%
s
m
al
l
e
r
c
o
m
p
a
r
e
d
t
o
t
h
e
f
u
zz
y
i
n
f
e
r
e
n
c
e
m
o
d
e
l
,
f
o
r
w
h
i
c
h
is
t
h
e
m
o
s
t
s
u
i
t
a
b
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e
i
n
t
e
r
m
s
o
f
o
p
t
i
m
i
z
a
t
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o
n
t
h
e
m
e
m
o
r
y
s
p
a
c
e
c
o
n
s
u
m
p
t
i
o
n
.
T
h
e
n
e
u
r
a
l
n
e
t
w
o
r
k
m
o
d
el
c
o
n
s
u
m
es
l
e
s
s
ti
m
e
i
n
i
ts
p
r
o
c
es
s
e
s
,
b
e
i
n
g
a
l
m
o
s
t
a
t
t
h
e
s
a
m
e
l
e
v
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l
as
a
n
e
m
b
e
d
d
e
d
o
n
e
w
i
th
o
u
t
a
n
y
p
r
o
c
e
s
s
i
n
g
a
l
g
o
r
i
t
h
m
(
M
2
)
.
T
h
e
a
d
v
a
n
t
a
g
e
o
f
i
m
p
le
m
e
n
t
i
n
g
i
n
f
e
r
e
n
c
e
m
o
d
e
l
s
i
n
s
e
n
s
o
r
n
o
d
e
s
a
l
l
o
w
s
t
h
e
m
t
o
m
a
k
e
d
e
c
is
i
o
n
s
to
s
a
v
e
e
n
e
r
g
y
b
a
s
e
d
o
n
t
h
e
i
r
k
n
o
w
l
e
d
g
e
o
f
t
h
e
i
r
e
n
v
i
r
o
n
m
e
n
t
,
b
e
h
a
v
i
n
g
l
i
k
e
c
o
g
n
i
t
i
v
e
d
e
v
i
c
es
.
A
s
f
u
tu
r
e
wo
r
k
,
d
ec
is
io
n
-
m
a
k
in
g
an
d
tr
an
s
m
is
s
io
n
tim
e
ev
alu
atio
n
co
u
ld
b
e
o
p
tim
ized
b
y
co
n
s
id
er
in
g
a
m
o
r
e
co
n
tin
u
o
u
s
o
u
tp
u
t
r
an
g
e
b
y
m
ig
r
atin
g
th
e
m
o
d
el
to
th
e
r
eg
r
ess
io
n
ty
p
e.
T
h
e
ar
ticle
h
as
lim
itatio
n
s
o
n
th
e
p
ar
am
et
er
s
u
s
ed
f
o
r
d
ec
is
io
n
m
ak
in
g
,
b
ec
au
s
e
it
d
id
n
o
t
co
n
s
id
er
en
v
i
r
o
n
m
e
n
tal
s
en
s
o
r
s
as
an
im
p
o
r
tan
t
f
ac
to
r
.
Fu
r
th
er
m
o
r
e
,
o
n
l
y
a
s
p
ec
if
ic
ty
p
e
o
f
f
u
zz
y
alg
o
r
ith
m
an
d
n
e
u
r
al
n
etwo
r
k
was e
v
alu
ated
.
ACK
N
O
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
was
co
n
d
u
cted
in
lab
o
r
ato
r
ies
I
NI
C
T
E
L
-
UNI
an
d
as
p
ar
t
o
f
th
e
Do
cto
r
ate
s
tu
d
ies
in
Sy
s
tem
s
E
n
g
in
ee
r
in
g
an
d
I
n
f
o
r
m
atics
at
th
e
Facu
lty
o
f
Sy
s
tem
s
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n
g
in
ee
r
i
n
g
a
n
d
I
n
f
o
r
m
a
tics
o
f
th
e
Natio
n
al
Un
iv
er
s
ity
of
San
Ma
r
c
o
s
(
U
NM
SM)
.
RE
F
E
R
E
NC
E
S
[1
]
S
.
I.
D.
G
u
e
rre
ro
,
J.
A.
C.
Ca
rre
r
o
,
a
n
d
O.
A.
C.
G
o
m
e
z
,
“
An
a
ly
sis
o
f
th
e
F
o
g
a
n
d
E
d
g
e
Co
m
p
u
ti
n
g
P
a
ra
d
ig
m
in
S
p
a
in
:
A
n
á
li
sis
d
e
l
P
a
ra
d
ig
m
a
F
o
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.
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]
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A.
Ku
m
a
r
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n
d
K.
Ja
y
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ra
m
a
n
,
“
Irri
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ter
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[3
]
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.
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[4
]
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.
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ll
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tt
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.
S
c
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tà,
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.
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rrit
o
,
R.
F
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ro
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a
n
d
M
.
Re
b
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o
,
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p
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ra
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[5
]
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.
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,
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.
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n
d
Y
.
S
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n
,
“
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[6
]
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B.
Tariq
a
n
d
M
.
T.
Laz
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re
sc
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,
“
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.
[7
]
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.
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u
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.
[8
]
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.
Ward
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n
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n
d
D.
S
it
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n
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k
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,
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n
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l:
M
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Re
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,
2
0
2
0
.
[9
]
F
.
S
h
a
h
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d
a
n
d
A.
Zam
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r,
“
A
n
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term
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[1
0
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.
K.
M
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.
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ll
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m
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tru
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mp
u
ter
s &
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e
c
trica
l
En
g
in
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,
p
.
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[1
1
]
J
.
W
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n
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,
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[1
2
]
R.
Ya
u
ri,
J.
Lez
a
m
a
,
a
n
d
M
.
Ri
o
s,
“
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3
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D
.
K
.
J
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2
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Au
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20
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724
[1
4
]
G
.
Cro
c
io
n
i,
“
Li
-
Io
n
Ba
tt
e
ries
P
a
ra
m
e
ter
Esti
m
a
ti
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n
Wi
t
h
Ti
n
y
Ne
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ra
l
Ne
two
rk
s
Em
b
e
d
d
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d
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n
In
telli
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n
t
I
o
T
M
icro
c
o
n
tro
ll
e
rs,”
IE
EE
Acc
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ss
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l.
8
,
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0
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.
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0
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6
.
[1
5
]
Z.
H.
Wan
g
a
n
d
G
.
J.
Ho
rn
g
,
“
A
v
e
h
icle
sa
fe
ty
m
o
n
it
o
ri
n
g
s
y
ste
m
b
a
se
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In
ter
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t
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fica
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y
sio
l
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g
ica
l
c
h
a
ra
c
teristics
,
”
Co
mp
u
ter
s
&
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e
c
trica
l
En
g
in
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rin
g
,
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l.
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9
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6
.
[1
6
]
N.
Ab
d
u
ll
a
h
,
e
t
a
l
.
,
“
To
wa
r
d
s
S
m
a
rt
Ag
ricu
lt
u
re
M
o
n
it
o
r
in
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Us
i
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g
F
u
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s,”
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.
[1
7
]
S
.
G
h
o
rp
a
d
e
,
M
.
Zen
n
a
ro
,
a
n
d
B.
S
.
Ch
a
u
d
h
a
ri,
“
To
wa
rd
s
g
re
e
n
c
o
m
p
u
ti
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g
:
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telli
g
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io
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n
sp
ire
d
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g
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n
t
fo
r
Io
T
-
e
n
a
b
led
wire
les
s
se
n
so
r
n
e
two
r
k
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
e
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Ne
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.
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.
[1
8
]
K.
B.
Ca
b
é
a
n
d
G
.
Xo
u
,
S
imp
le
M
L
P
-
Ne
u
r
a
lNetwo
rk
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i
b
ra
ry
Fo
r
M
icr
o
c
o
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tr
o
ll
e
rs
,
G
it
Hu
b
,
2
0
2
1
.
[On
li
n
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