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3212
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CC B
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E
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m
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ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
W
SN
is
o
n
e
o
f
t
h
e
m
ai
n
tec
h
n
o
lo
g
ies
d
e
v
o
ted
to
th
e
id
e
n
ti
f
ica
tio
n
,
s
e
n
s
i
n
g
,
an
d
co
n
tr
o
llin
g
o
f
p
h
y
s
ica
l
en
v
ir
o
n
m
e
n
tal
p
h
e
n
o
m
e
n
a
i
n
r
ea
l
-
ti
m
e
s
u
c
h
as
th
e
d
ete
ctio
n
an
d
m
ea
s
u
r
e
m
en
t
o
f
v
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r
atio
n
,
p
r
ess
u
r
e,
te
m
p
er
atu
r
e
a
n
d
s
o
u
n
d
.
[
1
-
3
]
.
T
h
e
W
SN
ca
n
b
e
in
co
r
p
o
r
ated
in
m
a
n
y
s
ec
to
r
s
li
k
e
s
tr
ee
t
p
a
r
k
in
g
,
s
m
ar
t
r
o
ad
s
,
an
d
in
d
u
s
tr
ial
m
o
n
ito
r
in
g
[
4
-
5
]
.
I
n
g
e
n
er
al,
W
SN
co
n
s
is
ts
o
f
s
ev
er
al
s
el
f
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o
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an
ized
s
en
s
o
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n
o
d
es
w
it
h
li
m
ited
en
er
g
y
,
co
m
p
u
ta
tio
n
al
ca
p
ac
i
ties
,
an
d
b
an
d
w
id
t
h
[
6
-
8
]
,
w
h
ich
ar
e
d
ep
lo
y
ed
in
ar
g
e
q
u
an
titi
e
s
o
f
s
e
n
s
o
r
s
in
d
ed
icate
d
en
v
ir
o
n
m
e
n
t
s
.
Un
d
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h
o
s
tile
co
n
d
itio
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s
,
ad
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s
tin
g
t
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'
s
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atter
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n
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er
o
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o
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is
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m
p
r
ac
tical.
An
au
to
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m
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s
en
er
g
y
s
o
u
r
ce
(
b
atter
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)
s
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p
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s
o
r
n
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d
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w
it
h
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m
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co
n
tr
o
ller
,
m
e
m
o
r
y
,
an
d
tr
an
s
ce
iv
er
[
2
-
7
]
.
T
h
e
b
ase
s
tatio
n
(
Sin
k
)
,
h
o
w
e
v
er
,
o
th
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s
d
ata
f
o
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p
r
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ce
s
s
in
g
an
d
s
en
d
s
i
t to
th
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ce
n
tr
al
co
m
p
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ter
.
A
W
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m
an
a
g
ed
b
y
o
n
e
o
r
m
o
r
e
b
ase
s
tatio
n
s
[
1
-
4
,
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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I
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P
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fu
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C
-
mea
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s
(
Ha
mid
B
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ko
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k
)
3213
I
n
W
SN
t
h
e
co
m
m
u
n
icatio
n
p
r
o
ce
s
s
r
eq
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a
co
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s
id
er
ab
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q
u
an
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it
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n
er
g
y
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E
n
er
g
y
c
o
n
s
u
m
p
ti
n
o
p
tim
izatio
n
i
s
o
n
e
o
f
t
h
e
p
r
o
m
i
n
en
t
p
r
o
b
le
m
s
f
ac
i
n
g
w
ir
ele
s
s
s
e
n
s
o
r
n
et
w
o
r
k
s
.
Fo
r
th
is
r
e
aso
n
,
i
m
p
r
o
v
i
n
g
th
e
n
et
w
o
r
k
's
en
er
g
y
o
u
tp
u
t
i
s
n
ec
ess
ar
y
i
n
o
r
d
er
to
in
cr
ea
s
e
t
h
e
s
y
s
te
m
's
li
f
eti
m
e
[
9
]
.
T
o
o
v
er
co
m
e
th
i
s
li
m
itat
io
n
a
s
et
o
f
tech
n
iq
u
es h
av
e
b
ee
n
d
ev
elo
p
ed
to
m
i
n
i
m
ize
th
e
co
m
m
u
n
icatio
n
en
er
g
y
co
n
s
u
m
p
tio
n
[
5
]
.
Du
e
to
th
eir
en
er
g
y
e
f
f
icie
n
c
y
,
au
to
m
ated
r
o
u
tin
g
s
tr
ateg
ie
s
s
u
c
h
as
s
i
n
g
le
-
h
o
p
,
m
u
l
ti
h
o
p
o
r
clu
s
ter
in
g
h
av
e
b
ee
n
w
id
el
y
i
m
p
le
m
en
ted
at
W
SN [
6
]
.
Sev
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al
r
esear
ch
er
s
h
a
v
e
p
r
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p
o
s
ed
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ith
m
s
to
in
cr
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s
e
th
e
l
if
et
i
m
e
o
f
n
et
wo
r
k
.
B
ased
o
n
th
e
n
et
w
o
r
k
la
y
o
u
t,
r
o
u
tin
g
in
W
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ca
n
t
y
p
icall
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e
class
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f
ied
in
to
f
lat
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o
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tin
g
,
h
ier
ar
ch
ical
r
o
u
tin
g
,
an
d
lo
ca
tio
n
r
o
u
tin
g
[
1
0
-
1
2
]
.
Am
o
n
g
s
u
ch
p
r
o
to
co
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r
o
to
co
ls
f
o
r
clu
s
ter
in
g
.
T
h
e
n
et
w
o
r
k
is
p
ar
titi
o
n
ed
in
to
s
m
all
ar
ea
s
in
th
e
clu
s
ter
in
g
alg
o
r
ith
m
,
an
d
ea
ch
p
ar
t is c
o
n
tr
o
lled
an
d
r
eg
u
lated
b
y
th
e
clu
s
ter
h
ea
d
(
C
H)
.
T
h
is
is
r
esp
o
n
s
ib
le
f
o
r
co
m
p
r
ess
i
n
g
t
h
e
co
llected
d
ata
an
d
tr
an
s
m
itti
n
g
it
to
th
e
b
ase
s
tatio
n
t
h
r
o
u
g
h
a
s
in
g
le
h
o
p
o
r
m
u
lti
-
h
o
p
th
at
ca
n
b
e
lin
k
ed
to
a
p
o
w
er
f
u
l
d
ev
ice
o
v
er
th
e
i
n
ter
n
et
o
r
a
s
atellite
o
f
th
e
in
f
o
r
m
atio
n
s
e
n
s
ed
b
y
t
h
e
ar
ea
n
o
d
es
[
1
2
-
1
6
]
.
T
h
e
clu
s
ter
i
n
g
p
r
o
to
co
ls
s
av
es
r
eso
u
r
ce
s
to
e
x
ten
d
t
h
e
n
et
w
o
r
k
's
lif
esp
a
n
[
1
6
]
.
T
h
at
ca
n
also
b
e
a
r
ea
lis
tic
attr
ib
u
te
f
o
r
lar
g
e
s
en
s
o
r
n
e
t
w
o
r
k
s
,
s
i
n
c
e
th
e
cl
u
s
t
e
r
h
e
a
d
s
a
r
e
e
as
i
e
r
t
o
h
an
d
l
e
th
an
th
e
w
h
o
l
e
n
e
tw
o
r
k
.
M
u
l
ti
p
l
e
c
lu
s
t
e
r
in
g
r
o
u
t
in
g
p
r
o
t
o
c
o
l
s
u
s
in
g
m
u
l
t
i
-
p
at
h
s
h
av
e
b
e
e
n
p
r
o
p
o
s
e
d
o
n
t
h
e
b
a
s
i
s
o
f
l
o
a
d
b
a
la
n
c
in
g
a
c
co
r
d
i
n
g
t
o
th
e
f
o
ll
o
w
in
g
o
r
d
e
r
L
E
A
C
H
[
16
-
19
]
,
DE
E
C
[
1
2
,
1
5
,
1
6
]
.
T
h
e
clu
s
ter
i
n
g
p
r
o
to
co
ls
ca
n
b
e
d
iv
id
ed
in
to
tw
o
t
y
p
e
s
:
t
h
e
cl
u
s
ter
i
n
g
a
lg
o
r
i
th
m
w
it
h
h
o
m
o
g
e
n
eo
u
s
s
c
h
e
m
es
in
w
h
ic
h
all
t
h
e
s
en
s
o
r
n
o
d
es
h
a
v
e
t
h
e
s
a
m
e
i
n
itial
en
er
g
y
;
a
n
d
th
e
h
eter
o
g
e
n
eo
u
s
cl
u
s
ter
i
n
g
p
r
o
to
co
ls
in
wh
ich
all
t
h
e
s
en
s
o
r
n
o
d
es a
r
e
d
eliv
er
ed
w
it
h
a
d
if
f
er
en
t a
m
o
u
n
t o
f
p
o
w
er
at
n
et
wo
r
k
s
tar
tu
p
[
1
8
].
I
n
th
is
p
ap
er
,
w
e
co
n
d
u
ct
a
n
an
al
y
s
is
o
f
t
h
e
p
er
f
o
r
m
a
n
ce
p
ar
a
m
eter
s
o
f
s
o
m
e
ef
f
ec
ti
v
e
h
ier
ar
ch
ical
r
o
u
tin
g
p
r
o
to
co
ls
,
th
e
lo
w
-
en
er
g
y
ad
ap
tiv
e
h
ier
ar
c
h
y
p
r
o
t
o
co
l
k
n
o
w
n
as
L
E
AC
H
[
6
]
,
th
e
s
tab
le
e
lectio
n
p
r
o
to
co
l
(
SEP)
[
2
0
]
,
th
e
d
is
tr
i
b
u
ted
en
er
g
y
-
e
f
f
icie
n
t
clu
s
ter
in
g
p
r
o
to
co
l
(
DE
E
C
)
[
1
2
]
,
an
d
o
u
r
p
o
r
p
o
s
itio
n
s
:
th
e
F
-
L
E
AC
H
p
r
o
to
co
l
an
d
th
e
F
-
DE
E
C
p
r
o
to
co
l.
T
h
e
r
est
o
f
th
e
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
w
s
:
T
h
e
r
elev
an
t
r
esear
ch
an
d
b
en
c
h
m
ar
k
i
n
g
ar
e
p
r
esen
ted
in
s
ec
t
io
n
2
.
Se
ctio
n
3
in
t
r
o
d
u
ce
s
i
n
tr
o
d
u
ce
s
t
h
e
F
-
L
E
A
C
H
p
r
o
to
co
l.
Sectio
n
4
d
escr
ib
es
th
e
F
-
D
E
E
C
p
r
o
to
co
l.
Sectio
n
5
est
ab
lis
h
es
m
o
d
el
s
an
d
th
e
an
al
y
s
i
s
o
f
t
h
e
r
es
u
lts
.
E
v
en
t
u
all
y
,
s
ec
tio
n
6
f
i
n
alize
s
th
e
p
ap
er
b
ased
o
n
th
e
f
in
d
i
n
g
s
o
b
tain
ed
an
d
p
o
s
s
ib
le
r
esea
r
ch
2.
RE
L
AT
E
D
WO
RK
AND
B
E
NCH
M
ARK
I
NG
I
n
th
e
r
ec
en
t
p
as
t,
a
n
u
m
b
er
o
f
clu
s
ter
i
n
g
a
lg
o
r
it
h
m
s
w
er
e
p
r
o
p
o
s
ed
f
o
r
W
SNs
,
a
m
o
n
g
th
ese
ar
e
p
r
o
p
o
s
ed
f
o
u
n
d
to
w
ar
d
s
ef
f
ec
t
iv
e
d
ata
co
m
m
u
n
icatio
n
an
d
d
ata
p
r
o
ce
s
s
in
g
w
it
h
o
p
ti
m
al
r
e
s
o
u
r
ce
u
s
a
g
e
i
n
t
h
e
W
SN.
I
n
th
is
p
ar
t,
w
e
d
escr
i
b
e
s
o
m
e
o
f
t
h
e
m
o
s
t
ef
f
ec
ti
v
e
r
o
u
tin
g
al
g
o
r
ith
m
s
o
f
W
SN
.
R
esear
ch
er
s
h
av
e
co
n
d
u
cted
a
v
ar
iet
y
o
f
w
o
r
k
s
f
o
cu
s
ed
o
n
ap
p
licatio
n
a
n
d
n
et
w
o
r
k
la
y
o
u
t
to
ad
v
an
ce
r
o
u
tin
g
p
r
o
to
co
ls
in
W
SN.
T
h
er
e
ar
e
also
s
ev
er
al
p
ar
am
e
t
er
s
to
co
n
s
id
er
w
h
e
n
d
esi
g
n
i
n
g
th
e
r
o
u
ti
n
g
p
r
o
to
co
l.
E
n
er
g
y
p
er
f
o
r
m
a
n
ce
i
s
o
n
e
o
f
th
e
m
o
s
t
o
b
v
io
u
s
v
ar
iab
les
d
ir
ec
tl
y
i
m
p
ac
ti
n
g
th
e
li
f
e
s
p
an
o
f
t
h
e
n
et
w
o
r
k
.
I
n
th
i
s
p
ap
er
,
w
e
p
r
o
v
id
e
s
o
m
e
ex
a
m
p
le
s
o
f
t
h
e
en
er
g
y
-
e
f
f
icie
n
t r
o
u
ti
n
g
p
r
o
to
co
ls
.
2
.
1
.
L
o
w
-
ener
g
y
a
da
ptiv
e
clus
t
er
ing
hiera
rc
hy
(
L
E
ACH
)
T
h
e
L
E
AC
H
p
r
o
to
co
l
[
8
,
1
8
]
i
s
th
e
f
ir
s
t
p
r
o
to
co
l
th
at
u
s
e
s
a
p
u
r
e
p
r
o
b
a
b
ilis
tic
m
o
d
el
to
p
ick
C
H
s
an
d
to
r
o
tate
th
e
C
Hs
p
er
io
d
ical
l
y
to
b
alan
ce
en
er
g
y
u
s
a
g
e.
T
h
e
d
y
n
a
m
ic
clu
s
ter
in
g
m
e
ch
an
i
s
m
h
as
b
ee
n
in
tr
o
d
u
ce
d
,
w
h
er
e
a
n
o
d
e
elec
ts
its
el
f
b
y
u
n
iq
u
e
p
r
o
b
ab
ilit
y
t
o
b
ec
o
m
e
a
C
H
an
d
tr
an
s
m
i
ts
i
ts
s
tat
u
s
to
all
n
o
d
es
[
3
,
2
1
]
.
I
n
ce
r
tain
in
s
ta
n
ce
s
,
h
o
w
e
v
er
,
in
e
f
f
icie
n
t
C
Hs
ar
e
s
e
lecta
b
le.
T
h
is
is
d
u
e
to
th
e
f
ac
t
th
at
L
E
A
C
H
o
n
l
y
r
elies
o
n
a
p
r
o
b
a
b
ilis
tic
m
o
d
el
.
So
m
e
C
Hs
m
a
y
b
e
v
er
y
clo
s
e
to
ea
ch
o
th
er
an
d
m
a
y
b
e
s
it
u
a
ted
at
th
e
W
SN
'
s
ed
g
e.
Su
c
h
i
n
ef
f
icie
n
t
h
ea
d
s
o
f
C
l
u
s
ter
s
ca
n
h
a
v
e
a
n
e
g
ati
v
e
i
m
p
ac
t
o
n
e
n
er
g
y
ef
f
ic
ien
c
y
i
n
th
e
n
et
w
o
r
k
.
T
h
e
L
E
AC
H
c
y
cle
m
o
v
e
s
to
cir
cles.
E
v
er
y
r
o
u
n
d
h
a
s
t
w
o
p
h
a
s
es:
t
h
e
f
ir
s
t
p
h
ase
is
s
et
-
u
p
f
o
r
o
r
g
an
izi
n
g
t
h
e
clu
s
ter
s
an
d
s
elec
ti
n
g
C
Hs
;
th
e
s
ec
o
n
d
p
h
ase
i
s
s
tab
le
-
s
tate
f
o
r
tr
an
s
m
itti
n
g
d
ata
to
t
h
e
b
ase
s
tatio
n
.
C
Hs
elec
t
io
n
s
ar
e
b
ased
o
n
th
e
d
esire
d
p
er
ce
n
ta
g
e
o
f
C
H
s
a
n
d
t
h
e
n
u
m
b
er
o
f
iter
atio
n
s
a
n
o
d
e
h
as
ta
k
e
n
o
n
C
Hs
f
u
n
ctio
n
[
2
2
,
2
3
]
.
A
n
o
d
e
s
is
th
er
e
f
o
r
e
a
r
a
n
d
o
m
v
al
u
e
b
et
w
ee
n
0
a
n
d
1
.
I
f
t
h
e
v
alu
e
is
b
elo
w
th
e
T
(
s
)
th
r
es
h
o
ld
,
th
e
n
o
d
e
w
il
l b
ec
o
m
e
C
H.
T
h
e
th
r
es
h
o
l
d
is
s
et
as:
(
)
=
{
1
−
(
(
1
)
)
0
∈
ℎ
}
(
1
)
w
h
er
e
p
is
th
e
tar
g
et
p
er
ce
n
tag
e
o
f
C
H
n
o
d
es
w
it
h
i
n
th
e
s
e
n
s
o
r
p
o
p
u
latio
n
an
d
r
is
th
e
c
u
r
r
en
t
r
o
u
n
d
n
u
m
b
er
,
w
h
er
e
G
i
s
t
h
e
s
et
o
f
n
o
d
es th
at
w
er
e
n
o
t
C
Hs i
n
t
h
e
la
s
t
1
/p
r
o
u
n
d
s
[
3
,
1
2
,
2
4
]
.
W
ith
all
th
e
ad
v
an
ta
g
es
o
f
t
h
e
L
E
AC
H
p
r
o
to
co
l,
b
y
d
is
p
er
s
i
n
g
t
h
e
cl
u
s
ter
h
ea
d
s
ac
r
o
s
s
t
h
e
n
et
w
o
r
k
,
it
s
u
f
f
er
s
f
r
o
m
t
h
e
d
ev
elo
p
m
e
n
t
o
f
q
u
ali
t
y
cl
u
s
ter
s
.
T
h
er
ef
o
r
e,
th
e
au
th
o
r
s
in
[
1
3
,
19
]
p
r
o
p
o
s
ed
L
E
AC
H
-
C
r
ec
o
m
m
e
n
d
a
ce
n
tr
alize
d
a
p
p
r
o
ac
h
to
s
elec
tin
g
C
Hs
f
o
r
a
n
i
m
p
r
o
v
ed
v
er
s
io
n
o
f
L
E
AC
H.
I
n
L
E
AC
H
-
C
all
n
o
d
es
ar
e
s
e
n
t
to
th
e
b
ase
s
t
atio
n
w
it
h
th
eir
id
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t 2
0
2
1
:
3
2
1
2
-
3221
321
4
en
er
g
y
lev
el,
an
d
GP
S
co
o
r
d
in
ates.
I
t
is
th
e
b
a
s
e
s
tatio
n
's
r
o
le
to
s
elec
t
a
f
e
w
n
o
d
es
as
C
H
b
a
s
ed
o
n
th
eir
en
er
g
y
lev
el.
Fo
r
th
e
cu
r
r
e
n
t
r
o
u
n
d
,
n
o
d
es
w
it
h
en
er
g
y
,
g
r
ea
ter
th
a
n
av
er
a
g
e
en
er
g
y
,
ar
e
s
elec
ted
as
C
H.
T
h
e
m
aj
o
r
d
o
w
n
s
id
e
o
f
th
is
ap
p
r
o
ac
h
is
t
h
at
it d
is
s
ip
ates t
h
e
en
er
g
y
o
f
all
n
o
d
es in
ea
c
h
r
o
u
n
d
f
o
r
th
e
tr
an
s
m
is
s
io
n
o
f
t
h
e
in
f
o
r
m
atio
n
to
B
S.
2
.
2
.
Sta
ble e
lect
io
n pro
t
o
co
l (
SE
P
)
I
t
m
a
k
e
s
L
E
AC
H
in
ter
esti
n
g
t
o
ex
p
er
im
e
n
t
w
it
h
t
h
e
u
s
e
o
f
o
th
er
p
r
o
b
ab
ilit
y
-
b
ased
m
o
d
els
s
u
c
h
as
A
s
tab
le
elec
tio
n
p
r
o
to
co
l
(
SEP
)
[
2
0
]
as
a
m
et
h
o
d
.
T
h
e
SEP
p
r
o
to
co
l
a
d
o
p
ted
th
e
s
i
m
ilar
p
r
o
ce
d
u
r
es
ad
o
p
ted
b
y
th
e
L
E
AC
H
p
r
o
to
co
l
f
o
r
th
e
s
elec
tio
n
o
f
cl
u
s
ter
h
ea
d
s
.
T
h
e
ch
o
ice
p
r
o
ce
s
s
o
f
clu
s
ter
h
e
ad
s
is
b
ased
o
n
th
e
w
ei
g
h
ted
p
r
o
b
ab
ilit
y
o
f
ea
c
h
n
o
d
e
b
ein
g
a
cl
u
s
ter
h
ea
d
[
7
]
.
T
h
e
SEP
p
r
o
to
c
o
l
tak
es
in
to
ac
co
u
n
t
t
h
e
h
eter
o
g
e
n
eities
i
n
ea
ch
s
e
n
s
o
r
n
o
d
e
in
th
e
in
itial
en
er
g
y
q
u
an
tit
y
.
T
h
e
SEP
p
r
o
to
co
l
ca
te
g
o
r
izes
th
e
s
e
n
s
o
r
n
o
d
es
in
to
t
w
o
s
e
ts
ac
co
r
d
in
g
to
th
e
in
itial
e
n
er
g
y
:
ad
v
an
ce
d
n
o
d
es
an
d
r
eg
u
lar
n
o
d
es.
A
d
v
an
ce
d
n
o
d
es
h
a
v
e
in
itial
e
n
er
g
y
t
h
at
is
v
er
y
h
i
g
h
th
an
r
e
g
u
lar
n
o
d
es,
th
e
ad
d
itio
n
al
en
er
g
y
f
ac
to
r
b
et
w
ee
n
ad
v
an
ce
d
an
d
s
ta
n
d
ar
d
n
o
d
es
is
d
en
o
ted
b
y
α
.
T
h
e
a
d
v
an
ce
d
n
o
d
es
ar
e
eq
u
ip
p
ed
w
it
h
(
1
+α
)
m
o
r
e
en
er
g
y
q
u
a
n
tit
y
t
h
an
t
h
e
r
eg
u
lar
n
o
d
es.
T
h
e
ad
v
an
ce
d
n
o
d
es
in
SEP
p
r
o
t
o
co
l
h
av
e
m
o
r
e
p
r
o
b
ab
ilit
y
o
f
b
ein
g
cl
u
s
ter
h
ea
d
th
a
n
r
e
g
u
lar
n
o
d
es.
T
h
e
SEP
u
s
es t
w
o
w
e
ig
h
ted
p
r
o
b
ab
ilit
ies o
f
elec
tio
n
:
O
n
e
f
o
r
r
eg
u
lar
n
o
d
es,
a
n
d
th
e
o
th
er
f
o
r
ad
v
an
ce
d
n
o
d
es
[
1
3
]
.
W
h
er
e
P
normal
is
t
h
e
w
e
ig
h
ted
p
r
o
b
ab
ilit
y
o
f
elec
t
io
n
f
o
r
n
o
r
m
al
n
o
d
es,
an
d
t
h
e
P
adv
is
t
h
e
w
eig
h
ted
p
r
o
b
a
b
ilit
y
o
f
elec
tio
n
f
o
r
th
e
ad
v
an
ce
d
n
o
d
es.
A
cc
o
r
d
in
g
l
y
,
th
e
w
ei
g
h
ted
p
r
o
b
ab
ilit
ies
o
f
th
e
u
s
u
a
l
n
o
d
e
an
d
ad
v
an
ce
d
n
o
d
e
ar
e
g
en
er
ated
a
cc
o
r
d
in
g
l
y
[
1
2
,
2
5
].
=
1
+
(
2
)
=
1
+
∗
(
1
+
)
(
3
)
W
h
er
e
m
is
th
e
p
r
o
p
o
r
tio
n
o
f
ad
v
an
ce
d
n
o
d
es
w
it
h
n
o
d
es
w
i
t
h
α
ti
m
e
s
m
o
r
e
en
er
g
y
t
h
a
n
th
e
n
o
r
m
al
n
o
d
es.
I
n
SEP
p
r
o
to
co
l,
ea
ch
n
o
d
e
ty
p
e
h
as
a
th
r
esh
o
ld
;
T(
snormal
)
is
th
e
n
o
r
m
al
n
o
d
e
th
r
esh
o
ld
an
d
T
(Sadv)
is
th
e
ad
v
a
n
ce
d
n
o
d
e
th
r
es
h
o
ld
.
C
o
n
s
eq
u
en
tl
y
,
t
h
e
n
o
r
m
al
an
d
ad
v
an
ce
d
th
r
es
h
o
ld
f
o
r
eq
u
ati
o
n
o
f
n
o
d
es is
:
-
Fo
r
n
o
r
m
al
n
o
d
es:
T
(
s
n
or
m
a
l
)
=
{
P
no
r
m
al
1
−
P
no
r
m
al
(
r
m
o
d
(
1
P
no
r
m
al
)
)
0
i
f
s
∈
G
′
o
t
her
w
i
s
e
}
(
4
)
w
h
er
e
G’
is
a
s
et
o
f
n
o
r
m
a
l
n
o
d
es
w
h
ich
ca
n
b
ec
o
m
e
C
H
a
n
d
m
i
s
t
h
e
p
r
o
p
o
r
tio
n
o
f
ad
v
an
ce
d
n
o
d
es
w
it
h
α
ti
m
e
s
m
o
r
e
en
er
g
y
t
h
a
n
th
e
n
o
r
m
al
n
o
d
es [
26
].
-
Fo
r
ad
v
an
ce
d
n
o
d
es:
T
(
s
a
dv
)
=
{
P
adv
1
−
P
adv
(
r
m
o
d
(
1
P
adv
)
)
0
i
f
s
∈
G
′′
o
t
her
w
i
s
e
}
(
5)
w
h
er
e
G"
is
a
s
et
o
f
ad
v
an
ce
d
n
o
d
es th
at
h
av
e
n
o
t b
ec
o
m
e
cl
u
s
ter
h
ea
d
s
w
it
h
in
t
h
e
las
t
r
o
u
n
d
.
2
.
3
.
T
he
di
s
t
ribute
d e
nerg
y
ef
f
icient
clus
t
er
ing
pro
t
o
co
l (
DE
E
C)
T
h
e
DE
E
C
p
r
o
to
co
l
is
an
e
n
er
g
y
-
i
n
ten
s
i
v
e,
d
is
tr
ib
u
ted
cl
u
s
ter
i
n
g
p
r
o
to
co
l
f
o
r
th
e
h
et
er
o
g
en
eo
u
s
w
ir
ele
s
s
s
e
n
s
o
r
n
et
w
o
r
k
.
Un
li
k
e
th
e
L
E
AC
H
p
r
o
to
co
l
an
d
th
e
SEP
p
r
o
to
co
l,
th
e
DE
E
C
p
r
o
to
co
l
ad
o
p
ts
an
en
h
a
n
ce
d
m
et
h
o
d
f
o
r
th
e
co
lle
ctio
n
o
f
C
Hs
u
s
ed
b
y
th
e
L
E
AC
H
p
r
o
to
co
l
an
d
th
e
SEP
p
r
o
t
o
co
l,
to
m
ea
s
u
r
e
th
e
p
r
o
b
a
b
ilit
y
o
f
th
e
o
r
ig
in
a
l
an
d
r
esid
u
al
en
er
g
y
le
v
el
o
f
th
e
n
o
d
es
[
8
,
2
7
]
.
T
h
e
C
Hs
ar
e
ch
o
s
en
b
y
a
p
r
o
b
a
b
ilit
y
o
n
th
e
b
asis
o
f
th
e
r
atio
o
f
ea
ch
n
o
d
e's
r
e
m
ai
n
i
n
g
en
er
g
y
to
th
e
av
er
a
g
e
n
et
w
o
r
k
e
n
er
g
y
.
Fo
r
ea
ch
n
o
d
e,
th
e
r
o
u
n
d
n
u
m
b
er
o
f
t
h
e
r
ev
o
l
v
in
g
ep
o
ch
is
d
if
f
er
en
t
d
ep
en
d
in
g
o
n
i
ts
o
r
ig
i
n
al
a
n
d
r
esid
u
a
l
en
er
g
y
.
T
h
e
DE
E
C
p
r
o
to
co
l
a
d
ap
ts
ea
ch
n
o
d
e's
r
o
tatin
g
ep
o
ch
to
its
ca
p
ac
it
y
.
N
o
d
es
ca
r
r
y
i
n
g
h
ig
h
i
n
itial
a
n
d
r
em
ai
n
i
n
g
en
er
g
y
h
av
e
g
r
ea
ter
ch
a
n
ce
s
o
f
b
ei
n
g
C
H
t
h
a
n
lo
w
-
e
n
er
g
y
n
o
d
es
[
2
3
,
2
8
]
.
T
h
is
m
et
h
o
d
allo
w
s
f
o
r
p
r
o
lo
n
g
in
g
t
h
e
n
et
w
o
r
k
l
if
e
ti
m
e
o
f
th
e
DE
E
C
p
r
o
to
co
l.
B
u
t
it
i
s
d
if
f
ic
u
lt to
p
r
o
v
id
e
a
g
lo
b
al
k
n
o
w
led
g
e
o
f
t
h
e
a
v
er
ag
e
e
n
er
g
y
o
f
ea
ch
n
o
d
e's
n
et
w
o
r
k
.
A
cc
o
r
d
in
g
to
(
7
)
,
th
e
DE
E
C
p
r
o
to
co
l
ass
u
m
es
a
n
id
ea
l
v
a
lu
e
f
o
r
th
e
n
et
w
o
r
k
li
f
et
i
m
e
u
s
ed
to
m
ea
s
u
r
e
th
e
r
e
f
er
en
c
e
en
er
g
y
t
h
at
ea
c
h
n
o
d
e
w
ill
ex
ten
d
d
u
r
i
n
g
ea
c
h
r
o
u
n
d
.
T
h
e
DE
E
C
h
as
th
e
d
r
a
w
b
ac
k
th
a
t
th
e
ad
v
a
n
ce
d
n
o
d
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ar
e
s
till
p
en
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d
w
h
e
n
t
h
e
r
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u
al
e
n
er
g
y
i
s
li
m
ited
an
d
w
h
e
n
it
is
eq
u
al
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(
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W
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(
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(
1
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W
h
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R
d
en
o
tes t
h
e
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tal
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o
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er
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(
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.
=
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(
1
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∗
+
∗
∗
∗
(
1
+
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(
8
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W
h
er
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R
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o
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4
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as
d
ev
elo
p
ed
b
y
Du
n
n
i
n
1
9
7
4
an
d
i
m
p
r
o
v
ed
b
y
B
ez
d
ek
in
1
9
8
7
,
h
as
b
ee
n
ex
te
n
s
i
v
el
y
s
t
u
d
ied
an
d
ap
p
lie
d
[
2
9
,
3
0
]
.
FC
M
is
a
clu
s
ter
in
g
alg
o
r
ith
m
th
at
i
s
u
n
s
u
p
er
v
is
ed
as
s
a
m
e
as
th
e
k
-
m
ea
n
s
al
g
o
r
ith
m
w
i
th
th
e
s
a
m
e
cl
u
s
ter
d
i
v
is
io
n
p
u
r
p
o
s
e.
N
o
n
eth
ele
s
s
,
k
-
m
ea
n
s
is
a
h
ar
d
s
et
b
ased
alg
o
r
ith
m
,
an
d
FC
M
i
s
an
al
g
o
r
ith
m
b
ase
d
o
n
th
e
n
o
n
-
cr
is
p
s
ap
p
r
o
ac
h
(
all
in
d
iv
id
u
als
a
r
e
lis
ted
in
t
w
o
g
r
o
u
p
s
:
1
o
r
0
)
[
3
0
-
3
2
]
.
T
h
is
alg
o
r
it
h
m
w
o
r
k
s
b
y
a
s
s
i
g
n
i
n
g
a
f
f
ilia
tio
n
to
ea
c
h
s
e
n
s
o
r
n
o
d
e
th
at
co
r
r
esp
o
n
d
s
to
ea
ch
clu
s
ter
ce
n
ter
.
T
h
is
p
r
o
ce
s
s
is
b
ased
o
n
th
e
d
is
tan
ce
b
et
w
ee
n
t
h
e
clu
s
ter
ce
n
ter
an
d
th
e
s
en
s
o
r
n
o
d
e.
T
h
er
ef
o
r
e,
th
e
clo
s
er
th
e
s
en
s
o
r
n
o
d
e
is
to
th
e
clu
s
ter
ce
n
ter
,
th
e
s
tr
o
n
g
er
its
m
e
m
b
er
s
h
ip
i
n
clu
s
ter
ce
n
ter
is
[
3
0
-
3
3
]
.
T
h
e
FC
M
alg
o
r
ith
m
r
ep
r
esen
t
s
an
iter
ativ
e
o
p
ti
m
izatio
n
al
g
o
r
ith
m
th
at
m
i
n
i
m
izes
t
h
e
f
o
llo
w
in
g
o
b
j
ec
tiv
e
f
u
n
ct
io
n
[
33
]
.
=
∑
∑
‖
−
‖
2
=
1
=
1
(
1
0
)
W
h
er
e
n
is
th
e
n
u
m
b
er
o
f
s
en
s
o
r
n
o
d
es,
c
is
th
e
n
u
m
b
er
o
f
clu
s
ter
s
,
is
th
e
ith
s
e
n
s
o
r
n
o
d
e,
i
s
th
e
j
th
clu
s
ter
ce
n
ter
,
u
ij
m
is
t
h
e
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
o
f
t
h
e
i
th
s
e
n
s
o
r
n
o
d
e
in
th
e
j
th
cl
u
s
ter
,
a
n
d
m
i
s
a
co
n
s
t
an
t
g
r
ea
ter
t
h
an
1
(
ty
p
icall
y
m
=
2
)
.
‖
x
i
−
CH
j
‖
2
r
ep
r
ese
n
ts
th
e
m
ea
s
u
r
e
o
f
th
e
E
u
clid
ea
n
d
is
tan
ce
b
et
w
ee
n
th
e
s
en
s
o
r
n
o
d
e
x
i
an
d
th
e
cl
u
s
ter
ce
n
ter
CH
j
.
T
h
e
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
u
ij
m
an
d
th
e
clu
s
ter
Hea
d
ar
e
d
ef
in
ed
as th
e
(
1
1
)
,
(
1
2
)
.
=
1
∑
(
‖
−
‖
‖
−
‖
)
2
−
1
=
1
(
11)
=
∑
.
=
1
∑
=
1
(
1
2
)
Fo
llo
w
i
n
g
ar
e
th
e
s
tep
s
o
f
th
e
f
u
zz
y
C
-
m
ea
n
s
al
g
o
r
ith
m
a.
I
n
itialize
m
e
m
b
er
s
h
ip
;
b.
Fin
d
t
h
e
f
u
zz
y
ce
n
tr
o
id
f
o
r
j
=
{1
,
2
,
3
.
.
.
c}
;
c.
Up
d
ate
th
e
f
u
zz
y
m
e
m
b
er
s
h
ip
;
d.
R
ep
ea
t step
s
I
I
an
d
I
I
I
u
n
til
(
,
)
is
n
o
lo
n
g
er
d
ec
r
ea
s
i
n
g
.
3.
F
U
Z
Z
Y
C
-
M
E
ANS
B
AS
E
D
H
I
E
RAR
CH
I
CA
L
RO
UT
I
N
G
AP
P
RO
ACH
(
F
-
L
E
A
CH
)
Her
e
w
e
p
r
ese
n
t
t
h
e
o
u
tli
n
e
o
f
a
f
u
zz
y
C
-
m
ea
n
s
b
ased
h
ier
ar
ch
ical
r
o
u
t
in
g
ap
p
r
o
ac
h
(F
-
L
E
AC
H)
.
T
h
e
s
en
s
o
r
n
o
d
es
ar
e
u
n
if
o
r
m
l
y
s
p
r
ea
d
o
v
er
an
ar
ea
o
f
1
0
0
m
2
to
tr
ac
k
th
e
en
v
ir
o
n
m
e
n
t
in
ce
s
s
an
tl
y
.
Se
n
s
o
r
n
o
d
e
s
en
s
in
g
d
ata
i
s
f
o
r
w
ar
d
ed
to
B
S o
u
t
s
id
e
o
f
t
h
e
d
ep
lo
y
m
e
n
t a
r
ea
.
E
ac
h
s
e
n
s
o
r
n
o
d
e
m
a
y
eit
h
er
w
o
r
k
in
s
e
n
s
i
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t 2
0
2
1
:
3
2
1
2
-
3221
3216
m
o
d
e
to
tr
ac
k
t
h
e
p
ar
a
m
eter
s
o
f
th
e
e
n
v
ir
o
n
m
en
t
an
d
tr
an
s
m
it
it
to
th
e
ass
o
ciate
d
C
H
o
r
in
C
H
m
o
d
e
to
co
llect,
co
m
p
r
es
s
,
an
d
s
e
n
d
d
ata
to
th
e
b
ase
s
tatio
n
.
Her
e
f
o
llo
w
s
o
m
e
ad
d
itio
n
al
as a
s
s
u
m
p
t
io
n
s
:
T
h
e
b
ase
s
tatio
n
h
as l
i
m
itles
s
p
o
w
er
an
d
co
m
p
u
ti
n
g
p
o
w
er
a
n
d
is
lo
ca
ted
o
u
ts
id
e
th
e
s
en
s
o
r
ar
ea
.
T
h
e
n
et
w
o
r
k
i
s
h
o
m
o
g
e
n
eo
u
s
an
d
all
s
en
s
o
r
n
o
d
es a
r
e
s
tatic
an
d
h
av
e
t
h
e
s
a
m
e
i
n
itial c
ap
a
cit
y
.
No
d
es h
av
e
t
h
e
ab
ilit
y
to
m
o
n
ito
r
th
e
tr
an
s
m
i
s
s
io
n
p
o
w
er
in
r
elatio
n
to
th
e
d
is
ta
n
ce
o
f
r
ec
e
iv
i
n
g
n
o
d
es.
L
i
n
k
s
ar
e
s
y
m
m
etr
ic
T
h
e
F
-
L
E
A
C
H
u
s
e
t
h
r
ee
alg
o
r
ith
m
s
.
First
l
y
,
i
t
s
tar
t
s
u
s
i
n
g
t
h
e
s
u
b
tr
ac
tiv
e
clu
s
ter
i
n
g
a
p
p
r
o
ac
h
to
ev
alu
a
te
t
h
e
co
r
r
ec
t n
u
m
b
er
o
f
clu
s
ter
s
[
3
2
]
.
T
h
e
FC
M
a
lg
o
r
i
th
m
i
s
t
h
en
i
m
p
le
m
e
n
ted
to
f
o
r
m
h
ig
h
l
y
u
n
if
o
r
m
clu
s
ter
i
n
g
d
is
p
er
s
io
n
o
f
n
o
d
es (
s
eg
m
en
ta
io
n
o
f
n
et
w
o
r
k
)
.
I
n
ea
ch
clu
s
ter
,
th
e
L
E
AC
H
p
r
o
to
co
l
is
u
s
ed
to
b
u
il
d
th
e
s
u
b
-
cl
u
s
ter
s
an
d
ch
o
o
s
e
th
e
h
ea
d
s
u
b
-
cl
u
s
ter
s
an
d
th
eir
m
e
m
b
er
s
,
w
h
ic
h
ap
p
ly
a
p
u
r
e
p
r
o
b
a
b
ilis
tic
m
o
d
el
f
o
r
C
Hs
s
e
lectio
n
a
n
d
p
er
io
d
i
ca
ll
y
r
o
tate
th
e
C
Hs
f
o
r
th
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
b
alan
ce
.
Fig
u
r
e
1
p
r
esen
ts
a
f
lo
w
ch
ar
t
o
f
th
e
F
-
L
E
AC
H
clu
s
ter
f
o
r
m
atio
n
p
r
o
ce
s
s
,
w
h
ich
co
m
b
i
n
es
t
h
e
c
lu
s
ter
i
n
g
o
f
f
u
zz
y
C
-
m
ea
n
s
,
L
E
AC
H,
an
d
th
e
s
u
b
tr
ac
tiv
e
clu
s
ter
in
g
m
eth
o
d
[
3
3
]
.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
t
h
e
clu
s
ter
f
o
r
m
atio
n
p
r
o
ce
s
s
o
f
th
e
F
-
L
E
A
C
H
4.
DIS
T
RIB
U
T
E
D
E
NE
R
G
Y
E
F
F
I
CI
E
NT
C
L
US
T
E
RI
NG
AL
G
O
R
I
T
H
M
B
ASE
D
O
N
F
U
Z
Z
Y
L
O
G
I
C
AP
P
RO
ACH
(
F
-
DE
E
C)
I
n
t
h
is
s
ec
tio
n
,
w
e
s
h
o
w
th
e
d
is
tr
ib
u
ted
e
n
er
g
y
e
f
f
icien
t
cl
u
s
t
er
in
g
alg
o
r
it
h
m
s
y
s
te
m
b
ased
o
n
a
f
u
zz
y
lo
g
ic
ap
p
r
o
ac
h
(F
-
DE
E
C
)
,
w
h
i
ch
f
o
c
u
s
e
s
o
n
t
h
r
ee
ap
p
r
o
ac
h
es
to
d
ea
l
w
ith
th
e
W
SN
e
n
er
g
y
co
n
s
er
v
at
io
n
is
s
u
e
[
2
6
]
.
T
h
e
s
en
s
o
r
n
o
d
es
ar
e
r
an
d
o
m
l
y
d
is
tr
ib
u
ted
i
n
t
h
e
ap
p
licatio
n
ar
ea
s
o
a
s
to
m
o
n
it
o
r
th
e
e
n
v
ir
o
n
m
e
n
t
in
ce
s
s
an
t
l
y
.
Her
e
f
o
llo
w
s
o
m
e
ad
d
itio
n
al
as
ass
u
m
p
tio
n
s
:
T
h
e
b
ase
s
tatio
n
h
as
li
m
itle
s
s
p
o
w
er
an
d
co
m
p
u
ti
n
g
p
o
w
er
.
A
ll
s
e
n
s
o
r
n
o
d
es
ar
e
s
tatic
an
d
h
a
v
e
th
e
s
a
m
e
i
n
itia
l
ca
p
ac
ity
.
No
d
es
h
a
v
e
t
h
e
ab
ilit
y
to
m
o
n
ito
r
th
e
tr
an
s
m
is
s
io
n
p
o
w
er
i
n
r
elatio
n
to
th
e
d
is
tan
ce
o
f
r
ec
eiv
in
g
n
o
d
es.
T
h
e
F
-
DE
E
C
m
o
d
e
l
is
b
ased
o
n
th
r
ee
alg
o
r
ith
m
s
.
Firstl
y
,
it
s
tar
t
s
to
u
s
e
t
h
e
s
u
b
tr
ac
ti
v
e
cl
u
s
ter
i
n
g
ap
p
r
o
ac
h
f
o
r
an
o
p
ti
m
al
n
u
m
b
er
o
f
w
id
e
clu
s
ter
s
.
Seco
n
d
l
y
,
t
h
e
r
o
le
o
f
th
e
ap
p
li
ca
tio
n
o
f
t
h
e
F
C
M
alg
o
r
it
h
m
i
s
to
f
o
r
m
h
i
g
h
l
y
u
n
i
f
o
r
m
cl
u
s
t
er
in
g
o
f
n
o
d
es
b
ased
o
n
an
o
p
ti
m
al
n
u
m
b
er
g
en
er
at
ed
b
y
t
h
e
s
u
b
tr
ac
tiv
e
cl
u
s
ter
in
g
m
e
th
o
d
.
I
n
ea
ch
cl
u
s
ter
,
th
e
DE
E
C
p
r
o
to
co
l
is
u
s
ed
f
o
r
t
h
e
co
n
s
tr
u
ctio
n
o
f
th
e
clu
s
ter
s
a
n
d
to
th
e
s
elec
tio
n
o
f
th
e
C
Hs a
n
d
th
eir
m
e
m
b
er
s
.
T
o
b
alan
ce
en
er
g
y
co
n
s
u
m
p
tio
n
,
it
u
s
e
s
a
p
u
r
e
p
r
o
b
ab
ilis
tic
m
o
d
el
to
s
elec
t
C
Hs
an
d
to
r
o
tate
th
e
C
Hs
p
er
io
d
icall
y
.
Fig
u
r
e
2
d
em
o
n
s
tr
ate
s
th
e
f
lo
w
c
h
ar
t o
f
th
e
F
-
DE
E
C
cl
u
s
ter
f
o
r
m
atio
n
p
r
o
ce
s
s
[
2
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f h
ie
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l c
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p
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w
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-
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(
Ha
mid
B
a
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ko
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k
)
3217
Fig
u
r
e
2
.
Flo
w
c
h
ar
t o
f
t
h
e
clu
s
ter
f
o
r
m
atio
n
p
r
o
ce
s
s
o
f
th
e
F
-
DE
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C
5.
SI
M
UL
AT
I
O
N
A
ND
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V
AL
UATI
O
N
T
o
ass
ess
th
e
F
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AC
H
a
n
d
F
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DE
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en
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g
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ef
f
icie
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c
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,
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co
m
p
ar
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th
e
m
a
in
p
er
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o
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ce
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f
t
h
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F
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L
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AC
H
a
n
d
F
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DE
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C
w
it
h
th
e
DE
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C
p
r
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l
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d
th
e
L
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p
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l.
A
ll
s
i
m
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latio
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s
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er
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ca
r
r
ied
o
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t
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T
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A
B
to
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alu
ate
t
h
e
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tire
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y
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te
m
a
n
d
th
e
n
u
m
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es
aliv
e.
W
e
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eg
an
b
y
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p
lain
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th
e
m
etr
ics u
s
ed
i
n
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i
m
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s
.
W
e
in
c
lu
d
e
t
h
e
d
etails
o
f
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al
g
o
r
ith
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ed
,
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n
d
th
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n
p
r
esen
t
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d
a
n
al
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ze
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th
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r
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r
s
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m
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,
1
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e
ass
u
m
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ib
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(
1
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)
m
; th
e
B
S is
p
o
s
itio
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ed
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ts
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e
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e
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at
th
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co
o
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d
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ate
(
-
5
0
,
5
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.
T
h
e
n
o
d
es d
o
n
o
t h
av
e
th
e
s
a
m
e
in
itial
ca
p
ac
it
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,
a
n
d
it
is
p
r
es
u
m
ed
t
h
at
t
h
e
b
ase
s
tatio
n
h
a
s
li
m
itle
s
s
p
o
w
er
.
T
h
r
o
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g
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o
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t
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ch
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n
d
,
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ch
n
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e
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en
d
s
.
4
0
0
0
-
b
it p
ac
k
ets to
th
e
b
a
s
e
s
tatio
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v
ia
th
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ea
d
o
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t
h
e
clu
s
ter
.
A
ll
th
e
r
esu
lts
ar
e
d
i
s
p
la
y
ed
i
n
Fi
g
u
r
es
3
-
7
.
F
ig
u
r
e
3
s
h
o
ws
th
e
n
u
m
b
er
o
f
d
ea
d
n
o
d
es
v
er
s
u
s
t
h
e
tr
an
s
m
is
s
io
n
s
er
ies.
T
h
e
b
lu
e
-
co
lo
r
ed
cu
r
v
e
s
h
o
w
s
t
h
e
r
es
u
lts
o
b
tain
ed
b
y
u
s
in
g
t
h
e
L
E
AC
H
p
r
o
to
co
l.
T
h
e
r
esu
lt
s
o
f
th
e
DE
E
C
p
r
o
to
co
l
s
h
o
w
t
h
e
r
ed
-
co
lo
r
ed
cu
r
v
e;
th
e
y
ello
w
-
co
lo
r
ed
cu
r
v
e
s
h
o
w
s
t
h
e
r
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lt
s
o
f
F
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L
E
AC
H
an
d
th
e
g
r
ee
n
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lo
r
s
h
o
w
s
t
h
e
r
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lt
s
o
f
F
-
DE
E
C
p
r
o
to
co
l.
Fig
u
r
e
4
s
h
o
w
s
th
e
n
u
m
b
er
o
f
liv
e
n
o
d
es
v
er
s
u
s
th
e
tr
an
s
m
is
s
io
n
r
o
u
n
d
.
T
h
is
f
i
g
u
r
e
h
as
4
cu
r
v
es:
T
h
e
r
ed
cu
r
v
e
s
h
o
w
s
th
e
r
esu
lts
o
b
tain
ed
in
th
e
ca
s
e
w
h
er
e
th
e
DE
E
C
p
r
o
to
co
l
is
ex
ec
u
ted
;
t
h
e
r
esu
lts
o
f
t
h
e
L
E
AC
H
p
r
o
t
o
c
o
l
s
h
o
w
n
b
y
t
h
e
b
l
u
e
c
u
r
v
e
.
T
h
e
y
e
l
l
o
w
c
u
r
v
e
s
h
o
w
s
t
h
e
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s
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l
t
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o
b
t
a
i
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d
b
y
F
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L
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A
C
H
a
n
d
t
h
e
g
r
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e
n
c
o
l
o
r
s
h
o
w
s
F
-
DE
E
C
p
r
o
to
co
l.
A
cc
o
r
d
in
g
to
Fig
u
r
es 3
an
d
4
,
w
e
ca
n
o
b
s
er
v
e
th
at
p
r
o
to
co
l p
r
o
lo
n
g
s
t
h
e
s
tab
ilit
y
p
er
io
d
co
m
p
ar
ed
to
F
-
L
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AC
H,
DE
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C
an
d
L
E
AC
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p
r
o
to
co
l.
I
n
ad
d
itio
n
to
th
at,
th
e
F
-
L
E
AC
H
g
iv
e
s
b
etter
r
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i
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s
tab
ilit
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p
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io
d
th
an
th
e
DE
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d
L
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AC
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A
cc
o
r
d
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to
Fig
u
r
es 3
an
d
4
,
w
e
ca
n
n
o
te
th
at
F
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DE
E
C
p
r
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to
co
l
ex
ten
d
s
th
e
s
tab
ili
t
y
d
u
r
atio
n
co
m
p
ar
ed
to
p
r
o
to
co
l
F
-
L
E
AC
H,
D
E
E
C
an
d
L
E
AC
H.
F
u
r
t
h
er
m
o
r
e,
th
e
F
-
L
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A
C
H
p
r
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etter
r
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n
s
tab
il
it
y
c
y
c
le
th
a
n
t
h
e
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C
an
d
L
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H.
T
h
e
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ir
s
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d
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e
f
o
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L
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p
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F
-
L
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p
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d
F
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p
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is
at
8
2
8
r
o
u
n
d
s
,
9
6
2
r
o
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n
d
s
,
1
6
3
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o
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n
d
s
,
1
8
9
6
r
o
u
n
d
s
r
esp
ec
ti
v
el
y
.
Sta
tis
t
ics
s
h
o
w
t
h
at
th
e
F
-
L
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A
C
H
p
r
o
to
co
l
an
d
th
e
F
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DE
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C
p
r
o
to
co
l
ar
e
b
etter
th
a
n
th
e
L
E
AC
H
p
r
o
to
co
l
an
d
DE
E
C
p
r
o
to
c
o
l
to
in
cr
ea
s
e
th
e
p
ar
a
m
eter
s
tab
ili
t
y
d
u
r
atio
n
.
T
h
e
last
d
ea
d
n
o
d
e
t
o
L
E
AC
H
p
r
o
to
co
l
is
at
2
7
2
0
r
o
u
n
d
s
.
Fo
r
DE
E
C
p
r
o
to
co
l,
o
n
e
n
o
d
e
is
liv
e
at
4
0
0
0
r
o
u
n
d
s
,
2
4
liv
in
g
n
o
d
es,
5
3
liv
i
n
g
n
o
d
es
f
o
r
DE
E
C
p
r
o
to
co
l,
r
esp
ec
tiv
el
y
F
-
L
E
AC
H
p
r
o
to
co
l
an
d
F
-
DE
E
C
p
r
o
to
co
l
.
T
h
er
ef
o
r
e,
in
th
e
in
s
tab
il
it
y
p
e
r
io
d
,
th
e
F
-
DE
E
C
p
r
o
to
co
l
p
r
o
v
es
to
b
e
m
o
r
e
en
er
g
y
-
e
f
f
icie
n
t
th
a
n
th
e
F
-
L
E
AC
H
p
r
o
to
co
l,
th
e
DE
E
C
p
r
o
to
co
l a
n
d
th
e
L
E
A
C
H
p
r
o
to
co
l.
W
ith
r
esp
ec
t
to
th
e
n
u
m
b
er
o
f
r
o
u
n
d
s
as
s
ee
n
i
n
Fi
g
u
r
e
4
,
w
e
h
av
e
es
ti
m
ated
th
e
n
et
w
o
r
k
lif
eti
m
e
i
n
ter
m
s
o
f
a
n
u
m
b
er
o
f
li
v
i
n
g
n
o
d
es.
T
h
e
aliv
e
n
o
d
es
ar
e
th
o
s
e
w
it
h
th
e
ir
r
eso
u
r
ce
s
a
s
n
o
t
n
u
l
l.
I
n
t
h
e
o
r
ig
i
n
al
L
E
AC
H
p
r
o
to
co
l,
all
n
o
d
es
i
n
th
e
n
et
w
o
r
k
ar
e
d
ea
d
f
o
r
th
e
2
7
2
0
th
r
o
u
n
d
.
B
u
t
f
o
r
t
h
e
DE
E
C
p
r
o
to
co
l
o
n
e
n
o
d
e
is
ali
v
e
af
ter
th
e
4
0
0
0
th
r
o
u
n
d
.
I
n
th
e
F
-
L
E
AC
H
p
r
o
to
co
l,
th
e
f
i
r
s
t
n
o
d
es
d
ie
in
1
6
3
0
th
r
o
u
n
d
s
an
d
af
ter
4
0
0
0
th
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t 2
0
2
1
:
3
2
1
2
-
3221
3218
r
o
u
n
d
s
th
e
2
4
n
o
d
es
ar
e
aliv
e.
T
h
e
f
ir
s
t
n
o
d
es
in
t
h
e
F
-
DE
E
C
p
r
o
to
c
o
l
d
ie
in
r
o
u
n
d
s
1
8
9
6
a
n
d
th
e
5
3
n
o
d
es
ar
e
aliv
e
a
f
ter
r
o
u
n
d
s
4
0
0
0
.
Am
o
n
g
all
t
h
e
s
e,
th
e
F
-
DE
E
C
p
r
o
to
co
l
o
f
f
er
s
th
e
lo
n
g
e
s
t
li
f
eti
m
e
o
f
th
e
n
e
t
w
o
r
k
th
a
t
allo
w
s
a
b
alan
ce
d
n
et
w
o
r
k
o
f
l
o
ad
s
to
b
e
r
ea
ch
ed
.
T
h
e
r
esu
lts
o
b
tain
ed
in
Fig
u
r
es 4
an
d
5
i
n
d
icate
d
th
at
th
e
F
-
DE
E
C
p
r
o
to
co
l
an
d
th
e
F
-
L
E
AC
H
p
r
o
to
co
l
s
u
p
p
o
r
te
d
W
SN
w
it
h
th
e
lo
n
g
est
li
f
eti
m
e
co
m
p
ar
ed
t
o
th
e
L
E
AC
H
p
r
o
to
co
l
an
d
DE
E
C
p
r
o
to
co
l.
I
t
c
an
b
e
o
b
s
er
v
ed
th
at
p
r
o
t
o
c
o
l
F
-
DE
E
C
an
d
p
r
o
to
co
l
F
-
L
E
AC
H
in
cr
ea
s
e
t
h
e
s
tab
ilit
y
d
u
r
atio
n
r
elati
v
e
to
p
r
o
to
co
l
L
E
AC
H
an
d
p
r
o
to
co
l
DE
E
C
.
T
h
e
F
-
DE
E
C
p
r
o
to
co
l
an
d
th
e
F
-
L
E
AC
H
p
r
o
to
co
l
p
r
o
v
e
th
er
ef
o
r
e
to
b
e
m
o
r
e
e
n
er
g
y
-
ef
f
icie
n
t t
h
an
t
h
e
DE
E
C
p
r
o
to
co
l a
n
d
th
e
L
E
AC
H
p
r
o
to
co
l.
Fig
u
r
e
3
.
Nu
m
b
er
o
f
d
ea
d
n
o
d
es v
er
s
u
s
tr
a
n
s
m
is
s
io
n
r
o
u
n
d
Fig
u
r
e
4
.
Nu
m
b
er
o
f
ali
v
e
n
o
d
es v
er
s
u
s
tr
a
n
s
m
is
s
io
n
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6
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.
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to
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5
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ir
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ates
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it
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f
f
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,
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b
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y
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d
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b
alan
ci
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g
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s
tab
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RE
F
E
R
E
NC
E
S
[1
]
No
u
re
d
d
in
e
,
S
e
d
d
ik
i,
Kh
e
li
f
a
,
Be
n
a
h
m
e
d
,
M
o
h
a
m
m
e
d
,
Be
lg
a
c
h
i,
“
A
p
p
ro
a
c
h
to
m
in
im
izin
g
c
o
n
su
m
p
ti
o
n
o
f
e
n
e
rg
y
in
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
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
te
r
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
3
,
p
p
.
2
5
5
1
-
2
5
6
1
,
2
0
2
0
.
[2
]
Ik
ra
m
D
.
,
A
b
d
e
n
n
a
c
e
u
r
B
.
,
A
b
d
e
lh
a
k
i
m
B.
,
“
A
n
e
n
h
a
n
c
e
d
e
n
e
r
g
y
-
e
ff
icie
n
t
ro
u
ti
n
g
p
r
o
to
c
o
l
f
o
r
w
irele
ss
se
n
so
r
n
e
tw
o
rk
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
&
Co
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
),
v
o
l.
1
0
,
n
o
.
5
,
p
p
.
5
4
6
2
-
5
4
6
9
,
2
0
2
0
.
[3
]
Ku
n
d
a
li
y
a
,
Brij
e
sh
,
Ha
d
ia,
S
.
K.
,
“
En
h
a
n
c
in
g
n
e
tw
o
rk
li
f
e
ti
m
e
with
a
n
im
p
ro
v
e
d
M
OD
-
L
E
A
CH,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
9
,
n
o
5
,
p
p
.
3
6
1
5
-
3
6
2
2
,
2
0
1
9
.
[4
]
S
a
v
it
h
a
,
S
.
,
L
in
g
a
re
d
d
y
,
S
.
C.
,
e
t
Ch
it
n
is,
S
a
n
jay
,
“
En
e
rg
y
e
ff
icie
n
t
c
lu
ste
rin
g
a
n
d
r
o
u
t
in
g
o
p
ti
m
iz
a
ti
o
n
m
o
d
e
l
f
o
r
m
a
x
i
m
izin
g
li
fe
ti
m
e
o
f
w
irel
e
ss
se
n
so
r
n
e
tw
o
rk
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
t
e
r
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
5
,
p
p
.
4
7
9
8
-
4
8
0
8
,
2
0
2
0
.
[5
]
M
o
h
a
m
m
e
d
,
S
a
m
a
r
Aw
a
d
,
A
b
d
El
sa
la
m
A
l
y
,
Kh
a
led
,
G
h
u
n
iem
,
A
te
f
M
o
h
a
m
m
e
d
,
“
A
n
En
h
a
n
c
e
m
e
n
t
P
r
o
c
e
ss
f
o
r
Re
d
u
c
in
g
E
n
e
rg
y
Co
n
su
m
p
ti
o
n
in
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Eme
r
g
in
g
T
re
n
d
s
i
n
En
g
i
n
e
e
rin
g
Res
e
a
rc
h
(
IJ
ET
ER
),
v
o
l.
8
,
n
o
6
,
p
p
.
2
7
6
5
-
2
7
6
9
,
2
0
2
0
.
[6
]
V
im
a
la
M
.
a
n
d
Ra
jee
v
Ra
n
jan
,
“
En
e
rg
y
e
ff
ici
e
n
t
c
lu
ste
rin
g
u
si
n
g
th
e
A
M
HC
(a
d
o
p
ti
v
e
m
u
lt
i
-
h
o
p
c
l
u
ste
rin
g
)
tec
h
n
iq
u
e
,
”
In
t
.
J
o
u
rn
a
l
o
f
El
e
c
tri
c
a
l
&
Co
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
),
v
o
l.
1
0
,
n
o
.
2
,
p
p
.
1
6
2
2
-
1
6
3
1
,
2
0
2
0
.
[7
]
V
e
h
b
i
Ça
ğ
rı
G
ü
n
g
ö
r
a
n
d
G
e
rh
a
rd
P
.
Ha
n
c
k
e
,
“
In
d
u
strial
W
irele
s
s
S
e
n
so
r
Ne
tw
o
rk
s:
A
p
p
li
c
a
ti
o
n
s
,
P
r
o
to
c
o
ls,
a
n
d
S
tan
d
a
rd
s,”
C
RC
Pre
ss
,
v
o
l.
8
3
,
p
p
.
1
0
2
7
-
1
0
4
0
,
2
0
1
3
.
[8
]
A
z
i
z
M
a
h
b
o
u
b
,
M
o
u
n
ir
A
rio
u
a
,
EL
M
o
k
h
tar
E
n
-
n
a
im
i
a
n
d
Im
a
d
Ezz
a
z
i,
“
P
e
rf
o
rm
a
n
c
e
Ev
o
lu
ti
o
n
o
f
En
e
rg
y
-
Eff
icie
n
t
Clu
ste
rin
g
A
l
g
o
rit
h
m
si
n
W
irele
ss
S
e
n
so
r
Ne
t
w
o
rk
,
”
J
o
u
rn
a
l
o
f
T
h
e
o
re
ti
c
a
l
a
n
d
Ap
p
li
e
d
In
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
(
J
AT
IT
)
,
v
o
l
.
8
3
,
n
o
.
2
,
p
p
.
1
8
7
-
1
9
4
,
2
0
1
6
.
[9
]
W
.
H.
He
in
z
e
l
m
a
n
,
A
.
Ch
a
n
d
ra
k
a
sa
n
a
n
d
H.
Ba
lak
rish
a
m
,
“
En
e
rg
y
-
e
ff
icie
n
t
c
o
m
m
u
n
ica
ti
o
n
p
r
o
to
c
o
l
f
o
r
w
irel
e
ss
m
icro
se
n
so
r
n
e
tw
o
r
k
s,”
Pro
c
.
o
f
t
h
e
3
3
r
d
A
n
n
u
a
l
Ha
wa
ii
In
t
.
C
o
n
f
.
o
n
S
y
ste
m S
c
ien
c
e
s
,
M
a
u
i
,
HI,
U
S
A
,
2
0
0
0
.
[1
0
]
A
z
i
z
M
a
h
b
o
u
b
,
M
o
u
n
ir
A
rio
u
a
,
EL
M
o
k
h
tar
E
n
-
Na
im
i
a
n
d
Im
a
d
Ezz
a
z
i,
“
M
u
lt
i
-
z
o
n
a
l
a
p
p
r
o
a
c
h
f
o
r
c
lu
ste
re
d
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,”
In
t
.
Co
n
f
.
o
n
El
e
c
trica
l
a
n
d
I
n
fo
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
ies
(
ICEIT
),
2
0
1
6
,
p
p
.
2
1
9
-
2
2
4
.
[1
1
]
P
ra
d
e
e
p
,
J.
P
.
M
a
h
e
s
Ku
m
a
r,
M
u
m
m
o
o
rth
y
,
“
Distrib
u
ted
En
t
r
o
p
y
En
e
rg
y
-
E
ff
icie
n
t
Clu
ste
rin
g
A
l
g
o
rit
h
m
F
o
r
He
tero
g
e
n
e
o
u
s
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
Ba
se
d
Ch
a
o
ti
c
F
iref
l
y
A
l
g
o
rit
h
m
Clu
ste
r
He
a
d
S
e
lec
ti
o
n
,
”
J
o
u
rn
a
l
o
f
Criti
c
a
l
Rev
iews
,
v
o
l.
7
,
n
o
8
,
p
p
.
1
2
0
8
-
1
2
1
5
,
2
0
2
0
.
[1
2
]
Ne
h
ra
,
V
i
b
h
a
,
S
h
a
rm
a
,
A
ja
y
K.,
T
rip
a
th
i,
Ra
ji
v
K.,
“
I
-
d
e
e
c
:
im
p
ro
v
e
d
d
e
e
c
f
o
r
b
lan
k
e
t
c
o
v
e
ra
g
e
i
n
h
e
tero
g
e
n
e
o
u
s
w
irele
ss
se
n
so
r
n
e
t
w
o
rk
s,”
J
o
u
rn
a
l
o
f
Amb
ie
n
t
In
telli
g
e
n
c
e
a
n
d
Hu
m
a
n
ize
d
C
o
mp
u
ti
n
g
,
v
o
l.
1
1
,
n
o
2
,
p
p
.
3
6
8
7
-
3
6
9
8
,
2
0
2
0
.
[1
3
]
S
y
e
d
Um
a
r,
Ye
rra
g
u
d
ip
a
d
u
S
u
b
b
a
ra
y
u
d
u
,
K.
Kira
n
Ku
m
a
r
a
n
d
N.
Ba
sh
w
a
n
th
,
“
De
sig
n
in
g
o
f
Dy
n
a
m
ic
Re
-
c
lu
ste
rin
g
L
e
a
c
h
P
ro
t
o
c
o
l
f
o
r
Ca
lcu
lati
n
g
T
o
tal
Re
sid
u
a
l
T
im
e
a
n
d
P
e
rf
o
rm
a
n
c
e
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
ter
E
n
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
3
,
p
p
.
1
2
8
6
-
1
2
9
2
,
2
0
1
7
.
[1
4
]
I
.
F
.
A
k
y
il
d
iz,
“
A
S
u
rv
e
y
o
n
S
e
n
so
r
Ne
tw
o
rk
s,”
IEE
E
Co
mm
u
.
M
a
g
a
zin
e
,
v
o
l.
4
0
,
n
o
.
8
,
p
p
.
1
0
2
-
1
1
4
,
2
0
0
2
.
[1
5
]
EF
F
A
H,
Emm
a
n
u
e
l
T
HI
A
RE,
Ou
sm
a
n
e
,
“
Re
a
li
stic
Clu
ste
r
-
Ba
se
d
En
e
rg
y
-
E
ff
icie
n
t
a
n
d
F
a
u
lt
-
T
o
lera
n
t
(RCEE
F
T
)
Ro
u
ti
n
g
P
r
o
to
c
o
l
f
o
r
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s
(W
S
Ns
),
”
Fu
t
u
re
o
f
I
n
f
o
rm
a
ti
o
n
a
n
d
Co
mm
u
n
ica
t
i
o
n
Co
n
fer
e
n
c
e
,
v
o
l.
1
1
2
9
,
p
p
.
3
2
0
-
3
3
7
,
2
0
2
0
.
[1
6
]
T
.
V
e
lm
u
ru
g
a
n
,
“
P
e
rf
o
rm
a
n
c
e
b
a
se
d
a
n
a
ly
sis
b
e
t
w
e
e
n
k
-
M
e
a
n
s
a
n
d
F
u
z
z
y
C
-
M
e
a
n
s
c
lu
ste
r
in
g
a
lg
o
rit
h
m
s
f
o
r
c
o
n
n
e
c
ti
o
n
o
rien
ted
tele
c
o
m
m
u
n
ica
ti
o
n
d
a
ta,”
A
p
p
li
e
d
S
o
f
t
Co
m
p
u
t
in
g
,
v
o
l.
1
9
,
p
p
.
1
3
4
-
1
4
6
,
2
0
1
4
[1
7
]
Ra
d
h
ik
a
,
M
.
S
iv
a
k
u
m
a
r,
P
.
,
“
En
e
r
g
y
o
p
ti
m
iz
e
d
m
i
c
ro
g
e
n
e
ti
c
a
lg
o
ri
th
m
b
a
se
d
L
E
A
C
H
p
ro
to
c
o
l
f
o
r
W
S
N,”
W
ir
e
les
s
Ne
two
rk
s
,
v
o
l.
2
7
,
p
p
.
2
7
-
4
0
,
2
0
2
0
[1
8
]
Ja
y
a
r
a
jan
,
P
.
,
Ka
n
a
g
a
c
h
id
a
m
b
a
re
sa
n
,
G
.
R.
,
S
u
n
d
a
ra
ra
jan
,
T
.
V
.
P
.
e
t
a
l
.
,
“
A
n
e
n
e
rg
y
-
a
wa
re
b
u
f
fe
r
m
a
n
a
g
e
m
e
n
t
(EA
BM
)
ro
u
ti
n
g
p
ro
t
o
c
o
l
f
o
r
W
S
N,”
T
h
e
J
o
u
r
n
a
l
o
f
S
u
p
e
rc
o
mp
u
ti
n
g
,
v
o
l.
7
6
,
n
o
6
,
p
p
.
4
5
4
3
-
4
5
5
5
,
2
0
2
0
.
[1
9
]
K.
Ka
p
it
a
n
o
v
a
,
S
.
H.
S
o
n
,
K.
-
D.
Ka
n
g
,
“
U
sin
g
f
u
z
z
y
lo
g
ic
f
o
r
ro
b
u
st
e
v
e
n
t
d
e
tec
ti
o
n
in
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,”
Ad
Ho
c
Ne
tw
o
rk
s
,
v
o
l.
1
0
,
n
o
.
4
,
p
p
.
7
0
9
-
7
2
2
,
2
0
1
2
.
[2
0
]
G
e
o
rg
io
s
S
m
a
r
a
g
d
a
k
is,
Ib
ra
h
im
M
a
tt
a
a
n
d
A
z
e
r
Be
sta
v
ro
s,
“
S
E
P
:
A
S
tab
le
El
e
c
ti
o
n
P
r
o
to
c
o
l
f
o
r
c
lu
ste
re
d
h
e
tero
g
e
n
e
o
u
s w
irele
ss
se
n
s
o
r
n
e
tw
o
rk
s,”
T
e
c
h
n
ica
l
Rep
o
rt
BUCS
-
TR
-
2
0
0
4
-
0
2
2
,
2
0
0
4
.
[2
1
]
S
.
D.
M
u
r
u
g
a
n
a
th
a
n
,
D.
C.
M
a
,
R.
I.
Bh
a
sin
,
a
n
d
A
.
O.
F
a
p
o
ju
w
o
,
“
A
c
e
n
tralize
d
e
n
e
rg
y
-
e
ff
icie
n
t
ro
u
ti
n
g
p
ro
t
o
c
o
l
f
o
r
w
irel
e
ss
se
n
so
r
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e
tw
o
rk
s,”
IE
EE
Co
mm
u
n
ica
ti
o
n
s M
a
g
a
zi
n
e
,
v
o
l.
4
3
,
n
o
.
3
,
p
p
.
S
8
-
1
3
,
2
0
0
5
.
[2
2
]
L
i
Qin
g
,
Qin
g
x
in
Zh
u
,
M
in
g
we
n
W
a
n
g
,
“
De
sig
n
o
f
a
d
istri
b
u
ted
e
n
e
rg
y
-
e
ff
icie
n
t
c
lu
ste
rin
g
a
lg
o
rit
h
m
f
o
r
h
e
tero
g
e
n
e
o
u
s w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,”
Co
mp
u
ter
Co
mm
u
n
ic
a
ti
o
n
s
,
v
o
l.
2
9
,
n
o
.
1
2
,
p
p
.
2
2
3
0
-
2
2
3
7
,
2
0
0
6
.
[2
3
]
P
.
Ty
a
g
i,
R
.
P
.
G
u
p
ta,
R
.
K
.
G
il
l,
“
Co
m
p
a
ra
ti
v
e
A
n
a
l
y
sis
o
f
Clu
ste
r
Ba
se
d
Ro
u
ti
n
g
P
r
o
t
o
c
o
ls
u
se
d
i
n
He
tero
g
e
n
e
o
u
s
W
irele
ss
S
e
n
so
r
Ne
t
w
o
rk
,
”
In
t
.
J
.
o
f
S
o
ft
Co
m
p
u
ti
n
g
a
n
d
En
g
in
e
e
ri
n
g
(
IJ
S
CE),
v
o
l
.
1
,
n
o
.
5
,
p
p
.
3
6
2
-
3
6
6
,
2
0
1
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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3221
[2
4
]
D.
G
o
y
a
let
a
n
d
M
.
R.
T
rip
a
t
h
y
,
“
Ro
u
ti
n
g
P
ro
t
o
c
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in
W
irele
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e
y
,
p
p
.
4
7
4
4
8
0
,
2
0
1
2
.
[2
5
]
A
z
i
z
M
a
h
b
o
u
b
,
M
o
u
n
irA
rio
u
a
,
E
L
M
o
k
h
tar
E
n
-
n
a
im
i,
Im
a
d
Ezz
a
z
i
a
n
d
A
h
m
e
d
El
Ou
a
lk
a
d
,
“
M
u
lt
i
-
z
o
n
a
l
a
p
p
ro
a
c
h
Clu
ste
rin
g
b
a
se
d
o
n
S
tab
le
El
e
c
ti
o
n
P
r
o
to
c
o
l
in
He
tero
g
e
n
e
o
u
s
W
irele
s
s
S
e
n
so
r
Ne
tw
o
rk
s,”
2
0
1
6
4
th
IEE
E
In
ter
n
a
t
io
n
a
l
C
o
ll
o
q
u
iu
m
o
n
In
f
o
rm
a
ti
o
n
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
(
CiS
t)
,
T
a
n
g
ier,
M
o
ro
c
c
o
,
2
0
1
6
,
p
p
.
9
1
2
-
9
1
7
.
[2
6
]
A
z
i
z
M
a
h
b
o
u
b
,
E
n
-
Na
im
i
El
M
o
k
h
tar,
M
o
u
n
ir
A
rio
u
a
,
Ha
m
id
Ba
rk
o
u
k
,
Yo
u
n
e
s
e
l
A
ss
a
ri,
A
h
m
e
d
El
Ou
a
lk
a
d
i,
“
A
n
e
n
e
rg
y
-
e
ff
ici
e
n
t
c
lu
ste
ri
n
g
p
r
o
to
c
o
l
u
sin
g
f
u
z
z
y
lo
g
ic
a
n
d
n
e
t
w
o
rk
se
g
m
e
n
tatio
n
f
o
r
h
e
tero
g
e
n
e
o
u
s
W
S
N,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
9
,
n
o
5
,
p
p
.
4
1
9
2
-
4
2
0
3
,
2
0
1
9
.
[2
7
]
S
.
S
in
g
h
,
A
.
M
a
li
k
,
R.
Ku
m
a
r,
“
En
e
rg
y
e
ff
icie
n
t
h
e
tero
g
e
n
e
o
u
s
DEEC
p
r
o
t
o
c
o
l
f
o
r
e
n
h
a
n
c
in
g
l
if
e
ti
m
e
in
W
S
Ns
,
”
En
g
i
n
e
e
rin
g
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
a
n
I
n
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
,
v
o
l.
2
0
,
n
o
.
1
,
p
p
.
3
4
5
-
3
5
3
,
2
0
1
7
.
[2
8
]
A
z
i
z
M
a
h
b
o
u
b
,
M
o
u
n
irA
rio
u
a
,
E
L
M
o
k
h
tar
En
-
n
a
im
i
a
n
d
Ha
m
id
b
a
rk
o
u
k
,
“
Distrib
u
ted
e
n
e
rg
y
e
ff
i
c
ien
t
c
lu
ste
rin
g
a
lg
o
rit
h
m
b
a
se
d
o
n
f
u
z
z
y
lo
g
ic
a
p
p
r
o
a
c
h
a
p
p
l
ied
f
o
r
h
e
tero
g
e
n
e
o
u
s
W
S
N,”
Pro
c
e
e
d
in
g
s
o
f
th
e
2
n
d
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
m
p
u
ti
n
g
a
n
d
W
ire
les
s Co
mm
u
n
ica
ti
o
n
S
y
ste
ms
(
I
CCW
CS
2
0
1
7
)
,
p
p
.
1
-
7
,
2
0
1
7
.
[2
9
]
Q.
-
T
.
L
a
m
,
M
.
-
F
.
Ho
r
n
g
,
T
.
-
T
.
Ng
u
y
e
n
,
J.
-
N.
L
in
,
a
n
d
J.
-
P
.
Hs
u
,
“
A
Hig
h
En
e
rg
y
Eff
icie
n
c
y
A
p
p
ro
a
c
h
Ba
se
d
o
n
F
u
z
z
y
Clu
ste
rin
g
T
o
p
o
lo
g
y
f
o
r
Lo
n
g
L
if
e
ti
m
e
in
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s,”
Ad
v
a
n
c
e
d
M
e
th
o
d
s
fo
r
Co
mp
u
t
a
ti
o
n
a
l
Co
ll
e
c
ti
v
e
In
telli
g
e
n
c
e
,
v
o
l.
4
5
7
,
p
p
.
3
6
7
-
3
7
6
,
2
0
1
3
.
[3
0
]
S
.
C
h
a
tt
o
p
a
d
h
y
a
y
,
D.
K.
P
ra
ti
h
a
r,
a
n
d
S
.
C.
De
S
a
rk
a
r,
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
f
u
z
z
y
c
-
m
e
a
n
s
a
lg
o
rit
h
m
a
n
d
e
n
tro
p
y
-
b
a
se
d
f
u
z
z
y
c
lu
ste
rin
g
a
lg
o
rit
h
m
s
,
”
Co
mp
u
ti
n
g
a
n
d
In
f
o
rm
a
ti
c
s
,
v
o
l.
3
0
,
n
o
.
4
,
p
p
.
7
0
1
-
7
2
0
,
2
0
1
1
.
[3
1
]
H.
W
a
n
g
,
Z.
X
u
,
a
n
d
W
.
P
e
d
ry
c
z
,
“
A
n
o
v
e
rv
ie
w o
n
th
e
ro
les
o
f
f
u
z
z
y
se
t
t
e
c
h
n
iq
u
e
s in
b
ig
d
a
ta p
ro
c
e
ss
in
g
:
T
r
e
n
d
s,
c
h
a
ll
e
n
g
e
s,
a
n
d
o
p
p
o
rt
u
n
it
ies
,
”
Kn
o
wled
g
e
-
Ba
se
d
S
y
ste
ms
,
v
o
l.
1
1
8
,
p
p
.
1
5
-
3
0
,
2
0
1
7
.
[3
2
]
A
z
i
z
M
a
h
b
o
u
b
,
M
o
u
n
irA
rio
u
a
,
E
L
M
o
k
h
tar
En
-
n
a
im
i
a
n
d
Ha
m
id
Ba
rk
o
u
k
,
“
F
u
z
z
y
C
-
M
e
a
n
s
b
a
s
e
d
Hie
ra
rc
h
ica
l
Ro
u
ti
n
g
a
p
p
ro
a
c
h
f
o
r
h
o
m
o
g
e
n
o
u
s
W
S
N,”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
M
e
d
it
e
rr
a
n
e
a
n
S
y
mp
o
siu
m
o
n
S
m
a
rt
Cit
y
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
3
7
,
p
p
.
2
6
5
-
2
7
5
,
2
0
1
7
.
[3
3
]
A
li
A
b
d
u
l
-
h
u
ss
ian
Ha
ss
a
n
,
W
a
h
id
a
h
M
d
S
h
a
h
,
M
o
h
d
F
a
iru
z
Isk
a
n
d
a
r
Oth
m
a
n
a
n
d
Ha
y
d
e
r
A
b
d
u
l
Hu
ss
ien
Ha
ss
a
n
,
“
Ev
a
lu
a
te
th
e
p
e
rf
o
rm
a
n
c
e
o
f
K
-
M
e
a
n
s
a
n
d
t
h
e
f
u
z
z
y
C
-
M
e
a
n
s
a
lg
o
rit
h
m
s
to
f
o
rm
a
ti
o
n
b
a
lan
c
e
d
c
lu
ste
rs
in
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
o
f
El
e
c
trica
l
&
Co
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
2
,
p
p
.
1
5
1
5
-
1
5
2
3
,
2
0
2
0
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
H
a
m
i
d
B
a
r
k
o
u
k
He
o
b
tain
e
d
M
a
ste
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
s
f
ro
m
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s,
Un
iv
e
rsit
y
o
f
A
b
d
e
lma
lek
E
ss
a
â
d
i
in
2
0
1
5
,
h
e
is
c
u
rr
e
n
tl
y
a
P
h
D
S
t
u
d
e
n
t
i
n
L
IS
T
(L
a
b
o
ra
to
ired
’In
f
o
rm
a
ti
q
u
e
S
y
ste
m
e
s
e
t
Tele
c
o
m
m
u
n
ica
ti
o
n
s),
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
s,
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s a
n
d
T
e
c
h
n
o
l
o
g
ies
,
T
a
n
g
ier,
M
o
ro
c
c
o
a
t
th
e
U
n
i
v
e
rsit
y
o
f
A
b
d
e
lma
lek
Essa
a
d
i.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
s,
w
ir
e
les
s
n
e
two
rk
in
g
a
n
d
c
o
m
m
u
n
ica
ti
o
n
.
He
is
a
n
A
u
th
o
r/Co
-
A
u
th
o
r
s
o
f
se
v
e
ra
l
A
rt
icle
s
a
n
d
Ch
a
p
ters
,
p
u
b
li
s
h
e
d
in
T
h
e
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls
a
n
d
i
n
th
e
c
o
n
f
e
re
n
c
e
p
ro
c
e
e
d
in
g
s a
s we
ll
a
s Ch
a
p
ters
o
f
b
o
o
k
s,
i
n
Co
m
p
u
ter
S
c
ien
c
e
s.
El
M
o
k
h
t
a
r
En
-
Na
i
m
i
is
a
F
u
ll
P
ro
f
e
ss
o
r
in
th
e
U
n
iv
e
rsity
o
f
A
b
d
e
lm
a
l
e
k
Essa
â
d
i,
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
T
e
c
h
n
o
l
o
g
ies
o
f
Tan
g
ier,
De
p
a
rtm
e
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
s.
(He
w
a
s
T
e
m
p
o
ra
r
y
P
ro
f
e
ss
o
r:
f
ro
m
2
0
0
0
to
2
0
0
3
a
n
d
P
r
o
f
e
ss
o
r
P
e
rm
a
n
e
n
t:
si
n
c
e
2
0
0
3
/2
0
0
4
a
n
d
a
c
tu
a
ll
y
,
He
is
a
F
u
ll
P
ro
f
e
ss
o
r).
He
is
a
He
a
d
o
f
Co
m
p
u
ter
S
c
ien
c
e
s
De
p
a
rtm
e
n
t,
sin
c
e
Oc
to
b
e
r
2
0
1
6
u
n
ti
l
n
o
w
.
He
w
a
s
re
sp
o
n
sib
le
f
o
r
a
Ba
c
h
e
lo
r
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
BS
T
Co
m
p
u
ter
En
g
in
e
e
rin
g
(“
L
ic
e
n
c
e
L
S
T
-
G
I”
),
f
ro
m
J
a
n
u
a
ry
2
0
1
2
t
o
Oc
to
b
e
r
2
0
1
6
.
He
is
a
lso
a
f
o
u
n
d
in
g
m
e
m
b
e
r
o
f
th
e
L
a
b
o
ra
to
ry
L
IS
T
(L
a
b
o
ra
to
ire
d
'
In
f
o
rm
a
ti
q
u
e
,
S
y
stè
m
e
s
e
t
Tél
é
c
o
m
m
u
n
ica
ti
o
n
s),
th
e
Un
iv
e
rsit
y
o
f
A
b
d
e
lma
lek
Es
sa
â
d
i,
F
S
T
o
f
T
a
n
g
ier,
M
o
ro
c
c
o
.
He
is
a
lso
a
n
Ex
p
e
rt
Ev
a
lu
a
to
r
w
it
h
th
e
A
NE
A
Q,
sin
c
e
th
e
a
c
a
d
e
m
ic
y
e
a
r
2
0
1
6
/2
0
1
7
u
n
ti
l
n
o
w
,
th
a
t
a
n
Ex
p
e
rt
o
f
t
h
e
P
riv
a
te
Estab
li
sh
m
e
n
ts
b
e
lo
n
g
i
n
g
t
o
t
h
e
territ
o
ry
o
f
th
e
U
A
E
a
n
d
a
l
so
a
n
Ex
p
e
rt
o
f
th
e
I
n
it
ial
o
r
F
u
n
d
a
m
e
n
tal
F
o
rm
a
ti
o
n
s
a
n
d
F
o
rm
a
ti
o
n
s
Co
n
ti
n
u
o
u
s
a
t
t
h
e
M
i
n
istry
o
f
Hig
h
e
r
Ed
u
c
a
ti
o
n
,
S
c
ien
ti
f
ic
Re
se
a
rc
h
a
n
d
Ex
e
c
u
ti
v
e
T
ra
in
in
g
a
n
d
a
lso
a
t
th
e
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E
Un
iv
e
rsit
y
a
n
d
th
e
F
S
T
T
a
n
g
ier
sin
c
e
2
0
1
2
/
2
0
1
3
u
n
ti
l
No
w
.
He
is
a
n
A
u
th
o
r/C
o
-
A
u
th
o
rs
o
f
se
v
e
ra
l
A
rti
c
les
,
p
u
b
li
s
h
e
d
in
T
h
e
In
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls
i
n
C
o
m
p
u
ter
S
c
ien
c
e
s,
in
p
a
rti
c
u
lar,
i
n
M
u
lt
i
-
Ag
e
n
t
S
y
ste
m
s
(M
A
S
),
Ca
se
s
Ba
s
e
d
Re
a
so
n
in
g
(CBR),
A
rti
f
icia
l
In
te
ll
ig
e
n
t
(A
I),
e
Lea
rn
in
g
,
M
OO
C,
Big
D
A
TA
,
D
a
ta
-
m
in
in
g
,
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
,
V
A
Ne
t,
M
A
Ne
t,
a
n
d
S
m
a
rt
Cit
y
.
He
is
a
lso
Dire
c
to
r
o
f
se
v
e
ra
l
Do
c
to
ra
l
T
h
e
se
s
in
Co
m
p
u
ter
S
c
ien
c
e
s.
In
a
d
d
it
io
n
,
h
e
is
a
n
a
ss
o
c
iate
m
e
m
b
e
r
o
f
th
e
IS
CN
-
In
stit
u
te
o
f
Co
m
p
lex
S
y
ste
m
s
in
No
rm
a
n
d
y
,
th
e
Un
iv
e
rsity
o
f
th
e
Ha
v
re
,
F
ra
n
c
e
,
sin
c
e
2
0
0
9
u
n
ti
l
No
w
.
Az
iz
M
a
h
b
o
u
b
is
a
P
ro
f
e
ss
o
r
in
th
e
Un
iv
e
rsity
o
f
A
b
d
e
l
m
a
lek
Essa
â
d
i,
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
T
e
c
h
n
o
lo
g
ies
o
f
T
a
n
g
ier,
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
s,
si
n
c
e
S
e
p
tem
b
e
r
2
0
1
9
u
n
t
il
N
o
w
.
He
is
a
m
e
m
b
e
r
o
f
th
e
L
a
b
o
ra
to
ry
L
IS
T
(L
a
b
o
ra
to
ire
d
'
In
f
o
rm
a
ti
q
u
e
,
S
y
st
è
m
e
s
e
t
Télé
c
o
m
m
u
n
ica
ti
o
n
s),
t
h
e
Un
iv
e
rsit
y
o
f
A
b
d
e
lma
lek
Ess
a
â
d
i,
F
S
T
o
f
T
a
n
g
ier,
M
o
ro
c
c
o
.
He
o
b
tain
e
d
M
a
ste
r
De
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
s
f
ro
m
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s,
Un
iv
e
rsity
o
f
A
b
d
e
lma
lek
Essa
â
d
i
in
2
0
0
8
.
He
o
b
tai
n
e
d
His
Do
c
to
ra
te
(
P
h
D)
in
C
o
m
p
u
ter
S
c
ien
c
e
s
f
ro
m
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
T
e
c
h
n
o
lo
g
ies
o
f
T
a
n
g
i
e
r,
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
s,
Un
iv
e
rsit
y
o
f
A
b
d
e
l
m
a
le
k
Essa
â
d
i
in
A
p
ril
2
0
1
9
.
His
re
se
a
rc
h
e
s
a
re
in
fi
e
ld
s
o
f
W
irele
ss
S
e
n
so
r
N
e
tw
o
rk
,
In
tern
e
t
o
f
T
h
in
g
s,
w
irele
ss
c
o
m
m
u
n
ica
ti
o
n
s
a
n
d
m
o
b
i
le
c
o
m
p
u
ti
n
g
.
He
h
a
s
se
r
v
e
d
a
s
in
v
it
e
d
re
v
ie
w
e
r.
He
h
a
s
p
u
b
li
sh
e
d
re
se
a
rc
h
p
a
p
e
rs
in
IJECE
Jo
u
rn
a
l,
JA
T
I
T
Jo
u
rn
a
l,
a
n
d
c
o
n
f
e
re
n
c
e
p
ro
c
e
e
d
in
g
s a
s w
e
ll
a
s c
h
a
p
ters
o
f
b
o
o
k
s.
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