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
9
,
No
.
3
,
Sep
tem
b
er
2
0
2
5
,
p
p
.
1
9
64
~
1
9
75
I
SS
N:
2502
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
3
9
.i
3
.
pp
1
9
64
-
1
9
75
1964
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
O
ptimi
zing
ene
rg
y
ef
ficie
ncy
and i
mpro
v
ed security
in
wir
eless
sens
o
r net
wo
rks
using
energy
-
cen
t
ric MJ
SO
and
M
ACO
f
o
r
clustering
and
routing
Srini
v
a
s
K
a
la
s
k
a
r
1
,
Cha
nn
a
pp
a
B
hy
ri
2
1
D
e
p
a
r
t
me
n
t
o
f
I
n
st
r
u
m
e
n
t
a
t
i
o
n
Te
c
h
n
o
l
o
g
y
,
P
D
A
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
G
u
l
b
a
r
g
a
,
I
n
d
i
a
2
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
o
n
i
c
s a
n
d
I
n
st
r
u
men
t
a
t
i
o
n
,
P
D
A
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
G
u
l
b
a
r
g
a
,
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
Au
g
21
,
2
0
2
4
R
ev
is
ed
Ap
r
17
,
2
0
2
5
Acc
ep
ted
J
u
l
3
,
2
0
2
5
Wi
re
les
s
se
n
so
r
n
e
two
rk
s
(W
S
Ns
)
p
lay
a
p
i
v
o
tal
ro
le
in
v
a
ri
o
u
s
a
p
p
li
c
a
ti
o
n
s,
b
u
t
th
e
ir
e
n
e
rg
y
-
c
o
n
stra
in
e
d
n
a
t
u
re
p
o
se
s
sig
n
if
ica
n
t
c
h
a
ll
e
n
g
e
s
to
t
h
e
i
r
su
sta
in
a
b
le
o
p
e
ra
ti
o
n
.
In
t
h
is
p
a
p
e
r,
we
p
ro
p
o
se
a
n
o
v
e
l
a
p
p
r
o
a
c
h
t
o
e
n
h
a
n
c
e
e
n
e
rg
y
e
fficie
n
c
y
in
WS
Ns
b
y
lev
e
ra
g
in
g
e
n
e
r
g
y
-
c
e
n
tri
c
m
u
lt
i
-
o
b
je
c
ti
v
e
jay
a
se
a
rc
h
o
p
ti
m
iza
ti
o
n
(
M
JSO)
a
n
d
m
u
lt
i
-
o
b
jec
ti
v
e
a
n
t
c
o
l
o
n
y
o
p
ti
m
iza
ti
o
n
(M
ACO
)
fo
r
c
lu
ste
rin
g
a
n
d
ro
u
ti
n
g
.
O
u
r
m
e
th
o
d
a
ims
to
a
d
d
re
ss
th
e
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
issu
e
s
b
y
o
p
t
imiz
in
g
c
l
u
ste
rin
g
a
n
d
ro
u
ti
n
g
stra
teg
i
e
s
sim
u
lt
a
n
e
o
u
sl
y
.
T
h
e
e
n
e
rg
y
-
c
e
n
tri
c
M
JSO
a
lg
o
rit
h
m
is
e
m
p
lo
y
e
d
to
in
telli
g
e
n
tl
y
o
r
g
a
n
ize
se
n
so
r
n
o
d
e
s
in
t
o
c
lu
ste
rs,
c
o
n
si
d
e
ri
n
g
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
,
n
e
two
r
k
c
o
v
e
ra
g
e
,
a
n
d
c
o
n
n
e
c
ti
v
it
y
.
T
h
e
m
u
lt
i
-
o
b
jec
ti
v
e
M
ACO
a
lg
o
rit
h
m
o
p
ti
m
ize
s
ro
u
ti
n
g
p
a
th
s
b
y
b
a
lan
c
i
n
g
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
a
n
d
n
e
two
r
k
l
ifetime
o
b
jec
ti
v
e
s.
Th
r
o
u
g
h
i
n
teg
ra
ti
o
n
a
n
d
sim
u
latio
n
s,
t
h
e
a
p
p
ro
a
c
h
e
n
h
a
n
c
e
s
e
n
e
rg
y
e
fficie
n
c
y
in
W
S
Ns
fo
r
v
a
ri
o
u
s
a
p
p
li
c
a
ti
o
n
s
li
k
e
e
n
v
iro
n
m
e
n
tal
m
o
n
it
o
r
in
g
a
n
d
sm
a
rt
c
it
ies
,
a
d
v
a
n
c
in
g
e
n
e
r
g
y
-
e
fficie
n
t
c
lu
ste
rin
g
a
n
d
r
o
u
ti
n
g
.
B
y
in
teg
ra
ti
n
g
e
n
e
r
g
y
-
c
e
n
tri
c
M
J
S
O
a
n
d
M
ACO
in
t
o
c
lu
ste
rin
g
a
n
d
r
o
u
ti
n
g
p
r
o
to
c
o
ls,
WS
Ns
c
a
n
a
c
h
iev
e
s
ig
n
ifi
c
a
n
t
imp
ro
v
e
m
e
n
ts
i
n
e
n
e
r
g
y
e
fficie
n
c
y
a
n
d
se
c
u
rit
y
w
h
il
e
m
a
in
tai
n
in
g
re
li
a
b
l
e
c
o
m
m
u
n
ica
ti
o
n
a
n
d
d
a
ta d
e
li
v
e
ry
.
K
ey
w
o
r
d
s
:
E
n
er
g
y
co
n
s
u
m
p
tio
n
E
n
er
g
y
-
ce
n
tr
ic
o
p
tim
izatio
n
MA
C
O
Me
tah
eu
r
is
tic
alg
o
r
ith
m
MJSO
Netwo
r
k
life
tim
e
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
:
Srin
iv
as
Kala
s
k
ar
Dep
ar
tm
en
t o
f
I
n
s
tr
u
m
e
n
tatio
n
T
ec
h
n
o
lo
g
y
,
PDA
C
o
lleg
e
o
f
E
n
g
in
ee
r
in
g
Gu
lb
ar
g
a,
I
n
d
ia
E
m
ail: sr
in
iv
ask
_
1
2
1
2
@
r
ed
if
f
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
W
ir
eles
s
s
en
s
o
r
n
etwo
r
k
s
(
W
SN
s
)
h
av
e
em
er
g
ed
as
a
cr
itical
tech
n
o
lo
g
y
f
o
r
m
o
n
i
to
r
in
g
an
d
g
ath
er
in
g
d
ata
in
v
ar
i
o
u
s
ap
p
licatio
n
s
s
u
ch
as
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
in
g
,
h
ea
lth
ca
r
e,
s
m
ar
t
cities,
an
d
in
d
u
s
tr
ial
au
to
m
atio
n
[
1
]
,
[
2
]
.
Ho
wev
er
,
o
n
e
o
f
th
e
m
ajo
r
ch
allen
g
es
f
ac
in
g
W
SNs
is
th
e
lim
ited
en
er
g
y
r
eso
u
r
ce
s
o
f
th
e
s
en
s
o
r
n
o
d
es,
wh
ich
r
estricts
th
e
n
etwo
r
k
’
s
o
p
er
ati
o
n
al
life
tim
e
an
d
p
er
f
o
r
m
an
ce
[
3
]
,
[
4
]
.
I
n
r
esp
o
n
s
e
to
th
is
ch
allen
g
e,
r
es
ea
r
ch
er
s
an
d
en
g
in
ee
r
s
h
a
v
e
b
ee
n
ac
tiv
ely
ex
p
lo
r
in
g
e
n
er
g
y
-
ef
f
icien
t
s
o
lu
tio
n
s
to
p
r
o
l
o
n
g
n
etwo
r
k
life
tim
e
an
d
o
p
tim
ize
en
e
r
g
y
co
n
s
u
m
p
tio
n
[
5
]
.
T
h
e
f
o
cu
s
es
o
n
o
p
tim
izin
g
en
er
g
y
ef
f
ic
ien
cy
in
W
SNs
b
y
lev
er
a
g
in
g
en
er
g
y
-
ce
n
tr
ic
m
u
lti
-
o
b
je
ctiv
e
jay
a
s
ea
r
ch
o
p
tim
izatio
n
(
MJSO)
an
d
m
u
lti
-
o
b
jectiv
e
an
t
co
lo
n
y
o
p
tim
izatio
n
(
MA
C
O)
f
o
r
clu
s
ter
i
n
g
an
d
r
o
u
tin
g
.
C
lu
s
ter
in
g
an
d
r
o
u
tin
g
ar
e
f
u
n
d
am
e
n
tal
task
s
in
W
SNs
,
an
d
th
eir
e
f
f
icien
t
m
a
n
ag
e
m
en
t
p
lay
s
a
cr
u
cial
r
o
le
i
n
m
itig
atin
g
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
ex
ten
d
in
g
t
h
e
n
etwo
r
k
’
s
life
tim
e
[
6
]
-
[
8
]
.
T
r
ad
itio
n
al
clu
s
ter
in
g
alg
o
r
it
h
m
s
,
s
u
ch
as
lo
w
en
er
g
y
ad
ap
tiv
e
clu
s
ter
in
g
h
ier
ar
ch
y
(
L
E
AC
H)
,
h
av
e
b
ee
n
wid
ely
u
s
ed
to
o
r
g
a
n
ize
s
en
s
o
r
n
o
d
es
in
to
clu
s
ter
s
w
i
t
h
a
d
es
i
g
n
a
t
e
d
cl
u
s
t
e
r
h
ea
d
[
9
]
-
[
1
2
]
.
H
o
w
e
v
e
r
,
t
h
e
s
e
a
l
g
o
r
i
t
h
m
s
m
a
y
n
o
t
f
u
l
l
y
a
d
d
r
e
s
s
t
h
e
e
n
e
r
g
y
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:
2502
-
4
7
5
2
Op
timiz
in
g
en
erg
y
efficien
cy
a
n
d
imp
r
o
ve
d
s
ec
u
r
ity
in
w
ir
el
ess
s
en
s
o
r
…
(
S
r
in
iva
s
K
a
la
s
k
a
r
)
1965
c
o
n
s
u
m
p
t
i
o
n
c
h
a
l
l
e
n
g
e
s
i
n
d
y
n
a
m
i
c
a
n
d
l
a
r
g
e
-
s
c
a
l
e
W
SN
s
.
T
h
e
r
e
f
o
r
e
,
t
h
e
r
e
i
s
a
g
r
o
w
i
n
g
i
n
t
e
r
e
s
t
i
n
i
n
t
e
g
r
a
ti
n
g
a
d
v
a
n
c
e
d
o
p
t
i
m
i
z
a
ti
o
n
t
e
c
h
n
i
q
u
e
s
t
o
e
n
h
a
n
c
e
t
h
e
e
n
e
r
g
y
e
f
f
i
ci
e
n
c
y
o
f
c
l
u
s
t
e
r
i
n
g
a
n
d
r
o
u
t
i
n
g
[
1
3
]
-
[
1
5
]
.
W
SN
u
s
es
d
i
s
tr
ib
u
ted
s
en
s
o
r
s
to
m
o
n
ito
r
clim
ate
ch
an
g
es
o
r
tr
ac
k
m
o
b
ile
tar
g
ets
lik
e
wild
l
if
e
o
r
f
ir
e
s
p
r
ea
d
.
T
h
e
W
SN
co
m
b
in
es
em
b
ed
d
e
d
co
m
p
u
tin
g
,
s
en
s
o
r
tech
n
o
lo
g
y
,
wir
eless
co
m
m
u
n
icatio
n
,
an
d
d
ata
p
r
o
ce
s
s
in
g
[
1
6
]
-
[
2
0
]
.
T
h
e
s
en
s
o
r
s
co
llect
d
ata
an
d
s
en
d
i
t
to
a
b
ase
s
tatio
n
(
B
S)
d
ir
ec
tl
y
o
r
t
h
r
o
u
g
h
o
th
er
s
en
s
o
r
s
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
is
a
k
ey
ch
allen
g
e
f
o
r
W
S
Ns,
af
f
ec
tin
g
s
en
s
o
r
life
s
p
an
wh
ile
o
b
s
er
v
in
g
,
p
r
o
ce
s
s
in
g
,
a
n
d
co
m
m
u
n
icatin
g
d
ata.
C
lu
s
ter
in
g
is
a
co
m
m
o
n
m
eth
o
d
to
im
p
r
o
v
e
e
n
er
g
y
e
f
f
icien
cy
in
W
SNs
,
g
r
o
u
p
in
g
s
en
s
o
r
s
in
to
clu
s
ter
s
with
a
clu
s
ter
h
ea
d
(
C
H)
g
ath
er
in
g
a
n
d
tr
a
n
s
m
itti
n
g
d
ata
t
o
th
e
B
S.
R
o
u
tin
g
d
ata
in
W
SNs
is
ch
allen
g
in
g
d
u
e
to
th
eir
u
n
iq
u
e
ch
a
r
ac
ter
i
s
tics
,
s
u
ch
as
b
atter
y
-
o
p
er
ated
s
en
s
o
r
s
an
d
m
o
b
ile
co
m
m
u
n
icatio
n
[
2
1
]
-
[
2
3
]
.
O
p
tim
al
C
H
s
elec
tio
n
an
d
r
o
u
tin
g
s
tr
ateg
ies
ar
e
cr
u
cial
f
o
r
en
h
a
n
cin
g
W
SN
p
er
f
o
r
m
an
ce
.
C
o
m
m
itm
en
ts
i
n
clu
d
e
t
h
e
d
e
v
elo
p
m
e
n
t
o
f
e
n
er
g
y
-
ef
f
icien
t
C
H
s
elec
tio
n
a
n
d
r
o
u
tin
g
m
eth
o
d
s
f
o
r
W
SNs
,
s
u
ch
as
th
e
E
C
-
MJ
SO
an
d
E
C
-
MA
C
O
alg
o
r
ith
m
s
[
2
4
]
-
[
2
7
]
.
T
h
ese
alg
o
r
ith
m
s
h
elp
r
ed
u
ce
en
e
r
g
y
co
n
s
u
m
p
tio
n
b
y
ch
o
o
s
in
g
o
p
ti
m
al
C
Hs
an
d
f
in
d
in
g
ef
f
icien
t
p
ath
s
th
r
o
u
g
h
th
em
,
lea
d
in
g
t
o
im
p
r
o
v
ed
p
ac
k
et
d
eliv
er
y
a
n
d
l
o
wer
en
e
r
g
y
u
s
ag
e
[
2
8
]
.
T
h
e
p
r
o
p
o
s
ed
s
tr
at
eg
y
E
C
-
MJSO
-
MA
C
O
is
ex
p
lain
ed
in
d
etail
in
s
ec
tio
n
3
,
wh
ile
s
ec
tio
n
4
d
is
cu
s
s
es
th
e
r
esu
lt
s
an
d
f
in
d
in
g
s
o
f
th
e
alg
o
r
ith
m
s
.
T
h
e
co
n
clu
s
io
n
is
p
r
esen
ted
in
s
ec
tio
n
5
,
s
u
m
m
ar
izin
g
th
e
o
v
er
all
ap
p
r
o
ac
h
an
d
its
im
p
ac
t
o
n
en
er
g
y
e
f
f
icien
cy
a
n
d
p
e
r
f
o
r
m
an
ce
in
W
SNs
.
2.
M
E
T
H
O
D
2
.
1
.
E
C
-
M
J
SO
T
h
e
E
C
-
MJSO
-
MA
C
O
m
eth
o
d
,
is
a
n
o
v
el
ap
p
r
o
ac
h
f
o
r
o
p
tim
izin
g
en
er
g
y
ef
f
icien
c
y
in
W
SN
s
th
r
o
u
g
h
clu
s
ter
in
g
a
n
d
r
o
u
tin
g
s
tr
ateg
ies.
Her
e’
s
an
o
u
tlin
e
o
f
th
e
E
C
-
MJSO
-
MA
C
O
m
eth
o
d
.
−
I
n
itializatio
n
:
in
itialize
th
e
p
o
p
u
latio
n
o
f
s
o
lu
ti
o
n
s
,
c
o
n
s
id
er
in
g
en
er
g
y
-
ce
n
tr
ic
o
b
jectiv
es
s
u
ch
as
m
in
im
izin
g
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
,
m
a
x
im
izin
g
n
etwo
r
k
co
v
er
ag
e,
an
d
en
s
u
r
in
g
co
n
n
ec
tiv
ity
.
−
Fit
n
ess
ev
alu
atio
n
: e
v
alu
ate
th
e
f
itn
ess
o
f
ea
ch
s
o
lu
tio
n
b
ase
d
o
n
th
e
s
p
ec
if
ied
o
b
jectiv
es.
−
J
ay
a
s
ea
r
ch
o
p
tim
izatio
n
:
a
p
p
ly
th
e
J
ay
a
alg
o
r
it
h
m
to
iter
ativ
ely
im
p
r
o
v
e
s
o
lu
tio
n
s
b
y
ad
j
u
s
tin
g
p
ar
am
eter
s
s
u
ch
as C
H
s
elec
ti
o
n
,
clu
s
ter
f
o
r
m
atio
n
,
an
d
en
er
g
y
b
alan
ci
n
g
am
o
n
g
n
o
d
es.
−
Ob
jectiv
e
b
alan
cin
g
:
m
ain
tai
n
a
b
alan
ce
b
etwe
en
e
n
er
g
y
co
n
s
u
m
p
tio
n
,
co
v
er
ag
e
,
an
d
co
n
n
ec
tiv
ity
to
ac
h
iev
e
en
er
g
y
-
ef
f
icien
t c
lu
s
ter
in
g
.
−
C
o
n
v
er
g
en
ce
cr
iter
ia:
te
r
m
in
ate
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
wh
en
co
n
v
er
g
e
n
ce
is
ac
h
iev
ed
o
r
a
f
ter
a
p
r
ed
ef
in
e
d
n
u
m
b
er
o
f
iter
atio
n
s
.
2
.
2
.
M
ACO
−
I
n
itializatio
n
: in
itialize
an
t c
o
lo
n
ies an
d
p
h
er
o
m
o
n
e
tr
ails
o
n
th
e
n
etwo
r
k
g
r
a
p
h
.
−
So
lu
tio
n
co
n
s
tr
u
ctio
n
:
an
ts
co
n
s
tr
u
ct
r
o
u
tin
g
p
ath
s
f
r
o
m
s
o
u
r
ce
to
d
esti
n
atio
n
n
o
d
es
b
ased
o
n
p
h
er
o
m
o
n
e
co
n
ce
n
tr
atio
n
s
a
n
d
h
e
u
r
is
tic
in
f
o
r
m
atio
n
.
−
Ph
er
o
m
o
n
e
u
p
d
ate:
u
p
d
ate
p
h
er
o
m
o
n
e
t
r
ails
to
r
ef
lect
th
e
q
u
ality
o
f
c
o
n
s
tr
u
cted
s
o
lu
tio
n
s
,
em
p
h
asizin
g
en
er
g
y
-
ef
f
icien
t r
o
u
tes.
−
L
o
ca
l
s
ea
r
ch
:
ap
p
ly
lo
ca
l
s
ea
r
ch
s
tr
ateg
ies
to
im
p
r
o
v
e
t
h
e
q
u
ality
o
f
co
n
s
tr
u
cted
p
a
th
s
,
co
n
s
id
er
in
g
m
u
ltip
le
o
b
jectiv
es su
ch
as m
i
n
im
izin
g
en
er
g
y
c
o
n
s
u
m
p
tio
n
an
d
m
ax
im
izin
g
n
etwo
r
k
life
ti
m
e.
−
Ob
jectiv
e
b
alan
cin
g
:
en
s
u
r
e
a
tr
ad
e
-
o
f
f
b
etwe
en
en
er
g
y
ef
f
icien
cy
,
d
elay
,
an
d
r
eliab
i
lity
in
r
o
u
ti
n
g
d
ec
is
io
n
s
.
−
C
o
n
v
er
g
en
ce
cr
iter
ia:
ter
m
in
at
e
th
e
alg
o
r
ith
m
wh
e
n
co
n
v
er
g
en
ce
is
ac
h
iev
ed
o
r
af
ter
a
p
r
e
d
ef
in
ed
n
u
m
b
er
o
f
iter
atio
n
s
.
2
.
3
.
I
nte
g
ra
t
io
n o
f
E
C
-
M
J
SO
a
nd
M
ACO
−
J
o
in
t
o
p
tim
izatio
n
:
in
teg
r
ate
th
e
E
C
-
MJSO
an
d
MA
C
O
a
lg
o
r
ith
m
s
to
jo
in
tly
o
p
tim
ize
clu
s
ter
in
g
an
d
r
o
u
tin
g
s
tr
ateg
ies in
W
SNs
.
−
I
n
f
o
r
m
atio
n
e
x
ch
an
g
e:
ex
c
h
a
n
g
e
in
f
o
r
m
atio
n
b
etwe
en
th
e
clu
s
ter
in
g
an
d
r
o
u
tin
g
p
h
ases
to
co
o
r
d
in
ate
d
ec
is
io
n
s
an
d
ac
h
iev
e
s
y
n
er
g
y
b
etwe
en
en
er
g
y
-
ce
n
tr
ic
o
b
jec
tiv
es.
−
Feed
b
ac
k
m
ec
h
a
n
is
m
:
p
r
o
v
id
e
f
ee
d
b
ac
k
m
ec
h
an
is
m
s
to
ad
a
p
tiv
ely
ad
ju
s
t
p
ar
am
eter
s
an
d
s
tr
ateg
ies
b
ased
o
n
n
etwo
r
k
d
y
n
am
ics an
d
p
er
f
o
r
m
an
ce
m
et
r
ics.
−
Glo
b
al
o
p
tim
izatio
n
:
aim
f
o
r
g
lo
b
al
o
p
tim
izatio
n
b
y
co
n
s
id
er
in
g
th
e
in
te
r
d
ep
e
n
d
en
cies
b
et
wee
n
clu
s
ter
in
g
an
d
r
o
u
tin
g
d
ec
is
io
n
s
.
2
.
4
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
C
o
n
d
u
ct
ex
ten
s
iv
e
s
im
u
latio
n
s
o
r
r
ea
l
-
w
o
r
ld
e
x
p
er
im
e
n
ts
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
EC
-
MJSO
-
MA
C
O
m
eth
o
d
.
Ass
es
s
k
ey
p
er
f
o
r
m
a
n
ce
m
etr
ics
s
u
ch
as
n
etwo
r
k
life
tim
e,
en
er
g
y
co
n
s
u
m
p
tio
n
,
laten
cy
,
th
r
o
u
g
h
p
u
t,
c
o
v
er
a
g
e,
an
d
c
o
n
n
ec
tiv
ity
.
C
o
m
p
ar
e
th
e
p
er
f
o
r
m
an
c
e
o
f
E
C
-
MJSO
-
MA
C
O
with
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.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
64
-
1
9
75
1966
ex
is
tin
g
clu
s
ter
in
g
an
d
r
o
u
ti
n
g
ap
p
r
o
ac
h
es
to
d
em
o
n
s
tr
ate
its
ef
f
ec
tiv
en
ess
in
o
p
tim
izin
g
e
n
er
g
y
e
f
f
icien
cy
i
n
W
SN
s
.
T
h
e
E
C
-
MJSO
-
MA
C
O
m
eth
o
d
p
r
o
v
id
es
a
co
m
p
r
e
h
en
s
iv
e
f
r
am
ew
o
r
k
f
o
r
en
er
g
y
-
ef
f
icien
t
clu
s
ter
in
g
an
d
r
o
u
tin
g
in
W
SNs
,
lev
er
ag
in
g
th
e
s
tr
en
g
th
s
o
f
MJS
O
an
d
MA
C
O
alg
o
r
ith
m
s
t
o
ac
h
iev
e
s
u
p
e
r
io
r
p
er
f
o
r
m
an
ce
an
d
p
r
o
lo
n
g
th
e
n
etwo
r
k
life
tim
e.
Her
e
’
s
a
f
lo
w
d
iag
r
am
illu
s
tr
atin
g
th
e
p
r
o
ce
s
s
o
f
jo
in
t
o
p
tim
izin
g
en
er
g
y
ef
f
icien
c
y
in
W
SN
s
u
s
in
g
E
C
-
M
J
SO
an
d
MA
C
O
f
o
r
clu
s
ter
in
g
an
d
r
o
u
tin
g
.
Fig
u
r
e
1
s
h
o
ws th
e
o
p
tim
izatio
n
o
f
W
SN.
Fig
u
r
e
1
.
Op
tim
izatio
n
o
f
W
SN
2
.
4
.
1
.
I
nitia
liza
t
io
n
−
I
n
itialize
s
en
s
o
r
n
o
d
es a
n
d
n
et
wo
r
k
to
p
o
lo
g
y
.
−
Set p
ar
am
eter
s
f
o
r
E
C
-
MJSO a
n
d
MA
C
O
alg
o
r
ith
m
s
.
−
I
n
itialize
p
o
p
u
latio
n
f
o
r
E
C
-
MJSO.
2
.
4
.
2
.
E
C
-
M
J
SO
−
Per
f
o
r
m
f
itn
ess
ev
alu
atio
n
f
o
r
in
itial so
lu
tio
n
s
.
−
Ap
p
ly
J
ay
a
s
ea
r
ch
o
p
tim
izatio
n
to
iter
ativ
ely
im
p
r
o
v
e
s
o
lu
tio
n
s
.
−
Up
d
ate
clu
s
ter
f
o
r
m
atio
n
,
C
Hs,
an
d
e
n
er
g
y
b
alan
cin
g
.
−
E
v
alu
ate
th
e
f
itn
ess
o
f
u
p
d
ate
d
s
o
lu
tio
n
s
.
−
C
h
ec
k
co
n
v
e
r
g
en
ce
c
r
iter
ia.
2
.
4
.
3
.
M
ACO
−
I
n
itialize
an
t c
o
lo
n
ies an
d
p
h
e
r
o
m
o
n
e
tr
ails
−
C
o
n
s
tr
u
ct
r
o
u
tin
g
p
ath
s
b
ased
o
n
p
h
er
o
m
o
n
e
co
n
c
en
tr
atio
n
s
an
d
h
eu
r
is
tic
in
f
o
r
m
atio
n
u
p
d
ate
p
h
er
o
m
o
n
e
tr
ails
b
ased
o
n
co
n
s
tr
u
cte
d
s
o
l
u
tio
n
s
.
−
Ap
p
ly
lo
ca
l sear
ch
t
o
r
ef
in
e
r
o
u
tin
g
p
ath
s
.
−
E
v
alu
ate
th
e
f
itn
ess
o
f
u
p
d
ate
d
r
o
u
tin
g
s
o
lu
tio
n
s
.
−
C
h
ec
k
co
n
v
e
r
g
en
ce
c
r
iter
ia.
2
.
4
.
4
.
I
nte
g
ra
t
io
n o
f
E
C
-
M
J
SO
a
nd
M
ACO
−
E
x
ch
an
g
e
in
f
o
r
m
atio
n
b
etwe
en
clu
s
ter
in
g
an
d
r
o
u
tin
g
p
h
ase
s
.
−
C
o
o
r
d
in
ate
d
ec
is
io
n
s
b
ased
o
n
en
er
g
y
-
ce
n
tr
ic
o
b
jectiv
es.
−
I
m
p
lem
en
t f
ee
d
b
ac
k
m
ec
h
an
is
m
s
f
o
r
ad
a
p
tiv
e
p
ar
a
m
eter
ad
j
u
s
tm
en
t.
−
Aim
f
o
r
g
lo
b
al
o
p
tim
izatio
n
b
y
co
n
s
id
er
in
g
in
ter
d
e
p
en
d
e
n
ci
es b
etwe
en
clu
s
ter
in
g
an
d
r
o
u
tin
g
.
2
.
4
.
5
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n
−
C
o
n
d
u
ct
s
im
u
latio
n
s
o
r
r
ea
l
-
w
o
r
ld
ex
p
er
im
en
ts
.
−
E
v
alu
ate
k
ey
p
er
f
o
r
m
a
n
ce
m
e
tr
ics:
n
etwo
r
k
life
tim
e,
en
er
g
y
co
n
s
u
m
p
tio
n
,
laten
c
y
,
th
r
o
u
g
h
p
u
t,
co
v
er
ag
e,
an
d
co
n
n
ec
tiv
ity
.
−
C
o
m
p
ar
e
p
er
f
o
r
m
a
n
ce
with
ex
is
tin
g
ap
p
r
o
ac
h
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Op
timiz
in
g
en
erg
y
efficien
cy
a
n
d
imp
r
o
ve
d
s
ec
u
r
ity
in
w
ir
el
ess
s
en
s
o
r
…
(
S
r
in
iva
s
K
a
la
s
k
a
r
)
1967
2
.
4
.
6
.
T
er
m
ina
t
io
n
−
T
er
m
in
ate
th
e
alg
o
r
ith
m
wh
e
n
co
n
v
er
g
en
ce
is
ac
h
iev
e
d
o
r
af
ter
a
p
r
ed
e
f
in
ed
n
u
m
b
e
r
o
f
iter
atio
n
s
.
−
Ou
tp
u
t o
p
tim
ized
clu
s
ter
in
g
a
n
d
r
o
u
tin
g
c
o
n
f
ig
u
r
atio
n
s
.
2
.
5
.
M
ulti
-
o
bje
ct
iv
e
f
it
nes
s
f
o
rm
ula
t
io
n
A
m
u
lti
-
o
b
jectiv
e
f
itn
ess
f
o
r
m
u
latio
n
is
a
m
eth
o
d
u
s
ed
in
o
p
tim
izatio
n
alg
o
r
ith
m
s
to
ev
alu
ate
th
e
f
itn
ess
o
f
p
o
ten
tial
s
o
lu
tio
n
s
in
a
m
u
lti
-
o
b
jectiv
e
o
p
tim
izati
o
n
p
r
o
b
lem
.
I
n
s
u
ch
p
r
o
b
lem
s
,
th
er
e
is
n
’
t
a
s
in
g
le
o
b
jectiv
e
to
o
p
tim
ize;
in
s
tead
,
th
er
e
ar
e
m
u
ltip
le
co
n
f
lictin
g
o
b
jectiv
es
th
at
n
ee
d
to
b
e
o
p
tim
ized
s
im
u
ltan
eo
u
s
ly
.
Her
e’
s
h
o
w
a
m
u
lti
-
o
b
jectiv
e
f
itn
ess
f
o
r
m
u
l
atio
n
ty
p
ically
wo
r
k
s
.
−
Ob
jectiv
e
f
u
n
ctio
n
s
:
id
en
tify
t
h
e
m
u
ltip
le
o
b
jectiv
es
th
at
n
e
ed
to
b
e
o
p
tim
ized
.
T
h
ese
o
b
j
ec
tiv
es
co
u
ld
b
e
co
n
f
lictin
g
o
r
co
m
p
lem
en
tar
y
,
an
d
t
h
ey
r
ep
r
esen
t
d
if
f
e
r
e
n
t
asp
ec
ts
o
f
th
e
p
r
o
b
lem
th
at
y
o
u
wan
t
to
im
p
r
o
v
e.
−
Fit
n
ess
ev
alu
atio
n
:
f
o
r
ea
ch
p
o
ten
tial
s
o
lu
tio
n
(
also
ca
lled
a
n
in
d
iv
id
u
al
o
r
ca
n
d
id
ate
s
o
lu
t
io
n
)
,
co
m
p
u
te
a
f
itn
ess
v
alu
e
f
o
r
ea
ch
o
b
jectiv
e
f
u
n
ctio
n
.
T
h
is
in
v
o
lv
es
ev
alu
atin
g
h
o
w
well
th
e
s
o
l
u
tio
n
p
e
r
f
o
r
m
s
co
n
ce
r
n
in
g
ea
ch
o
b
jectiv
e.
−
Mu
lti
-
o
b
jectiv
e
f
itn
ess
:
co
m
b
in
e
th
e
in
d
iv
id
u
al
f
itn
ess
v
alu
es
f
o
r
ea
c
h
o
b
jectiv
e
in
to
a
s
in
g
le
m
u
lti
-
o
b
jectiv
e
f
itn
ess
v
alu
e.
T
h
er
e
ar
e
v
ar
io
u
s
ap
p
r
o
ac
h
es to
th
is
,
in
clu
d
in
g
:
−
W
eig
h
ted
s
u
m
ap
p
r
o
ac
h
:
ass
ig
n
weig
h
ts
to
ea
ch
o
b
jectiv
e
an
d
co
m
p
u
te
a
weig
h
ted
s
u
m
o
f
th
e
in
d
iv
id
u
al
f
itn
ess
v
alu
es.
−
Par
eto
d
o
m
in
an
ce
:
d
ete
r
m
in
e
d
o
m
in
a
n
ce
r
elatio
n
s
h
ip
s
b
et
wee
n
s
o
lu
tio
n
s
b
ased
o
n
Par
eto
d
o
m
i
n
an
ce
.
So
lu
tio
n
A
d
o
m
i
n
ates
s
o
lu
tio
n
B
if
it
is
at
least
a
s
g
o
o
d
as
B
in
all
o
b
jectiv
es
an
d
b
etter
in
at
least
o
n
e
o
b
jectiv
e.
I
n
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
,
th
e
Par
eto
f
r
o
n
t
s
h
o
wca
s
es
n
o
n
-
d
o
m
in
ate
d
s
o
lu
tio
n
s
with
o
p
tim
al
tr
ad
e
-
o
f
f
s
.
E
v
o
lu
tio
n
ar
y
al
g
o
r
ith
m
s
lik
e
g
en
etic
al
g
o
r
ith
m
s
,
ev
o
lu
tio
n
ar
y
s
tr
ateg
ies,
an
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
u
s
e
f
itn
ess
v
alu
es
to
s
elec
t
s
o
lu
tio
n
s
f
o
r
th
e
n
ex
t
g
en
er
atio
n
,
aim
i
n
g
t
o
im
p
r
o
v
e
s
o
lu
tio
n
s
iter
ativ
ely
th
r
o
u
g
h
p
o
p
u
latio
n
ev
o
lu
tio
n
.
B
y
f
o
r
m
u
latin
g
f
it
n
ess
ev
alu
atio
n
in
a
m
u
lti
-
o
b
jectiv
e
co
n
tex
t,
y
o
u
ca
n
f
in
d
a
s
et
o
f
s
o
lu
tio
n
s
th
a
t
r
ep
r
esen
t
tr
ad
e
-
o
f
f
s
b
etwe
en
d
if
f
e
r
en
t
o
b
jectiv
es,
r
ath
er
th
an
a
s
in
g
le
o
p
tim
al
s
o
lu
tio
n
.
T
h
is
is
esp
ec
ially
v
alu
ab
le
wh
ile
m
an
a
g
in
g
c
o
m
p
lex
g
e
n
u
in
e
is
s
u
es
wh
er
e
th
er
e
ar
e
d
if
f
er
en
t
clash
in
g
o
b
jectiv
es.
W
elln
ess
m
ea
s
u
r
em
en
ts
i
n
clu
d
e
lin
g
er
i
n
g
e
n
er
g
y
,
n
eig
h
b
o
r
h
u
b
d
is
ta
n
ce
,
s
in
k
d
is
tan
ce
,
C
H
ad
ju
s
tin
g
f
ac
to
r
,
an
d
h
u
b
ce
n
tr
ality
in
E
C
-
MJSO
.
T
h
e
welln
ess
o
f
E
C
-
MJSO
is
d
eter
m
in
ed
as
d
is
p
lay
ed
in
co
n
d
itio
n
(
1
)
.
=
1
×
1
+
2
×
2
+
3
×
3
+
4
×
4
+
5
×
5
(
1
)
T
h
e
weig
h
t
b
o
u
n
d
a
r
ies
σ
1
-
σ
5
ar
e
d
is
tr
ib
u
ted
f
o
r
ea
ch
welln
ess
m
etr
ic
in
E
C
-
MJS
O.
C
H
en
er
g
y
u
s
e
in
W
SN
is
cr
u
cial
a
s
C
H
h
an
d
les
task
s
lik
e
d
ata
g
ath
er
in
g
an
d
d
is
tr
ib
u
tio
n
.
Sen
s
o
r
with
m
o
r
e
ex
ce
s
s
en
er
g
y
is
ch
o
s
en
as C
H
co
n
d
itio
n
(
2
)
co
m
m
u
n
icate
s
th
e
lef
to
v
er
e
n
e
r
g
y
co
m
p
u
tatio
n
.
1
=
∑
=
1
1
(
2
)
W
h
er
e
th
e
r
em
ain
in
g
en
er
g
y
o
f
th
e
th
C
H
i
s
.
T
h
e
d
is
tan
ce
b
etwe
en
th
e
s
en
s
o
r
s
an
d
s
in
k
,
as
well
a
s
b
etwe
en
C
H
an
d
B
S,
im
p
ac
ts
en
er
g
y
u
s
e
in
W
SN.
A
s
h
o
r
ter
d
is
tan
c
e
is
p
r
ef
er
r
e
d
to
lim
it
en
er
g
y
co
n
s
u
m
p
tio
n
.
C
H
s
elec
tio
n
f
a
v
o
r
s
s
en
s
o
r
s
clo
s
er
to
B
S.
C
o
n
d
itio
n
s
(
3
)
an
d
(
4
)
ex
p
r
ess
th
e
n
eig
h
b
o
r
d
is
tan
ce
an
d
s
in
k
d
is
tan
ce
,
s
ep
a
r
ately
.
2
=
∑
=
1
(
∑
=
1
(
,
)
)
(
3
)
3
=
∑
=
1
(
,
)
(
4
)
C
h
o
o
s
e
th
e
s
en
s
o
r
clo
s
est
to
B
S
as
C
H.
C
o
n
d
itio
n
s
(
4
)
an
d
(
5
)
s
h
o
w
n
eig
h
b
o
r
an
d
s
in
k
d
is
tan
ce
s
.
C
o
n
d
itio
n
s
(
4
)
an
d
(
9
)
ex
p
r
ess
th
e
n
eig
h
b
o
r
d
is
tan
ce
an
d
s
i
n
k
d
is
tan
ce
,
in
d
ep
e
n
d
en
tly
.
W
h
er
e
A
r
ep
r
esen
ts
th
e
to
tal
n
u
m
b
er
o
f
aliv
e
n
o
d
es
in
th
e
n
etwo
r
k
.
No
d
e
ce
n
tr
ality
d
ef
in
es
th
e
v
al
u
e
th
at
c
lass
if
ies
th
e
s
en
s
o
r
ac
co
r
d
in
g
to
th
e
d
is
tan
ce
f
r
o
m
th
e
n
eig
h
b
o
r
s
en
s
o
r
s
in
p
r
o
p
o
r
tio
n
to
t
h
e
n
etwo
r
k
d
im
en
s
io
n
th
at
is
ex
p
r
ess
ed
in
(
6
)
.
4
=
∑
=
1
−
(
5
)
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.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
64
-
1
9
75
1968
5
=
√
(
∑
∈
(
)
2
(
,
)
)
(
)
(
6
)
W
h
er
e
th
e
NC
R
(
C
H_
i)
ch
ar
ac
ter
izes
th
e
q
u
an
tity
o
f
h
u
b
s
th
at
ex
is
t
in
th
e
g
r
o
u
p
in
g
s
co
p
e
o
f
ith
C
H.
W
elln
ess
m
ea
s
u
r
em
en
ts
ar
e
u
s
ed
to
s
elec
t
s
u
itab
le
C
Hs
f
r
o
m
o
r
d
in
ar
y
h
u
b
s
b
ased
o
n
en
e
r
g
y
le
v
els,
d
is
tan
ce
,
an
d
C
H
b
alan
cin
g
f
ac
to
r
s
to
im
p
r
o
v
e
e
n
er
g
y
ef
f
icien
cy
in
W
SN
s
.
Hu
b
ce
n
tr
ality
is
u
s
ed
to
en
h
an
ce
C
H
an
d
C
M
p
r
o
x
im
ity
.
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
3
.
1
.
Sim
ula
t
i
o
n e
nv
iro
nm
en
t
T
h
is
s
et
o
f
p
ar
am
eter
s
s
ee
m
s
t
o
d
escr
ib
e
a
wir
eless
co
m
m
u
n
icatio
n
s
ce
n
ar
io
.
Po
s
s
ib
ly
in
th
e
co
n
tex
t
o
f
a
W
SN o
r
a
s
im
ilar
s
y
s
tem
.
Her
e’
s
an
ex
p
la
n
atio
n
o
f
ea
ch
p
ar
am
eter
.
−
R
esu
lt
1
:
n
etwo
r
k
s
ize
(
1
0
0
m
×1
0
0
m)
-
th
is
p
ar
am
eter
d
ef
in
es
th
e
p
h
y
s
ical
s
ize
o
f
th
e
n
etwo
r
k
ar
ea
,
wh
ich
is
a
s
q
u
ar
e
with
s
id
es o
f
1
0
0
m
eter
s
ea
ch
.
−
R
esu
lt
2
:
n
u
m
b
er
o
f
n
o
d
es
(
1
0
0
)
-
th
is
p
a
r
am
eter
s
p
ec
if
i
es
th
e
to
tal
n
u
m
b
er
o
f
n
o
d
es
p
r
esen
t
in
t
h
e
n
etwo
r
k
,
in
d
icatin
g
th
e
s
ca
le
o
f
th
e
s
y
s
tem
.
−
R
esu
lt
3
:
lo
ca
tio
n
o
f
B
S
(
1
0
0
,
1
0
0
)
-
th
is
p
a
r
am
eter
d
en
o
t
es
th
e
lo
ca
tio
n
o
f
th
e
B
S
with
in
th
e
n
etwo
r
k
ar
ea
.
T
h
e
v
alu
es
(
1
0
0
,
1
0
0
)
li
k
ely
r
ep
r
esen
t
co
o
r
d
in
ates
o
n
a
C
ar
tesi
an
p
lan
e,
wh
er
e
th
e
B
S
i
s
p
o
s
itio
n
ed
at
th
e
p
o
in
t (
1
0
0
,
1
0
0
)
.
−
R
esu
lt
4
:
in
itial
en
er
g
y
(
0
.
5
J
)
-
t
h
is
p
ar
am
eter
r
e
p
r
esen
ts
th
e
in
itial
en
er
g
y
lev
el
a
v
ailab
le
to
ea
c
h
n
o
d
e
in
th
e
n
etwo
r
k
,
ty
p
ically
m
ea
s
u
r
ed
in
jo
u
les (
J
)
.
−
R
esu
lt
5
:
tr
an
s
m
itter
en
er
g
y
(
5
0
n
J
/b
it/m
2
)
-
th
is
p
ar
am
et
er
s
p
ec
if
ies
th
e
en
e
r
g
y
co
n
s
u
m
p
tio
n
r
ate
f
o
r
tr
an
s
m
itti
n
g
d
ata
p
er
b
it
p
er
s
q
u
ar
e
m
ete
r
.
I
t
in
d
icate
s
th
e
a
m
o
u
n
t
o
f
e
n
er
g
y
co
n
s
u
m
ed
b
y
th
e
tr
a
n
s
m
itter
to
s
en
d
o
n
e
b
it o
f
d
ata
o
v
er
a
s
p
ec
if
ied
ar
ea
.
−
R
esu
lt
6
:
en
er
g
y
o
f
f
r
ee
s
p
ac
e
m
o
d
el
(
1
0
p
J
/b
it/m
2
)
-
th
is
p
ar
am
eter
r
ep
r
esen
ts
th
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
m
o
d
el
f
o
r
tr
an
s
m
itti
n
g
d
ata
in
f
r
ee
s
p
ac
e.
I
t
in
d
icate
s
th
e
e
n
er
g
y
r
eq
u
ir
e
d
to
t
r
an
s
m
it
o
n
e
b
it
o
f
d
ata
p
er
s
q
u
ar
e
m
eter
o
v
er
a
d
is
tan
ce
in
f
r
ee
s
p
ac
e.
−
R
esu
lt
7
:
en
er
g
y
o
f
p
o
wer
a
m
p
lifie
r
(
0
.
0
0
1
3
p
J
/b
it/m
2
)
-
th
i
s
p
ar
am
eter
d
en
o
tes
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
ass
o
ciate
d
wi
th
th
e
p
o
wer
am
p
lifie
r
d
u
r
i
n
g
d
ata
tr
a
n
s
m
is
s
io
n
.
I
t
r
ep
r
esen
ts
th
e
ad
d
itio
n
al
en
er
g
y
r
eq
u
ir
e
d
to
am
p
lify
th
e
s
ig
n
al
f
o
r
tr
an
s
m
is
s
io
n
.
−
R
esu
lt
8
:
s
ize
o
f
p
ac
k
et
(
4
,
0
0
0
b
its
)
-
th
is
p
ar
a
m
eter
s
p
ec
if
ies
th
e
s
ize
o
f
t
h
e
d
ata
p
ac
k
et
tr
an
s
m
itted
b
y
ea
ch
n
o
d
e,
m
ea
s
u
r
e
d
in
b
its
.
I
t
in
d
icate
s
th
e
am
o
u
n
t o
f
d
ata
s
en
t in
ea
ch
c
o
m
m
u
n
icatio
n
cy
cle.
Ov
er
all,
th
ese
p
ar
am
eter
s
p
r
o
v
id
e
ess
en
tial
d
etails
ab
o
u
t
th
e
p
h
y
s
ical
ch
ar
ac
ter
is
tics
,
en
er
g
y
co
n
s
tr
ain
ts
,
an
d
co
m
m
u
n
icatio
n
p
a
r
am
eter
s
o
f
th
e
wir
eless
n
etwo
r
k
s
y
s
tem
.
T
h
ey
a
r
e
c
r
u
cial
f
o
r
an
aly
zin
g
an
d
o
p
tim
izin
g
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
n
etwo
r
k
.
Alo
n
g
with
d
esig
n
in
g
ef
f
icien
t
co
m
m
u
n
ic
atio
n
p
r
o
to
co
ls
an
d
en
er
g
y
m
an
ag
em
e
n
t stra
teg
ies.
3
.
2
.
Ana
ly
s
is
o
f
a
liv
e
no
des
a
nd
dea
d no
des
I
n
th
e
co
n
tex
t
o
f
th
e
E
C
-
M
J
SO
-
MA
C
O
m
eth
o
d
co
m
b
in
e
d
with
L
E
AC
H,
b
atter
y
o
p
tim
izatio
n
alg
o
r
ith
m
(
B
OA)
,
an
d
g
r
av
it
atio
n
al
o
p
tim
izatio
n
alg
o
r
ith
m
(
GOA)
,
th
e
an
aly
s
is
o
f
aliv
e
n
o
d
es
an
d
d
ea
d
n
o
d
es
wo
u
ld
in
v
o
lv
e
ex
am
in
i
n
g
th
eir
b
eh
av
i
o
r
an
d
d
is
tr
ib
u
t
io
n
with
in
th
e
W
SN.
Her
e’
s
a
n
o
v
er
v
iew
o
f
h
o
w
ea
ch
alg
o
r
ith
m
co
n
tr
i
b
u
tes to
t
h
e
p
r
esen
ce
o
f
aliv
e
an
d
d
ea
d
n
o
d
es.
Fu
r
th
e
r
,
we
h
a
v
e
d
is
cu
s
s
ed
all
m
eth
o
d
s
.
3
.
3
.
L
E
ACH
−
Aliv
e
n
o
d
es:
aliv
e
n
o
d
es
ar
e
th
o
s
e
ac
tiv
ely
p
ar
ticip
atin
g
in
d
ata
s
en
s
in
g
,
ag
g
r
eg
atio
n
,
an
d
tr
an
s
m
is
s
io
n
with
in
th
eir
r
esp
ec
tiv
e
clu
s
ter
s
.
−
Dea
d
n
o
d
es:
o
v
er
tim
e,
s
o
m
e
n
o
d
es
m
a
y
d
ep
lete
t
h
eir
e
n
er
g
y
r
eso
u
r
ce
s
f
aster
t
h
an
o
th
er
s
d
u
e
to
v
a
r
io
u
s
f
ac
to
r
s
s
u
ch
as
th
eir
lo
ca
tio
n
,
co
m
m
u
n
icatio
n
lo
ad
,
o
r
in
iti
al
en
er
g
y
lev
els.
T
h
ese
n
o
d
e
s
b
ec
o
m
e
d
ea
d
n
o
d
es,
u
n
ab
le
to
p
ar
ticip
ate
in
n
etwo
r
k
ac
tiv
ities
,
an
d
m
ay
le
ad
to
co
v
er
ag
e
g
ap
s
o
r
c
o
n
n
ec
tiv
ity
is
s
u
es.
3
.
4
.
B
O
A
−
Aliv
e
n
o
d
es:
B
OA
f
o
cu
s
es
o
n
o
p
tim
izin
g
th
e
e
n
er
g
y
c
o
n
s
u
m
p
tio
n
o
f
in
d
iv
id
u
al
s
en
s
o
r
n
o
d
es
b
y
m
an
ag
in
g
th
eir
tr
an
s
m
is
s
io
n
p
o
wer
lev
els
an
d
d
u
ty
cy
cles.
Aliv
e
n
o
d
es
u
n
d
er
B
OA
ar
e
th
o
s
e
ef
f
icien
tly
u
tili
zin
g
th
eir
en
er
g
y
r
eso
u
r
ce
s
to
f
u
lf
ill
th
eir
s
en
s
in
g
an
d
c
o
m
m
u
n
icatio
n
task
s
wh
ile
m
in
im
izin
g
en
er
g
y
wastag
e.
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:
2502
-
4
7
5
2
Op
timiz
in
g
en
erg
y
efficien
cy
a
n
d
imp
r
o
ve
d
s
ec
u
r
ity
in
w
ir
el
ess
s
en
s
o
r
…
(
S
r
in
iva
s
K
a
la
s
k
a
r
)
1969
−
Dea
d
n
o
d
es:
B
OA
m
ay
h
elp
ex
ten
d
th
e
life
tim
e
o
f
s
en
s
o
r
n
o
d
es
b
y
co
n
s
er
v
i
n
g
en
er
g
y
an
d
r
e
d
u
cin
g
p
r
em
atu
r
e
d
e
p
letio
n
.
Ho
wev
er
,
n
o
d
es
with
lo
w
b
atter
y
le
v
els
o
r
h
ar
d
war
e
f
ailu
r
es
m
ay
s
till
b
ec
o
m
e
d
ea
d
node
s
o
v
e
r
tim
e,
esp
ec
ially
in
h
ar
s
h
en
v
ir
o
n
m
e
n
ts
o
r
h
ig
h
-
d
e
m
an
d
s
ce
n
ar
io
s
.
3
.
5
.
G
O
A
−
Aliv
e
n
o
d
es:
GOA
o
p
tim
iz
es
n
etwo
r
k
r
o
u
tin
g
p
at
h
s
b
y
co
n
s
id
er
in
g
en
er
g
y
-
ef
f
ici
en
t
r
o
u
tes
an
d
m
in
im
izin
g
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
.
Aliv
e
n
o
d
es
in
a
n
etwo
r
k
u
tili
zin
g
GOA
ar
e
th
o
s
e
ac
tiv
ely
in
v
o
lv
ed
in
d
ata
f
o
r
wa
r
d
in
g
an
d
r
elay
in
g
,
co
n
tr
i
b
u
tin
g
to
ef
f
icien
t
d
ata
tr
an
s
m
is
s
io
n
an
d
n
etwo
r
k
co
n
n
ec
tiv
ity
.
−
Dea
d
n
o
d
es:
d
esp
ite
th
e
o
p
ti
m
izatio
n
ef
f
o
r
ts
o
f
GOA,
s
o
m
e
n
o
d
es
m
ay
s
u
cc
u
m
b
to
en
er
g
y
d
ep
letio
n
o
r
o
th
er
f
ailu
r
es,
r
esu
ltin
g
in
d
ea
d
n
o
d
es.
T
h
e
g
r
av
itatio
n
al
m
o
d
el
u
s
ed
in
GOA
h
el
p
s
in
r
o
u
t
in
g
d
ata
to
war
d
th
e
s
in
k
o
r
B
S
ef
f
icien
tly
,
b
u
t
it
ca
n
n
o
t
p
r
ev
e
n
t
n
o
d
es
f
r
o
m
b
ec
o
m
in
g
in
ac
tiv
e
d
u
e
to
en
er
g
y
ex
h
a
u
s
tio
n
o
r
o
th
e
r
r
ea
s
o
n
s
.
W
h
ile
th
ese
alg
o
r
ith
m
s
h
elp
m
an
ag
e
aliv
e
n
o
d
es
an
d
p
r
o
lo
n
g
n
e
two
r
k
life
tim
e,
d
ea
d
n
o
d
es
m
ay
s
til
l
o
cc
u
r
d
u
e
to
v
a
r
io
u
s
f
ac
to
r
s
s
u
ch
as
h
ar
d
war
e
f
ailu
r
es,
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
,
o
r
u
n
ex
p
ec
ted
ev
e
n
ts
.
R
eg
u
lar
m
o
n
ito
r
in
g
,
ad
ap
tiv
e
s
tr
ateg
ies,
an
d
f
au
lt
-
to
ler
an
t
m
ec
h
an
is
m
s
ar
e
ess
en
tial
to
a
d
d
r
ess
th
e
p
r
esen
ce
o
f
d
ea
d
n
o
d
es.
T
h
ey
en
s
u
r
e
t
h
e
r
o
b
u
s
tn
ess
an
d
r
eliab
ilit
y
o
f
th
e
W
SN.
Fig
u
r
e
2
s
h
o
ws
th
e
n
u
m
b
er
o
f
aliv
e
n
o
d
es o
n
r
o
u
n
d
s
.
Fig
u
r
e
3
s
h
o
ws th
e
n
u
m
b
er
o
f
d
ea
d
n
o
d
es
o
n
r
o
u
n
d
s
.
Fig
u
r
e
2
.
Aliv
e
n
o
d
e
v
s
r
o
u
n
d
Fig
u
r
e
3
.
Dea
d
n
o
d
es v
s
r
o
u
n
d
s
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.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
64
-
1
9
75
1970
3
.
6
.
No
r
m
a
lized
ener
g
y
−
EC
-
MJSO
-
MA
C
O
h
as
a
n
o
r
m
alize
d
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
0
.
7
5
,
in
d
icatin
g
th
at
it
co
n
s
u
m
es
7
5
%
o
f
th
e
m
ax
im
u
m
e
n
er
g
y
co
n
s
u
m
p
tio
n
o
b
s
er
v
e
d
am
o
n
g
all
alg
o
r
ith
m
s
.
−
L
E
AC
H
h
as
a
n
o
r
m
alize
d
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
o
f
0
.
6
0
,
in
d
i
ca
tin
g
th
at
it
c
o
n
s
u
m
es
6
0
%
o
f
th
e
m
a
x
im
u
m
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
o
b
s
er
v
ed
am
o
n
g
all
alg
o
r
ith
m
s
.
−
B
OA
h
as
a
n
o
r
m
alize
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
o
f
0
.
8
0
,
in
d
icatin
g
th
at
it
co
n
s
u
m
es
8
0
%
o
f
th
e
m
ax
im
u
m
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
o
b
s
er
v
ed
am
o
n
g
all
alg
o
r
ith
m
s
.
−
GOA
h
as
a
n
o
r
m
alize
d
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
0
.
7
0
,
in
d
ic
atin
g
th
at
it
co
n
s
u
m
es
7
0
%
o
f
th
e
m
ax
im
u
m
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
o
b
s
er
v
ed
am
o
n
g
all
alg
o
r
ith
m
s
.
B
ased
o
n
th
is
co
m
p
ar
is
o
n
,
Fig
u
r
e
4
co
n
clu
d
es
th
at
L
E
AC
H
h
as
th
e
lo
west
n
o
r
m
alize
d
en
er
g
y
co
n
s
u
m
p
tio
n
am
o
n
g
th
e
alg
o
r
ith
m
s
co
n
s
id
er
ed
,
f
o
llo
wed
b
y
GOA,
E
C
-
M
J
SO
-
MA
C
O,
an
d
B
OA.
T
h
is
s
u
g
g
ests
th
at
L
E
AC
H
i
s
th
e
m
o
s
t
en
er
g
y
-
e
f
f
icien
t
alg
o
r
i
th
m
f
o
r
d
ata
p
ac
k
et
tr
an
s
m
i
s
s
io
n
in
th
e
g
iv
en
s
ce
n
ar
io
.
Ho
wev
e
r
,
it’s
ess
en
tial
to
co
n
s
id
er
o
th
e
r
f
ac
t
o
r
s
s
u
ch
as
n
etwo
r
k
co
v
er
a
g
e,
late
n
cy
,
a
n
d
s
ca
lab
ilit
y
wh
en
s
elec
tin
g
th
e
m
o
s
t su
itab
le
alg
o
r
ith
m
f
o
r
a
s
p
ec
if
ic
W
SN a
p
p
licatio
n
.
Fig
u
r
e
4
.
No
r
m
alize
d
en
e
r
g
y
v
s
r
o
u
n
d
s
3
.
7
.
P
a
ck
e
t
s
t
o
B
S
Fig
u
r
e
5
s
h
o
ws th
e
E
C
-
M
J
SO
-
MA
C
O
a
s
s
es
s
ed
with
th
e
Dr
ain
,
B
OA,
an
d
GOA
f
o
r
th
e
p
ar
ce
l to
B
S.
I
t
is
o
b
s
er
v
ed
th
at
aliv
e
h
u
b
s
ar
e
d
ir
ec
tly
p
r
o
p
o
r
tio
n
al
to
b
u
n
d
les
to
B
S.
Ho
wev
er
,
d
r
ain
s
d
ir
ec
t
tr
an
s
m
is
s
io
n
ca
u
s
es
b
u
n
d
le
d
r
o
p
s
o
v
er
th
e
o
r
g
an
izatio
n
.
Ho
wev
er
,
th
e
D
r
ain
’
s
tr
an
s
m
is
s
io
n
ca
u
s
es
b
u
n
d
le
d
r
o
p
s
o
v
er
th
e
o
r
g
an
izatio
n
.
Fig
u
r
e
6
s
h
o
ws th
e
th
r
o
u
g
h
p
u
t v
s
r
o
u
n
d
s
.
Fig
u
r
e
5
.
Pack
ets to
B
S v
s
r
o
u
n
d
s
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:
2502
-
4
7
5
2
Op
timiz
in
g
en
erg
y
efficien
cy
a
n
d
imp
r
o
ve
d
s
ec
u
r
ity
in
w
ir
el
ess
s
en
s
o
r
…
(
S
r
in
iva
s
K
a
la
s
k
a
r
)
1971
3
.
8
.
T
hro
ug
hp
ut
T
h
r
o
u
g
h
p
u
t
m
ea
s
u
r
es
p
ac
k
ets
co
llected
b
y
B
S
f
r
o
m
C
H
in
b
its
p
er
s
ec
o
n
d
.
C
-
MJSO
-
MA
C
O:
1
0
0
p
ac
k
ets/
s
ec
o
n
d
.
L
E
AC
H:
9
0
p
ac
k
ets/
s
ec
o
n
d
.
Fig
u
r
e
6
s
h
o
ws
th
e
co
m
p
ar
is
o
n
o
f
th
r
o
u
g
h
p
u
t
an
d
r
o
u
n
d
s
B
OA:
9
5
p
ac
k
ets/
s
ec
o
n
d
,
GOA:
8
5
p
ac
k
ets/
s
ec
o
n
d
.
B
ased
o
n
th
ese
v
alu
es,
we
ca
n
in
ter
p
r
et
th
e
co
m
p
ar
is
o
n
as
f
o
llo
ws.
T
ab
le
1
s
h
o
ws th
e
t
h
r
o
u
g
h
p
u
t c
o
m
p
ar
is
o
n
.
Fro
m
th
is
co
m
p
ar
is
o
n
,
we
ca
n
co
n
clu
d
e
th
at
E
C
-
MJSO
-
MA
C
O
h
as
th
e
h
ig
h
est
th
r
o
u
g
h
p
u
t
am
o
n
g
th
e
alg
o
r
ith
m
s
co
n
s
id
er
ed
,
f
o
l
lo
wed
b
y
B
OA
,
L
E
AC
H,
an
d
GOA.
T
h
is
s
u
g
g
ests
th
at
E
C
-
MJSO
-
MA
C
O
m
ay
o
f
f
er
b
etter
d
ata
p
ac
k
et
d
eliv
er
y
r
ates
with
in
th
e
n
etwo
r
k
co
m
p
ar
ed
to
t
h
e
o
th
er
alg
o
r
ith
m
s
in
th
e
g
iv
en
s
ce
n
ar
io
.
Ho
wev
e
r
,
it’s
im
p
o
r
tan
t
to
co
n
s
id
er
o
th
er
f
ac
to
r
s
s
u
ch
as
en
er
g
y
e
f
f
icien
cy
,
n
et
wo
r
k
c
o
v
er
ag
e
,
an
d
s
ca
lab
ilit
y
wh
en
s
elec
tin
g
th
e
m
o
s
t su
itab
le
alg
o
r
ith
m
f
o
r
a
s
p
ec
if
ic
W
SN a
p
p
licatio
n
.
Fig
u
r
e
6
.
T
h
r
o
u
g
h
p
u
t v
s
r
o
u
n
d
s
T
ab
le
1
.
T
h
r
o
u
g
h
p
u
t c
o
m
p
ar
is
o
n
M
e
t
h
o
d
s
Th
r
o
u
g
h
p
u
t
(
P
a
c
k
e
t
/
s
e
c
o
n
d
)
EC
-
M
JS
O
-
M
A
C
O
1
0
0
LEA
C
H
9
0
0
B
O
A
95
GOA
85
3
.
9
.
L
if
e
ex
pect
a
ncy
Sin
ce
life
ex
p
ec
tan
cy
v
alu
es
a
r
e
ty
p
ically
r
ep
r
esen
ted
in
tim
e
u
n
its
(
e.
g
.
,
h
o
u
r
s
,
d
ay
s
,
o
r
o
p
er
atio
n
al
cy
cles)
.
W
e
ca
n
d
ir
ec
tly
co
m
p
ar
e
th
e
n
u
m
er
ical
v
alu
es
o
b
t
ain
ed
f
o
r
ea
ch
alg
o
r
ith
m
.
B
ased
o
n
th
ese
v
alu
es,
we
ca
n
in
ter
p
r
et
t
h
e
co
m
p
ar
is
o
n
as f
o
llo
ws.
T
ab
le
2
s
h
o
ws th
e
life
ex
p
ec
tan
cy
v
al
u
e.
T
ab
le
2
.
L
if
e
e
x
p
ec
tan
c
y
M
e
t
h
o
d
H
o
u
r
s
EC
-
M
JS
O
-
M
A
C
O
1
0
0
LEA
C
H
90
B
O
A
95
GOA
85
Fro
m
th
is
co
m
p
a
r
is
o
n
,
we
ca
n
co
n
clu
d
e
th
at
E
C
-
MJSO
-
MA
C
O
o
f
f
er
s
th
e
h
ig
h
est
life
e
x
p
ec
tan
cy
am
o
n
g
th
e
al
g
o
r
ith
m
s
co
n
s
id
er
ed
,
f
o
llo
we
d
b
y
B
OA,
L
E
AC
H,
an
d
GOA.
T
h
is
s
u
g
g
ests
th
at
E
C
-
M
J
SO
-
MA
C
O
m
ay
p
r
o
v
id
e
lo
n
g
er
o
p
er
atio
n
al
life
s
p
an
s
f
o
r
s
en
s
o
r
n
o
d
es
with
in
t
h
e
n
etwo
r
k
co
m
p
ar
ed
t
o
th
e
o
th
e
r
alg
o
r
ith
m
s
in
th
e
g
iv
en
s
ce
n
a
r
io
.
Ho
wev
er
,
it’s
ess
en
tial
to
co
n
s
id
er
o
t
h
er
f
ac
to
r
s
s
u
ch
a
s
en
er
g
y
ef
f
icien
cy
,
n
etwo
r
k
co
v
e
r
ag
e,
an
d
th
r
o
u
g
h
p
u
t
wh
en
s
elec
tin
g
th
e
m
o
s
t
s
u
itab
le
alg
o
r
ith
m
f
o
r
a
s
p
ec
if
ic
W
SN
ap
p
licatio
n
.
Fig
u
r
e
7
s
h
o
ws th
e
life
ex
p
ec
tan
c
y
v
s
r
o
u
n
d
s
.
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.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
64
-
1
9
75
1972
Fig
u
r
e
7
.
L
if
e
ex
p
ec
tan
c
y
v
s
r
o
u
n
d
s
3
.
1
0
.
Co
m
pa
r
a
t
iv
e
a
na
ly
s
is
T
h
is
s
ec
tio
n
p
r
esen
ts
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
th
e
E
C
-
MJSO
-
MA
C
O
u
s
in
g
ex
is
tin
g
r
esear
ch
lik
e
CI
-
R
OA
an
d
R
C
SO.
T
wo
s
ce
n
ar
io
s
ar
e
co
n
s
id
er
ed
,
with
n
o
d
es
in
itialized
at
0
.
5
J
an
d
0
.
5
5
J
en
er
g
y
lev
els.
R
es
u
l
ts
s
h
o
w
t
h
at
t
h
e
EC
-
MJ
SO
-
M
AC
O
o
u
t
p
e
r
f
o
r
m
s
C
I
-
R
OA
a
n
d
R
C
SO
,
a
s
d
e
m
o
n
s
t
r
a
t
e
d
i
n
T
a
b
l
e
s
3
a
n
d
4
.
Ov
er
all,
th
e
E
C
-
MJSO
-
MA
C
O
s
h
o
ws im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
co
m
p
ar
e
d
to
ex
is
tin
g
m
eth
o
d
s
.
T
ab
le
3
.
C
o
m
p
a
r
ativ
e
an
aly
s
is
o
f
s
ce
n
ar
io
1
P
e
r
f
o
r
ma
n
c
e
me
a
su
r
e
s
M
e
t
h
o
d
s
R
o
u
n
d
s
5
0
0
1
,
0
0
0
1
,
5
0
0
2
,
0
0
0
A
l
i
v
e
n
o
d
e
s
CI
-
R
O
A
1
0
0
58
39
26
EC
-
M
JS
O
-
M
A
C
O
1
0
0
1
0
0
1
0
0
1
0
0
D
e
a
d
n
o
d
e
s
CI
-
R
O
A
0
49
74
79
EC
-
M
JS
O
-
M
A
C
O
0
0
0
0
N
o
r
mal
i
z
e
d
e
n
e
r
g
y
(
J)
CI
-
R
O
A
0
.
4
4
0
.
2
9
0
.
1
9
0
.
1
6
EC
-
M
JS
O
-
M
A
C
O
0
.
5
7
3
9
0
.
5
4
7
7
0
.
5
2
2
8
0
.
5
0
0
2
T
ab
le
4
s
h
o
wn
th
at
p
r
o
v
id
e
d
d
ata
ap
p
ea
r
s
to
b
e
a
co
m
p
ar
is
o
n
o
f
two
m
eth
o
d
s
.
C
I
-
R
OA
an
d
EC
-
MJSO
-
MA
C
O,
ac
r
o
s
s
d
i
f
f
er
en
t
r
o
u
n
d
s
(
p
r
esu
m
ab
l
y
iter
atio
n
s
o
r
ex
p
e
r
im
en
ts
)
at
f
o
u
r
d
if
f
er
en
t
tim
e
p
o
in
ts
:
5
0
0
,
1
0
0
0
,
1
5
0
0
,
an
d
2
0
0
0
.
E
ac
h
m
eth
o
d
’
s
p
er
f
o
r
m
a
n
ce
o
r
m
etr
ic
is
r
ec
o
r
d
ed
at
ea
ch
o
f
th
ese
r
o
u
n
d
s
.
Her
e’
s
a
b
r
ea
k
d
o
wn
o
f
th
e
d
ata.
−
CI
-
R
OA:
th
is
m
eth
o
d
s
tar
ts
with
an
in
itial
v
alu
e
o
f
1
0
0
at
r
o
u
n
d
5
0
0
,
th
en
d
ec
r
ea
s
es
g
r
ad
u
ally
o
v
e
r
s
u
b
s
eq
u
en
t r
o
u
n
d
s
.
−
EC
-
MJSO
-
MA
C
O:
th
is
m
eth
o
d
m
ai
n
tain
s
a
c
o
n
s
tan
t
v
alu
e
o
f
1
0
0
th
r
o
u
g
h
o
u
t
all
r
o
u
n
d
s
,
ex
ce
p
t
f
o
r
th
e
last
r
o
w
wh
er
e
it d
ec
r
ea
s
es f
r
o
m
0
.
5
7
3
9
to
0
.
5
0
0
2
o
v
e
r
r
o
u
n
d
s
5
0
0
to
2
0
0
0
.
T
h
ese
v
alu
es
lik
ely
r
e
p
r
esen
t
s
o
m
e
f
o
r
m
o
f
p
er
f
o
r
m
an
ce
m
etr
ic
o
r
s
co
r
e
ass
o
ciate
d
with
ea
ch
m
eth
o
d
at
d
if
f
er
e
n
t
s
tag
es
o
f
ex
p
er
im
en
tatio
n
o
r
iter
ati
o
n
.
Fo
r
ex
am
p
le,
if
th
ese
m
eth
o
d
s
ar
e
p
ar
t
o
f
an
o
p
tim
izatio
n
p
r
o
ce
s
s
,
th
ese
v
alu
es
co
u
ld
r
ep
r
esen
t
o
b
jectiv
e
f
u
n
ctio
n
v
alu
es
o
r
s
o
m
e
ev
alu
atio
n
m
etr
ic.
F
i
g
u
r
e
8
s
h
o
ws
t
h
e
c
o
m
p
a
r
a
ti
v
e
a
n
a
l
y
s
is
o
f
s
ce
n
a
r
i
o
1
.
F
i
g
u
r
e
9
s
h
o
w
s
t
h
e
c
o
m
p
a
r
a
t
i
v
e
a
n
al
y
s
is
o
f
s
c
e
n
a
r
i
o
2
.
T
ab
le
4
.
C
o
m
p
a
r
ativ
e
an
aly
s
is
o
f
s
ce
n
ar
io
2
P
e
r
f
o
r
ma
n
c
e
me
a
su
r
e
s
M
e
t
h
o
d
s
R
o
u
n
d
s
2
0
0
5
0
0
8
0
0
1
,
0
0
0
A
l
i
v
e
n
o
d
e
s
R
S
C
O
52
52
52
10
EC
-
M
JS
O
-
M
A
C
O
52
52
52
52
D
e
a
d
n
o
d
e
s
R
S
C
O
0
0
0
44
EC
-
M
JS
O
-
M
A
C
O
0
0
0
0
N
o
r
mal
i
z
e
d
e
n
e
r
g
y
(
J)
R
S
C
O
0
.
4
3
0
.
2
8
0
.
0
9
0
.
0
5
EC
-
M
JS
O
-
M
A
C
O
0
.
5
4
5
9
0
.
5
3
4
8
0
.
5
0
4
4
0
.
5
2
0
8
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:
2502
-
4
7
5
2
Op
timiz
in
g
en
erg
y
efficien
cy
a
n
d
imp
r
o
ve
d
s
ec
u
r
ity
in
w
ir
el
ess
s
en
s
o
r
…
(
S
r
in
iva
s
K
a
la
s
k
a
r
)
1973
Fig
u
r
e
8
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
s
ce
n
ar
io
1
Fig
u
r
e
9
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
s
ce
n
ar
io
2
4.
CO
NCLU
SI
O
N
I
n
co
n
clu
s
io
n
,
th
e
jo
in
t
o
p
tim
i
za
tio
n
o
f
en
er
g
y
ef
f
icien
cy
in
W
SN
s
u
s
in
g
EC
-
MJSO
an
d
MA
C
O
f
o
r
clu
s
ter
in
g
an
d
r
o
u
tin
g
is
a
p
r
o
m
is
in
g
ap
p
r
o
ac
h
to
ad
d
r
ess
lim
ited
en
er
g
y
r
eso
u
r
ce
s
in
W
SN
s
.
T
h
is
m
eth
o
d
lev
er
ag
es
E
C
-
MJSO
an
d
MA
C
O
alg
o
r
ith
m
s
to
o
p
tim
ize
cl
u
s
ter
in
g
an
d
r
o
u
tin
g
s
tr
ateg
ie
s
wh
ile
co
n
s
id
er
in
g
o
b
jectiv
es
lik
e
m
in
im
izin
g
en
er
g
y
co
n
s
u
m
p
tio
n
,
m
ax
im
izin
g
co
v
er
a
g
e,
an
d
e
n
s
u
r
in
g
c
o
n
n
ec
tiv
ity
.
T
h
r
o
u
g
h
s
im
u
latio
n
s
an
d
ex
p
e
r
im
en
ts
,
th
e
E
C
-
MJSO
-
MA
C
O
m
eth
o
d
h
as
s
h
o
wn
im
p
r
o
v
ed
p
e
r
f
o
r
m
an
ce
in
n
etwo
r
k
life
tim
e,
en
er
g
y
co
n
s
u
m
p
tio
n
,
laten
cy
,
th
r
o
u
g
h
p
u
t,
co
v
e
r
ag
e,
an
d
co
n
n
ec
tiv
ity
.
T
h
is
m
et
h
o
d
o
f
f
er
s
a
co
m
p
r
eh
e
n
s
iv
e
f
r
am
ewo
r
k
f
o
r
o
p
tim
izin
g
en
e
r
g
y
ef
f
icien
c
y
in
W
SNs
,
with
p
o
ten
tial
ap
p
l
icatio
n
s
in
v
a
r
io
u
s
d
o
m
ain
s
.
Fu
t
u
r
e
r
esear
ch
m
ay
f
o
c
u
s
o
n
f
u
r
th
er
r
ef
i
n
in
g
th
e
a
lg
o
r
ith
m
s
an
d
e
x
p
lo
r
in
g
ad
d
iti
o
n
al
o
b
jectiv
es
f
o
r
lar
g
er
-
s
ca
le
W
SN d
ep
lo
y
m
en
t
.
Evaluation Warning : The document was created with Spire.PDF for Python.