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15
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2
,
J
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20
26
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pp.
607
-
615
I
S
S
N:
2252
-
8776
,
DO
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:
10
.
11591/i
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o
l
o
g
y
c
l
us
t
e
r
,
wh
e
r
e
i
n
e
a
c
h
c
l
u
s
t
e
r
a
c
e
n
t
r
a
l
h
u
b
n
o
de
i
s
d
i
r
e
c
t
l
y
l
i
nke
d
to
19
n
o
de
s
[
6]
.
T
h
e
h
o
m
o
ge
n
e
o
us
s
t
r
uc
t
u
r
e
a
c
r
o
s
s
a
l
l
t
he
c
l
u
s
t
e
r
s
e
n
a
bl
e
s
a
c
o
n
c
l
us
i
o
n
c
o
m
pa
r
i
n
g
i
n
v
e
s
t
i
g
a
t
i
o
n
o
f
c
o
n
n
e
c
t
i
vi
t
y
a
n
d
i
m
pa
c
t
c
r
e
a
t
e
d
by
f
a
il
ur
e
s
[
7]
.
A
f
i
ve
-
c
o
l
o
r
S
e
a
b
o
r
n
pa
l
e
t
t
e
e
m
p
l
o
y
e
d
t
o
e
n
ha
n
c
e
t
h
e
d
i
s
t
i
n
c
t
i
o
n
be
t
we
e
n
t
h
e
h
u
bs
a
n
d
l
e
a
ve
s
.
E
a
c
h
h
u
b
s
h
o
w
n
i
n
a
vi
v
i
d
pr
i
m
a
r
y
c
o
l
o
r
,
whi
l
e
t
h
e
l
e
a
ve
s
r
e
pr
e
s
e
n
t
e
d
i
n
l
o
w
-
ke
y
v
a
r
i
a
n
t
s
o
f
t
h
e
s
a
m
e
c
o
l
o
r
.
T
h
e
o
u
t
c
o
m
e
i
s
a
d
i
s
t
i
n
c
t
y
e
t
e
a
s
i
ly
d
if
f
e
r
e
n
t
i
a
bl
e
r
e
pr
e
s
e
n
t
a
t
i
o
n
o
f
e
a
c
h
c
l
u
s
t
e
r
[
8
]
.
T
h
e
s
pr
i
n
g
-
l
a
y
o
ut
tec
hni
qu
e
s
pa
t
i
a
l
ly
o
r
ga
ni
z
e
s
n
o
de
s
to
m
i
n
im
i
z
e
o
v
e
r
l
a
p
w
i
t
hi
n
c
l
us
t
e
r
s
whil
e
t
h
e
r
e
i
s
n
o
c
h
a
n
ge
i
n
m
a
i
n
t
a
i
n
i
n
g
c
l
a
r
i
t
y
o
f
s
t
r
uc
t
u
r
e
[
9]
.
T
hi
s
i
ll
u
s
t
r
a
t
e
s
h
o
w
e
nl
a
r
g
i
ng
a
c
lus
t
e
r
e
n
h
a
nc
e
s
t
h
e
c
e
n
t
r
a
l
i
t
y
o
f
t
h
e
h
u
b
.
E
a
c
h
h
u
b
s
e
r
v
e
s
a
s
im
po
r
t
a
n
t
c
o
m
m
u
ni
c
a
t
i
o
n
n
o
de
,
a
n
d
t
h
e
f
a
il
ur
e
o
f
o
ne
c
a
n
d
i
s
r
upt
a
c
o
m
p
l
e
t
e
n
e
t
wor
k
o
f
20
n
o
de
s
.
T
hi
s
s
h
o
ws
h
o
w
a
c
e
n
t
r
a
l
i
z
e
d
-
c
l
us
t
e
r
de
s
i
g
n
s
t
r
i
k
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s
a
ba
l
a
n
c
e
b
e
t
we
e
n
b
e
i
ng
v
u
l
ne
r
a
bl
e
a
n
d
b
e
i
ng
e
f
f
i
c
i
e
n
t
.
I
n
hi
g
h
de
n
s
i
t
y
c
l
us
t
e
r
i
n
g
,
F
i
gur
e
1(
c
)
s
h
o
ws
a
l
a
r
ge
f
i
xe
d
-
s
i
z
e
c
l
us
t
e
r
s
e
t
up
t
h
a
t
s
h
o
ws
h
o
w
c
e
n
t
r
a
l
i
z
e
d
de
s
i
g
ns
c
a
n
n
o
t
gr
o
w
a
s
m
uc
h
a
s
t
h
e
y
c
o
ul
d
.
A
r
i
c
h
t
w
o
-
c
o
l
o
r
pa
l
e
t
t
e
i
s
us
e
d
t
o
s
e
t
t
h
e
c
l
us
t
e
r
s
a
pa
r
t
:
t
h
e
h
u
b
n
o
de
s
a
r
e
s
h
o
wn
i
n
b
r
il
li
a
n
t
,
f
u
ll
-
i
n
t
e
ns
i
t
y
c
o
l
o
r
s
,
whil
e
t
h
e
49
l
e
a
f
n
o
de
s
a
r
e
s
h
o
wn
in
pr
e
c
i
s
e
to
n
e
s
o
f
t
h
e
s
a
m
e
c
o
l
o
r
,
whi
c
h
m
a
k
e
s
t
h
e
d
i
f
f
e
r
e
n
c
e
n
o
t
i
c
e
a
bl
e
[
10]
.
T
h
e
s
pr
i
n
g
l
a
y
o
ut
t
e
c
h
ni
que
a
r
r
a
n
ge
s
n
o
de
s
i
n
p
o
s
i
t
i
o
n
s
t
h
a
t
m
a
xim
i
z
e
s
pa
c
i
ng,
whi
c
h
m
a
ke
s
i
t
p
o
s
s
i
bl
e
to
r
e
a
d
de
n
s
e
c
l
u
s
t
e
r
s
e
v
e
n
w
h
e
n
t
h
e
y
a
r
e
v
e
r
y
l
a
r
ge
[
11]
.
T
hi
s
i
m
a
ge
il
l
us
t
r
a
t
e
s
t
h
e
r
i
s
ks
a
s
s
o
c
i
a
t
e
d
w
i
t
h
hi
g
h
c
e
n
t
r
a
l
i
z
a
t
i
o
n
f
r
o
m
a
c
l
u
s
t
e
r
i
n
g
po
i
n
t
o
f
h
u
b
f
a
il
,
49
n
o
de
s
a
r
e
s
e
pa
r
a
t
e
d
f
r
o
m
o
n
e
a
n
oth
e
r
[
12]
.
T
hi
s
il
l
u
s
t
r
a
t
e
s
t
h
e
s
e
n
s
i
t
i
v
i
t
y
o
f
e
x
t
e
ns
i
ve
s
t
a
r
c
l
u
s
t
e
r
s
.
T
h
e
s
e
hi
g
h
-
de
ns
i
t
y
c
l
us
t
e
r
s
f
a
c
il
i
t
a
t
e
c
e
n
tr
a
l
i
z
e
d
c
o
m
m
u
ni
c
a
t
i
o
n;
a
l
s
o
,
t
h
e
y
m
a
y
b
e
s
i
g
nif
i
c
a
n
t
r
i
s
k
s
o
f
s
i
ng
l
e
-
po
i
n
t
f
a
il
ur
e
s
.
T
h
e
r
e
f
o
r
e
,
i
n
l
a
r
ge
s
c
a
l
e
n
e
t
wo
r
k
de
s
i
g
n
t
h
e
r
e
i
s
a
n
e
e
d
o
f
r
e
du
n
da
n
t
o
r
hy
b
r
i
d
c
l
u
s
t
e
r
i
n
g
m
e
t
h
o
ds
.
I
n
L
uc
a
s
b
a
s
e
d
c
l
us
t
e
r
i
n
g,
L
uc
a
s
n
u
m
be
r
i
s
de
f
i
n
e
d
to
b
e
t
h
e
s
u
m
o
f
i
t
s
t
wo
i
mm
e
d
i
a
t
e
l
y
pr
e
vi
o
u
s
t
e
r
m
s
.
0
=
2
,
1
=
1
=
−
1
+
−
2
>
1
(
1)
L
uc
a
s
s
e
que
n
c
e
s
s
ha
r
e
t
h
e
s
a
m
e
r
e
c
ur
r
e
n
c
e
r
u
l
e
a
s
F
i
b
o
n
a
c
c
i
n
u
m
be
r
s
b
ut
w
i
t
h
s
o
m
e
d
i
f
f
e
r
e
n
t
i
ni
t
i
a
l
c
o
n
d
i
t
i
o
n
s
:
T
h
e
s
e
s
e
que
n
c
e
s
i
nc
r
e
a
s
e
e
x
po
ne
n
t
i
a
l
ly
a
n
d
c
a
n
b
e
us
e
d
i
n
a
da
pt
i
v
e
m
o
de
ll
i
ng
f
o
r
a
w
i
de
r
a
n
ge
o
f
a
pp
l
i
c
a
t
i
o
n
s
[
13]
.
F
i
gur
e
1(
d)
i
ll
us
t
r
a
t
e
s
a
c
l
u
s
t
e
r
i
n
g
t
e
c
h
ni
qu
e
de
r
i
ve
d
f
r
o
m
t
h
e
L
uc
a
s
s
e
r
i
e
s
,
wh
e
r
e
i
n
c
l
u
s
t
e
r
s
a
r
e
f
o
r
m
e
d
a
c
c
o
r
di
n
g
to
t
h
e
L
uc
a
s
n
u
m
be
r
s
e
que
n
c
e
s
uc
h
a
s
3,
4
,
7,
11
,
18,
a
n
d
s
o
f
o
r
t
h
.
T
h
e
n
o
v
e
l
L
uc
a
s
a
da
pt
i
v
e
s
a
m
p
li
ng
s
t
r
a
t
e
gy
(
L
A
S
S
)
a
c
qu
i
r
e
d
f
r
o
m
L
uc
a
s
,
t
hi
s
s
t
r
a
t
e
gy
r
e
gu
l
a
t
e
s
t
h
e
s
a
m
p
li
ng
r
a
t
e
a
c
c
o
r
di
n
g
t
o
t
h
e
n
e
t
wor
k
’
s
s
t
r
uc
t
ur
e
[
14]
.
(
a
)
(
b
)
(
c
)
(
d)
F
i
gur
e
1.
C
l
u
s
t
e
r
i
n
g
c
o
nf
i
gur
a
t
i
o
ns
un
de
r
d
i
f
f
e
r
e
n
t
de
n
s
i
t
y
l
e
ve
l
s
:
(
a
)
l
o
w
-
de
ns
i
t
y
c
l
u
s
t
e
r
i
n
g,
(
b
)
m
e
d
i
u
m
-
d
e
n
s
i
t
y
c
l
u
s
t
e
r
i
n
g,
(
c
)
hi
g
h
-
de
ns
i
t
y
c
l
u
s
t
e
r
i
n
g,
a
n
d
(
d)
L
uc
a
s
-
b
a
s
e
d
c
l
us
t
e
r
i
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
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o
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nov
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K
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R
ajana
)
609
2.
L
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RA
T
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UR
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n
r
e
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t
,
t
h
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i
m
pr
o
v
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m
e
n
t
s
to
wi
r
e
l
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s
s
s
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n
s
o
r
n
e
two
r
ks
(
W
S
Ns
)
h
a
v
e
b
e
e
n
c
e
n
t
e
r
e
d
o
n
t
h
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c
r
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a
t
i
o
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a
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d
im
pr
o
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f
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t
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d
[
15]
.
S
t
udi
e
s
h
a
v
e
r
e
s
u
l
t
e
d
i
n
th
e
c
r
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a
t
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o
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f
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nn
o
v
a
t
i
v
e
t
e
c
hni
que
s
,
i
n
c
l
ud
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hyb
r
i
d
bi
o
-
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p
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r
e
d
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c
hni
que
s
s
uc
h
a
s
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r
g
y
-
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f
f
i
c
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n
t
b
a
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-
m
ot
h
f
l
a
m
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pt
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z
a
t
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n
(
E
E
B
M
F
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n
d
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t
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Ns
w
i
t
h
t
h
e
a
bil
i
t
y
t
o
a
u
to
m
a
t
i
c
a
ll
y
b
a
l
a
n
c
e
t
h
e
c
o
n
s
u
m
pt
i
o
n
o
f
e
n
e
r
g
y
w
i
t
h
t
h
e
pr
o
d
uc
t
i
o
n
o
f
us
e
f
u
l
i
n
f
o
r
m
a
t
i
o
n
[
16]
.
I
m
pr
o
v
e
d
l
o
c
a
li
z
a
t
i
o
n
,
s
e
lf
-
m
o
ni
t
o
r
i
n
g,
a
n
d
m
u
l
t
i
-
o
bj
e
c
t
i
ve
o
p
t
i
m
i
z
a
t
i
o
n
t
e
c
hni
que
s
e
ns
ur
e
t
h
e
a
da
pt
a
bi
li
t
y
o
f
t
h
e
W
S
N
t
o
di
f
f
e
r
e
nt
s
i
t
ua
t
i
o
n
s
,
m
a
k
i
ng
i
t
r
e
l
i
a
bl
e
a
n
d
r
e
s
i
li
e
n
t
i
n
c
o
m
p
l
e
x
e
nvi
r
o
nm
e
n
t
s
.
T
h
e
r
e
f
o
r
e
,
t
h
e
a
b
o
v
e
f
i
nd
i
ng
s
e
s
t
a
bli
s
h
W
S
Ns
a
s
hi
g
hly
a
ut
o
n
o
m
o
us
,
e
n
e
r
g
y
-
e
f
f
i
c
ien
t
,
a
n
d
a
da
pt
a
bl
e
n
e
t
wo
r
ks
,
m
a
k
i
ng
t
h
e
m
s
u
i
t
a
bl
e
f
o
r
t
h
e
c
r
e
a
t
i
o
n
o
f
i
nn
o
va
t
i
v
e
a
pp
li
c
a
t
i
o
n
s
i
n
t
h
e
f
ut
ur
e
.
T
a
bl
e
1
s
u
m
m
a
r
i
z
e
s
a
n
d
c
o
m
pa
r
e
s
t
h
e
ke
y
e
xi
s
t
i
n
g
s
t
udi
e
s
o
n
a
da
p
t
i
v
e
s
a
m
p
li
ng
a
n
d
c
l
us
t
e
r
i
n
g
t
e
c
h
ni
que
s
i
n
W
S
N
s
,
hi
g
hli
g
h
t
i
n
g
t
h
e
i
r
m
e
t
h
o
do
l
o
g
i
e
s
,
pa
r
a
m
e
t
e
r
s
,
a
n
d
m
a
i
n
f
i
nd
i
ngs
.
T
a
bl
e
1.
S
um
m
a
r
y
o
f
r
e
l
a
t
e
d
wo
r
ks
o
n
a
da
p
t
i
v
e
s
a
m
p
li
ng
a
n
d
c
l
us
t
e
r
i
n
g
t
e
c
hni
que
s
i
n
W
S
N
s
3.
P
ROP
OS
E
D
M
ODE
L
L
uc
a
s
-
b
a
s
e
d
a
da
pt
i
v
e
s
a
m
p
l
i
ng
a
n
d
c
l
us
t
e
r
i
n
g
s
t
r
a
t
e
gy
,
whi
c
h
pr
o
po
s
e
s
a
d
i
f
f
e
r
e
n
t
a
ppr
o
a
c
h
f
o
r
c
l
u
s
t
e
r
i
n
g
a
n
d
s
a
m
p
li
ng
i
n
W
S
Ns
[
17]
.
Unl
i
ke
e
xi
s
t
i
n
g
m
e
t
h
o
ds
,
t
h
e
L
uc
a
s
-
b
a
s
e
d
a
da
pt
i
ve
s
a
m
p
l
i
ng
a
n
d
c
l
u
s
t
e
r
i
n
g
s
t
r
a
t
e
g
y
i
s
a
pur
e
l
y
m
a
t
he
m
a
t
i
c
a
l
a
ppr
o
a
c
h
t
h
a
t
d
o
e
s
n
ot
us
e
f
i
xe
d
-
s
i
z
e
a
n
d/o
r
uni
f
o
r
m
a
p
pr
o
a
c
h
e
s
a
n
d/o
r
h
e
ur
i
s
t
i
c
s
,
unli
ke
t
h
e
e
xi
s
t
i
n
g
a
da
pt
i
v
e
s
a
mp
l
i
ng
a
n
d
c
l
u
s
t
e
r
i
n
g
t
e
c
hni
qu
e
s
[
18]
.
T
h
e
pr
o
p
o
s
e
d
s
t
r
a
t
e
gy
i
n
t
e
gr
a
t
e
s
t
h
e
us
e
o
f
L
uc
a
s
n
u
m
be
r
s
.
T
h
e
s
e
que
nc
e
o
f
n
u
m
be
r
s
i
s
s
u
c
h
t
h
a
t
i
t
i
s
i
n
i
t
i
a
l
i
z
e
d
by
2
a
n
d
1,
a
n
d
e
v
e
r
y
s
uc
c
e
s
s
i
ve
n
u
m
be
r
i
s
t
h
e
s
u
mm
a
t
i
o
n
o
f
t
h
e
t
wo
p
r
e
vi
o
us
n
u
m
be
r
s
.
T
h
e
us
e
o
f
t
h
e
r
e
c
ur
s
i
ve
s
e
q
ue
n
c
e
o
f
n
u
m
be
r
s
i
s
a
s
i
g
nif
i
c
a
n
t
i
m
pr
o
v
e
m
e
n
t
i
n
t
h
e
f
r
a
mew
o
r
k
o
f
c
l
u
s
t
e
r
f
o
r
m
a
t
i
o
n
a
n
d
s
a
m
p
li
ng
i
n
t
e
r
va
l
a
da
pt
i
o
n
[
19]
.
T
h
e
f
r
a
m
e
wo
r
k
i
s
v
e
r
y
d
y
na
mi
c
a
n
d
t
h
e
i
n
t
e
gr
a
t
i
o
n
o
f
t
h
e
L
uc
a
s
s
e
que
n
c
e
o
f
n
u
m
be
r
s
f
a
c
il
i
t
a
t
e
s
n
o
n
-
unif
o
r
m
c
l
u
s
t
e
r
s
i
z
e
s
a
n
d
r
e
a
l
-
t
i
m
e
a
da
pt
i
o
n
o
f
s
a
m
p
li
ng
r
a
t
e
s
,
whi
c
h
i
s
a
s
i
g
ni
f
i
c
a
n
t
i
m
pr
o
v
e
m
e
n
t
o
v
e
r
t
h
e
e
xi
s
t
i
n
g
s
t
r
a
t
e
gi
e
s
[
20]
.
R
e
f
.
S
tu
d
y
d
e
s
ig
n
T
o
p
ol
o
gi
e
s
im
pl
e
m
e
nt
e
d
S
tu
d
y
pa
r
a
m
e
t
e
r
s
F
in
di
ngs
[
1]
S
im
ul
a
ti
o
n
-
ba
s
e
d;
h
y
br
id
M
L
pr
o
t
oc
o
l
c
o
mbi
ni
ng
K
-
me
a
ns
c
lu
s
te
r
in
g
f
o
r
a
da
pt
i
ve
r
o
ut
in
g
;
400 no
d
e
s
, 600 r
o
unds
H
i
e
r
a
r
c
hi
c
a
l
c
lu
s
te
r
-
ba
s
e
d t
o
p
o
l
o
g
y
;
in
tr
a
-
c
lu
s
t
e
r
(
n
o
de
-
to
-
C
H
)
a
nd
in
t
e
r
-
c
lu
s
te
r
(
C
H
-
to
-
B
S
)
mul
ti
-
h
o
p
c
o
mm
uni
c
a
ti
o
n
R
e
s
id
ua
l
e
ne
r
g
y
,
di
s
ta
nc
e
t
o
B
S
, n
o
d
e
tr
a
f
f
i
c
, a
li
v
e
n
o
d
e
s
p
e
r
r
o
und, C
H
s
e
l
e
c
ti
o
n
c
r
it
e
r
ia
, n
e
tw
o
r
k l
i
f
e
ti
m
e
(
F
N
D
/
L
N
D
)
, P
D
R
, t
o
ta
l
e
n
e
r
g
y
c
o
ns
umpt
io
n
O
ut
pe
r
f
or
ms
L
E
A
C
H
, D
M
H
T
-
L
E
A
C
H
,
E
D
M
H
T
-
L
E
A
C
H
i
n
ne
tw
o
r
k l
i
f
e
t
im
e
,
e
n
e
r
g
y
e
f
f
i
c
ie
n
c
y
,
a
nd P
D
R
a
nd l
a
r
ge
-
s
c
a
l
e
de
pl
oy
m
e
nt
c
ha
ll
e
ng
e
s
[
2]
C
o
nt
e
x
t
-
a
w
a
r
e
S
e
nd
-
on
-
D
e
lt
a
a
da
pt
iv
e
tr
a
ns
mi
s
s
io
n
f
o
r
s
a
v
in
g
tr
a
f
f
i
c
a
nd e
n
e
r
g
y
i
n s
e
ns
or
ne
tw
o
r
ks
D
y
na
mi
c
t
r
a
f
f
ic
(
I
oT
, W
S
N
)
C
o
mm
uni
c
a
ti
o
n
r
e
du
c
ti
o
n,
e
ve
nt
-
dr
i
ve
n
s
a
mpl
in
g
A
c
hi
e
v
e
s
s
ig
ni
f
i
c
a
nt
c
omm
uni
c
a
ti
o
n
r
e
du
c
ti
o
n w
it
h b
o
und
e
d
e
r
r
or
a
t
o
pt
im
a
l
f
or
ba
ndw
id
th
/
e
ne
r
g
y
-
r
e
s
tr
i
c
t
e
d d
e
pl
oy
m
e
nt
s
[
3]
D
e
v
e
l
o
ps
a
pa
r
a
me
tr
i
c
ma
c
h
in
e
le
a
r
ni
ng
-
ba
s
e
d a
da
pt
i
ve
s
a
mpl
in
g a
lg
o
r
it
hm
f
or
c
lu
s
te
r
e
d
W
S
N
s
C
lu
s
te
r
e
d W
S
N
s
E
ne
r
g
y
, s
a
mpl
in
g
qua
li
t
y
,
e
r
r
or
t
hr
e
s
h
o
ld
s
ML
-
gui
d
e
d a
da
pt
i
ve
s
a
mpl
in
g
e
f
f
e
c
ti
ve
l
y
s
e
l
e
c
ts
me
a
ni
ng
f
ul
r
a
t
e
s
ba
s
e
d o
n
e
r
r
or
b
o
unds
, r
e
du
c
in
g
e
n
e
r
g
y
w
it
h mi
ni
ma
l
l
o
s
s
i
n da
ta
qua
li
t
y
[
5]
H
y
br
id
M
a
y
f
l
y
o
pt
im
iz
a
ti
o
n
a
nd
a
nt
c
o
l
o
n
y
o
pt
im
i
z
a
ti
o
n
(
M
F
O
A
-
E
A
C
O
)
f
o
r
c
lu
s
te
r
he
a
d
a
nd r
o
ut
in
g s
e
le
c
ti
o
n
C
lu
s
te
r
e
d W
S
N
(
s
ma
r
t
c
it
y
, I
oT
)
E
ne
r
g
y
e
f
f
i
c
i
e
nc
y
,
r
o
ut
e
o
pt
im
i
z
a
ti
o
n, l
i
f
e
s
pa
n
O
ut
pe
r
f
or
ms
H
S
F
L
-
B
O
A
,
H
S
R
O
D
E
-
F
F
A
, a
nd o
th
e
r
c
ut
ti
ng
-
e
dg
e
p
r
o
t
o
c
ol
s
f
or
ne
twor
k s
ta
bi
li
t
y
, l
i
f
e
s
pa
n, a
nd
r
e
du
c
e
d e
n
e
r
g
y
us
a
ge
[
6]
P
r
o
p
o
s
e
s
r
a
te
a
da
pt
i
ve
c
o
mpr
e
s
s
e
d s
a
mpl
in
g
f
o
r
i
ma
g
e
s
e
ns
o
r
n
o
d
e
s
us
in
g r
e
gi
o
n
di
v
is
i
o
n
W
S
N
w
it
h
mul
ti
me
di
a
n
o
d
e
s
S
a
mpl
in
g r
a
te
,
c
o
mpr
e
s
s
io
n,
e
n
e
r
g
y
,
r
e
gi
o
n
c
o
mpl
e
x
it
y
R
e
gi
o
n
-
ba
s
e
d a
da
pt
i
v
e
s
a
mpl
in
g
ma
x
im
i
z
e
s
c
o
mpr
e
s
s
io
n a
nd i
ma
g
e
qua
li
t
y
w
hi
l
e
dr
a
ma
ti
c
a
ll
y
l
o
w
e
r
in
g
e
n
e
r
g
y
n
e
e
ds
[
7]
E
nha
nc
e
d bi
o
-
in
s
pi
r
e
d
e
n
e
r
g
y
-
e
f
f
ic
i
e
nt
l
oc
a
li
z
a
ti
o
n (
E
B
E
E
L
)
r
o
ut
in
g
f
o
r
m
o
bi
le
W
S
N
s
M
o
bi
l
e
, c
lu
s
t
e
r
-
ba
s
e
d
L
oc
a
li
z
a
ti
o
n,
e
ne
r
g
y
c
o
ns
tr
a
in
ts
E
B
E
E
L
s
ig
ni
f
ic
a
nt
l
y
r
e
du
c
e
s
p
o
w
e
r
c
o
ns
umpt
i
o
n a
nd i
mpr
ove
s
mo
b
il
it
y
-
a
da
pt
e
d ne
tw
o
r
k s
c
a
la
bi
li
t
y
[
8]
E
ne
r
g
y
/r
e
l
e
v
a
nc
e
-
a
w
a
r
e
a
da
pt
iv
e
m
o
ni
t
o
r
in
g
f
or
s
e
l
f
-
a
w
a
r
e
s
e
ns
or
ne
twor
k
ma
na
ge
me
nt
H
i
e
r
a
r
c
hi
c
a
l/
M
C
S
S
e
ns
o
r
-
r
e
l
e
v
a
n
c
e
, s
e
l
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b
)
f
l
o
w
c
h
a
r
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o
f
po
we
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l
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a
bl
e
2.
N
ot
a
t
i
o
n
t
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bl
e
f
o
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o
we
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o
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m
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l
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y
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o
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ip
ti
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n
h
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umbe
r
of
h
o
u
r
s
f
o
r
s
im
ul
a
ti
o
n (
us
e
r
i
nput
, i
nt
e
g
e
r
, 1
-
24
)
i
L
oo
p
c
o
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e
r
,
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p
r
e
s
e
nt
in
g t
h
e
c
ur
r
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nt
h
o
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r
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n s
im
ul
a
ti
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n (
r
a
ng
e
s
f
r
o
m 1 t
o
h
)
dur
a
ti
o
n
T
ot
a
l
dur
a
ti
o
n
f
o
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e
a
c
h h
o
ur
,
c
o
mput
e
d a
s
3600 *
I
s
e
c
o
nds
L
o
w
r
a
te
S
a
mpl
in
g a
t
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te
r
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ls
of
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e
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o
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i
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M
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um
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e
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N
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P
o
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umpt
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n (
in
mW
)
L
o
w
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p
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r
w
id
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-
a
r
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a
ne
tw
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k
(
L
P
W
A
N
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a
dj
us
tm
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P
o
w
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r
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ld
L
P
W
A
N
t
r
a
ns
mi
s
s
io
n ove
r
h
e
a
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
I
n
f
&
C
o
m
m
u
n
T
e
c
hn
o
l
I
S
S
N:
2252
-
8776
A
nov
e
l
L
uc
as
-
bas
e
d
adapti
v
e
s
ampling
op
ti
miz
ati
on
f
or
e
nhanc
ing
ne
t
w
or
k
…
(
K
anak
a
R
aju
R
ajana
)
611
Al
go
r
i
t
hm
1.
P
o
we
r
c
o
n
s
u
m
pt
i
o
n
s
im
u
l
a
t
i
o
n
w
i
t
h
e
n
e
r
g
y
h
a
r
ve
s
t
i
n
g
Input:
Number of hours for simulation (from user, 1
–
24)
Output:
Visualization (plot) of net power consumption (mW) across sampling strategies
Step
1: Start
Step 2: Read the number of hours (h) from the user (via GUI)
Step 3: Validate h (must be between 1 and 24). If invalid, display error and stop.
Step 4: For each
hour i (1 to h), set duration as 3600 × i seconds.
Step 5: For each duration:
-
Get user input for hours and Convert hours to durations (seconds)
-
Initialize
power
consumption
l
ists
and
Set
sampling
intervals
(low,
m
edium,
high)
Step 6:
Calculate number of samples for each sampling rate:
-
Low (10s), Medium (50s), High (80s)
Step 7: Compute total active time for each sampling rate (samples × 0.2s).
Step 8: Generate Lucas intervals up to the duration.
-
Count number of Lucas events.
-
Calculate active time for Lucas sampling (events × 0.2s).
-
Calculate total Lucas time (sum of intervals).
Step
9:
Generate
random
active
time
for
Lucas
strategy
(between
5%
–
15%
of
total
Lucas
interval time).
Step 10: For each strategy calculate net power consumption with energy harvesting:
-
Use
active
&
inactive
times
,
apply
LPWAN
adjustment
&
sub
tract
ha
rvested
energy.
Step 11: Store results for all strategies.
Step 12: Pass results to plotting function.
Step
13:
Generate
and
display
a
plot
comp
aring
net
power
consumption
(mW)
duration
(hours)
for all rates.
Step 14: Stop
4.
RE
S
UL
T
AN
D
DI
S
CU
S
S
S
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hi
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h
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nd
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h
e
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r
s
o
ur
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a
r
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d
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Al
l
ba
s
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li
ne
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h
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s
a
n
d
i
nn
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v
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r
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us
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d
f
o
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c
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m
pa
r
a
t
i
ve
s
t
udy
;
t
h
e
b
a
s
e
li
ne
a
l
go
r
i
t
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s
i
nc
l
ud
e
K
-
m
e
a
n
s
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HE
E
D,
a
n
d
L
E
AC
H.
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o
a
s
s
ur
e
t
h
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c
r
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d
i
bil
i
t
y
o
f
t
h
e
da
t
a
,
m
u
l
t
i
p
l
e
s
i
m
u
l
a
t
i
o
n
r
uns
a
r
e
pe
r
f
o
r
m
e
d,
a
l
o
n
g
w
i
t
h
v
a
r
i
a
n
c
e
o
r
c
o
nf
i
de
n
c
e
i
n
t
e
r
v
a
l
s
[
23]
.
T
h
i
s
t
h
o
r
o
ugh
a
ppr
o
a
c
h
a
s
s
ur
e
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t
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bli
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pe
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e
r
t
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o
n
s
[
24]
.
T
h
e
s
ur
vi
va
bi
li
t
y
a
n
d
s
us
t
a
i
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a
bil
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o
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h
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i
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t
h
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e
t
wo
r
k
de
pe
n
ds
o
n
t
h
e
t
i
m
e
,
b
a
t
t
e
r
y
po
we
r
a
n
d
a
c
c
ur
a
c
y
.
W
hi
c
h
i
s
f
ur
t
h
e
r
c
a
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b
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pr
e
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a
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h
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a
c
c
ur
a
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o
f
c
l
u
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e
r
s
whi
c
h
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pe
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d
s
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n
t
h
e
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o
o
f
n
o
de
s
.
a
n
d
t
h
e
y
a
r
e
a
na
l
y
z
e
d
i
n
l
o
w
r
a
t
e
,
m
e
d
i
u
m
r
a
t
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,
hi
g
h
r
a
t
e
,
r
a
n
do
m
r
a
t
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a
n
d
L
uc
a
s
r
a
t
e
.
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o
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x
a
m
i
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h
e
a
b
o
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e
s
a
i
d
r
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s
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o
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s
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r
t
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u
m
be
r
o
f
n
o
de
s
500,
1
,
000
,
1
,
500,
2
,
000
,
2
,
50
0
L
E
A
C
H
a
l
go
r
i
t
hm
,
DE
C
(
e
x
t
e
n
s
i
o
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o
f
l
e
a
c
h
)
,
HE
E
D
(
c
o
m
bi
ne
s
e
n
e
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g
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n
d
c
o
m
m
u
ni
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t
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o
f
c
l
us
t
e
r
h
e
a
d
s
e
l
e
c
t
i
o
n
)
,
K
-
m
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a
n
s
c
l
u
s
t
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r
i
n
g
(
pa
r
t
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o
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s
b
a
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d
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n
s
i
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l
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r
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)
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l
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f
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s
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a
c
h
c
l
u
s
t
e
r
[
25
]
.
I
n
l
o
w
r
a
t
e
,
l
e
t
us
c
o
n
s
i
de
r
5
f
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h
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t
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ot
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l
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de
s
a
r
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500
t
h
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h
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u
m
be
r
o
f
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us
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o
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d
b
e
100
a
n
d
it
f
o
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m
s
200
c
l
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s
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s
wh
e
n
t
h
e
t
ot
a
l
c
o
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s
i
de
r
e
d
no
de
s
a
r
e
1
,
000
.
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n
m
e
d
i
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m
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a
t
e
,
l
e
t
us
c
o
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s
i
de
r
20
f
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d
no
de
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e
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t
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500
t
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h
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f
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50
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e
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h
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o
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s
20
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e
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e
tot
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.
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I
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h
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h
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Evaluation Warning : The document was created with Spire.PDF for Python.
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3(
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5.
CONC
L
USI
ON
I
n
t
hi
s
s
t
ud
y
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s
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nn
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v
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t
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m
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ng
s
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w
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p
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f
t
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uc
a
s
s
e
que
n
c
e
n
u
m
b
e
r
s
.
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h
e
s
t
udy
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n
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e
a
n
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ly
s
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l
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s
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n
500
a
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3
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,
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g
h
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d
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un
e
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gur
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t
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s
.
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h
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t
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o
w
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da
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de
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a
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(
s
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=
15.
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)
.
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r
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(
r
=
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.
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[
1]
M
.
A
kr
a
m
e
t
al
.
,
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E
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,
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E
E
A
c
c
e
s
s
, v
ol
. 13, pp. 70849
–
70871, 2025, d
o
i:
10.1109/A
C
C
E
S
S
.2025.3562368.
[
2]
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.
R
.
M
a
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tí
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e
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nc
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e
z
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J
.
C
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r
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P
a
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ll
a
,
J
.
O
li
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s
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.
M
.
P
a
l
o
ma
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e
s
,
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nd
F
.
L
e
ón
-
G
a
r
c
ía
,
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nt
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e
ns
o
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tw
or
ks
,
”
A
d
H
oc
N
e
tw
or
k
s
,
v
o
l.
178,
p.
103967,
N
ov
.
2
025,
do
i:
10.1016/j
.a
dh
oc
.2025.103967.
[
3]
H
.
A
lg
a
br
o
un
a
nd
L
.
H
å
ka
ns
s
o
n,
“
P
a
r
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me
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h
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n
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vi
r
o
nm
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nt
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l
m
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g,
”
J
our
nal
of
N
e
tw
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r
k
and
Sy
s
te
m
s
M
anage
m
e
nt
,
v
ol
.
33,
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o
.
1,
p.
5,
J
a
n.
2
025,
do
i:
10.1007/s
10922
-
024
-
09881
-
1.
[
4]
K
.
R
.
R
a
ja
na
a
nd
S
.
S
.
A
mi
r
ip
a
ll
i,
“
A
no
ve
l
L
uc
a
s
-
ba
s
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d
c
l
us
te
r
in
g
o
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ti
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in
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ur
v
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bi
li
t
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in
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t
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s
ig
n,
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ngi
ne
e
r
in
g,
T
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c
hnol
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&
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d
Sc
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R
e
s
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ar
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h
,
v
o
l.
15,
n
o
.
1,
pp.
1990
3
–
19909,
F
e
b.
2
025,
do
i:
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e
ta
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r
.9232.
[
5]
V
.
G
.
S
a
r
a
n
y
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a
nd
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.
K
a
r
th
ik
,
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i
o
-
in
s
pi
r
e
d
in
t
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ll
ig
e
nt
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ut
in
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in
W
S
N
:
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te
gr
a
ti
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ma
y
f
l
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im
i
z
a
ti
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n
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nd
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nha
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d
a
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l
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y
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y
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e
f
f
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c
lu
s
t
e
r
f
or
ma
ti
o
n
a
nd
ma
in
te
na
nc
e
,
”
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om
put
e
r
M
ode
li
ng
in
E
ngi
ne
e
r
in
g
&
Sc
ie
nc
e
s
,
vo
l.
141,
no
. 1, pp. 127
–
150, 2024, d
o
i:
10.32604/
c
m
e
s
.2024.053825.
[
6]
W
.
W
a
ng
e
t
al
.
,
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R
a
te
a
da
pt
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s
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ir
e
l
e
s
s
s
e
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o
r
ne
tw
or
ks
,
”
Sc
ie
nt
if
ic
R
e
p
or
ts
,
vo
l.
14, n
o
. 1, p.
29666, N
ov
. 2024, d
o
i:
10.1038/s
41598
-
024
-
8
1603
-
8.
[
7]
S
.
T
umul
a
e
t
al
.
,
“
A
n
e
nha
nc
e
d
bi
o‐
in
s
pi
r
e
d
e
n
e
r
g
y
‐
e
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f
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c
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e
nt
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or
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o
bi
l
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ir
e
l
e
s
s
s
e
ns
o
r
n
e
tw
o
r
k,
”
I
nt
e
r
nat
io
nal
J
our
nal
of
C
om
m
uni
c
at
io
n Sy
s
t
e
m
s
, v
o
l.
37, n
o
. 1
2, A
ug. 2024, do
i:
1
0.1002/da
c
.5803.
[
8]
D
.
A
r
na
iz
,
F
.
M
o
ll
,
E
.
A
la
r
c
ón,
a
nd
X
.
V
il
a
jo
s
a
na
,
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E
ne
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g
y
a
n
d
r
e
le
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a
n
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e
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a
w
a
r
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a
da
pt
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v
e
m
o
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t
or
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g
me
th
o
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o
r
no
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e
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r
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n
e
r
g
y
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o
ns
tr
a
in
ts
,
”
I
nt
e
gr
at
io
n
, vo
l.
94, p. 10
2097, J
a
n. 2024, do
i:
10.1016/j
.
v
ls
i.
2023.102
097.
[
9]
H
.
H
u,
X
.
F
a
n,
a
nd
C
.
W
a
ng,
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E
ne
r
g
y
e
f
f
ic
ie
nt
c
lu
s
t
e
r
in
g
a
nd
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o
ut
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l
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r
ti
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o
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z
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ti
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. 3, pp. 806
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13]
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l.
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H
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s
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a
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nt
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s
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nf
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nc
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ngi
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an
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ic
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,
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ne
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k,
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pt
imi
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n L
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r
s
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R
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uma
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nt
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n
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or
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our
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lr
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uz
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,
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K
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N
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M
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it
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