I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
202
1
,
p
p
.
9
3
8
~
9
44
I
SS
N:
2
5
02
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ee
cs.v
2
1
.i
2
.
p
p
9
3
8
-
9
44
938
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
O
pti
m
isa
tion
of
L
EACH
pro
toco
l
b
a
sed
on
a
g
a
m
e
th
eo
ry
clustering
a
ppro
a
ch
for
w
ireless
sens
o
r
netw
o
rk
s
Ya
s
s
ine
O
u
kes
s
o
u
1
,
M
o
ha
m
ed
B
a
s
la
m
2
,
M
o
ha
m
ed
O
u
k
e
s
s
o
u
3
1,
3
A
p
p
li
e
d
M
a
t
h
e
m
a
ti
c
s
a
n
d
S
c
ien
ti
f
ic
Co
m
p
u
ti
n
g
L
a
b
o
ra
to
ry
,
F
a
c
u
lt
y
of
S
c
ien
c
e
s
a
n
d
T
e
c
h
n
ics
,
S
u
l
t
a
n
M
o
u
lay
S
li
m
a
n
e
Un
iv
e
rsit
y
,
Be
n
i
M
e
ll
a
l,
M
o
ro
c
c
o
2
In
f
o
rm
a
ti
o
n
P
r
o
c
e
ss
in
g
a
n
d
De
c
i
sio
n
S
u
p
p
o
rt
L
a
b
o
ra
to
ry
,
F
a
c
u
lt
y
of
S
c
ien
c
e
s
a
n
d
T
e
c
h
n
ics
,
S
u
lt
a
n
M
o
u
lay
S
li
m
a
n
e
Un
iv
e
rsit
y
,
Be
n
i
-
M
e
l
lal,
M
o
ro
c
c
o
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ju
n
2
2
,
2
0
2
0
R
ev
i
s
ed
A
u
g
23
,
2
0
2
0
A
cc
ep
ted
Sep
7
,
2
0
2
0
T
h
e
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
c
lu
s
terin
g
ro
u
ti
n
g
m
e
c
h
a
n
is
m
is
th
e
b
e
st
m
u
lt
i
-
hop
a
lg
o
rit
h
m
u
se
d
to
a
g
g
re
g
a
t
e
d
a
ta
f
ro
m
se
n
so
rs
to
th
e
b
a
se
sta
ti
o
n
.
T
h
e
re
f
o
re
th
e
e
lec
ted
n
o
d
e
s
re
f
u
se
to
be
a
c
lu
ste
rs
h
e
a
d
s
CH
a
n
d
h
a
v
e
a
se
lf
ish
a
n
d
n
o
n
c
o
o
p
e
ra
ti
v
e
b
e
h
a
v
io
u
rs
in
each
g
ro
u
p
c
lu
ste
r.
A
ll
th
a
t
d
u
e
to
th
e
h
ig
h
e
lec
tri
c
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
,
a
n
d
e
sp
e
c
ially
th
a
t
th
e
m
o
st
e
x
isti
n
g
se
n
so
rs
a
re
p
o
w
e
re
d
by
b
a
tt
e
ries
.
In
t
h
is
p
a
p
e
r,
we
w
il
l
a
n
a
ly
s
e
th
e
se
lf
ish
n
e
ss
b
e
h
a
v
io
u
r
by
u
sin
g
th
e
m
o
st
k
n
o
w
in
g
m
a
th
e
m
a
ti
c
a
l
m
o
d
e
l
G
a
m
e
th
e
o
ry
to
i
m
p
ro
v
e
th
e
in
tera
c
ti
o
n
d
e
c
isio
n
m
a
k
in
g
f
o
r
th
e
CH
s
e
le
c
ti
o
n
,
a
n
d
m
a
k
e
a
c
o
m
p
a
riso
n
w
it
h
L
E
A
CH
p
ro
t
o
c
o
l.
K
ey
w
o
r
d
s
:
C
lu
s
ter
h
ea
d
Ga
m
e
t
h
eo
r
y
L
E
AC
H
Nash
eq
u
ilib
r
iu
m
W
SN
T
h
is
is
an
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Yass
i
n
e
O
u
k
e
s
s
o
u
A
p
p
lied
Ma
th
e
m
atic
s
an
d
Sci
en
ti
f
ic
C
o
m
p
u
ti
n
g
L
ab
o
r
ato
r
y
Facu
lt
y
o
f
Sc
ien
ce
s
an
d
T
ec
h
n
ics
Su
lta
n
Mo
u
la
y
Sl
i
m
a
n
e
U
n
i
v
e
r
s
it
y
,
B
en
i M
ella
l,
Mo
r
o
cc
o
E
m
ail:
O
u
k
y
a
s
s
i
n
e
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
W
ir
eless
s
e
n
s
o
r
n
et
w
o
r
k
(
W
SN)
[1
,
2
]
h
av
e
to
d
ay
a
w
id
e
civ
il
a
n
d
m
i
litar
y
ap
p
licatio
n
s
,
s
u
c
h
as
in
d
u
s
tr
ial
ap
p
licatio
n
s
,
tr
an
s
p
o
r
tatio
n
an
d
lo
g
is
tic
s
,
ag
r
ic
u
l
tu
r
e
an
d
an
i
m
al
tr
ac
k
in
g
,
s
m
ar
t
b
u
ild
in
g
s
,
s
m
ar
t
g
r
id
s
,
h
ea
lt
h
ca
r
e,
s
ec
u
r
it
y
a
n
d
s
u
r
v
eilla
n
ce
,
an
d
it
r
ef
er
s
to
a
n
et
w
o
r
k
of
s
e
n
s
o
r
s
u
s
ed
to
m
o
n
ito
r
an
d
r
ec
o
r
d
en
v
ir
o
n
m
e
n
tal
p
h
y
s
ical
co
n
d
it
io
n
s
an
d
a
g
g
r
e
g
ate
th
e
co
llect
ed
d
ata
to
a
ce
n
tr
al
lo
ca
tio
n
.
T
h
e
s
en
s
o
r
n
o
d
es
h
a
v
e
f
o
u
r
m
ai
n
f
u
n
ctio
n
s
:
s
e
n
s
i
n
g
,
p
r
o
ce
s
s
i
n
g
,
co
m
m
u
n
icat
io
n
a
n
d
p
o
w
er
u
n
it
s
.
T
h
e
s
en
s
in
g
m
o
d
u
le
ca
n
m
ea
s
u
r
es
v
ia
a
p
r
o
b
e
th
e
p
h
y
s
ica
l
v
ar
iatio
n
s
t
h
e
n
s
en
d
t
h
e
co
l
lecte
d
d
ata
to
th
e
p
r
o
ce
s
s
in
g
s
y
s
te
m
t
h
at
co
n
tai
n
s
a
m
icr
o
co
n
tr
o
ller
.
Af
ter
t
h
at
th
e
co
m
m
u
n
icatio
n
s
y
s
te
m
v
ia
a
r
ad
io
m
o
d
u
le:
tr
an
s
m
itter
/r
ec
ei
v
er
an
te
n
n
as
an
d
n
et
w
o
r
k
p
r
o
ce
s
s
i
n
g
u
n
it
w
il
l
r
ela
y
t
h
e
p
r
o
ce
s
s
ed
d
ata
to
a
b
ase
s
tatio
n
f
o
r
a
f
u
r
t
h
er
u
s
e.
T
h
u
s
,
t
h
e
d
ata
tr
an
s
f
er
can
be
d
o
n
e
d
ir
ec
tl
y
if
th
e
b
o
th
u
n
it
s
,
n
o
d
e
an
d
b
as
e
s
tatio
n
ar
e
in
t
h
e
s
a
m
e
co
m
m
u
n
icat
io
n
r
a
n
g
e;
if
n
o
t
a
m
u
lti
-
h
o
p
r
o
u
tin
g
p
r
o
to
co
l
[
3
-
6
]
w
il
l
be
u
s
ed
to
e
n
h
a
n
ce
t
h
e
d
eli
v
er
y
of
th
e
d
ata
p
ac
k
et
s
.
Fi
n
all
y
t
h
e
p
o
w
er
m
a
n
a
g
e
m
e
n
t
s
u
b
s
y
s
te
m
is
r
esp
o
n
s
ib
le
of
m
o
n
ito
r
i
n
g
of
t
h
e
r
ea
l
ti
m
e
r
esid
u
al
b
atter
y
e
n
er
g
y
t
h
en
will
be
r
ep
o
r
ted
v
ia
th
e
co
m
m
u
n
icatio
n
s
y
s
te
m
to
th
e
ce
n
tr
al
u
n
i
t
or
b
r
o
ad
ca
s
ted
th
e
t
h
e
o
t
h
er
n
e
t
w
o
r
k
n
o
d
es.
Un
li
k
e
t
h
e
o
ld
cla
s
s
ic
n
et
w
o
r
k
s
,
t
h
e
W
SN
m
o
te
s
ar
e
d
ep
lo
y
ed
in
an
u
n
atte
n
d
ed
en
v
ir
o
n
m
e
n
t
,
w
i
th
li
m
ited
p
o
w
er
ca
p
ab
ilit
ie
s
w
it
h
s
m
al
l
or
ir
r
ep
lace
ab
le
b
atter
ies.
T
h
u
s
th
e
n
ec
es
s
it
y
of
a
n
e
w
en
er
g
y
e
f
f
icie
n
y
r
o
u
t
in
g
a
lg
o
r
ith
m
s
ar
e
m
an
d
ato
r
y
in
ca
s
e
if
t
h
ese
n
o
d
es
ar
e
co
o
p
er
ati
v
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Op
timis
a
tio
n
o
f LE
A
C
H
p
r
o
to
co
l b
a
s
ed
o
n
a
g
a
me
th
e
o
r
y
clu
s
teri
n
g
a
p
p
r
o
a
c
h
fo
r
… (
Ya
s
s
in
e
Ou
ke
s
s
o
u
)
939
T
h
e
w
ir
eless
s
e
n
s
o
r
n
et
w
o
r
k
clu
s
ter
in
g
co
n
s
eq
u
en
tel
y
is
th
e
b
est
r
o
u
ti
n
g
m
ec
h
a
n
i
s
m
ai
m
in
g
r
ed
u
cin
g
th
e
th
r
o
u
g
h
p
u
t
lo
ad
an
d
by
th
e
w
a
y
e
n
h
an
ci
n
g
s
en
s
o
r
s
b
atter
ies
co
n
s
u
m
p
tio
n
,
esp
ec
iall
y
in
lo
w
p
o
w
er
w
id
e
ar
ea
n
et
w
o
r
k
s
L
P
W
A
N
[7
-
9
]
tech
n
o
lo
g
ies.
T
h
e
r
o
u
tin
g
tec
h
n
iq
u
e
is
d
o
n
e
by
d
iv
id
i
n
g
th
e
n
et
w
o
r
k
n
o
d
es
in
to
s
m
all
g
r
o
u
p
s
f
o
r
m
i
n
g
t
h
e
c
lu
s
ter
s
a
n
d
th
en
ea
c
h
cl
u
s
ter
elec
t
a
cl
u
s
ter
h
ea
d
(
C
H)
f
o
r
co
llectin
g
d
ata
an
d
s
e
n
d
it
b
ac
k
to
th
e
g
eta
w
a
y
-
s
i
n
k
.
A
v
ar
io
u
s
ap
p
r
o
ac
h
es
ar
e
p
r
o
p
o
s
ed
b
ased
on
L
E
AC
H
r
o
u
tin
g
p
r
o
to
co
l
[
10
-
17
].
L
E
AC
H
is
a
h
ier
ar
c
h
ical
r
o
u
t
in
g
p
r
o
to
co
l,
iter
ated
by
r
o
u
n
d
s
.
W
h
en
t
h
e
cl
u
s
ter
s
ar
e
o
r
g
an
ized
,
t
h
e
r
o
u
n
d
b
eg
i
n
s
w
it
h
a
s
et
-
up
p
h
ase,
an
d
t
h
e
n
f
o
llo
w
ed
by
a
s
t
ea
d
y
-
s
tate
p
h
a
s
e
w
h
e
n
d
ata
tr
an
s
f
er
s
to
t
h
e
s
i
n
k
.
Du
r
in
g
t
h
e
s
et
-
up
s
tag
e,
e
v
er
y
n
o
d
e
ch
o
o
s
es
w
h
et
h
er
or
n
o
t
to
tu
r
n
in
to
a
C
H,
in
v
ie
w
of
a
d
eter
m
in
ed
th
r
es
h
o
ld
an
d
a
p
r
o
d
u
ce
d
r
an
d
o
m
n
u
m
b
er
m
s
o
m
e
w
h
er
e
in
th
e
r
an
g
e
of
0
an
d
1.
if
m
is
les
s
th
a
n
T
(
n
)
th
en
th
e
n
o
d
e
b
ec
o
m
e
cl
u
s
ter
h
ea
d
f
o
r
th
e
cu
r
r
en
t
r
o
u
n
d
,
an
d
ad
v
er
tis
e
its
s
tat
u
s
to
th
e
o
th
er
n
o
d
es
in
to
th
e
cl
u
s
ter
.
E
ls
e
th
e
n
o
d
es
p
ick
th
e
ac
ce
s
s
ib
le
ad
j
ac
en
t
C
H.
T
h
e
th
r
esh
o
l
d
v
alu
e
is
e
x
p
r
ess
ed
as
f
o
llo
ws
(
1
)
:
{
(
.
/
)
(
1
)
W
h
er
e
G
is
th
e
s
et
of
n
o
d
es
n
o
t
s
elec
ted
in
th
e
las
t
1
/p
r
o
u
n
d
s
as
cl
u
s
ter
h
ea
d
an
d
r
is
th
e
r
o
u
n
d
n
u
m
b
er
.
On
ce
t
h
e
clu
s
ter
in
g
h
ea
d
o
p
e
r
atio
n
s
ar
e
d
o
n
e.
T
h
e
s
tead
y
-
s
tate
p
h
ase
s
tar
ts
by
ea
c
h
CH
ar
r
an
g
e
a
T
DM
A
f
r
a
m
e
by
a
llo
ca
tin
g
a
ti
m
e
s
lo
t
to
each
m
e
m
b
er
n
o
d
e,
th
e
n
t
h
e
co
llecti
n
g
a
n
d
p
r
o
ce
s
s
in
g
of
t
h
e
r
et
u
r
n
ed
d
at
a
w
il
l
be
b
eg
in
a
n
d
in
t
h
e
last
s
t
ag
e
w
ill
be
tr
an
s
f
er
r
ed
to
th
e
s
in
k
,
as
it
is
s
h
o
w
n
in
F
ig
u
r
e
1.
Fig
u
r
e
1
.
A
r
ch
itectu
r
e
of
L
E
AC
H
p
r
o
to
co
l
In
th
e
o
t
h
er
h
a
n
d
,
th
e
cl
u
s
ter
h
ea
d
s
co
n
s
u
m
e
m
o
r
e
e
n
er
g
y
f
o
r
r
elay
i
n
g
d
ata;
th
er
e
f
o
r
e,
s
o
m
e
n
o
d
es
h
av
e
a
s
el
f
i
s
h
n
es
s
b
eh
a
v
io
r
an
d
r
ef
u
s
e
to
be
CH
f
o
r
s
av
i
n
g
th
eir
e
n
er
g
ie
s
an
d
by
th
e
wa
y
f
o
r
m
in
g
a
n
o
n
-
co
o
p
er
ativ
e
m
o
d
el.
R
ec
en
tl
y
t
h
e
Ga
m
e
T
h
eo
r
y
[
1
8
]
h
as
b
ee
n
u
s
ed
f
o
r
an
al
y
zi
n
g
th
e
s
el
f
i
s
h
n
e
s
s
p
h
e
n
o
m
e
n
o
n
of
s
e
n
s
o
r
s
a
n
d
p
r
o
p
o
s
e
a
n
ew
cl
u
s
ter
in
g
m
ec
h
a
n
i
s
m
,
c
lu
s
ter
ed
r
o
u
tin
g
f
o
r
s
elf
is
h
s
e
n
s
o
r
s
(
C
R
O
SS
)
[
1
9
]
,
w
h
er
e
ea
c
h
m
o
te
is
p
r
ese
n
ted
as
a
p
la
y
er
ca
p
ab
le
of
h
ea
r
i
n
g
all
o
th
er
p
la
y
er
s
m
e
s
s
a
g
es
an
d
k
n
o
w
in
g
th
eir
n
u
m
b
er
s
.
E
ac
h
m
o
te
f
in
d
s
t
h
e
n
an
eq
u
ilib
r
iu
m
v
alu
e
b
ased
on
th
e
r
an
g
e
of
p
la
y
er
s
,
w
h
ich
d
eter
m
i
n
e
s
w
h
et
h
er
or
n
o
t
a
p
la
y
er
b
ec
o
m
es
a
C
H.
In
th
is
p
ap
er
we
r
ep
r
ese
n
t
th
e
g
a
m
e
th
eo
r
y
m
o
d
el
a
n
d
p
r
o
p
o
s
e
th
e
g
a
m
e
alg
o
r
ith
m
b
ased
on
Na
s
h
E
q
u
ilib
r
iu
m
f
o
r
d
esig
n
in
g
t
h
e
n
o
n
-
co
o
p
er
ativ
e
en
v
ir
o
n
m
en
t
an
d
o
p
ti
m
ize
t
h
e
s
elf
i
s
h
n
e
s
s
is
s
u
e
in
Sectio
n
2.
T
h
e
s
i
m
u
latio
n
an
d
p
er
f
o
r
m
an
ce
r
esu
lt
s
an
al
y
s
is
co
m
p
ar
i
s
o
n
ar
e
d
etailed
in
Sectio
n
3
.
T
h
e
S
ec
tio
n
4
co
n
t
ain
s
t
h
e
as
s
es
s
m
e
n
t
of
th
e
i
m
p
le
m
en
ted
al
g
o
r
ith
m
a
n
d
th
e
r
ep
r
esen
tatio
n
of
th
e
f
u
tu
r
e
w
o
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2021
:
9
38
-
9
44
940
2.
P
RO
P
O
SE
D
G
AM
E
T
H
E
O
RY
AL
G
O
RI
T
H
M
2
.
1
.
G
a
m
e
m
o
del
re
pre
s
ent
a
t
io
n
As
we
h
av
e
s
ee
n
ab
o
v
e,
in
t
h
e
s
elf
i
s
h
n
es
s
en
v
ir
o
n
m
e
n
t
th
e
n
o
d
es
ar
e
r
ef
u
s
i
n
g
to
be
CH
in
o
r
d
er
to
s
av
e
t
h
eir
en
er
g
y
.
So
we
w
ill
i
n
tr
o
d
u
ce
o
u
r
m
o
d
el
f
o
r
r
eso
lv
i
n
g
t
h
i
s
p
h
en
o
m
e
n
o
n
as
f
o
llo
ws:
First,
we
s
c
h
e
m
atize
t
h
e
CH
d
ec
lar
atio
n
as
a
g
a
m
e,
an
d
t
h
en
we
as
s
u
m
e
a
n
o
n
-
co
o
p
er
ativ
e
g
a
m
e
m
o
d
el,
w
h
er
e
each
m
o
te
h
o
p
e
m
ax
i
m
izes
it
s
g
ai
n
by
c
h
o
o
s
in
g
a
s
tr
ateg
y
d
ep
en
d
in
g
to
o
th
e
r
s
m
o
tes
c
h
o
ices.
By
t
h
is
w
a
y
we
d
ef
i
n
e
th
e
g
a
m
e
as
*
+
w
h
er
e
an
d
N
is
a
s
et
of
m
o
tes;
th
e
s
tr
ateg
y
s
p
ac
e:
*
+
w
h
er
e
AC
H
is
An
n
o
u
n
cin
g
cl
u
s
ter
h
ea
d
an
d
R
C
H
is
R
e
f
u
s
e
to
be
clu
s
ter
h
ea
d
;
is
th
e
u
tili
t
y
of
t
h
e
n
o
d
e
.
We
d
ef
in
e
th
e
u
t
ilit
y
f
u
n
ctio
n
as
f
o
llo
w
(
2
)
:
{
(
2
)
W
h
er
e
g
is
t
h
e
b
en
e
f
i
t
g
ets
by
th
e
m
o
te
w
h
en
it
r
ef
u
s
e
to
be
clu
s
ter
h
ea
d
a
n
d
o
th
er
o
n
e
is
b
e.
c
is
t
h
e
co
s
t
a
n
o
d
e
p
ay
s
w
h
en
it
s
elec
t
to
be
a
clu
s
ter
h
ea
d
.
In
[
20
],
th
e
(
)
p
ar
am
eter
co
s
t
is
d
ep
en
d
i
n
g
to
t
h
e
n
u
m
b
er
n
of
n
o
d
es
in
t
h
e
cl
u
s
t
er
an
d
th
e
d
is
tan
ce
d
b
et
w
ee
n
th
e
CH
a
n
d
s
i
n
k
.
Fo
r
th
e
r
est
of
t
h
e
p
ap
er
we
c
o
n
s
id
er
th
at
c
is
co
n
s
ta
n
t,
an
d
g
is
t
h
e
r
esid
u
al
e
n
er
g
y
on
th
e
n
o
d
e,
w
h
ic
h
is
d
ef
i
n
ed
in
[
21
]
as
(
3
)
:
(
3
)
W
h
er
e
an
d
ar
e
th
e
in
it
ial
en
er
g
y
a
n
d
th
e
e
n
er
g
y
d
r
ain
ed
af
te
r
each
r
o
u
n
d
of
th
e
n
o
d
e
r
esp
ec
tiv
el
y
.
We
ass
u
m
e
th
a
t
a
m
i
x
ed
s
tr
at
eg
y
Na
s
h
E
q
u
ilib
r
iu
m
can
be
f
o
u
n
d
to
allo
w
each
p
la
y
er
to
ch
o
o
s
e
h
is
s
tr
ateg
y
r
a
n
d
o
m
l
y
f
o
llo
w
in
g
a
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
t
io
n
.
L
et
's
s
et
p
as
t
h
e
p
r
o
b
ab
ilit
y
of
a
n
o
d
e
an
n
o
u
n
ci
n
g
a
C
H,
a
n
d
th
e
n
th
e
ch
a
n
ce
of
r
e
f
u
s
i
n
g
w
ill
be
th
e
.
We
co
n
s
id
er
th
e
f
o
llo
w
i
n
g
u
t
ilit
y
v
al
u
es
if
we
h
a
v
e
N
n
o
d
e
p
lay
er
s
a
n
d
at
least
o
n
e
node
d
ec
lar
es
C
H:
{
(
(
)
)
(
4
)
As
we
d
escr
ib
ed
b
ef
o
r
e,
th
e
g
a
m
e
f
o
llo
w
s
a
d
i
s
tr
ib
u
tio
n
p
r
o
b
a
b
ilit
y
so
a
m
ix
ed
s
tr
at
eg
y
Na
s
h
E
q
u
ilib
r
iu
m
w
i
ll
be
co
n
cl
u
d
ed
by
t
h
e
e
x
p
r
ess
io
n
:
=
y
s
o
l
v
in
g
it
we
g
et
t
h
e
b
elo
w
e
x
p
r
ess
io
n
(
5
)
,
(
6
)
:
=
(
(
)
)
(
5
)
We
h
av
e
s
o
:
.
/
(
6
)
2
.
2
.
I
G
T
L
E
ACH
a
lg
o
rit
h
m
A
ut
hor
s
in
[
22]
co
unse
l
ed
a
pr
ogr
es
si
v
e
se
l
ect
i
on
t
echni
que
w
her
ei
n
t
he
net
w
or
k
w
a
s
di
v
i
ded
i
nt
o
ar
eas
and
a
t
e
m
por
ar
y
choi
ce
di
st
r
i
but
i
on
t
echni
que
changed
i
nt
o
use
d.
Th
e
ar
ea
nodes
j
deci
de
f
i
r
st
,
t
hen
t
he
r
egi
on
nodes
deci
de
r
i
ght
now
af
t
er
w
ar
ds,
in
or
der
t
hat
t
he
v
i
ci
ni
t
y
nodes
ca
n
bear
in
m
i
nd
t
he
pr
es
ence
of
cl
ust
er
heads
f
r
om
t
he
pr
ecede
nt
r
egi
on
and
f
or
es
t
al
l
se
eki
ng
to
be
a
cl
ust
er
head
ev
er
y
t
i
m
e
t
her
e
'
s
a
cl
ose
nei
ghbour
.
In
our
i
m
pr
ov
ed
gam
e
t
heor
y
L
EA
C
H
al
gor
i
t
hm
I
G
TL
EA
C
H
,
we
pr
opose
an
ot
her
pr
ogr
es
si
v
e
r
egi
ons
di
v
i
si
on
t
echni
que
f
or
cl
ust
er
f
or
m
i
ng;
w
her
e
t
he
nodes
ar
e
uni
f
or
m
l
y
di
st
r
i
but
ed
in
a
r
ect
angl
e
,
as
show
n
in
F
i
g
ur
e
2.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Op
timis
a
tio
n
o
f LE
A
C
H
p
r
o
to
co
l b
a
s
ed
o
n
a
g
a
me
th
e
o
r
y
clu
s
teri
n
g
a
p
p
r
o
a
c
h
fo
r
… (
Ya
s
s
in
e
Ou
ke
s
s
o
u
)
941
Fig
u
r
e
2
.
C
ells
n
et
w
o
r
k
s
u
b
d
iv
is
io
n
m
ap
We
s
t
a
r
t
by
d
i
v
i
d
i
n
g
t
h
e
r
e
c
t
a
n
g
l
e
i
n
t
o
c
e
l
l
s
.
L
e
t
us
m
a
k
e
W
a
n
d
L
t
h
e
w
i
d
t
h
a
n
d
l
e
n
g
t
h
of
t
h
e
r
e
c
t
a
n
g
l
e
,
N
is
t
h
e
n
u
m
b
e
r
of
s
e
n
s
o
r
n
o
d
e
s
,
a
n
d
is
t
h
e
d
e
s
i
r
e
d
p
e
r
c
e
n
t
a
g
e
of
c
l
u
s
t
e
r
h
e
a
d
s
C
H
.
We
s
e
t
t
h
e
n
:
(
√
)
(
7
)
W
h
er
e
is
th
e
n
u
m
b
er
of
s
e
g
m
en
ts
in
each
s
id
e
of
th
e
s
q
u
ar
e
,
l
et
us
m
ak
e
:
{
(
8
)
We
s
et
th
e
zo
n
e
as
ce
ll
0,
th
e
zo
n
e
as
th
e
u
n
io
n
of
t
h
e
ce
ll
0
an
d
ce
ll
1,
so
we
m
a
k
e
(
9
)
:
⋃
,
-
(
9
)
T
h
e
s
elec
tio
n
p
r
o
ce
d
u
r
e
w
i
ll
u
s
e
t
h
e
s
a
m
e
co
n
ce
p
t
as
L
E
AC
H.
In
th
e
f
ir
s
t
s
ta
g
e,
each
m
o
te
co
llects
its
co
r
r
esp
o
n
d
in
g
cl
u
s
ter
d
ata
by
p
er
f
o
r
m
in
g
t
h
e
n
ei
g
h
b
o
r
r
elatio
n
s
h
ip
p
r
o
ce
s
s
;
by
s
en
d
in
g
t
h
e
HE
L
L
O
p
ac
k
ets
an
d
r
ec
ei
v
in
g
t
h
e
A
C
K
m
es
s
ag
e
s
t
h
at
co
n
tai
n
s
t
h
e
r
esid
u
al
p
o
w
er
of
all
t
h
e
cl
u
s
t
er
n
o
d
es
[
2
3
]
.
A
f
ter
th
at
each
n
o
d
e
g
e
n
er
ates
a
r
an
d
o
m
n
u
m
b
er
b
et
w
ee
n
0
an
d
1,
an
d
if
it
is
less
th
a
n
a
n
e
w
th
r
es
h
o
ld
,
it
w
ill
an
n
o
u
n
ce
its
el
f
as
C
H.
We
as
s
u
m
e
t
h
at
ea
c
h
n
o
d
e
ca
lcu
late
th
e
n
e
w
th
r
es
h
o
ld
in
i
ts
s
et
up
p
h
ase
[
2
4
]
as
f
o
llo
w
(
1
0
)
:
(
(
)
)
(
10)
W
h
er
e
G
is
t
h
e
s
e
t
of
n
o
d
es
s
elec
ted
in
t
h
e
las
t
r
o
u
n
d
as
cl
u
s
ter
h
ea
d
s
,
an
d
is
t
h
e
p
r
o
b
ab
ilit
y
f
o
u
n
d
by
r
eso
l
v
i
n
g
t
h
e
Mi
x
ed
Stra
te
g
y
Na
s
h
E
q
u
ilib
r
iu
m
.
As
we
h
av
e
s
ee
n
in
t
h
e
clu
s
ter
f
o
r
m
i
n
g
p
ar
t
th
a
t
u
s
e
o
u
r
zo
n
e/ce
ll
d
iv
is
io
n
tech
n
iq
u
e.
T
h
e
p
r
o
ce
s
s
s
elec
tio
n
in
ea
c
h
r
o
u
n
d
w
ill
p
r
o
g
r
es
s
iv
e
l
y
s
t
ar
t
f
r
o
m
zo
n
e
k
to
zo
n
e
,
an
d
d
u
r
in
g
t
h
at
ti
m
e
t
h
e
m
o
tes
of
t
h
e
zo
n
e
k
p
la
y
th
e
g
a
m
e
a
n
d
tak
e
in
co
n
s
i
d
er
atio
n
th
e
s
tr
ateg
ie
s
s
elec
ted
by
t
h
e
p
r
ev
i
o
u
s
zo
n
e
k
-
1.
3.
P
E
RF
O
F
O
RM
ANCE
SI
M
UL
AT
I
O
N
RE
SU
L
T
S
A
NAL
YSI
S
E
x
p
lo
itin
g
t
h
e
M
A
T
L
A
B
S
i
m
u
lato
r
[
2
5
]
,
we
h
a
v
e
m
ad
e
a
co
m
p
ar
is
o
n
b
et
w
ee
n
L
E
AC
H
p
r
o
to
co
l
an
d
o
u
r
I
m
p
r
o
v
ed
Ga
m
e
T
h
e
o
r
y
L
E
AC
H
p
r
o
to
co
l.
So
in
th
e
f
ir
s
t
s
i
m
u
latio
n
,
we
ch
ec
k
t
h
e
e
n
tire
en
er
g
y
d
r
ain
ed
by
all
t
h
e
n
o
d
es
d
u
r
i
n
g
a
p
er
io
d
of
th
e
ti
m
e.
T
h
e
s
ec
o
n
d
s
i
m
u
latio
n
is
th
e
li
f
eti
m
e
of
t
h
e
s
e
n
s
o
r
s
,
r
elate
d
to
th
e
d
is
s
ip
atio
n
p
o
w
er
p
r
ev
io
u
s
l
y
e
v
al
u
ated
.
In
th
e
last
o
n
e,
we
h
av
e
e
v
al
u
ated
th
e
th
r
o
u
g
h
p
u
t
g
en
er
ated
by
all
t
h
e
n
o
d
es
a
n
d
d
is
tr
ib
u
ted
to
th
e
s
i
n
g
le
ac
ce
s
s
g
ate
w
a
y
(
s
i
n
k
)
.
3
.
1
.
N
et
w
o
rk
c
o
nfig
ura
t
io
n
We
ass
u
m
e
t
h
at
th
e
s
i
n
k
h
av
e
al
w
a
y
s
e
n
o
u
g
h
p
o
w
er
,
an
d
lo
ca
ted
in
th
e
ce
n
ter
of
t
h
e
r
ec
tan
g
le
,
a
n
d
all
th
e
m
o
tes
ar
e
f
i
x
ed
in
t
h
eir
p
o
s
itio
n
s
lo
ca
tio
n
s
,
as
it
is
s
h
o
w
n
in
Fi
g
u
r
e
3.
B
y
th
e
w
a
y
w
e
s
et
s
o
m
e
n
et
w
o
r
k
p
ar
a
m
eter
s
i
n
T
ab
le
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2021
:
9
38
-
9
44
942
Fig
u
r
e
3.
CH
s
elec
tio
n
s
i
m
u
lat
io
n
in
p
r
o
g
r
ess
Tabl
e
1
.
N
et
w
or
k
si
m
ul
at
i
on
par
am
et
er
s
P
a
r
a
me
t
e
r
V
a
l
u
e
N
u
mb
e
r
of
n
o
d
e
s
1
0
0
Ne
tw
o
rk
si
z
e
1
0
0
0
m×
1
0
0
0
m
I
n
i
t
i
a
l
e
n
e
r
g
y
of
e
a
c
h
n
o
d
e
2J
S
i
mu
l
a
t
i
o
n
t
i
me
1
0
0
0
s
D
e
si
r
e
d
p
e
r
c
e
n
t
a
g
e
of
c
l
u
s
t
e
r
h
e
a
d
s
0
.
1
P
a
c
k
e
t
si
z
e
f
o
r
c
l
u
st
e
r
h
e
a
d
6
4
0
0
b
i
t
s
P
a
c
k
e
t
si
z
e
f
o
r
n
o
r
mal
n
o
d
e
p
e
r
r
o
u
n
d
2
0
0
b
i
t
s
3
.
2
.
P
er
f
o
r
m
a
nce
r
esu
lt
s
In
t
he
f
i
r
s
t
si
m
ul
at
i
on
t
es
t
,
F
i
g
ur
e
4
show
s
us
a
si
gni
f
i
cant
gap
in
t
ot
al
ener
gy
di
ss
i
pat
e
d
st
ar
t
i
ng
f
r
om
about
250
se
c
onds
b
et
w
een
our
i
m
pr
ov
ed
gam
e
t
heor
y
L
EA
C
H
al
gor
i
t
h
m
I
G
TL
EA
C
H
an
d
L
EA
C
H
pr
ot
ocol
,
t
hus
al
l
ow
i
ng
m
or
e
m
ot
es
t
hat
ar
e
al
i
v
e
by
keepi
ng
t
hei
r
l
i
f
et
i
m
e
uni
f
or
m
l
y
decr
eas
i
ng
in
t
i
m
e
F
i
g
ur
e
5.
T
h
e
last
s
i
m
u
latio
n
r
esu
lt
Fi
g
u
r
e
6
s
h
o
w
s
u
s
a
s
tab
le
u
n
i
f
o
r
m
to
tal
t
h
r
o
u
g
h
p
u
t
o
f
o
u
r
I
G
T
L
E
AC
H
al
g
o
r
ith
m
,
w
h
ich
is
e
x
p
lain
ed
b
y
th
e
r
es
is
tan
ce
to
d
ea
th
d
o
n
e
b
y
t
h
e
m
o
tes.
I
n
o
th
er
ca
s
e,
if
w
e
in
cr
ea
s
e
th
e
n
u
m
b
er
o
f
n
o
d
es
to
4
0
0
m
o
te
s
w
e
g
et
m
o
r
e
lif
eti
m
e
e
f
f
icien
c
y
g
ap
Fig
u
r
e
7
.
T
h
is
m
ea
n
s
th
at
o
u
r
al
g
o
r
ith
m
is
m
o
r
e
ef
f
i
cien
t i
n
ter
m
s
o
f
n
o
d
es d
en
s
it
y
.
Fig
u
r
e
4.
C
o
m
p
ar
is
o
n
of
n
et
wo
r
k
en
er
g
y
co
n
s
u
m
p
tio
n
s
of
L
E
AC
H
an
d
I
GT
L
E
A
C
H
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
Op
timis
a
tio
n
o
f LE
A
C
H
p
r
o
to
co
l b
a
s
ed
o
n
a
g
a
me
th
e
o
r
y
clu
s
teri
n
g
a
p
p
r
o
a
c
h
fo
r
… (
Ya
s
s
in
e
Ou
ke
s
s
o
u
)
943
Fig
u
r
e
5.
C
o
m
p
ar
is
o
n
of
n
et
wo
r
k
lif
e
s
p
a
n
s
of
L
E
AC
H
an
d
I
GT
L
E
A
C
H
Fig
u
r
e
6.
C
o
m
p
ar
is
o
n
of
n
et
wo
r
k
th
r
o
u
g
h
p
u
ts
of
L
E
AC
H
an
d
I
G
T
L
E
AC
H
Fig
u
r
e
7.
C
o
m
p
ar
is
o
n
of
n
et
wo
r
k
lif
e
s
p
a
n
s
of
L
E
AC
H
an
d
I
GT
L
E
A
C
H
w
it
h
400
n
o
d
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2021
:
9
38
-
9
44
944
4.
CO
NCLU
SI
O
N
Desp
ite
th
a
t
th
e
cl
u
s
ter
in
g
is
t
h
e
b
est
tec
h
n
iq
u
e
to
av
o
id
th
e
n
o
n
-
d
is
tr
ib
u
tio
n
of
CH
s
elec
t
io
n
in
t
h
e
w
ir
ele
s
s
s
e
n
s
o
r
n
et
w
o
r
k
s
,
th
e
s
elf
is
h
n
es
s
b
eh
av
io
r
an
d
th
e
en
er
g
y
s
to
r
ag
e
ch
a
llen
g
e
of
n
o
d
es
af
f
ec
t
it
s
ef
f
icien
c
y
.
In
t
h
is
w
o
r
k
we
h
a
v
e
p
r
o
p
o
s
e
d
a
n
e
w
clu
s
ter
in
g
g
a
m
e
s
elec
tio
n
p
r
o
ce
s
s
of
C
H,
w
h
er
e
th
e
n
et
w
o
r
k
en
er
g
y
d
r
ai
n
ed
s
lo
w
l
y
t
h
a
n
t
h
e
f
a
m
o
u
s
L
E
AC
H
m
ec
h
a
n
i
s
m
,
co
n
s
eq
u
e
n
tl
y
b
etter
p
er
f
o
r
m
a
n
ce
s
s
h
o
w
n
in
s
i
m
u
lat
io
n
co
m
p
ar
is
o
n
s
.
In
o
u
r
f
u
t
u
r
e
w
o
r
k
,
we
p
la
n
to
m
o
r
e
o
p
tim
ize
th
e
eq
u
ilib
r
iu
m
eq
u
atio
n
w
it
h
o
u
t
u
s
in
g
th
e
s
u
b
d
iv
i
s
io
n
tech
n
iq
u
e,
b
e
ca
u
s
e
t
h
e
cl
u
s
ter
in
g
f
o
r
m
i
n
g
m
et
h
o
d
h
as
a
C
P
U
p
r
o
ce
s
s
o
r
s
tr
ess
i
m
p
ac
t
on
th
e
n
o
d
es,
w
h
ich
i
n
cr
ea
s
e
th
eir
p
o
w
er
co
n
s
u
m
p
t
io
n
.
So
f
o
r
th
at,
we
w
i
ll
i
n
tr
o
d
u
ce
a
n
e
w
e
n
er
g
y
p
ar
a
m
eter
s
,
an
d
also
n
o
t
co
n
s
id
er
in
g
th
e
co
s
t
as
co
n
s
tan
t
as
we
h
av
e
s
ee
n
in
t
h
e
s
ec
tio
n
3
t
h
at
is
d
ep
en
d
to
th
e
d
is
tan
ce
f
r
o
m
th
e
s
i
n
k
an
d
th
e
n
u
m
b
er
of
n
o
d
es
in
th
e
cl
u
s
ter
.
RE
F
E
R
E
NC
E
S
[1
]
M.
A.
M
a
ti
n
a
n
d
M.
M.
Isla
m
,
“
Ov
e
rv
i
e
w
of
w
ir
e
les
s
s
e
n
so
r
n
e
tw
o
rk
,
”
in
W
ir
e
les
s
S
e
n
so
r
Ne
two
rk
s
-
T
e
c
h
n
o
lo
g
y
and
Pro
t
o
c
o
ls
,
In
T
e
c
h
,
2
0
1
2
.
[2
]
D.
-
S.
Kim
a
n
d
H.
T
ra
n
-
Da
n
g
,
“
A
n
o
v
e
r
v
ie
w
on
w
ir
e
les
s
se
n
so
r
n
e
tw
o
rk
s,
”
in
Co
mp
u
ter
Co
mm
u
n
ica
ti
o
n
s
and
Ne
two
rk
s
,
Ch
a
m
:
S
p
rin
g
e
r
In
tern
a
ti
o
n
a
l
P
u
b
li
sh
i
n
g
,
p
p
.
1
0
1
-
1
1
3
,
2
0
1
9
.
[3
]
A.
G
u
e
r
m
a
z
i,
A.
Be
lg
h
it
h
,
a
n
d
M.
A
b
id
,
“
M
u
lt
i
-
h
o
p
ro
u
ti
n
g
f
o
r
d
istri
b
u
te
d
c
lu
ste
rin
g
p
ro
t
o
c
o
ls
in
w
id
e
w
ir
e
les
s
se
n
so
r
n
e
tw
o
rk
s,”
in
2
0
1
5
IEE
E/
ACS
1
2
t
h
In
ter
n
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
of
Co
mp
u
ter
S
y
ste
ms
and
A
p
p
l
ica
ti
o
n
s
(
AICCS
A)
,
p
p
.
1
-
6
,
2
0
1
5
.
[4
]
S.
Ra
n
i
a
n
d
S.
H.
A
h
m
e
d
,
M
u
lt
i
-
h
o
p
R
o
u
ti
n
g
in
W
ire
les
s
S
e
n
so
r
Ne
two
rk
s
,
S
in
g
a
p
o
re
:
S
p
rin
g
e
r
S
i
n
g
a
p
o
re
,
2
0
1
6
.
[5
]
A.
V
in
it
h
a
,
M.
S.
S.
R
u
k
m
in
i,
a
n
d
D
h
irajsu
n
e
h
ra
,
“
S
e
c
u
re
a
n
d
e
n
e
rg
y
a
w
a
r
e
m
u
lt
i
-
h
o
p
r
o
u
ti
n
g
p
ro
to
c
o
l
in
W
S
N
u
sin
g
T
a
y
lo
r
-
b
a
se
d
h
y
b
rid
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
,
”
J.
Kin
g
S
a
u
d
U
n
iv.
-
C
o
mp
u
t.
I
n
f.
S
c
i
.,
2
0
1
9
.
[6
]
M.
N.
Ja
m
b
li
,
e
t
a
l
,
“
Ev
a
lu
a
ti
o
n
of
c
lu
ste
rin
g
a
n
d
m
u
lt
i
-
h
o
p
ro
u
ti
n
g
p
ro
to
c
o
ls
in
M
o
b
il
e
Ad
-
hoc
S
e
n
so
r
Ne
tw
o
rk
s,”
in
2
0
1
5
9
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
on
IT
in
Asi
a
(
CIT
A)
,
p
p
.
1
-
5
,
2
0
1
5
.
[
7
]
O.
Y
a
s
s
i
n
e
,
M.
B
a
s
l
a
m
,
a
n
d
M.
O
u
k
e
s
s
o
u
,
“
L
p
w
a
n
i
e
e
e
8
0
2
.
1
1
a
h
a
n
d
l
o
r
a
w
a
n
c
a
p
a
c
i
t
y
s
i
m
u
l
a
t
i
o
n
a
n
a
l
y
s
i
s
c
o
m
p
a
r
i
s
o
n
u
s
i
n
g
ns
-
3
,
”
in
2018
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
on
O
p
t
i
m
i
z
a
t
i
o
n
and
A
p
p
l
i
c
a
t
i
o
n
s
(
I
C
O
A
)
,
pp.
1
-
4
,
2
0
1
8
.
[8
]
K.
M
e
k
k
i,
E.
Ba
ji
c
,
F.
Ch
a
x
e
l,
a
n
d
F.
M
e
y
e
r,
“A
c
o
m
p
a
ra
ti
v
e
stu
d
y
of
L
P
WA
N
te
c
h
n
o
l
o
g
ies
f
o
r
larg
e
-
s
c
a
le
Io
T
d
e
p
lo
y
m
e
n
t,
”
ICT
Exp
re
ss
,
v
o
l.
5,
n
o
.
1,
p
p
.
1
-
7,
2
0
1
9
.
[9
]
J.
Ru
b
io
-
A
p
a
ricio
,
F.
Ce
rd
a
n
-
Ca
r
tag
e
n
a
,
J.
S
u
a
rd
iaz
-
M
u
ro
,
a
n
d
J.
Yb
a
rra
-
M
o
re
n
o
,
“
De
sig
n
a
n
d
i
m
p
lem
e
n
tatio
n
of
a
m
i
x
e
d
Io
T
L
P
WA
N
n
e
t
w
o
rk
a
r
c
h
it
e
c
tu
re
,
”
S
e
n
s
o
rs
(
Ba
se
l)
,
v
o
l.
19,
n
o
.
3,
2
0
1
9
.
[1
0
]
Y.
L
iu
,
Q.
W
u
,
T.
Z
h
a
o
,
Y.
T
ie,
F.
Ba
i
,
a
n
d
M.
Ji
n
,
“
A
n
im
p
ro
v
e
d
e
n
e
rg
y
-
e
ff
icie
n
t
ro
u
t
in
g
p
ro
t
o
c
o
l
f
o
r
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,”
S
e
n
so
rs
(
Ba
se
l)
,
v
o
l.
19,
n
o
.
20,
2
0
1
9
.
[1
1
]
A.
Iq
b
a
l,
M.
A
k
b
a
r,
N.
Ja
v
a
id
,
S.
Bo
u
k
,
M.
Ilah
i
,
a
n
d
R.
Kh
a
n
,
“
A
d
v
a
n
c
e
d
L
E
A
CH:
A
sta
ti
c
c
lu
ste
rin
g
-
b
a
se
d
h
e
tero
n
e
o
u
s
ro
u
ti
n
g
p
ro
to
c
o
l
f
o
r
W
S
Ns
,
”
ArXi
v
,
2
0
1
3
.
[1
2
]
L.
Tan
g
a
n
d
S.
L
iu
,
“
Im
p
ro
v
e
m
e
n
t
on
L
EA
CH
ro
u
ti
n
g
a
lg
o
r
it
h
m
f
o
r
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
s,”
in
2
0
1
1
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
on
I
n
ter
n
e
t
Co
mp
u
ti
n
g
a
n
d
I
n
fo
rm
a
ti
o
n
S
e
rv
ice
s
,
pp.
1
9
9
-
2
0
2
,
2
0
1
1
.
[1
3
]
W.
Hu
a
n
g
,
Y.
L
in
g
,
a
n
d
W.
Z
h
o
u
,
“
A
n
im
p
ro
v
e
d
L
E
A
CH
ro
u
ti
n
g
a
lg
o
rit
h
m
f
o
r
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
,
”
In
t.
J.
W
ire
l.
In
f.
Ne
tw.
,
v
o
l.
25,
n
o
.
3,
p
p
.
3
2
3
-
3
3
1
,
2
0
1
8
.
[1
4
]
A.
O.
A
b
u
S
a
le
m
a
n
d
N.
S
h
u
d
if
a
t,
“
En
h
a
n
c
e
d
L
EA
CH
p
ro
to
c
o
l
f
o
r
in
c
re
a
sin
g
a
li
f
e
ti
m
e
of
W
S
Ns
,
”
Per
s.
Ub
iq
u
it
o
u
s
Co
m
p
u
t
.,
v
o
l.
2
3
,
no.
5
-
6,
p
p
.
901
-
9
0
7
,
2
0
1
9
.
[1
5
]
Zh
a
o
,
Jia
n
li
&
YA
N
G
,
L
iro
n
g
,
"
L
E
A
CH
-
A:
An
a
d
a
p
ti
v
e
m
e
th
o
d
f
o
r
im
p
ro
v
in
g
L
E
A
C
H
p
ro
to
c
o
l
,
"
S
e
n
so
rs
a
n
d
T
ra
n
sd
u
c
e
rs
,
v
o
l.
1
6
2
,
n
o
.
1
,
p
p
.
136
-
1
4
0
,
2
0
1
4
.
[1
6
]
Z.
Zh
a
o
,
G.
L
i,
a
n
d
M.
X
u
,
“
A
n
i
m
p
ro
v
e
d
a
l
g
o
rit
h
m
b
a
se
d
on
L
EA
CH
ro
u
ti
n
g
p
ro
t
o
c
o
l,
”
2
0
1
9
IEE
E
1
9
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mm
u
n
ica
t
io
n
T
e
c
h
n
o
l
o
g
y
(
ICCT
)
,
X
i'
a
n
,
C
h
in
a
,
2
0
1
9
,
p
p
.
1
2
4
8
-
1
2
5
1
,
2
0
1
9
.
[1
7
]
M.
Am
ru
,
M.
Ja
b
iru
ll
a
h
,
a
n
d
A.
C.
Krish
n
a
,
“
A
n
im
p
ro
v
e
d
n
e
t
w
o
rk
c
o
d
in
g
b
a
se
d
L
EA
CH
p
ro
t
o
c
o
l
f
o
r
e
n
e
rg
y
e
ffe
c
ti
v
e
n
e
ss
in
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,
”
in
In
telli
g
e
n
t
S
y
ste
ms
Refe
re
n
c
e
L
ib
ra
ry
,
Ch
a
m
:
S
p
rin
g
e
r
In
tern
a
ti
o
n
a
l
P
u
b
l
ish
i
n
g
,
p
p
.
1
2
5
-
1
3
6
,
2
0
2
0
.
[1
8
]
C.
Hilb
e
,
A.
T
ra
u
lse
n
,
a
n
d
K.
S
ig
m
u
n
d
,
“
P
a
rtn
e
rs
or
riv
a
ls?
S
trate
g
ies
f
o
r
th
e
it
e
ra
ted
p
riso
n
e
r’s
d
il
e
m
m
a
,”
Ga
me
s
Eco
n
.
Beh
a
v
.,
v
o
l.
9
2
,
p
p
.
41
-
5
2
,
2
0
1
5
.
[1
9
]
H.
-
Y.
S
h
i,
W.
-
L.
W
a
n
g
,
N.
-
M.
Kw
o
k
,
a
n
d
S.
-
Y.
C
h
e
n
,
“
G
a
m
e
th
e
o
ry
f
o
r
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s:
A
su
rv
e
y
,
”
S
e
n
so
rs
,
v
o
l
.
1
2
,
n
o
.
7,
p
p
.
9
0
5
5
-
9
0
9
7
,
2
0
1
2
.
[2
0
]
Q.
L
iu
a
n
d
M.
L
iu
,
“
En
e
rg
y
-
e
ff
ic
ien
t
c
lu
ste
rin
g
a
lg
o
rit
h
m
b
a
se
d
on
g
a
m
e
th
e
o
ry
f
o
r
w
irel
e
ss
s
e
n
so
r
n
e
tw
o
rk
s,”
In
t.
J.
Distrib
.
S
e
n
s.
Ne
tw
.,
v
o
l.
1
3
,
n
o
.
1
1
,
p.
1
5
5
0
1
4
7
7
1
7
7
4
3
7
0
,
2
0
1
7
.
[2
1
]
Z.
Xu
,
Y.
Yi
n
,
X.
C
h
e
n
,
a
n
d
J.
W
a
n
g
,
“A
g
a
m
e
-
th
e
o
ry
b
a
se
d
c
l
u
ste
rin
g
a
p
p
r
o
a
c
h
f
o
r
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
s,”
NGCIT
2
0
1
3
,
A
S
TL
,
p
p
.
5
8
-
66
,
2
0
1
3
.
[2
2
]
L.
S
HEN
a
n
d
X.
S
HI
,
"
A
lo
c
a
ti
o
n
b
a
se
d
c
lu
ste
rin
g
a
lg
o
ri
th
m
f
o
r
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s
,
"
In
ter
n
a
ti
o
n
a
l
j
o
u
rn
a
l
of
In
telli
g
e
n
t
Co
n
tro
l
a
n
d
S
y
ste
ms
,
v
o
l.
1
3
,
n
o
.
3
,
p
p
.
2
0
8
-
2
1
3
,
2
0
0
8
[2
3
]
S.
M
a
g
o
tra
a
n
d
K.
Ku
m
a
r,
“
De
te
c
ti
o
n
of
HELL
O
f
lo
o
d
a
tt
a
c
k
on
L
E
A
CH
p
ro
to
c
o
l,
”
in
2
0
1
4
IE
EE
In
ter
n
a
ti
o
n
a
l
Ad
v
a
n
c
e
C
o
mp
u
ti
n
g
Co
n
fer
e
n
c
e
(IA
CC)
,
p
p
.
1
9
3
-
1
9
8
,
2
0
1
4
.
[2
4
]
E.
F.
Yo
u
ss
e
f
,
F.
M
o
h
a
m
m
e
d
,
a
n
d
E.
A
b
e
d
e
ll
a
h
,
“
Im
p
ro
v
e
m
e
n
t
of
lea
c
h
ro
u
ti
n
g
a
lg
o
rit
h
m
b
a
se
d
on
th
e
u
se
of
g
a
m
e
th
e
o
ry
,
”
in
Pro
c
e
e
d
in
g
s
of
th
e
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
on
In
ter
n
e
t
of
th
i
n
g
s
a
n
d
Cl
o
u
d
Co
mp
u
ti
n
g
-
ICC
’1
6
,
p
p
.
1
-
5
,
2
0
1
6
.
[2
5
]
[
On
li
n
e
]
.
A
v
a
il
a
b
le:
ww
w
.
m
a
th
wo
rk
s.co
m
[
A
c
c
e
se
d
:
Oc
to
b
e
r
20
,
2
0
1
9
]
Evaluation Warning : The document was created with Spire.PDF for Python.