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SS
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[
1
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T
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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-
4864
I
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Sy
s
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,
Vo
l.
14
,
No
.
1
,
Ma
r
c
h
2
0
2
5
:
2
2
1
-
230
222
W
SN
ap
p
licatio
n
,
s
en
s
o
r
n
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d
e
(
SN)
s
en
s
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d
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4
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f
ie
ld
[
5
]
.
T
h
e
NL
is
d
ef
i
n
ed
as
p
o
s
itio
n
d
eter
m
i
n
atio
n
t
h
e
o
f
th
e
u
n
k
n
o
w
n
SN
s
k
n
o
w
n
as
tar
g
et
n
o
d
es
u
s
i
n
g
t
h
e
k
n
o
w
n
lo
ca
tio
n
o
f
th
e
SNs
ter
m
ed
as
an
ch
o
r
n
o
d
e
ac
co
r
d
in
g
to
th
e
q
u
a
n
titi
e
s
li
k
e
ar
r
iv
al
ti
m
e,
ti
m
e
v
ar
ia
n
ce
o
f
ar
r
iv
al,
tr
ian
g
u
la
tio
n
a
n
d
m
ax
i
m
a
l
lik
e
lih
o
o
d
ar
r
iv
al
an
g
le,
an
d
s
o
o
n
[
6
]
.
T
h
e
NL
is
s
u
e
o
f
W
SN
s
h
o
u
ld
b
e
s
o
lv
ed
b
y
ap
p
l
y
i
n
g
g
lo
b
al
p
o
s
itio
n
in
g
s
y
s
te
m
(
GP
S)
w
ith
SNs
,
b
u
t
it
is
n
o
t
f
a
v
o
u
r
ed
o
w
i
n
g
to
its
s
ize,
en
er
g
y
an
d
co
s
t
p
r
o
b
le
m
s
.
H
en
ce
,
s
u
p
er
io
r
an
d
ef
f
ec
tu
al
a
lter
n
ati
v
e
i
s
r
eq
u
i
r
ed
f
o
r
lo
ca
lizin
g
t
h
e
S
Ns
[
7
]
.
T
h
e
n
o
n
-
GP
S
-
b
ased
lo
ca
lizatio
n
s
y
s
te
m
is
class
i
f
ied
in
t
o
r
an
g
e
-
f
r
ee
an
d
r
an
g
e
-
b
ased
m
o
d
els.
I
n
r
ec
en
t
ti
m
e
s
,
NL
in
W
SN
ca
n
b
e
m
a
n
a
g
ed
as
a
m
u
ltid
i
m
en
s
io
n
a
l,
an
d
m
u
l
ti
m
o
d
al
o
p
ti
m
izatio
n
p
r
o
b
lem
s
ar
e
o
v
er
co
m
e
b
y
p
o
p
u
latio
n
-
b
ased
s
to
ch
as
tic
al
g
o
r
ith
m
s
[
8
]
.
I
n
th
i
s
w
o
r
k
,
s
ev
er
al
m
eta
h
e
u
r
is
tic
ap
p
r
o
ac
h
es
ar
e
u
tili
z
ed
to
r
e
s
o
lv
e
th
e
N
L
i
s
s
u
es
i
n
W
SN.
T
h
is
m
e
th
o
d
w
a
s
s
u
cc
ee
d
ed
in
d
r
am
atica
ll
y
d
ec
lin
in
g
th
e
lo
ca
lizatio
n
er
r
o
r
s
.
I
t
attem
p
ts
to
r
eso
lv
e
an
o
p
ti
m
izatio
n
is
s
u
e
u
s
i
n
g
tr
ial
an
d
er
r
o
r
w
h
er
e
th
e
f
ea
s
ib
le
s
o
l
u
tio
n
is
p
r
o
ce
s
s
e
d
,
an
d
th
e
n
ea
r
b
y
t
h
e
f
i
n
es
t
s
o
lu
tio
n
is
d
etec
ted
[
9
]
.
Pre
s
en
tl
y
,
d
if
f
er
en
t
o
p
tim
izatio
n
tec
h
n
iq
u
es
s
u
c
h
as
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
,
cu
ck
o
o
s
ea
r
ch
(
C
S),
g
en
etic
al
g
o
r
ith
m
(
GA
)
,
b
u
tter
f
l
y
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
B
O
A
)
,
g
r
av
i
tatio
n
al
s
ea
r
c
h
alg
o
r
it
h
m
(
GS
A
)
,
an
d
ar
tif
icial
b
ee
co
lo
n
y
(
A
B
C
)
,
w
h
ic
h
ar
e
ef
f
e
ctiv
el
y
ap
p
lied
to
s
p
ec
if
y
t
h
e
p
o
s
itio
n
o
f
u
n
k
n
o
w
n
n
o
d
e
in
W
SN [
1
0
]
.
T
h
is
ar
ticle
o
f
f
er
s
th
e
m
o
d
elin
g
o
f
c
h
i
m
p
o
p
ti
m
izatio
n
alg
o
r
ith
m
n
o
d
e
lo
ca
lizatio
n
s
y
s
te
m
in
W
SN
s
(
MCO
A
N
L
-
W
SN)
tech
n
iq
u
e
.
T
h
e
MCOA
N
L
-
W
SN
m
et
h
o
d
i
m
p
le
m
e
n
t
s
a
m
o
d
elin
g
ar
ch
itect
u
r
e
th
a
t
in
co
r
p
o
r
ates
th
e
d
is
ti
n
cti
v
e
f
e
atu
r
es
o
f
c
h
i
m
p
o
p
ti
m
izatio
n
alg
o
r
ith
m
(
C
O
A
)
,
r
ec
o
g
n
ized
f
o
r
its
s
ti
m
u
latio
n
f
r
o
m
t
h
e
co
o
p
er
ativ
e
h
u
n
ti
n
g
b
eh
av
io
r
o
f
ch
i
m
p
a
n
ze
es,
in
to
th
e
N
L
p
r
o
ce
d
u
r
e.
T
h
e
s
y
s
te
m
i
s
ap
p
lied
m
at
h
e
m
a
tical
m
o
d
eli
n
g
f
o
r
s
ig
n
if
y
i
n
g
th
e
co
llab
o
r
ativ
e
s
ch
e
m
e
s
o
f
n
o
d
es
in
en
h
a
n
ci
n
g
t
h
eir
lo
ca
tio
n
s
.
A
d
d
itio
n
al
l
y
,
th
e
C
O
A
-
b
ased
lo
ca
lizatio
n
m
o
d
el
is
co
n
s
id
er
ed
f
o
r
a
d
ap
tin
g
d
y
n
a
m
icall
y
to
th
e
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
a
n
d
d
y
n
a
m
ic
t
y
p
e
o
f
W
SNs
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
M
C
O
A
N
L
-
W
SN
s
y
s
te
m
ca
n
b
e
m
ea
s
u
r
ed
v
ia
w
id
e
-
r
a
n
g
in
g
s
i
m
u
latio
n
s
,
co
n
s
id
er
in
g
cr
u
cial
m
e
tr
ics
li
k
e
s
ca
lab
ilit
y
,
en
er
g
y
e
f
f
icien
c
y
,
an
d
lo
ca
lizatio
n
ac
cu
r
ac
y
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
Z
h
an
g
et
a
l
.
[
1
1
]
g
o
al
is
to
im
p
r
o
v
e
th
e
n
o
d
e
u
tili
za
tio
n
o
f
u
n
d
er
w
ater
W
SN
u
t
ilizi
n
g
in
telli
g
en
t
o
p
tim
i
ze
r
s
y
s
te
m
s
an
d
r
o
b
o
t
co
llab
o
r
atio
n
to
o
l.
T
h
e
r
esear
ch
u
s
e
s
th
e
c
h
e
m
ical
r
ea
ctio
n
o
p
ti
m
izer
(
C
R
O)
m
o
d
el
th
at
i
n
co
r
p
o
r
ates
th
e
p
r
o
f
its
o
f
in
h
er
ited
m
eth
o
d
s
,
s
i
m
u
la
tio
n
a
n
n
ea
li
n
g
m
et
h
o
d
,
an
d
An
t
co
lo
n
y
alg
o
r
ith
m
(
A
C
A
)
.
T
h
e
C
R
O
m
o
d
el
i
s
i
m
p
r
o
v
ed
o
v
er
an
ar
ch
i
tect
u
r
e
alter
atio
n
r
o
le.
Mo
r
eo
v
er
,
th
e
au
to
n
o
m
y
an
d
f
lex
ib
ilit
y
o
f
r
o
b
o
ts
ar
e
l
ev
er
ag
ed
.
Z
h
an
g
et
a
l
.
[
1
2
]
d
ev
elo
p
ed
th
e
h
y
b
r
id
s
y
s
te
m
s
h
u
f
f
led
f
r
o
g
leap
in
g
alg
o
r
ith
m
(
S
F
L
A
)
-
W
O
A
(
SW
OA
)
d
ep
en
d
e
n
t
u
p
o
n
t
h
e
w
h
al
e
o
p
ti
m
izatio
n
alg
o
r
it
h
m
(
W
O
A
)
a
n
d
SF
L
A
.
T
h
e
SW
O
A
p
r
o
ce
s
s
i
n
teg
r
ate
s
th
e
b
en
ef
it
s
o
f
W
O
A
an
d
SF
L
A
;
it
r
ec
o
llects
th
e
ex
cl
u
s
iv
e
e
v
o
lu
tio
n
tec
h
n
iq
u
e
o
f
W
OA
a
n
d
th
e
o
u
t
s
ta
n
d
in
g
co
-
ev
o
l
u
tio
n
ab
ilit
y
o
f
SF
L
A
.
F
u
r
th
er
m
o
r
e,
u
tili
zi
n
g
th
e
m
u
ta
tio
n
,
cr
o
s
s
o
v
er
an
d
co
llectio
n
p
r
o
ce
s
s
es
o
f
th
e
d
if
f
er
en
ce
e
v
o
l
u
tio
n
(
DE
)
p
r
o
ce
d
u
r
e
to
i
m
p
r
o
v
e
th
is
h
y
b
r
id
s
y
s
te
m
,
t
h
e
SW
O
A
-
b
ased
SF
L
A
-
W
O
A
DE
m
o
d
el
h
as
b
ee
n
p
r
o
j
ec
ted
.
Yan
g
et
a
l
.
[
1
3
]
in
tr
o
d
u
ce
d
a
n
ew
h
y
b
r
i
d
ch
i
m
p
o
p
tim
izer
an
d
h
u
n
g
er
g
a
m
es
s
ea
r
c
h
(
C
h
OA
-
HG
S)
s
y
s
te
m
s
.
I
n
th
is
m
o
d
el,
at
p
r
im
ar
y
,
th
e
C
h
O
A
w
a
s
u
tili
ze
d
in
o
r
d
er
t
o
s
elec
t
clu
s
ter
h
ea
d
(
C
H)
an
d
p
r
o
f
ess
io
n
all
y
as
s
e
m
b
l
y
cl
u
s
t
er
s
.
T
h
en
,
th
e
HGS
-
b
ased
r
o
u
te
m
et
h
o
d
h
as
b
ee
n
e
m
p
lo
y
ed
in
o
r
d
er
to
d
ef
in
e
th
e
s
y
s
te
m
’
s
b
est
w
a
y
s
.
T
h
e
p
r
o
j
ec
ted
m
o
d
el
in
teg
r
ates
t
h
e
ad
v
an
ta
g
e
s
o
f
r
o
u
tin
g
an
d
clu
s
ter
in
g
,
s
u
b
s
eq
u
e
n
t f
o
r
o
p
tim
u
m
s
y
s
te
m
p
er
io
d
an
d
en
er
g
y
ef
f
icac
y
.
R
ed
d
y
et
a
l
.
[
1
4
]
d
ev
elo
p
ed
a
n
en
er
g
y
e
f
f
icien
t
clu
s
ter
h
ea
d
(
C
H)
ass
o
r
t
m
en
t
u
til
izin
g
a
n
i
m
p
r
o
v
ed
v
er
s
io
n
o
f
t
h
e
g
r
e
y
w
o
l
f
o
p
ti
m
izatio
n
(
E
E
C
HI
GW
O)
p
r
o
ce
d
u
r
e
to
ea
s
e
t
h
e
i
n
eq
u
it
y
a
m
o
n
g
e
x
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
,
ab
s
en
ce
o
f
p
o
p
u
lace
v
ar
iet
y
,
a
n
d
th
e
ea
r
l
y
u
n
io
n
o
f
th
e
s
i
m
p
le
GW
O
s
y
s
te
m
.
T
h
is
tech
n
iq
u
e
r
ef
lects
r
esid
u
al
en
er
g
y
,
s
i
n
k
d
is
tan
ce
,
CH
b
ala
n
ce
f
ea
t
u
r
e,
an
d
n
o
r
m
al
in
tr
a
clu
s
ter
s
p
a
ce
as
th
e
li
m
its
i
n
ch
o
o
s
in
g
t
h
e
C
H.
A
s
tr
u
g
g
le
to
co
o
r
d
in
ate
an
d
f
o
cu
s
th
e
u
n
d
er
w
ater
s
en
s
o
r
s
in
s
ta
n
tan
eo
u
s
l
y
i
n
a
m
u
l
ti
-
h
o
p
at
m
o
s
p
h
er
e
w
as
m
ea
s
u
r
ed
[
1
5
]
.
T
h
is
s
tu
d
y
r
ec
o
g
n
ized
a
lin
k
a
m
id
s
en
s
o
r
s
p
o
in
t
-
to
-
p
o
in
t
f
o
cu
s
ed
lin
k
s
f
o
llo
w
ed
b
y
lo
g
icall
y
b
u
ild
th
e
m
et
h
o
d
f
o
r
th
e
o
r
g
a
n
izatio
n
as
a
u
til
it
y
o
f
as
s
o
r
t
m
e
n
t,
d
elay
,
an
d
ti
m
e
s
ta
m
p
s
.
T
h
en
,
th
e
m
et
h
o
d
co
n
v
e
y
ed
th
e
u
n
co
n
s
tr
ai
n
ed
o
p
ti
m
izer
is
s
u
e
f
o
r
lo
ca
lizatio
n
b
y
u
ti
lizi
n
g
a
g
r
ad
ien
t
m
o
d
el.
A
s
y
s
te
m
w
it
h
co
m
p
ac
t
an
d
p
ar
allel
m
o
d
els
th
at
is
cr
ea
ted
o
n
w
h
a
le
o
p
ti
m
izatio
n
al
g
o
r
ith
m
(
P
C
W
O
A
)
m
et
h
o
d
is
p
r
o
j
ec
te
d
f
o
r
en
h
an
cin
g
ef
f
ec
ti
v
e
n
es
s
o
f
th
e
d
is
tan
ce
v
ec
to
r
-
h
o
p
(
DV
-
Ho
p
)
[
1
6
]
.
T
h
e
co
m
p
ac
t
m
et
h
o
d
k
ee
p
s
m
e
m
o
r
y
in
ta
k
e
b
y
d
ec
r
ea
s
i
n
g
th
e
u
n
iq
u
e
p
o
p
u
lace
.
Si
m
ilar
m
et
h
o
d
s
i
m
p
r
o
v
e
th
e
ca
p
ab
ilit
y
to
ex
its
f
r
o
m
lo
ca
l
o
p
ti
m
izer
a
n
d
en
h
a
n
ce
ac
cu
r
ate
n
es
s
.
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
Mo
d
elin
g
o
f c
h
imp
o
p
timiz
a
tio
n
a
lg
o
r
ith
m
n
o
d
e
l
o
ca
liz
a
tio
n
s
ch
eme
in
…
(
S
r
ip
r
iya
A
r
u
n
a
ch
a
la
m
)
223
3.
T
H
E
P
RO
P
O
SE
D
M
O
DE
L
I
n
th
is
ar
ticle,
w
e
f
o
cu
s
o
n
d
esig
n
an
d
d
ev
elo
p
m
e
n
t
o
f
th
e
MCO
A
N
L
-
W
S
N
tech
n
iq
u
e.
T
h
e
m
ai
n
ai
m
o
f
th
e
M
C
O
A
N
L
-
W
SN
al
g
o
r
ith
m
is
i
m
p
le
m
e
n
ted
f
o
r
lo
ca
lizin
g
th
e
u
n
k
n
o
w
n
n
o
d
es
i
n
th
e
n
et
w
o
r
k
u
s
i
n
g
a
m
etah
e
u
r
is
tic
o
p
ti
m
izat
io
n
a
lg
o
r
ith
m
.
Fi
g
u
r
e
1
d
em
o
n
s
tr
at
es th
e
w
o
r
k
f
lo
w
o
f
M
C
O
A
N
L
-
W
SN te
c
h
n
iq
u
e.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
MCO
A
N
L
-
W
SN te
c
h
n
iq
u
e
3
.
1
.
M
o
delin
g
o
f
chi
m
p o
pti
m
i
za
t
io
n a
lg
o
rit
h
m
Hu
m
an
s
as
w
el
l
as
ch
i
m
p
a
n
ze
es
h
av
e
n
u
m
er
o
u
s
s
i
m
ilar
it
ies
lik
e
DN
A
,
s
o
cial
p
er
f
o
r
m
an
ce
s
an
d
in
tellect
u
al
ap
tit
u
d
es
a
n
d
t
h
es
e
ar
e
p
r
o
m
i
n
en
t
ch
a
n
g
es
a
m
o
n
g
th
e
m
[
1
7
]
.
Fo
r
in
s
ta
n
ce
,
h
u
m
a
n
s
h
a
v
e
m
o
r
e
s
tr
aig
h
t
p
o
s
tu
r
e,
a
lar
g
e
b
r
ain
,
an
d
less
h
air
w
h
en
co
m
p
ar
e
d
w
i
th
ch
i
m
p
a
n
ze
es.
F
u
r
th
er
m
o
r
e,
h
u
m
an
s
h
a
v
e
in
n
o
v
ati
v
e
m
en
ta
l
s
k
ills
lik
e
lan
g
u
a
g
e,
cu
l
tu
r
e,
an
d
d
i
f
f
icu
lt
p
r
o
b
lem
r
eso
l
v
in
g
k
n
o
w
le
d
g
e
w
h
ic
h
ar
e
n
o
t
p
r
esen
t
in
ch
i
m
p
an
ze
e
s
.
T
ec
h
n
ica
l
s
ig
n
p
r
o
p
o
s
es
th
at
ch
i
m
p
a
n
ze
e
s
ca
n
ab
le
to
r
ec
o
g
n
ize
d
ef
in
ite
m
en
tal
p
r
o
ce
d
u
r
es lik
e
v
is
io
n
b
u
t t
h
e
y
d
o
n
o
t o
b
s
er
v
e
v
ie
w
s
a
n
d
o
th
er
s
m
in
d
s
t
h
a
n
h
u
m
an
s
.
C
h
i
m
p
a
n
ze
es
f
o
r
m
d
i
f
f
icu
l
t
s
o
cial
clu
s
ter
s
w
it
h
a
clas
s
i
f
ie
d
s
tr
u
ct
u
r
e,
r
o
b
u
s
t
b
o
n
d
s
,
an
d
n
u
m
er
o
u
s
s
o
cial
p
er
f
o
r
m
a
n
ce
s
li
k
e
g
r
o
o
m
i
n
g
as
w
ell
as
co
m
m
u
n
icatio
n
.
C
u
r
r
en
t
r
es
u
lts
r
ec
o
m
m
e
n
d
th
at
th
e
y
s
u
r
v
i
v
e
in
a
f
is
s
io
n
‐
f
u
s
io
n
cu
lt
u
r
e
an
d
d
is
p
la
y
r
ese
m
b
la
n
ce
s
to
h
u
m
a
n
s
th
at
co
n
tai
n
s
to
o
l‐
m
a
k
in
g
as
w
ell
as
s
u
p
p
o
r
tiv
e
h
u
n
ti
n
g
.
B
u
t
s
tr
u
g
g
les
ca
n
r
is
e
an
d
lead
to
v
io
le
n
t
p
er
f
o
r
m
an
ce
s
a
n
d
r
eg
io
n
al
ar
g
u
m
en
ts
.
I
n
ad
d
itio
n
,
th
e
y
h
av
e
r
e
m
ar
k
ab
le
p
r
o
b
lem
-
cr
ac
k
in
g
an
d
co
g
n
iti
v
e
s
k
il
ls
,
cu
s
to
m
f
a
m
il
y
ele
m
e
n
ts
as
w
ell
a
s
in
ter
r
elatin
g
w
it
h
n
ea
r
b
y
g
r
o
u
p
s
.
I
n
c
h
i
m
p
an
ze
e
g
r
o
u
p
s
,
c
h
i
m
p
lead
er
is
t
h
e
m
ai
n
i
n
d
iv
id
u
al
w
h
o
g
r
ip
s
a
d
o
m
in
a
n
t
p
o
s
itio
n
w
it
h
i
n
s
o
cial
h
ier
ar
ch
y
.
T
h
e
lead
er
d
is
p
lay
s
m
ai
n
b
eh
a
v
io
r
an
d
p
la
y
s
a
v
ital
p
ar
t
in
m
a
k
in
g
d
ec
is
io
n
,
u
p
h
o
ld
s
s
o
cial
p
r
o
m
is
e
s
,
s
o
lv
es
f
ig
h
t
s
,
i
m
p
o
r
tan
t
in
m
ati
n
g
,
an
d
p
r
o
tects
g
r
o
u
p
’
s
ter
r
ito
r
y
.
T
h
e
ch
i
m
p
lead
er
’
s
f
ea
t
u
r
es sa
f
eg
u
ar
d
ex
i
s
ten
ce
a
n
d
r
ep
r
o
d
u
ctiv
e
ac
h
ie
v
e
m
e
n
t
o
f
th
eir
g
r
o
u
p
.
C
h
i
m
p
a
n
ze
es
ar
e
ty
p
es
o
f
ex
c
ess
i
v
e
atten
tio
n
d
u
e
to
th
eir
ex
tr
ao
r
d
in
ar
y
co
n
n
ec
tio
n
s
,
co
m
m
u
n
icat
io
n
as
w
ell
a
s
ap
tit
u
d
e.
T
h
eir
s
o
c
ial
p
er
f
o
r
m
a
n
ce
a
n
d
p
r
o
b
lem
s
o
lv
i
n
g
s
k
il
ls
cr
ea
te
th
e
m
a
n
attr
ac
tiv
e
to
p
ic
o
f
r
esear
ch
f
o
r
s
cie
n
tis
ts
.
T
h
er
e
ar
e
4
s
ep
ar
ate
r
o
les in
ch
i
m
p
an
ze
e
s
o
cieties th
a
t
m
e
n
tio
n
ed
:
−
Dr
iv
er
s
ar
e
m
ai
n
ch
i
m
p
an
ze
e
s
th
at
g
u
id
e
th
eir
g
r
o
u
p
’
s
ac
tio
n
s
an
d
ac
ti
v
itie
s
.
T
h
ey
p
la
y
a
cr
itical
p
ar
t
in
m
ak
in
g
d
ec
is
io
n
an
d
f
o
r
m
in
g
ac
tio
n
s
li
k
e
lead
in
g
g
r
o
u
p
to
v
icti
m
.
−
C
h
a
s
er
s
ar
e
aler
t
an
d
f
as
t
ch
i
m
p
a
n
ze
e
s
th
at
s
h
i
n
e
i
n
h
u
n
t
an
d
ch
asi
n
g
p
er
f
o
r
m
a
n
ce
s
.
T
h
e
y
ar
e
ch
ief
l
y
b
en
ef
icia
l in
c
h
asi
n
g
s
p
ec
if
ic
t
ar
g
ets li
k
e
p
r
e
y
d
u
r
in
g
h
u
n
ti
n
g
.
−
B
ar
r
ier
s
ar
e
r
o
b
u
s
t
an
d
s
elf
-
ass
u
r
ed
ch
i
m
p
an
ze
e
s
th
at
d
ef
en
d
th
eir
g
r
o
u
p
.
T
h
e
y
g
en
er
a
te
b
ar
r
ier
s
o
r
p
r
o
b
lem
s
to
av
er
t
in
tr
u
d
er
s
o
r
th
r
ea
ts
f
r
o
m
e
n
ter
in
g
g
r
o
u
p
ter
r
ito
r
y
.
T
h
eir
m
ai
n
r
o
le
is
t
o
s
af
eg
u
ar
d
th
eir
g
r
o
u
p
an
d
its
ter
r
ito
r
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.
14
,
No
.
1
,
Ma
r
c
h
2
0
2
5
:
2
2
1
-
230
224
−
A
ttac
k
er
s
p
r
o
tect
t
h
eir
g
r
o
u
p
as
w
ell
as
e
m
p
lo
y
th
eir
v
io
le
n
t
p
er
f
o
r
m
a
n
ce
to
p
r
ed
ict
th
e
p
r
ey
’
s
e
s
ca
p
e
w
a
y
.
T
h
e
y
ca
n
s
en
d
p
r
e
y
b
ac
k
n
ea
r
to
h
u
n
ter
s
o
r
d
o
w
n
i
n
to
t
h
e
lo
w
er
ca
n
o
p
y
.
C
o
r
r
esp
o
n
d
in
g
C
h
O
A
,
w
e
h
a
v
e
5
p
ar
ts
as:
a.
Dr
iv
i
n
g
an
d
ch
a
s
i
n
g
p
r
e
y
:
tec
h
n
iq
u
e
o
f
d
r
iv
in
g
an
d
ch
as
in
g
p
r
ey
b
y
c
h
i
m
p
s
is
d
ef
i
n
ed
.
T
h
is
b
eh
a
v
io
r
is
ex
p
r
ess
ed
in
(
1
)
an
d
(
2
)
,
w
h
er
e
s
ig
n
if
ies d
is
ta
n
ce
a
m
o
n
g
ch
i
m
p
an
d
p
r
e
y
lo
ca
tio
n
s
:
=
|
.
(
)
−
.
ℎ
(
)
|
,
(
1
)
ℎ
(
+
1
)
=
(
)
−
.
.
(
2
)
h
er
e
,
m
ea
n
s
e
x
i
s
ti
n
g
i
ter
atio
n
,
,
,
an
d
d
escr
ib
es
co
n
s
ta
n
t
v
ec
t
o
r
s
,
d
en
o
tes
p
r
ey
p
o
s
itio
n
,
a
n
d
ℎ
m
ea
n
s
ch
i
m
p
p
o
s
itio
n
.
T
h
e
co
n
s
ta
n
t
s
ar
e
ca
lcu
lated
b
y
e
m
p
l
o
y
i
n
g
(
4
)
an
d
(
5
)
:
=
2
1
−
,
(
3
)
=
2
.
2
,
(
4
)
=
ℎ
;
(
5
)
m
u
ltip
le
s
el
f
-
g
o
v
er
n
i
n
g
c
h
i
m
p
clu
s
ter
s
w
it
h
d
is
s
i
m
ilar
p
lan
s
u
p
g
r
ad
e
f
o
r
lo
ca
l
an
d
g
lo
b
al
h
u
n
t
s
.
T
h
is
ex
p
an
d
s
a
n
d
b
alan
ce
s
s
ea
r
c
h
p
er
f
o
r
m
an
ce
.
d
en
o
tes
k
e
y
p
ar
am
eter
i
n
o
p
ti
m
izer
alg
o
r
i
th
m
,
f
lex
ib
le
b
alan
ce
a
m
o
n
g
ex
p
lo
itatio
n
an
d
ex
p
lo
r
atio
n
.
I
t
m
o
n
ito
r
s
an
al
g
o
r
ith
m
’
s
p
er
f
o
r
m
a
n
ce
in
ex
p
lo
r
in
g
s
o
lu
tio
n
s
an
d
u
p
d
ated
b
y
u
s
in
g
v
ar
io
u
s
p
lan
s
f
o
r
lo
ca
l
an
d
g
lo
b
al
h
u
n
ts
to
en
h
a
n
ce
o
p
tim
izatio
n
.
L
ib
er
ated
clu
s
ter
s
i
m
p
r
o
v
e
e
x
p
lo
r
atio
n
,
b
alan
ce
g
lo
b
al‐
lo
ca
l
s
ea
r
ch
,
a
n
d
g
r
ip
d
if
f
ic
u
lt
o
p
ti
m
izatio
n
.
C
h
i
m
p
s
ca
n
ab
le
to
ch
a
n
g
e
lo
ca
tio
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s
b
y
e
m
p
lo
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in
g
r
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d
o
m
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s
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h
is
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r
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d
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r
e
s
p
r
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m
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s
p
ac
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C
h
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m
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s
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lan
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en
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o
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atter
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h
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led
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n
d
a
m
en
ta
l d
eter
m
i
n
is
tic
p
r
o
ce
d
u
r
es.
b.
A
ttac
k
i
n
g
tech
n
iq
u
e
(
ex
p
lo
it
atio
n
p
h
ase)
:
ch
i
m
p
s
tr
av
el
p
r
ey
’
s
p
o
s
itio
n
v
ia
d
r
iv
in
g
,
o
b
s
tr
u
ctiv
e,
ch
asi
n
g
,
an
d
s
u
r
r
o
u
n
d
i
n
g
.
A
t
t
ac
k
er
ch
i
m
p
s
lead
ch
asi
n
g
t
h
a
t
is
m
ai
n
l
y
s
u
p
p
o
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ted
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y
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r
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ar
r
ier
s
a
s
w
ell
a
s
h
u
n
ter
ch
i
m
p
s
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In
(
6
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-
(
1
4
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d
ef
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ite
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n
ter
ac
tio
n
s
as
(
6)
-
(
9
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A
t
t
=
|
1
A
t
t
−
1
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(
6
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=
|
2
−
2
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7
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ℎ
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3
ℎ
−
3
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(
8
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=
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4
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4
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9
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u
p
g
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ad
e
p
o
s
itio
n
s
i
s
p
r
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ce
d
u
r
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o
f
ad
j
u
s
tin
g
lo
ca
tio
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s
o
f
ch
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m
p
:
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=
−
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(
)
(
10
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2
=
2
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)
(
11
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3
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−
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(
ℎ
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(
12
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4
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(
13
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o
v
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u
p
g
r
ad
ed
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o
s
itio
n
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(
+
1
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2
+
3
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4
4
(
14
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c.
Sear
ch
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o
r
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r
e
y
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lo
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o
m
f
ea
t
u
r
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w
i
th
in
s
p
an
o
f
[
−
2
,
2
]
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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ch
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m
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ased
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ch
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h
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(
4
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o
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d
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(
5
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im
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t a
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o
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itio
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e.
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cial
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ce
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(
s
e
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u
al
m
o
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as
d
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b
ef
o
r
e,
ch
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l
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il
th
e
ir
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d
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ir
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en
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ai
n
l
y
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m
at
in
g
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n
d
g
r
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o
m
i
n
g
.
So
,
th
eir
co
n
ce
n
tr
atio
n
m
o
v
es
a
w
a
y
f
r
o
m
ch
a
s
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g
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h
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A
e
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eten
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eh
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ap
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la
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et
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s
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r
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h
c
h
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m
p
p
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s
itio
n
as
(
1
5
)
:
ℎ
im
(
+
1
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=
{
(
)
−
.
<
0
.
5
ℎ
_
≥
0
.
5
(
15
)
u
p
g
r
ad
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o
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itio
n
t
y
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icall
y
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h
en
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d
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tili
ze
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h
ao
tic
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al
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e
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f
≥
0
.
5
th
at
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an
d
o
m
i
n
[
0
,
1
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an
d
w
e
e
s
ti
m
ate
ch
ao
tic
v
al
u
e
b
y
o
n
e
o
f
6
m
ap
s
.
3
.
2
.
P
r
o
ce
s
s
inv
o
lv
ed
in M
C
O
ANL
-
WSN
m
et
ho
d
T
h
e
MCOA
N
L
-
W
SN
al
g
o
r
ith
m
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n
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d
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th
e
s
u
b
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e
n
t
s
tag
e
s
to
id
en
tify
th
e
s
e
n
s
o
r
in
W
SN.
R
an
d
o
m
l
y
p
lace
an
ch
o
r
n
o
d
e
(
A
N)
an
d
tar
g
et
n
o
d
es
(
T
N)
at
th
e
d
ev
ice
p
o
r
tio
n
[
1
8
]
.
E
v
er
y
A
N
w
a
s
s
p
atiall
y
lo
ca
lized
a
n
d
ass
i
s
t
ed
f
o
r
r
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o
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n
izin
g
t
h
e
p
o
s
iti
o
n
o
f
alt
er
n
at
iv
e
n
o
d
es.
E
ac
h
tar
g
et
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n
d
A
Ns
en
co
m
p
as
s
tr
an
s
m
i
s
s
io
n
r
an
g
e
.
−
Dis
ta
n
ce
a
m
o
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e
A
N
s
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d
T
Ns
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e
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d
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ated
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e
Gau
s
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ian
n
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e.
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h
e
T
N
w
as
e
m
p
lo
y
ed
to
m
ea
s
u
r
e
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̂
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d
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ates
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e
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al
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ce
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m
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u
ted
a
m
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g
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t t
h
e
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o
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itio
n
s
o
f
T
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(
,
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an
d
B
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co
n
(
,
)
:
=
√
(
−
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2
+
(
−
)
2
(
16
)
n
o
w
,
co
n
tr
o
ls
t
h
e
n
o
is
e
th
at
f
o
llo
w
s
t
h
e
esti
m
ated
d
is
ta
n
ce
in
±
(
/
100
)
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d
m
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n
s
t
h
e
s
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n
d
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n
n
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tio
n
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h
th
e
p
r
ed
ictab
le
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is
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ce
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h
e
p
r
ef
er
r
ed
n
o
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e
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n
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m
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w
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it p
r
o
ce
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s
3
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at
th
e
C
R
o
f
T
N.
−
Fo
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th
e
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t
h
e
MC
O
A
N
L
-
W
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s
y
s
te
m
co
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e
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m
o
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e
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s
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C
R
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,
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(
1
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(
17
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w
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th
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ased
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ates
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n
p
r
o
b
le
m
s
d
es
cr
ib
e
4
‐
s
id
ed
d
etac
h
m
en
t
a
m
o
n
g
s
t
T
N
an
d
A
N
:
(
,
)
=
1
(
∑
√
(
−
)
2
+
(
−
)
2
=
1
−
̂
)
2
(
18
)
h
er
e
≥
3
s
h
o
w
s
t
h
e
A
N
co
u
n
t
s
s
ta
b
le
a
tr
an
s
m
i
s
s
io
n
r
ad
iu
s
o
f
T
N.
−
W
h
ile
th
e
h
i
g
h
e
s
t
r
ep
etitio
n
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u
n
t
s
w
il
l
b
e
o
b
tain
ed
,
an
d
f
o
llo
w
ed
b
y
o
p
ti
m
u
m
lo
ca
tio
n
co
o
r
d
in
atio
n
(
,
)
is
d
eter
m
i
n
ed
b
y
th
e
MCO
A
N
L
-
W
SN
s
y
s
te
m
.
T
h
e
lo
ca
lizin
g
er
r
o
r
h
as
b
ee
n
d
eter
m
i
n
ed
af
te
r
m
ea
s
u
r
in
g
t
h
e
lo
ca
lizab
le
T
N
.
T
h
is
m
i
g
h
t
b
e
ev
al
u
ated
as
a
m
ea
n
4
‐
s
id
ed
o
f
co
ld
n
ess
i
n
t
h
e
n
o
d
e
(
,
)
m
atch
e
s
i
n
co
o
r
d
in
ates o
f
th
e
r
ea
l n
o
d
e
(
,
)
:
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.
14
,
No
.
1
,
Ma
r
c
h
2
0
2
5
:
2
2
1
-
230
226
1
=
1
1
∑
√
(
−
)
2
+
(
−
)
2
=
1
(
19
)
−
Stag
e
s
2
-
6
r
ef
er
s
r
eiter
ated
til
th
e
T
N
ca
n
lo
ca
lizatio
n
.
T
h
e
lo
ca
lizatio
n
m
o
d
el
w
a
s
d
ep
en
d
en
t
u
p
o
n
th
e
m
is
tak
e
-
co
n
tr
o
l
1
,
an
d
th
e
q
u
an
tit
y
o
f
u
n
lo
ca
lized
p
r
o
m
in
e
n
ce
s
is
d
escr
ib
ed
as
=
−
.
T
h
e
d
ec
r
ea
s
ed
s
co
r
e
o
f
1
an
d
in
d
icate
s
a
co
n
tr
o
lled
m
et
h
o
d
.
Fin
all
y
,
v
ar
io
u
s
m
et
h
o
d
s
f
o
r
lo
ca
tin
g
n
o
d
es
i
n
W
SNs
s
h
o
w
i
m
p
o
r
tan
t
i
m
p
r
o
v
e
m
en
t
s
i
n
p
r
ec
is
io
n
,
en
er
g
y
s
av
in
g
,
an
d
f
le
x
ib
ilit
y
,
ea
ch
p
r
o
v
id
in
g
s
p
ec
if
ic
a
d
v
an
ta
g
es
f
o
r
d
if
f
er
en
t
n
et
wo
r
k
s
itu
atio
n
s
a
n
d
n
ee
d
s
[
1
9
]
-
[
2
2
]
.
A
d
d
itio
n
all
y
,
th
e
s
u
cc
e
s
s
o
f
lo
ca
lizatio
n
m
eth
o
d
s
ca
n
b
e
g
r
ea
tl
y
a
f
f
ec
ted
b
y
en
v
ir
o
n
m
e
n
ta
l
co
n
d
itio
n
s
an
d
th
e
ch
a
n
g
in
g
n
atu
r
e
o
f
s
en
s
o
r
n
et
w
o
r
k
s
.
Fo
r
ex
a
m
p
le,
in
ca
s
es
w
h
er
e
n
o
d
es
ex
p
er
ien
ce
m
o
v
e
m
e
n
t
o
r
d
if
f
er
en
t
co
m
m
u
n
ica
tio
n
s
etti
n
g
s
,
th
e
r
eliab
ili
t
y
o
f
th
ese
m
eth
o
d
s
b
ec
o
m
es
cr
u
cial.
R
esear
ch
er
s
ar
e
s
tar
tin
g
to
lo
o
k
i
n
to
h
y
b
r
id
m
et
h
o
d
s
th
at
m
er
g
e
s
ev
e
r
al
lo
ca
lizatio
n
tech
n
iq
u
e
s
to
i
m
p
r
o
v
e
ac
cu
r
ac
y
ad
ap
tiv
el
y
i
n
r
esp
o
n
s
e
to
ch
a
n
g
i
n
g
n
et
w
o
r
k
s
tr
u
ct
u
r
es
[
2
3
]
.
T
h
ese
ap
p
r
o
ac
h
es
n
o
t
o
n
l
y
ta
ck
le
t
h
e
ch
al
len
g
es
lin
k
ed
to
f
ix
ed
an
ch
o
r
lo
ca
tio
n
s
b
u
t
also
f
u
r
th
er
en
h
a
n
ce
en
er
g
y
u
s
e,
as
d
em
o
n
s
tr
ated
b
y
r
ec
en
t
d
ev
elo
p
m
e
n
ts
th
at
u
s
e
m
o
b
ile
a
n
ch
o
r
s
w
it
h
co
n
v
e
n
tio
n
al
tr
ilater
atio
n
t
ec
h
n
iq
u
es.
Fu
r
t
h
er
m
o
r
e,
i
n
co
r
p
o
r
atin
g
m
ac
h
i
n
e
lear
n
in
g
m
et
h
o
d
s
i
n
to
lo
ca
liz
atio
n
tas
k
s
h
a
s
b
ee
n
p
r
o
m
i
s
i
n
g
i
n
b
o
o
s
tin
g
d
ec
is
io
n
-
m
a
k
i
n
g
s
k
ill
s
,
f
ac
ilit
ati
n
g
r
ea
l
-
ti
m
e
ch
a
n
g
es
b
ased
o
n
o
b
s
er
v
ed
d
ata
tr
en
d
s
,
w
h
ic
h
co
u
ld
h
elp
lo
w
er
lo
ca
lizatio
n
m
is
tak
e
s
ev
e
n
m
o
r
e.
T
h
is
s
h
i
f
t
to
w
ar
d
s
ad
ap
tab
le
an
d
s
m
ar
t s
y
s
te
m
s
in
d
icate
s
a
s
ig
n
if
ican
t c
h
a
n
g
e
in
h
o
w
W
S
Ns tac
k
le
NL
is
s
u
e
s
,
o
p
en
in
g
u
p
o
p
p
o
r
tu
n
ities
f
o
r
m
o
r
e
r
o
b
u
s
t a
p
p
licatio
n
s
i
n
v
a
r
io
u
s
ar
ea
s
[
2
4
]
,
[
2
5
]
.
4.
E
XP
E
R
I
M
E
NT
A
L
VAL
I
D
AT
I
O
N
T
h
e
lo
ca
lizatio
n
r
esu
lts
o
f
th
e
MCO
A
N
L
-
W
SN
tech
n
iq
u
e
ca
n
b
e
in
v
es
tig
a
ted
in
ter
m
s
o
f
d
is
tin
ct
m
ea
s
u
r
es.
I
n
T
ab
le
1
an
d
Fig
u
r
e
2
,
a
d
etailed
av
er
ag
e
lo
ca
li
za
tio
n
er
r
o
r
(
AL
E
)
r
es
u
lt
o
f
th
e
MCO
A
N
L
-
W
S
N
s
y
s
te
m
is
p
r
o
v
id
ed
w
ith
r
ec
en
t
o
n
e
s
[
1
8
]
.
T
h
e
r
esu
lt
s
i
m
p
l
y
th
a
t
t
h
e
m
o
d
i
f
ied
g
r
a
m
Sc
h
m
id
t
D
V
-
Ho
p
alg
o
r
ith
m
(
MG
DV
-
Ho
p
)
an
d
W
SN
-
DV
-
Ho
p
m
o
d
els
h
av
e
s
h
o
w
n
w
o
r
s
e
r
esu
lt
s
w
it
h
in
cr
ea
s
ed
AL
E
v
alu
e
s
.
Nex
t,
th
e
v
ir
t
u
al
p
ar
titi
o
n
an
d
d
is
tan
ce
co
r
r
ec
tio
n
(
VP
DC
)
an
d
elite
o
p
p
o
s
itio
n
al
f
ar
m
la
n
d
f
er
tili
t
y
o
p
tim
izatio
n
b
ased
n
o
d
e
lo
ca
l
izatio
n
tec
h
n
iq
u
e
f
o
r
w
ir
eles
s
n
et
w
o
r
k
s
(
E
OF
FON
L
W
N)
m
o
d
els
h
a
v
e
tr
ied
to
ex
h
ib
it
s
li
g
h
tl
y
d
ec
r
ea
s
ed
AL
E
v
alu
es
.
A
lt
h
o
u
g
h
th
e
C
ML
OA
-
N
L
A
m
o
d
el
h
as
ex
h
ib
ite
d
r
ea
s
o
n
ab
le
A
L
E
v
alu
e,
t
h
e
MCO
AN
L
-
W
SN
t
ec
h
n
iq
u
e
h
i
g
h
li
g
h
ted
its
s
u
p
r
e
m
ac
y
w
i
th
leas
t
AL
E
v
a
lu
e
s
o
f
4
.
2
4
%,
4
.
5
4
%,
3
.
5
5
%,
2
.
8
7
%,
2
.
8
8
%,
1
.
7
7
%,
an
d
1
.
3
2
% u
n
d
er
5
-
35
B
ea
co
n
n
o
d
es,
co
r
r
esp
o
n
d
in
g
l
y
.
T
ab
le
1
.
A
L
E
r
es
u
lt o
f
M
C
O
ANL
-
W
SN
m
o
d
el
co
m
p
ar
ed
w
i
th
o
th
er
al
g
o
r
ith
m
s
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n
d
er
v
ar
i
o
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s
B
ea
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n
n
o
d
es
N
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o
f
B
e
a
c
o
n
n
o
d
e
s
W
N
D
-
DV
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H
o
p
M
G
D
V
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H
o
p
V
P
D
C
EO
F
F
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L
W
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M
L
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C
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5
4
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4
8
6
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2
Fig
u
r
e
2
.
AL
E
o
u
tco
m
e
o
f
M
C
O
A
N
L
-
W
SN
m
o
d
el
u
n
d
er
v
ar
io
u
s
B
ea
co
n
n
o
d
es
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
Mo
d
elin
g
o
f c
h
imp
o
p
timiz
a
tio
n
a
lg
o
r
ith
m
n
o
d
e
l
o
ca
liz
a
tio
n
s
ch
eme
in
…
(
S
r
ip
r
iya
A
r
u
n
a
ch
a
la
m
)
227
A
co
m
p
r
eh
e
n
s
iv
e
lo
ca
lizatio
n
ti
m
e
(
L
T
)
o
u
tp
u
ts
o
f
th
e
M
C
O
A
N
L
-
W
SN
s
y
s
te
m
w
as
d
eter
m
i
n
ed
w
it
h
r
ec
en
t
m
eth
o
d
s
in
T
ab
le
2
an
d
Fig
u
r
e
3
.
T
h
ese
ac
co
m
p
lis
h
ed
o
u
tco
m
e
s
h
o
w
ca
s
e
s
th
at
th
e
MG
DV
-
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p
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DV
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tech
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e
s
ar
e
d
is
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la
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p
o
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m
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w
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th
i
m
p
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v
ed
L
T
v
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e
s
.
T
h
en
,
th
e
VP
D
C
an
d
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OFFON
L
W
N
alg
o
r
it
h
m
s
ar
e
clo
s
ed
t
o
s
h
o
w
m
o
d
er
at
el
y
r
ed
u
ce
d
lo
ca
lizatio
n
er
r
o
r
(
L
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)
v
alu
e
s
.
W
h
ile
th
e
C
M
L
O
A
-
N
L
A
m
e
th
o
d
p
r
o
v
id
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etter
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v
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e,
t
h
e
M
C
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A
N
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W
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y
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te
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e
m
p
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ized
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1
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i
n
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0
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m
in
,
0
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1
1
1
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1
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ased
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B
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d
es.
T
ab
le
2
.
L
T
o
u
tc
o
m
e
o
f
MCO
A
NL
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W
SN sy
s
te
m
co
m
p
ar
e
d
t
o
o
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s.
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is
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lso
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m
e
m
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c
a
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b
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c
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ted
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