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n
et
w
o
r
k
is
f
u
ll
y
u
til
ized
.
A
t
t
h
e
s
a
m
e
ti
m
e;
t
h
e
y
m
u
s
t
n
o
t
b
e
lo
ca
ted
to
o
f
ar
to
av
o
id
th
e
f
o
r
m
atio
n
o
f
co
v
er
ag
e
h
o
l
es
(
ar
ea
o
u
ts
id
e
s
en
s
in
g
r
an
g
e
o
f
s
en
s
o
r
s
)
.
R
an
d
o
m
d
ep
lo
y
m
e
n
t
m
et
h
o
d
d
is
tr
ib
u
tes
s
en
s
o
r
n
o
d
es
s
to
c
h
asti
ca
ll
y
a
n
d
i
n
d
ep
en
d
en
tl
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
ho
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
c
lo
s
e
to
ea
ch
o
th
er
w
h
ile
o
t
h
er
s
ar
e
to
o
f
ar
ap
ar
t.
I
n
b
o
th
s
it
u
atio
n
s
c
o
v
er
a
g
e
p
r
o
b
lem
w
i
ll
ar
is
e,
t
h
e
s
e
n
s
i
n
g
ca
p
ab
ilit
ies
o
f
t
h
e
s
en
s
o
r
s
ar
e
w
a
s
ted
an
d
t
h
e
co
v
er
ag
e
i
s
n
o
t
m
a
x
i
m
ized
i
n
th
e
f
ir
s
t
co
n
d
itio
n
,
w
h
ile
in
t
h
e
l
ater
b
lin
d
s
p
o
ts
w
il
l
b
e
f
o
r
m
ed
.
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
a
l
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
a
r
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
icie
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
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
t
h
e
p
lace
m
en
t
o
f
th
e
s
en
s
o
r
s
o
n
t
h
e
r
eg
io
n
o
f
i
n
ter
est
(
R
OI
)
.
T
h
er
e
ar
e
m
ai
n
l
y
th
r
ee
s
tr
ateg
ie
s
f
o
r
s
o
l
v
i
n
g
co
v
er
a
g
e
p
r
o
b
le
m
s
n
a
m
el
y
;
f
o
r
ce
,
g
r
id
an
d
co
m
p
u
ta
tio
n
al
g
eo
m
etr
y
b
ased
[
8
]
.
T
o
d
eter
m
in
e
t
h
e
o
p
ti
m
a
l
p
o
s
itio
n
o
f
t
h
e
s
e
n
s
o
r
s
f
o
r
ce
b
ased
m
et
h
o
d
s
u
s
e
a
ttra
ctio
n
a
n
d
r
ep
u
ls
io
n
f
o
r
ce
s
.
W
h
il
e
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
a
n
d
Dela
u
n
a
y
tr
ian
g
u
latio
n
f
r
o
m
th
e
co
m
p
u
tatio
n
al
g
eo
m
etr
y
ap
p
r
o
ac
h
ar
e
u
s
ed
in
W
S
N
co
v
er
ag
e
o
p
ti
m
izatio
n
m
eth
o
d
.
Hen
ce
,
th
ese
s
tr
ateg
ie
s
ar
e
e
m
p
lo
y
ed
in
co
m
b
in
at
io
n
w
it
h
P
SO to
ac
h
ie
v
e
b
etter
r
esu
lts
.
Dif
f
er
en
t
m
e
th
o
d
s
i
m
p
le
m
en
t
ed
u
n
d
er
Vir
t
u
al
Fo
r
ce
B
ase
d
m
eth
o
d
ar
e
VF
(
V
ir
tu
al
Fo
r
ce
)
,
P
SO,
VFP
SO
(
Vir
tu
al
Fo
r
ce
Dir
ec
ted
P
ar
ticle
S
w
ar
m
Op
ti
m
iza
tio
n
)
&
VF
C
P
SO
(
Vir
t
u
al
F
o
r
ce
Dir
ec
ted
C
o
-
ev
o
lu
tio
n
ar
y
P
ar
ticle
S
w
ar
m
Op
ti
m
izatio
n
)
.
VFC
P
SO
h
a
s
b
etter
p
er
f
o
r
m
an
ce
w
i
th
r
esp
ec
t
to
co
m
p
u
tat
io
n
ti
m
e
a
n
d
ef
f
ec
ti
v
en
e
s
s
t
h
an
t
h
e
VF,
P
SO a
n
d
VFP
SO
m
et
h
o
d
s
[
9
]
.
Dif
f
er
en
t
m
et
h
o
d
s
i
m
p
le
m
e
n
t
ed
u
n
d
er
Gr
id
B
ased
m
et
h
o
d
ar
e
P
SO,
B
P
SO
(
B
in
ar
y
P
SO
)
,
DB
P
SO
(
Dis
cr
ete
B
in
ar
y
P
SO)
a
n
d
MD
B
P
SO
(
Mo
d
if
ied
Dis
cr
et
e
B
in
ar
y
P
SO)
.
MD
B
P
SO
p
r
o
v
id
es
p
lace
m
e
n
t
o
f
s
en
s
o
r
s
to
in
cr
ea
s
e
th
e
co
v
er
ag
e
o
n
s
e
n
s
o
r
f
ield
also
it
i
s
m
o
r
e
u
s
ef
u
l,
s
ca
lab
le,
d
u
r
ab
le,
m
ax
i
m
u
m
co
v
er
ag
e
an
d
m
i
n
i
m
u
m
n
et
w
o
r
k
co
s
t a
s
co
m
p
ar
ed
to
o
th
er
m
et
h
o
d
s
[
1
0
]
.
Dif
f
er
en
t
m
et
h
o
d
s
i
m
p
le
m
e
n
t
ed
u
n
d
er
Vo
r
o
n
o
i
Dia
g
r
a
m
(
s
u
b
t
y
p
e
o
f
C
o
m
p
u
tatio
n
a
l
Ge
o
m
etr
y
B
ased
m
et
h
o
d
)
ar
e
W
SNP
SO
v
or
,
W
S
NP
SO
per
a
n
d
W
SNP
SO
con
.
I
n
a
lar
g
er
R
OI
,
W
S
NP
SO
con
m
a
n
a
g
es
to
en
s
u
r
e
m
a
x
i
m
u
m
d
is
tan
ce
m
o
v
ed
to
b
e
less
th
a
n
th
r
es
h
o
ld
v
alu
e
[
1
1
]
.
As t
h
e
VORo
n
o
i b
ased
m
et
h
o
d
(
VOR);
a
s
e
n
s
o
r
m
o
v
e
s
to
it
s
f
ar
th
e
s
t V
o
r
o
n
o
i
v
er
te
x
w
h
e
n
it
d
etec
ts
a
co
v
er
ag
e
h
o
le[
1
2
]
.
T
h
is
co
n
s
u
m
es
m
o
r
e
a
m
o
u
n
t
o
f
en
er
g
y
a
s
co
m
p
ar
ed
to
Gr
id
&
Fo
r
ce
B
ased
s
en
s
o
r
d
ep
lo
y
m
en
t
m
et
h
o
d
.
Hen
ce
,
t
h
is
p
ap
er
d
is
cu
s
s
e
s
p
er
f
o
r
m
a
n
ce
a
n
al
y
s
i
s
o
f
r
a
n
d
o
m
,
Gr
id
B
ased
MD
B
P
SO
(
Mo
d
if
ied
Dis
cr
ete
B
in
ar
y
P
ar
ticle
S
w
a
r
m
Op
ti
m
iza
tio
n
)
,
Fo
r
ce
B
ased
VFC
P
SO
an
d
C
o
m
b
i
n
atio
n
o
f
Gr
id
&
Fo
r
ce
B
ased
Sen
s
o
r
Dep
lo
y
m
e
n
t M
e
th
o
d
s
b
ased
o
n
in
ter
v
al
an
d
p
ac
k
et
s
ize.
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
ec
i
f
ic
cr
iter
ia
an
d
an
al
y
zi
n
g
it
s
p
er
f
o
r
m
an
ce
u
n
d
er
d
if
f
er
en
t
s
c
en
ar
io
s
[
1
3
]
.
Net
w
o
r
k
Si
m
u
l
a
to
r
2
is
u
s
ed
f
o
r
s
i
m
u
lat
io
n
o
f
t
h
e
m
et
h
o
d
s
.
Sectio
n
2
d
is
c
u
s
s
e
s
R
a
n
d
o
m
Dep
lo
y
m
e
n
t.
Sectio
n
3
elab
o
r
ates
Gr
id
B
ased
MD
B
P
SO
Dep
lo
y
m
en
t
w
h
er
e
as
Sectio
n
4
d
is
cu
s
s
es
Fo
r
ce
B
ased
VFC
P
SO
Dep
lo
y
m
e
n
t;
Sectio
n
5
ex
p
lain
s
C
o
m
b
i
n
atio
n
o
f
Gr
id
&
Fo
r
ce
B
ased
Dep
l
o
y
m
e
n
t
an
d
Sectio
n
6
d
is
cu
s
s
s
i
m
u
latio
n
r
esu
lt
s
.
Fin
all
y
t
h
e
co
n
clu
d
i
n
g
r
e
m
ar
k
s
ar
e
g
iv
e
n
in
Sectio
n
7
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ac
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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
tain
ar
ea
s
i
n
th
e
s
en
s
in
g
f
ield
b
u
t
leav
i
n
g
o
th
er
ar
ea
s
d
ep
r
iv
ed
o
f
n
o
d
es.
T
h
er
e
ar
e
b
ig
co
v
er
ag
e
h
o
le
s
as
t
h
e
n
et
w
o
r
k
s
ize
g
r
o
w
s
.
U
n
ev
e
n
n
o
d
e
to
p
o
lo
g
y
m
a
y
b
r
in
g
ab
o
u
t
u
n
b
a
la
n
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.
T
h
ese
li
m
itatio
n
s
m
o
ti
v
ate
th
e
e
s
tab
lis
h
m
en
t
o
f
a
p
lan
n
in
g
s
y
s
te
m
t
h
at
o
p
ti
m
ize
s
th
e
s
en
s
o
r
r
eo
r
g
an
izatio
n
p
r
o
ce
s
s
to
en
h
an
ce
t
h
e
co
v
er
ag
e
a
f
ter
in
it
ial
r
an
d
o
m
d
ep
lo
y
m
en
t
3.
G
RI
D
B
ASE
D
M
DB
P
SO
DE
P
L
O
YM
E
NT
Mo
d
if
ied
Dis
cr
ete
B
in
ar
y
P
SO
(
MD
B
P
SO)
is
i
m
p
le
m
e
n
ted
f
o
r
i
m
p
r
o
v
i
n
g
t
h
e
co
v
er
ag
e
w
h
ile
d
ep
lo
y
i
n
g
th
e
s
en
s
o
r
n
et
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
th
e
m
u
lt
i
-
v
alu
ed
p
r
o
b
le
m
s
[
1
0
]
.
I
n
b
in
ar
y
P
SO
m
o
d
el,
V
id
d
ef
i
n
es
t
h
e
p
r
o
b
ab
ilit
y
o
f
v
a
l
u
e
o
f
o
n
e
X
id
.
P
o
s
itio
n
o
f
ea
ch
p
ar
ticle
d
ef
in
e
s
in
r
eg
io
n
o
f
o
n
e
an
d
ze
r
o
(
0
,
1
)
w
h
ile
V
id
i
s
d
ef
in
ed
as
p
r
o
b
ab
ilit
y
f
u
n
ctio
n
s
o
it
is
li
m
i
ted
in
th
e
r
an
g
e
o
f
o
n
e
an
d
ze
r
o
(
0
,
1
)
.
T
h
er
ef
o
r
e,
th
e
p
ar
ticle
p
o
s
itio
n
ca
n
b
e
u
p
d
at
e
b
y
u
s
i
n
g
eq
u
a
tio
n
(
3
)
.
I
n
t
h
i
s
t
h
e
n
e
w
p
o
s
it
io
n
co
m
p
o
n
e
n
t
h
a
s
to
b
e
e
x
c
h
a
n
g
ed
w
ith
a
v
al
u
e
o
f
p
r
o
b
a
b
ilit
y
o
b
tai
n
ed
b
y
ap
p
l
y
in
g
m
o
d
i
f
ied
s
ig
m
o
id
tr
an
s
f
o
r
m
atio
n
to
th
e
v
elo
cit
y
co
m
p
o
n
e
n
t
(
s
ee
eq
u
atio
n
(
2
)
)
.
T
h
e
v
alu
e
o
f
v
id
ca
n
b
e
h
ig
h
,
lo
w
o
r
ze
r
o
.
I
f
th
e
v
alu
e
o
f
V
id
is
h
i
g
h
t
h
e
p
ar
tic
l
e’
s
p
o
s
itio
n
is
u
n
f
it
t
h
er
ef
o
r
e
it
ca
u
s
es
th
e
v
al
u
e
o
f
X
id
to
c
h
an
g
e
f
r
o
m
0
to
1
o
r
v
ice
v
er
s
a.
I
f
th
e
v
alu
e
o
f
v
id
is
lo
w
f
o
r
X
id
it
d
ec
r
ea
s
es
th
e
p
r
o
b
ab
ilit
y
o
f
ch
a
n
g
es
in
t
h
e
v
alu
e
o
f
X
id
.
An
d
th
e
v
alu
e
o
f
X
id
is
u
n
c
h
a
n
g
ed
i
f
th
e
v
alu
e
o
f
v
id
i
s
ze
r
o
ac
co
r
d
in
g
to
eq
u
atio
n
(
3
)
.
Velo
cit
y
o
f
ea
c
h
p
ar
ticle
ca
n
b
e
m
o
d
if
ied
b
y
t
h
e
f
o
llo
w
i
n
g
e
q
u
atio
n
[
1
1
]
:
id
(
+1
)
=(
∗
id
(
)
)
+(
1
∗
an
d
(
)
∗
(
idbest
–
X
id
(
)
)
)
+(
2
∗
an
d
(
)
∗
(
idbest
−X
id
(
)))
(
1
)
w
h
er
e
d
=
1
,
2
,
…N
d
W
h
er
e,
c
1
&
c
2
ar
e
w
ei
g
h
t
in
g
f
a
cto
r
o
r
lear
n
in
g
co
ef
f
ic
ien
t
s
.
Usu
al
l
y
c
1
i
s
eq
u
al
to
c
2
,
an
d
t
h
e
y
ar
e
i
n
th
e
r
an
g
e
(
1
,
2
)
.
i
d
en
o
tes
th
e
p
ar
ticle
an
d
d
d
en
o
tes
th
e
d
im
en
s
io
n
s
ea
r
ch
s
p
ac
e
w
i
s
w
e
ig
h
tin
g
f
u
n
ctio
n
o
r
lear
n
in
g
co
e
f
f
ic
ien
t
s
,
u
s
u
a
ll
y
is
a
n
u
m
b
er
in
t
h
e
r
an
g
e
(
0
,
1
)
,
r
a
n
d
(
)
is
r
a
n
d
o
m
f
u
n
ctio
n
in
t
h
e
r
an
g
e
o
f
(
0
,
1
)
,
x(
t)
is
c
u
r
r
en
t p
o
s
itio
n
o
f
p
ar
ticle,
p
b
est
is
b
est o
f
p
ar
ticle
an
d
g
b
est i
s
b
est o
f
t
h
e
g
r
o
u
p
.
T
h
e
f
i
n
al
v
al
u
e
f
o
r
v
elo
cit
y
o
f
ea
ch
p
ar
ticle
is
li
m
ited
to
a
v
o
id
th
e
d
i
v
er
g
e
n
ce
:
V
id
Є [
-
v
m
ax
,
v
m
a
x
]
.
T
y
p
icall
y
,
th
i
s
p
r
o
ce
s
s
i
s
iter
ated
f
o
r
a
ce
r
tain
n
u
m
b
er
o
f
t
i
m
e
s
tep
s
,
o
r
u
n
til
s
o
m
e
ac
ce
p
tab
le
s
o
lu
tio
n
h
a
s
b
ee
n
f
o
u
n
d
b
y
th
e
m
et
h
o
d
.
Sig
(
V
id
)
=1
/ (
1
+e
-
v
id
)
S’
(
V
id
)
=
2
˟
|
Si
g
(
V
id
)
-
0
.
5
|
(
2
)
I
f
r
an
d
<
s
’
(
V
id
(
t+1
)
t
h
e
n
X
id
(
t+1
)
=
ex
ch
an
g
e
(
X
id
)
else
X
id
(
t+1
)
=
(
X
id
)
(
3
)
T
h
e
m
o
d
if
ied
s
i
g
m
o
id
also
m
ap
s
th
e
v
al
u
es
o
f
v
elo
cities
f
r
o
m
(
-
∞
to
+∞)
to
(
0
to
1
)
th
i
s
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
er
o
f
iter
atio
n
s
.
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
M
DB
P
SO b
ased
d
ep
l
o
y
m
e
n
t o
f
s
e
n
s
o
r
n
o
d
es:
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
ass
i
g
n
t
h
is
p
o
s
itio
n
to
p
er
s
o
n
al
b
est
p
o
s
itio
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
th
e
p
ar
ticle
p
w
it
h
m
in
i
m
u
m
f
it
n
es
s
f
r
o
m
P
an
d
ass
ig
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
i
d
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
ess
i
s
less
t
h
an
p
er
s
o
n
al
b
est
th
en
u
p
d
ate
th
e
p
er
s
o
n
al
b
est
f
itn
e
s
s
an
d
p
o
s
it
io
n
&
f
i
n
d
th
e
b
est p
ar
ticle
in
p
ar
ticle
v
ec
to
r
P
.
10.
I
f
th
e
f
it
n
e
s
s
o
f
p
ar
ticle
p
is
le
s
s
th
a
n
g
lo
b
al
b
est
f
it
n
es
s
th
e
n
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
it
n
es
s
.
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
8
7
–
1
2
9
5
1290
1
1
.
I
f
t
h
e
g
lo
b
al
b
est
f
itn
e
s
s
is
ze
r
o
th
at
i
n
d
icate
s
t
h
at
f
u
ll
co
v
er
ag
e
is
o
cc
u
p
ied
b
y
s
e
n
s
o
r
s
th
er
ef
o
r
e
s
to
p
th
e
iter
atio
n
s
.
12.
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
.
4.
F
O
RCE B
ASE
D
VF
CP
SO
DE
P
L
O
YM
E
NT
Her
e,
VF
m
eth
o
d
is
co
m
b
i
n
ed
w
it
h
co
-
e
v
o
lu
tio
n
ar
y
p
ar
ticle
s
w
ar
m
o
p
ti
m
iza
tio
n
(
C
P
SO)
f
o
r
i
m
p
r
o
v
i
n
g
th
e
p
er
f
o
r
m
an
ce
o
f
d
y
n
a
m
ic
d
ep
lo
y
m
en
t
o
p
ti
m
i
za
tio
n
.
T
h
e
C
P
SO
m
et
h
o
d
is
an
i
m
p
r
o
v
ed
P
SO
m
et
h
o
d
in
s
p
ir
ed
b
y
t
h
e
co
-
e
v
o
lu
tio
n
o
f
p
o
p
u
latio
n
s
[
1
4
]
,
w
h
ic
h
u
s
es
m
u
l
tip
le
s
w
ar
m
s
t
o
o
p
tim
ize
d
if
f
er
e
n
t
co
m
p
o
n
e
n
t
s
o
f
th
e
s
o
l
u
tio
n
v
ec
to
r
s
[
1
5
]
.
T
h
e
v
ir
tu
al
f
o
r
ce
is
in
tr
o
d
u
ce
d
to
d
ir
ec
t
th
e
p
ar
ticles
f
li
g
h
t
to
th
e
o
p
tim
a
l
s
o
l
u
tio
n
s
a
n
d
e
n
h
a
n
ce
th
e
p
er
f
o
r
m
a
n
ce
o
f
C
P
SO,
i.e
.
,
u
n
d
er
t
h
e
g
u
id
an
ce
o
f
v
ir
t
u
a
l
f
o
r
ce
,
C
P
SO
ca
n
co
n
v
er
g
e
m
o
r
e
r
ap
id
ly
a
n
d
ac
cu
r
atel
y
to
t
h
e
o
p
ti
m
al
r
es
u
lt
s
[
9
]
.
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
VFC
P
SO b
a
s
ed
d
ep
l
o
y
m
e
n
t o
f
s
e
n
s
o
r
n
o
d
e
s:
1.
A
ll t
h
e
S
Ns ar
e
r
an
d
o
m
l
y
s
ca
t
ter
ed
in
th
e
R
OI
w
h
ile
i
n
itial
iz
in
g
.
2.
Sen
s
o
r
d
etec
tio
n
ar
ea
s
s
h
o
u
l
d
b
e
o
v
er
lap
p
ed
in
o
r
d
er
to
co
m
p
e
n
s
ate
f
o
r
p
o
ten
tial
lo
w
d
etec
tio
n
p
r
o
b
a
b
ilit
y
in
t
h
e
ar
ea
w
h
ic
h
i
s
f
ar
f
r
o
m
a
SN.
3.
T
h
e
VF
m
et
h
o
d
is
a
s
elf
o
r
g
a
n
izi
n
g
m
et
h
o
d
w
h
ich
co
n
s
id
er
s
th
at
th
e
o
b
j
ec
ts
;
in
cl
u
d
in
g
S
Ns,
o
b
s
tacle
s
&
ar
ea
s
o
f
p
r
e
f
er
en
tia
l
co
v
er
ag
e
w
h
ich
n
ee
d
g
r
ea
ter
ce
r
tai
n
t
y
w
ill
e
x
er
t
v
ir
tu
al
a
ttra
ctiv
e
&
r
ep
u
l
s
iv
e
f
o
r
ce
s
o
n
ea
ch
o
t
h
er
.
4.
T
h
e
to
tal
f
o
r
ce
ex
er
ted
o
n
s
i
ca
n
b
e
ex
p
r
ess
ed
as
⃗
⃗
∑
⃗
⃗
⃗
⃗
∑
⃗
⃗
⃗
⃗
⃗
⃗
⃗
∑
⃗
⃗
⃗
⃗
⃗
⃗
⃗
w
h
er
e
F
ij
is
f
o
r
ce
ex
er
ted
o
n
s
en
s
o
r
s
i
b
y
s
j
,
F
i
Am
b
e
t
h
e
f
o
r
ce
ex
er
ted
o
n
s
en
s
o
r
s
i
d
u
e
t
o
p
r
ef
er
en
tial
co
v
er
ag
e
ar
ea
A
m
,
F
i
Rn
b
e
t
h
e
r
ep
u
ls
iv
e
f
o
r
ce
o
n
s
i
d
u
e
to
o
b
s
tacle
R
n
,
k
is
n
u
m
b
er
o
f
SNs
,
M
i
s
n
u
m
b
er
o
f
o
b
s
tacle
s
&
N
i
s
n
u
m
b
er
o
f
p
r
ef
er
en
tial c
o
v
er
ag
e
ar
ea
s
5.
T
h
e
n
e
w
lo
ca
tio
n
o
f
SN
is
ca
l
cu
lated
ac
co
r
d
in
g
to
th
e
o
r
ie
n
t
atio
n
&
m
ag
n
it
u
d
e
o
f
t
h
e
to
tal
f
o
r
ce
ex
er
ted
o
n
it.
w
h
er
e,
Ma
x
Step
is
th
e
p
r
ed
ef
i
n
ed
s
i
n
g
le
m
ax
i
m
u
m
d
is
t
an
ce
,
F
x
,
F
y
ar
e
x
-
a
n
d
y
-
co
o
r
d
in
ate
f
o
r
ce
s
r
esp
ec
tiv
el
y
.
6.
P
SO
is
in
tr
o
d
u
ce
d
in
o
r
d
er
to
ca
lcu
late
v
elo
cit
y
&
p
o
s
iti
o
n
o
f
th
e
p
ar
ticle.
Her
e,
v
e
l
o
cit
y
o
f
ea
c
h
p
ar
ticle
is
u
p
d
ated
ac
co
r
d
in
g
to
n
o
t
o
n
l
y
t
h
e
h
is
to
r
ical
o
p
tim
al
s
o
lu
tio
n
s
,
b
u
t
al
s
o
th
e
v
ir
tu
al
f
o
r
ce
s
o
f
SNs
i
n
VFP
SO
m
eth
o
d
.
(
)
(
)
(
)
(
)
(
(
)
(
)
)
(
)
(
(
)
̂
(
)
)
(
)
(
)
7.
Fo
r
i
m
p
r
o
v
in
g
s
ea
r
c
h
i
n
g
ab
i
lit
y
o
f
P
SO
in
h
i
g
h
d
i
m
e
n
s
io
n
al
p
r
o
b
lem
,
t
h
e
s
ea
r
c
h
s
p
ac
e
ca
n
b
e
p
ar
titi
o
n
ed
in
to
lo
w
er
d
i
m
en
s
i
o
n
al
s
u
b
s
p
ac
es
b
y
s
p
lit
t
in
g
t
h
e
s
o
lu
tio
n
v
ec
to
r
s
i
n
to
s
m
aller
v
ec
to
r
s
[
1
6
]
.
T
h
is
is
co
-
ev
o
l
u
tio
n
ar
y
P
SO (
C
P
SO)
.
8.
I
n
s
tead
o
f
ad
o
p
tin
g
o
n
e
s
w
ar
m
to
f
i
n
d
t
h
e
o
p
ti
m
al
n
-
d
i
m
e
n
s
io
n
al
v
ec
to
r
,
in
C
P
SO,
th
e
v
ec
to
r
is
s
p
lit
in
to
it
s
co
m
p
o
n
en
ts
s
o
t
h
at
ea
ch
s
w
ar
m
atte
m
p
ts
to
o
p
ti
m
ize
a
s
i
n
g
le
co
m
p
o
n
e
n
t
o
f
t
h
e
s
o
lu
tio
n
v
ec
to
r
,
ess
e
n
tiall
y
a
1
-
D
o
p
ti
m
izatio
n
p
r
o
b
lem
.
9.
Ho
w
e
v
er
,
th
e
f
u
n
ctio
n
b
ei
n
g
o
p
ti
m
ized
s
till
r
eq
u
ir
es a
n
n
d
im
en
s
io
n
a
l v
ec
to
r
to
ev
al
u
ate.
10.
T
h
is
co
n
tex
t
v
ec
to
r
is
co
n
s
tr
u
cted
b
y
ta
k
i
n
g
t
h
e
g
lo
b
al
b
est
p
ar
ticle
f
r
o
m
ea
ch
o
f
t
h
e
s
w
ar
m
s
a
n
d
co
n
ca
ten
ati
n
g
th
e
m
to
f
o
r
m
s
u
ch
an
n
-
d
i
m
e
n
s
io
n
al
v
ec
to
r
.
11.
T
h
e
f
itn
e
s
s
f
u
n
ctio
n
o
f
all
p
ar
ticles in
s
w
ar
m
i
s
ca
lcu
lated
.
VFC
P
SO
m
et
h
o
d
h
as
g
o
o
d
g
l
o
b
al
s
ea
r
ch
i
n
g
an
d
r
eg
io
n
a
l
c
o
n
v
er
g
e
n
ce
ab
ilit
ies
i
n
t
h
e
p
r
o
ce
d
u
r
e
o
f
o
p
tim
izatio
n
,
a
n
d
it
ca
n
i
m
p
le
m
en
t
th
e
d
y
n
a
m
ic
d
ep
lo
y
m
e
n
t
o
f
h
y
b
r
id
W
SNs
w
it
h
m
o
b
il
e
s
en
s
o
r
n
o
d
es
an
d
s
tatio
n
ar
y
s
e
n
s
o
r
s
n
o
d
es r
ap
id
l
y
,
ef
f
ec
ti
v
el
y
an
d
r
o
b
u
s
tl
y
.
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
Gri
d
a
n
d
F
o
r
ce
B
a
s
ed
S
en
s
o
r
Dep
lo
yme
n
t Meth
o
d
s
in
W
ir
el
ess
S
en
s
o
r
…
(
A
p
a
r
n
a
P
r
a
d
ee
p
La
tu
r
ka
r
)
1291
5.
CO
M
B
I
NATI
O
N
O
F
G
RID
& F
O
RCE B
ASE
D
DE
P
L
O
YM
E
NT
Her
e,
co
n
ce
p
t
o
f
Fo
r
ce
B
ased
VFC
P
SO
i
s
co
m
b
i
n
ed
w
it
h
Gr
id
B
ased
MD
B
P
SO
f
o
r
im
p
r
o
v
in
g
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
d
y
n
a
m
ic
d
ep
lo
y
m
e
n
t o
p
ti
m
izat
io
n
.
Fo
llo
w
i
n
g
ar
e
th
e
s
tep
s
i
n
v
o
lv
ed
in
its
i
m
p
le
m
e
n
tatio
n
:
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
ass
i
g
n
t
h
is
p
o
s
itio
n
to
p
er
s
o
n
al
b
est
p
o
s
itio
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
u
s
in
g
Gr
id
B
ased
MD
B
P
S
O
m
et
h
o
d
.
5.
Mo
d
if
y
t
h
e
f
i
tn
e
s
s
f
u
n
ctio
n
o
f
all
p
ar
ticles in
s
w
ar
m
u
s
i
n
g
V
FC
P
SO
m
e
th
o
d
.
6.
E
v
alu
a
te
th
e
p
o
s
itio
n
&
v
elo
ci
t
y
o
f
t
h
e
p
ar
ticle.
7.
I
f
t
h
e
g
lo
b
al
b
est
f
itn
e
s
s
is
ze
r
o
th
at
i
n
d
icate
s
t
h
at
f
u
ll
co
v
er
ag
e
is
o
cc
u
p
ied
b
y
s
e
n
s
o
r
s
th
er
ef
o
r
e
s
to
p
th
e
iter
atio
n
s
.
8.
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
.
6.
R
E
SU
L
T
S&
AN
AL
Y
SI
S
T
h
is
p
ap
er
d
is
cu
s
s
es
p
er
f
o
r
m
an
ce
a
n
al
y
s
is
o
f
r
an
d
o
m
;
g
r
i
d
b
ased
MD
B
P
SO
(
Mo
d
if
ied
Dis
cr
ete
B
in
ar
y
P
ar
ticle
S
w
ar
m
Op
ti
m
i
za
tio
n
)
,
Fo
r
ce
B
ased
VF
C
P
SO a
n
d
C
o
m
b
i
n
atio
n
o
f
Gr
id
&
Fo
r
ce
B
ased
s
e
n
s
o
r
d
ep
lo
y
m
en
t
m
et
h
o
d
s
b
ased
o
n
in
ter
v
al
&
p
ac
k
et
s
i
ze.
Fig
u
r
e
1
.
C
o
m
p
ar
is
o
n
o
f
4
m
e
th
o
d
s
f
o
r
in
ter
v
al
v
s
n
o
r
m
alize
d
o
v
er
h
ea
d
s
Fig
u
r
e
2
.
C
o
m
p
ar
is
o
n
o
f
4
m
e
th
o
d
s
f
o
r
in
ter
v
al
v
s
p
ac
k
et
s
d
r
o
p
p
e
d
0
.
0
0
1
0
.
0
0
2
0
.
0
0
3
0
.
0
0
4
0
.
0
0
5
0
.
0
0
6
0
.
0
0
7
0
.
0
0
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
No
rm
a
lized_
O
H
(
Co
un
t
)
No
r
m
al
ized
_
OH_
C
o
m
b
in
e
d
No
r
m
al
ized
_
OH_
Fo
r
ce
No
r
m
al
ized
_
OH_
Gr
id
No
r
m
al
ized
_
OH_
R
an
d
o
m
Int
er
val
(
Sec)
0
20
40
60
80
1
0
0
1
2
0
1
4
0
1
6
0
1
8
0
0
.
5
0
.
6
0
.
7
0
.
8
0
.
9
P
a
ck
et
s
Dro
pp
ed
(
Co
un
t
)
Pack
ets
_
Dr
o
p
p
ed
_
C
o
m
b
in
ed
Pack
ets
_
Dr
o
p
p
ed
_
Fo
r
ce
Pack
ets
_
Dr
o
p
p
ed
_
Gr
i
d
Pack
ets
_
Dr
o
p
p
ed
_
R
an
d
o
m
Int
er
val
(
Sec)
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
8
7
–
1
2
9
5
1292
Fig
u
r
e
3
.
C
o
m
p
ar
is
o
n
o
f
4
m
e
th
o
d
s
f
o
r
in
ter
v
al
v
s
t
h
r
o
u
g
h
p
u
t
Fig
u
r
e
4
.
C
o
m
p
ar
is
o
n
o
f
4
m
e
th
o
d
s
f
o
r
in
ter
v
al
v
s
l
i
f
eti
m
e
I
n
F
ig
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ased
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ased
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e
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at
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ased
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et
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s
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a
n
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m
;
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ased
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s
cr
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in
a
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m
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n
)
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ased
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7.
CO
NCLU
SI
O
N
W
SN
h
as
is
s
u
e
s
s
u
ch
a
s
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v
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ag
e,
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n
n
ec
ti
v
it
y
,
n
et
w
o
r
k
l
if
e
ti
m
e
an
d
s
c
h
ed
u
li
n
g
&
d
ata
ag
g
r
e
g
atio
n
.
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o
n
n
ec
ti
v
it
y
a
n
d
co
v
er
ag
e
p
r
o
b
le
m
s
ar
e
ca
u
s
ed
b
y
t
h
e
li
m
i
ted
co
m
m
u
n
i
ca
tio
n
a
n
d
s
e
n
s
i
n
g
r
an
g
e.
C
o
v
er
ag
e
i
s
s
u
e
ca
n
b
e
s
o
lv
ed
at
t
h
e
ti
m
e
o
f
s
en
s
o
r
d
ep
lo
y
m
e
n
t
it
s
elf
b
y
s
tr
ate
g
ical
l
y
d
ep
lo
y
i
n
g
s
en
s
o
r
n
o
d
es.
T
h
er
e
ar
e
m
ai
n
l
y
th
r
ee
s
tr
ateg
ie
s
f
o
r
s
o
l
v
in
g
co
v
er
a
g
e
p
r
o
b
lem
s
n
a
m
e
l
y
;
f
o
r
ce
,
g
r
id
an
d
co
m
p
u
tat
io
n
al
g
eo
m
etr
y
b
ased
.
T
h
ese
s
tr
at
eg
ies
ar
e
e
m
p
lo
y
ed
in
co
m
b
in
atio
n
w
i
th
P
SO
to
ac
h
ie
v
e
b
etter
r
esu
lt
s
.
R
esear
ch
er
s
h
av
e
p
r
ev
io
u
s
l
y
w
o
r
k
ed
o
n
t
h
ese
all
tec
h
n
iq
u
e
s
s
ep
ar
atel
y
.
B
u
t
n
o
o
n
e
h
as
d
o
n
e
i
m
p
le
m
e
n
tatio
n
o
f
th
ese
tec
h
n
iq
u
e
s
o
n
co
m
m
o
n
p
latf
o
r
m
w
it
h
s
a
m
e
W
SN
p
ar
am
eter
s
.
I
n
th
i
s
p
ap
er
,
p
er
f
o
r
m
a
n
ce
an
a
l
y
s
is
o
f
r
an
d
o
m
;
g
r
id
b
ased
MD
B
P
S
O
(
Mo
d
if
ied
Dis
cr
ete
B
in
ar
y
P
ar
ticle
S
w
ar
m
Op
ti
m
izat
io
n
)
,
Fo
r
ce
B
ased
VFC
P
SO
an
d
C
o
m
b
in
a
tio
n
o
f
Gr
id
&
Fo
r
ce
B
ased
s
en
s
o
r
d
ep
lo
y
m
e
n
t
m
e
th
o
d
s
b
ased
o
n
in
ter
v
a
l
an
d
p
ac
k
e
t
s
ize
an
d
its
ef
f
ec
t
is
o
b
s
er
v
e
d
o
n
p
ar
am
eter
s
v
iz.
n
o
r
m
ali
ze
d
o
v
er
h
ea
d
,
p
ac
k
ets
d
r
o
p
p
ed
,
th
r
o
u
g
h
p
u
t
an
d
lif
eti
m
e
s
o
as
to
ch
ec
k
t
h
e
r
o
b
u
s
tn
e
s
s
o
f
ea
ch
o
f
th
e
m
.
Fro
m
all
ab
o
v
e
f
ig
u
r
es
i
t
ca
n
b
e
co
n
clu
d
ed
t
h
at
C
o
m
b
i
n
atio
n
o
f
Gr
id
&
Fo
r
ce
B
ased
Sen
s
o
r
Dep
lo
y
m
e
n
t
m
eth
o
d
p
er
f
o
r
m
s
b
etter
th
a
n
R
a
n
d
o
m
;
Gr
id
B
ased
MD
B
P
SO,
Fo
r
ce
B
ased
VFC
P
SO
m
eth
o
d
s
.
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
Distr
ib
u
ti
o
n
w
it
h
G
e
n
e
ti
c
A
lg
o
rit
h
m
in
W
irele
ss
S
e
n
so
r
Ne
t
w
o
rk
”
In
Ad
v
a
n
c
e
in
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
irel
e
ss
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
t
ics
,
V
o
l.
6
,
Iss
u
e
1
,
2
0
1
7
,
p
p
.
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
,
V
o
l
.
6
,
Iss
u
e
1
,
2
0
1
7
,
p
p
.
8
1
-
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
m
y
o
f
S
c
ien
c
e
,
En
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
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.
[6
]
J.
Ke
n
n
e
d
y
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n
d
R.
Eb
e
rh
a
rt,
“
Pa
rticle
swa
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o
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n
,
”
in
P
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o
c
.
IEE
E
In
t.
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o
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ra
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rk
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l.
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7
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v
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c
.
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1
9
9
5
.
[7
]
Ra
g
h
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d
ra
V
.
Ku
lk
a
rn
i,
G
a
n
e
sh
Ku
m
a
r,
“
P
a
rti
c
le
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wa
r
m
Op
ti
m
iz
a
ti
o
n
in
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ss
-
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e
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so
r
Ne
tw
o
rk
s:
A
Brie
f
S
u
rv
e
y
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IEE
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T
ra
n
s
a
c
ti
o
n
s o
n
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y
ste
ms
,
M
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n
&
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ti
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s
-
Pa
rt
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p
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if
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id
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if
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an
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acket
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e
(
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yt
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Gri
d
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Dep
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S
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A
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1295
[8
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No
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a
A
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.
A
z
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z
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Ka
m
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ru
lza
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z
iz,
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a
n
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iah
W
a
n
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m
a
il
,
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v
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ra
g
e
S
trate
g
i
e
s
f
o
r
W
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ss
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e
n
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r
Ne
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o
rk
s”
W
o
rld
Aca
d
e
my
o
f
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e
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g
i
n
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n
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e
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g
y
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l.
:
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6
,
p
p
.
1
3
5
-
1
4
0
,
2
3
-
02
-
2
0
0
9
.
[9
]
X
u
e
W
a
n
g
,
S
h
e
n
g
W
a
n
g
a
n
d
Ju
n
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Jie
M
a
,
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A
n
I
m
p
ro
v
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d
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-
e
v
o
lu
ti
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ry
P
a
rti
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le
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wa
r
m
O
p
ti
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iza
ti
o
n
f
o
r
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irele
ss
S
e
n
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r
Ne
t
w
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rk
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w
it
h
Dy
n
a
m
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c
De
p
lo
y
m
e
n
t” ,
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e
n
so
rs
,
I
S
S
N 1
4
2
4
-
8
2
2
0
,
p
p
3
5
4
-
3
7
0
,
2
0
0
7
.
[1
0
]
Ne
h
a
Ja
in
,
Ka
n
c
h
a
n
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h
a
rm
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,
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M
o
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a
r
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se
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m
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s”
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In
ter
n
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ti
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a
l
J
o
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rn
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l
o
f
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e
c
tro
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ics
a
n
d
Co
m
p
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ter
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c
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e
En
g
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IJ
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ECS
)
,
2
0
1
1
;
IS
S
N
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2
2
7
7
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1
9
5
6
,
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l.
1
,
p
p
.
1
5
4
8
-
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5
5
4
,
.
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1
]
No
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A
z
li
n
a
A
b
A
z
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n
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r
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rk
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v
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ra
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A
lg
o
rit
h
m
s
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se
d
On
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a
rti
c
le
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w
a
rm
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ti
m
iza
ti
o
n
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tes
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1
8
(
2
),
4
1
-
5
2
,
2
0
1
3
.
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2
]
S
h
iri
n
Kh
e
z
ri,
Ka
rim
F
a
e
z
,
Am
ja
d
Os
m
a
n
i
,
“
M
o
d
if
ied
Disc
re
te
Bin
a
ry
P
S
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b
a
se
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S
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n
so
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lac
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m
e
n
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in
W
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o
rk
s
”
.
in
In
ter
n
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t
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l
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o
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fer
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e
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mp
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ta
ti
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n
telli
g
e
n
c
e
a
n
d
Co
mm
u
n
ica
t
io
n
S
y
ste
ms
,
IEE
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DO
I
1
0
.
1
1
0
9
/CICN.
2
0
1
0
.
4
9
2
0
0
2
0
1
0
.
[1
3
]
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
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f
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lec
trica
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g
i
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n
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fo
rm
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t
ics
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l.
6
,
Iss
u
e
1
,
2
0
1
7
,
p
p
.
6
2
-
69.
[1
4
]
S
h
i,
Y.;
Kro
h
li
n
g
,
R.
A
.
,
“
Co
-
e
v
o
lu
ti
o
n
a
ry
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
i
z
a
ti
o
n
t
o
so
lv
e
m
in
-
m
a
x
p
ro
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le
m
s
”
.
Pro
c
.
2
0
0
2
Co
n
g
r
.
Evo
l
u
t.
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m
p
u
t
.
2
0
0
2
,
1
6
8
2
-
1
6
8
7
.
[1
5
]
V
a
n
d
e
n
Be
rg
h
,
F
.
;
E
n
g
e
lb
r
e
c
h
t,
A
.
P
.
,
“
A
c
o
o
p
e
ra
ti
v
e
a
p
p
ro
a
c
h
to
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
t
io
n
”
.
IEE
E
T
r
a
n
s.
o
n
Evo
lu
t.
C
o
mp
u
t.
2
0
0
4
,
8
,
2
2
5
-
2
3
9
.
[1
6
]
P
o
tt
e
r,
M
.
A
.
;
De
Jo
n
g
,
K.
A
.
,
“
A
c
o
o
p
e
ra
ti
v
e
c
o
e
v
o
lu
ti
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n
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ry
a
p
p
ro
a
c
h
t
o
f
u
n
c
ti
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n
o
p
ti
m
iza
ti
o
n
”
.
T
h
ir
d
P
a
ra
ll
.
Pro
b
.
S
o
lv.
Na
t
.
1
9
9
4
,
2
4
9
–
2
5
7
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
A
p
a
rn
a
P
ra
d
e
e
p
L
a
tu
rk
a
r,
M
.
E.
(El
e
c
tro
n
ics
)
a
n
d
w
o
rk
in
g
a
s
A
s
sista
n
t
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ro
f
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ss
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r
in
P
ES
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o
d
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r
n
Co
ll
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g
e
o
f
En
g
in
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e
ri
n
g
,
P
u
n
e
,
M
a
h
a
ra
sh
tra,
I
n
d
ia.
Re
se
a
r
c
h
in
tere
sts
a
re
in
c
o
m
m
u
n
ica
ti
o
n
a
n
d
w
irele
s
s
s
e
n
so
r
n
e
tw
o
rk
.
P
o
sta
l
A
d
d
re
ss
:
F
la
t
No
.
3
0
1
,
A
V
AL
O
N,
S
,
No
.
1
8
/
1
,
1
9
,
2
0
/3
,
Ne
a
r
F
ire
Brig
a
d
e
,
S
u
n
Cit
y
Ro
a
d
,
Of
f
S
in
h
g
a
d
R
o
a
d
,
W
a
d
g
a
o
n
Bu
d
r
u
k
,
P
u
n
e
–
4
1
1
0
5
1
.
S
rid
h
a
ra
n
B
h
a
v
a
n
i
is
P
h
.
D.
a
n
d
w
o
rk
in
g
a
s P
r
o
f
e
ss
o
r
&
He
a
d
o
f
El
e
c
tro
n
ics
&
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
De
p
a
rtm
e
n
t
in
Ka
rp
a
g
a
m
A
c
a
d
e
m
y
o
f
Hig
h
e
r
Ed
u
c
a
ti
o
n
,
C
o
im
b
a
to
re
,
T
a
m
il
n
a
d
u
,
In
d
ia.
Re
se
a
rc
h
in
tere
sts a
re
in
ima
g
e
p
ro
c
e
ss
in
g
,
e
m
b
e
d
d
e
d
sy
ste
m
s,
V
L
S
I
a
n
d
w
irele
ss
n
e
tw
o
rk
s.
P
o
sta
l
A
d
d
re
ss
:
P
o
ll
a
c
h
i
M
a
i
n
Ro
a
d
,
L
&
T
B
y
P
a
ss
Ro
a
d
Ju
n
c
ti
o
n
Eac
h
a
n
a
ri
P
o
st,
Eac
h
a
n
a
ri,
Co
im
b
a
to
re
,
T
a
m
il
Na
d
u
6
4
1
0
2
1
De
e
p
a
li
P
a
ra
g
A
d
h
y
a
p
a
k
,
M
.
E.
(El
e
c
tro
n
ics
:
Dig
it
a
l
S
y
ste
m
s)
a
n
d
w
o
rk
in
g
a
s
A
ss
istan
t
P
r
o
f
e
ss
o
r
in
P
ES
’s
M
o
d
e
rn
C
o
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
P
u
n
e
,
M
a
h
a
ra
sh
tra,
In
d
ia.
Re
se
a
rc
h
in
tere
sts
a
re
in
c
o
m
m
u
n
ica
ti
o
n
a
n
d
w
irele
ss
m
u
lt
i
m
e
d
ia
se
n
so
r
n
e
tw
o
rk
.
P
o
sta
l
A
d
d
re
ss
:
H.
No
.
4
5
4
,
Ka
n
a
k
a
d
it
y
a
P
ra
sa
d
,
A
b
h
in
a
v
Na
g
a
r,
Eas
t
S
a
n
g
a
v
i,
P
u
n
e
–
4
1
1
0
2
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.