I
nte
rna
t
io
na
l J
o
urna
l o
f
Rec
o
nfig
ura
ble a
nd
E
m
be
dd
e
d Sy
s
t
e
m
s
(
I
J
R
E
S)
Vo
l.
15
,
No
.
3
,
N
o
v
e
m
b
er
20
26
,
p
p
.
5
6
3
~
5
7
1
I
SS
N:
2089
-
4864
,
DOI
:
1
0
.
1
1
5
9
1
/i
j
r
es
.
v
1
5
.
i
3
.
pp
5
6
3
-
571
563
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
r
es.ia
esco
r
e.
co
m
Energ
y
-
eff
ici
ent
c
lustering
opti
m
i
z
a
tion for he
terog
e
neo
us
w
ireless
senso
r ne
tw
o
rk
s w
ith
K
-
n
e
a
rest neighbo
rs
a
l
g
o
rith
m
Adil H
il
m
a
ni
1
,
Ya
s
s
ine
Sa
bri
2
,
Abderra
hi
m
M
a
iza
t
e
3
,
Sih
a
m
Ao
ua
d
4
,
F
o
ua
d Ay
o
ub
5
1
L
a
b
o
r
a
t
o
r
y
f
o
r
S
y
st
e
m A
n
a
l
y
si
s,
I
n
f
o
r
mat
i
o
n
P
r
o
c
e
ssi
n
g
,
a
n
d
I
n
t
e
g
r
a
t
e
d
M
a
n
a
g
e
me
n
t
(
L
A
S
T
I
M
I
)
, H
i
g
h
S
c
h
o
o
l
o
f
Te
c
h
n
o
l
o
g
y
S
a
l
é
,
M
o
h
a
mm
e
d
V
U
n
i
v
e
r
si
t
y
,
R
a
b
a
t
,
M
o
r
o
c
c
o
2
L
a
b
o
r
a
t
o
r
y
o
f
M
e
t
r
o
l
o
g
y
a
n
d
I
n
f
o
r
m
a
t
i
o
n
P
r
o
c
e
ssi
n
g
(
L
M
TI
),
F
a
c
u
l
t
y
o
f
S
c
i
e
n
c
e
s,
I
b
n
Z
o
h
r
U
n
i
v
e
r
si
t
y
,
A
g
a
d
i
r
,
M
o
r
o
c
c
o
3
R
e
se
a
r
c
h
a
n
d
I
n
n
o
v
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
M
a
n
a
g
e
me
n
t
(
R
I
T
M
)
,
EST
C
/
C
ED
-
E
N
S
EM
,
U
n
i
v
e
r
si
t
y
H
a
ssa
n
I
I
,
C
a
sab
l
a
n
c
a
,
M
o
r
o
c
c
o
4
S
mart
S
y
st
e
ms
L
a
b
o
r
a
t
o
r
y
,
N
a
t
i
o
n
a
l
S
c
h
o
o
l
of
C
o
mp
u
t
e
r
S
c
i
e
n
c
e
a
n
d
S
y
st
e
ms
A
n
a
l
y
si
s,
M
o
h
a
me
d
V
U
n
i
v
e
r
si
t
y
,
R
a
b
a
t
,
M
o
r
o
c
c
o
5
Eq
u
i
p
e
d
e
R
e
c
h
e
r
c
h
e
a
p
p
l
i
q
u
é
e
e
n
t
e
c
h
n
o
l
o
g
i
e
s d
e
l
'i
n
f
o
r
mat
i
o
n
e
t
d
e
l
a
c
o
mm
u
n
i
c
a
t
i
o
n
T
e
a
m,
L
a
b
o
r
a
t
o
i
r
e
d
e
R
e
c
h
e
r
c
h
e
e
n
En
se
i
g
n
e
me
n
t
,
A
p
p
r
e
n
t
i
ssag
e
,
M
a
t
h
é
mat
i
q
u
e
s e
t
I
n
f
o
r
mat
i
q
u
e
,
R
e
g
i
o
n
a
l
C
e
n
t
e
r
f
o
r
Ed
u
c
a
t
i
o
n
a
n
d
T
r
a
i
n
i
n
g
P
r
o
f
e
ssi
o
n
s,
K
e
n
i
t
r
a
,
M
o
r
o
c
c
o
Art
icle
I
nf
o
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
A
p
r
2
0
,
2
0
2
6
R
ev
i
s
ed
J
u
l 3
,
2
0
2
6
A
cc
ep
ted
J
u
l 1
8
,
2
0
2
6
W
irele
ss
se
n
so
r
n
e
tw
o
rk
s
(
W
S
Ns
)
c
o
n
sist
o
f
a
u
to
n
o
m
o
u
s
se
n
sin
g
n
o
d
e
s
w
h
o
se
o
p
e
ra
ti
o
n
re
li
e
s
o
n
li
m
it
e
d
e
n
e
rg
y
r
e
so
u
rc
e
s.
T
o
e
x
ten
d
th
e
o
p
e
ra
ti
o
n
a
l
li
f
e
ti
m
e
o
f
su
c
h
sy
ste
m
s,
o
p
ti
m
izin
g
p
o
w
e
r
c
o
n
su
m
p
ti
o
n
is
e
ss
e
n
ti
a
l.
Clu
ste
rin
g
p
lay
s
a
k
e
y
ro
le
in
e
n
h
a
n
c
in
g
n
e
tw
o
rk
st
a
b
il
it
y
a
n
d
im
p
ro
v
in
g
c
o
m
m
u
n
ica
ti
o
n
e
ff
ic
ien
c
y
.
T
h
is
w
o
rk
p
ro
p
o
se
s
a
n
im
p
ro
v
e
d
c
lu
ste
rin
g
stra
teg
y
b
a
se
d
o
n
th
e
K
-
n
e
a
re
st
n
e
ig
h
b
o
rs
(KN
N)
a
lg
o
rit
h
m
to
re
d
u
c
e
tran
sm
issio
n
d
istan
c
e
s
in
h
e
tero
g
e
n
e
o
u
s n
e
tw
o
rk
s.
B
y
in
teg
ra
ti
n
g
th
i
s
a
p
p
ro
a
c
h
i
n
to
h
ier
a
rc
h
ica
l
p
ro
t
o
c
o
ls
su
c
h
a
s
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
En
g
in
e
e
ri
n
g
(DEE
C),
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
Ce
n
ter
(EE
C),
Ce
n
ter
f
o
r
A
d
v
a
n
c
e
d
M
a
teria
ls
a
n
d
P
ro
c
e
ss
e
s
(CA
M
P
)
,
t
h
e
m
e
th
o
d
re
f
in
e
s
b
o
t
h
n
o
d
e
se
lec
ti
o
n
a
n
d
c
l
u
ste
r
o
rg
a
n
iza
ti
o
n
.
S
im
u
latio
n
re
su
lt
s
o
b
tai
n
e
d
u
sin
g
n
e
tw
o
rk
sim
u
lato
r
2
(NS
2
)
d
e
m
o
n
stra
te
th
a
t
th
e
m
o
d
if
ied
p
r
o
to
c
o
ls
sig
n
if
ica
n
tl
y
re
d
u
c
e
e
n
e
rg
y
d
issip
a
ti
o
n
w
h
il
e
i
n
c
re
a
sin
g
o
v
e
ra
ll
n
e
tw
o
rk
li
f
e
ti
m
e
.
T
h
is co
m
b
in
e
d
a
p
p
ro
a
c
h
im
p
ro
v
e
s
s
y
ste
m
e
f
f
i
c
ien
c
y
a
n
d
p
ro
v
id
e
s a
ro
b
u
st
so
lu
ti
o
n
f
o
r
s
u
s
tain
a
b
le
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
in
W
S
Ns
,
m
e
e
ti
n
g
th
e
re
q
u
irem
e
n
ts o
f
h
ig
h
ly
d
e
m
a
n
d
in
g
a
p
p
li
c
a
ti
o
n
s.
K
ey
w
o
r
d
s
:
E
f
f
icien
t c
lu
s
ter
in
g
E
n
er
g
y
e
f
f
icie
n
c
y
K
-
n
ea
r
est n
e
ig
h
b
o
r
s
alg
o
r
ith
m
P
o
w
er
co
n
s
u
m
p
tio
n
R
o
u
ti
n
g
p
r
o
to
co
l
W
ir
eless
s
en
s
o
r
n
et
w
o
r
k
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
d
il Hil
m
a
n
i
L
ab
o
r
ato
r
y
f
o
r
S
y
s
te
m
An
al
y
s
is
,
I
n
f
o
r
m
atio
n
P
r
o
ce
s
s
i
n
g
,
a
n
d
I
n
teg
r
ated
Ma
n
a
g
e
m
e
n
t
(
L
AST
I
MI
)
Hig
h
Sc
h
o
o
l o
f
T
ec
h
n
o
lo
g
y
S
alé
,
Mo
h
a
m
m
ed
V
Un
i
v
er
s
i
t
y
R
ab
at,
Mo
r
o
cc
o
E
m
ail: a
d
il
h
il
m
an
i@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
u
s
e
o
f
w
ir
eles
s
s
e
n
s
o
r
n
et
w
o
r
k
s
(
W
SNs
)
h
as
b
ec
o
m
e
w
id
esp
r
ea
d
in
s
tr
ate
g
ic
s
ec
to
r
s
s
u
c
h
a
s
in
d
u
s
tr
ial
au
to
m
atio
n
,
h
ea
lt
h
c
ar
e,
an
d
en
v
ir
o
n
m
e
n
tal
m
o
n
it
o
r
in
g
.
T
h
ese
in
f
r
as
tr
u
ct
u
r
es
r
el
y
o
n
au
to
n
o
m
o
u
s
s
en
s
in
g
u
n
its
th
at
tr
an
s
m
it
d
at
a
to
a
ce
n
tr
alize
d
b
ase
s
tatio
n
(
B
S)
n
o
d
e.
E
n
er
g
y
p
r
eser
v
ati
o
n
r
e
m
ai
n
s
a
m
aj
o
r
tech
n
ical
ch
a
llen
g
e,
as
t
h
e
o
p
er
atio
n
al
li
f
eti
m
e
o
f
t
h
ese
n
et
w
o
r
k
s
is
s
tr
ictl
y
co
n
s
tr
ain
ed
b
y
l
i
m
ited
b
atter
y
r
eso
u
r
ce
s
.
Desp
ite
ad
v
an
ce
s
i
n
r
o
u
tin
g
an
d
clu
s
ter
in
g
o
p
ti
m
izatio
n
,
i
m
p
r
o
v
i
n
g
t
h
e
d
u
r
ab
ilit
y
o
f
th
ese
s
y
s
te
m
s
r
e
m
ain
s
a
m
aj
o
r
r
esear
ch
f
o
cu
s
.
T
h
e
cr
itical
p
o
in
t
f
o
r
t
h
ese
n
e
t
w
o
r
k
s
i
s
to
g
u
ar
an
tee
co
n
s
ta
n
t
d
ata
tr
an
s
m
is
s
io
n
w
i
th
o
u
t
p
r
e
m
at
u
r
el
y
d
ep
letin
g
en
er
g
y
r
eso
u
r
ce
s
.
C
o
n
v
e
n
tio
n
al
cl
u
s
ter
in
g
tech
n
iq
u
es
o
f
te
n
ex
h
ib
it
w
ea
k
n
e
s
s
e
s
in
lo
ad
d
is
tr
ib
u
t
io
n
b
et
w
ee
n
n
o
d
es,
lead
in
g
to
lo
ca
lized
f
ailu
r
es
t
h
at
co
m
p
r
o
m
is
e
t
h
e
o
v
er
all
in
te
g
r
it
y
o
f
th
e
n
et
w
o
r
k
.
T
o
o
v
er
co
m
e
th
e
s
e
s
h
o
r
tco
m
in
g
s
,
it is
i
m
p
er
ati
v
e
to
d
ev
elo
p
m
o
r
e
b
alan
ce
d
p
ar
titi
o
n
in
g
s
tr
ate
g
ies [
1
]
–
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4864
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
15
,
No
.
3
,
No
v
e
m
b
er
202
6
:
563
-
571
564
T
h
is
s
cien
ti
f
ic
co
n
tr
ib
u
tio
n
p
r
o
p
o
s
es
in
teg
r
at
i
n
g
ar
ti
f
icial
i
n
telli
g
e
n
ce
,
v
ia
th
e
K
-
n
ea
r
es
t
n
eig
h
b
o
r
s
(
KNN)
alg
o
r
ith
m
,
in
to
co
m
m
u
n
ica
tio
n
p
r
o
to
co
ls
f
o
r
W
SNs
.
B
y
ad
ap
tin
g
th
e
Dep
ar
t
m
e
n
t
o
f
E
lectr
ical
an
d
E
lectr
o
n
ic
E
n
g
i
n
ee
r
in
g
(
DE
E
C
)
,
E
lectr
ical
E
n
g
i
n
ee
r
i
n
g
C
e
n
ter
(
E
E
C
)
,
C
e
n
ter
f
o
r
A
d
v
a
n
ce
d
Ma
ter
ials
an
d
P
r
o
ce
s
s
es
(
C
A
MP
)
m
o
d
els,
w
e
i
n
tr
o
d
u
ce
o
p
ti
m
ized
v
er
s
i
o
n
s
(
DE
E
C
-
KNN,
E
E
C
-
KN
N
,
an
d
C
A
MP
-
KNN)
th
at
r
ef
in
e
t
h
e
s
elec
tio
n
o
f
c
lu
s
ter
h
ea
d
s
(
CH
)
.
T
h
ese
d
is
tr
ib
u
ted
m
o
d
els
ai
m
to
r
ed
u
ce
co
m
m
u
n
icatio
n
d
is
tan
ce
s
a
n
d
m
a
x
i
m
ize
t
h
e
n
e
t
w
o
r
k
's o
p
er
atio
n
al
lif
e
ti
m
e
[
5]
–
[
1
0
]
.
T
h
e
o
r
g
an
izatio
n
o
f
th
i
s
ar
tic
le
is
as
f
o
llo
w
s
:
s
ec
tio
n
2
r
e
v
ie
w
s
r
elate
d
r
esear
c
h
,
w
h
i
le
s
ec
tio
n
3
d
etails
th
e
p
r
o
p
o
s
ed
s
y
s
te
m
ar
ch
itect
u
r
e.
Sectio
n
4
p
r
ese
n
ts
t
h
e
tech
n
ical
s
p
ec
if
icat
io
n
s
o
f
o
u
r
p
r
o
to
co
l,
f
o
llo
w
ed
b
y
s
ec
tio
n
5
,
w
h
ic
h
an
al
y
ze
s
t
h
e
p
er
f
o
r
m
a
n
ce
o
b
tain
ed
th
r
o
u
g
h
s
i
m
u
latio
n
.
Fin
all
y
,
s
ec
tio
n
6
co
n
clu
d
es t
h
e
s
t
u
d
y
b
y
o
u
tli
n
i
n
g
f
u
tu
r
e
r
esear
c
h
d
ir
ec
tio
n
s
.
2.
RE
L
AT
E
D
WO
RK
S
T
h
e
en
er
g
y
e
f
f
icien
c
y
p
r
o
b
lem
in
W
S
Ns
h
a
s
m
o
t
iv
ated
t
h
e
d
ev
elo
p
m
e
n
t
o
f
m
u
ltip
le
r
o
u
ti
n
g
s
tr
ateg
y
f
a
m
ilies
,
s
u
c
h
as
h
i
er
ar
c
h
ical
,
d
ata
-
d
r
iv
en
,
lo
ca
tio
n
-
a
w
ar
e,
an
d
f
lo
w
-
o
p
ti
m
ized
ap
p
r
o
ac
h
es.
E
ac
h
ca
te
g
o
r
y
ai
m
s
to
i
m
p
r
o
v
e
co
m
m
u
n
ica
tio
n
e
f
f
icien
c
y
w
h
i
le
r
ed
u
ci
n
g
e
n
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
e
x
ten
d
i
n
g
n
et
w
o
r
k
lif
eti
m
e.
T
h
is
s
t
u
d
y
s
p
ec
i
f
ic
all
y
i
n
v
e
s
ti
g
ates
h
ier
ar
ch
ical
r
o
u
tin
g
p
r
o
to
co
ls
,
in
cl
u
d
in
g
DE
E
C
,
E
E
C
,
an
d
C
A
MP
,
w
h
ic
h
ar
e
d
esig
n
ed
to
o
p
tim
ize
e
n
er
g
y
u
s
ag
e
t
h
r
o
u
g
h
s
tr
u
ct
u
r
ed
clu
s
ter
in
g
m
ec
h
a
n
i
s
m
s
.
T
h
ese
m
o
d
el
s
o
p
ti
m
ize
cl
u
s
ter
lead
e
r
s
elec
tio
n
b
ased
o
n
n
o
d
e
r
esid
u
al
en
er
g
y
an
d
t
h
e
o
v
er
all
n
et
w
o
r
k
s
tate.
T
h
eir
p
r
im
ar
y
o
b
j
ec
tiv
e
i
s
to
ex
ten
d
n
et
w
o
r
k
li
f
eti
m
e
w
h
ile
m
ai
n
ta
in
i
n
g
e
f
f
icie
n
t c
o
m
m
u
n
ica
tio
n
[
1
1
]
–
[
1
5
]
.
T
h
e
KNN
alg
o
r
ith
m
h
a
s
also
b
ee
n
u
s
ed
in
W
SN
s
to
i
m
p
r
o
v
e
CH
s
elec
tio
n
an
d
en
er
g
y
m
a
n
ag
e
m
e
n
t,
in
cl
u
d
in
g
i
n
t
h
r
esh
o
ld
-
s
e
n
s
i
tiv
e
en
er
g
y
-
ef
f
icie
n
t
n
et
w
o
r
k
(
T
E
E
N)
-
b
ased
ap
p
r
o
ac
h
es
[
1
6
]
,
h
y
b
r
id
m
o
d
els
w
it
h
m
eta
h
eu
r
i
s
tics
[
1
7
]
,
in
tr
u
s
io
n
d
etec
tio
n
s
y
s
te
m
s
[
1
8
]
,
[
1
9
]
,
an
d
co
m
b
i
n
ed
d
ee
p
lear
n
i
n
g
f
r
a
m
e
w
o
r
k
s
[
2
0
]
.
Oth
er
s
t
u
d
ies
h
a
v
e
ex
p
lo
r
ed
m
u
lti
-
o
b
j
ec
tiv
e
o
p
tim
izat
io
n
an
d
m
ac
h
i
n
e
lear
n
i
n
g
m
et
h
o
d
s
s
u
ch
as
SVM
to
en
h
a
n
ce
en
er
g
y
e
f
f
icie
n
c
y
a
n
d
n
et
w
o
r
k
p
er
f
o
r
m
a
n
ce
[
2
1
]
–
[
2
3
]
.
Ho
w
e
v
er
,
m
o
s
t
e
x
is
t
in
g
ap
p
r
o
ac
h
es
o
p
ti
m
ize
a
s
i
n
g
le
cr
iter
i
o
n
o
r
d
ep
en
d
o
n
s
p
ec
i
f
ic
p
r
o
t
o
co
ls
an
d
r
ar
ely
co
n
s
id
er
jo
in
tl
y
r
esid
u
al
en
er
g
y
,
n
o
d
e
ce
n
tr
al
it
y
,
co
m
m
u
n
icatio
n
lo
ad
,
an
d
t
r
an
s
m
is
s
io
n
q
u
ali
t
y
.
I
n
co
n
tr
ast,
o
u
r
ap
p
r
o
ac
h
u
s
es
a
m
u
lti
-
cr
iter
ia
K
NN
m
o
d
el
to
o
p
ti
m
ize
t
h
ese
f
ac
to
r
s
s
i
m
u
lt
an
eo
u
s
l
y
,
r
ed
u
c
in
g
en
er
g
y
co
n
s
u
m
p
tio
n
,
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
,
an
d
tr
an
s
m
i
s
s
io
n
co
s
t
w
h
ile
i
m
p
r
o
v
i
n
g
n
e
t
w
o
r
k
s
tab
il
it
y
a
n
d
lif
eti
m
e.
3.
SYST
E
M
O
VE
RVI
E
W
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
s
y
s
t
e
m
m
o
d
el
u
s
ed
i
n
th
i
s
s
tu
d
y
.
I
t
f
ir
s
t
p
r
esen
t
s
th
e
n
e
t
w
o
r
k
ar
ch
itect
u
r
e,
th
en
t
h
e
ad
o
p
ted
en
er
g
y
d
is
s
ip
atio
n
m
o
d
el.
Fin
all
y
,
th
e
m
ai
n
ass
u
m
p
tio
n
s
an
d
d
ef
i
n
i
tio
n
s
n
ec
e
s
s
ar
y
f
o
r
u
n
d
er
s
ta
n
d
in
g
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
ar
e
o
u
tlin
ed
.
3
.
1
.
Net
wo
rk
m
o
del
Ou
r
al
g
o
r
ith
m
co
n
s
id
er
s
a
s
t
atic
W
SN
co
m
p
o
s
ed
o
f
a
BS
an
d
n
f
i
x
ed
s
e
n
s
o
r
n
o
d
es
r
an
d
o
m
l
y
d
ep
lo
y
ed
o
v
er
a
t
w
o
-
d
i
m
e
n
s
io
n
al
(
2
D)
ar
ea
.
No
d
es
r
e
m
ain
s
tatio
n
ar
y
af
ter
d
ep
lo
y
m
e
n
t
a
n
d
ar
e
h
o
m
o
g
en
eo
u
s
,
s
h
ar
in
g
t
h
e
s
a
m
e
i
n
itial
e
n
er
g
y
a
n
d
s
i
m
ilar
co
m
m
u
n
icatio
n
a
n
d
p
r
o
ce
s
s
i
n
g
ca
p
ab
ilit
ies.
E
ac
h
n
o
d
e
is
a
w
ar
e
o
f
it
s
lo
ca
tio
n
,
r
esid
u
al
e
n
er
g
y
,
a
n
d
r
o
le
i
n
cl
u
s
ter
f
o
r
m
atio
n
.
T
h
e
BS
h
a
s
u
n
li
m
ited
e
n
er
g
y
a
n
d
co
m
p
u
tatio
n
al
p
o
w
er
an
d
ca
n
d
ir
ec
tl
y
co
m
m
u
n
ic
ate
w
i
th
all
s
en
s
o
r
n
o
d
es f
o
r
d
ata
co
llectio
n
.
3
.
2
.
E
nerg
y
c
o
ns
u
m
ptio
n a
n
a
ly
s
is
m
o
de
T
h
is
s
tu
d
y
ad
o
p
ts
th
e
lo
w
-
e
n
er
g
y
ad
ap
tiv
e
c
lu
s
ter
in
g
h
ier
a
r
ch
y
(
L
E
A
C
H)
r
ef
er
en
ce
e
n
e
r
g
y
m
o
d
el
f
o
r
W
SNs
to
e
v
alu
a
te
en
er
g
y
d
i
s
s
ip
atio
n
[2
4
]
–
[2
7
]
.
T
h
is
m
o
d
el
co
n
s
id
er
s
t
w
o
c
ases
b
ased
o
n
t
h
e
tr
an
s
m
is
s
io
n
d
i
s
tan
ce
b
et
w
ee
n
n
o
d
es
.
W
h
e
n
t
h
e
d
is
ta
n
ce
i
s
b
elo
w
t
h
e
cr
itical
t
h
r
es
h
o
ld
0
,
th
e
f
r
ee
-
s
p
ac
e
p
r
o
p
ag
atio
n
m
o
d
el
is
ap
p
lie
d
,
ass
u
m
i
n
g
a
c
lear
an
d
u
n
o
b
s
tr
u
cted
co
m
m
u
n
icatio
n
c
h
an
n
el.
A
b
o
v
e
t
h
i
s
th
r
es
h
o
ld
,
th
e
m
u
ltip
at
h
m
o
d
el
is
ap
p
lied
to
ac
co
u
n
t
f
o
r
p
r
o
p
ag
atio
n
ef
f
ec
t
s
s
u
c
h
as
r
ef
lectio
n
an
d
d
if
f
r
ac
tio
n
.
T
h
e
th
r
esh
o
ld
is
d
ef
i
n
ed
b
y
:
0
=
√
ℇ
ℇ
(
1
)
I
n
th
is
ex
p
r
ess
io
n
,
ℇ
an
d
ℇ
r
esp
e
ctiv
el
y
d
en
o
te
th
e
a
m
p
l
if
icati
o
n
co
ef
f
icie
n
ts
r
elate
d
to
th
e
f
r
ee
-
s
p
ac
e
an
d
m
u
ltip
ath
p
r
o
p
ag
atio
n
m
o
d
els.
T
h
e
to
tal
tr
an
s
m
i
s
s
io
n
en
er
g
y
r
eq
u
ir
ed
b
y
a
n
o
d
e
to
tr
an
s
m
it
a
p
ac
k
et
o
f
(
m
)
b
its
to
a
d
esti
n
at
io
n
lo
c
ated
at
a
d
is
tan
ce
d
is
d
escr
ib
ed
b
y
(
2
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
erg
y
-
efficien
t c
lu
s
teri
n
g
o
p
t
imiz
a
tio
n
fo
r
h
etero
g
en
eo
u
s
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
w
i
th
…
(
A
d
il Hilma
n
i
)
565
=
{
+
ℇ
2
,
<
0
+
ℇ
4
,
≥
0
(
2
)
Her
e,
r
ep
r
esen
ts
t
h
e
e
n
er
g
y
co
n
s
u
m
ed
b
y
th
e
o
n
b
o
ar
d
elec
tr
o
n
ic
cir
cu
it
s
f
o
r
p
r
o
ce
s
s
in
g
ea
c
h
b
it,
w
h
et
h
er
d
u
r
i
n
g
tr
an
s
m
i
s
s
io
n
o
r
r
ec
ep
tio
n
.
Un
li
k
e
tr
an
s
m
is
s
io
n
,
t
h
e
r
ec
ep
tio
n
en
er
g
y
f
o
r
a
m
es
s
a
g
e
o
f
m
b
its
is
co
n
s
id
er
ed
to
b
e
p
r
o
p
o
r
tio
n
al
to
its
s
ize
a
n
d
in
d
ep
en
d
en
t o
f
t
h
e
d
is
ta
n
ce
d
,
as sh
o
w
n
in
(
3
)
.
=
(
3
)
4.
P
RO
T
O
CO
L
P
RO
P
O
SE
D
T
h
e
p
r
o
p
o
s
ed
p
r
o
to
co
l
is
b
ased
o
n
t
h
r
ee
m
ai
n
s
tep
s
:
cl
u
s
ter
f
o
r
m
a
tio
n
,
CH
s
e
lectio
n
,
an
d
d
ata
g
r
o
u
p
in
g
w
it
h
tr
an
s
m
is
s
io
n
.
T
h
is
s
tr
u
ct
u
r
e
i
m
p
r
o
v
e
s
co
m
m
u
n
icat
io
n
e
f
f
ic
ien
c
y
b
y
o
r
g
an
izi
n
g
n
o
d
es
in
to
clu
s
ter
s
,
r
ed
u
cin
g
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
ag
g
r
eg
a
tin
g
d
ata
b
ef
o
r
e
s
en
d
in
g
it.
T
h
e
f
in
al
tr
an
s
m
is
s
io
n
to
th
e
BS
is
p
er
f
o
r
m
ed
th
r
o
u
g
h
o
p
tim
ized
in
tr
a
-
an
d
in
ter
-
clu
s
ter
r
o
u
ti
n
g
p
ath
s
,
en
s
u
r
in
g
r
eliab
le
d
ata
d
eliv
er
y
[
2
8
]
,
[
2
9
]
.
4
.
1
.
T
he
clus
t
er
f
o
r
m
a
t
io
n
C
lu
s
ter
f
o
r
m
at
io
n
is
a
cr
u
cial
s
tep
in
o
p
tim
iz
in
g
th
e
e
n
er
g
y
e
f
f
icie
n
c
y
o
f
W
SN
s
.
I
n
th
is
p
h
ase,
th
e
g
o
al
is
to
g
r
o
u
p
th
e
n
o
d
es
o
f
th
e
n
et
w
o
r
k
i
n
to
clu
s
ter
s
u
s
i
n
g
th
e
KNN
alg
o
r
ith
m
.
C
l
u
s
ter
f
o
r
m
at
io
n
in
v
o
lv
e
s
s
ev
er
al
k
e
y
s
tep
s
t
h
at
ar
e
d
etailed
b
elo
w
.
W
e
w
il
l
f
o
cu
s
o
n
ca
lcu
lati
n
g
d
is
ta
n
ce
s
,
s
elec
tin
g
th
e
KNN
,
in
iti
al
izin
g
t
h
e
clu
s
ter
s
,
an
d
as
s
ig
n
i
n
g
n
o
d
es to
clu
s
ter
s
.
T
h
e
p
r
o
ce
d
u
r
e
b
eg
in
s
b
y
m
e
asu
r
i
n
g
t
h
e
s
p
atial
d
is
ta
n
ce
b
et
w
ee
n
ea
ch
n
o
d
e
an
d
its
n
e
ig
h
b
o
r
in
g
n
o
d
es
w
it
h
in
t
h
e
n
et
w
o
r
k
i
n
f
r
astru
ct
u
r
e.
Fo
r
t
w
o
n
o
d
es
an
d
,
lo
ca
ted
at
p
o
s
itio
n
s
=
(
,
)
an
d
=
(
,
)
r
esp
ec
tiv
el
y
,
t
h
i
s
d
is
tan
ce
i
s
c
o
m
p
u
ted
u
s
in
g
t
h
e
s
ta
n
d
ar
d
E
u
clid
ea
n
f
o
r
m
u
la
(
4
)
.
(
,
)
=
√
(
−
)
2
+
(
−
)
2
(
4
)
On
ce
th
e
s
e
m
ea
s
u
r
e
m
e
n
ts
ar
e
o
b
tain
ed
,
ea
ch
n
o
d
e
ev
alu
at
es
a
co
s
t
f
u
n
ctio
n
(
,
)
f
o
r
all
o
th
er
n
o
d
es
w
h
er
e
≠
.
T
h
is
h
y
b
r
id
m
e
tr
ic
s
i
m
u
lta
n
eo
u
s
l
y
co
n
s
id
er
s
g
e
o
g
r
ap
h
ical
p
r
o
x
i
m
it
y
an
d
t
h
e
r
e
m
ain
in
g
en
er
g
y
le
v
els.
T
h
e
r
esu
l
tin
g
v
alu
es
ar
e
th
e
n
s
o
r
ted
in
a
s
ce
n
d
in
g
o
r
d
er
,
allo
w
in
g
n
o
d
e
to
s
elec
t
t
h
e
KN
N
s
w
it
h
t
h
e
lo
w
est co
s
t
v
al
u
es (
5
)
an
d
(
6
)
.
=
{
,
≠
,
(
,
)
ℎ
}
(
5
)
(
,
)
=
e
xp
(
(
,
)
)
+
e
xp
(
|
−
|
)
(
6
)
I
n
th
e
s
e
ex
p
r
ess
io
n
s
,
an
d
d
en
o
te
th
e
r
esp
ec
tiv
e
e
n
er
g
y
lev
e
ls
,
w
h
ile
an
d
ar
e
s
ca
lin
g
p
ar
a
m
eter
s
.
Af
ter
id
en
ti
f
y
in
g
th
e
n
ei
g
h
b
o
r
s
,
th
e
s
y
s
te
m
in
itialize
s
th
e
clu
s
ter
s
b
y
r
an
d
o
m
l
y
s
elec
ti
n
g
K
n
o
d
es
to
ac
t
as
in
itial c
l
u
s
ter
ce
n
ter
s
(
ce
n
tr
o
id
s
)
.
T
o
im
p
r
o
v
e
n
et
w
o
r
k
s
tab
ilit
y
an
d
b
alan
ce
th
e
w
o
r
k
lo
ad
,
c
lu
s
ter
ce
n
ter
s
ar
e
n
o
t
co
m
p
u
t
ed
u
s
in
g
a
s
i
m
p
le
ar
it
h
m
e
tic
m
ea
n
b
u
t
t
h
r
o
u
g
h
an
e
n
er
g
y
-
w
ei
g
h
ted
f
o
r
m
u
latio
n
.
T
h
is
en
s
u
r
es
th
at
n
o
d
es
w
it
h
h
i
g
h
er
r
esid
u
al
en
er
g
y
h
a
v
e
a
s
tr
o
n
g
e
r
in
f
l
u
e
n
ce
o
n
t
h
e
ce
n
tr
o
id
p
o
s
itio
n
.
T
h
e
w
ei
g
h
ted
ce
n
ter
o
f
a
clu
s
ter
is
th
e
n
d
ef
in
ed
b
y
(
7
)
.
=
∑
(
,
)
∈
∑
∈
(
7
)
T
h
is
s
tr
ateg
y
f
a
v
o
r
s
p
o
s
itio
n
i
n
g
CH
s
n
ea
r
n
o
d
es
w
it
h
h
ig
h
er
en
er
g
y
r
eser
v
e
s
.
As
a
r
esu
l
t,
it
r
ed
u
ce
s
th
e
r
is
k
o
f
ea
r
l
y
b
atter
y
d
ep
let
io
n
,
th
er
eb
y
e
x
te
n
d
in
g
th
e
o
v
er
all
o
p
er
atio
n
al
li
f
eti
m
e
o
f
t
h
e
s
y
s
te
m
.
4
.
2
.
T
he
clus
t
er
hea
d select
i
o
n
T
h
e
C
H
s
elec
tio
n
p
h
ase
is
a
c
r
itical
s
tep
f
o
r
e
n
s
u
r
in
g
n
et
w
o
r
k
s
u
s
tai
n
ab
ilit
y
.
T
h
e
p
r
o
p
o
s
e
d
m
eth
o
d
r
elies
o
n
a
m
u
l
ti
-
cr
iter
ia
ev
alu
atio
n
to
id
en
tify
t
h
e
m
o
s
t
s
u
it
ab
le
n
o
d
es
f
o
r
th
is
r
o
le.
I
t
u
s
es
a
f
itn
es
s
f
u
n
ctio
n
th
at
co
m
b
i
n
es
t
h
r
ee
k
e
y
f
ac
t
o
r
s
:
r
esid
u
al
en
er
g
y
,
s
p
atial
ce
n
tr
alit
y
w
it
h
i
n
th
e
cl
u
s
ter
,
an
d
co
m
m
u
n
icatio
n
lo
ad
.
Fo
r
ea
ch
n
o
d
e
i
a
s
co
r
e
i
s
co
m
p
u
ted
ac
co
r
d
in
g
to
(
8
)
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4864
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
15
,
No
.
3
,
No
v
e
m
b
er
202
6
:
563
-
571
566
(
)
=
+
−
(
8
)
w
h
er
e
is
th
e
r
esid
u
al
en
er
g
y
o
f
n
o
d
e
an
d
is
th
e
in
itia
l
en
e
r
g
y
o
f
t
h
e
n
o
d
e
(
u
s
ed
f
o
r
n
o
r
m
aliza
tio
n
)
.
r
ep
r
esen
ts
th
e
s
p
atial
ce
n
tr
ali
t
y
o
f
n
o
d
e
iii
w
it
h
i
n
its
cl
u
s
ter
an
d
is
ca
lcu
lated
as f
o
llo
w
s
:
I
n
th
is
m
o
d
el,
th
e
r
atio
r
ep
r
esen
t
s
th
e
n
o
r
m
a
lized
r
esid
u
a
l
en
er
g
y
w
it
h
r
esp
ec
t
to
th
e
n
o
d
e’
s
in
itial
ca
p
ac
it
y
.
T
h
e
ter
m
m
e
asu
r
es
t
h
e
s
p
atial
ce
n
tr
alit
y
o
f
n
o
d
e
i
d
ef
in
ed
as
th
e
in
v
er
s
e
o
f
its
av
er
ag
e
d
is
tan
ce
to
o
th
er
n
o
d
es
w
it
h
i
n
clu
s
ter
K
,
as s
h
o
w
n
i
n
(
9
)
.
=
−
1
|
|
∑
(
,
)
∈
(
9
)
Her
e,
d
en
o
tes
th
e
to
tal
n
u
m
b
er
o
f
n
o
d
es
in
t
h
e
cl
u
s
ter
,
an
d
(
,
)
r
ep
r
esen
ts
t
h
e
E
u
clid
ea
n
d
is
ta
n
ce
b
et
w
ee
n
n
o
d
es
an
d
n
o
d
e
.
Fin
all
y
,
m
o
d
els
th
e
co
m
m
u
n
i
ca
tio
n
lo
ad
,
d
ef
in
ed
as
th
e
n
u
m
b
er
o
f
n
o
d
es
lo
ca
ted
w
it
h
in
t
h
e
r
ad
io
co
m
m
u
n
ica
tio
n
r
an
g
e
.
=
∑
(
(
,
)
)
≤
)
∈
,
≠
(1
0
)
T
h
e
in
d
icato
r
(
.
)
eq
u
als
1
if
t
h
e
p
r
o
x
i
m
it
y
co
n
d
itio
n
is
s
a
tis
f
ied
,
an
d
0
o
th
er
w
is
e.
O
n
ce
all
ev
alu
a
tio
n
s
ar
e
co
m
p
leted
,
th
e
s
y
s
te
m
s
e
lects
as
CH
th
e
n
o
d
e
w
it
h
th
e
h
i
g
h
est
s
co
r
e
ac
co
r
d
in
g
to
th
e
f
o
llo
w
in
g
s
elec
tio
n
r
u
le
(
1
1
)
.
=
a
r
g
∈
_
(
)
(1
1
)
T
h
is
d
y
n
a
m
ic
s
elec
tio
n
s
tr
at
eg
y
e
n
s
u
r
es
a
b
alan
ce
d
d
is
tr
ib
u
tio
n
o
f
w
o
r
k
lo
ad
,
th
er
eb
y
p
r
eser
v
in
g
en
er
g
y
r
eso
u
r
ce
s
an
d
s
i
g
n
i
f
ican
t
l
y
e
x
t
en
d
in
g
th
e
o
p
er
atio
n
al
li
f
eti
m
e
o
f
th
e
n
e
t
w
o
r
k
in
f
r
as
tr
u
ct
u
r
e
.
4
.
3
.
D
a
t
a
a
g
g
re
g
a
t
io
n a
nd
t
r
a
ns
m
is
s
io
n
T
h
is
s
ec
tio
n
p
r
esen
ts
an
o
p
tim
ized
m
et
h
o
d
o
lo
g
y
f
o
r
d
ata
a
g
g
r
e
g
atio
n
an
d
tr
an
s
m
i
s
s
io
n
in
clu
s
ter
ed
W
SNs
.
T
h
e
o
b
j
ec
tiv
e
is
to
en
h
an
ce
e
n
er
g
y
e
f
f
icien
c
y
a
n
d
ex
ten
d
n
et
w
o
r
k
l
if
et
i
m
e
b
y
ad
o
p
tin
g
a
m
u
lti
-
h
o
p
co
m
m
u
n
icatio
n
s
tr
ateg
y
.
I
n
t
h
is
s
c
h
e
m
e,
CH
s
ac
t
a
s
r
ela
y
n
o
d
es
to
f
o
r
w
ar
d
d
ata
to
th
e
BS
,
co
n
s
id
er
in
g
k
e
y
p
ar
am
eter
s
s
u
c
h
as r
es
id
u
al
e
n
er
g
y
,
n
e
ig
h
b
o
r
h
o
o
d
d
en
s
it
y
,
tr
af
f
ic
lo
ad
,
an
d
lin
k
r
eliab
ili
t
y
.
T
h
e
co
n
n
ec
tiv
it
y
b
et
w
ee
n
t
wo
CH
s
i
an
d
j
is
ev
alu
ated
u
s
i
n
g
t
h
e
lin
k
q
u
alit
y
f
ac
to
r
,
d
e
f
i
n
ed
in
(1
2
).
=
1
1
+
,
×
ℎ
(1
2
)
I
n
th
is
ex
p
r
es
s
io
n
,
,
d
en
o
tes
th
e
p
ac
k
et
lo
s
s
p
r
o
b
ab
ilit
y
b
et
wee
n
n
o
d
es,
r
ep
r
esen
ts
th
e
ef
f
ec
tiv
e
s
ig
n
al
-
to
-
n
o
is
e
r
atio
,
an
d
ℎ
is
th
e
p
r
ed
ef
in
ed
r
eliab
ilit
y
th
r
e
s
h
o
ld
f
o
r
s
tab
le
co
m
m
u
n
icatio
n
.
A
h
ig
h
er
v
alu
e
o
f
im
p
lie
s
en
h
a
n
ce
d
lin
k
q
u
alit
y
.
T
o
s
elec
t
th
e
m
o
s
t
s
u
itab
le
r
ela
y
,
a
co
s
t
f
u
n
c
tio
n
i
s
co
m
p
u
ted
f
o
r
ea
c
h
n
e
ig
h
b
o
r
in
g
CH
.
T
h
is
m
etr
ic
co
m
b
in
e
s
r
esid
u
al
e
n
er
g
y
,
th
e
n
u
m
b
er
o
f
n
ei
g
h
b
o
r
s
,
lin
k
q
u
alit
y
,
an
d
th
e
d
is
tan
ce
to
th
e
B
S
(
,
)
.
=
1
+
1
+
(
,
)
−
(1
3
)
T
o
av
o
id
co
n
s
is
ten
tl
y
o
v
er
lo
ad
in
g
t
h
e
n
o
d
es
w
it
h
th
e
lo
w
e
s
t
co
s
t,
o
u
r
ap
p
r
o
ac
h
in
tr
o
d
u
ce
s
a
p
r
o
b
a
b
ilis
tic
r
ela
y
s
elec
tio
n
m
ec
h
a
n
i
s
m
.
T
h
i
s
m
e
th
o
d
en
a
b
les
a
m
o
r
e
b
alan
ce
d
d
is
tr
ib
u
tio
n
o
f
tr
a
f
f
ic
ac
r
o
s
s
th
e
n
et
w
o
r
k
.
=
−
∑
−
ℕ
(1
4
)
Her
e,
is
an
ad
j
u
s
t
m
e
n
t
co
ef
f
i
cie
n
t
u
s
ed
to
b
alan
ce
d
eter
m
i
n
is
t
ic
s
elec
tio
n
an
d
p
r
o
b
ab
ilis
tic
b
eh
av
io
r
,
w
h
ile
ℕ
r
ep
r
esen
ts
th
e
s
et
o
f
ad
j
ac
en
t
CH
s
.
Fi
n
all
y
,
th
e
r
ela
y
n
o
d
e
f
o
r
C
H
is
s
e
lecte
d
ac
co
r
d
in
g
t
o
th
e
f
o
llo
w
in
g
m
i
n
i
m
izatio
n
r
u
le.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
erg
y
-
efficien
t c
lu
s
teri
n
g
o
p
t
imiz
a
tio
n
fo
r
h
etero
g
en
eo
u
s
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
w
i
th
…
(
A
d
il Hilma
n
i
)
567
=
a
r
g
∈
ℕ
(1
5
)
T
h
an
k
s
to
t
h
is
co
n
f
i
g
u
r
atio
n
,
d
ata
r
o
u
tin
g
d
y
n
a
m
icall
y
f
a
v
o
r
s
CH
s
w
i
th
h
i
g
h
er
r
esid
u
al
en
er
g
y
,
s
tr
o
n
g
er
co
n
n
ec
ti
v
it
y
,
an
d
lo
w
er
co
n
g
est
io
n
,
en
s
u
r
i
n
g
b
alan
ce
d
lo
ad
d
is
tr
ib
u
tio
n
a
n
d
ex
ten
d
i
n
g
th
e
o
v
er
all
n
et
w
o
r
k
li
f
eti
m
e.
5.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
AND
DIS
CUSS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
a
n
d
co
m
p
ar
es
t
h
e
s
i
m
u
latio
n
s
o
f
th
e
o
r
ig
i
n
al
DE
E
C
,
EEC
a
n
d
C
A
MP
p
r
o
to
co
ls
w
it
h
t
h
eir
i
m
p
r
o
v
ed
v
er
s
io
n
s
b
y
o
u
r
p
r
o
p
o
s
ed
p
r
o
to
co
l
b
ased
o
n
KNN
al
g
o
r
ith
m
.
T
ab
le
1
s
h
o
w
s
th
at
b
o
th
s
i
m
u
latio
n
s
u
s
ed
th
e
s
a
m
e
p
ar
a
m
eter
s
.
T
ab
le
1
.
Sim
u
latio
n
p
ar
a
m
eter
P
a
r
a
me
t
e
r
V
a
l
u
e
S
t
u
d
y
a
r
e
a
d
i
me
n
s
i
o
n
s
1
00
×
1
00
T
o
t
a
l
n
u
m
b
e
r
o
f
se
n
so
r
n
o
d
e
s (n
)
4
0
0
N
u
mb
e
r
o
f
r
o
u
n
d
s
max
5
0
0
0
C
H
e
l
e
c
t
i
o
n
p
r
o
b
a
b
i
l
i
t
y
(
p
)
0
.
1
B
S
c
o
o
r
d
i
n
a
t
e
s
2
0
0
×
2
0
0
El
e
c
t
r
o
n
i
c
e
n
e
r
g
y
p
e
r
b
i
t
5
0
n
J
/
b
i
t
A
mp
l
i
f
i
c
a
t
i
o
n
c
o
e
f
f
i
c
i
e
n
t
(
f
r
e
e
-
sp
a
c
e
,
ℇ
)
1
0
p
J
/
b
i
t
/
m
2
A
mp
l
i
f
i
c
a
t
i
o
n
c
o
e
f
f
i
c
i
e
n
t
(
mu
l
t
i
p
a
t
h
,
ℇ
)
0
.
0
0
1
3
p
J/
b
i
t
/
m
2
R
e
c
e
p
t
i
o
n
e
n
e
r
g
y
p
e
r
b
i
t
5
0
n
J
/
b
i
t
D
a
t
a
p
a
c
k
e
t
si
z
e
5
0
0
b
y
t
e
s
T
o
t
a
l
si
mu
l
a
t
i
o
n
d
u
r
a
t
i
o
n
6
0
0
s
T
h
e
n
et
w
o
r
k
to
p
o
lo
g
y
a
n
al
y
s
i
s
p
r
esen
ted
i
n
Fi
g
u
r
e
1
clea
r
l
y
d
e
m
o
n
s
tr
ates
t
h
e
e
f
f
ec
ti
v
en
ess
o
f
t
h
e
p
r
o
p
o
s
ed
KNN
-
b
ased
clu
s
ter
i
n
g
s
tr
ate
g
y
i
n
i
m
p
r
o
v
i
n
g
th
e
o
v
er
all
o
r
g
an
izatio
n
o
f
th
e
W
SN
.
C
o
m
p
ar
ed
to
th
e
o
r
ig
in
al
p
r
o
to
co
ls
,
th
e
en
h
a
n
ce
d
v
er
s
io
n
s
(
DE
E
C
-
KNN,
EEC
-
K
NN,
an
d
C
AM
P
-
KN
N)
ac
h
iev
e
a
m
o
r
e
o
p
tim
ized
s
p
atial
d
is
tr
ib
u
tio
n
o
f
CH
s
,
s
i
g
n
i
f
ican
tl
y
r
ed
u
cin
g
th
eir
a
v
er
ag
e
d
i
s
tan
ce
to
th
e
BS
.
T
h
is
i
m
p
r
o
v
ed
p
lace
m
en
t
eli
m
i
n
ate
s
in
e
f
f
icie
n
t
o
r
in
ac
tiv
e
C
Hs
lo
ca
ted
at
th
e
n
et
w
o
r
k
p
er
ip
h
er
y
,
lead
in
g
to
m
o
r
e
b
alan
ce
d
clu
s
ter
f
o
r
m
atio
n
an
d
i
m
p
r
o
v
e
d
co
v
er
ag
e
co
n
s
is
ten
c
y
ac
r
o
s
s
th
e
d
ep
lo
y
m
e
n
t a
r
ea
.
Fig
u
r
e
1
.
P
er
f
o
r
m
a
n
ce
r
esu
lt
s
o
f
th
e
p
r
o
p
o
s
ed
DE
E
C
-
KNN,
E
E
C
-
K
NN,
an
d
C
A
MP
-
KNN
i
m
p
le
m
en
ta
tio
n
s
(
X
co
o
r
d
in
ate
(
m
)
,
Y
co
o
r
d
in
ate
(
m
)
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4864
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
15
,
No
.
3
,
No
v
e
m
b
er
202
6
:
563
-
571
568
I
n
ter
m
s
o
f
en
er
g
y
e
f
f
icie
n
c
y
(
Fig
u
r
e
2
)
,
th
e
r
esu
lt
s
r
ev
ea
l
a
m
ar
k
ed
r
ed
u
ctio
n
i
n
t
h
e
av
er
ag
e
en
er
g
y
co
n
s
u
m
p
tio
n
p
er
s
en
s
o
r
n
o
d
e.
T
h
is
g
ain
is
m
a
in
l
y
attr
ib
u
ted
to
th
e
o
p
tim
ized
clu
s
ter
in
g
p
r
o
ce
s
s
an
d
th
e
m
o
r
e
s
elec
ti
v
e
C
H
elec
tio
n
m
ec
h
a
n
is
m
,
w
h
ich
to
g
et
h
er
m
i
n
i
m
i
ze
u
n
n
ec
es
s
ar
y
lo
n
g
-
d
is
ta
n
ce
tr
an
s
m
is
s
io
n
s
an
d
d
is
tr
ib
u
te
co
m
m
u
n
ica
tio
n
tas
k
s
m
o
r
e
ev
e
n
l
y
ac
r
o
s
s
t
h
e
n
e
t
wo
r
k
.
A
s
a
r
es
u
lt,
th
e
o
v
er
all
en
er
g
y
lo
ad
is
b
etter
b
alan
ce
d
,
co
n
tr
ib
u
ti
n
g
to
i
m
p
r
o
v
ed
n
et
w
o
r
k
s
u
s
ta
in
ab
ili
t
y
.
Fig
u
r
e
2
.
C
o
m
p
ar
is
o
n
o
f
a
v
er
a
g
e
p
er
-
n
o
d
e
en
er
g
y
co
n
s
u
m
p
ti
o
n
b
et
w
ee
n
o
r
ig
i
n
al
an
d
KNN
-
m
o
d
i
f
ied
p
r
o
to
co
ls
T
h
e
p
er
f
o
r
m
an
ce
ev
a
lu
at
io
n
i
n
ter
m
s
o
f
r
eliab
ilit
y
a
n
d
n
et
wo
r
k
lif
eti
m
e
(
Fig
u
r
e
3
)
f
u
r
th
er
h
ig
h
li
g
h
ts
th
e
ad
v
a
n
tag
e
s
o
f
t
h
e
p
r
o
p
o
s
e
d
m
et
h
o
d
.
T
h
e
KNN
-
en
h
a
n
ce
d
p
r
o
t
o
co
ls
co
n
s
is
te
n
tl
y
m
ai
n
t
ain
a
h
i
g
h
er
p
ac
k
et
d
eliv
er
y
r
atio
(F
i
g
u
r
e
3
(
a)
)
,
ev
en
u
n
d
er
in
cr
ea
s
i
n
g
n
et
w
o
r
k
d
en
s
it
y
,
w
h
er
e
tr
ad
itio
n
a
l
ap
p
r
o
ac
h
es
t
y
p
icall
y
ex
p
er
ien
ce
p
er
f
o
r
m
a
n
ce
d
eg
r
ad
atio
n
d
u
e
to
co
n
g
est
io
n
an
d
u
n
e
v
e
n
en
er
g
y
d
ep
leti
o
n
.
Mo
r
eo
v
er
,
th
e
m
o
n
ito
r
i
n
g
o
f
ac
ti
v
e
n
o
d
es
o
v
er
ti
m
e
co
n
f
ir
m
s
a
s
i
g
n
if
ican
t
ex
te
n
s
io
n
o
f
t
h
e
n
et
w
o
r
k
l
if
e
ti
m
e
(F
ig
u
r
e
3
(
b
)
)
,
in
d
icati
n
g
t
h
at
t
h
e
p
r
o
p
o
s
ed
s
tr
ateg
y
e
f
f
ec
ti
v
el
y
m
iti
g
ate
s
p
r
e
m
at
u
r
e
n
o
d
e
f
ail
u
r
es.
(
a)
(
b
)
Fig
u
r
e
3
.
E
v
alu
atio
n
o
f
n
et
w
o
r
k
p
er
f
o
r
m
an
ce
;
(
a)
p
ac
k
et
d
el
iv
er
y
r
atio
an
d
(
b
)
n
u
m
b
er
o
f
ac
tiv
e
n
o
d
es
Fin
all
y
,
th
e
s
ca
lab
ilit
y
a
s
s
e
s
s
m
en
t
(
Fig
u
r
e
4
)
co
n
f
ir
m
s
th
e
r
o
b
u
s
tn
es
s
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
w
h
e
n
th
e
n
et
w
o
r
k
s
ize
in
cr
ea
s
es
f
r
o
m
1
0
0
to
8
0
0
n
o
d
es.
Desp
ite
th
e
g
r
o
w
i
n
g
co
m
p
le
x
it
y
o
f
t
h
e
to
p
o
lo
g
y
,
t
h
e
s
y
s
te
m
m
ai
n
tai
n
s
s
tab
le
p
er
f
o
r
m
an
ce
i
n
ter
m
s
o
f
cl
u
s
ter
i
n
g
q
u
alit
y
,
e
n
er
g
y
e
f
f
ic
ien
c
y
,
a
n
d
co
m
m
u
n
icatio
n
r
eliab
ilit
y
.
T
h
ese
r
es
u
lts
co
lle
ctiv
el
y
v
a
lid
ate
th
e
p
r
o
p
o
s
ed
KNN
-
b
ased
p
r
o
to
co
l
as
a
s
ca
lab
le
an
d
en
er
g
y
-
ef
f
icien
t so
lu
tio
n
f
o
r
r
eliab
le
d
ata
tr
an
s
m
is
s
io
n
a
n
d
lo
n
g
-
ter
m
o
p
er
atio
n
in
d
en
s
e
W
SN
en
v
ir
o
n
m
en
ts
.
T
h
e
s
i
m
u
latio
n
r
esu
lts
co
n
f
ir
m
t
h
e
s
u
p
er
io
r
it
y
o
f
th
e
KNN
-
o
p
ti
m
ized
v
er
s
io
n
s
o
f
DE
E
C
,
E
E
C
,
an
d
C
A
MP
co
m
p
ar
ed
to
th
eir
co
n
v
e
n
tio
n
al
co
u
n
ter
p
ar
ts
.
T
h
ese
im
p
r
o
v
e
m
e
n
ts
ar
e
m
ai
n
l
y
r
ef
lecte
d
in
b
etter
r
eso
u
r
ce
m
an
a
g
e
m
en
t,
h
ig
h
er
n
et
w
o
r
k
r
o
b
u
s
t
n
ess
,
an
d
i
m
p
r
o
v
ed
o
v
er
all
en
er
g
y
ef
f
icie
n
c
y
(F
i
g
u
r
e
4
(
a)
)
.
B
y
o
p
tim
izin
g
cl
u
s
ter
f
o
r
m
atio
n
an
d
r
ef
i
n
in
g
C
H
s
elec
tio
n
,
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
s
ig
n
if
ica
n
tl
y
r
ed
u
ce
s
e
n
er
g
y
co
n
s
u
m
p
tio
n
w
h
ile
en
h
a
n
cin
g
r
eliab
ilit
y
.
I
t
also
ac
h
iev
e
s
a
h
ig
h
er
p
ac
k
et
d
eliv
er
y
r
atio
(F
ig
u
r
e
4
(
b
)
)
,
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
erg
y
-
efficien
t c
lu
s
teri
n
g
o
p
t
imiz
a
tio
n
fo
r
h
etero
g
en
eo
u
s
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
w
i
th
…
(
A
d
il Hilma
n
i
)
569
ex
ten
d
s
n
et
w
o
r
k
li
f
eti
m
e
(F
ig
u
r
e
4
(
c
)
)
.
T
h
ese
ad
v
an
tag
es
r
em
ai
n
co
n
s
i
s
ten
t
ev
en
i
n
h
i
g
h
-
d
en
s
it
y
n
et
w
o
r
k
s
ce
n
ar
io
s
,
d
e
m
o
n
s
tr
ati
n
g
th
e
e
f
f
ec
tiv
e
n
e
s
s
o
f
t
h
e
m
eth
o
d
f
o
r
r
eliab
le
an
d
en
er
g
y
-
e
f
f
icie
n
t
W
SN r
o
u
tin
g
.
(
a)
(
b)
(
c)
Fig
u
r
e
4
.
A
s
ca
lab
ili
t
y
a
n
al
y
s
i
s
;
(
a)
an
al
y
s
is
o
f
en
er
g
y
co
n
s
u
m
p
tio
n
,
(
b
)
ev
alu
at
io
n
o
f
n
et
wo
r
k
lif
eti
m
e,
an
d
(
c)
p
ac
k
et
d
eliv
er
y
r
atio
p
er
f
o
r
m
an
ce
6.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
o
p
o
s
ed
an
in
n
o
v
ativ
e
ap
p
r
o
ac
h
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
class
ic
W
SN
p
r
o
to
co
ls
,
in
cl
u
d
in
g
:
DE
E
C
,
E
E
C
,
an
d
C
A
MP
,
th
r
o
u
g
h
t
h
e
i
n
te
g
r
ati
o
n
o
f
th
e
KNN
al
g
o
r
ith
m
.
T
h
is
m
et
h
o
d
en
ab
les
b
etter
id
en
tific
atio
n
o
f
ac
ti
v
e
n
o
d
es
an
d
m
o
r
e
e
f
f
ic
ien
t
m
an
a
g
e
m
e
n
t
o
f
clu
s
ter
lead
e
r
s
,
th
u
s
p
r
o
m
o
ti
n
g
o
p
tim
a
l
clu
s
ter
o
r
g
an
izatio
n
.
C
lu
s
ter
lead
er
s
elec
tio
n
is
b
a
s
ed
o
n
r
esid
u
al
e
n
er
g
y
a
n
d
p
r
o
x
i
m
it
y
to
t
h
e
BS
,
r
ed
u
cin
g
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
co
m
m
u
n
icat
io
n
d
ela
y
s
.
Si
m
u
latio
n
r
esu
lt
s
s
h
o
w
a
s
i
g
n
i
f
ican
t
in
cr
ea
s
e
in
n
et
w
o
r
k
li
f
eti
m
e
an
d
a
d
ec
r
ea
s
e
in
th
e
n
u
m
b
er
o
f
d
ea
d
n
o
d
es
co
m
p
ar
ed
to
r
ef
er
en
ce
p
r
o
to
c
o
ls
.
T
h
ese
i
m
p
r
o
v
e
m
en
t
s
d
e
m
o
n
s
tr
ate
t
h
e
ef
f
ec
ti
v
e
n
ess
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
in
en
h
a
n
ci
n
g
th
e
s
tab
ilit
y
a
n
d
p
er
f
o
r
m
a
n
ce
o
f
W
SNs
.
F
u
tu
r
e
w
o
r
k
co
u
ld
ex
te
n
d
th
is
s
o
l
u
tio
n
to
d
y
n
a
m
ic
en
v
ir
o
n
m
e
n
ts
an
d
r
ea
l
-
w
o
r
ld
d
ep
lo
y
m
en
t
s
to
ev
al
u
ate
it
s
r
o
b
u
s
t
n
ess
u
n
d
er
v
ar
y
i
n
g
co
n
d
i
ti
o
n
s
.
F
UNDIN
G
I
NF
O
RM
AT
I
O
N
Au
t
h
o
r
s
s
tate
n
o
f
u
n
d
i
n
g
i
n
v
o
l
v
ed
.
AUTHO
R
CO
NT
RIB
UT
I
O
NS ST
A
T
E
M
E
NT
T
h
is
jo
u
r
n
al
u
s
e
s
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
t
o
r
ec
o
g
n
ize
in
d
i
v
id
u
al
au
t
h
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
t
h
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
lla
b
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
A
d
il Hil
m
a
n
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
Yass
i
n
e
Sab
r
i
✓
✓
✓
✓
✓
A
b
d
er
r
ah
i
m
Ma
izate
✓
✓
✓
Sih
a
m
A
o
u
ad
✓
✓
✓
✓
✓
Fo
u
ad
Ay
o
u
b
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
si
s
I
:
I
n
v
e
st
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
si
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
T
h
e
au
th
o
r
s
d
ec
lar
e
th
at
t
h
e
y
h
av
e
n
o
co
n
f
lict o
f
i
n
ter
est r
el
ated
to
th
e
p
u
b
licatio
n
o
f
t
h
is
ar
ticle.
DATA AV
AI
L
AB
I
L
I
T
Y
Data
av
ailab
ilit
y
is
n
o
t
ap
p
licab
le
to
th
is
p
ap
er
as
n
o
n
ew
d
ata
w
er
e
cr
ea
ted
o
r
an
aly
ze
d
in
th
i
s
s
tu
d
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4864
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
15
,
No
.
3
,
No
v
e
m
b
er
202
6
:
563
-
571
570
RE
F
E
R
E
NC
E
S
[
1
]
M.
A
l
a
g
a
r
sam
y
,
S
.
S
.
S
i
n
n
a
samy
,
I
.
G
o
p
a
l
,
R
.
K
u
p
p
u
samy
,
A
.
B
.
H
a
f
f
i
s
h
t
h
u
l
l
a
h
,
a
n
d
K.
S
u
r
i
y
a
n
,
"
A
n
e
f
f
e
c
t
i
v
e
g
o
ssi
p
r
o
u
t
i
n
g
b
a
se
d
w
i
r
e
l
e
ss
s
e
n
so
r
n
e
t
w
o
r
k
f
r
a
mew
o
r
k
f
o
r
f
o
r
e
st
f
i
r
e
d
e
t
e
c
t
i
o
n
,
"
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
R
e
c
o
n
f
i
g
u
r
a
b
l
e
a
n
d
Em
b
e
d
d
e
d
S
y
s
t
e
m
s (I
J
RES
)
,
v
o
l
.
1
2
,
n
o
.
3
,
p
p
.
3
9
2
-
4
0
2
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
r
e
s.v
1
2
.
i
3
p
p
3
9
2
-
4
0
2
.
[
2
]
S.
T
u
mu
l
a
e
t
a
l
.,
"
A
n
o
p
p
o
r
t
u
n
i
st
i
c
e
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
d
y
n
a
mi
c
se
l
f
-
c
o
n
f
i
g
u
r
a
t
i
o
n
c
l
u
st
e
r
i
n
g
a
l
g
o
r
i
t
h
m
i
n
W
S
N
-
b
a
se
d
I
o
T
n
e
t
w
o
r
k
s
,
"
I
n
t
e
r
n
a
t
i
o
n
a
l
j
o
u
r
n
a
l
o
f
c
o
m
m
u
n
i
c
a
t
i
o
n
s
y
st
e
m
s
,
v
o
l
.
3
7
,
n
o
.
1
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
2
/
d
a
c
.
5
6
3
3
.
[
3
]
W
.
B
.
N
e
d
h
a
m
a
n
d
A
.
K
.
M
.
A
l
-
Q
u
r
a
b
a
t
,
"
A
c
o
mp
r
e
h
e
n
si
v
e
r
e
v
i
e
w
o
f
c
l
u
st
e
r
i
n
g
a
p
p
r
o
a
c
h
e
s
f
o
r
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
i
n
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
s
,
"
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
C
o
m
p
u
t
e
r
A
p
p
l
i
c
a
t
i
o
n
s
i
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
7
2
,
n
o
.
2
,
p
p
1
3
9
-
1
6
0
,
A
u
g
.
2
0
2
4
,
d
o
i
:
1
0
.
1
5
0
4
/
I
JC
A
T
.
2
0
2
3
.
1
3
3
0
3
5
.
[
4
]
H.
M
o
h
a
p
a
t
r
a
,
A
.
K
.
R
a
t
h
,
R
.
K
L
e
n
k
a
,
R
.
K
.
N
a
y
a
k
,
a
n
d
R.
T
r
i
p
a
t
h
y
,
"
T
o
p
o
l
o
g
i
c
a
l
l
o
c
a
l
i
z
a
t
i
o
n
a
p
p
r
o
a
c
h
f
o
r
e
f
f
i
c
i
e
n
t
e
n
e
r
g
y
man
a
g
e
me
n
t
o
f
W
S
N
,
"
Ev
o
l
u
t
i
o
n
a
r
y
I
n
t
e
l
l
i
g
e
n
c
e
,
v
o
l
.
1
7
,
p
p.
7
1
7
–
7
2
7
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
2
0
6
5
-
021
-
0
0
6
3
1
-
9
.
[
5
]
T
.
-
N
.
T
r
a
n
,
T
.
-
L
.
N
g
u
y
e
n
,
V
.
T
.
H
o
a
n
g
,
a
n
d
M
.
V
o
z
n
a
k
,
"
S
e
n
so
r
C
l
u
s
t
e
r
i
n
g
U
si
n
g
a
K
-
M
e
a
n
s
A
l
g
o
r
i
t
h
m
i
n
C
o
mb
i
n
a
t
i
o
n
w
i
t
h
O
p
t
i
mi
z
e
d
U
n
ma
n
n
e
d
A
e
r
i
a
l
V
e
h
i
c
l
e
T
r
a
j
e
c
t
o
r
y
i
n
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
,
"
S
e
n
s
o
rs
,
v
o
l
.
23
,
n
o
.
4
,
p
p
.
1
-
25
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
0
4
2
3
4
5
.
[
6
]
P
.
G
o
u
,
B
.
G
u
o
,
M
.
G
u
o
,
a
n
d
S
.
M
a
o
,
"
V
K
EC
E
-
3
D
:
E
n
e
r
g
y
-
Ef
f
i
c
i
e
n
t
C
o
v
e
r
a
g
e
En
h
a
n
c
e
me
n
t
i
n
T
h
r
e
e
-
D
i
me
n
si
o
n
a
l
H
e
t
e
r
o
g
e
n
e
o
u
s
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
B
a
se
d
o
n
3
D
-
V
o
r
o
n
o
i
a
n
d
K
-
M
e
a
n
s
A
l
g
o
r
i
t
h
m
,
"
S
e
n
s
o
rs
,
v
o
l
.
23
,
n
o
.
2
,
p
p
.
1
-
26
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s2
3
0
2
0
5
7
3
.
[
7
]
I
.
A
.
S
h
a
h
a
n
d
M
.
A
h
me
d
,
"
F
K
-
m
e
a
n
s
R
A
:
F
u
z
z
y
K
-
M
e
a
n
s C
l
u
s
t
e
r
i
n
g
R
o
u
t
i
n
g
A
l
g
o
r
i
t
h
m
f
o
r
L
o
a
d
B
a
l
a
n
c
i
n
g
i
n
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
,
"
Wi
r
e
l
e
ss P
e
rso
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
3
0
,
p
p
.
1
0
7
1
–
1
0
8
3
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
2
7
7
-
0
2
3
-
1
0
3
2
0
-
8
.
[
8
]
R
.
K
a
r
i
m,
M
.
Z
a
h
e
d
i
,
D
.
D
e
,
a
n
d
A
.
D
a
s,
"
M
K
F
F
:
mi
d
-
p
o
i
n
t
K
-
me
a
n
s
b
a
s
e
d
c
l
u
s
t
e
r
i
n
g
i
n
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
f
o
r
f
o
r
e
st
f
i
r
e
p
r
e
d
i
c
t
i
o
n
,
"
Mi
c
ro
s
y
st
e
m
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
3
0
,
p
p.
4
6
9
–
4
8
0
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s
0
0
5
4
2
-
0
2
3
-
0
5
5
7
8
-
8
.
[
9
]
A
.
M
o
h
a
m
me
d
a
n
d
Y
.
Z
a
n
g
,
"
A
S
u
r
v
e
y
o
f
R
o
u
t
i
n
g
P
r
o
t
o
c
o
l
s
o
f
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
,"
S
u
st
a
i
n
a
b
l
e
Wi
re
l
e
ss
S
e
n
s
o
r
N
e
t
w
o
r
k
s
,
M
a
y
2
0
1
0
,
d
o
i
:
1
0
.
5
7
7
2
/
1
3
9
4
2
.
[
1
0
]
S
.
B
i
n
a
n
d
G
.
S
u
n
,
"
O
p
t
i
m
a
l
E
n
e
r
g
y
R
e
so
u
r
c
e
s
A
l
l
o
c
a
t
i
o
n
M
e
t
h
o
d
o
f
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
f
o
r
I
n
t
e
l
l
i
g
e
n
t
R
a
i
l
w
a
y
S
y
st
e
ms
,"
S
e
n
s
o
rs
,
v
o
l
.
2
0
,
n
o
.
2
,
p
p
.
1
-
20
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
0
0
2
0
4
8
2
.
[
1
1
]
A
.
Ju
w
a
i
e
d
a
n
d
L
.
Jac
k
o
w
sk
a
-
S
t
r
u
m
i
t
t
o
,
"
A
n
a
l
y
si
s
o
f
c
l
u
st
e
r
h
e
a
d
s
p
o
s
i
t
i
o
n
s
i
n
st
a
b
l
e
e
l
e
c
t
i
o
n
p
r
o
t
o
c
o
l
f
o
r
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
,
"
i
n
2
0
1
8
I
n
t
e
r
n
a
t
i
o
n
a
l
I
n
t
e
r
d
i
s
c
i
p
l
i
n
a
ry
Ph
D
W
o
rks
h
o
p
(
I
I
P
h
D
W)
,
Ś
w
i
n
o
u
ś
c
i
e
,
P
o
l
a
n
d
,
2
0
1
8
,
p
p
.
3
6
7
-
3
7
0
,
d
o
i
:
1
0
.
1
1
0
9
/
I
I
P
H
D
W
.
2
0
1
8
.
8
3
8
8
3
9
2
.
[
1
2
]
S
.
L
o
mb
a
r
d
o
,
E
.
Y
.
W
u
,
a
n
d
J
.
H
.
S
t
a
t
h
i
s,
"
El
e
c
t
r
o
n
e
n
e
r
g
y
d
i
ss
i
p
a
t
i
o
n
m
o
d
e
l
o
f
g
a
t
e
d
i
e
l
e
c
t
r
i
c
p
r
o
g
r
e
ssi
v
e
b
r
e
a
k
d
o
w
n
i
n
n
-
a
n
d
p
-
c
h
a
n
n
e
l
f
i
e
l
d
e
c
t
t
r
a
n
si
s
t
o
r
s
,"
J
o
u
r
n
a
l
o
f
A
p
p
l
i
e
d
P
h
y
s
i
c
s
,
v
o
l
.
8
,
p
.
0
8
5
7
0
1
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
6
3
/
1
.
4
9
8
5
7
9
4
.
[
1
3
]
L
.
Q
i
n
g
,
Q
.
Z
h
u
,
a
n
d
M
.
W
a
n
g
,
"
D
e
si
g
n
o
f
a
d
i
s
t
r
i
b
u
t
e
d
e
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
c
l
u
st
e
r
i
n
g
a
l
g
o
r
i
t
h
m
f
o
r
h
e
t
e
r
o
g
e
n
e
o
u
s
w
i
r
e
l
e
ss
s
e
n
so
r
n
e
t
w
o
r
k
s,
"
C
o
m
p
u
t
e
r
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
2
9
,
n
o
.
1
2
,
p
p
.
2
2
3
0
-
2
2
3
7
,
A
u
g
.
2
0
0
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
mco
m.
2
0
0
6
.
0
2
.
0
1
7
.
[
1
4
]
H
.
L
i
n
,
L
.
W
a
n
g
a
n
d
R
.
K
o
n
g
,
"
En
e
r
g
y
Ef
f
i
c
i
e
n
t
C
l
u
s
t
e
r
i
n
g
P
r
o
t
o
c
o
l
f
o
r
L
a
r
g
e
-
S
c
a
l
e
S
e
n
so
r
N
e
t
w
o
r
k
s,"
I
EEE
S
e
n
s
o
rs
J
o
u
rn
a
l
,
v
o
l
.
1
5
,
n
o
.
1
2
,
p
p
.
7
1
5
0
-
7
1
6
0
,
D
e
c
.
2
0
1
5
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
1
5
.
2
4
7
1
8
4
3
.
[
1
5
]
M
.
S
a
j
w
a
n
,
D
.
G
o
sai
n
,
a
n
d
A
.
K
.
S
h
a
r
ma
,
"
C
A
M
P
:
c
l
u
st
e
r
a
i
d
e
d
m
u
l
t
i
-
p
a
t
h
r
o
u
t
i
n
g
p
r
o
t
o
c
o
l
f
o
r
w
i
r
e
l
e
ss
sen
so
r
n
e
t
w
o
r
k
s
,"
Wi
re
l
e
ss
N
e
t
w
o
r
k
s
,
v
o
l
.
2
5
,
p
p
.
2
6
0
3
–
2
6
2
0
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
7
6
-
0
1
8
-
1
6
8
9
-
0.
[
1
6
]
A
.
Ju
w
a
i
e
d
,
L
.
Jac
k
o
w
sk
a
-
S
t
r
u
mi
ł
ł
o
,
a
n
d
A
.
S
i
e
r
sz
e
ń
,
"
M
o
d
i
f
i
e
d
T
EEN
P
r
o
t
o
c
o
l
i
n
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
U
si
n
g
K
N
N
A
l
g
o
r
i
t
h
m
,
"
in
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
I
m
a
g
e
Pro
c
e
ss
i
n
g
a
n
d
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
2
0
1
8
,
p
p
.
1
6
1
-
1
6
8
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
0
3
0
-
0
3
6
5
8
-
4
_
1
9
.
[
1
7
]
M
.
H
.
B
e
h
i
r
y
a
n
d
M
.
A
l
y
,
"
C
y
b
e
r
a
t
t
a
c
k
d
e
t
e
c
t
i
o
n
i
n
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
s
u
s
i
n
g
a
h
y
b
r
i
d
f
e
a
t
u
r
e
r
e
d
u
c
t
i
o
n
t
e
c
h
n
i
q
u
e
w
i
t
h
A
I
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
me
t
h
o
d
s
,"
J
o
u
r
n
a
l
o
f
Bi
g
D
a
t
a
,
v
o
l
.
1
1
,
n
o
.
1
,
p
p
.
1
-
39
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
8
6
/
s
4
0
5
3
7
-
0
2
3
-
0
0
8
7
0
-
w
.
[
1
8
]
L
.
F
a
n
,
L
.
L
i
u
,
H.
G
a
o
,
Z.
M
a
,
a
n
d
Y
.
W
u
,
"
S
e
c
u
r
e
K
-
N
e
a
r
e
st
n
e
i
g
h
b
o
r
q
u
e
r
i
e
s
i
n
t
w
o
-
t
i
e
r
e
d
mo
b
i
l
e
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
s
,"
D
i
g
i
t
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
a
n
d
N
e
t
w
o
rk
s
,
v
o
l
.
7
,
p
p
.
2
4
7
–
2
5
6
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
d
c
a
n
.
2
0
2
0
.
0
9
.
0
0
6
.
[
1
9
]
M
.
C
h
u
,
H
.
H
a
u
sse
c
k
e
r
,
a
n
d
F
.
Z
h
a
o
,
"
S
c
a
l
a
b
l
e
I
n
f
o
r
mat
i
o
n
-
D
r
i
v
e
n
S
e
n
so
r
Q
u
e
r
y
i
n
g
a
n
d
R
o
u
t
i
n
g
f
o
r
a
d
h
o
c
H
e
t
e
r
o
g
e
n
e
o
u
s
S
e
n
so
r
N
e
t
w
o
r
k
s
,"
T
h
e
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
H
i
g
h
Pe
r
f
o
rm
a
n
c
e
C
o
m
p
u
t
i
n
g
,
v
o
l
.
16
,
n
o
.
3
,
2
9
3
–
3
1
3
,
2
0
0
2
,
d
o
i
:
1
0
.
1
1
7
7
/
1
0
9
4
3
4
2
0
0
2
0
1
6
0
0
3
0
9
0
1
.
[
2
0
]
A
.
S
i
d
d
i
q
a
n
d
Y
.
J.
G
h
a
z
w
a
n
i
,
"
H
y
b
r
i
d
O
p
t
i
mi
z
e
d
D
e
e
p
N
e
u
r
a
l
N
e
t
w
o
r
k
-
B
a
se
d
I
n
t
r
u
si
o
n
N
o
d
e
D
e
t
e
c
t
i
o
n
a
n
d
M
o
d
i
f
i
e
d
E
n
e
r
g
y
Ef
f
i
c
i
e
n
t
C
e
n
t
r
a
l
i
z
e
d
C
l
u
st
e
r
i
n
g
R
o
u
t
i
n
g
P
r
o
t
o
c
o
l
f
o
r
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
,
"
I
EEE
T
r
a
n
sa
c
t
i
o
n
s
o
n
C
o
n
su
m
e
r
E
l
e
c
t
r
o
n
i
c
s
,
v
o
l
.
7
0
,
n
o
.
3
,
p
p
.
6
3
0
3
-
6
3
1
3
,
A
u
g
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
T
C
E
.
2
0
2
4
.
3
4
3
0
9
7
4
.
[
2
1
]
M
.
I
q
b
a
l
,
M
.
N
a
e
e
m,
A.
A
n
p
a
l
a
g
a
n
,
A
.
A
h
me
d
,
a
n
d
M
.
A
z
a
m,
"
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
O
p
t
i
mi
z
a
t
i
o
n
:
M
u
l
t
i
-
O
b
j
e
c
t
i
v
e
P
a
r
a
d
i
g
m
,"
S
e
n
so
rs
,
v
o
l
.
1
5
,
p
p
.
1
7
5
7
2
–
1
7
6
2
0
,
2
0
1
5
,
d
o
i
:
1
0
.
3
3
9
0
/
s
1
5
0
7
1
7
5
7
2
.
[
2
2
]
A
.
A
r
b
e
l
a
e
z
,
D
.
M
e
h
t
a
,
B.
O
’
S
u
l
l
i
v
a
n
,
a
n
d
L
.
Q
u
e
sad
a
,
"
A
c
o
n
s
t
r
a
i
n
t
-
b
a
se
d
p
a
r
a
l
l
e
l
l
o
c
a
l
se
a
r
c
h
f
o
r
t
h
e
e
d
g
e
-
d
i
sj
o
i
n
t
r
o
o
t
e
d
d
i
s
t
a
n
c
e
-
c
o
n
s
t
r
a
i
n
e
d
mi
n
i
mu
m
s
p
a
n
n
i
n
g
t
r
e
e
p
r
o
b
l
e
m
,"
J
o
u
rn
a
l
o
f
H
e
u
r
i
st
i
c
s
,
v
o
l
.
2
4
,
p
p
.
359
–
3
9
4
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
7
3
2
-
0
1
7
-
9
3
4
2
-
0.
[
2
3
]
J.
S
.
R
a
i
k
w
a
l
a
n
d
K
.
S
a
x
e
n
a
,
"
P
e
r
f
o
r
man
c
e
Ev
a
l
u
a
t
i
o
n
o
f
S
V
M
a
n
d
K
-
N
e
a
r
e
st
N
e
i
g
h
b
o
r
A
l
g
o
r
i
t
h
m
o
v
e
r
M
e
d
i
c
a
l
D
a
t
a
se
t
,
"
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
C
o
m
p
u
t
e
r
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
5
0
,
n
o
.
1
4
,
p
p
.
35
–
39
,
Ju
l
.
2
0
1
2
,
d
o
i
:
1
0
.
5
1
2
0
/
7
8
4
2
-
1
0
5
5
.
[
2
4
]
M
.
G
a
mal
,
N
.
E
.
M
e
k
k
y
,
H
.
H
.
S
o
l
i
man
,
a
n
d
N
.
A
.
H
i
k
a
l
,
"
E
n
h
a
n
c
i
n
g
t
h
e
L
i
f
e
t
i
me
o
f
W
i
r
e
l
e
ss
S
e
n
so
r
N
e
t
w
o
r
k
s
U
si
n
g
F
u
z
z
y
L
o
g
i
c
L
EA
C
H
Te
c
h
n
i
q
u
e
-
B
a
se
d
P
a
r
t
i
c
l
e
S
w
a
r
m
O
p
t
i
mi
z
a
t
i
o
n
,
"
I
EE
E
A
c
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
3
6
9
3
5
-
3
6
9
4
8
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
2
.
3
1
6
3
2
5
4
.
[
2
5
]
G
.
A
r
y
a
,
A
.
B
a
g
w
a
r
i
,
a
n
d
D
.
S
.
C
h
a
u
h
a
n
,
"
P
e
r
f
o
r
man
c
e
A
n
a
l
y
si
s
o
f
D
e
e
p
L
e
a
r
n
i
n
g
-
B
a
se
d
R
o
u
t
i
n
g
P
r
o
t
o
c
o
l
f
o
r
a
n
Ef
f
i
c
i
e
n
t
D
a
t
a
T
r
a
n
smissi
o
n
i
n
5
G
W
S
N
C
o
mm
u
n
i
c
a
t
i
o
n
,
"
I
EE
E
A
c
c
e
ss
,
v
o
l
.
1
0
,
p
p
.
9
3
4
0
-
9
3
5
6
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
2
.
3
1
4
2
0
8
2
.
[
2
6
]
M
.
D
o
h
l
e
r
a
n
d
T
.
N
a
k
a
mu
r
a
,
"
5
G
M
o
b
i
l
e
a
n
d
W
i
r
e
l
e
ss
C
o
mm
u
n
i
c
a
t
i
o
n
s
T
e
c
h
n
o
l
o
g
y
,"
C
a
m
b
ri
d
g
e
U
n
i
v
e
r
si
t
y
Pre
ss
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
7
/
C
B
O
9
7
8
1
3
1
6
4
1
7
7
4
4
.
[
2
7
]
S
.
S
.
W
a
g
h
,
A
.
M
o
r
e
,
a
n
d
P
.
R
.
K
h
a
r
o
t
e
,
"
P
e
r
f
o
r
man
c
e
e
v
a
l
u
a
t
i
o
n
o
f
I
EEE
8
0
2
.
1
5
.
4
p
r
o
t
o
c
o
l
u
n
d
e
r
c
o
e
x
i
st
e
n
c
e
o
f
W
i
F
i
8
0
2
.
1
1
b
,
"
Pr
o
c
e
d
i
a
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
5
7
,
p
p
.
7
4
5
–
7
5
1
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
r
o
c
s.
2
0
1
5
.
0
7
.
4
6
7
.
[
2
8
]
M
.
S
a
a
d
a
t
i
,
S
.
M
.
M
a
z
i
n
a
n
i
,
A
.
A
.
K
h
a
z
a
e
i
,
a
n
d
S
.
J.
S
.
M
.
C
h
a
b
o
k
,
"
En
e
r
g
y
e
f
f
i
c
i
e
n
t
c
l
u
st
e
r
i
n
g
f
o
r
d
e
n
se
w
i
r
e
l
e
ss
se
n
so
r
n
e
t
w
o
r
k
b
y
a
p
p
l
y
i
n
g
G
r
a
p
h
N
e
u
r
a
l
N
e
t
w
o
r
k
s
w
i
t
h
c
o
v
e
r
a
g
e
me
t
r
i
c
s,
"
Ad
H
o
c
N
e
t
w
o
rks
,
v
o
l
.
1
5
6
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
d
h
o
c
.
2
0
2
4
.
1
0
3
4
3
2
.
[
2
9
]
A
.
A
b
R
a
h
m
a
n
a
n
d
S
.
I
.
S
.
H
a
mi
m
,
"
I
mp
r
o
v
e
d
K
-
m
e
a
n
s
c
l
u
st
e
r
i
n
g
a
n
d
a
d
a
p
t
i
v
e
d
i
st
a
n
c
e
t
h
r
e
sh
o
l
d
f
o
r
e
n
e
r
g
y
r
e
d
u
c
t
i
o
n
i
n
W
S
N
-
I
o
T
s
,
"
Arra
y
,
v
o
l
.
2
7
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
r
r
a
y
.
2
0
2
5
.
1
0
0
4
3
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
E
n
erg
y
-
efficien
t c
lu
s
teri
n
g
o
p
t
imiz
a
tio
n
fo
r
h
etero
g
en
eo
u
s
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
w
i
th
…
(
A
d
il Hilma
n
i
)
571
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Ad
il
H
il
m
a
n
i
re
c
e
iv
e
d
h
is d
i
p
l
o
m
a
a
s
m
u
c
h
a
s a
N
e
t
w
o
rk
a
n
d
T
e
lec
o
m
m
u
n
ica
ti
o
n
e
n
g
in
e
e
r
f
ro
m
th
e
su
p
e
ri
o
r
e
n
g
i
n
e
e
rin
g
sc
h
o
o
l,
f
ro
m
th
e
Un
iv
e
rsity
o
f
S
e
v
il
le
in
S
p
a
in
.
I
n
2
0
2
1
,
h
e
h
a
d
h
is
d
o
c
to
ra
te
i
n
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
a
t
th
e
Na
ti
o
n
a
l
S
c
h
o
o
l
o
f
El
e
c
tri
c
it
y
a
n
d
M
e
c
h
a
n
i
c
s
(ENS
EM
)
i
n
Ca
sa
b
lan
c
a
-
M
o
ro
c
c
o
.
C
u
rre
n
t
ly
,
h
e
is
a
p
ro
f
e
ss
o
r
o
f
Hig
h
S
c
h
o
o
l
o
f
T
e
c
h
n
o
lo
g
y
S
a
lé,
M
o
h
a
m
m
e
d
V
Un
iv
e
rsity
,
Ra
b
a
t,
M
o
ro
c
c
o
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
d
il
h
il
m
a
n
i@g
m
a
il
.
c
o
m
.
Ya
ss
i
n
e
S
a
b
r
i
b
o
rn
o
n
Oc
to
b
e
r
2
8
th
,
1
9
8
4
,
i
n
Ra
b
a
t,
M
o
r
o
c
c
o
,
h
e
re
c
e
i
v
e
d
a
B.
S
c
.
in
sc
ien
c
e
,
tec
h
n
o
lo
g
y
,
a
n
d
m
a
th
e
m
a
ti
c
s
f
ro
m
M
o
h
a
m
e
d
5
Un
iv
e
r
sity
o
f
Ra
b
a
t
in
2
0
0
6
a
n
d
a
n
M
.
S
c
.
i
n
E
n
g
in
e
e
rin
g
f
ro
m
th
e
Un
iv
e
rsity
Ch
o
u
a
ib
Do
u
a
k
a
ly
in
2
0
0
8
.
He
e
a
rn
e
d
h
is
P
h
.
D
.
i
n
W
S
N
tec
h
n
o
l
o
g
y
a
t
th
e
L
a
b
o
ra
to
ry
o
f
S
c
ie
n
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
b
riy
a
ss
in
o
@g
m
a
il
.
c
o
m
.
Abd
e
r
r
a
h
i
m
M
a
iza
te
a
f
ter
c
o
m
p
letin
g
h
is
d
ip
lo
m
a
in
Ne
tw
o
rk
a
n
d
T
e
le
c
o
m
m
u
n
ica
ti
o
n
e
n
g
in
e
e
rin
g
f
ro
m
th
e
Un
iv
e
rsit
y
o
f
S
e
v
il
le
in
S
p
a
in
,
h
e
w
e
n
t
o
n
to
o
b
tain
h
is
d
o
c
to
ra
te
i
n
c
o
m
p
u
ter.
His
re
se
a
rc
h
in
tere
sts
li
e
in
th
e
a
r
e
a
s
o
f
m
o
b
il
e
n
e
tw
o
rk
s
a
n
d
c
o
m
p
u
ti
n
g
,
w
irele
ss
se
n
so
r
n
e
tw
o
rk
s,
a
n
d
e
m
b
e
d
d
e
d
sy
ste
m
s
so
f
t
wa
re
f
o
r
Io
T
.
He
is
a
m
e
m
b
e
r
o
f
IEE
E.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
a
i
z
a
t
e
@
h
o
tm
a
i
l
.
c
o
m
.
S
ih
am
Ao
u
a
d
b
o
rn
i
n
T
a
n
g
ier,
M
o
r
o
c
c
o
in
1
9
8
1
,
h
o
l
d
s
a
P
h
.
D.
i
n
Co
m
p
u
ter
En
g
in
e
e
rin
g
f
ro
m
M
o
h
a
m
m
a
d
ia
S
c
h
o
o
l
o
f
En
g
in
e
e
rs
EM
I
in
2
0
1
4
.
S
h
e
o
b
tai
n
e
d
h
e
r
n
e
tw
o
rk
e
n
g
in
e
e
rin
g
d
e
g
re
e
f
ro
m
th
e
Na
ti
o
n
a
l
S
c
h
o
o
l
o
f
A
p
p
li
e
d
S
c
ien
c
e
s
ENS
A
Tan
g
ier
in
2
0
0
5
.
Cu
rre
n
t
ly
,
sh
e
w
o
rk
s,
S
DN
,
A
I,
v
irt
u
a
li
z
a
ti
o
n
,
c
l
o
u
d
c
o
m
p
u
ti
n
g
,
a
n
d
se
c
u
rit
y
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sih
a
m
.
a
o
u
a
d
@e
n
sia
s.u
m
5
.
a
c
.
m
a
.
Fo
u
a
d
Ay
o
u
b
b
o
rn
in
Ke
n
i
tra,
M
o
ro
c
c
o
,
in
1
9
7
9
.
He
o
b
tain
e
d
h
is
B.
S
c
.
d
e
g
re
e
in
M
a
th
e
m
a
ti
c
a
l
S
c
ien
c
e
s
in
S
e
p
tem
b
e
r
1
9
9
8
.
He
t
h
e
n
p
u
rs
u
e
d
h
is
g
ra
d
u
a
te
stu
d
ies
a
t
M
o
h
a
m
m
e
d
V
Un
iv
e
rsit
y
,
F
a
c
u
lt
y
o
f
S
c
i
e
n
c
e
s,
Ra
b
a
t,
M
o
ro
c
c
o
,
wh
e
re
h
e
e
a
rn
e
d
h
is M
a
ste
r
o
f
S
c
ien
c
e
a
n
d
P
h
.
D.
d
e
g
re
e
s
in
E
n
g
in
e
e
rin
g
,
T
e
lec
o
m
m
u
n
ica
ti
o
n
s,
a
n
d
M
u
lt
im
e
d
ia
in
2
0
0
5
a
n
d
2
0
1
1
,
re
sp
e
c
ti
v
e
l
y
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
y
o
u
b
f
o
u
a
d
n
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.