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o
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two
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k
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UM
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Mo
b
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ch
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CC B
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C
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Ku
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task
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ally
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s
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d
ata
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task
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an
a
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.
I
f
th
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s
en
s
o
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ca
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n
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co
m
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ter
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o
f
f
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war
d
in
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d
ata
to
s
in
k
[
1
]
.
W
ir
eless
Sen
s
o
r
N
etwo
r
k
s
(
W
SN
s
)
h
av
e
em
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k
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ab
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t
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a
p
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in
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an
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esp
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s
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On
e
im
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in
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in
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p
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o
f
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o
d
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.
T
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s
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p
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b
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m
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with
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GPS
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ch
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co
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p
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Tr
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M.
G.
K
a
vith
a
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35
f
o
r
la
r
g
e
-
s
ca
le
W
SNs
.
Hen
ce
a
v
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f
ap
p
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es
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av
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b
ee
n
d
ev
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f
o
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s
en
s
o
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n
etwo
r
k
lo
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lizatio
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[
2
]
.
L
o
ca
tio
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d
is
co
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y
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em
er
g
i
n
g
as
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im
p
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task
s
a
s
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cu
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ate
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[
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4
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o
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o
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etwo
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k
s
is
p
er
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o
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m
e
d
in
f
o
llo
win
g
s
tep
s
.
Fir
s
t,
d
is
tan
ce
esti
m
at
io
n
:
t
h
is
p
h
ase
in
v
o
lv
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m
ea
s
u
r
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m
en
t
t
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h
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es
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ate
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e
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d
is
tan
ce
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etwe
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n
o
d
es
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Po
s
itio
n
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m
p
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tatio
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:
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t
co
n
s
is
ts
o
f
alg
o
r
ith
m
s
to
ca
lcu
late
th
e
co
o
r
d
in
ates
o
f
th
e
u
n
k
n
o
wn
n
o
d
e
with
r
esp
ec
t
to
th
e
lo
ca
tio
n
o
f
k
n
o
wn
an
ch
o
r
n
o
d
es
o
r
o
th
er
n
e
i
g
h
b
o
r
in
g
n
o
d
es
.
L
o
ca
lizatio
n
alg
o
r
ith
m
s
r
eq
u
i
r
e
tech
n
iq
u
es
f
o
r
lo
ca
tio
n
esti
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atin
g
d
ep
e
n
d
in
g
o
n
th
e
b
ea
co
n
n
o
d
es’
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tio
n
.
T
h
ese
ar
e
ca
ll
ed
m
u
lti
-
later
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n
(
ML
)
tech
n
iq
u
es.
I
ter
ativ
e
M
L
:
So
m
e
n
o
d
es
m
ay
n
o
t
b
e
i
n
th
e
d
ir
ec
t
r
an
g
e
o
f
th
r
ee
b
e
ac
o
n
s
.
On
ce
a
n
o
d
e
esti
m
ates
i
ts
lo
ca
tio
n
,
it
s
en
d
s
o
u
t
a
b
ea
co
n
,
wh
ich
en
a
b
le
s
s
o
m
e
o
th
er
n
o
d
es
to
n
o
w
r
ec
eiv
e
atlea
s
t
th
r
ee
b
ea
co
n
s
.
I
ter
ativ
el
y
,
all
n
o
d
es
in
th
e
n
etwo
r
k
ca
n
esti
m
ate
t
h
eir
lo
ca
tio
n
b
u
t
l
o
ca
tio
n
esti
m
atio
n
m
ay
n
o
t
b
e
ac
cu
r
ate
as
e
r
r
o
r
s
m
ay
p
r
o
p
a
g
ate.
C
o
llab
o
r
ativ
e
ML
is
w
h
e
n
two
o
r
m
o
r
e
n
o
d
es
ca
n
n
o
t
r
ec
eiv
e
atlea
s
t
th
r
ee
b
ea
co
n
s
ea
ch
,
th
ey
co
llab
o
r
ate
with
ea
ch
o
th
er
.
Fig
u
r
e
1
s
h
o
w
s
n
o
d
es
A
an
d
B
h
av
e
th
r
ee
n
eig
h
b
o
r
s
ea
ch
.
Of
th
e
s
ix
p
ar
ticip
atin
g
n
o
d
es,
f
o
u
r
ar
e
b
ea
co
n
s
,
wh
o
s
e
p
o
s
itio
n
s
ar
e
k
n
o
wn
.
Pro
x
im
ity
tech
n
iq
u
e
is
u
s
ed
wh
e
n
th
er
e
is
n
o
r
an
g
e
in
f
o
r
m
atio
n
a
v
ailab
le.
I
t
r
ev
ea
ls
wh
eth
er
o
r
n
o
t
a
n
o
d
e
is
in
r
an
g
e
o
r
n
ea
r
to
a
r
ef
er
en
ce
p
o
i
n
t.
L
o
ca
lizatio
n
alg
o
r
ith
m
s
u
s
in
g
th
is
tech
n
iq
u
e
d
eter
m
in
e
if
a
n
o
d
e
is
in
p
r
o
x
im
ity
to
a
r
ef
er
e
n
ce
p
o
in
t
b
y
en
ab
lin
g
th
e
r
e
f
er
en
ce
to
tr
an
s
m
it
p
er
io
d
ic
b
ea
co
n
s
ig
n
al
s
an
d
wh
eth
er
th
e
n
o
d
e
is
ab
le
to
r
ec
eiv
e
at
least
ce
r
tain
v
alu
e
o
f
th
e
b
ea
co
n
s
i
g
n
als
s
et
a
s
th
r
esh
o
ld
.
I
n
a
p
er
io
d
,
t
if
it
r
ec
eiv
es
n
b
ea
co
n
s
g
r
ea
ter
th
an
th
e
s
et
th
r
esh
o
ld
th
en
it
is
in
p
r
o
x
im
i
ty
to
th
at
r
ef
er
en
ce
p
o
in
t
[
5
,
6
]
.
L
o
ca
lizatio
n
alg
o
r
ith
m
s
:
i
t
d
eter
m
in
es
h
o
w
th
e
in
f
o
r
m
atio
n
co
n
ce
r
n
i
n
g
d
is
tan
ce
s
an
d
p
o
s
itio
n
s
,
is
m
a
n
ip
u
lat
ed
in
o
r
d
er
t
o
allo
w
m
o
s
t o
r
al
l
n
o
d
es
o
f
W
SN
to
esti
m
ate
th
eir
p
o
s
itio
n
.
Op
tim
ally
th
e
lo
ca
lizatio
n
alg
o
r
ith
m
s
m
ay
in
v
o
lv
e
alg
o
r
ith
m
s
to
r
e
d
u
ce
th
e
er
r
o
r
s
.
Fig
u
r
e
1
.
Netwo
r
k
ar
ch
itectu
r
e
I
n
th
is
p
ap
er
,
we
p
r
o
p
o
s
e
a
m
o
b
ile
an
c
h
o
r
ass
is
ted
lo
ca
lizatio
n
alg
o
r
ith
m
b
ased
o
n
T
r
ilater
atio
n
m
eth
o
d
(
L
UM
AT
)
with
th
e
o
b
jectiv
es
o
f
m
ax
im
izin
g
lo
ca
l
izatio
n
r
atio
,
en
er
g
y
ef
f
icien
c
y
an
d
lo
ca
lizatio
n
ac
cu
r
ac
y
.
L
UM
AT
u
s
es
lo
g
e
(
n
)
n
u
m
b
e
r
o
f
m
o
b
ile
a
n
ch
o
r
n
o
d
es
as
th
e
r
e
f
er
en
ce
n
o
d
es,
wh
ich
m
o
v
e
i
n
th
e
s
en
s
in
g
f
ield
a
n
d
b
r
o
ad
ca
s
t
th
eir
cu
r
r
en
t
p
o
s
itio
n
p
er
io
d
icall
y
.
Sen
s
o
r
n
o
d
es
r
ec
eiv
e
th
e
p
o
s
itio
n
in
f
o
r
m
atio
n
o
f
th
e
m
o
b
ile
a
n
c
h
o
r
n
o
d
es
an
d
lo
ca
lize
th
e
m
s
elv
es
b
y
u
s
in
g
T
r
ilater
atio
n
alg
o
r
ith
m
.
T
h
e
r
e
s
u
lts
o
f
s
im
u
latio
n
s
an
d
m
ea
s
u
r
em
e
n
ts
s
h
o
w
th
at
L
UM
AT
is
a
p
r
ac
tical
m
eth
o
d
th
at
ca
n
b
e
u
s
ed
in
r
ea
l
-
wo
r
ld
s
y
s
tem
,
an
d
is
also
a
m
eth
o
d
o
f
p
r
i
n
cip
le
s
im
p
le,
l
ess
co
m
p
u
tin
g
an
d
co
m
m
u
n
ic
atio
n
,
lo
w
co
s
t,
an
d
h
ig
h
ac
c
u
r
ac
y
.
T
h
e
r
est
o
f
th
e
p
ap
er
is
o
r
g
an
ize
d
as
f
o
llo
ws:
th
e
n
ex
t
s
ec
tio
n
s
u
r
v
e
y
s
r
elate
d
wo
r
k
s
o
n
p
r
ev
io
u
s
lo
ca
lizatio
n
r
esear
ch
,
esp
ec
ially
ab
o
u
t
m
eth
o
d
s
b
a
s
ed
o
n
m
o
b
ile
lo
ca
lizatio
n
with
m
o
r
e
d
etails
in
o
r
d
er
to
clar
if
y
o
u
r
wo
r
k
.
I
n
s
ec
tio
n
I
I
I
,
we
d
escr
ib
e
th
e
L
UM
AT
m
eth
o
d
.
Sectio
n
I
V
r
ep
o
r
ts
o
u
r
s
im
u
latio
n
an
d
ex
p
er
im
en
tal
r
esu
lts
.
Fin
ally
,
we
p
r
esen
t o
u
r
co
n
cl
u
s
io
n
in
s
ec
tio
n
V.
2.
R
E
L
AT
E
D
WO
RK
A
g
en
er
al
s
u
r
v
ey
ab
o
u
t
lo
ca
liz
atio
n
f
o
r
wir
eless
s
en
s
o
r
n
etw
o
r
k
s
is
f
o
u
n
d
in
wh
ich
is
a
b
r
o
ad
r
esear
ch
ar
ea
in
th
e
p
ast
s
ev
er
al
y
ea
r
s
.
A
b
r
ief
s
u
r
v
ey
ab
o
u
t
v
ar
io
u
s
r
an
g
e
-
f
r
ee
ap
p
r
o
ac
h
es
an
d
lo
ca
lizatio
n
m
eth
o
d
s
wh
ich
in
v
o
l
v
e
m
o
b
ile
r
ef
er
e
n
ce
n
o
d
es
ar
e
p
r
o
v
id
ed
h
er
e.
E
n
er
g
y
co
n
s
u
m
p
tio
n
p
h
en
o
m
en
o
n
h
as
alwa
y
s
b
ee
n
n
o
ticed
in
s
in
k
-
b
ase
d
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
T
h
is
p
ap
e
r
e
x
p
lo
r
es
an
en
er
g
y
ef
f
icien
t
r
o
u
tin
g
p
r
o
to
co
l
with
a
m
o
b
ile
s
in
k
b
ased
o
n
th
e
s
h
o
r
test
p
ath
d
ata
tr
a
n
s
m
is
s
io
n
m
o
d
e
[
7
]
.
Acc
o
r
d
in
g
to
t
h
e
p
o
s
itio
n
o
f
th
e
s
in
k
n
o
d
e
an
d
th
e
c
o
m
m
o
n
n
o
d
es'
I
D
in
th
e
n
etwo
r
k
,
we
ca
lcu
late
th
e
co
o
r
d
i
n
ate
v
alu
e
o
f
ea
c
h
n
o
d
e
in
th
e
n
etwo
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
9
,
No
.
1
,
Ma
r
ch
2
0
2
0
:
34
–
42
36
B
y
co
m
p
ar
i
n
g
t
h
e
co
o
r
d
i
n
ate
v
alu
es
to
c
h
o
o
s
e
t
h
e
s
h
o
r
test
p
ath
to
f
o
r
war
d
t
h
e
d
ata.
Sim
u
latio
n
r
esu
lts
s
h
o
w
th
at
th
is
m
eth
o
d
ca
n
p
r
o
lo
n
g
t
h
e
life
tim
e
o
f
th
e
n
etwo
r
k
,
im
p
r
o
v
e
th
e
en
e
r
g
y
u
tili
za
tio
n
r
a
tio
an
d
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
m
o
r
e
b
ala
n
ce
d
.
C
lu
s
ter
in
g
m
eth
o
d
o
lo
g
y
p
lay
a
s
ig
n
if
ican
t
r
o
le
in
im
p
r
o
v
in
g
t
h
e
life
tim
e
o
f
s
en
s
o
r
n
etwo
r
k
.
T
h
er
e
h
as
b
ee
n
v
ar
i
o
u
s
h
ier
ar
ch
ical
clu
s
ter
in
g
t
h
a
t
h
as
b
ee
n
d
ev
elo
p
ed
in
r
ec
e
n
t
tim
e
b
y
en
h
a
n
cin
g
th
e
p
r
o
to
co
l
[
8
]
.
T
h
e
d
r
a
w
b
a
ck
o
f
th
ese
p
r
o
to
c
o
ls
is
en
er
g
y
o
f
clu
s
ter
h
ea
d
d
eg
r
a
d
es
v
e
r
y
f
ast
d
u
e
t
o
lo
n
g
d
is
tan
ce
tr
an
s
m
is
s
io
n
an
d
p
ac
k
et
f
ailu
r
e
lik
elih
o
o
d
is
n
o
t
c
o
n
s
id
er
ed
f
o
r
in
ter
an
d
in
tr
a
c
lu
s
ter
tr
an
s
m
is
s
io
n
.
T
o
ad
d
r
ess
th
e
e
n
er
g
y
ef
f
icien
cy
is
s
u
e
o
f
e
x
is
tin
g
ap
p
r
o
ac
h
th
is
wo
r
k
p
r
o
p
o
s
ed
p
ac
k
et
f
ailu
r
e
lik
elih
o
o
d
esti
m
atio
n
m
o
d
el
an
d
h
o
p
s
elec
tio
n
o
p
tim
izatio
n
m
o
d
el
f
o
r
i
n
ter
clu
s
ter
tr
an
s
m
is
s
io
n
.
Po
s
itio
n
al
ac
cu
r
ac
y
is
v
er
y
im
p
o
r
tan
t
in
d
icato
r
f
o
r
ass
ess
in
g
th
e
lo
ca
tio
n
o
f
p
er
f
o
r
m
a
n
ce
.
Mo
r
e
lo
ca
lizatio
n
is
h
ig
h
p
r
ec
is
io
n
lo
ca
tio
n
o
f
th
e
p
e
r
f
o
r
m
an
ce
i
s
b
etter
[
9
]
.
A
co
n
clu
s
io
n
m
i
g
h
t
elab
o
r
ate
o
n
th
e
im
p
o
r
tan
ce
o
f
th
e
wo
r
k
o
r
s
u
g
g
est ap
p
licatio
n
s
an
d
ex
ten
s
io
n
s
.
I
n
ad
d
itio
n
,
th
e
ac
cu
r
ac
y
o
f
th
e
lo
ca
tio
n
o
f
th
e
am
o
r
p
h
o
u
s
alg
o
r
ith
m
is
s
u
p
er
io
r
to
th
at
o
f
o
t
h
er
alg
o
r
ith
m
s
an
d
t
h
er
e
is
n
o
t
a
lar
g
e
in
cr
ea
s
e
o
f
en
er
g
y
co
n
s
u
m
p
tio
n
,
wh
ich
is
wh
y
it
is
s
u
itab
le
f
o
r
th
e
lo
ca
tio
n
o
f
n
etwo
r
k
n
o
d
es
lar
g
e
s
ca
le.
E
n
h
an
cin
g
t
h
e
life
tim
e
o
f
th
e
s
en
s
o
r
n
etwo
r
k
an
d
at
th
e
s
am
e
tim
e
m
ain
tain
in
g
p
r
o
p
e
r
s
ec
u
r
ity
is
an
im
p
o
r
tan
t
asp
ec
t
in
wir
eless
s
en
s
o
r
n
etwo
r
k
s
[
1
0
]
.
I
n
t
h
is
p
ap
er
we
u
s
e
th
e
co
n
ce
p
t
o
f
cl
u
s
ter
ed
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
C
lu
s
ter
in
g
is
a
k
ey
co
n
ce
p
t
f
o
r
en
h
a
n
cin
g
th
e
s
en
s
o
r
n
etwo
r
k
’
s
life
tim
e.
C
lu
s
ter
wir
eless
s
en
s
o
r
n
etwo
r
k
s
h
av
e
m
ain
ly
two
b
en
ef
its
th
an
n
o
n
clu
s
ter
ed
W
SNs
.
T
h
ey
a
r
e:
r
e
d
u
cin
g
f
lo
w
o
f
p
ac
k
ets
th
r
o
u
g
h
t
h
e
n
etwo
r
k
an
d
s
av
in
g
en
er
g
y
b
y
p
laci
n
g
u
n
u
s
ed
n
o
d
es
in
s
leep
m
o
d
e.
J
i
an
g
[
1
1
]
p
r
o
p
o
s
ed
a
n
o
v
el
lo
ca
lizatio
n
ap
p
r
o
ac
h
wh
er
e
u
n
k
n
o
wn
n
o
d
es
th
r
o
u
g
h
th
eir
n
ea
r
an
ch
o
r
n
o
d
es
to
o
b
t
ain
th
eir
p
o
s
itio
n
.
I
n
o
r
d
er
to
r
ed
u
ce
er
r
o
r
d
u
r
i
n
g
lo
ca
lizatio
n
,
a
n
ew
m
ea
n
s
was
u
s
ed
to
ap
p
r
o
x
im
ate
th
e
d
is
tan
ce
b
etwe
en
u
n
k
n
o
wn
n
o
d
es
an
d
a
n
ch
o
r
n
o
d
es
wh
e
n
it
is
lar
g
er
th
a
n
n
o
d
e
’
s
co
m
m
u
n
icatio
n
r
a
d
iu
s
.
I
n
cl
u
d
in
g
th
is
,
s
elf
-
ad
a
p
tin
g
g
en
eti
c
alg
o
r
ith
m
is
p
r
o
p
o
s
ed
t
o
c
alcu
late
th
e
s
im
ilar
p
o
s
itio
n
o
f
n
o
d
es,
it m
a
k
es th
e
lo
ca
lizatio
n
er
r
o
r
m
u
c
h
lo
wer
th
an
th
e
c
o
m
m
o
n
m
eth
o
d
.
Yetk
in
an
d
G
u
n
g
o
r
[
1
2
]
p
r
o
p
o
s
ed
a
n
ew
R
ec
eiv
ed
Sig
n
al
Stre
n
g
th
I
n
d
icato
r
(
R
SS
I
)
b
ased
f
in
g
er
p
r
i
n
t
tech
n
iq
u
e
wh
ich
u
s
es
lo
g
ical
in
f
er
en
ce
s
.
Her
e
clo
s
ed
ar
ea
was
d
iv
id
ed
in
to
th
e
ce
lls
o
f
1
x
1
m
t.
T
h
e
R
SS
I
ch
ar
ac
ter
is
tics
o
f
ea
ch
ce
ll
wer
e
r
ec
o
r
d
ed
in
to
a
d
atab
ase
i
n
o
r
d
e
r
to
p
r
ep
ar
e
a
r
ad
io
m
a
p
.
At
r
ea
l
tim
e,
th
e
R
SS
I
s
o
f
an
ch
o
r
n
o
d
es
r
ec
ei
v
ed
f
r
o
m
b
ase
s
tatio
n
wer
e
co
m
p
ar
ed
with
r
ad
io
m
a
p
ac
co
r
d
in
g
to
lo
g
ical
alg
o
r
ith
m
s
.
I
n
th
is
s
ch
em
e,
th
e
tar
g
et
lo
ca
lizatio
n
was
ca
r
r
i
ed
o
u
t
m
ath
e
m
atica
lly
.
W
ei
Z
h
ag
[
1
3
]
p
r
o
p
o
s
ed
a
two
-
p
h
ase
r
o
b
u
s
t
lo
ca
lizatio
n
alg
o
r
ith
m
b
ased
o
n
C
o
n
s
is
ten
cy
o
f
B
ea
co
n
s
in
Gr
id
.
I
n
th
e
f
ir
s
t
-
p
h
ase,
a
v
o
tin
g
m
eth
o
d
b
ased
o
n
th
e
co
n
s
is
ten
cy
o
f
b
ea
co
n
s
in
th
e
g
r
id
i
s
u
s
ed
to
f
ilter
o
u
t
p
ar
t
o
f
th
e
s
u
s
p
icio
u
s
n
o
d
es.
I
n
t
h
e
s
ec
o
n
d
-
p
h
ase,
it wa
s
ad
o
p
te
d
th
e
lo
s
s
f
u
n
ctio
n
in
M
-
esti
m
atio
n
o
f
R
o
b
u
s
t Statis
tic
s
to
o
b
tain
a
r
o
b
u
s
t so
lu
tio
n
with
th
e
r
em
ain
ed
n
o
d
es.
Z
h
an
g
an
d
Ho
n
g
Pei
[
1
4
]
ex
p
lo
r
e
d
a
two
-
hop
C
o
llab
o
r
ativ
e
M
u
ltil
ater
al
L
o
ca
lizatio
n
Alg
o
r
ith
m
(
C
ML
A)
.
T
h
is
al
g
o
r
ith
m
was
im
p
lem
en
ted
th
r
o
u
g
h
ev
en
t
-
d
r
iv
en
s
ch
em
es.
I
t
is
also
in
tr
o
d
u
ce
d
a
n
ew
m
eth
o
d
wh
ic
h
is
u
s
ed
to
esti
m
ate
th
e
d
is
tan
ce
s
b
etwe
e
n
two
h
o
p
n
o
d
es,
a
p
p
lies
an
ch
o
r
n
o
d
es
with
in
two
h
o
p
s
to
lo
ca
lize
u
n
k
n
o
wn
n
o
d
es,
an
d
u
s
es
th
e
m
in
im
u
m
r
an
g
e
er
r
o
r
esti
m
atio
n
to
co
m
p
u
te
co
o
r
d
in
ates
o
f
u
n
k
n
o
wn
n
o
d
es.
I
f
an
y
u
n
k
n
o
wn
n
o
d
e
ca
n
n
o
t
b
e
lo
ca
lized
t
h
r
o
u
g
h
two
h
o
p
an
ch
o
r
s
n
o
d
es
,
it
was
lo
ca
lized
b
y
an
ch
o
r
s
an
d
lo
ca
lized
n
o
d
es w
ith
in
two
h
o
p
s
.
C
h
en
g
p
ei
[
1
5
]
im
p
lem
e
n
ted
a
W
SN
lo
ca
lizatio
n
m
eth
o
d
b
ased
o
n
p
lan
t g
r
o
wth
s
im
u
latio
n
alg
o
r
ith
m
(
PGSA).
T
h
is
alg
o
r
ith
m
is
a
b
io
n
ic
r
an
d
o
m
alg
o
r
ith
m
th
at
ch
ar
ac
ter
izes
th
e
g
r
o
wth
m
ec
h
an
is
m
o
f
p
lan
t
p
h
o
to
tr
o
p
is
m
.
B
ased
o
n
s
im
u
latio
n
an
aly
s
is
,
th
is
alg
o
r
ith
m
(
PGSA)
i
s
s
im
p
le,
f
ast
co
n
v
er
g
en
ce
an
d
r
o
b
u
s
tn
ess
,
wh
ich
is
m
o
r
e
s
u
itab
le
f
o
r
th
e
lar
g
e
-
s
ca
le
en
v
ir
o
n
m
en
t.
L
o
n
g
C
h
en
g
[
1
6
]
p
r
esen
ted
a
co
m
p
r
eh
en
s
iv
e
an
aly
s
is
o
f
th
ese
ch
allen
g
es:
lo
ca
liza
tio
n
in
n
o
n
-
lin
e
-
of
-
s
ig
h
t,
n
o
d
e
s
elec
tio
n
cr
iter
ia
f
o
r
lo
ca
lizatio
n
in
en
er
g
y
-
co
n
s
tr
ain
ed
n
etwo
r
k
,
s
ch
ed
u
lin
g
th
e
s
en
s
o
r
n
o
d
e
to
o
p
tim
iz
e
th
e
tr
ad
e
o
f
f
b
etwe
en
l
o
ca
lizatio
n
p
er
f
o
r
m
an
ce
an
d
en
er
g
y
co
n
s
u
m
p
tio
n
,
co
o
p
er
ativ
e
n
o
d
e
l
o
ca
lizatio
n
,
an
d
lo
ca
lizatio
n
alg
o
r
ith
m
i
n
h
eter
o
g
en
eo
u
s
n
etwo
r
k
.
I
n
clu
d
in
g
th
is
it
was
in
tr
o
d
u
ce
d
th
at
th
e
e
v
alu
atio
n
cr
iter
ia
f
o
r
lo
ca
lizatio
n
in
wir
eless
s
en
s
o
r
n
etwo
r
k
.
Og
u
ejif
o
r
[
1
7
]
im
p
lem
en
ted
a
lo
ca
lizatio
n
s
y
s
tem
th
at
u
s
es
a
R
SS
I
tr
ilater
atio
n
ap
p
r
o
ac
h
i
n
a
wir
eless
s
en
s
o
r
n
etwo
r
k
.
T
h
e
s
y
s
tem
p
o
s
itio
n
esti
m
atio
n
ac
cu
r
ac
y
was
also
ev
alu
ated
.
Fin
ally
it
wa
s
c
o
n
clu
d
ed
t
h
at
f
o
r
th
e
p
r
o
p
o
s
ed
s
y
s
tem
to
wo
r
k
th
e
r
e
m
u
s
t
b
e
th
e
av
ailab
ilit
y
o
f
a
t
least
th
r
ee
an
ch
o
r
n
o
d
es
with
in
th
e
n
etwo
r
k
an
d
wh
en
ev
er
an
ch
o
r
n
o
d
es
b
r
o
ad
ca
s
t
p
ac
k
ets
co
n
tain
i
n
g
th
eir
l
o
ca
tio
n
s
an
d
o
th
er
s
en
s
ed
p
a
r
am
eter
s
,
th
e
b
lin
d
n
o
d
e
with
in
t
h
e
b
r
o
ad
ca
s
t
r
an
g
e
ca
n
alwa
y
s
esti
m
ate
its
d
is
t
an
ce
to
th
e
a
n
ch
o
r
n
o
d
es,
an
d
if
p
er
ad
v
en
tu
r
e
th
e
b
lin
d
n
o
d
es r
ec
eiv
e
p
ac
k
ets f
r
o
m
at
least th
r
ee
an
ch
o
r
s
,
th
e
b
lin
d
n
o
d
e
ca
n
lo
ca
lize
its
p
o
s
itio
n
.
Xiajo
u
n
Z
h
u
[
1
8
]
ex
am
in
e
d
two
ca
n
d
id
ate
s
o
lu
tio
n
s
d
ev
elo
p
e
d
f
r
o
m
e
x
is
tin
g
id
ea
s
,
with
o
n
e
ass
u
m
in
g
th
at
n
o
d
es
ca
n
h
ea
r
f
r
o
m
ea
c
h
o
th
er
if
a
n
d
o
n
ly
if
th
ey
ar
e
with
in
tr
an
s
m
is
s
io
n
r
an
g
e,
a
n
d
th
e
o
th
e
r
ass
u
m
in
g
clo
s
er
n
o
d
es
o
b
s
er
v
e
lar
g
er
R
SS
I
.
B
o
th
ca
n
d
id
ate
s
o
lu
tio
n
s
d
o
n
o
t
wo
r
k
well
i
n
p
r
ac
t
ice.
Af
ter
c
h
an
g
i
n
g
“
clo
s
e
r
”
to
“
th
e
clo
s
est
”
an
d
“
lar
g
er
”
to
“
th
e
lar
g
est
”
in
t
h
e
s
ec
o
n
d
ap
p
r
o
ac
h
,
it
was
f
o
u
n
d
t
h
at
th
e
n
ew
ass
u
m
p
tio
n
is
q
u
ite
r
eliab
le
in
p
r
ac
tice.
R
am
a
an
d
Par
v
a
d
h
a
[
1
9
]
p
r
o
p
o
s
ed
a
f
u
zz
y
lo
g
ic
-
b
ased
r
estrictio
n
s
y
s
tem
s
u
itab
le
f
o
r
r
em
o
te
s
e
n
s
o
r
h
u
b
s
th
at
ar
e
p
o
r
ta
b
le
i
n
u
p
r
o
ar
i
o
u
s
,
s
av
ag
e
s
itu
atio
n
s
.
T
h
e
co
n
s
titu
en
t
f
r
am
ewo
r
k
s
u
s
ed
f
u
zz
y
m
u
lti
later
atio
n
an
d
a
g
r
id
p
r
e
d
ictio
n
to
p
r
o
ce
s
s
th
e
ar
ea
o
f
a
h
u
b
as a
zo
n
e.
T
h
e
s
ig
n
al
s
tr
en
g
th
is
th
r
o
wn
in
to
b
in
s
wh
ich
en
co
d
e
th
e
im
p
r
ec
is
io
n
.
L
aslo
[
2
0
]
p
r
esen
ted
W
SN
b
as
ed
f
in
g
er
p
r
in
tin
g
lo
ca
lizatio
n
m
eth
o
d
.
T
h
e
R
SS
I
v
alu
es
o
f
th
e
c
o
m
m
u
n
icatio
n
lin
k
s
b
etwe
en
th
e
p
r
e
v
io
u
s
ly
s
itu
ated
s
en
s
o
r
s
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
Tr
ila
tera
tio
n
b
a
s
ed
lo
ca
liz
a
tio
n
meth
o
d
u
s
in
g
mo
b
ile
a
n
ch
o
r
in
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
(
M.
G.
K
a
vith
a
)
37
th
e
m
o
b
ile
s
en
s
o
r
wer
e
r
e
co
r
d
ed
in
a
n
in
d
o
o
r
en
v
ir
o
n
m
e
n
t
t
h
r
o
u
g
h
th
e
ex
p
e
r
im
en
t.
Usi
n
g
th
e
r
ec
o
r
d
ed
R
SS
I
v
alu
es
a
f
ee
d
-
f
o
r
war
d
ty
p
e
o
f
n
eu
r
al
n
etwo
r
k
was
tr
ain
ed
.
T
h
e
r
esu
lt
o
f
th
e
tr
ain
in
g
is
a
n
eu
r
al
n
etwo
r
k
ca
p
a
b
le
o
f
p
er
f
o
r
m
in
g
in
d
o
o
r
lo
ca
lizatio
n
.
T
h
e
ac
c
u
r
ac
y
o
f
th
e
lo
ca
lizatio
n
b
etwe
en
th
e
r
ea
l
an
d
t
h
e
ca
lcu
lated
v
alu
es
was m
ea
s
u
r
ed
with
E
u
clid
ea
n
d
is
tan
ce
an
d
d
em
o
n
s
tr
ated
wit
h
th
e
cu
m
u
lativ
e
d
is
tr
ib
u
tio
n
f
u
n
ctio
n
.
Priti
an
d
T
y
ag
i
[
2
1
]
p
r
o
p
o
s
ed
a
tech
n
iq
u
e
ca
lled
Mu
ltid
i
m
en
s
io
n
al
s
ca
lin
g
wh
ich
co
m
p
u
tes
th
e
p
o
s
itio
n
o
f
n
o
d
es
wh
ich
a
r
e
i
n
th
e
co
m
m
u
n
icatio
n
r
an
g
e
o
f
ea
ch
o
th
e
r
.
T
h
is
an
aly
s
is
tech
n
iq
u
e
f
in
d
o
u
t
th
e
r
elativ
e
p
o
s
itio
n
o
f
n
o
d
es
with
ac
cu
r
ac
y
s
u
f
f
icien
t
e
n
o
u
g
h
f
o
r
m
o
s
t
o
f
t
h
e
ap
p
licatio
n
s
s
o
as
to
s
o
lv
e
th
e
p
r
o
b
lem
o
f
r
ec
r
ea
tio
n
.
Ma
r
tin
an
d
R
am
alak
s
h
m
i
[
2
2
]
d
ev
elo
p
ed
a
lo
ca
lizatio
n
s
y
s
tem
th
at
ca
r
r
ies
h
ig
h
-
lo
ca
tio
n
esti
m
atio
n
ac
cu
r
ac
y
at
lo
w
co
s
t.
T
h
e
s
y
s
tem
u
s
ed
s
p
atio
tem
p
o
r
al
p
r
o
p
er
ties
o
f
well
-
co
n
tr
o
lled
ev
en
ts
in
th
e
n
etwo
r
k
;
lig
h
t
in
th
is
ca
s
e,
to
o
b
tain
lo
ca
tio
n
s
o
f
s
en
s
o
r
n
o
d
es.
T
h
e
s
y
s
tem
was
to
d
etec
t
t
h
e
m
u
ltip
le
ev
en
ts
in
th
e
n
etwo
r
k
an
d
to
in
cr
ea
s
e
th
e
ar
ea
o
f
th
e
s
en
s
o
r
f
ield
b
y
i
n
cr
ea
s
in
g
th
e
n
u
m
b
er
o
f
n
o
d
e
s
.
B
y
h
an
d
lin
g
th
is
k
in
d
o
f
d
etec
tio
n
o
f
m
u
ltip
le
ev
en
ts
in
th
e
n
etwo
r
k
at
o
n
ce
,
m
ain
ly
th
e
tim
e
was
s
av
ed
.
Sach
in
Desh
p
an
d
e
[
2
3
]
p
r
esen
ted
th
e
m
eth
o
d
o
lo
g
y
th
at
g
iv
es
a
s
o
lu
tio
n
to
co
m
p
u
t
e
th
e
s
tate
p
ar
am
eter
s
o
f
th
e
ad
v
er
s
ar
y
tar
g
et
a
n
d
tr
ac
k
s
it a
n
d
ass
o
ciate
th
e
s
am
e
with
th
e
lo
ca
tio
n
in
th
e
p
e
r
ip
h
er
y
o
f
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
Nir
m
ala
[
2
4
]
d
is
cu
s
s
ed
a
n
ew
t
ec
h
n
iq
u
e
th
at
aim
s
to
lo
ca
lize
all
th
e
s
en
s
o
r
n
o
d
es
in
th
e
n
et
wo
r
k
u
s
in
g
tr
ilater
atio
n
,
an
d
a
s
ec
u
r
ity
p
r
o
to
co
l w
as u
s
ed
f
o
r
p
r
o
v
id
i
n
g
co
n
f
id
en
tial
ity
a
n
d
au
th
en
ticat
io
n
b
etwe
en
an
ch
o
r
n
o
d
es
an
d
s
en
s
o
r
n
o
d
es.
B
aih
u
a
an
d
Gu
o
li [
2
5
]
p
r
o
p
o
s
ed
a
n
ew
m
eth
o
d
,
b
ased
o
n
r
ad
ial
d
is
tan
ce
m
o
d
u
latio
n
,
to
d
etec
t
an
d
lo
ca
te
m
o
v
i
n
g
o
b
ject
f
r
o
m
to
p
v
iew
an
g
le.
T
h
is
m
eth
o
d
h
as
ad
v
a
n
tag
es
o
f
ex
tr
ac
tin
g
in
f
o
r
m
atio
n
d
ir
ec
tly
f
r
o
m
th
e
m
o
v
in
g
o
b
j
ec
t
ch
ar
ac
ter
is
tics
o
f
m
o
v
em
e
n
t
an
d
s
p
atial
p
o
s
itio
n
,
s
m
all
co
m
p
u
tatio
n
,
g
o
o
d
r
o
b
u
s
tn
ess
,
co
n
v
en
ien
t
co
n
f
ig
u
r
atio
n
,
n
o
n
-
co
n
tact
etc.
I
t
ca
n
lo
ca
te
th
e
m
o
v
in
g
o
b
ject
with
s
im
p
le
in
f
o
r
m
atio
n
af
ter
m
o
d
u
latin
g
an
d
en
co
d
in
g
th
e
p
er
ce
p
ti
o
n
ar
ea
o
f
s
en
s
o
r
s
.
Dan
an
d
Dan
iel
[
2
6
]
p
r
o
p
o
s
ed
an
o
th
er
a
n
ch
o
r
n
o
d
e
lo
ca
lizatio
n
tec
h
n
iq
u
e
t
h
at
ca
n
b
e
u
s
ed
wh
e
n
GPS
d
ev
ices
ca
n
n
o
t
ac
co
m
p
lis
h
t
h
eir
m
is
s
io
n
o
r
a
r
e
co
n
s
id
er
ed
to
b
e
to
o
e
x
p
en
s
i
v
e.
T
h
is
n
o
v
el
tec
h
n
iq
u
e
was
b
ased
o
n
th
e
f
u
s
io
n
o
f
v
id
e
o
an
d
c
o
m
p
ass
d
ata
ac
q
u
ir
ed
b
y
th
e
an
ch
o
r
n
o
d
e
s
an
d
is
esp
ec
ially
s
u
itab
le
f
o
r
v
i
d
eo
-
o
r
m
u
ltime
d
ia
-
b
ase
d
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
Div
y
a
[
2
7
]
p
r
o
p
o
s
ed
a
m
o
b
ilit
y
co
n
tr
o
l
s
ch
em
e
a
n
d
we
ex
p
lo
r
e
d
th
e
im
p
ac
t
o
f
m
o
b
ilit
y
o
v
er
t
h
e
p
er
f
o
r
m
an
ce
o
f
wir
eless
s
en
s
o
r
n
etwo
r
k
.
T
wo
d
if
f
e
r
en
t
p
r
o
to
co
ls
wer
e
u
s
ed
f
o
r
th
e
p
er
f
o
r
m
an
ce
a
n
aly
s
is
o
f
p
r
o
p
o
s
ed
m
o
b
ilit
y
c
o
n
tr
o
l
s
ch
em
e
an
d
th
e
im
p
ac
t
o
f
th
is
m
e
th
o
d
o
v
er
th
e
s
elec
ted
p
r
o
to
c
o
ls
.
I
t
was
an
al
y
ze
d
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
to
c
o
ls
o
n
th
e
b
asis
o
f
d
if
f
e
r
en
t
p
a
r
am
eter
s
lik
e
T
h
r
o
u
g
h
p
u
t,
Pac
k
et
Deliv
er
y
R
atio
,
R
o
u
tin
g
L
o
ad
an
d
en
er
g
y
c
o
n
s
u
m
p
tio
n
.
J
an
g
Pin
g
Sh
eu
[
2
8
]
p
r
o
p
o
s
ed
d
is
tr
ib
u
ted
lo
ca
liza
tio
n
s
ch
em
e
wh
er
e
ea
ch
n
o
r
m
al
n
o
d
e
g
ath
er
s
th
e
n
ec
ess
ar
y
in
f
o
r
m
atio
n
v
ia
two
-
h
o
p
f
lo
o
d
i
n
g
an
d
is
th
u
s
s
ca
lab
le.
Asi
d
e
f
r
o
m
th
is
,
ea
ch
n
o
r
m
al
n
o
d
e
u
s
es
a
s
im
p
lifie
d
ap
p
r
o
ac
h
an
d
th
e
p
r
o
p
o
s
ed
im
p
r
o
v
ed
g
r
id
-
s
ca
n
alg
o
r
ith
m
to
f
in
d
th
e
in
itial
esti
m
ated
lo
ca
tio
n
s
o
f
th
e
n
o
r
m
al
n
o
d
e,
t
h
u
s
r
ed
u
cin
g
th
e
c
o
m
p
u
tatio
n
c
o
s
t.
I
t
also
in
tr
o
d
u
ce
d
a
v
ec
to
r
-
b
ased
r
ef
in
em
en
t
s
ch
em
e
to
co
r
r
ec
t t
h
e
in
itial
esti
m
ated
lo
ca
tio
n
o
f
th
e
n
o
r
m
al
n
o
d
e,
t
h
u
s
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
o
f
th
e
esti
m
ated
lo
ca
tio
n
.
T
h
e
n
o
d
es
wh
ich
ar
e
awa
r
e
o
f
th
eir
lo
ca
tio
n
s
u
s
in
g
s
p
ec
ial
p
o
s
itio
n
in
g
d
ev
ices
ar
e
ca
lled
an
ch
o
r
n
o
d
es
o
r
r
e
f
er
en
ce
n
o
d
es.
Oth
er
n
o
d
es
th
at
d
o
n
o
t
in
itially
k
n
o
w
t
h
eir
lo
ca
tio
n
s
ar
e
ca
lled
u
n
k
n
o
wn
n
o
d
es
o
r
s
en
s
o
r
n
o
d
es.
Gen
er
ally
,
an
u
n
k
n
o
w
n
n
o
d
e
esti
m
ates
its
lo
ca
tio
n
b
y
r
an
g
e
-
b
ased
o
r
r
a
n
g
e
-
f
r
ee
m
eth
o
d
s
if
th
r
ee
o
r
m
o
r
e
an
c
h
o
r
s
ar
e
av
ailab
le
in
its
2
-
d
im
en
s
io
n
al
co
v
er
ag
e
f
ie
ld
[
2
9
]
.
I
n
all
th
ese
b
etter
lo
ca
lizat
io
n
p
r
ec
is
io
n
is
ac
h
iev
ed
with
th
e
in
c
r
ea
s
ed
n
u
m
b
er
o
f
an
ch
o
r
n
o
d
es.
T
h
e
m
a
in
p
r
o
b
lem
with
th
e
in
c
r
ea
s
ed
n
u
m
b
er
o
f
an
ch
o
r
s
is
th
at
th
ey
ar
e
f
ar
m
o
r
e
ex
p
e
n
s
iv
e
th
an
th
e
r
est
o
f
th
e
s
en
s
o
r
s
.
T
h
e
p
r
ice
o
f
th
e
wh
o
le
n
e
two
r
k
will
in
cr
ea
s
e
ev
en
if
o
n
ly
1
0
%
o
f
th
e
n
o
d
es
ar
e
an
ch
o
r
n
o
d
es.
Af
te
r
th
e
u
n
k
n
o
wn
/ statio
n
ar
y
n
o
d
es h
a
v
e
b
ee
n
lo
ca
lized
,
th
e
an
ch
o
r
s
b
ec
o
m
e
u
s
eless
.
Fo
r
t
h
is
r
ea
s
o
n
,
it
is
n
ec
ess
ar
y
to
c
o
n
s
id
er
an
o
p
tim
al
n
u
m
b
e
r
o
f
m
o
b
ile
an
ch
o
r
s
to
lo
ca
lize
th
e
s
en
s
o
r
n
etwo
r
k
[
3
0
]
.
T
h
e
m
ain
id
ea
o
f
l
o
ca
lizat
io
n
u
s
in
g
a
m
o
b
ile
an
ch
o
r
n
o
d
e
is
as
f
o
llo
ws
a
m
o
b
ile
an
c
h
o
r
n
o
d
e
tr
av
er
s
es
th
e
s
en
s
o
r
n
etwo
r
k
b
y
b
r
o
a
d
c
asti
n
g
an
ch
o
r
p
ac
k
ets
th
at
co
n
tain
th
e
co
o
r
d
in
ates
o
f
th
e
a
n
ch
o
r
n
o
d
e
af
ter
th
e
d
ep
lo
y
m
e
n
t
o
f
s
en
s
o
r
n
etwo
r
k
.
Sen
s
o
r
n
o
d
es
th
at
r
ec
eiv
e
a
n
c
h
o
r
p
ac
k
ets
p
o
s
s
ib
ly
will
in
f
e
r
th
eir
d
is
tan
ce
f
r
o
m
a
m
o
b
ile
an
ch
o
r
n
o
d
e
a
n
d
u
s
e
th
ese
m
ea
s
u
r
em
en
ts
as c
o
n
s
tr
ain
ts
to
co
n
s
tr
u
ct
an
d
m
ain
tain
p
o
s
itio
n
esti
m
ates.
T
h
ese
m
eth
o
d
s
h
av
e
a
c
o
m
m
o
n
f
ea
t
u
r
e
s
u
c
h
as
th
e
y
u
s
e
r
an
g
e
-
b
ased
ap
p
r
o
ac
h
es.B
ased
o
n
th
ese
an
aly
s
es,
lo
ca
liz
atio
n
u
s
in
g
an
o
p
tim
al
n
u
m
b
er
o
f
m
o
b
ile
an
ch
o
r
n
o
d
es
wo
u
ld
b
e
m
o
r
e
ec
o
n
o
m
y
.
I
n
ad
d
itio
n
,
it
is
n
ec
ess
ar
y
to
co
n
s
id
er
th
e
co
n
s
tr
ain
ts
in
co
m
p
u
tin
g
an
d
m
em
o
r
y
p
o
wer
o
f
s
en
s
o
r
s
with
an
o
p
tim
al
n
u
m
b
er
o
f
m
o
b
ile
an
ch
o
r
s
f
o
r
ef
f
icien
t lo
ca
lizatio
n
in
wir
eless
s
e
n
s
o
r
n
etwo
r
k
s
.
3.
RE
S
E
ARCH
M
E
T
H
O
D
3
.
1
.
Sy
s
t
e
m
en
v
iro
nm
ent
I
n
th
e
s
im
u
latio
n
,
th
e
co
n
s
id
er
ed
W
SN
co
n
s
is
ts
o
f
two
ty
p
es
o
f
s
en
s
o
r
s
in
clu
d
i
n
g
s
tatic
s
en
s
o
r
n
o
d
es
an
d
lo
g
e
(
n
)
n
u
m
b
er
o
f
m
o
b
ile
an
ch
o
r
n
o
d
es.
Static sen
s
o
r
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o
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es a
r
e
r
a
n
d
o
m
ly
d
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two
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d
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en
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io
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co
o
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d
in
ate
s
y
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tem
.
T
h
e
lo
ca
tio
n
s
o
f
s
tatic
s
en
s
o
r
s
ar
e
u
n
k
n
o
w
n
s
in
ce
th
ey
d
o
n
o
t
h
a
v
e
GPS
f
ac
ilit
y
.
T
h
e
m
o
b
ile
an
ch
o
r
n
o
d
es
ar
e
eq
u
ip
p
ed
wit
h
GPS
r
ec
eiv
er
to
d
eter
m
in
e
th
eir
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ca
tio
n
s
wh
en
th
ey
n
av
ig
a
te
o
v
er
th
e
s
en
s
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
9
,
No
.
1
,
Ma
r
ch
2
0
2
0
:
34
–
42
38
r
eg
io
n
.
T
h
e
an
c
h
o
r
n
o
d
es
wh
ich
ar
e
m
o
b
ile
in
n
atu
r
e
an
d
s
en
s
o
r
s
ar
e
ab
le
to
r
ec
eiv
e
m
ess
ag
es
f
r
o
m
th
ese
an
ch
o
r
s
in
t
h
e
s
en
s
in
g
r
eg
i
o
n
.
3.
2
.
B
ea
co
n
m
ess
a
g
es
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el
th
e
an
c
h
o
r
n
o
d
es
p
er
io
d
ically
s
en
d
m
ess
ag
e
p
ac
k
ets
co
n
tain
in
g
th
eir
p
o
s
itio
n
co
o
r
d
in
ates
wh
ile
n
a
v
ig
atin
g
in
th
e
s
en
s
in
g
ar
ea
.
T
h
ese
m
ess
ag
es
ar
e
ca
lled
b
ea
co
n
m
ess
ag
es.
W
h
en
an
u
n
k
n
o
wn
s
en
s
o
r
n
o
d
e
r
ec
eiv
e
s
at
leas
t
th
r
ee
s
u
ch
m
ess
ag
e
p
ac
k
ets,
it
ca
lcu
lates
i
ts
p
o
s
itio
n
co
o
r
d
i
n
ates
u
s
in
g
tr
ilater
atio
n
m
eth
o
d
.
W
e
ass
u
m
e
th
at
t
h
e
d
is
tan
ce
b
etwe
en
th
e
m
o
b
ile
an
ch
o
r
n
o
d
es,
an
y
u
n
k
n
o
wn
n
o
d
e
is
esti
m
ated
u
s
in
g
R
SS
I
tech
n
iq
u
e.
3.
3
.
E
nerg
y
m
o
del
T
h
e
m
o
b
ile
an
ch
o
r
h
as
s
u
f
f
ici
en
t
in
itial
en
er
g
y
f
o
r
m
o
v
in
g
an
d
b
r
o
a
d
ca
s
tin
g
an
ch
o
r
p
ac
k
ets
d
u
r
in
g
lo
ca
lizatio
n
is
th
e
b
asic
ass
u
m
p
tio
n
we
co
n
s
id
er
r
e
g
ar
d
in
g
en
er
g
y
lev
el.
N
u
m
b
er
o
f
b
its
tr
an
s
m
itted
in
a
m
ess
ag
e
an
d
d
is
tan
ce
tr
av
elle
d
af
f
ec
t th
e
e
n
er
g
y
lev
el
o
f
an
ch
o
r
n
o
d
es.
3.
4
.
L
UM
AT
M
et
ho
d
3
.
4
.
1
.
Net
wo
r
k
s
eg
m
ent
a
t
io
n
Ass
u
m
e
th
e
s
en
s
in
g
r
eg
io
n
is
d
iv
id
ed
in
to
s
ev
er
al
h
ex
ag
o
n
s
.
T
h
e
m
o
b
ile
an
ch
o
r
n
o
d
es
tr
av
er
s
e
th
e
en
tire
r
eg
io
n
in
o
r
d
er
to
co
v
e
r
all
s
en
s
o
r
n
o
d
es.
Sin
ce
t
h
e
s
en
s
in
g
r
eg
io
n
is
in
ir
r
e
g
u
lar
s
h
ap
e
s
o
m
e
o
f
th
e
r
eg
io
n
s
m
ay
n
o
t
b
e
co
v
e
r
ed
b
y
h
ex
a
g
o
n
al
s
p
ac
e.
3
.
4
.
2
.
M
o
bil
e
a
ncho
r
no
de
T
h
e
an
c
h
o
r
n
o
d
es
r
an
d
o
m
ly
tr
av
er
s
e
ar
o
u
n
d
th
e
en
tire
n
etwo
r
k
,
wh
ich
p
er
io
d
ically
b
r
o
a
d
ca
s
t
m
ess
ag
es.
T
h
e
m
o
b
ile
an
c
h
o
r
tr
av
er
s
es
th
e
en
tire
r
eg
io
n
at
th
e
s
p
ee
d
V
an
d
b
r
o
ad
ca
s
ts
its
cu
r
r
en
t
l
o
ca
tio
n
(x
i
,
y
i
)
with
a
n
in
ter
v
al
L
an
d
a
co
m
m
u
n
icatio
n
r
an
g
e
as
d
ep
icted
in
Fig
u
r
e
2
.
T
h
e
p
s
eu
d
o
co
d
e
f
o
r
m
o
b
ile
an
ch
o
r
is
d
escr
ib
e
d
b
elo
w.
I
n
p
u
t: {
(
x
,
y
)
–
c
o
o
r
d
i
n
ates o
f
an
ch
o
r
n
o
d
es,
L
-
in
ter
v
al}
Ou
tp
u
t: M
-
m
ess
ag
e
Pro
ce
s
s
:
Set in
itial tim
er
=
0
// b
r
o
ad
ca
s
tin
g
p
o
s
itio
n
s
with
an
in
ter
v
al
L
I
f
(
tim
er
% T
=
=
0
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th
e
n
(x
i
, y
i
)
=
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s
itio
n
(
xi
,
yi
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Msg
=
Ma
k
eM
ess
ag
e
(
x
i
, y
i
)
B
r
o
ad
ca
s
t (
m
s
g
)
;
E
n
d
if
3
.
4
.
3
.
Unk
no
wn no
des
Un
k
n
o
wn
n
o
d
es r
ec
eiv
e
p
o
s
itio
n
s
o
f
th
e
m
o
b
ile
a
n
ch
o
r
n
o
d
e
s
co
n
tin
u
o
u
s
ly
,
an
d
s
av
e
t
h
e
c
o
o
r
d
in
ates
with
in
a
ce
r
tain
p
er
io
d
o
f
tim
e
“T
”,
i.e
.
th
e
tim
e
th
at
m
o
b
ile
a
n
ch
o
r
n
o
d
es
co
m
p
lete
all
in
f
o
r
m
atio
n
b
r
o
a
d
ca
s
tin
g
in
th
e
s
en
s
in
g
r
eg
io
n
.
I
f
a
n
o
d
e
r
ec
eiv
es
s
ev
er
al
m
ess
ag
es
at
a
tim
e
it
co
n
s
id
er
s
th
e
m
es
s
ag
es
h
av
in
g
h
ig
h
er
s
ig
n
al
s
tr
en
g
th
an
d
co
n
tin
u
es
f
in
d
in
g
its
lo
ca
tio
n
.
T
r
ilater
ati
o
n
m
et
h
o
d
is
u
s
ed
to
esti
m
ate
p
o
s
itio
n
s
(
x
esst
,
y
est
)
o
f
u
n
k
n
o
w
n
n
o
d
es.
Fig
u
r
e
2
.
Sy
s
tem
en
v
ir
o
n
m
en
t
with
m
o
b
ile
an
c
h
o
r
n
o
d
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2
2
5
2
-
8
8
1
4
Tr
ila
tera
tio
n
b
a
s
ed
lo
ca
liz
a
tio
n
meth
o
d
u
s
in
g
mo
b
ile
a
n
ch
o
r
in
w
ir
eles
s
s
en
s
o
r
n
etw
o
r
k
s
(
M.
G.
K
a
vith
a
)
39
T
h
e
f
o
llo
win
g
is
th
e
p
s
eu
d
o
c
o
d
e
f
o
r
u
n
k
n
o
w
n
n
o
d
es
I
n
p
u
t: T
–
tim
e
p
er
i
o
d
,
M
-
m
e
s
s
ag
e
Ou
tp
u
t: (
x
est
,
y
est
)
Pro
ce
s
s
:
Set n
u
m
b
er
o
f
r
ec
eiv
e
d
m
ess
ag
es n
=
0
Set
clo
ck
in
itializatio
n
tim
er
=
0
// r
ec
eiv
e
m
ess
ag
es c
o
n
tin
u
o
u
s
ly
with
in
a
tim
e
p
er
io
d
Of
T
W
h
ile
(
tim
er
!
=
T
)
{
m
s
g
=
R
ec
eiv
eM
ess
ag
e
(
)
n
++
R
ec
o
r
d
Po
s
itio
n
(
n
,
m
s
g
)
}
C
alcu
late
(
x
est
, y
est
)
3
.
4
.
4
.
T
rila
t
er
a
t
io
n
An
ex
am
p
le
o
f
th
e
tr
ilater
atio
n
is
s
h
o
wn
in
Fig
u
r
e
3
.
Un
k
n
o
wn
n
o
d
e
(
,
)
r
ec
eiv
es
th
r
ee
an
ch
o
r
p
ac
k
ets f
r
o
m
th
e
m
o
b
ile
an
c
h
o
r
,
n
a
m
ely
,
(
,
)
,
(
,
)
,
an
d
(
,
)
.
Dis
tan
ce
s
b
etwe
en
,
,
,
an
d
ar
e
,
,
an
d
r
esp
ec
tiv
ely
.
Sin
ce
th
e
u
n
k
n
o
wn
n
o
d
e
is
with
in
th
e
r
eg
u
lar
tr
ian
g
le
wh
i
ch
is
co
m
p
o
s
ed
o
f
,
,
an
d
,
u
n
k
n
o
wn
n
o
d
e
will c
alcu
late
its
lo
ca
tio
n
b
y
u
s
in
g
:
(
−
)
2
+
(
−
)
2
=
2
(
−
)
2
+
(
−
)
2
=
2
(
−
)
2
+ (
− y
)
2
=
2
Fig
u
r
e
3
.
T
r
ilater
atio
n
b
ased
l
o
ca
lizatio
n
4.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
4
.
1
.
E
v
a
lua
t
i
o
n
cr
it
er
ia
a.
L
o
ca
lizatio
n
R
atio
L
o
ca
lizatio
n
r
atio
is
th
e
r
atio
o
f
th
e
n
u
m
b
er
o
f
u
n
k
n
o
wn
n
o
d
es
lo
ca
lized
to
th
e
t
o
tal
n
u
m
b
er
o
f
u
n
k
n
o
wn
n
o
d
es.
T
h
is
m
etr
ic
also
in
d
icate
s
th
e
co
v
er
a
g
e
d
e
g
r
ee
o
f
th
e
m
o
v
em
en
t
p
ath
.
L
o
ca
li
za
tio
n
r
atio
is
d
ef
in
ed
as
L
ratio
= N
l
/ N
u
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
9
,
No
.
1
,
Ma
r
ch
2
0
2
0
:
34
–
42
40
W
h
er
e
is
th
e
n
u
m
b
er
o
f
l
o
c
aliza
b
le
u
n
k
n
o
wn
n
o
d
es a
n
d
is
th
e
to
tal
n
u
m
b
er
o
f
u
n
k
n
o
wn
n
o
d
es.
b.
L
o
ca
lizatio
n
Acc
u
r
ac
y
T
h
e
lo
ca
lizatio
n
er
r
o
r
o
f
u
n
k
n
o
wn
n
o
d
e
u
is
d
ef
i
n
ed
as
e
u
=
(
x
u
–
a
u
)
2
+
(
y
u
–
b
u
)
2
+
(
z
u
–
c
u
)
2
R
W
h
er
e
(x
u
,
y
u
,
z
u
)
ar
e
r
ea
l
c
o
o
r
d
in
ates
o
f
an
u
n
k
n
o
wn
n
o
d
e
u
,
(
a
u
,
b
u
,
cu
)
ar
e
esti
m
ate
d
co
o
r
d
in
ates
o
f
an
u
n
k
n
o
wn
n
o
d
e
u
,
an
d
R
is
th
e
co
m
m
u
n
icatio
n
r
an
g
e
o
f
s
en
s
o
r
n
o
d
es.
c.
Path
L
en
g
th
T
o
s
av
e
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
tim
e
f
o
r
lo
ca
lizatio
n
,
th
e
p
ath
len
g
th
o
f
th
e
m
o
b
ile
an
c
h
o
r
n
o
d
e
s
h
o
u
ld
b
e
as sh
o
r
t a
s
p
o
s
s
ib
le.
d.
Scalab
ilit
y
Scalab
ilit
y
m
ea
n
s
th
at
th
e
lo
ca
lizatio
n
p
er
f
o
r
m
an
ce
is
in
d
e
p
e
n
d
en
t o
f
th
e
u
n
k
n
o
wn
n
o
d
es d
en
s
ity
.
4
.
2
.
Sim
ula
t
i
o
ns
a
nd
a
na
ly
s
is
Hu
n
d
r
ed
s
en
s
o
r
n
o
d
es
ar
e
r
an
d
o
m
ly
d
ep
l
o
y
ed
in
a
1
0
0
m
×
1
0
0
m
s
q
u
ar
e
r
eg
io
n
as
s
h
o
wn
in
Fig
u
r
e
4
.
E
ac
h
s
en
s
o
r
ca
n
co
m
m
u
n
icate
with
th
e
m
o
b
ile
an
c
h
o
r
n
o
d
e
s
if
d
is
tan
ce
b
etwe
en
th
em
is
s
m
aller
th
an
s
en
s
o
r
r
an
g
e
R
.
F
ig
u
r
e
5
to
Fig
u
r
e
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s
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.
RE
F
E
R
E
NC
E
S
[1
]
Z
.
Hu
,
D
.
G
u
,
Z
.
S
o
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g
,
a
n
d
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.
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,
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telli
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M
e
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c
s,
2
0
0
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.
[2
]
K.F
.
S
su
,
C.
H.
O
u
,
a
n
d
H.
C
.
Jia
u
,
“
Lo
c
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k
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T
r
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n
Ve
h
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l
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.
[3
]
Yu
ro
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g
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Ou
y
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n
g
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e
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g
y
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o
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n
d
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il
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M
a
k
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o
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o
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ter
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fer
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v
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n
telli
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ts
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2
0
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.
[4
]
Jin
fa
n
g
Jia
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g
,
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u
a
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n
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Hu
i
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,
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S
h
u
,
a
n
d
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o
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se
n
G
u
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i,
“
LM
AT:
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c
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ti
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with
a
M
o
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le
An
c
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se
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tera
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les
s S
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e
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o
m
p
ro
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in
g
s
,
2
0
1
1
.
[5
]
S
a
y
y
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d
M
a
ji
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a
z
in
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n
i
a
n
d
F
a
tem
e
h
F
a
rn
ia,
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sta
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v
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ter
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o
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Co
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ter
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v
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l
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2
,
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o
.
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0
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.
[6
]
Ku
o
-
F
e
n
g
S
su
,
Ch
ia
-
H
o
Ou
,
a
n
d
He
wiji
n
Ch
risti
n
e
Jia
u
,
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c
a
l
iza
ti
o
n
Wi
t
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o
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il
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h
o
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n
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les
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r
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k
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ter
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o
u
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o
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s
,
v
o
l.
8
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o.
6
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2
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.
[7
]
M
in
g
Ch
e
n
,
X
u
m
in
X
u
,
S
h
a
o
h
u
i
Zh
a
n
g
,
a
n
d
G
u
o
fu
F
e
n
g
,
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n
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icie
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L
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NIKA
(T
e
lec
o
mm
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n
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m
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ti
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g
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e
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tro
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ics
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l
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8
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p
p
.
2
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0
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2
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.
[8
]
M
a
d
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P
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ti
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d
Ch
irag
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h
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rm
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,
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WS
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k
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d
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o
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rn
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f
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trica
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2
,
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p
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7
.
[9
]
R
.
Kh
a
d
im,
M
.
Err
it
a
li
,
a
n
d
A
.
M
a
a
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e
n
,
“
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F
re
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c
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(
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0
]
M
.
Ali
Hu
ss
a
in
,
“
En
e
r
g
y
E
fficie
n
t
In
tru
si
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De
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k
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,
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.
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-
141
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0
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5
.
[1
1
]
N.
Jia
n
g
,
S
.
Jin
,
Y.
G
u
o
,
a
n
d
Y
.
He
,
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o
f
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ter
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2
]
Ye
tk
in
Tata
r
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n
d
G
u
n
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d
iri
m
,
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Altern
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In
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2
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8
-
1
2
9
4
,
2
0
1
3
.
[1
3
]
Wei
Zh
a
n
g
,
We
n
q
i
n
g
Li
u
,
Yu
n
fa
n
g
Ch
e
n
,
a
n
d
Zey
u
Ni
,
“
Ro
b
u
st
S
e
c
u
re
Lo
c
a
li
z
a
ti
o
n
o
f
WS
N
Ba
se
d
o
n
C
o
n
siste
n
c
y
o
f
Be
a
c
o
n
s i
n
G
rid
,
”
J
o
u
r
n
a
l
o
f
C
o
mp
u
t
a
ti
o
n
a
l
I
n
fo
rm
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ti
o
n
S
y
ste
ms
, v
o
l
.
1
0
,
n
o.
6
,
p
p
.
2
2
8
3
-
2
2
9
5
,
2
0
1
4
.
[1
4
]
S
h
a
o
p
in
g
Zh
a
n
g
a
n
d
Ho
n
g
P
e
i,
“
A
Two
-
h
o
p
C
o
ll
a
b
o
ra
ti
v
e
Lo
c
a
li
z
a
ti
o
n
Alg
o
rit
h
m
fo
r
Wi
re
les
s
S
e
n
so
r
Ne
two
r
k
s
,
”
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ic
a
t
io
n
C
o
mp
u
ti
n
g
E
lec
tro
n
ics
a
n
d
C
o
n
tro
l)
,
v
o
l
.
1
1
,
n
o
.
5
,
p
p
.
2
4
3
2
-
2
4
4
1
,
2
0
1
3
.
[1
5
]
C
.
Tan
g
,
R
.
i
L
iu
,
a
n
d
J
.
Ni,
“
A N
o
v
e
l
Wi
re
les
s S
e
n
so
r
Ne
two
rk
L
o
c
a
li
z
a
ti
o
n
Ap
p
ro
a
c
h
:
L
o
c
a
li
z
a
ti
o
n
b
a
se
d
o
n
P
lan
t
G
ro
wth
S
imu
latio
n
a
lg
o
rit
h
m
”
El
e
k
tro
n
ika
IR
El
e
k
tro
tec
h
n
ik
a
,
v
o
l
.
9
,
n
o.
8
,
p
p
.
97
-
1
0
0
,
2
0
1
3
.
[1
6
]
Lo
n
g
C
h
e
n
g
,
C
h
e
n
g
d
o
n
g
W
u
,
Yu
n
z
h
o
u
Zh
a
n
g
,
Ha
o
Wu
,
M
e
n
g
x
in
Li
,
a
n
d
Ca
rste
n
M
a
p
le,
“
A
S
u
r
v
e
y
o
f
L
o
c
a
li
z
a
ti
o
n
in
Wi
r
e
les
s S
e
n
s
o
r
Ne
two
rk
,
”
In
t
e
rn
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Distrib
u
ted
S
e
n
so
r Ne
two
rk
s
,
p
p
.
1
-
13
,
2
0
1
2
.
[1
7
]
O.S
.
Og
u
e
ji
o
f
o
r,
V.N.
O
k
o
r
o
g
u
,
Ad
e
wa
le
Ab
e
,
B.
Os
u
e
su
,
“
Ou
t
d
o
o
r
L
o
c
a
li
z
a
ti
o
n
S
y
ste
m
Us
in
g
R
S
S
I
M
e
a
su
re
m
e
n
t
o
f
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
,
”
In
t
e
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
n
o
v
a
ti
v
e
T
e
c
h
n
o
l
o
g
y
a
n
d
Ex
p
lo
ri
n
g
E
n
g
i
n
e
e
rin
g
,
v
o
l.
2
,
no.
2
,
p
p
.
1
-
6
,
2
0
1
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
9
,
No
.
1
,
Ma
r
ch
2
0
2
0
:
34
–
42
42
[1
8
]
Xia
o
ju
n
Zh
u
,
Xia
o
b
i
n
g
W
u
,
a
n
d
G
u
ih
a
i
C
h
e
n
,
“
Re
lati
v
e
lo
c
a
li
z
a
ti
o
n
f
o
r
wire
les
s
se
n
so
r
n
e
tw
o
rk
s
wit
h
l
in
e
a
r
to
p
o
lo
g
y
,
”
El
se
v
ier
, v
o
l
.
3
6
,
p
p
.
1
5
8
1
-
1
5
9
1
,
2
0
1
3
.
[1
9
]
M.
Ra
m
a
P
ra
b
h
a
a
n
d
R.
P
a
rv
a
d
h
a
De
v
i,
“
Eff
icie
n
t
No
d
e
Lo
c
a
li
z
a
ti
o
n
in
M
o
b
il
e
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
d
Res
e
a
rc
h
i
n
Co
m
p
u
ter
S
c
ien
c
e
&
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
2,
no.
1
,
p
p
.
2
4
6
-
2
4
9
,
2
0
1
4
.
[2
0
]
Las
lo
G
o
g
o
lak
,
S
z
il
v
e
sz
ter
P
let
l,
a
n
d
Dra
g
a
n
K
u
k
o
lj
,
“
Ne
u
ra
l
Ne
two
rk
-
b
a
se
d
In
d
o
o
r
L
o
c
a
li
z
a
ti
o
n
i
n
WS
N
En
v
ir
o
n
m
e
n
ts
,
”
Acta
P
o
lyte
c
h
n
ic
a
Hu
n
g
a
ric
a
,
v
o
l
.
1
0
,
n
o.
6
,
p
p
.
2
2
1
-
2
3
5
,
2
0
1
3
.
[2
1
]
P
rit
i
Na
rwa
l
a
n
d
S
.
S
.
Ty
a
g
i
,
“
P
o
siti
o
n
Est
ima
ti
o
n
u
si
n
g
Lo
c
a
li
z
a
ti
o
n
Tec
h
n
i
q
u
e
i
n
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
s
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
A
p
p
li
c
a
ti
o
n
o
r In
n
o
v
a
ti
o
n
i
n
E
n
g
i
n
e
e
rin
g
&
M
a
n
a
g
e
me
n
t
,
v
o
l.
2
,
n
o
.
6
,
p
p
.
110
-
1
1
5
,
2
0
1
3
.
[2
2
]
M
.
Vic
to
r
a
n
d
K.
Ra
m
a
lak
sh
m
i,
“
M
u
lt
i
p
le E
v
e
n
t
-
Dri
v
e
n
No
d
e
Lo
c
a
li
z
a
ti
o
n
i
n
Wi
re
les
s S
e
n
so
r
Ne
tw
o
rk
s
,
”
I
n
t
.
J
.
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
in
Co
mp
u
ter
En
g
i
n
e
e
rin
g
&
T
e
c
h
n
o
lo
g
y
, v
o
l.
2
,
n
o
.
3
,
p
p
.
1
0
7
3
-
1
0
7
7
,
2
0
1
3
.
[2
3
]
S
a
c
h
in
De
sh
p
a
n
d
e
,
Um
e
sh
Ku
lk
a
rn
i
a
n
d
M
rit
u
n
j
a
y
k
u
m
a
r
Oj
h
a
,
“
Targ
e
t
Trac
k
in
g
in
Wi
re
les
s
S
e
n
so
r
Ne
two
r
k
,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Eme
rg
i
n
g
T
e
c
h
n
o
l
o
g
y
a
n
d
Ad
v
a
n
c
e
d
En
g
i
n
e
e
rin
g
,
v
o
l.
3
,
no.
9
,
p
p
.
1
7
7
-
181
,
2
0
1
3
.
[2
4
]
M
.
B.
Nirm
a
la,
Na
y
a
n
a
,
a
n
d
A
.
S
.
M
a
n
j
u
n
a
th
,
“
Lo
c
a
li
z
a
ti
o
n
o
f
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
s
Us
in
g
Ro
b
u
st
Esti
m
a
te
d
Tru
st E
v
a
lu
a
ti
o
n
M
o
d
e
l”,
In
t
.
J
o
u
rn
a
l
o
f
S
c
ien
ti
fi
c
E
n
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
,
v
o
l.
2
,
n
o
.
7
,
p
p
.
7
2
9
-
7
3
2
,
2
0
1
3
.
[2
5
]
Ba
ih
u
a
S
h
e
n
a
n
d
G
u
o
li
Wan
g
,
“
Distrib
u
te
d
Targ
e
t
L
o
c
a
li
z
a
ti
o
n
a
n
d
Trac
k
i
n
g
wit
h
Wi
re
les
s
P
y
r
o
e
lec
tri
c
S
e
n
so
r
Ne
two
rk
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
S
ma
rt
S
e
n
si
n
g
a
n
d
I
n
telli
g
e
n
t
S
y
ste
ms
, v
o
l.
6
,
n
o
.
4
,
p
p
.
1
4
0
0
-
1
4
1
8
,
2
0
1
3
.
[2
6
]
Da
n
P
e
sc
a
ru
a
n
d
Da
n
iel
-
I
o
a
n
C
u
riac
,
“
An
c
h
o
r
No
d
e
Lo
c
a
li
z
a
ti
o
n
fo
r
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
s
Us
in
g
Vid
e
o
a
n
d
Co
m
p
a
ss
In
fo
rm
a
ti
o
n
F
u
sio
n
,
”
S
e
n
so
rs
,
v
o
l.
1
4
,
p
p
.
4
2
1
1
-
4
2
2
4
,
2
0
1
4
.
[2
7
]
Div
y
a
B
h
a
rti
,
M
a
n
jee
t
Be
h
n
iwa
l,
a
n
d
Aja
y
Ku
m
a
r
S
h
a
rm
a
,
“
P
e
rfo
rm
a
n
c
e
An
a
ly
sis
a
n
d
M
o
b
il
it
y
M
a
n
a
g
e
m
e
n
t
in
Wi
re
les
s
S
e
n
so
r
Ne
two
r
k
,
”
I
n
te
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
i
n
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
S
o
ft
w
a
re
En
g
i
n
e
e
rin
g
,
v
o
l.
3
,
n
o
.
7
,
p
p
.
1
3
3
3
-
1
3
4
2
,
2
0
1
3
.
[2
8
]
Ja
n
g
-
P
in
g
S
h
e
u
,
P
e
i
-
C
h
u
n
Ch
e
n
,
a
n
d
Ch
i
h
-
S
h
u
n
Hs
u
,
“
A
Distri
b
u
ted
Lo
c
a
li
z
a
ti
o
n
S
c
h
e
m
e
fo
r
W
irele
ss
S
e
n
so
r
Ne
two
rk
s
with
Im
p
ro
v
e
d
G
rid
-
S
c
a
n
a
n
d
Ve
c
to
r
-
Ba
se
d
Re
fin
e
m
e
n
t
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
M
o
b
i
le
Co
mp
u
ti
n
g
,
v
o
l.
7
,
n
o.
9
,
p
p
.
1
1
1
0
-
1
1
2
3
,
2
0
0
8
.
[2
9
]
K.
Vin
o
t
h
Ku
m
a
r
a
n
d
S
.
B
h
a
v
a
n
i
,
“
Lo
c
a
li
z
a
ti
o
n
b
a
se
d
Op
ti
m
ize
d
En
e
rg
y
Ro
u
ti
n
g
f
o
r
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
s
,
”
M
id
d
le E
a
st
J
o
u
r
n
a
l
o
f
S
c
ien
ti
fi
c
Res
e
a
rc
h
(M
EJS
R)
,
v
o
l
.
23
,
n
o
.
0
5
,
2
0
1
5
.
[3
0
]
K.
Vin
o
t
h
Ku
m
a
r,
T
.
Ja
y
a
sa
n
k
a
r,
V.
S
ri
n
iv
a
sa
n
,
a
n
d
M.
P
ra
b
h
a
k
a
ra
n
,
“
EOM
RP
:
En
e
rg
y
Op
ti
m
ize
d
M
u
lt
ip
a
th
Ro
u
ti
n
g
P
ro
t
o
c
o
l
f
o
r
Wi
re
les
s
S
e
n
so
r
Ne
two
rk
s
,
”
I
n
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
Pri
n
ti
n
g
,
P
a
c
k
a
g
in
g
&
Al
li
e
d
S
c
ien
c
e
s
,
v
o
l.
4
,
n
o
.
1
,
p
p
.
3
3
6
-
3
4
3
,
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.