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[
1
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W
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[
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y
w
it
h
i
n
t
h
e
f
ield
.
I
t
is
u
s
u
all
y
f
o
r
d
an
g
er
o
u
s
o
r
ab
o
m
in
ab
le
s
u
c
h
as
b
attl
e
f
ield
,
f
o
e
m
ilit
ar
y
an
d
d
is
as
t
er
ap
p
licatio
n
o
r
in
h
o
s
p
itab
le
ar
ea
s
w
h
er
e
n
et
w
o
r
k
s
ize
is
lar
g
e.
Dr
o
p
p
in
g
s
en
s
o
r
s
f
r
o
m
a
p
lan
e
w
o
u
ld
b
e
an
ex
a
m
p
le
o
f
r
an
d
o
m
d
ep
lo
y
m
en
t.
R
a
n
d
o
m
d
ep
lo
y
m
en
t
co
u
ld
ca
u
s
e
s
o
m
e
o
f
t
h
e
s
en
s
o
r
s
b
ein
g
d
ep
lo
y
ed
to
o
clo
s
e
to
ea
ch
o
t
h
er
w
h
ile
o
th
er
s
ar
e
to
o
f
ar
ap
ar
t.
T
r
a
d
itio
n
al
an
al
y
tical
o
p
ti
m
i
za
tio
n
tech
n
iq
u
e
s
r
eq
u
ir
e
m
o
r
e
co
m
p
u
tatio
n
al
ef
f
o
r
ts
,
wh
ich
g
r
o
w
ex
p
o
n
en
t
iall
y
a
s
th
e
p
r
o
b
le
m
s
ize
in
cr
ea
s
e
s
.
A
n
o
p
ti
m
izatio
n
m
et
h
o
d
w
h
ic
h
r
eq
u
ir
es
m
o
d
er
ate
m
e
m
o
r
y
w
i
th
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
y
et
p
r
o
d
u
ce
s
g
o
o
d
r
esu
lts
is
ex
p
ec
ted
,
esp
ec
ially
f
o
r
i
m
p
le
m
e
n
tatio
n
o
n
a
n
in
d
iv
id
u
al
s
e
n
s
o
r
n
o
d
e.
S
w
ar
m
o
p
ti
m
izat
io
n
m
e
th
o
d
s
ar
e
co
m
p
u
tatio
n
all
y
e
f
f
ici
e
n
t
alter
n
ati
v
es
to
an
al
y
tica
l
m
et
h
o
d
s
av
ai
lab
le.
P
ar
ticle
Sw
ar
m
Op
ti
m
izatio
n
(
P
SO)
is
a
p
o
p
u
lar
m
u
l
tid
i
m
e
n
s
io
n
al
o
p
t
i
m
izatio
n
tec
h
n
iq
u
e
[
6
]
.
Stre
n
g
th
s
o
f
th
e
P
SO
ar
e
ea
s
e
o
f
i
m
p
le
m
e
n
tat
io
n
,
h
ig
h
q
u
alit
y
o
f
s
o
l
u
tio
n
s
,
co
m
p
u
tat
io
n
al
ef
f
icie
n
c
y
an
d
s
p
ee
d
o
f
co
n
v
er
g
e
n
ce
[
7
]
.
T
h
e
P
SO
b
ased
s
en
s
o
r
d
ep
lo
y
m
en
t
m
e
th
o
d
tr
ies
to
f
in
d
t
h
e
o
p
tim
a
l
p
o
s
itio
n
s
o
f
s
e
n
s
o
r
to
co
v
er
th
e
co
m
p
lete
r
eg
io
n
o
f
i
n
ter
est
(
R
OI
)
.
P
SO
m
e
th
o
d
u
s
es
a
f
it
n
e
s
s
f
u
n
ctio
n
as
a
n
o
b
j
ec
tiv
e
to
b
e
m
in
i
m
ized
.
T
h
e
ai
m
in
t
h
e
s
e
n
s
o
r
d
ep
lo
y
m
en
t
is
to
f
u
ll
y
co
v
er
th
e
r
eg
io
n
o
f
in
ter
e
s
t
u
s
in
g
m
in
i
m
u
m
n
u
m
b
er
o
f
n
o
d
es.
T
h
is
m
et
h
o
d
iter
ativ
el
y
e
v
al
u
ate
s
t
h
e
co
v
er
ag
e
as i
ts
f
it
n
ess
f
u
n
c
t
io
n
.
T
h
e
co
v
er
ag
e
o
p
tim
izat
io
n
s
t
r
ateg
ies
ar
e
i
m
p
le
m
en
ted
d
u
r
in
g
d
ep
lo
y
m
en
t
p
h
ase
an
d
c
o
v
er
ag
e
is
ca
lcu
lated
b
ased
o
n
th
e
p
lace
m
en
t
o
f
th
e
s
e
n
s
o
r
s
o
n
th
e
r
eg
io
n
o
f
i
n
ter
est
(
R
OI
)
.
T
h
e
y
ar
e
ca
teg
o
r
ized
in
to
th
r
ee
g
r
o
u
p
s
,
n
a
m
el
y
;
f
o
r
ce
b
ased
,
g
r
id
b
ased
o
r
co
m
p
u
tati
o
n
al
g
eo
m
etr
y
b
ased
ap
p
r
o
ac
h
[
8
]
.
T
o
d
eter
m
i
n
e
th
e
o
p
ti
m
al
p
o
s
itio
n
o
f
th
e
s
e
n
s
o
r
s
f
o
r
ce
b
ased
m
et
h
o
d
s
u
s
e
attr
ac
tio
n
an
d
r
ep
u
l
s
io
n
f
o
r
ce
s
.
W
h
ile
g
r
id
b
ased
m
et
h
o
d
s
u
s
e
g
r
id
p
o
in
ts
f
o
r
th
e
s
a
m
e
o
b
j
ec
tiv
e.
Vo
r
o
n
o
i
d
iag
r
a
m
an
d
Dela
u
n
a
y
tr
ia
n
g
u
latio
n
f
r
o
m
t
h
e
co
m
p
u
tatio
n
al
g
eo
m
etr
y
ap
p
r
o
ac
h
ar
e
co
m
m
o
n
l
y
u
s
ed
i
n
W
SN c
o
v
er
ag
e
o
p
ti
m
izat
io
n
m
et
h
o
d
.
MD
B
P
SO is a
Gr
id
B
ased
m
e
th
o
d
f
o
r
d
ep
lo
y
m
e
n
t
o
f
s
e
n
s
o
r
n
o
d
es.
I
t
i
s
ex
p
ec
ted
t
h
at
MD
B
P
SO
B
ased
ap
p
r
o
ac
h
w
il
l
ac
h
iev
e
m
ax
i
m
u
m
co
v
er
a
g
e
f
o
r
th
e
W
ir
eles
s
Se
n
s
o
r
Net
w
o
r
k
(
W
SN)
d
u
e
to
s
tr
ate
g
ic
d
e
p
lo
y
m
e
n
t
o
f
SN
s
a
s
co
m
p
ar
ed
to
th
e
o
th
er
co
v
er
a
g
e
s
tr
ateg
ie
s
s
u
c
h
as
f
o
r
ce
an
d
co
m
p
u
tatio
n
al
g
eo
m
etr
y
b
ased
ap
p
r
o
ac
h
.
R
an
d
o
m
d
ep
lo
y
m
e
n
t;
g
r
id
b
as
ed
P
SO
an
d
MD
B
P
SO
b
ased
d
ep
lo
y
m
en
t
h
a
s
b
ee
n
i
m
p
le
m
en
ted
an
d
test
ed
w
ith
v
ar
iab
le
g
r
id
s
ize,
n
u
m
b
er
o
f
n
o
d
es a
n
d
s
en
s
in
g
r
an
g
e
w
i
th
s
tatio
n
ar
y
s
e
n
s
o
r
n
o
d
es.
T
h
e
Net
w
o
r
k
s
i
m
u
la
to
r
h
elp
s
t
h
e
d
e
v
elo
p
er
to
cr
ea
te
a
n
d
s
i
m
u
la
te
n
e
w
m
o
d
els
o
n
a
n
ar
b
itra
r
y
n
et
w
o
r
k
b
y
s
p
ec
if
y
i
n
g
b
o
th
t
h
e
b
eh
av
io
r
o
f
th
e
n
et
w
o
r
k
n
o
d
es
an
d
th
e
co
m
m
u
n
icatio
n
ch
a
n
n
el
s
.
I
t
p
r
o
v
id
es
a
v
ir
tu
a
l
en
v
ir
o
n
m
e
n
t
f
o
r
an
as
s
o
r
t
m
en
t
o
f
d
esira
b
le
f
ea
tu
r
es
s
u
c
h
a
s
m
o
d
eli
n
g
a
n
et
w
o
r
k
b
ased
o
n
a
s
p
e
ci
f
ic
cr
iter
ia
an
d
an
al
y
zin
g
it
s
p
er
f
o
r
m
an
ce
u
n
d
er
d
if
f
er
e
n
t
s
ce
n
ar
io
s
[
9
]
.
Net
w
o
r
k
s
i
m
u
lato
r
2
is
u
s
ed
f
o
r
s
i
m
u
lat
io
n
o
f
th
e
m
et
h
o
d
s
.
Sectio
n
2
d
is
cu
s
s
es
r
an
d
o
m
d
ep
lo
y
m
e
n
t.
Sectio
n
3
elab
o
r
ates
P
SO
b
ased
d
ep
lo
y
m
en
t
w
h
er
ea
s
Sectio
n
4
d
is
cu
s
s
e
s
MD
B
P
SO
b
ased
d
ep
lo
y
m
e
n
t,
Sectio
n
5
co
n
tain
s
s
i
m
u
latio
n
r
es
u
lts
.
Fin
all
y
t
h
e
co
n
cl
u
d
in
g
r
e
m
ar
k
s
ar
e
g
iv
e
n
i
n
Sectio
n
6
.
2.
RAND
O
M
DE
P
L
O
YM
E
NT
Ma
n
y
s
ce
n
ar
io
s
ad
o
p
t
r
an
d
o
m
d
ep
lo
y
m
e
n
t
f
o
r
p
r
ac
tical
r
ea
s
o
n
s
s
u
c
h
as
d
ep
lo
y
m
e
n
t
co
s
t
an
d
ti
m
e.
B
u
t
it
d
o
es
n
o
t
g
u
ar
an
tee
f
u
ll
co
v
e
r
a
g
e
b
ec
au
s
e
it
i
s
s
to
ch
ast
ic
i
n
n
at
u
r
e,
h
e
n
ce
o
f
ten
r
e
s
u
lt
in
g
i
n
ac
cu
m
u
lat
io
n
o
f
n
o
d
es
at
ce
r
t
ain
ar
ea
s
i
n
t
h
e
s
e
n
s
in
g
f
ield
b
u
t
leav
in
g
o
th
er
ar
ea
s
d
ep
r
iv
ed
o
f
n
o
d
es.
I
n
b
o
t
h
s
itu
a
tio
n
s
co
v
er
a
g
e
p
r
o
b
le
m
w
il
l
ar
is
e,
t
h
e
s
e
n
s
in
g
ca
p
ab
ili
ties
o
f
t
h
e
s
en
s
o
r
s
ar
e
w
asted
i
n
t
h
e
f
ir
s
t
co
n
d
it
io
n
an
d
th
e
co
v
er
ag
e
is
n
o
t
m
a
x
i
m
ized
,
w
h
ile
in
t
h
e
later
,
b
lin
d
s
p
o
ts
w
ill
b
e
f
o
r
m
ed
.
T
h
er
e
ar
e
b
ig
co
v
er
ag
e
h
o
les
as
th
e
n
et
w
o
r
k
s
ize
g
r
o
w
s
.
U
n
e
v
en
n
o
d
e
to
p
o
l
o
g
y
m
a
y
b
r
in
g
ab
o
u
t
u
n
b
alan
ce
d
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
lead
to
a
s
h
o
r
t
s
y
s
te
m
li
f
eti
m
e.
F
ig
u
r
e
1
s
h
o
w
s
R
an
d
o
m
Se
n
s
o
r
d
ep
lo
y
m
en
t
w
it
h
s
e
n
s
i
n
g
r
ad
iu
s
0
.
5
m
,
g
r
id
s
ize
0
.
5
m
X
0
.
5
m
.
R
OI
:
1
0
m
eter
X
1
0
m
eter
,
Nu
m
b
e
r
o
f
n
o
d
es:
1
0
0
T
a
b
les
an
d
Fi
g
u
r
e
s
ar
e
p
r
esen
ted
ce
n
ter
,
as s
h
o
w
n
b
elo
w
a
n
d
cit
ed
in
th
e
m
an
u
s
cr
ip
t.
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3
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u
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8
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u
r
e
1
.
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an
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m
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e
n
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id
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ize
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b
er
o
f
n
o
d
es g
o
es o
n
in
cr
ea
s
in
g
% c
o
v
er
ag
e
al
s
o
g
o
es o
n
i
n
c
r
ea
s
in
g
.
3.
P
SO
B
ASE
D
DE
P
L
O
YM
E
NT
T
h
ese
li
m
itat
io
n
s
m
o
ti
v
ate
t
h
e
estab
lis
h
m
e
n
t
o
f
a
p
lan
n
in
g
s
y
s
te
m
th
a
t
o
p
ti
m
izes
t
h
e
s
e
n
s
o
r
r
eo
r
g
an
izatio
n
p
r
o
ce
s
s
to
en
h
an
ce
th
e
co
v
er
ag
e
r
ate
af
ter
in
itial
r
an
d
o
m
d
ep
lo
y
m
en
t.
T
h
is
m
et
h
o
d
tr
ies
to
f
i
n
d
th
e
o
p
ti
m
al
p
o
s
itio
n
s
o
f
s
en
s
o
r
to
co
v
er
th
e
co
m
p
lete
r
eg
io
n
o
f
in
ter
est
(
R
OI
)
.
T
h
e
p
ar
ticles
m
o
v
e
i
n
li
m
ited
r
eg
io
n
to
f
o
r
m
u
n
i
f
o
r
m
l
y
d
is
tr
ib
u
ted
s
en
s
o
r
n
et
wo
r
k
.
P
SO
m
eth
o
d
u
s
e
s
a
f
it
n
ess
f
u
n
c
tio
n
as
an
o
b
j
ec
tiv
e
to
b
e
m
i
n
i
m
ized
.
T
h
e
ai
m
i
n
th
e
s
en
s
o
r
d
ep
lo
y
m
en
t
is
to
f
u
ll
y
co
v
er
th
e
r
eg
io
n
o
f
in
ter
e
s
t
u
s
in
g
m
i
n
i
m
u
m
n
u
m
b
er
o
f
n
o
d
es.
T
h
is
m
et
h
o
d
iter
ativ
el
y
e
v
al
u
ates
th
e
co
v
er
a
g
e
as
its
f
it
n
e
s
s
f
u
n
ctio
n
.
Se
n
s
o
r
p
lace
m
en
t
p
r
o
b
le
m
is
v
ie
w
ed
as
d
is
cr
ete
p
r
o
b
lem
a
s
th
e
r
e
g
io
n
o
f
i
n
ter
est
i
s
d
iv
id
ed
in
t
o
f
in
ite
n
u
m
b
er
o
f
g
r
id
s
.
T
h
e
g
r
id
b
ased
s
tr
ateg
y
is
u
s
ed
in
t
h
is
m
et
h
o
d
to
ev
alu
a
te
th
e
co
v
er
a
g
e
esti
m
a
te
o
f
th
e
n
e
t
w
o
r
k
.
Fo
llo
w
i
n
g
ar
e
th
e
s
tep
s
i
n
v
o
lv
ed
in
i
m
p
le
m
en
tatio
n
o
f
P
SO
b
ased
d
ep
lo
y
m
e
n
t o
f
s
en
s
o
r
n
o
d
es:
1.
I
n
itialize
t
h
e
p
o
s
itio
n
a
n
d
v
elo
cit
y
v
ec
to
r
s
&
as
s
i
g
n
r
a
n
d
o
m
v
alu
e
s
to
it.
2.
E
v
alu
a
te
th
e
f
it
n
e
s
s
o
f
p
ar
ticl
e
p
an
d
ass
i
g
n
it
to
p
er
s
o
n
al
f
it
n
es
s
o
f
p
ar
ticle
p
.
Fi
n
d
th
e
p
ar
ticle
p
w
it
h
m
i
n
i
m
u
m
f
it
n
ess
f
r
o
m
P
an
d
ass
ig
n
i
ts
p
o
s
it
io
n
v
ec
to
r
t
o
g
lo
b
al
b
est p
o
s
itio
n
v
ec
to
r
an
d
its
b
est
f
it
n
es
s
as g
lo
b
al
b
est f
it
n
ess
.
3.
Fo
r
n
u
m
b
er
o
f
i
ter
atio
n
s
&
ea
ch
p
ar
ticle
p
r
ep
ea
t step
s
1
to
3.
4.
C
alcu
late
n
e
w
v
elo
cit
y
u
s
in
g
e
q
u
atio
n
(
+1
)
=
(
∗
(
)
)
+(
1
∗
1
∗
(
(
)
–
(
)
)
)
+
(
2
∗
2
∗
(
(
)
−
(
)
)
)
(
1
)
E
q
u
atio
n
(
1
)
u
p
d
ates a
p
ar
ticle’
s
v
elo
cit
y
.
5.
I
f
n
e
w
v
elo
cit
y
i
s
g
r
ea
ter
t
h
an
m
ax
i
m
u
m
v
e
lo
cit
y
t
h
e
n
u
s
e
m
ax
i
m
u
m
v
elo
cit
y
a
s
n
e
w
v
elo
c
it
y
.
6.
A
p
p
l
y
p
o
s
itio
n
u
p
d
a
te
eq
u
atio
n
(
+1
)
=(
)
+
(
+1
)
(
2
)
&
ev
al
u
ate
t
h
e
f
itn
e
s
s
o
f
p
ar
ticle
p
.
7.
I
f
t
h
e
n
e
w
f
it
n
ess
is
less
th
a
n
p
er
s
o
n
al
b
est
t
h
en
u
p
d
ate
t
h
e
p
er
s
o
n
al
b
est
f
it
n
es
s
an
d
p
o
s
it
io
n
&
f
in
d
th
e
b
est p
ar
ticle
in
p
ar
ticle
v
ec
to
r
P
.
8.
I
f
t
h
e
f
it
n
ess
o
f
p
ar
ticle
p
is
l
ess
t
h
a
n
g
lo
b
al
b
est
f
it
n
es
s
t
h
en
u
p
d
ate
th
e
g
lo
b
al
b
est
p
o
s
itio
n
v
ec
to
r
an
d
g
lo
b
al
b
est
f
itn
e
s
s
.
I
f
th
e
g
lo
b
al
b
est
f
itn
e
s
s
is
ze
r
o
th
is
in
d
icate
s
th
at
f
u
l
l
co
v
er
ag
e
is
ac
h
iev
ed
th
er
ef
o
r
e
s
to
p
th
e
iter
atio
n
s
.
9.
C
r
ea
te
n
n
o
d
es a
n
d
ass
i
g
n
x
a
n
d
y
co
o
r
d
in
ate
v
al
u
es
f
r
o
m
g
l
o
b
al
b
est p
o
s
itio
n
v
ec
to
r
&
th
en
s
to
p
.
Fig
u
r
e
2
s
h
o
w
s
P
SO
b
ased
d
ep
lo
y
m
e
n
t
w
it
h
s
e
n
s
in
g
r
ad
iu
s
1
m
,
g
r
id
s
ize
1
m
X
1
m
.
R
OI
:
1
0
m
eter
X
1
0
m
eter
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
a
n
d
o
m,
P
S
O
a
n
d
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O
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a
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en
s
o
r
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lo
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Wir
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s
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(
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p
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a
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ee
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u
r
e
2
.
Sen
s
i
n
g
r
ad
iu
s
=
1
m
an
d
g
r
id
s
ize
=
1
m
X
1
m
,
R
OI
: 1
0
m
X
1
0
m
T
ab
le
2
.
E
f
f
ec
t o
f
Se
n
s
i
n
g
A
r
e
a
o
n
Nu
m
b
er
o
f
No
d
es
&
I
ter
atio
n
Se
n
si
n
g
R
a
d
i
u
s
=
1
m
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r
i
d
S
i
z
e
1
m
X
1
m
S
e
n
si
n
g
A
r
e
a
N
o
d
e
s
I
t
e
r
a
t
i
o
n
s
4x4
4
13
5x5
4
33
6x6
10
168
7x7
11
2
7
6
8
X
8
19
390
9x9
22
462
10x10
40
757
T
ab
le
2
s
h
o
w
s
t
h
e
e
f
f
ec
t
o
f
s
e
n
s
i
n
g
ar
ea
o
n
n
u
m
b
er
o
f
n
o
d
es
an
d
iter
atio
n
s
r
eq
u
ir
ed
.
I
t
ca
n
b
e
co
n
cl
u
d
ed
t
h
at
as sen
s
i
n
g
ar
ea
g
o
es o
n
in
cr
ea
s
in
g
n
u
m
b
er
o
f
n
o
d
es a
n
d
iter
atio
n
s
r
eq
u
ir
ed
also
g
o
es o
n
i
n
cr
ea
s
in
g
.
4.
M
D
B
P
SO
B
ASE
D
DE
P
L
O
Y
M
E
NT
Mo
d
if
ied
Dis
cr
ete
B
in
ar
y
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
(
M
DB
P
SO)
is
i
m
p
le
m
e
n
ted
f
o
r
i
m
p
r
o
v
in
g
th
e
co
v
er
ag
e
w
h
ile
d
ep
lo
y
i
n
g
th
e
s
en
s
o
r
n
e
t
w
o
r
k
.
MD
B
P
SO
o
p
er
ates in
d
is
cr
ete
p
r
o
b
lem
s
p
ac
e
f
o
r
t
h
e
m
u
lti
-
v
alu
ed
p
r
o
b
lem
s
.
T
o
s
o
lv
e
t
h
e
p
r
o
b
lem
o
f
p
o
o
r
co
n
v
er
g
en
ce
m
o
d
if
ied
s
i
g
m
o
id
f
u
n
ctio
n
is
u
s
ed
.
T
h
e
s
a
m
e
v
elo
cit
y
u
p
d
ate
eq
u
ati
o
n
(
1
)
is
u
s
ed
f
o
r
th
is
m
eth
o
d
.
Ho
w
e
v
er
,
th
e
p
o
s
itio
n
u
p
d
ate
eq
u
atio
n
is
d
if
f
er
en
t
f
r
o
m
eq
u
atio
n
(
2
)
in
th
e
f
o
llo
w
i
n
g
m
an
n
er
.
T
h
e
v
elo
cit
y
i
s
f
ir
s
t
tr
an
s
f
o
r
m
ed
i
n
to
a
n
u
m
b
er
b
et
w
ee
n
(
0
,
M
-
1
)
u
s
in
g
th
e
f
o
llo
w
i
n
g
s
i
g
m
o
id
tr
an
s
f
o
r
m
atio
n
g
i
v
e
n
b
y
= (
−
1
)
/ (
1
+
−
)
(
3
)
[
8
]
T
h
e
p
o
s
itio
n
s
o
f
th
e
p
ar
ticles
ar
e
d
is
cr
ete
v
alu
es
b
et
w
ee
n
(
0
,
M
-
1
)
.
No
te
th
at
f
o
r
a
g
iv
e
n
S
id
th
er
e
is
a
p
r
o
b
ab
ilit
y
o
f
h
av
in
g
an
y
n
u
m
b
er
b
et
w
ee
n
(
0
,
M
-
1
)
.
T
h
e
s
ig
m
o
id
tr
an
s
f
o
r
m
atio
n
p
r
o
p
o
s
ed
in
eq
u
atio
n
(
3
)
in
th
e
b
in
ar
y
P
SO
m
ap
s
th
e
v
alu
e
o
f
v
elo
cit
y
f
r
o
m
(
-
∞
to
+∞)
to
(
0
t
o
1
)
[
1
0
]
.
B
u
t
th
is
ca
u
s
e
s
th
e
p
o
o
r
co
n
v
er
g
e
n
ce
o
f
t
h
e
m
et
h
o
d
;
as
th
e
n
eg
a
tiv
e
a
s
w
ell
as
p
o
s
itiv
e
v
elo
cities
ar
e
m
ap
p
ed
to
s
a
m
e
v
a
lu
e
s
o
f
s
ig
m
o
id
f
u
n
c
tio
n
s
o
w
h
e
n
d
ec
id
in
g
t
h
e
n
e
w
p
o
s
itio
n
m
et
h
o
d
h
as
n
o
w
a
y
to
d
eter
m
i
n
e
in
w
h
ich
d
ir
ec
tio
n
to
m
o
v
e.
T
h
is
ca
u
s
es
m
e
th
o
d
to
tr
ap
in
to
ce
r
tai
n
s
o
l
u
tio
n
.
A
n
e
w
m
o
d
i
f
ied
s
i
g
m
o
id
tr
an
s
f
o
r
m
atio
n
is
p
r
o
p
o
s
ed
in
th
is
m
et
h
o
d
to
o
v
er
co
m
e
t
h
i
s
p
r
o
b
lem
.
T
h
e
m
o
d
if
ied
s
i
g
m
o
id
tr
an
s
f
o
r
m
at
io
n
is
g
i
v
en
a
s
:
′
=2
∗
|
−
0
.
5
|
(
4
)
T
h
e
m
o
d
if
ied
s
i
g
m
o
id
a
ls
o
m
ap
s
th
e
v
a
lu
e
s
o
f
v
elo
cities
f
r
o
m
(
-
∞
to
+∞)
to
(
0
to
1
)
.
T
h
is
f
u
n
ctio
n
ca
n
b
e
u
s
ed
w
i
th
t
h
e
s
i
g
n
o
f
v
elo
cit
y
f
o
r
th
e
d
ir
ec
tio
n
an
d
h
elp
s
th
e
m
et
h
o
d
to
co
n
v
er
g
e
w
it
h
i
n
f
in
i
te
n
u
m
b
e
r
o
f
iter
atio
n
s
.
T
h
e
h
i
g
h
v
al
u
e
o
f
v
elo
cit
y
i
n
d
icate
s
th
at
th
e
p
ar
ticles
p
o
s
itio
n
i
s
u
n
f
it
s
o
it
ca
u
s
e
s
t
h
e
p
o
s
itio
n
v
alu
e
to
b
e
ch
an
g
ed
an
d
lo
w
v
alu
e
o
f
v
elo
cit
y
d
ec
r
ea
s
e
s
th
e
p
r
o
b
ab
ilit
y
o
f
c
h
an
g
e
s
in
p
o
s
itio
n
.
Fi
n
all
y
,
if
t
h
e
v
elo
cit
y
is
ze
r
o
,
t
h
e
p
o
s
it
io
n
is
p
er
f
ec
t
[
1
0
]
.
T
h
e
p
o
s
itio
n
o
f
p
ar
ticle
is
ca
lcu
lated
u
s
in
g
s
ig
m
o
id
v
alu
e
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
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m
p
Sci,
Vo
l.
10
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
7
8
–
1
2
8
6
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n
u
m
b
er
o
f
g
r
id
p
o
in
t
s
g
i
v
en
b
y
:
=
′
∗
−
1
(
5
)
.
T
h
e
p
o
s
itio
n
is
u
p
d
ated
o
n
l
y
if
v
al
u
e
o
f
s
ig
m
o
id
f
u
n
ctio
n
i
s
n
o
t
ze
r
o
.
A
ze
r
o
v
al
u
e
o
f
s
i
g
m
o
id
f
u
n
ctio
n
in
d
icate
s
th
a
t n
o
ch
a
n
g
e
in
p
o
s
itio
n
is
r
eq
u
ir
ed
.
1.
Ass
u
m
e
th
e
n
u
m
b
er
o
f
n
o
d
es i
s
n
.
2.
I
n
itialize
t
h
e
p
o
s
itio
n
a
n
d
v
elo
cit
y
v
ec
to
r
s
.
3.
Ass
i
g
n
r
an
d
o
m
v
al
u
es
to
p
o
s
itio
n
v
ec
to
r
an
d
as
s
i
g
n
th
is
p
o
s
itio
n
to
p
er
s
o
n
a
l
b
est
p
o
s
it
io
n
v
ec
to
r
o
f
p
ar
ticle
p
.
4.
E
v
alu
a
te
th
e
f
it
n
es
s
o
f
p
ar
ticle
p
an
d
ass
ig
n
t
h
is
f
it
n
es
s
to
p
er
s
o
n
al
f
i
tn
e
s
s
o
f
p
ar
ticle
p
.
5.
Fin
d
t
h
e
p
ar
ticle
p
w
i
th
m
i
n
i
m
u
m
f
i
tn
e
s
s
f
r
o
m
P
an
d
as
s
i
g
n
its
p
o
s
itio
n
v
ec
to
r
to
g
lo
b
al
b
est
p
o
s
itio
n
v
ec
to
r
g
lo
b
al
b
est p
o
s
itio
n
an
d
its
b
est f
i
tn
e
s
s
to
g
lo
b
al
b
est
f
itn
es
s
.
6.
A
p
p
l
y
v
elo
cit
y
u
p
d
ate
eq
u
atio
n
to
ca
lcu
late
n
e
w
v
e
lo
cit
y
.
7.
I
f
n
e
w
v
elo
cit
y
i
s
g
r
ea
ter
t
h
an
m
ax
i
m
u
m
v
e
lo
cit
y
t
h
e
n
u
s
e
m
ax
i
m
u
m
v
elo
cit
y
a
s
n
e
w
v
elo
c
it
y
.
8.
C
alcu
late
s
i
g
m
o
id
v
alu
e
&
n
e
w
p
o
s
itio
n
.
E
v
al
u
ate
th
e
f
it
n
es
s
f
u
n
c
tio
n
o
f
p
ar
ticle
p
.
9.
I
f
th
e
n
e
w
f
it
n
e
s
s
i
s
les
s
t
h
an
p
er
s
o
n
al
b
est
t
h
en
u
p
d
ate
th
e
p
er
s
o
n
al
b
est
f
itn
e
s
s
a
n
d
p
o
s
it
io
n
&
f
i
n
d
t
h
e
b
est p
ar
ticle
in
p
ar
ticle
v
ec
to
r
P
.
10.
I
f
th
e
f
i
tn
e
s
s
o
f
p
ar
ticle
p
is
le
s
s
t
h
an
g
lo
b
al
b
est
f
it
n
es
s
t
h
e
n
u
p
d
ate
t
h
e
g
lo
b
al
b
est
p
o
s
iti
o
n
v
ec
to
r
an
d
g
lo
b
al
b
est f
it
n
es
s
.
11.
I
f
th
e
g
lo
b
al
b
est
f
it
n
es
s
is
ze
r
o
th
at
i
n
d
icate
s
t
h
at
f
u
ll c
o
v
er
ag
e
is
o
cc
u
p
ied
b
y
s
e
n
s
o
r
s
t
h
e
r
ef
o
r
e
s
to
p
th
e
iter
atio
n
s
.
1
2
.
C
r
ea
te
n
n
o
d
es a
n
d
ass
i
g
n
x
a
n
d
y
co
o
r
d
in
ate
v
al
u
es
f
r
o
m
g
l
o
b
al
b
est p
o
s
itio
n
v
ec
to
r
&
th
en
s
to
p
.
Fig
u
r
e
3
s
h
o
w
s
MD
B
P
SO
b
as
ed
d
ep
lo
y
m
e
n
t
w
it
h
s
e
n
s
i
n
g
r
ad
iu
s
1
m
,
g
r
id
s
ize
1
m
X
1
m
.
R
OI
:
9
m
eter
X
9
m
e
ter
.
Fig
u
r
e
3
.
Sen
s
i
n
g
r
ad
iu
s
=
1
m
an
d
g
r
id
s
ize
=
1
m
X
1
m
,
R
OI
: 9
m
X
9
m
T
ab
le
3
.
E
f
f
ec
t o
f
Se
n
s
i
n
g
A
r
e
a
o
n
Nu
m
b
er
o
f
No
d
es
&
I
ter
atio
n
Se
n
si
n
g
R
a
d
i
u
s
=
1
m
G
r
i
d
S
i
z
e
1
m
X
1
m
S
e
n
si
n
g
A
r
e
a
N
o
d
e
s
I
t
e
r
a
t
i
o
n
s
4x4
3
8
5x5
4
11
6x6
7
23
7x7
1
0
38
8
X
8
17
45
9x9
21
149
10x10
37
280
T
ab
le
3
s
h
o
w
s
th
e
e
f
f
ec
t
o
f
s
e
n
s
i
n
g
ar
ea
o
n
n
u
m
b
er
o
f
n
o
d
e
s
an
d
iter
atio
n
s
r
eq
u
ir
ed
.
I
t
ca
n
b
e
co
n
clu
d
ed
th
at
as sen
s
i
n
g
ar
ea
g
o
es o
n
in
cr
ea
s
in
g
n
u
m
b
er
o
f
n
o
d
es a
n
d
iter
atio
n
s
r
eq
u
ir
ed
also
g
o
es o
n
i
n
cr
ea
s
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
a
n
d
o
m,
P
S
O
a
n
d
MDBP
S
O
b
a
s
ed
S
en
s
o
r
Dep
lo
yme
n
t in
Wir
ele
s
s
…
(
A
p
a
r
n
a
P
r
a
d
ee
p
La
tu
r
ka
r
)
1283
5.
RE
SU
L
T
S&
AN
AL
Y
SI
S
Her
e,
th
e
r
esu
lts
o
f
R
a
n
d
o
m
,
P
SO
an
d
MD
B
P
SO
b
ased
s
e
n
s
o
r
d
ep
lo
y
m
en
t
m
et
h
o
d
ar
e
p
r
esen
ted
.
T
h
e
m
et
h
o
d
s
ar
e
s
i
m
u
lated
f
o
r
th
e
d
i
f
f
er
en
t
g
r
id
s
izes
&
s
e
n
s
in
g
ar
ea
s
s
tar
ti
n
g
f
r
o
m
4
m
X
4
m
to
1
0
m
X
1
0
m
w
it
h
s
e
n
s
i
n
g
r
ad
iu
s
o
f
1
m
.
I
t
ca
n
b
e
o
b
s
er
v
ed
f
r
o
m
th
e
T
ab
le
4
an
d
F
ig
u
r
e
4
th
a
t
th
e
MD
B
P
SO
r
eq
u
ir
es
v
er
y
les
s
n
u
m
b
er
o
f
iter
atio
n
s
as
co
m
p
ar
ed
to
P
SO
b
ased
s
e
n
s
o
r
d
ep
lo
y
m
en
t.
T
h
is
tr
e
n
d
is
f
o
llo
w
ed
e
v
e
n
i
f
s
e
n
s
i
n
g
ar
ea
g
o
es
o
n
in
cr
ea
s
i
n
g
f
r
o
m
4
X4
to
1
0
X1
0
.
Fig
u
r
e
4
.
C
o
m
p
ar
is
o
n
o
f
P
SO
&
MD
B
P
SO f
o
r
s
en
s
i
n
g
ar
ea
v
s
n
u
m
b
er
o
f
iter
atio
n
s
T
ab
le
4
.
E
f
f
ec
t o
f
Se
n
s
i
n
g
A
r
e
a
o
n
N
u
m
b
er
o
f
I
ter
atio
n
s
i
n
P
SO &
MD
B
P
SO
S
e
n
si
n
g
R
a
d
i
u
s
=
1
m
G
r
i
d
S
i
z
e
=
>
1
m X
1
m
S
e
n
si
n
g
A
r
e
a
(
mx
m)
N
u
mb
e
r
o
f
I
t
e
r
a
t
i
o
n
s
i
n
P
S
O
N
u
mb
e
r
o
f
I
t
e
r
a
t
i
o
n
s
i
n
M
D
B
P
S
O
4
x
4
13
8
5
x
5
33
11
6
x
6
1
6
8
23
7
x
7
2
7
6
38
8
X
8
3
9
0
45
9
x
9
4
6
2
1
4
9
1
0
x
1
0
7
5
7
2
8
0
Her
e
in
Fi
g
u
r
e
5
&
T
ab
le
5
,
r
ec
tan
g
u
lar
s
e
n
s
in
g
ar
ea
is
co
n
s
id
er
ed
an
d
v
ar
ied
f
r
o
m
5
X
3
to
1
0
X4
an
d
n
u
m
b
er
o
f
n
o
d
es r
eq
u
ir
ed
f
o
r
R
an
d
o
m
,
P
SO a
n
d
MD
B
P
SO s
y
s
te
m
s
ar
e
ca
lc
u
lated
.
Fig
u
r
e
5
.
C
o
m
p
ar
is
o
n
o
f
r
a
n
d
o
m
P
S
O
&
MD
B
P
SO f
o
r
s
en
s
in
g
ar
ea
v
s
n
u
m
b
er
o
f
n
o
d
es
f
o
r
r
ec
tan
g
u
lar
g
r
id
ar
e
0
20
0
40
0
60
0
80
0
4x4
5x5
6x6
7x7
8X
8
9x9
10
x10
N
u
m
b
e
r
o
f Ite
r
ation
s
S
e
n
si
n
g
A
r
e
a
P
SO
v
s
.
M
DB
P
SO
Sens
ing
ra
diu
s
:
1
m
,
g
rid size:
1
m
X
1
m
PSO
MD
BPSO
0
5
10
15
20
25
30
35
40
5
x
3
5
x
4
6
x
3
6
x
4
7
x
3
7
x
4
8
x
3
8
x
4
9
x
3
9
x
4
1
0
x
3
1
0
x
4
No
d
es
R
an
d
o
m
No
d
es
PS
O
No
d
es
MD
B
PS
O
Ran
dom
Vs P
SO
Vs M
DB
P
SO
Sensi
ng
A
r
ea
Nu
m
b
er
o
f
No
d
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
10
,
No
.
3
,
J
u
n
e
2
0
1
8
:
1
2
7
8
–
1
2
8
6
1284
T
ab
le
5
.
C
o
m
p
ar
is
o
n
o
f
R
a
n
d
o
m
P
SO
&
MD
B
P
SO
f
o
r
Sen
s
in
g
A
r
ea
v
s
Nu
m
b
er
o
f
No
d
es
f
o
r
R
ec
tan
g
u
lar
Gr
id
A
r
ea
S
e
n
si
n
g
R
a
d
i
u
s
=
1
me
t
e
r
G
r
i
d
S
i
z
e
=
>
1
m X
1
m
S
e
n
si
n
g
A
r
e
a
(
mx
m)
N
o
d
e
s :
R
a
n
d
o
m
N
o
d
e
s :
PSO
N
o
d
e
s :
M
D
B
P
S
O
5
x
3
8
3
3
5
x
4
15
4
4
6
x
3
12
3
3
6
x
4
13
6
4
7
x
3
13
4
4
7
x
4
21
7
5
8
x
3
16
5
4
8
x
4
26
8
6
9
x
3
21
5
5
9
x
4
31
9
7
1
0
x
3
26
6
5
1
0
x
4
35
11
8
Fro
m
F
i
g
u
r
e
5
&
T
ab
le
5
,
it c
a
n
b
e
co
n
clu
d
ed
th
at
n
u
m
b
er
o
f
n
o
d
es r
eq
u
ir
ed
f
o
r
P
SO a
n
d
MD
B
P
SO a
r
e
al
m
o
s
t e
q
u
a
l b
u
t a
r
e
al
w
a
y
s
le
s
s
t
h
an
t
h
at
o
f
R
an
d
o
m
d
ep
lo
y
m
en
t.
Als
o
,
in
s
o
m
e
ca
s
e
s
s
u
c
h
as 6
X4
,
7
X4
,
8
X3
,
8
X4
,
9
X4
,
1
0
X3
an
d
1
0
X4
n
u
m
b
er
o
f
n
o
d
es r
eq
u
ir
ed
in
MD
B
P
SO a
r
e
less
th
a
n
t
h
at
o
f
P
SO.
H
er
e,
s
q
u
ar
e
s
en
s
i
n
g
ar
ea
is
co
n
s
id
e
r
ed
an
d
v
ar
ied
f
r
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m
b
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th
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to
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h
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n
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m
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o
f
iter
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n
s
r
eq
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ir
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f
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r
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m
p
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co
v
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g
e
in
MD
B
P
SO
is
also
less
th
an
t
h
at
r
e
q
u
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in
P
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n
MD
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P
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th
e
s
en
s
o
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d
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tr
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lap
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s
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less
th
a
n
t
h
at
o
f
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SO.
T
h
u
s
,
it
ca
n
b
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s
a
id
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at
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f
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b
etter
th
an
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SO b
ased
s
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s
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ep
lo
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m
e
n
t.
RE
F
E
R
E
NC
E
S
[1
]
Zh
a
o
J.,
W
e
n
Y.,
S
h
a
n
g
R.
a
n
d
W
a
n
g
G
.
,
“
Op
ti
m
izin
g
S
e
n
so
r
No
d
e
Distri
b
u
ti
o
n
w
it
h
G
e
n
e
ti
c
M
e
th
o
d
in
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
,
”
Ad
v
a
n
c
e
i
n
Ne
u
ra
l
Ne
tw
o
rk
,
p
p
.
2
4
2
-
2
4
7
,
2
0
0
4
.
[2
]
T
a
m
i
z
h
a
ra
si,
A
.
,
S
e
lv
a
th
a
i,
J.J.,
Ka
v
iP
riy
a
,
A
.
,
M
a
a
rli
n
,
R.
,
Ha
rin
e
th
a
,
M
.
,
“
En
e
rg
y
a
w
a
re
h
e
u
rist
ic
a
p
p
ro
a
c
h
f
o
r
c
lu
ste
r
h
e
a
d
se
lec
ti
o
n
in
w
irele
s
s
se
n
so
r
n
e
tw
o
rk
s”
Bu
ll
e
ti
n
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
In
fo
rm
a
ti
c
s
(
BE
EI)
,
2
0
1
7
;
6
(
1
)
:
7
0
-
7
5
.
[3
]
S
a
in
i,
R.
K
.
,
Rit
ik
a
,
V
ij
a
y
,
S
.
,
“
D
a
ta
f
lo
w
in
w
irele
ss
s
e
n
so
r
n
e
tw
o
rk
p
ro
to
c
o
l
sta
c
k
b
y
u
sin
g
b
e
ll
m
a
n
-
f
o
rd
ro
u
ti
n
g
a
lg
o
rit
h
m
”
,
Bu
ll
e
ti
n
o
f
E
lec
trica
l
En
g
i
n
e
e
rin
g
a
n
d
I
n
fo
rm
a
ti
c
s
(
BE
EI)
,
2
0
1
7
;
6
(
1
):
81
-
8
7
.
[4
]
Am
it
a
b
h
a
G
h
o
sh
a
n
d
S
a
jal
K.
Da
s,
Ch
a
p
ter
9
,
“
C
o
v
e
ra
g
e
a
n
d
Co
n
n
e
c
ti
v
it
y
Iss
u
e
s
in
W
ir
e
les
s
S
e
n
so
r
Ne
tw
o
rk
s,”
Un
iv
e
rsit
y
o
f
Tex
a
s at
A
rli
n
g
to
n
.
[5
]
No
r
Az
li
n
a
A
b
.
Az
iz,
Ka
m
a
ru
lza
m
a
n
A
b
.
A
z
iz,
a
n
d
W
a
n
Zak
i
a
h
W
a
n
Is
m
a
il
,
“
Co
v
e
ra
g
e
S
trate
g
ie
s
f
o
r
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s,”
W
o
rld
Aca
d
e
my
o
f
S
c
ien
c
e
,
E
n
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
Vo
l.
:
2
6
,
p
p
.
1
3
5
-
1
4
0
,
2
3
-
02
-
2
0
0
9
.
[6
]
J.
Ke
n
n
e
d
y
a
n
d
R.
Eb
e
rh
a
rt,
“
P
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
in
Pr
o
c
.
IEE
E
In
t
.
Co
n
f.
Ne
u
ra
l
Ne
tw
o
rk
,
v
o
l.
4
,
p
p
.
1
9
4
2
–
1
9
4
8
,
2
7
N
o
v
.
–
1
De
c
.
,
1
9
9
5
.
[7
]
Ra
g
h
a
v
e
n
d
ra
V
.
Ku
lk
a
rn
i,
G
a
n
e
sh
Ku
m
a
r,
“
P
a
rti
c
le
S
wa
r
m
Op
ti
m
iz
a
ti
o
n
in
W
irele
ss
-
S
e
n
so
r
Ne
tw
o
rk
s:
A
Brie
f
S
u
rv
e
y
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
y
ste
ms
,
M
a
n
&
Cy
b
e
rn
e
ti
c
s
-
Pa
rt
-
C,
p
p
.
1
-
7
,
M
a
rc
h
2
0
1
0
.
[8
]
Bh
u
v
n
e
sh
G
a
u
r,
P
a
rd
e
e
p
Ku
m
a
r,
“
W
irele
ss
S
e
n
so
r
De
p
lo
y
m
e
n
t
Us
in
g
M
o
d
if
ied
Disc
re
te
Bin
a
r
y
P
S
O
M
e
th
o
d
”
,
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
I
n
n
o
v
a
ti
v
e
Res
e
a
rc
h
in
El
e
c
trica
l,
El
e
c
tro
n
ics
,
In
str
u
me
n
ta
ti
o
n
a
n
d
C
o
n
tr
o
l
En
g
in
e
e
rin
g
,
IS
S
N 2
3
2
1
–
2
0
0
4
,
IS
S
N 2
3
2
1
–
5
5
2
6
,
Vo
l.
1
,
Iss
u
e
3
,
p
p
.
8
2
-
8
9
,
Ju
n
e
2
0
1
3
.
[9
]
T
o
o
r,
A
.
S
.
,
Ja
in
,
A
.
K.,
“
A
su
r
v
e
y
o
n
w
irele
s
s
n
e
tw
o
rk
si
m
u
lato
rs”
,
Bu
ll
e
ti
n
o
f
E
lec
trica
l
E
n
g
i
n
e
e
rin
g
a
n
d
In
fo
rm
a
t
ics
(
BE
EI)
,
Vo
l.
6
,
Iss
u
e
1
,
2
0
1
7
,
p
p
.
6
2
-
6
9
.
[1
0
]
Ne
h
a
Ja
in
,
Ka
n
c
h
a
n
S
h
a
rm
a
,
“
M
o
d
if
ied
Disc
re
te
Bin
a
r
y
P
S
O
b
a
se
d
S
e
n
so
r
P
lac
e
m
e
n
t
f
o
r
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