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
.
1
0
0
2
~
1
0
1
0
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
1
0
0
2
-
1
0
1
0
1002
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
Lev
enberg
–
M
a
rqua
rt
lo
g
istic de
ep
neura
l learning
b
a
sed
energy
ef
fi
cient
and lo
a
d bala
nced
ro
uting in MA
NE
T
A.
Sa
ng
ee
t
ha
1
,
T
.
Ra
j
endra
n
2
1
Re
se
a
rc
h
S
c
h
o
lar,
P
e
riy
a
r
U
n
iv
e
rsity
,
S
a
lem
,
I
n
d
ia
2
Ka
n
g
e
y
a
m
Arts an
d
S
c
ien
c
e
Co
l
leg
e
,
Bh
a
ra
th
iar U
n
iv
e
rsit
y
,
I
n
d
ia
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
an
2
,
2
0
2
1
R
ev
is
ed
May
18
,
2
0
2
1
Acc
ep
ted
J
u
n
1
,
2
0
2
1
As
th
e
a
d
v
e
n
t
o
f
n
e
w
tec
h
n
o
lo
g
i
e
s
g
ro
ws
,
th
e
d
e
p
l
o
y
m
e
n
t
o
f
m
o
b
il
e
a
d
h
o
c
n
e
two
rk
s (M
AN
ET
)
b
e
c
o
m
e
s in
c
re
a
sin
g
ly
p
o
p
u
lar i
n
m
a
n
y
a
p
p
li
c
a
ti
o
n
a
re
a
s.
In
a
d
d
it
i
o
n
,
a
ll
th
e
n
o
d
e
s
in
M
AN
ET
a
re
b
a
tt
e
ry
o
p
e
ra
ted
a
n
d
th
e
n
o
d
e
m
o
b
il
it
y
a
ffe
c
ts
t
h
e
p
a
th
sta
b
il
it
y
a
n
d
c
re
a
tes
e
x
c
e
ss
iv
e
traffic
lea
d
s
to
h
ig
h
e
r
u
ti
li
z
a
ti
o
n
o
f
e
n
e
rg
y
,
d
a
ta
lo
ss
wh
ich
d
e
g
ra
d
e
s
th
e
p
e
rf
o
rm
a
n
c
e
o
f
ro
u
ti
n
g
.
S
o
,
i
n
th
is
p
a
p
e
r
we
p
ro
p
o
se
Lev
e
n
b
e
rg
–
M
a
rq
u
a
rd
t
l
o
g
isti
c
d
e
e
p
n
e
u
ra
l
lea
rn
in
g
b
a
se
d
e
n
e
rg
y
e
fficie
n
t
a
n
d
l
o
a
d
b
a
lan
c
e
d
ro
u
ti
n
g
(
LL
DN
L
-
EE
LBR)
wh
ich
is
a
m
a
c
h
i
n
e
lea
rn
in
g
m
e
t
h
o
d
to
d
e
e
p
l
y
a
n
a
l
y
z
e
th
e
m
o
b
il
e
n
o
d
e
s
to
c
a
lcu
late
re
sid
u
a
l
lo
a
d
a
n
d
e
n
e
rg
y
a
n
d
i
t
a
lso
u
se
s
lo
g
isti
c
a
c
ti
v
a
ti
o
n
f
u
n
c
ti
o
n
to
se
lec
t
th
e
m
o
b
i
le
n
o
d
e
h
a
v
in
g
h
ig
h
e
r
re
sid
u
a
l
e
n
e
r
g
y
a
n
d
re
sid
u
a
l
lo
a
d
t
o
ro
u
te
th
e
d
a
ta
p
a
c
k
e
t.
Ex
p
e
rime
n
tal
e
v
a
lu
a
ti
o
n
s
o
f
th
re
e
m
e
th
o
d
s
(LL
DN
L
-
EE
LBR,
m
u
l
ti
p
a
t
h
b
a
tt
e
ry
a
n
d
m
o
b
il
it
y
-
a
wa
re
ro
u
t
in
g
sc
h
e
m
e
(M
BM
A
-
OLS
R)
a
n
d
o
p
p
o
rt
u
n
isti
c
r
o
u
ti
n
g
wit
h
g
ra
d
ie
n
t
f
o
rwa
rd
i
n
g
fo
r
M
AN
ET
s
(ORG
M
A))
w
e
re
d
o
n
e
a
n
d
th
e
re
su
lt
re
v
e
a
ls
th
a
t
LL
DN
L
-
EE
LBR
m
e
th
o
d
is
ab
le
to
in
c
re
a
se
th
e
th
r
o
u
g
h
p
u
t
a
n
d
m
i
n
imiz
e
s
th
e
d
e
lay
a
n
d
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
in
M
AN
ET
w
h
e
n
c
o
m
p
a
re
d
to
w
o
rk
s
u
n
d
e
r
c
o
n
si
d
e
ra
ti
o
n
.
K
ey
w
o
r
d
s
:
E
n
er
g
y
u
tili
za
tio
n
L
o
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
R
esid
u
al
lo
ad
an
d
en
e
r
g
y
T
h
r
o
u
g
h
p
u
t
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
:
A.
San
g
ee
th
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
s
c
ie
n
ce
Go
v
er
n
m
e
n
t A
r
ts
an
d
Scien
ce
C
o
lleg
e
Mo
d
ak
k
u
r
ich
i,
E
r
o
d
e,
I
n
d
ia
E
m
ail
:
s
an
g
ee
th
ak
n
g
@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
d
ig
ital
r
ev
o
lu
tio
n
in
th
e
la
s
t
d
ec
ad
e
h
as
o
p
e
n
ed
a
g
ate
w
ay
o
f
n
ew
o
p
p
o
r
tu
n
ities
wh
er
e
MA
NE
T
p
lay
s
a
v
ital
r
o
le
in
-
ca
s
e
o
f
d
is
aster
to
tr
ad
itio
n
al
co
m
m
u
n
i
ca
tio
n
m
o
d
els
i.e
.
,
m
o
b
ile
n
et
wo
r
k
s
an
d
in
te
r
n
et
cr
is
is
.
MA
NE
T
is
an
u
n
s
tr
u
ctu
r
ed
n
etwo
r
k
o
f
m
o
b
ile
n
o
d
es
co
n
n
ec
ted
v
ia
r
ad
io
lin
k
s
.
E
v
er
y
d
ev
ice
in
MA
NE
T
m
o
v
es
in
a
r
a
n
d
o
m
d
ir
ec
tio
n
a
n
d
c
h
an
g
es
t
h
eir
c
o
n
n
ec
tio
n
s
t
o
o
t
h
er
d
ev
ice
to
r
o
u
te
d
ata
p
ac
k
et
s
.
T
h
e
u
n
b
ala
n
ce
d
lo
ad
d
is
tr
ib
u
t
io
n
am
o
n
g
n
o
d
es
d
ir
ec
ted
to
a
p
o
wer
d
is
s
ip
atio
n
wh
ich
ca
u
s
es
lin
k
f
ailu
r
e
an
d
af
f
ec
ts
th
e
n
etwo
r
k
p
e
r
f
o
r
m
a
n
ce
.
T
h
e
r
ef
o
r
e
,
en
er
g
y
an
d
lo
ad
b
ala
n
ce
d
r
o
u
tin
g
is
an
i
m
p
o
r
tan
t
p
r
o
b
lem
.
Sev
er
al
r
esear
ch
es
h
av
e
b
ee
n
d
o
n
e
t
o
p
er
f
o
r
m
en
er
g
y
e
f
f
i
cien
t
an
d
lo
a
d
b
alan
ce
d
r
o
u
ti
n
g
.
B
u
t
it
f
ailed
t
o
d
ec
r
ea
s
e
th
e
tim
e
an
d
en
er
g
y
co
n
s
u
m
p
tio
n
d
u
r
in
g
d
ata
p
ac
k
et
r
o
u
tin
g
a
n
d
d
ata
d
eliv
er
y
.
W
h
en
tr
an
s
m
itti
n
g
d
ata
f
r
o
m
s
o
u
r
ce
to
d
esti
n
atio
n
,
th
e
r
esid
u
al
en
er
g
y
an
d
s
tab
ilit
y
o
f
th
e
lin
k
m
u
s
t
b
e
an
al
y
ze
d
.
T
h
e
r
esid
u
al
en
er
g
y
is
ca
lcu
lated
b
y
s
u
b
tr
ac
tin
g
to
tal
tr
an
s
m
is
s
io
n
en
er
g
y
(
s
u
m
o
f
r
ec
eiv
in
g
en
e
r
g
y
an
d
tr
an
s
m
itti
n
g
en
er
g
y
)
f
r
o
m
in
itial
en
er
g
y
o
f
th
e
n
o
d
e.
Du
r
in
g
d
ata
tr
an
s
m
is
s
io
n
,
th
e
r
o
u
te
d
is
co
v
er
y
p
r
o
ce
s
s
f
in
d
s
o
u
t
th
e
n
o
d
e
with
h
ig
h
er
r
esid
u
al
e
n
e
r
g
y
.
I
f
th
e
n
o
d
es
r
esid
u
al
lo
a
d
is
lo
w,
th
e
d
ata
tr
an
s
m
is
s
io
n
p
r
o
ce
s
s
b
ec
o
m
es
v
er
y
s
lo
w,
a
n
d
it
a
u
to
m
atica
lly
ca
u
s
es
tr
af
f
ic
wh
ic
h
d
ec
r
ea
s
es
th
e
th
r
o
u
g
h
p
u
t.
Hen
ce
,
to
i
n
cr
ea
s
e
th
r
o
u
g
h
p
u
t
an
d
ex
ten
d
th
e
n
etwo
r
k
life
tim
e,
b
o
th
r
esid
u
al
en
er
g
y
an
d
lo
a
d
is
v
er
y
ess
en
tial.
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
Leve
n
b
erg
–
Ma
r
q
u
a
r
t lo
g
is
tic
d
ee
p
n
e
u
r
a
l le
a
r
n
in
g
b
a
s
ed
en
erg
y
efficien
t a
n
d
lo
a
d
…
(
A
.
S
a
n
g
ee
t
h
a
)
1003
A
m
u
ltip
ath
b
atter
y
an
d
m
o
b
ilit
y
-
awa
r
e
r
o
u
tin
g
s
ch
em
e
[
1
]
was
d
esig
n
ed
to
ch
o
o
s
e
th
e
s
t
ab
le
p
ath
s
to
d
esti
n
atio
n
.
B
u
t
th
is
s
ch
em
e
f
ailed
to
r
ed
u
ce
th
e
en
d
-
to
-
en
d
d
elay
.
A
r
eliab
le
an
d
p
r
a
ctica
l
o
p
p
o
r
tu
n
is
tic
r
o
u
tin
g
p
r
o
to
co
l
f
o
r
MA
NE
T
s
[
2
]
in
c
r
ea
s
es
p
ac
k
et
d
eliv
e
r
y
r
atio
w
h
er
ea
s
en
er
g
y
c
o
n
s
u
m
p
tio
n
d
u
r
in
g
d
ata
p
ac
k
et
r
o
u
tin
g
was
n
o
t
r
ed
u
ce
d
.
A
f
o
r
war
d
in
g
s
tr
ateg
y
was
in
tr
o
d
u
ce
d
in
[
3
]
to
r
ed
u
ce
th
e
to
p
o
lo
g
y
c
o
n
tr
o
l
tr
af
f
ic.
Ho
wev
er
,
th
e
r
o
u
tin
g
tim
e
was
n
o
t
r
ed
u
ce
d
.
B
an
d
wid
th
-
s
atis
f
ied
a
n
d
co
d
in
g
-
awa
r
e
r
o
u
tin
g
p
r
o
to
co
l
[
4
]
r
ed
u
ce
s
th
e
to
tal
b
an
d
wid
th
co
n
s
u
m
p
tio
n
;
th
e
r
o
u
tin
g
ef
f
icien
cy
was
n
o
t
im
p
r
o
v
ed
.
Secu
r
e
o
p
tim
ized
lin
k
s
tate
r
o
u
tin
g
p
r
o
to
co
l
[
5
]
f
in
d
s
a
s
ec
u
r
e
lin
k
f
o
r
th
e
r
o
u
tin
g
.
Ho
wev
e
r
,
th
i
s
s
y
s
tem
d
o
es
n
o
t
co
n
s
id
er
an
y
ex
ter
n
al
attac
k
s
d
u
r
in
g
p
ac
k
et
tr
an
s
m
is
s
io
n
.
ME
QSA
-
OL
SR
v
2
[
6
]
s
o
lv
es th
e
p
r
o
b
lem
o
f
lim
ited
en
er
g
y
r
eso
u
r
ce
s
,
n
o
d
e
m
o
b
ilit
y
an
d
n
etwo
r
k
tr
af
f
ic
in
MA
NE
T
an
d
wir
eless
s
en
s
o
r
n
etwo
r
k
(
W
SN)
.
Ho
wev
er
,
th
r
o
u
g
h
p
u
t
was
lo
wer
.
A
p
ar
ticle
s
war
m
o
p
tim
izatio
n
ap
p
r
o
ac
h
[
7
]
ca
r
r
ies
o
u
t
en
er
g
y
-
o
p
tim
ized
r
o
u
tin
g
in
MA
NE
T
.
B
u
t
p
ac
k
et
d
eliv
er
y
r
atio
was
n
o
t
im
p
r
o
v
ed
.
I
n
least
co
m
m
o
n
m
u
ltip
le
b
ased
r
o
u
tin
g
[
8
]
f
o
r
lo
a
d
-
b
alan
ci
n
g
in
MA
NE
T
,
p
ac
k
et
lo
s
s
r
ate
was
n
o
t
s
o
lv
ed
.
A
p
o
wer
a
n
d
lo
a
d
-
aw
ar
e
r
o
u
tin
g
s
ch
em
e
b
ased
o
n
th
e
d
y
n
am
ic
s
o
u
r
ce
r
o
u
tin
g
(
DSR
)
p
r
o
to
co
l
s
ch
e
m
e
[
9
]
in
c
r
ea
s
es
th
e
o
p
er
atio
n
al
life
o
f
n
o
d
es
an
d
th
e
n
etwo
r
k
life
tim
e.
B
u
t
en
er
g
y
u
tili
za
tio
n
was
h
ig
h
er
.
A
n
o
v
el
ap
p
r
o
ac
h
was
in
tr
o
d
u
ce
d
to
in
cr
ea
s
e
th
e
n
etwo
r
k
p
er
f
o
r
m
a
n
ce
th
r
o
u
g
h
d
is
tr
ib
u
tio
n
o
f
lo
ad
am
o
n
g
th
e
f
r
ee
n
o
d
es
,
b
u
t
s
tab
le
,
r
eliab
le
an
d
lo
ad
b
alan
ce
d
r
o
u
tin
g
was
n
o
t
p
er
f
o
r
m
ed
[
1
0
]
-
[
1
2
]
.
A
s
tab
le
a
n
d
en
e
r
g
y
ef
f
icien
t
r
o
u
tin
g
a
lg
o
r
ith
m
[
1
3
]
with
ap
p
licatio
n
o
f
lear
n
in
g
au
to
m
a
ta
th
eo
r
y
r
esu
lts
in
lo
wer
p
ac
k
et
d
eliv
er
y
r
atio
.
A
n
o
v
el
tech
n
iq
u
e
was
in
tr
o
d
u
ce
d
in
[
1
4
]
,
[
1
5
]
with
th
e
in
ten
tio
n
o
f
i
n
cr
ea
s
in
g
lo
ad
b
alan
cin
g
ca
p
ac
ity
.
B
u
t
th
e
n
u
m
b
er
o
f
d
ata
lo
s
t
was
h
ig
h
e
r
an
d
n
etwo
r
k
life
tim
e
was
n
o
t
im
p
r
o
v
ed
.
J
o
in
t
co
s
t
an
d
s
ec
u
r
ed
n
o
d
e
d
is
jo
in
t
en
er
g
y
-
ef
f
icien
t
m
u
ltip
ath
r
o
u
tin
g
[
1
6
]
im
p
r
o
v
es
s
ec
u
r
it
y
in
d
ata
r
o
u
tin
g
a
n
d
r
ed
u
ce
s
en
er
g
y
u
tili
za
tio
n
an
d
ex
ec
u
ti
o
n
tim
e
d
u
r
in
g
m
u
ltip
ath
r
o
u
ti
n
g
.
Ho
wev
er
,
p
er
f
o
r
m
an
ce
o
f
en
er
g
y
ef
f
icien
cy
is
n
o
t
s
u
f
f
icien
t.
Min
im
al
e
n
er
g
y
co
n
s
u
m
p
tio
n
with
o
p
tim
ize
d
r
o
u
tin
g
[
1
7
]
u
s
es
m
in
im
al
e
n
er
g
y
d
u
r
in
g
p
ac
k
et
tr
an
s
m
is
s
io
n
.
Ho
wev
er
,
it
in
cr
e
ases
th
e
ex
ec
u
tio
n
tim
e.
An
in
tellig
en
t
e
n
er
g
y
-
awa
r
e
r
o
u
tin
g
p
r
o
to
co
l
f
o
r
MA
NE
T
[
1
8
]
d
id
n
o
t
co
n
s
id
er
th
e
l
o
ad
o
f
n
o
d
es
d
u
r
in
g
tr
a
n
s
m
is
s
io
n
.
R
eliab
le
an
d
e
n
er
g
y
ef
f
icien
t
p
r
o
to
co
l
d
ep
en
d
i
n
g
o
n
d
is
tan
ce
an
d
r
em
ain
in
g
en
er
g
y
[
1
9
]
r
e
d
u
ce
s
en
er
g
y
co
n
s
u
m
p
tio
n
.
B
u
t
it
f
ailed
in
ter
m
s
o
f
p
ac
k
et
d
eliv
er
y
r
atio
.
An
Ad
Ho
c
o
n
d
em
an
d
m
u
ltip
ath
d
is
tan
ce
v
ec
to
r
r
o
u
tin
g
p
r
o
to
co
l
u
s
in
g
f
itn
ess
f
u
n
ctio
n
[
2
0
]
f
in
d
s
o
u
t
t
h
e
o
p
tim
al
p
ath
f
r
o
m
s
o
u
r
ce
to
d
esti
n
atio
n
.
B
u
t
it
f
ailed
to
a
d
d
r
ess
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
n
etwo
r
k
life
t
im
e.
Af
s
ar
an
d
Yo
u
n
is
in
[
2
1
]
,
Ma
u
r
y
a
et
a
l
.
[
2
2
]
,
cr
o
s
s
-
lay
er
d
esig
n
f
o
r
W
SNs
with
en
er
g
y
s
ca
v
en
g
in
g
an
d
tr
an
s
f
er
ca
p
a
b
ilit
ies
an
d
d
elay
a
war
e
en
er
g
y
ef
f
icien
t
r
o
u
tin
g
wer
e
in
tr
o
d
u
ce
d
to
r
ed
u
ce
th
e
en
er
g
y
u
tili
za
tio
n
.
Ho
wev
er
,
en
e
r
g
y
ef
f
icien
cy
o
f
th
ese
m
e
th
o
d
s
was
n
o
t
s
u
f
f
ic
ien
t.
Secu
r
e
en
e
r
g
y
ef
f
icien
t
r
o
u
tin
g
p
r
o
to
co
l
[
2
3
]
s
elec
ts
a
s
ec
u
r
e
r
o
u
te
p
ath
.
B
u
t
it
f
ailed
to
attain
m
in
im
al
p
ac
k
et
lo
s
s
r
ate.
A
clu
s
ter
in
g
ap
p
r
o
ac
h
an
d
en
er
g
y
ef
f
icien
t
tech
n
iq
u
e
was
im
p
lem
en
ted
in
[
2
4
]
,
[
2
5
]
f
o
r
in
cr
ea
s
in
g
th
e
n
et
wo
r
k
life
tim
e
b
y
co
n
s
id
er
in
g
th
e
m
o
b
ilit
y
,
en
er
g
y
o
f
m
o
b
ile
n
o
d
e
s
an
d
r
ed
u
n
d
a
n
t
p
ac
k
et
g
en
e
r
a
tio
n
.
Ho
wev
er
,
th
e
en
er
g
y
ef
f
icien
c
y
is
n
o
t
s
u
f
f
icien
t.
T
o
o
v
er
co
m
e
th
e
a
b
o
v
e
-
m
e
n
tio
n
ed
is
s
u
es
s
u
ch
as
h
ig
h
er
en
er
g
y
co
n
s
u
m
p
tio
n
d
u
r
i
n
g
d
ata
tr
an
s
m
is
s
io
n
,
less
er
p
ac
k
et
d
eliv
er
y
r
atio
an
d
n
etwo
r
k
life
tim
e
,
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
in
tr
o
d
u
ce
d
in
t
h
is
p
ap
er
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
e
p
ap
er
is
p
lan
n
e
d
is
b
ein
g
as:
s
ec
tio
n
2
d
ep
icts
r
esear
ch
m
eth
o
d
s
.
Sectio
n
3
ex
p
lo
r
es
r
esu
lt
an
d
d
is
cu
s
s
io
n
.
I
n
s
ec
tio
n
4
,
t
h
e
co
n
clu
s
io
n
o
f
th
is
p
ap
er
is
p
r
esen
ted
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
P
r
o
blem
f
o
rm
ula
t
io
n
I
n
MA
NE
T
,
ea
ch
a
n
d
ev
e
r
y
n
o
d
e
ac
t
as
a
h
o
s
t
an
d
r
o
u
ter
.
Du
e
to
er
r
o
r
-
p
r
o
n
e
wir
eless
co
n
n
ec
tio
n
s
,
d
y
n
am
ic
ch
a
n
g
in
g
s
tr
u
ctu
r
e,
d
ata
r
o
u
tin
g
with
o
u
t
an
y
d
is
co
n
n
ec
tio
n
s
is
a
co
m
p
lex
task
.
Fu
r
th
er
m
o
r
e,
an
in
ter
m
ed
iate
n
o
d
e
is
u
tili
ze
d
f
o
r
lo
n
g
er
d
u
r
atio
n
r
esu
lts
i
n
tr
af
f
ic
co
n
g
esti
o
n
.
T
h
is
tr
af
f
ic
ca
u
s
es
h
ig
h
er
laten
cy
an
d
r
ed
u
ce
s
th
e
en
e
r
g
y
o
f
n
o
d
es.
T
h
u
s
,
en
er
g
y
ef
f
icien
t
an
d
lo
ad
b
alan
ce
d
d
ata
r
o
u
tin
g
is
an
im
p
o
r
tan
t
is
s
u
e
to
b
e
co
n
s
id
er
ed
.
T
h
er
e
f
o
r
e,
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
in
tr
o
d
u
ce
d
b
y
u
s
in
g
L
ev
e
n
b
er
g
–
Ma
r
q
u
ar
d
t
lo
g
is
tic
d
ee
p
n
eu
r
a
l
lear
n
in
g
co
n
ce
p
ts
wh
ich
p
r
o
lo
n
g
s
n
etwo
r
k
life
tim
e
an
d
in
cr
ea
s
es
th
r
o
u
g
h
p
u
t
in
MA
NE
T
b
y
s
elec
tin
g
n
o
d
es
with
h
ig
h
er
r
esid
u
al
en
er
g
y
a
n
d
r
esid
u
al
lo
a
d
f
o
r
r
o
u
tin
g
.
2.
2
.
T
he
pro
po
s
ed
L
L
D
NL
-
E
E
L
B
R
m
et
ho
d
I
n
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
,
L
L
DFFNL
alg
o
r
ith
m
attain
s
g
r
ea
ter
co
n
s
id
er
atio
n
b
ec
au
s
e
it
is
f
ast
an
d
r
eq
u
ir
es
r
elativ
ely
less
co
m
p
u
tatio
n
al
tim
e
an
d
is
v
er
y
u
s
ef
u
l
f
o
r
r
ea
l
-
wo
r
ld
a
p
p
licatio
n
s
.
T
h
e
L
L
DFFNL
is
an
ar
tific
ial
in
tel
lig
en
ce
f
u
n
cti
o
n
th
at
em
p
lo
y
s
th
e
wo
r
k
in
g
s
o
f
h
u
m
a
n
b
r
ain
to
f
i
n
d
o
u
t th
e
b
est m
o
b
ile
n
o
d
es.
T
h
is
m
eth
o
d
c
o
m
p
r
is
es o
f
f
o
u
r
lay
er
s
,
n
am
ely
o
n
e
i
n
p
u
t la
y
er
,
two
h
id
d
en
lay
e
r
an
d
o
n
e
o
u
tp
u
t la
y
er
.
F
i
g
u
r
e
1
s
h
o
w
s
t
h
e
s
t
r
u
c
t
u
r
e
o
f
L
L
D
F
F
N
L
a
l
g
o
r
i
t
h
m
i
n
w
h
i
c
h
M
A
N
E
T
i
s
o
r
g
a
n
i
z
e
d
i
n
t
h
e
f
o
r
m
o
f
g
r
a
p
h
i
c
a
l
r
e
p
r
e
s
e
n
t
a
t
i
o
n
a
s
‘
(
,
)
’
.
H
e
r
e
,
‘
’
r
e
p
r
e
s
e
n
t
s
a
t
o
t
a
l
n
u
m
b
e
r
o
f
m
o
b
i
l
e
n
o
d
e
s
‘
=
1
,
2
,
3
,
…
,
’
d
i
s
p
e
n
s
e
d
i
n
a
s
q
u
a
r
e
a
r
e
a
o
f
‘
∗
’
w
i
t
h
i
n
t
h
e
c
o
m
m
u
n
i
c
a
t
i
o
n
r
a
n
g
e
‘
’
a
n
d
‘
’
i
n
d
i
c
a
t
e
s
a
c
o
n
n
e
c
t
i
o
n
b
e
t
w
e
e
n
n
o
d
e
s
i
n
t
h
e
n
e
t
w
o
r
k
.
Am
o
n
g
th
e
s
ev
er
al
m
o
b
ile
n
o
d
es,
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
id
en
tifie
s
en
er
g
y
ef
f
icien
t
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
:
1
0
0
2
-
1
0
1
0
1004
an
d
lo
ad
b
alan
ce
d
m
o
b
ile
n
o
d
es
an
d
r
o
u
te
th
e
d
ata
p
ac
k
et
s
.
L
L
DFFNL
co
n
s
i
s
ts
o
f
s
ev
e
r
al
lay
er
s
an
d
ea
ch
lay
er
in
L
L
DFFNL
u
tili
ze
s
th
e
o
u
tp
u
t
f
r
o
m
o
n
e
lay
er
as
in
p
u
t
to
th
e
n
ex
t
lay
e
r
an
d
d
ee
p
i
n
d
icate
s
th
e
n
u
m
b
er
o
f
lay
er
s
.
Fu
r
th
er
,
L
L
DFFNL
co
n
tain
s
a
s
u
b
s
tan
tial
cr
ed
it
ass
ig
n
m
en
t
p
ath
(
C
AP)
d
ep
th
.
C
AP
ex
p
lain
s
ca
u
s
al
co
n
n
ec
tio
n
b
etwe
en
th
e
in
p
u
t
an
d
o
u
tp
u
t
lay
er
.
Dep
t
h
o
f
th
e
C
AP
is
th
e
n
u
m
b
er
o
f
h
id
d
e
n
lay
e
r
s
p
lu
s
o
n
e.
L
L
DFFNL
is
a
f
ee
d
f
o
r
war
d
n
etwo
r
k
i
n
wh
ich
d
ata
f
lo
w
f
r
o
m
i
n
p
u
t
lay
e
r
to
o
u
tp
u
t
lay
er
with
o
u
t
b
ac
k
p
r
o
p
ag
atio
n
.
I
t
co
n
s
tr
u
cts
a
m
ap
o
f
v
ir
tu
al
n
eu
r
o
n
s
wit
h
m
o
b
ile
n
o
d
es
an
d
in
itializes
r
an
d
o
m
weig
h
ts
to
ea
ch
n
o
d
e.
T
h
e
in
p
u
t
lay
er
r
ec
eiv
es
th
e
n
u
m
b
e
r
o
f
m
o
b
ile
n
o
d
es
as
in
p
u
t.
Af
ter
o
b
tai
n
in
g
in
p
u
t,
h
id
d
e
n
la
y
er
s
d
ee
p
ly
ex
am
in
es
ea
ch
n
o
d
e
a
n
d
ca
lcu
lates
th
eir
r
esid
u
al
en
er
g
y
an
d
lo
ad
wh
ich
is
s
en
t
to
th
e
o
u
tp
u
t
lay
er
.
T
h
e
weig
h
ts
an
d
th
ese
in
p
u
ts
ar
e
m
u
ltip
lied
in
th
e
o
u
tp
u
t
lay
er
an
d
g
i
v
e
a
p
r
ed
ictio
n
r
esu
lt
‘
0
’
o
r
‘
1
’
u
s
in
g
a
lo
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
.
I
f
th
e
n
etwo
r
k
d
id
n
’
t
ex
ac
tly
f
i
n
d
o
u
ts
th
e
b
est
n
o
d
es,
an
L
L
DFFNL
ad
ju
s
t
s
th
e
weig
h
ts
b
ased
o
n
th
eir
er
r
o
r
.
W
e
u
s
e
th
e
r
o
o
t
m
ea
n
s
q
u
ar
ed
m
eth
o
d
to
r
e
d
u
ce
er
r
o
r
s
.
Mo
r
eo
v
e
r
,
th
e
b
ac
k
p
r
o
p
ag
at
io
n
is
ap
p
lied
in
L
L
DFFNL
to
tr
ain
th
e
m
u
ltil
ay
er
n
eu
r
al
n
etwo
r
k
b
y
ch
an
g
in
g
th
e
weig
h
ts
in
th
e
n
o
d
es
to
im
p
r
o
v
e
r
o
u
tin
g
p
er
f
o
r
m
an
ce
ac
co
r
d
in
g
to
th
e
er
r
o
r
co
r
r
ec
tio
n
lear
n
in
g
f
u
n
ct
io
n
.
I
n
th
is
m
an
n
er
,
th
e
L
L
DFFNL
ac
cu
r
ately
s
elec
ts
th
e
b
est n
o
d
es in
MA
NE
T
to
o
b
tain
r
eliab
le
d
ata
r
o
u
tin
g
.
Fig
u
r
e
1
.
Stru
ctu
r
e
o
f
L
L
DFFNL
alg
o
r
ith
m
Fig
u
r
e
2
d
e
p
icts
a
ty
p
ical
MA
NE
T
n
etwo
r
k
with
8
n
o
d
es.
T
h
e
d
ata
h
as
to
b
e
t
r
an
s
m
itted
f
r
o
m
n
o
d
e
a
(
s
o
u
r
ce
)
to
n
o
d
e
H
(
d
esti
n
atio
n
)
th
r
o
u
g
h
an
o
p
tim
ized
p
ath
.
T
h
e
f
ir
s
t
s
tep
id
e
n
tifie
s
th
e
elig
ib
le
n
o
d
es
th
r
o
u
g
h
L
L
DN
-
E
E
L
B
R
m
eth
o
d
o
lo
g
y
.
Am
o
n
g
th
e
elig
i
b
le
n
o
d
es,
an
ef
f
icien
t
p
ath
wo
u
l
d
b
e
i
d
en
tifie
d
an
d
d
ata
will b
e
tr
an
s
f
er
r
ed
th
r
o
u
g
h
th
is
p
ath
.
Fig
u
r
e
2
.
Path
d
is
co
v
er
y
i
n
L
L
DFFNL
alg
o
r
ith
m
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
Leve
n
b
erg
–
Ma
r
q
u
a
r
t lo
g
is
tic
d
ee
p
n
e
u
r
a
l le
a
r
n
in
g
b
a
s
ed
en
erg
y
efficien
t a
n
d
lo
a
d
…
(
A
.
S
a
n
g
ee
t
h
a
)
1005
L
et
u
s
co
n
s
id
er
th
e
m
o
b
ile
n
o
d
es
‘
=
1
,
2
,
3
,
…
,
’
as
in
p
u
t.
T
h
u
s
,
th
e
n
e
u
r
o
n
s
o
p
er
atio
n
i
n
th
e
in
p
u
t la
y
er
‘
(
)
’
o
b
tain
e
d
as,
(
)
=
∑
+
(
1
)
Fro
m
(
1
)
,
‘
(
)
’
r
ep
r
esen
ts
n
eu
r
o
n
ac
tiv
ities
in
in
p
u
t
lay
er
at
a
tim
e
‘
’
an
d
‘
’
d
en
o
tes
an
i
n
p
u
t
m
o
b
ile
n
o
d
e
an
d
‘
’
r
ef
e
r
s
to
weig
h
t
b
etwe
en
th
e
i
n
p
u
t
an
d
h
id
d
e
n
lay
e
r
a
n
d
‘
’
in
d
icate
s
th
e
b
ias
ter
m
.
Af
ter
g
ettin
g
th
e
in
p
u
t,
t
h
e
n
e
u
r
o
n
in
in
p
u
t
la
y
er
‘
’
co
m
b
i
n
e
an
in
p
u
t
m
o
b
ile
n
o
d
e
‘
’
with
weig
h
ts
‘
’
an
d
b
ias
ter
m
‘
’
.
C
o
n
s
eq
u
e
n
tly
,
th
e
in
p
u
t
lay
er
f
o
r
war
d
in
p
u
t
m
o
b
ile
n
o
d
es
to
th
e
h
id
d
e
n
lay
e
r
.
I
n
L
L
DFFNL,
th
e
f
ir
s
t h
id
d
en
la
y
er
d
eter
m
i
n
es th
e
r
esid
u
al
en
er
g
y
o
f
ea
ch
i
n
p
u
t m
o
b
ile
n
o
d
e
u
s
in
g
ex
p
r
ess
io
n
,
1
(
)
=
∑
(
)
1
,
(
2
)
Fro
m
(
2
)
,
‘
1
(
)
’
r
ef
er
s
to
o
u
tp
u
t
o
f
f
ir
s
t
h
id
d
en
lay
er
at
tim
e
‘
’
.
Her
e,
‘
(
)
’
r
ep
r
esen
ts
th
e
in
p
u
t
ac
q
u
ir
ed
f
r
o
m
th
e
in
p
u
t
lay
er
i.e
.
n
u
m
b
e
r
o
f
m
o
b
ile
n
o
d
es
‘
=
1
,
2
,
3
,
…
,
’
wh
er
ea
s
‘
1
’
is
th
e
weig
h
t
o
f
f
ir
s
t
h
id
d
en
lay
er
an
d
‘
’
d
e
n
o
tes
th
e
r
esid
u
al
en
er
g
y
o
f
m
o
b
ile
n
o
d
es.
W
h
en
ev
er
th
e
r
esid
u
al
en
e
r
g
y
o
f
m
o
b
ile
n
o
d
es
is
m
in
im
al,
lin
k
f
ailu
r
e
o
cc
u
r
s
an
d
th
er
e
f
o
r
e
th
e
d
ata
p
ac
k
et
r
o
u
tin
g
is
f
ailed
.
Fro
m
th
at,
id
en
tific
atio
n
o
f
h
i
g
h
er
r
esid
u
al
en
er
g
y
is
ess
en
tial
to
att
ain
th
e
r
eliab
le
d
ata
tr
a
n
s
m
is
s
io
n
.
T
h
e
r
esid
u
al
en
er
g
y
o
f
m
o
b
ile
n
o
d
e
‘
’
is
c
alcu
lated
as,
=
−
(
+
)
(
3
)
Fro
m
(
3
)
,
‘
’
is
th
e
in
itial
en
er
g
y
o
f
m
o
b
ile
n
o
d
e
‘
’
an
d
‘
’
,
‘
’
s
ig
n
if
ies
en
er
g
y
u
tili
ze
d
b
y
a
n
o
d
e
to
tr
a
n
s
m
it
an
d
r
ec
ei
v
e
t
h
e
d
ata
p
ac
k
ets.
T
h
u
s
,
th
e
a
m
o
u
n
t
o
f
en
er
g
y
c
o
n
s
u
m
e
d
b
y
a
n
o
d
e
‘
’
to
s
en
d
an
d
r
ec
eiv
e
p
ac
k
ets is
m
ath
em
atica
lly
m
ea
s
u
r
ed
u
s
in
g
as,
=
−
(
4
)
Fro
m
(
4
)
,
‘
’
r
ep
r
esen
t
th
e
to
ta
l
en
er
g
y
lev
el
o
f
a
m
o
b
ile
n
o
d
e.
Nex
t,
th
e
r
esid
u
al
e
n
er
g
y
o
f
all
m
o
b
ile
n
o
d
es
is
s
en
t
to
th
e
s
ec
o
n
d
h
id
d
en
lay
er
.
T
h
e
r
esid
u
al
lo
ad
o
f
m
o
b
ile
n
o
d
e
is
co
m
p
u
ted
in
t
h
e
s
ec
o
n
d
h
id
d
en
la
y
er
‘
2
(
)
’
at
tim
e
‘
t’
u
s
in
g
,
2
(
)
=
∑
1
(
)
2
,
(
5
)
Fro
m
(
5
)
,
‘
2
(
)
’
in
d
icat
es
th
e
o
u
tp
u
t
o
f
s
ec
o
n
d
h
id
d
en
lay
er
an
d
‘
1
(
)
’
is
th
e
r
esid
u
al
e
n
er
g
y
wh
ich
is
o
b
tain
ed
f
r
o
m
t
h
e
f
i
r
s
t
h
id
d
en
lay
e
r
.
Her
e
‘
2
’
r
ep
r
esen
ts
th
e
weig
h
t
o
f
s
ec
o
n
d
h
id
d
en
lay
er
an
d
‘
’
r
ef
er
s
to
r
esid
u
al
lo
ad
o
f
n
o
d
es.
T
h
e
lo
ad
o
n
ea
c
h
m
o
b
ile
n
o
d
e
‘
’
is
ca
lcu
lated
as,
=
(
6
)
Fro
m
(
6
)
,
‘
’
r
ep
r
esen
t
t
h
e
to
tal
lo
ad
ca
p
ac
ity
o
f
m
o
b
ile
n
o
d
e
an
d
‘
’
in
d
icate
s
th
at
t
h
e
am
o
u
n
t
o
f
d
ata
p
ac
k
ets b
ein
g
ca
r
r
ied
b
y
n
o
d
e
‘
’
.
Fo
llo
wed
b
y
,
th
e
r
esid
u
al
lo
ad
o
f
a
n
o
d
e
is
esti
m
ated
u
s
in
g
as,
=
−
(
7
)
Fro
m
(
7
)
,
th
e
r
esid
u
al
lo
a
d
is
ca
lcu
lated
f
o
r
ea
ch
m
o
b
ile
n
o
d
e
‘
’
in
n
etwo
r
k
.
Su
b
s
eq
u
e
n
tly
,
th
e
h
id
d
en
la
y
er
s
en
d
s
r
esid
u
al
e
n
er
g
y
a
n
d
r
esid
u
al
lo
ad
r
esu
lts
to
th
e
o
u
tp
u
t
lay
er
.
T
h
e
b
est
m
o
b
ile
n
o
d
es
ar
e
s
elec
ted
in
o
u
tp
u
t la
y
er
u
s
in
g
ex
p
r
ess
io
n
,
(
)
=
2
(
)
(
8
)
Fro
m
(
8
)
,
‘
(
)
’
r
ep
r
esen
ts
o
u
tp
u
t
lay
er
r
esu
lt
at
tim
e
‘
’
an
d
‘
’
in
d
icate
s
th
e
weig
h
t
b
etwe
en
th
e
h
id
d
e
n
an
d
o
u
tp
u
t
lay
er
a
n
d
‘
’
is
th
e
lo
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
.
T
h
e
L
L
DNL
-
E
E
L
B
R
T
ec
h
n
iq
u
e
u
s
ed
th
e
lo
g
is
tic
r
eg
r
ess
io
n
f
u
n
cti
o
n
as
ac
tiv
atio
n
f
u
n
ctio
n
al
g
o
r
ith
m
s
to
in
cr
ea
s
e
th
e
r
o
u
tin
g
ef
f
icien
c
y
in
MA
NE
T
.
T
h
e
lo
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
‘
’
r
esu
lt is
m
ath
em
atica
lly
f
o
r
m
u
lated
as,
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
:
1
0
0
2
-
1
0
1
0
1006
=
{
(
>
)
,
ℎ
(
)
=
1
ℎ
,
(
)
=
0
(
9
)
Fro
m
(
9
)
,
‘
’
r
ep
r
esen
t
th
r
esh
o
l
d
v
alu
e
ass
ig
n
ed
f
o
r
r
esid
u
al
e
n
er
g
y
an
d
r
esid
u
al
lo
a
d
.
I
n
L
L
DFFNL,
th
e
o
u
t
p
u
t
o
f
l
o
g
is
tic
s
ig
m
o
i
d
f
u
n
ctio
n
r
an
g
es
b
etwe
en
‘
0
’
an
d
‘
1
’
.
T
h
e
o
u
t
p
u
t
o
f
t
h
e
lo
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
is
d
e
p
icted
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
L
o
g
is
tic
ac
tiv
atio
n
f
u
n
ctio
n
o
u
tp
u
t
Fro
m
th
at,
th
e
o
u
tp
u
t la
y
er
r
es
u
lt is
m
ath
em
atica
lly
o
b
tain
ed
as,
(
)
=
{
1
,
0
,
(
1
0
)
Fro
m
(
1
0
)
,
‘
(
)
=
1
’
in
d
icate
s
th
at
th
e
m
o
b
ile
n
o
d
e
‘
’
s
elec
ted
to
p
er
f
o
r
m
d
ata
r
o
u
tin
g
wh
er
ea
s
‘
(
)
=
0
’
s
ig
n
if
ies
th
e
m
o
b
ile
n
o
d
e
‘
’
is
n
o
t
ch
o
s
en
.
Fo
r
ea
ch
p
r
e
d
ictio
n
r
esu
lt
o
f
m
o
b
ile
n
o
d
e,
t
h
e
er
r
o
r
is
ca
lcu
lated
as
d
if
f
er
en
ti
atio
n
b
etwe
en
tar
g
et
o
u
tp
u
t
‘
´
(
)
’
an
d
o
b
tain
e
d
o
u
t
p
u
t
‘
(
)
’
u
s
in
g
r
o
o
t
-
m
ea
n
-
s
q
u
ar
ed
er
r
o
r
u
s
in
g
,
(
)
=
√
∑
(
,
(
)
−
(
)
)
2
=
1
(
1
1
)
Fro
m
(
1
1
)
,
r
o
o
t
-
m
ea
n
-
s
q
u
ar
ed
er
r
o
r
‘
’
at
tim
e
‘
’
in
th
e
o
u
tp
u
t
lay
er
is
d
eter
m
in
ed
.
L
L
DFFNL
alg
o
r
ith
m
d
e
cr
ea
s
e
th
e
er
r
o
r
b
y
m
ea
n
s
o
f
ch
an
g
in
g
weig
h
ts
a
n
d
b
iases
in
th
e
n
etwo
r
k
.
Su
b
s
eq
u
en
tly
,
th
e
weig
h
ts
ar
e
u
p
d
ated
as,
∆
=
−
(
)
;
∆
=
−
(
)
;
∆
=
−
(
)
(1
2)
Fro
m
(
1
2
)
,
weig
h
t
v
alu
es
o
n
in
p
u
t
‘
∆
’
,
h
id
d
en
‘
∆
’
an
d
o
u
tp
u
t
lay
er
‘
∆
’
is
u
p
d
ated
ac
co
r
d
in
g
to
th
eir
r
o
o
t
-
m
ea
n
-
s
q
u
ar
ed
er
r
o
r
.
Her
e,
‘
’
r
ef
er
s
to
th
e
lear
n
in
g
r
ate
wh
ich
co
n
tr
o
ls
th
e
c
h
an
g
e
i
n
weig
h
t f
r
o
m
o
n
e
iter
atio
n
to
a
n
o
th
er
.
I
t
is
f
o
r
m
u
lated
as,
(
)
(
)
≡
√
∑
(
,
(
)
−
(
)
)
2
=
1
(
1
3
)
Fro
m
(
1
3
)
,
th
e
o
u
t
p
u
t
er
r
o
r
‘
(
)
’
o
f
L
L
DFFNL
is
m
in
im
ized
t
o
s
elec
t
th
e
b
est
n
o
d
e
f
o
r
r
o
u
tin
g
.
T
h
e
ab
o
v
e
p
r
o
ce
s
s
o
f
L
L
DFFNL
alg
o
r
ith
m
is
r
ep
ea
ted
u
n
til
th
e
r
o
o
t
-
m
ea
n
-
s
q
u
ar
ed
e
r
r
o
r
is
lo
wer
.
T
h
e
alg
o
r
ith
m
ic
p
r
o
ce
s
s
o
f
L
L
DFFNL
is
ex
p
lain
ed
in
th
is
s
ec
tio
n
.
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
Leve
n
b
erg
–
Ma
r
q
u
a
r
t lo
g
is
tic
d
ee
p
n
e
u
r
a
l le
a
r
n
in
g
b
a
s
ed
en
erg
y
efficien
t a
n
d
lo
a
d
…
(
A
.
S
a
n
g
ee
t
h
a
)
1007
// Levenberg
-
Marquardt Logistic Deep Feedforward Neural LearningAl
gorithm
Input: Large Number of Mobile Nodes ‘
=
1
,
2
,
3
,
…
,
’ in MANET; Number of Data Packets ‘
=
1
,
2
,
…
,
’
ut: Attain Energy Efficient and Load Balanced Routing
Step 1: Begin
Step 2: Construct an artificialnetwork with Random weights
Step 3: While (‘
(
)
’ is lower) do
Step 4: Number of mobile nodes are taken as inputusing (1)// input layer
Step 5: For each ‘
’
Step 6: Determine ‘
’using (2)// First hidden layer
Step 7: Measure ‘
’ using (5) // Second hidden layer
Step 8: End For
Step 9: Find the best nodes ‘
’ in network using (8) // output layer
Step 10: Calculate ‘
(
)
’ using (11)
Step 11: Update ‘
∆
’, ‘
∆
’, ‘
∆
’ using (12)
Step 1
2: Find ‘
(
)
’ using (13) // Levenberg
-
Marquardt algorithm
Step 13: End While
Step 14: If (
(
)
=
1
) then
Step 15: ‘
’ is selected for routing
Step 16: Else
Step 17: ‘
’ is not selected for routing
Step 18: End If
step 19: End
Alg
o
r
ith
m
1
.
L
ev
en
b
er
g
-
Ma
r
q
u
ar
d
t lo
g
is
tic
d
ee
p
f
ee
d
f
o
r
war
d
n
eu
r
al
lea
r
n
in
g
3.
RE
SU
L
T
S
A
ND
D
IS
CU
SS
I
O
N
T
o
esti
m
ate
th
e
p
er
f
o
r
m
an
ce
o
f
p
r
o
p
o
s
ed
,
L
L
DNL
-
E
E
L
B
R
Me
th
o
d
is
im
p
lem
en
ted
in
n
etwo
r
k
s
im
u
lato
r
-
3
.
2
6
with
v
ar
y
in
g
s
p
ee
d
o
f
n
o
d
es,
d
ata
r
ate
an
d
n
u
m
b
er
o
f
m
o
b
ile
m
o
d
es.
T
h
e
p
er
f
o
r
m
an
ce
o
f
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
c
alcu
lated
in
ter
m
s
o
f
th
r
o
u
g
h
p
u
t,
en
er
g
y
co
n
s
u
m
p
tio
n
,
e
n
d
to
en
d
d
elay
a
n
d
p
ac
k
et
lo
s
s
r
ate
an
d
th
e
r
esu
lt
is
co
m
p
ar
e
d
ag
ain
s
t
with
tw
o
ex
is
tin
g
m
eth
o
d
s
n
a
m
ely
m
u
ltip
ath
b
atter
y
an
d
m
o
b
ilit
y
-
awa
r
e
r
o
u
t
in
g
s
ch
e
m
e
(
MBMA
-
OL
SR
)
an
d
r
el
iab
le
an
d
p
r
ac
tical
o
p
p
o
r
tu
n
is
tic
r
o
u
tin
g
with
g
r
ad
ien
t f
o
r
war
d
i
n
g
f
o
r
MA
N
E
T
s
n
am
ed
as ORGMA
an
d
an
aly
ze
d
with
th
e
h
elp
o
f
tab
les an
d
g
r
ap
h
s
.
3
.
1
.
Sim
ula
t
i
o
n e
nv
iro
nm
en
t
I
n
th
is
s
im
u
latio
n
,
a
MA
NE
T
with
5
0
to
5
0
0
m
o
b
ile
n
o
d
es
i
s
r
an
d
o
m
l
y
s
ca
tter
ed
with
in
a
1
2
0
0
m
x
1
2
0
0
m
ar
ea
.
T
h
e
n
o
d
es
ar
e
m
o
v
in
g
in
d
if
f
e
r
en
t
d
ir
ec
tio
n
s
with
a
s
p
ee
d
v
ar
y
i
n
g
f
r
o
m
0
to
2
5
m
/s
with
th
e
p
au
s
e
tim
e
o
f
1
0
s
.
An
in
itial
am
o
u
n
t o
f
en
e
r
g
y
is
co
n
s
id
er
e
d
as
1
0
0
J
.
T
h
e
m
o
b
ile
n
o
d
es m
o
v
e
u
s
in
g
r
an
d
o
m
wa
y
p
o
in
t m
o
b
ilit
y
m
o
d
el.
T
h
e
r
o
u
tin
g
p
r
o
t
o
co
l u
s
ed
is
DSR
an
d
th
e
d
ata
p
ac
k
et
s
ize
is
5
1
2
b
y
tes.
T
h
e
m
o
b
ile
n
o
d
es
ar
e
in
th
e
tr
a
n
s
m
is
s
io
n
r
an
g
e
2
0
to
2
0
0
m
eter
s
an
d
th
e
MA
C
lay
er
p
r
o
to
co
l
u
s
ed
is
8
0
2
.
1
1
.
I
t
u
s
es
co
n
s
tan
t
b
it
r
ate
(
C
B
R
)
tr
af
f
ic
an
d
r
o
u
tin
g
b
a
n
d
wid
th
o
f
2
Mb
p
s
.
E
ac
h
s
im
u
latio
n
h
as
b
ee
n
r
u
n
1
0
tim
es
to
o
b
tain
th
e
o
p
tim
al
r
esu
lts
f
o
r
c
o
m
p
ar
is
o
n
s
.
T
h
e
s
im
u
latio
n
p
ar
am
eter
s
ettin
g
s
ar
e
d
escr
ib
e
d
in
T
ab
le
1
.
T
ab
le
1
.
Simu
latio
n
p
ar
am
eter
s
S
i
mu
l
a
t
i
o
n
P
a
r
a
m
e
t
e
r
s
V
a
l
u
e
s
S
i
mu
l
a
t
o
r
N
S
3
.
2
6
N
u
mb
e
r
o
f
mo
b
i
l
e
n
o
d
e
s
5
0
–
500
S
i
mu
l
a
t
i
o
n
a
r
e
a
1
2
0
0
m
×
1
2
0
0
m
S
i
mu
l
a
t
i
o
n
Ti
me
2
0
0
se
c
S
i
mu
l
a
t
i
o
n
i
t
e
r
a
t
i
o
n
10
I
n
i
t
i
a
l
e
n
e
r
g
y
1
0
0
J
P
a
c
k
e
t
si
z
e
5
1
2
b
y
t
e
s
N
u
mb
e
r
o
f
d
a
t
a
p
a
c
k
e
t
s
2
0
,
4
0
,
6
0
,
8
0
,
1
0
0
,
1
2
0
,
1
4
0
,
1
6
0
,
1
8
0
,
2
0
0
Tr
a
n
sm
i
ssi
o
n
r
a
n
g
e
o
f
n
o
d
e
s
2
0
m
t
o
2
0
0
m
M
o
v
e
me
n
t
R
a
n
d
o
m w
a
y
p
o
i
n
t
M
a
x
i
m
u
m
sp
e
e
d
2
5
m
/
s
R
o
u
t
i
n
g
p
r
o
t
o
c
o
l
D
y
n
a
mi
c
s
o
u
r
c
e
r
o
u
t
i
n
g
(
D
S
R
)
3
.
2
.
Sim
ula
t
i
o
n r
esu
lt
s
I
n
th
is
s
ec
tio
n
,
th
e
s
im
u
latio
n
r
esu
lt
o
f
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
co
m
p
ar
e
d
ag
ain
s
t
MBMA
-
OL
SR
an
d
OR
GM
A.
T
h
e
r
ep
o
r
ted
r
e
s
u
lts
ar
e
o
b
tain
ed
with
s
ev
er
al
r
u
n
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.
23
,
No
.
2
,
Au
g
u
s
t
20
21
:
1
0
0
2
-
1
0
1
0
1008
3
.
2
.
1
.
E
f
f
ec
t
o
f
t
hro
ug
hp
ut
T
h
e
th
r
o
u
g
h
p
u
t
is
d
ef
in
e
d
a
s
th
e
n
u
m
b
er
o
f
d
ata
p
ac
k
et
s
th
at
ar
e
s
u
cc
ess
f
u
lly
r
ea
ch
ed
to
th
e
d
esti
n
atio
n
at
a
s
p
ec
if
ic
am
o
u
n
t o
f
tim
e.
T
h
e
th
r
o
u
g
h
p
u
t is
m
ath
em
atica
lly
o
b
tain
ed
u
s
in
g
,
ℎ
ℎ
=
(
1
4
)
Fro
m
(
1
4
)
,
‘
’
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
d
ata
p
ac
k
ets
s
u
cc
ess
f
u
lly
tr
an
s
m
itted
to
th
e
d
esti
n
atio
n
with
o
u
t
an
y
in
f
o
r
m
atio
n
lo
s
s
at
tim
e
‘
’
.
T
h
e
th
r
o
u
g
h
p
u
t
is
m
ea
s
u
r
ed
in
ter
m
s
o
f
p
ac
k
ets
p
er
s
ec
o
n
d
(
p
p
s
)
.
Fig
u
r
e
4
p
r
esen
ts
th
e
t
h
r
o
u
g
h
p
u
t
with
r
esp
ec
t
to
d
if
f
er
e
n
t
n
u
m
b
e
r
o
f
d
ata
p
ac
k
ets.
T
h
e
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
ac
h
iev
es
1
7
6
p
p
s
th
r
o
u
g
h
p
u
t
wh
en
tak
in
g
2
0
0
p
a
ck
ets
as
in
p
u
t
wh
er
ea
s
MBM
A
-
OL
SR
,
O
R
GM
A
g
ets
1
6
6
p
p
s
an
d
1
7
0
p
p
s
.
Fro
m
th
e
a
v
er
ag
e
v
alu
e
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
i
n
cr
ea
s
es
th
e
th
r
o
u
g
h
p
u
t
r
ate
b
y
1
6
%
as
co
m
p
ar
e
d
t
o
MBMA
-
OL
SR
an
d
8
%
as
co
m
p
a
r
ed
to
OR
GM
A.
Hen
ce
,
L
L
DN
L
-
E
E
L
B
R
m
eth
o
d
s
ig
n
if
ican
tly
im
p
r
o
v
es th
e
s
u
c
ce
s
s
f
u
l d
eliv
er
y
o
f
p
ac
k
ets wh
en
co
m
p
a
r
ed
to
e
x
is
tin
g
m
eth
o
d
s
.
3
.
2
.
2
.
E
f
f
ec
t
o
f
end t
o
end dela
y
E
n
d
to
e
n
d
d
elay
m
ea
s
u
r
es
th
e
am
o
u
n
t
o
f
tim
e
tak
e
n
to
s
u
c
ce
s
s
f
u
lly
d
eliv
er
p
ac
k
ets
to
a
d
esti
n
atio
n
.
T
h
er
ef
o
r
e,
e
n
d
t
o
e
n
d
d
elay
is
d
eter
m
in
ed
as
th
e
d
is
tin
ctio
n
b
etwe
en
th
e
ar
r
iv
al
tim
e
an
d
s
en
d
in
g
tim
e
o
f
a
p
ac
k
et.
I
t is ca
lcu
lated
in
te
r
m
s
o
f
m
illi
s
ec
o
n
d
s
(
m
s
)
.
T
h
e
en
d
-
to
-
en
d
d
elay
is
ca
lcu
lated
as,
=
−
(
1
5
)
Fro
m
(
1
5
)
,
‘
’
p
o
i
n
t
o
u
ts
ar
r
iv
al
tim
e
o
f
a
p
ac
k
et
wh
er
ea
s
‘
’
r
ep
r
esen
ts
th
e
s
en
d
i
n
g
tim
e
o
f
a
p
ac
k
et.
Fig
u
r
e
5
d
e
p
icts
th
e
co
m
p
ar
is
o
n
o
f
en
d
-
to
-
e
n
d
d
elay
.
T
h
e
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
o
b
tain
s
5
7
m
s
en
d
to
en
d
d
elay
wh
e
n
co
n
s
id
er
i
n
g
1
0
0
p
ac
k
ets
as
in
p
u
t
wh
er
ea
s
MBMA
-
OL
SR
,
O
R
GM
A
a
t
tain
s
6
4
m
s
an
d
6
2
m
s
.
C
lear
ly
,
L
L
DNL
-
E
E
L
B
R
tak
es
a
m
in
im
al
am
o
u
n
t
o
f
tim
e
to
r
ea
ch
d
esti
n
atio
n
.
As
a
r
esu
lt,
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
s
u
b
s
tan
tially
r
ed
u
ce
s
th
e
en
d
-
to
-
en
d
d
elay
b
y
1
7
%
wh
en
co
m
p
ar
e
d
to
MB
MA
-
OL
S
R
an
d
1
3
% a
s
co
m
p
ar
e
d
to
OR
GM
A.
Fig
u
r
e
4
.
T
h
r
o
u
g
h
p
u
t v
er
s
u
s
n
u
m
b
er
o
f
p
ac
k
et
Fig
u
r
e
5
.
E
n
d
to
en
d
d
elay
v
e
r
s
u
s
n
u
m
b
er
o
f
p
ac
k
et
3
.
2
.
3
.
E
f
f
ec
t
o
f
pa
ck
e
t
lo
s
s
ra
t
e
Pack
et
lo
s
s
r
ate
m
ea
s
u
r
es
th
e
r
atio
b
etwe
en
t
h
e
n
u
m
b
er
o
f
p
ac
k
ets
lo
s
t
an
d
th
e
to
tal
n
u
m
b
er
o
f
d
ata
p
ac
k
ets s
en
t.
T
h
e
p
ac
k
et
lo
s
s
r
ate
is
d
eter
m
in
ed
in
p
er
ce
n
tag
e
(
%).
T
h
e
p
ac
k
et
lo
s
s
r
ate
is
esti
m
ated
as,
=
∗
100
(
1
6
)
Fro
m
(
1
6
)
,
‘
’
d
esig
n
ate
th
e
n
u
m
b
er
o
f
d
r
o
p
p
e
d
an
d
‘
s
ig
n
if
ies
to
tal
n
u
m
b
er
o
f
d
ata
p
ac
k
ets
s
en
t.
Fig
u
r
e
6
p
o
r
tr
ay
s
th
e
im
p
ac
ts
o
f
p
ac
k
et
lo
s
s
r
ate
with
v
ar
y
in
g
n
u
m
b
e
r
s
o
f
p
ac
k
ets.
Ob
v
io
u
s
ly
,
L
L
DNL
-
E
E
L
B
R
Me
th
o
d
g
ets
1
4
%
p
ac
k
et
lo
s
s
r
ate
wh
er
ea
s
M
B
MA
-
OL
S
R
,
OR
GM
A
ac
q
u
ir
es
2
3
%
an
d
1
8
%
r
esp
ec
tiv
ely
.
As
co
m
p
ar
e
d
to
MBMA
-
OL
S
R
an
d
OR
GM
A,
th
e
p
er
ce
n
ta
g
e
o
f
r
ed
u
ctio
n
i
n
p
ac
k
et
lo
s
s
r
ate
o
f
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
3
6
%
an
d
2
5
%
o
n
av
e
r
ag
e.
T
h
er
ef
o
r
e,
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
ac
h
i
ev
es
g
o
o
d
r
ed
u
ctio
n
i
n
p
ac
k
et
r
ate
d
u
e
to
en
er
g
y
lim
itatio
n
an
d
tr
af
f
ic.
3
.
2
.
4
.
E
f
f
ec
t
o
f
ener
g
y
utiliza
t
io
n
T
h
e
en
er
g
y
u
tili
za
tio
n
d
eter
m
i
n
es
th
e
am
o
u
n
t
o
f
en
er
g
y
r
eq
u
ir
ed
to
s
u
cc
ess
f
u
lly
d
eliv
er
d
ata
p
ac
k
ets
to
th
e
d
esti
n
atio
n
f
r
o
m
a
s
o
u
r
ce
n
o
d
e.
T
h
e
e
n
er
g
y
u
tili
za
tio
n
is
m
ea
s
u
r
ed
in
ter
m
s
o
f
J
o
u
les
(
J
)
.
T
h
e
en
er
g
y
co
n
s
u
m
p
tio
n
is
ca
lcu
lated
as,
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
Leve
n
b
erg
–
Ma
r
q
u
a
r
t lo
g
is
tic
d
ee
p
n
e
u
r
a
l le
a
r
n
in
g
b
a
s
ed
en
erg
y
efficien
t a
n
d
lo
a
d
…
(
A
.
S
a
n
g
ee
t
h
a
)
1009
=
∗
(
)
(
1
7
)
Fro
m
(
1
7
)
,
‘
’
is
th
e
to
tal
n
u
m
b
er
o
f
m
o
b
ile
n
o
d
es
an
d
‘
(
)
’
r
ep
r
esen
ts
th
e
e
n
er
g
y
u
s
ed
b
y
a
s
in
g
le
n
o
d
e
.
Fig
u
r
e
7
illu
s
tr
ates
en
er
g
y
u
tili
za
tio
n
with
r
esp
ec
t
to
v
ar
io
u
s
n
u
m
b
er
s
o
f
m
o
b
i
le
n
o
d
es.
L
L
DNL
-
E
E
L
B
R
Me
th
o
d
co
n
s
u
m
es
4
8
J
en
er
g
y
wh
e
n
c
o
n
d
u
ctin
g
s
i
m
u
latio
n
p
r
o
ce
s
s
with
3
0
0
m
o
b
ile
n
o
d
es
wh
e
r
ea
s
MBMA
-
OL
S
R
an
d
O
R
GM
A
u
s
es
7
7
J
an
d
7
0
J
r
e
s
p
ec
tiv
ely
.
T
o
tally
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
m
in
im
izes
th
e
en
er
g
y
u
tili
za
tio
n
b
y
3
3
%
co
m
p
ar
ed
to
MBMA
-
OL
SR
an
d
2
7
%
co
m
p
ar
ed
to
OR
GM
A
o
n
av
er
ag
e
.
T
h
u
s
,
L
L
DNL
-
E
E
L
B
R
co
n
s
id
er
ab
ly
r
ed
u
ce
s
t
h
e
ex
t
r
a
am
o
u
n
t
o
f
en
er
g
y
u
tili
ze
d
to
r
eb
r
o
ad
ca
s
ti
n
g
d
ata
d
u
e
to
lin
k
f
ailu
r
e,
co
n
g
esti
o
n
o
n
m
o
b
ile
n
o
d
es
an
d
en
h
an
ce
s
th
e
s
u
cc
e
s
s
f
u
l
d
eliv
er
y
o
f
p
ac
k
ets
to
th
e
d
esti
n
atio
n
n
o
d
e.
T
ab
le
2
clea
r
ly
s
u
m
m
ar
izes th
at,
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
p
er
f
o
r
m
s
b
etter
t
h
an
o
t
h
er
two
m
eth
o
d
s
in
ter
m
s
o
f
th
r
o
u
g
h
p
u
t,
d
elay
,
p
ac
k
et
lo
s
s
r
ate
an
d
en
e
r
g
y
co
n
s
u
m
p
tio
n
.
Fig
u
r
e
6
.
Pack
et
lo
s
s
r
ate
v
e
r
s
u
s
n
u
m
b
er
o
f
p
ac
k
ets
Fig
u
r
e
7
.
E
n
er
g
y
u
tili
za
tio
n
v
e
r
s
u
s
n
u
m
b
er
o
f
m
o
b
ile
n
o
d
es
T
ab
le
2
.
R
esu
lt C
o
m
p
ar
is
o
n
P
a
r
a
me
t
e
r
/
M
e
t
h
o
d
s
M
B
M
A
-
O
LSR
O
R
G
M
A
LLD
N
L
-
EEL
B
R
Th
r
o
u
g
h
p
u
t
(
p
p
s)
8
8
(
p
p
s)
9
1
(
p
p
s)
9
5
(
p
p
s)
En
d
-
to
-
En
d
d
e
l
a
y
(
ms)
6
6
(
ms)
6
4
(
ms)
5
7
(
ms)
P
a
c
k
e
t
l
o
ss ra
t
e
(
%)
2
4
%
2
0
%
1
4
%
En
e
r
g
y
c
o
n
su
m
p
t
i
o
n
(
J)
74J
68J
49J
4.
CO
NCLU
SI
O
N
T
h
e
g
o
al
o
f
th
e
p
r
o
p
o
s
ed
L
L
DNL
-
E
E
L
B
R
m
eth
o
d
is
en
h
a
n
cin
g
th
e
r
o
u
tin
g
ef
f
icien
c
y
o
f
MA
NE
T
th
r
o
u
g
h
lo
a
d
b
alan
ci
n
g
an
d
en
er
g
y
m
in
im
izatio
n
.
B
y
u
s
in
g
th
is
m
eth
o
d
,
th
e
s
u
cc
ess
f
u
l
d
eliv
er
y
o
f
d
ata
p
ac
k
et
in
cr
ea
s
es
s
ig
n
if
ican
tly
with
a
m
in
im
al
d
ela
y
b
y
s
elec
tin
g
th
e
n
o
d
es
with
a
h
ig
h
er
r
esid
u
al
en
er
g
y
an
d
lo
ad
.
Als
o
,
it
d
ec
r
ea
s
es
p
at
h
f
ailu
r
es
an
d
tr
af
f
ic
c
o
n
g
esti
o
n
b
ec
au
s
e
o
f
e
n
er
g
y
c
o
n
s
tr
ictio
n
s
wh
ich
au
to
m
atica
lly
r
ed
u
ce
s
th
e
p
a
ck
et
lo
s
s
r
ate.
T
h
is
m
eth
o
d
f
u
r
th
er
e
n
h
an
ce
s
th
e
s
tab
ilit
y
an
d
r
eliab
ilit
y
o
f
r
o
u
tin
g
b
y
r
ed
u
cin
g
en
e
r
g
y
co
n
s
u
m
p
tio
n
th
er
e
b
y
in
c
r
e
asin
g
th
e
n
etwo
r
k
life
tim
e.
Su
b
s
eq
u
en
tly
,
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
ev
alu
ate
d
in
ter
m
s
o
f
Qo
s
p
ar
a
m
eter
s
an
d
c
o
m
p
ar
ed
with
MBMA
-
OL
SR
an
d
OR
GM
A.
Simu
latio
n
r
esu
lt
d
em
o
n
s
tr
ates
th
at
L
L
DNL
-
E
E
L
B
R
m
e
th
o
d
g
iv
es
b
etter
p
er
f
o
r
m
a
n
ce
with
an
en
h
an
ce
m
en
t
o
f
th
r
o
u
g
h
p
u
t
an
d
m
in
im
izes
en
er
g
y
co
n
s
u
m
p
tio
n
.
Fu
tu
r
e
wo
r
k
ca
n
b
e
p
r
ec
ed
ed
to
s
o
lv
e
th
e
p
r
o
b
lem
o
f
r
o
u
te
p
ath
d
is
co
v
er
y
with
o
u
t
an
y
lin
k
b
r
ea
k
s
d
u
r
i
n
g
m
u
ltip
ath
tr
an
s
m
is
s
io
n
u
n
d
e
r
d
if
f
er
en
t
lo
a
d
co
n
d
itio
n
s
.
I
n
a
d
d
itio
n
,
th
e
s
ec
u
r
ity
is
co
n
s
id
er
ed
in
f
u
t
u
r
e
wo
r
k
f
o
r
ef
f
ec
ti
v
e
tr
an
s
f
er
o
f
d
ata
with
o
u
t p
r
io
r
k
n
o
wled
g
e
o
f
th
e
n
etwo
r
k
o
r
th
e
d
eg
r
ee
o
f
tr
u
s
two
r
th
in
ess
o
f
th
e
in
ter
m
e
d
iate
n
o
d
es.
RE
F
E
R
E
NC
E
S
[1
]
W.
A.
Ja
b
b
a
r,
M
.
Ism
a
il
,
a
n
d
R
.
No
rd
i
n
,
‘‘E
n
e
rg
y
a
n
d
m
o
b
il
it
y
c
o
n
sc
io
u
s
m
u
l
ti
p
a
t
h
ro
u
ti
n
g
sc
h
e
m
e
fo
r
ro
u
te
sta
b
il
it
y
a
n
d
l
o
a
d
b
a
lan
c
in
g
in
M
AN
ET
s,”
S
imu
l
a
ti
o
n
M
o
d
e
ll
in
g
Pra
c
ti
c
e
a
n
d
T
h
e
o
ry
,
v
o
l.
7
7
,
p
p
.
2
4
5
-
2
7
1
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
sim
p
a
t
.
2
0
1
7
.
0
7
.
0
0
1
.
[
2
]
D
.
K
a
n
g
,
H
y
u
n
g
-
S
i
n
K
i
m
,
C
.
J
o
o
,
a
n
d
S
.
B
a
h
k
,
”
O
RG
M
A
:
Re
l
i
a
b
l
e
O
p
p
o
r
t
u
n
i
s
t
i
c
R
o
u
t
i
n
g
w
i
t
h
G
r
a
d
ie
n
t
F
o
r
w
a
r
d
i
n
g
f
o
r
M
A
N
E
T
s
,
”
C
o
m
p
u
t
e
r
Ne
t
w
o
rk
s
,
E
l
s
e
v
ie
r
,
v
o
l
.
1
3
1
,
p
p
.
5
2
-
6
4
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
m
n
e
t
.
2
0
1
7
.
1
2
.
0
0
1
.
[3
]
H.
Am
ra
o
u
i,
A.
Ha
b
b
a
n
i,
a
n
d
A.
Ha
jam
i,
”
A
F
o
rwa
rd
in
g
G
a
m
e
A
p
p
r
o
a
c
h
fo
r
Re
d
u
c
in
g
To
p
o
lo
g
y
Co
n
tr
o
l
Traff
ic
in
M
AN
ET
s,”
Ara
b
ia
n
J
o
u
rn
a
l
fo
r
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
S
p
ri
n
g
e
r
,
v
o
l
.
4
3
,
p
p
.
6
9
4
5
-
6
9
6
1
,
2
0
1
8
,
d
o
i:
1
0
.
1
0
0
7
/s1
3
3
6
9
-
0
1
7
-
2
9
1
0
-
7.
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
:
1
0
0
2
-
1
0
1
0
1010
[4
]
Y.
Ch
e
n
,
E
.
H.
W
u
,
C.
Li
n
,
a
n
d
G
.
C
h
e
n
,
“
Ba
n
d
wid
th
-
S
a
ti
s
fied
a
n
d
C
o
d
i
n
g
-
Aw
a
re
M
u
lt
ica
st
P
ro
t
o
c
o
l
in
M
AN
ET
s,”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
M
o
b
i
le
Co
mp
u
ti
n
g
,
v
o
l.
1
7
,
n
o
.
8
,
p
p
.
1
7
7
8
-
1
7
9
0
,
1
Au
g
.
2
0
1
8
,
d
o
i:
1
0
.
1
1
0
9
/T
M
C
.
2
0
1
7
.
2
7
7
8
2
6
2
.
[5
]
T.
S
in
g
h
,
J.
S
in
g
h
,
a
n
d
S
.
S
h
a
r
m
a
,
“
En
e
rg
y
e
fficie
n
t
se
c
u
re
d
ro
u
ti
n
g
p
r
o
to
c
o
l
fo
r
M
AN
E
Ts,
”
W
ire
les
s
Ne
two
rk
,
v
o
l.
2
3
,
p
p
.
1
0
0
1
-
1
0
0
9
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/s1
1
2
7
6
-
0
1
5
-
1
1
7
6
-
9.
[6
]
W.
A.
Ja
b
b
a
r,
W
.
K.
S
a
a
d
,
a
n
d
M
.
Ism
a
il
,
“
M
EQS
A
-
OLS
R
v
2
:
A
M
u
l
ti
c
rit
e
ria
-
Ba
se
d
Hy
b
rid
M
u
lt
ip
a
t
h
P
ro
t
o
c
o
l
fo
r
En
e
r
g
y
-
Eff
icie
n
t
a
n
d
Qo
S
-
Aw
a
re
Da
ta
Ro
u
ti
n
g
i
n
M
AN
E
T
-
WS
N
Co
n
v
e
rg
e
n
c
e
S
c
e
n
a
rio
s
o
f
Io
T
,
”
IEE
E
Acc
e
ss
,
v
o
l.
6
,
p
p
.
7
6
5
4
6
-
7
6
5
7
2
,
2
0
1
8
,
d
o
i:
1
0
.
1
1
0
9
/ACCE
S
S
.
2
0
1
8
.
2
8
8
2
8
5
3
.
[7
]
S
.
Ja
m
a
li
,
L.
Re
z
a
e
i,
a
n
d
S
.
J.
G
u
d
a
k
a
h
riz,
“
An
E
n
e
rg
y
-
e
fficie
n
t
R
o
u
ti
n
g
P
ro
to
c
o
l
fo
r
M
AN
ET
s:
a
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
A
p
p
r
o
a
c
h
,
”
J
o
u
rn
a
l
o
f
A
p
p
l
ied
Res
e
a
rc
h
a
n
d
T
e
c
h
n
o
l
o
g
y
,
E
l
se
v
ier
,
v
o
l.
1
1
,
p
p
.
8
0
3
-
8
1
2
,
2
0
1
3
.
[8
]
A.
Bh
a
tt
a
c
h
a
ry
a
a
n
d
K.
S
in
h
a
,
“
An
e
fficie
n
t
p
r
o
to
c
o
l
f
o
r
l
o
a
d
-
b
a
lan
c
e
d
m
u
lt
i
p
a
th
ro
u
ti
n
g
i
n
m
o
b
il
e
a
d
h
o
c
n
e
two
rk
s,”
A
d
Ho
c
Ne
tw
o
rk
s,
El
s
e
v
ier
,
v
o
l.
6
3
,
p
p
.
1
0
4
-
1
1
4
,
Au
g
.
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
a
d
h
o
c
.
2
0
1
7
.
0
5
.
0
0
8
.
[9
]
H.
A.
Ali
,
M
.
F
.
Are
e
d
,
a
n
d
D.
I.
El
e
we
ly
,
“
An
o
n
-
d
e
m
a
n
d
p
o
we
r
a
n
d
lo
a
d
-
a
wa
re
m
u
lt
i
-
p
a
th
n
o
d
e
-
d
isjo
i
n
t
so
u
rc
e
ro
u
ti
n
g
sc
h
e
m
e
imp
lem
e
n
tatio
n
u
sin
g
NS
-
2
f
o
r
m
o
b
il
e
a
d
-
h
o
c
n
e
two
rk
s,”
S
im
u
la
t
io
n
M
o
d
e
ll
in
g
Pr
a
c
ti
c
e
a
n
d
T
h
e
o
ry
,
v
o
l.
8
0
,
p
p
.
5
0
-
6
5
,
Ja
n
.
2
0
1
8
,
d
o
i:
1
0
.
1
0
1
6
/j
.
sim
p
a
t
.
2
0
1
7
.
0
9
.
0
0
5
.
[1
0
]
G
.
P
a
th
a
k
a
n
d
K.
K
u
m
a
r,
”
Traffic
a
wa
re
lo
a
d
b
a
lan
c
in
g
in
AO
M
DV
fo
r
m
o
b
il
e
Ad
-
h
o
c
n
e
tw
o
r
k
s,”
J
o
u
rn
a
l
o
f
Co
mm
u
n
ica
ti
o
n
s a
n
d
In
f
o
rm
a
ti
o
n
Ne
two
rk
s,
S
p
ri
n
g
e
r
,
v
o
l.
2
,
p
p
.
1
2
3
-
1
3
0
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
0
7
/s
4
1
6
5
0
-
0
1
7
-
0
0
1
2
-
z.
[1
1
]
J.
Zh
o
u
,
H.
Tan
,
Y.
De
n
g
,
L.
C
u
i
,
a
n
d
D.
D.
Li
u
,
“
An
t
c
o
lo
n
y
-
b
a
se
d
e
n
e
rg
y
c
o
n
tro
l
ro
u
ti
n
g
p
ro
to
c
o
l
fo
r
m
o
b
il
e
a
d
h
o
c
n
e
two
r
k
s
u
n
d
e
r
d
i
ffe
re
n
t
n
o
d
e
m
o
b
i
li
ty
m
o
d
e
ls,”
EURA
S
I
P
J
o
u
rn
a
l
o
n
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s
a
n
d
Ne
two
rk
in
g
,
S
p
ri
n
g
e
r
,
v
o
l.
1
0
5
,
p
p
.
1
-
8
,
2
0
1
6
,
d
o
i:
1
0
.
1
1
8
6
/s1
3
6
3
8
-
0
1
6
-
0
6
0
0
-
x.
[
1
2
]
S
.
C
h
e
t
t
i
b
i
a
n
d
S
.
C
h
i
k
h
i
,
‘
‘
D
y
n
a
m
i
c
f
u
z
z
y
l
o
g
i
c
a
n
d
re
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
f
o
r
a
d
a
p
t
i
v
e
e
n
e
r
g
y
e
f
f
ic
i
e
n
t
r
o
u
t
i
n
g
i
n
m
o
b
i
l
e
a
d
-
h
o
c
n
e
t
w
o
r
k
s
,
”
A
p
p
l
i
e
d
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
3
8
,
p
p
.
3
2
1
-
3
2
8
,
J
a
n
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
s
o
c
.
2
0
1
5
.
0
9
.
0
0
3
.
[1
3
]
S
.
Ha
o
,
H.
Z
h
a
n
g
,
a
n
d
M
.
S
o
n
g
,
"
A
S
tab
le
a
n
d
En
e
r
g
y
-
Eff
icie
n
t
Ro
u
ti
n
g
Al
g
o
r
it
h
m
Ba
se
d
o
n
Lea
rn
in
g
Au
t
o
m
a
ta
Th
e
o
ry
fo
r
M
AN
ET
,
"
i
n
J
o
u
rn
a
l
o
f
C
o
mm
u
n
ic
a
ti
o
n
s a
n
d
In
fo
rm
a
t
i
o
n
Ne
tw
o
rk
s
,
v
o
l.
3
,
n
o
.
2
,
p
p
.
4
3
-
5
7
,
Ju
n
.
2
0
1
8
,
d
o
i:
1
0
.
1
0
0
7
/s4
1
6
5
0
-
0
1
8
-
0
0
1
2
-
7.
[1
4
]
M
.
Ra
t
h
,
B
.
P
a
t
i,
B
.
K.
P
a
tt
a
n
a
y
a
k
,
C
.
R.
P
a
n
ig
ra
h
i,
a
n
d
J.
L.
S
a
rk
a
r,
”
Lo
a
d
b
a
lan
c
e
d
ro
u
ti
n
g
sc
h
e
m
e
fo
r
M
AN
ET
s
with
p
o
we
r
a
n
d
d
e
lay
o
p
ti
m
iza
ti
o
n
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
mm
u
n
ica
ti
o
n
Ne
tw
o
rk
s
a
n
d
Distr
ib
u
te
d
S
y
ste
ms
,
vol
.
1
9
,
n
o
.
4
,
p
p
.
3
9
4
-
4
0
5
,
2
0
1
7
,
d
o
i:
1
0
.
1
5
0
4
/IJCND
S
.
2
0
1
7
.
0
8
7
3
8
6
.
[1
5
]
M
.
A.
S
a
lem
a
n
d
R.
Ya
d
a
v
,
“
Eff
icie
n
t
Lo
a
d
Ba
lan
c
i
n
g
R
o
u
t
i
n
g
Tec
h
n
iq
u
e
fo
r
M
o
b
il
e
Ad
Ho
c
Ne
two
rk
s,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
Co
m
p
u
ter
S
c
ie
n
c
e
a
n
d
A
p
p
li
c
a
t
io
n
s
,
v
o
l.
7
,
n
o
.
5
,
p
p
.
2
4
9
-
2
5
4
,
2
0
1
6
.
[1
6
]
S
.
Ru
ss
ia
a
n
d
R.
A
n
it
a
,
“
Jo
i
n
t
c
o
st
a
n
d
se
c
u
re
d
n
o
d
e
d
isjo
i
n
t
e
n
e
r
g
y
e
fficie
n
t
m
u
l
ti
p
a
t
h
ro
u
ti
n
g
i
n
m
o
b
il
e
a
d
h
o
c
n
e
two
rk
,
”
W
ire
les
s Ne
two
rk
s
,
v
o
l
.
2
3
,
n
o
.
7
,
p
p
.
2
3
0
7
-
2
3
1
6
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
0
7
/s1
1
2
7
6
-
0
1
6
-
1
2
8
8
-
x.
[1
7
]
R.
Ha
v
in
a
l,
G
.
V.
Atti
m
a
ra
d
,
a
n
d
M
.
N.
G
.
P
ra
sa
d
,
“
M
ECOR:
M
in
ima
l
En
e
rg
y
Co
n
s
u
m
p
ti
o
n
with
Op
ti
m
ize
d
Ro
u
ti
n
g
in
M
AN
ET
,
”
W
ire
les
s
p
e
rs
o
n
a
l
c
o
mm
u
n
ica
ti
o
n
,
v
o
l.
8
8
,
n
o
.
4
,
p
p
.
9
6
3
-
9
8
3
,
2
0
1
6
,
d
o
i
:
0
.
1
0
0
7
/s1
1
2
7
7
-
016
-
3
2
2
3
-
y.
[1
8
]
S
.
K.
Da
s
a
n
d
S
.
Tr
ip
a
th
i,
“
I
n
tell
ig
e
n
t
e
n
e
rg
y
-
a
wa
re
e
fficie
n
t
ro
u
ti
n
g
fo
r
M
AN
ET
,
”
W
ire
les
s
Ne
two
rk
s,
S
p
rin
g
e
r
,
v
o
l.
2
4
,
n
o
.
4
,
p
p
.
1
1
3
9
-
1
1
5
9
,
2
0
1
8
,
d
o
i:
1
0
.
1
0
0
7
/s1
1
2
7
6
-
0
1
6
-
1
3
8
8
-
7.
[1
9
]
B.
H.
AlQa
rn
i
a
n
d
A.
S
.
AlM
o
g
r
e
n
,
“
Re
li
a
b
le
a
n
d
En
e
rg
y
Eff
icie
n
t
P
ro
to
c
o
l
fo
r
M
AN
ET
M
u
lt
ica
sti
n
g
,
”
J
o
u
r
n
a
l
o
f
Co
mp
u
ter
Ne
tw
o
rk
s a
n
d
Co
mm
u
n
ica
ti
o
n
s
,
2
0
1
6
,
d
o
i:
1
0
.
1
1
5
5
/
2
0
1
6
/9
1
4
6
1
6
8
.
[2
0
]
A.
Tah
a
,
R.
Alsa
q
o
u
r,
M
.
U
d
d
i
n
,
M
.
Ab
d
e
lh
a
q
,
a
n
d
T.
S
a
b
a
,
“
E
n
e
rg
y
Eff
icie
n
t
M
u
lt
i
p
a
th
Ro
u
ti
n
g
P
ro
t
o
c
o
l
f
o
r
M
o
b
i
le
Ad
-
Ho
c
Ne
two
r
k
Us
i
n
g
t
h
e
F
it
n
e
ss
F
u
n
c
ti
o
n
,
”
IE
EE
Acc
e
ss
,
v
o
l.
5
,
p
p
.
1
0
3
6
9
-
1
0
3
8
1
,
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/ACCES
S
.
2
0
1
7
.
2
7
0
7
5
3
7
.
[
2
1
]
M
.
M
.
A
f
s
a
r
a
n
d
M
.
Y
o
u
n
i
s
,
“
C
R
o
s
s
-
l
a
y
e
r
d
e
s
i
g
n
f
o
r
W
S
N
s
w
i
t
h
E
n
e
r
g
y
S
c
a
v
e
n
g
i
n
g
a
n
d
T
r
a
n
s
f
e
r
c
a
p
a
b
i
l
i
t
i
e
s
(
C
R
E
S
T
)
,
”
J
o
u
r
n
a
l
o
f
n
e
t
w
o
r
k
a
n
d
c
o
m
p
u
t
e
r
a
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
4
5
,
p
.
1
0
2
3
9
0
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
n
c
a
.
2
0
1
9
.
0
6
.
0
1
0
.
[2
2
]
S
.
M
a
u
ry
a
,
V.
K.
Ja
in
,
a
n
d
D.
R.
Ch
o
wd
h
u
ry
,
“
De
lay
a
wa
re
e
n
e
rg
y
e
fficie
n
t
re
li
a
b
le
r
o
u
ti
n
g
f
o
r
d
a
t
a
tran
sm
issi
o
n
in
h
e
ter
o
g
e
n
e
o
u
s
m
o
b
il
e
sin
k
wi
re
les
s
se
n
so
r
n
e
two
rk
,
”
J
o
u
r
n
a
l
o
f
n
e
tw
o
rk
a
n
d
c
o
mp
u
ter
a
p
p
li
c
a
ti
o
n
s
,
v
o
l
.
1
4
4
,
p
p
.
1
1
8
-
1
3
7
,
Oc
t.
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jn
c
a
.
2
0
1
9
.
0
6
.
0
1
2
.
[2
3
]
P
.
Vijay
a
k
u
m
a
r,
R.
Ra
jas
h
re
e
,
a
n
d
P
.
S
a
n
d
h
y
a
,
“
A
S
e
c
u
re
Ro
u
t
e
Disc
o
v
e
ry
P
r
o
to
c
o
l
fo
r
A
OD
V
Ba
se
d
M
o
b
i
le
Ad
h
o
c
Ne
two
rk
s
Us
in
g
Hy
p
e
re
ll
ip
ti
c
C
u
rv
e
Cry
p
to
g
ra
p
h
y
,
”
Eme
rg
in
g
T
re
n
d
s
in
El
e
c
trica
l,
Co
mm
u
n
ica
ti
o
n
s
a
n
d
In
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
ies
,
v
o
l.
3
9
4
,
p
p
.
2
0
9
-
2
1
7
,
2
0
1
7
,
d
o
i:
1
0
.
1
0
0
7
/9
7
8
-
9
8
1
-
10
-
1
5
4
0
-
3
_
2
2
.
[
2
4
]
N
.
K
h
a
t
o
o
n
,
“
M
o
b
i
l
i
t
y
A
w
a
r
e
En
e
r
g
y
E
f
f
i
c
i
e
n
t
C
l
u
s
t
e
r
i
n
g
f
o
r
M
A
N
E
T
:
A
B
i
o
-
I
n
s
p
i
re
d
A
p
p
r
o
a
c
h
w
i
t
h
P
a
r
t
i
c
le
S
w
a
r
m
O
p
t
i
m
i
z
a
t
i
o
n
,
”
W
ir
e
l
e
s
s
C
o
mm
u
n
i
c
a
t
i
o
n
s
a
n
d
M
o
b
i
l
e
C
o
m
p
u
t
i
n
g
,
v
o
l
.
2
0
1
7
,
2
0
1
7
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
1
7
/
1
9
0
3
1
9
0
.
[
2
5
]
R.
N.
Ja
d
o
o
n
,
W.
Y.
Z
h
o
u
,
I
.
A.
Kh
a
n
,
M
.
A.
Kh
a
n
,
a
n
d
W.
Ja
d
o
o
n
,
‘‘E
EHRT:
En
e
rg
y
E
ff
icie
n
t
Tec
h
n
iq
u
e
fo
r
Ha
n
d
li
n
g
Re
d
u
n
d
a
n
t
Traffic
i
n
Z
o
n
e
-
Ba
se
d
Ro
u
ti
n
g
fo
r
Wi
re
les
s
S
e
n
so
r
Ne
two
r
k
,
”
W
ire
les
s
Co
mm
u
n
ica
t
io
n
s
a
n
d
M
o
b
il
e
Co
m
p
u
ti
n
g
,
v
o
l.
2
0
1
9
,
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
5
/
2
0
1
9
/7
5
0
2
1
4
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.