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m
p
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s M
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with
AI
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MG
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K
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:
Ar
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in
tellig
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K
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m
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MG
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L
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Mu
ltil
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ail: h
.
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ac
.
m
a
1.
I
NT
RO
D
UCT
I
O
N
W
ir
eles
s
s
en
s
o
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etwo
r
k
s
(
W
SNs
)
ar
e
th
e
ess
en
tial
co
m
p
o
n
en
t
o
f
t
h
e
n
ew
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
s
y
s
tem
s
,
en
ab
lin
g
ap
p
licatio
n
s
r
an
g
in
g
f
r
o
m
r
ea
l
-
tim
e
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
i
n
g
[
1
]
to
life
-
cr
itical
h
ea
lth
ca
r
e
s
y
s
tem
s
[
2
]
,
[
3
]
an
d
lar
g
e
-
s
ca
le
in
d
u
s
tr
ial
au
to
m
atio
n
[
4
]
,
an
d
m
ilit
ar
y
a
p
p
licatio
n
s
[
5
]
.
Desp
ite
th
eir
v
er
s
atility
,
W
SN
s
f
ac
e
a
f
u
n
d
am
en
tal
co
n
s
tr
ain
t:
th
e
s
ev
e
r
e
en
er
g
y
lim
itatio
n
s
o
f
b
atte
r
y
-
p
o
we
r
ed
s
en
s
o
r
n
o
d
es
[
6
]
.
T
h
is
co
n
s
tr
ain
t
d
i
r
ec
tly
im
p
ac
ts
n
etwo
r
k
lo
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g
e
v
ity
,
r
eliab
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,
an
d
s
ca
lab
ilit
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,
m
ak
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g
e
n
er
g
y
ef
f
icien
cy
th
e
f
o
r
em
o
s
t p
r
io
r
it
y
in
W
SN p
r
o
to
co
l
d
esig
n
[
7
]
.
T
o
m
itig
ate
e
n
er
g
y
waste,
clu
s
ter
-
b
ased
p
r
o
to
co
ls
h
a
v
e
d
o
m
in
ated
W
SN
r
esear
ch
[
8
]
.
T
h
e
s
em
in
al
lo
w
-
en
er
g
y
ad
ap
tiv
e
cl
u
s
ter
in
g
h
ier
ar
ch
y
(
L
E
AC
H)
p
r
o
to
c
o
l
[
9
]
,
[
1
0
]
in
tr
o
d
u
ce
d
a
r
ev
o
lu
tio
n
ar
y
a
p
p
r
o
ac
h
:
d
y
n
am
ic
cl
u
s
ter
in
g
with
r
o
tatin
g
clu
s
ter
h
ea
d
s
(
C
Hs)
to
b
al
an
ce
en
er
g
y
c
o
n
s
u
m
p
tio
n
as
s
h
o
wn
in
Fig
u
r
e
1
.
W
h
ile
ef
f
ec
tiv
e,
L
E
AC
H’
s
r
an
d
o
m
ized
C
H
s
elec
tio
n
o
f
t
en
ca
u
s
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en
er
g
y
im
b
alan
ce
,
ac
ce
ler
atin
g
n
o
d
e
ex
h
au
s
tio
n
in
h
ig
h
-
tr
af
f
ic
zo
n
es a
n
d
d
eg
r
ad
in
g
n
etwo
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k
ef
f
i
cien
cy
[
1
1
]
,
[
1
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
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g
I
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N:
2088
-
8
7
0
8
AI
-
MG
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LEA
C
H:
in
ve
s
tig
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tio
n
o
f MG
-
LEA
C
H
in
w
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…
(
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Ou
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5081
T
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ig
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atin
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ar
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eter
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o
d
e
d
e
n
s
ity
to
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ak
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m
o
r
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i
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f
o
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e
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d
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s
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u
r
e
2
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Desp
ite
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p
r
o
v
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en
ts
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th
er
e
is
s
till
p
len
ty
o
f
r
o
o
m
f
o
r
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tim
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ar
ticu
lar
ly
to
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o
u
n
t
o
f
th
e
d
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n
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d
f
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th
er
im
p
r
o
v
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en
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f
f
icien
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[
1
3
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.
I
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ar
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in
tell
ig
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(
AI
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in
to
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f
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m
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co
ls
[
1
4
]
.
Un
lik
e
co
n
v
en
tio
n
al
r
u
le
-
b
ased
m
ec
h
an
is
m
s
,
wh
ich
ar
e
in
h
er
en
tly
s
tatic,
AI
-
d
r
iv
en
s
tr
ateg
ies
p
ar
ticu
lar
ly
th
o
s
e
b
ased
o
n
r
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
en
a
b
le
r
ea
l
-
tim
e
o
p
tim
izatio
n
o
f
n
etwo
r
k
p
a
r
am
eter
s
,
ef
f
ec
tiv
el
y
ad
d
r
ess
in
g
cr
itical
is
s
u
es
s
u
ch
as
en
er
g
y
ef
f
icien
c
y
,
s
ca
lab
ilit
y
,
an
d
r
esil
ien
ce
.
B
u
i
l
d
i
n
g
o
n
t
h
is
p
a
r
a
d
i
g
m
,
w
e
p
r
o
p
o
s
e
A
I
-
MG
-
L
E
AC
H
,
a
n
i
n
t
e
l
li
g
e
n
t
e
x
t
e
n
s
i
o
n
o
f
t
h
e
MG
-
L
E
AC
H
p
r
o
t
o
c
o
l
t
h
a
t
i
n
c
o
r
p
o
r
a
t
es
a
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
-
p
o
w
e
r
e
d
C
H
s
el
e
c
ti
o
n
m
e
c
h
a
n
i
s
m
.
W
e
f
o
cu
s
o
u
r
s
tu
d
y
o
n
d
esig
n
in
g
,
im
p
lem
en
tin
g
,
an
d
ev
alu
atin
g
th
is
n
o
v
el
clu
s
ter
in
g
p
r
o
t
o
co
l
to
e
n
h
an
ce
C
H
s
elec
tio
n
p
er
f
o
r
m
an
ce
.
T
h
e
p
r
o
to
c
o
l
lev
er
ag
es
b
o
t
h
h
is
to
r
ical
an
d
r
ea
l
-
tim
e
m
etr
ics
in
clu
d
in
g
r
esid
u
al
en
er
g
y
,
n
o
d
e
d
en
s
ity
,
co
m
m
u
n
icatio
n
co
s
ts
,
an
d
lin
k
q
u
ality
to
id
en
tify
o
p
tim
al
C
Hs
with
h
ig
h
p
r
ec
is
io
n
as
s
h
o
wn
in
Fig
u
r
e
3
.
B
y
r
ep
lacin
g
s
tatic
o
r
p
r
o
b
a
b
ilis
tic
d
ec
is
io
n
-
m
ak
in
g
with
p
r
ed
ictiv
e
in
tellig
en
ce
,
AI
-
MG
-
L
E
AC
H
r
ed
u
ce
s
en
er
g
y
co
n
s
u
m
p
tio
n
b
y
2
5
%
an
d
ex
ten
d
s
n
etwo
r
k
life
tim
e
b
y
4
0
%
co
m
p
ar
e
d
to
MG
-
L
E
A
C
H
(
s
ee
s
ec
tio
n
4
)
.
Mo
r
eo
v
er
,
it
m
ain
tain
s
d
ata
d
eliv
er
y
r
ates
e
x
ce
ed
in
g
9
5
%
e
v
en
u
n
d
er
h
i
g
h
-
m
o
b
ilit
y
s
ce
n
a
r
io
s
,
d
em
o
n
s
tr
atin
g
r
o
b
u
s
t
a
d
ap
tab
ilit
y
to
d
y
n
a
m
ic
co
n
d
itio
n
s
.
T
h
ese
ad
v
an
ce
s
h
ig
h
li
g
h
t
t
h
e
tr
a
n
s
f
o
r
m
ativ
e
p
o
ten
tial
o
f
AI
in
W
SN
s
,
en
ab
lin
g
th
e
d
ev
elo
p
m
en
t
o
f
s
elf
-
h
ea
lin
g
,
s
elf
-
o
p
t
im
izin
g
n
etwo
r
k
s
th
at
m
o
v
e
b
ey
o
n
d
in
c
r
em
en
tal
im
p
r
o
v
em
e
n
ts
to
ac
h
iev
e
a
u
to
n
o
m
o
u
s
o
p
er
atio
n
.
Fig
u
r
e
1
.
W
SN a
r
ch
itectu
r
e
s
h
o
win
g
s
en
s
o
r
n
o
d
es,
C
H,
an
d
a
ce
n
tr
al
s
in
k
,
all
co
n
n
ec
ted
to
th
e
in
ter
n
et
f
o
r
d
iv
er
s
e
ap
p
licatio
n
s
Fig
u
r
e
2
.
B
lo
ck
d
iag
r
am
illu
s
t
r
atin
g
th
e
f
u
n
d
a
m
en
tal
co
m
p
o
n
en
ts
o
f
a
W
SN
Fig
u
r
e
3
.
Stru
ctu
r
e
o
f
th
e
L
E
A
C
H
p
r
o
to
co
l in
W
SN
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
0
8
0
-
5
0
9
0
5082
Ou
r
s
tu
d
y
p
r
o
v
i
d
es
a
s
y
s
tem
a
tic
co
m
p
ar
is
o
n
b
etwe
en
MG
-
L
E
AC
H
an
d
AI
-
MG
-
L
E
AC
H
,
ass
es
s
in
g
h
o
w
m
ac
h
i
n
e
lear
n
in
g
en
h
a
n
ce
s
clu
s
ter
h
ea
d
s
elec
tio
n
in
W
SN
s
.
Fo
cu
s
in
g
o
n
en
e
r
g
y
ef
f
icien
cy
,
n
etwo
r
k
life
tim
e
an
d
d
ata
r
eliab
ilit
y
m
etr
ics,
we
d
em
o
n
s
tr
ate
th
r
o
u
g
h
e
x
ten
s
iv
e
s
im
u
latio
n
s
th
e
p
r
o
t
o
co
l'
s
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
an
d
ad
a
p
tab
ilit
y
in
d
y
n
am
ic
c
o
n
d
itio
n
s
.
T
h
e
p
ap
e
r
also
ex
p
l
o
r
es
p
ath
way
s
to
war
d
s
elf
-
o
p
tim
izin
g
n
etwo
r
k
ar
ch
it
ec
tu
r
es.
T
h
e
p
ap
er
is
s
tr
u
ctu
r
e
d
in
to
d
is
tin
ct
s
ec
tio
n
:
s
ec
tio
n
2
c
o
v
er
s
r
elev
an
t
r
esear
ch
,
s
ec
tio
n
3
g
iv
es
d
etai
ls
o
f
th
e
o
f
f
er
ed
p
r
o
to
c
o
l
in
d
etail,
s
ec
tio
n
4
d
is
cu
s
s
es
th
e
s
im
u
latio
n
f
in
d
in
g
s
an
d
th
eir
a
n
aly
s
is
,
an
d
s
ec
tio
n
5
co
n
clu
d
es th
e
wo
r
k
with
u
p
c
o
m
in
g
p
er
s
p
ec
tiv
es.
2.
RE
L
AT
E
D
WO
RK
Pre
v
io
u
s
s
cien
tific
r
esear
ch
o
n
th
e
im
p
r
o
v
e
m
en
t
o
f
b
u
n
d
li
n
g
p
r
o
t
o
co
ls
in
W
NS
b
y
m
ea
n
s
o
f
th
e
in
clu
s
io
n
o
f
AI
is
cited
.
T
h
e
L
E
AC
H
p
r
o
to
co
l
was
p
r
esen
ted
b
y
Hein
ze
lm
a
n
et
a
l.
[
1
5
]
as
a
p
io
n
ee
r
in
g
ap
p
r
o
ac
h
to
clu
s
ter
in
g
n
etwo
r
k
s
er
v
ices
in
n
etwo
r
k
n
etwo
r
k
s
.
I
t
h
as
p
r
o
v
id
ed
th
e
b
asis
f
o
r
m
an
y
s
u
b
s
eq
u
e
n
t
s
tu
d
ies
to
o
p
tim
ize
clu
s
ter
h
ea
d
(
C
H)
s
elec
tio
n
i
n
o
r
d
er
t
o
r
ed
u
ce
e
n
er
g
y
co
n
s
u
m
p
tio
n
.
Var
ian
ts
s
u
ch
as
L
E
AC
H
-
C
(
L
E
A
C
H
c
en
tr
aliz
ed
)
an
d
L
E
AC
H
-
F (
f
ix
ed
clu
s
ter
in
g
)
h
av
e
ex
p
lo
r
e
d
ce
n
tr
alize
d
m
eth
o
d
s
an
d
th
e
f
o
r
m
atio
n
o
f
f
ix
ed
clu
s
ter
s
to
i
m
p
r
o
v
e
en
er
g
y
ef
f
icien
cy
.
L
iu
an
d
R
av
is
h
an
k
a
r
[
1
6
]
p
r
o
p
o
s
ed
th
e
L
E
AC
H
-
GA
p
r
o
to
c
o
l
as
p
ar
t
o
f
th
ei
r
s
tu
d
y
to
en
h
an
ce
th
e
L
E
AC
H
ap
p
r
o
ac
h
.
Usi
n
g
g
en
etic
alg
o
r
ith
m
s
to
o
p
tim
ize
th
e
s
elec
tio
n
o
f
clu
s
ter
lead
er
s
(
C
H)
,
th
eir
g
o
al
was
to
p
er
f
ec
t
th
is
p
r
o
ce
s
s
b
y
in
teg
r
atin
g
v
ar
io
u
s
f
ac
to
r
s
,
in
o
r
d
er
to
len
g
th
en
th
e
life
o
f
W
SN.
Gu
p
ta
,
R
io
r
d
an
,
Sam
p
alli
(
2
0
1
1
)
wo
r
k
s
o
n
“
F
u
zz
y
-
L
E
AC
H
”
,
it
is
an
o
th
e
r
v
ar
ian
t
wh
er
e
f
u
zz
y
lo
g
ic
is
u
s
ed
to
m
a
k
e
s
m
ar
ter
d
ec
is
io
n
s
wh
en
s
elec
tin
g
C
H.
T
h
is
p
r
o
to
co
l
v
ar
i
o
u
s
p
a
r
am
eter
s
s
u
ch
as
d
en
s
ity
o
f
n
o
d
e,
en
er
g
y
o
f
r
esid
u
al
an
d
d
is
tan
ce
to
th
e
b
ase
s
tatio
n
to
o
p
tim
ize
th
e
clu
s
t
er
in
g
p
r
o
ce
s
s
[
1
7
]
.
T
h
is
p
ap
er
p
r
esen
ted
b
y
W
an
g
et
a
l.
[
1
8
]
p
r
esen
ts
th
e
en
er
g
y
-
ef
f
icien
t
d
is
tr
ib
u
ted
a
d
ap
tiv
e
co
o
p
er
ativ
e
r
o
u
tin
g
(
E
DAC
R
)
f
o
r
war
d
i
n
g
p
r
o
to
co
l
f
o
r
wir
eless
m
u
ltime
d
ia
s
en
s
o
r
n
etwo
r
k
s
(
W
MSN)
to
o
p
tim
ize
p
o
we
r
an
d
q
u
ality
o
f
s
er
v
ice.
B
y
u
s
in
g
r
ein
f
o
r
ce
m
en
t le
ar
n
i
n
g
,
B
DU
im
p
r
o
v
es e
n
er
g
y
ef
f
icie
n
cy
wh
ile
en
s
u
r
in
g
q
u
ality
o
f
s
er
v
ice,
m
ee
tin
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
p
r
o
to
co
ls
.
T
h
e
p
ap
er
b
y
B
eh
er
a
et
a
l.
[
1
9
]
p
u
b
lis
h
ed
o
n
J
u
ly
2
2
,
2
0
2
2
,
p
r
o
v
id
es
an
in
-
d
ep
th
an
al
y
s
is
o
f
b
o
th
class
ical
an
d
b
io
-
in
s
p
ir
ed
r
o
u
tin
g
p
r
o
to
co
ls
b
ased
o
n
L
E
AC
H,
o
f
f
er
i
n
g
in
s
ig
h
ts
f
o
r
r
esear
ch
er
s
in
to
v
ar
io
u
s
ar
ch
itectu
r
es,
in
n
o
v
ativ
e
s
tr
ateg
ies,
an
d
en
h
a
n
ce
d
p
e
r
f
o
r
m
a
n
ce
.
T
h
e
s
tu
d
y
c
o
n
clu
d
es
th
at
th
e
L
E
AC
H
-
MA
C
p
r
o
to
co
l
is
well
-
s
u
ited
to
n
et
wo
r
k
s
in
wh
ic
h
lo
n
g
ev
ity
p
r
e
s
en
ts
a
cr
itical
is
s
u
e.
T
h
e
M
G
-
L
E
AC
H
p
r
o
to
co
l
p
r
o
v
es
ad
v
a
n
tag
eo
u
s
f
o
r
b
o
th
lar
g
e
an
d
s
m
all
-
s
ca
le
n
etwo
r
k
s
,
wh
ile
th
e
L
E
AC
H
-
KH
p
r
o
to
co
l,
with
its
h
ig
h
p
ac
k
et
d
eliv
er
y
r
atio
(
PDR
)
,
is
id
ea
l f
o
r
n
etwo
r
k
s
wh
er
e
r
elia
b
ilit
y
is
th
e
p
r
im
ar
y
f
o
cu
s
.
3.
P
RO
P
O
SE
D
P
RO
T
O
CO
L
A
k
ey
lim
itatio
n
o
f
th
e
L
E
A
C
H
p
r
o
to
co
l
is
its
r
e
q
u
ir
em
e
n
t
to
a
p
p
o
in
t
a
n
ew
C
H
f
o
r
ev
er
y
tim
e,
wh
ich
co
n
s
u
m
es
s
ig
n
if
ican
t
en
er
g
y
d
u
r
in
g
th
e
clu
s
ter
f
o
r
m
atio
n
p
r
o
ce
s
s
.
T
h
is
lead
s
to
ex
ce
s
s
iv
e
en
er
g
y
ex
p
en
d
itu
r
e
d
u
e
to
th
e
in
cr
ea
s
ed
r
o
u
ti
n
g
o
v
er
h
ea
d
,
m
a
k
in
g
it
u
n
s
u
itab
le
f
o
r
I
o
T
d
ev
ices
with
lim
ited
p
o
wer
r
eso
u
r
ce
s
[
2
0
]
.
T
o
ad
d
r
ess
th
is
,
Ah
m
m
ad
an
d
Alab
ad
y
[
7
]
h
av
e
s
u
g
g
ested
s
o
m
e
im
p
r
o
v
ed
C
H
r
e
b
u
ild
in
g
tech
n
iq
u
es.
I
n
th
is
ap
p
r
o
ac
h
,
a
th
r
esh
o
ld
v
alu
e
is
ca
lcu
lated
,
an
d
a
n
ew
C
H
an
d
clu
s
ter
ar
e
f
o
r
m
ed
o
n
ly
wh
en
th
e
cu
r
r
e
n
t
C
H’
s
en
er
g
y
lev
el
f
alls
b
elo
w
th
e
th
r
esh
o
ld
,
th
er
eb
y
m
in
im
izin
g
u
n
n
ec
ess
ar
y
e
n
er
g
y
co
n
s
u
m
p
tio
n
d
u
r
in
g
clu
s
ter
f
o
r
m
atio
n
a
n
d
a
d
v
er
tis
em
en
t
m
ess
ag
e
tr
an
s
m
is
s
io
n
.
Oth
er
wis
e,
th
e
s
am
e
C
H
co
n
tin
u
es
in
to
th
e
n
ex
t r
o
u
n
d
[
2
1
]
.
T
h
e
o
p
tim
al
m
in
im
u
m
en
e
r
g
y
le
v
el
f
o
r
C
H
r
ep
lace
m
en
t is d
eter
m
i
n
ed
as
(
1
)
:
(
)
=
{
−
∗
(
)
×
[
]
2
∶
(
)
=
1
1
∶
(
)
=
0
(
1
)
h
er
e
is
th
e
aim
ed
r
ate
o
f
C
Hs
,
is
th
e
im
m
ed
iate
s
tep
,
(
)
is
th
e
lis
t
o
f
n
o
d
es
th
at
d
id
n
o
t
ch
an
g
e
to
C
H
in
last
r
o
u
n
d
s
,
[
]
2
is
th
e
n
o
d
e
’
s
en
er
g
y
s
ep
ar
ated
b
y
ea
r
lier
en
er
g
y
t
o
p
ick
th
e
n
o
d
e
h
av
in
g
h
ig
h
est
lev
el
o
f
r
esid
u
al
en
er
g
y
.
Du
r
in
g
th
e
r
an
d
o
m
d
ep
lo
y
m
en
t
o
f
n
o
d
es,
ev
er
y
is
h
a
v
in
g
GPS
u
n
it
tr
a
n
s
m
its
its
p
o
s
itio
n
d
ir
ec
tly
to
t
h
e
b
ase
s
tatio
n
(
B
S).
T
h
e
B
S
u
s
es
th
is
in
f
o
r
m
at
io
n
d
u
r
in
g
th
e
s
et
co
n
s
tr
u
ctio
n
p
h
ase,
a
o
n
e
-
tim
e
p
r
o
ce
s
s
th
at
co
n
s
u
m
es m
in
im
a
l e
n
er
g
y
.
T
h
e
s
etu
p
an
d
s
tead
y
-
s
tate
p
h
ases
f
o
llo
w
th
e
s
im
ilar
p
r
in
cip
les
lik
e
L
E
AC
H
b
u
t
ar
e
ap
p
lied
in
d
ep
en
d
en
tly
to
ea
c
h
g
r
o
u
p
.
T
h
ese
g
r
o
u
p
s
o
p
er
ate
alter
n
ately
,
b
ased
o
n
a
d
u
ty
c
y
cle
d
ef
in
ed
b
y
th
e
B
S
d
u
r
in
g
th
e
s
etu
p
p
h
ase.
Fo
r
in
s
tan
ce
,
wh
en
s
u
b
-
g
r
o
u
p
(
G1
)
is
ac
tiv
e,
s
u
b
-
g
r
o
u
p
(
G2
)
r
em
ain
s
in
s
leep
m
o
d
e.
T
h
e
m
in
im
u
m
n
u
m
b
er
o
f
s
u
b
-
g
r
o
u
p
s
is
two
,
b
u
t th
is
d
ep
e
n
d
s
o
n
th
e
n
etwo
r
k
’
s
n
o
d
e
d
e
n
s
ity
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
MG
-
LEA
C
H:
in
ve
s
tig
a
tio
n
o
f MG
-
LEA
C
H
in
w
ir
ele
s
s
…
(
Hich
a
m
Ou
ld
z
ir
a
)
5083
Simu
latio
n
s
o
f
MG
-
L
E
AC
H
d
em
o
n
s
tr
ate
s
ig
n
if
ican
tly
h
ig
h
er
e
f
f
icien
cy
co
m
p
a
r
ed
t
o
L
E
AC
H,
p
ar
ticu
lar
ly
in
ex
ten
d
i
n
g
n
etwo
r
k
life
tim
e.
Per
f
o
r
m
a
n
ce
was
ev
alu
ated
u
n
d
er
v
ar
y
in
g
i
n
itial
n
o
d
e
e
n
er
g
y
an
d
p
ar
am
eter
p
v
al
u
es.
MG
-
L
E
AC
H
ca
n
b
e
in
teg
r
ated
with
L
E
AC
H
-
b
ased
v
ar
ian
ts
th
at
ad
d
r
ess
lim
itatio
n
s
,
s
u
ch
as
co
n
s
id
er
in
g
r
esid
u
al
e
n
er
g
y
an
d
o
th
er
cr
itical
p
ar
am
eter
s
.
L
E
AC
H
wo
r
k
s
in
two
p
h
ases
as
s
h
o
wn
i
n
Fig
u
r
e
4
.
−
C
lu
s
ter
s
etu
p
p
h
ase:
n
o
d
es
el
ec
t
th
em
s
elv
es
as
C
Hs
u
s
in
g
a
r
an
d
o
m
ized
r
o
tatio
n
m
ec
h
a
n
is
m
.
E
ac
h
n
o
d
e
b
ec
o
m
es a
C
H
with
a
p
r
o
b
ab
il
ity
p
.
−
Stead
y
-
s
tate
p
h
ase:
n
o
n
-
clu
s
ter
-
h
ea
d
n
o
d
es
ex
ch
an
g
e
d
ata
b
y
th
eir
C
Hs,
wh
o
co
llect
th
e
d
ata
an
d
tr
an
s
m
it
it
to
th
e
b
ase
s
tatio
n
(
s
in
k
)
.
Ho
wev
er
,
L
E
AC
H
s
u
f
f
er
s
f
r
o
m
s
ev
er
al
lim
itatio
n
s
,
in
clu
d
in
g
u
n
ev
e
n
clu
s
ter
h
ea
d
d
is
tr
ib
u
tio
n
,
u
n
ev
e
n
en
e
r
g
y
d
is
s
ip
atio
n
b
etwe
en
n
o
d
es
an
d
ea
r
ly
n
o
d
e
d
ea
th
,
r
ed
u
ci
n
g
n
etwo
r
k
life
.
I
n
co
m
p
ar
ativ
e
s
tu
d
ies,
MG
-
L
E
AC
H
h
as
b
ee
n
s
h
o
wn
to
o
u
tp
er
f
o
r
m
L
E
AC
H
in
ter
m
s
o
f
n
etwo
r
k
life
,
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
d
a
ta
d
eliv
er
y
r
ates.
T
ab
le
1
h
ig
h
lig
h
ts
a
h
ig
h
-
le
v
el
co
m
p
ar
is
o
n
o
f
th
eir
ef
f
icien
cy
.
Fig
u
r
e
4
.
Op
e
r
atio
n
al
p
h
ases
o
f
a
clu
s
ter
-
b
ased
W
SN p
r
o
to
c
o
l
[
1
6
]
T
ab
le
1
.
C
o
m
p
a
r
ativ
e
p
ar
a
m
eter
f
o
r
MG
-
L
E
AC
H
an
d
L
E
AC
H
P
a
r
a
me
t
e
r
LEA
C
H
MG
-
LEA
C
H
C
l
u
st
e
r
h
e
a
d
s
e
l
e
c
t
i
o
n
R
a
n
d
o
m
i
z
e
d
,
e
q
u
a
l
p
r
o
b
a
b
i
l
i
t
y
B
a
se
d
o
n
r
e
s
i
d
u
a
l
e
n
e
r
g
y
En
e
r
g
y
e
f
f
i
c
i
e
n
c
y
M
o
d
e
r
a
t
e
H
i
g
h
e
r
N
e
t
w
o
r
k
l
i
f
e
t
i
m
e
Lo
w
e
r
Lo
n
g
e
r
d
u
e
t
o
e
n
e
r
g
y
-
a
w
a
r
e
C
H
se
l
e
c
t
i
o
n
D
a
t
a
a
g
g
r
e
g
a
t
i
o
n
B
a
si
c
O
p
t
i
mi
z
e
d
M
u
l
t
i
-
h
o
p
c
o
mm
u
n
i
c
a
t
i
o
n
No
Y
e
s
C
o
m
p
l
e
x
i
t
y
Lo
w
H
i
g
h
e
r
MG
-
L
E
AC
H
en
h
an
ce
s
th
e
o
r
ig
in
al
L
E
AC
H
p
r
o
to
co
l
b
y
in
co
r
p
o
r
atin
g
e
n
er
g
y
-
awa
r
e
m
u
lti
-
h
o
p
co
m
m
u
n
icatio
n
an
d
a
n
o
p
tim
ized
C
H
s
elec
tio
n
p
r
o
ce
s
s
,
r
esu
ltin
g
in
im
p
r
o
v
ed
e
n
er
g
y
ef
f
ici
en
cy
an
d
ex
ten
d
e
d
n
etwo
r
k
life
tim
e.
T
h
ese
a
d
v
a
n
ce
m
en
ts
,
h
o
wev
er
,
co
m
e
with
in
cr
ea
s
ed
p
r
o
to
co
l
c
o
m
p
lex
i
ty
,
m
ak
in
g
it
b
etter
s
u
ited
f
o
r
s
ce
n
ar
io
s
th
at
p
r
io
r
itize
en
er
g
y
co
n
s
er
v
atio
n
an
d
h
av
e
ad
e
q
u
ate
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
T
h
e
en
h
an
ce
d
C
H
s
elec
tio
n
an
d
m
u
lti
-
h
o
p
co
m
m
u
n
icatio
n
in
tr
o
d
u
ce
h
ig
h
er
d
em
a
n
d
s
o
n
p
r
o
ce
s
s
in
g
p
o
wer
an
d
m
em
o
r
y
,
p
o
ten
tially
lead
in
g
to
d
elay
s
an
d
r
eq
u
ir
i
n
g
m
o
r
e
s
o
p
h
is
ticated
r
o
u
tin
g
m
ec
h
an
is
m
s
.
Alth
o
u
g
h
d
y
n
am
ic
C
H
s
elec
tio
n
im
p
r
o
v
es
lo
ad
d
is
tr
ib
u
tio
n
,
it
also
ad
d
s
en
er
g
y
o
v
er
h
ea
d
d
u
e
to
th
e
ad
d
itio
n
al
co
m
p
u
tatio
n
s
an
d
co
m
m
u
n
icat
io
n
r
eq
u
ir
ed
.
I
n
co
r
p
o
r
atin
g
ar
tific
ial
in
tellig
en
ce
in
to
th
e
MG
-
L
E
AC
H
p
r
o
to
c
o
l
in
tr
o
d
u
ce
s
s
m
ar
ter
d
ec
is
io
n
-
m
ak
in
g
an
d
f
u
r
t
h
er
o
p
tim
izes
th
e
p
er
f
o
r
m
an
ce
o
f
W
SNs
.
B
y
lev
er
ag
in
g
m
ac
h
in
e
lear
n
in
g
(
ML
)
o
r
AI
-
b
ased
m
o
d
els,
th
e
en
h
an
ce
d
v
er
s
io
n
o
f
MG
-
L
E
AC
H,
ter
m
ed
AI
-
MG
-
L
E
AC
H,
ca
n
d
y
n
am
icall
y
ad
ap
t
to
c
h
an
g
i
n
g
n
etwo
r
k
c
o
n
d
itio
n
s
an
d
o
p
tim
ize
C
H
s
elec
tio
n
,
d
ata
a
g
g
r
e
g
atio
n
,
an
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
m
o
r
e
ef
f
ec
tiv
ely
.
AI
m
o
d
els,
esp
ec
ially
th
o
s
e
b
ased
o
n
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
R
L
)
,
n
eu
r
al
n
etwo
r
k
s
,
o
r
d
ee
p
lear
n
in
g
,
ca
n
p
r
ed
ict
n
etwo
r
k
b
eh
a
v
io
r
s
,
a
d
ju
s
t
p
r
o
to
co
ls
d
y
n
am
ically
,
an
d
im
p
r
o
v
e
t
h
e
o
v
er
all
ef
f
icien
cy
o
f
e
n
er
g
y
u
tili
za
tio
n
,
lead
in
g
to
b
etter
p
e
r
f
o
r
m
a
n
ce
in
co
m
p
ar
is
o
n
t
o
M
G
-
L
E
AC
H.
I
n
AI
-
MG
-
L
E
AC
H,
m
ac
h
in
e
l
ea
r
n
in
g
en
h
an
ce
s
th
e
C
H
s
ele
ctio
n
p
r
o
ce
s
s
b
y
co
n
s
id
er
i
n
g
p
ar
am
eter
s
in
clu
d
in
g
n
o
d
e
d
en
s
ity
,
r
esid
u
al
en
er
g
y
an
d
d
is
tan
ce
to
t
h
e
B
S.
AI
p
r
ed
icts
en
er
g
y
d
ep
letio
n
tr
en
d
s
a
n
d
en
s
u
r
es
ef
f
icien
t
C
H
s
elec
tio
n
,
im
p
r
o
v
in
g
n
etwo
r
k
l
o
n
g
e
v
ity
.
Fig
u
r
e
5
illu
s
tr
ates
th
e
K
-
m
ea
n
s
clu
s
ter
in
g
p
r
o
ce
s
s
u
s
ed
to
p
r
ep
r
o
ce
s
s
n
o
d
e
d
is
tr
ib
u
tio
n
d
ata
[
2
2
]
,
w
h
ich
f
ee
d
s
in
t
o
th
e
AI
m
o
d
el
f
o
r
o
p
tim
ized
C
H
s
elec
tio
n
.
B
y
an
aly
zin
g
h
is
to
r
ical
d
ata
an
d
s
p
atial
r
elatio
n
s
h
ip
s
,
th
is
ap
p
r
o
ac
h
p
r
ev
en
ts
r
ed
u
n
d
an
t
C
H
ch
o
ices
an
d
r
ef
i
n
es f
u
tu
r
e
d
ec
is
io
n
s
wh
ile
m
ain
tain
in
g
n
etwo
r
k
r
o
b
u
s
tn
ess
th
r
o
u
g
h
ad
a
p
tiv
e
clu
s
ter
m
ain
ten
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
0
8
0
-
5
0
9
0
5084
Fig
u
r
e
5
.
K
-
m
ea
n
s
clu
s
ter
in
g
p
r
o
ce
s
s
T
o
illu
s
tr
ate
th
e
AI
-
en
h
a
n
c
ed
d
ec
is
io
n
-
m
a
k
in
g
a
n
d
th
e
m
o
d
if
icatio
n
s
m
a
d
e
to
th
e
o
r
ig
in
al
MG
-
L
E
AC
H
eq
u
atio
n
s
,
th
e
C
H
s
elec
t
io
n
p
r
o
b
a
b
ilit
y
b
ec
o
m
es
d
y
n
am
ic
with
AI
in
teg
r
atio
n
[
2
3
]
.
Usi
n
g
m
ac
h
in
e
lear
n
in
g
,
th
e
p
r
o
b
ab
i
lity
o
f
n
o
d
e
b
ein
g
s
elec
ted
as
a
C
H
i
s
n
o
w
ad
ap
tab
le
an
d
ca
n
b
e
ex
p
r
ess
ed
b
ased
o
n
c
h
an
g
in
g
n
etwo
r
k
co
n
d
itio
n
s
(
2
).
(
)
=
(
(
)
,
(
)
,
(
)
,
)
(
2
)
wh
er
e
(
)
p
r
esen
ts
th
e
r
esid
u
al
e
n
er
g
y
o
f
n
o
d
e
,
(
)
is
th
e
d
is
tan
ce
o
f
n
o
d
e
to
th
e
b
ase
s
tatio
n
,
(
)
is
th
e
h
is
to
r
ical
d
ata
(
s
u
ch
as
th
e
n
u
m
b
e
r
o
f
r
o
u
n
d
n
o
d
e
h
as
b
ee
n
a
C
H)
,
r
ep
r
esen
t
s
ad
d
itio
n
al
en
v
ir
o
n
m
en
tal
o
r
tr
af
f
ic
f
ac
to
r
s
et
(
)
is
th
e
AI
m
o
d
el
(
e.
g
.
,
a
n
eu
r
al
n
etwo
r
k
)
th
at
d
y
n
a
m
ically
ad
ju
s
ts
th
e
p
r
o
b
a
b
ilit
y
b
ased
o
n
th
ese
p
ar
am
eter
s
[
2
4
]
.
AI
m
o
d
els
ca
n
p
r
ed
ict
th
e
p
o
wer
co
n
s
u
m
p
tio
n
o
f
ea
ch
n
o
d
e
d
u
r
in
g
tr
an
s
m
is
s
io
n
as (
3
):
−
(
)
=
(
(
)
,
,
,
)
(
3
)
wh
er
e:
−
(
)
is
th
e
p
r
ed
icted
tr
an
s
m
is
s
io
n
en
er
g
y
f
o
r
n
o
d
e
,
is
th
e
tr
af
f
ic
lo
ad
o
n
n
o
d
e
,
is
th
e
av
er
ag
e
d
is
tan
ce
to
o
t
h
er
n
o
d
es
o
r
C
Hs,
r
ep
r
esen
ts
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
(
s
u
ch
as
s
ig
n
al
in
ter
f
er
en
ce
o
r
o
b
s
tacle
s
)
an
d
AI
is
th
e
tr
ain
ed
AI
m
o
d
el
th
at
u
s
es
th
ese
f
ac
to
r
s
to
p
r
ed
ict
f
u
tu
r
e
en
er
g
y
co
n
s
u
m
p
tio
n
[
2
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
MG
-
LEA
C
H:
in
ve
s
tig
a
tio
n
o
f MG
-
LEA
C
H
in
w
ir
ele
s
s
…
(
Hich
a
m
Ou
ld
z
ir
a
)
5085
T
h
e
p
r
o
p
o
s
ed
AI
-
MG
-
L
E
AC
H
p
r
o
to
c
o
l
was
e
v
alu
ated
th
r
o
u
g
h
MA
T
L
AB
s
im
u
latio
n
s
co
m
p
ar
in
g
its
p
er
f
o
r
m
an
ce
with
L
E
AC
H
an
d
MG
-
L
E
AC
H
in
a
1
0
0
×1
0
0
m
W
SN
with
1
0
0
r
an
d
o
m
ly
d
ep
lo
y
ed
n
o
d
es
an
d
a
ce
n
tr
al
b
ase
s
tatio
n
.
Key
f
ea
tu
r
es in
clu
d
e:
−
Hy
b
r
id
en
e
r
g
y
m
o
d
el
(
f
r
ee
-
s
p
ac
e/m
u
lti
-
p
ath
p
r
o
p
ag
atio
n
)
−
AI
-
o
p
tim
ized
C
H
s
elec
tio
n
u
s
in
g
r
esid
u
al
e
n
er
g
y
,
n
o
d
e
d
e
n
s
ity
,
an
d
b
ase
s
tatio
n
d
is
tan
ce
−
K
-
m
ea
n
s
f
o
r
clu
s
ter
i
n
itializati
o
n
−
500
-
r
o
u
n
d
s
im
u
latio
n
s
tr
ac
k
in
g
: e
n
er
g
y
ef
f
icien
c
y
,
p
ac
k
et
d
eliv
er
y
r
atio
(
PDR
)
,
laten
cy
,
n
etwo
r
k
life
tim
e.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Sim
u
la
ti
o
n
r
es
u
lts
:
I
n
s
im
u
l
ate
d
e
n
v
ir
o
n
m
e
n
ts
,
A
I
-
MG
-
L
E
A
C
H
h
as
s
h
o
w
n
s
i
g
n
if
ic
an
t
im
p
r
o
v
em
en
ts
in
n
et
wo
r
k
p
e
r
f
o
r
m
a
n
ce
m
e
tr
i
cs
c
o
m
p
a
r
e
d
t
o
MG
-
L
E
AC
H
an
d
L
E
AC
H:
I
n
Fig
u
r
e
6
,
we
s
h
o
w
th
e
r
esid
u
al
en
er
g
y
o
f
th
e
n
o
d
es
o
f
a
wir
eless
s
en
s
o
r
n
etwo
r
k
o
v
er
5
0
0
tu
r
n
s
,
co
m
p
a
r
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
th
r
ee
p
r
o
to
co
ls
:
L
E
AC
H
(
r
ed
)
,
M
G
-
L
E
AC
H
(
b
lu
e)
an
d
AI
-
MG
-
L
E
AC
H
(
g
r
ee
n
)
.
R
esid
u
al
en
er
g
y
r
ef
lects
th
e
am
o
u
n
t
o
f
en
e
r
g
y
r
em
ain
i
n
g
in
th
e
n
etwo
r
k
s
en
s
o
r
n
o
d
es
af
ter
ea
ch
cy
cle,
g
iv
in
g
an
o
v
er
v
iew
o
f
th
e
p
o
wer
co
n
s
u
m
p
tio
n
ef
f
icien
cy
o
f
ea
c
h
p
r
o
t
o
co
l.
Fig
u
r
e
6
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
co
m
p
ar
is
o
n
(
r
esid
u
al
en
e
r
g
y
v
s
.
r
o
u
n
d
s
)
f
o
r
L
E
AC
H,
MG
-
L
E
AC
H,
an
d
AI
-
MG
-
L
E
AC
H
T
h
e
“
en
er
g
y
co
n
s
u
m
p
tio
n
o
v
er
tim
e
”
g
r
ap
h
s
h
o
ws
th
at
L
E
AC
H
ex
h
ib
its
th
e
m
o
s
t
r
ap
id
en
er
g
y
d
ep
letio
n
,
i
n
d
icatin
g
p
o
o
r
l
o
ad
b
ala
n
cin
g
a
n
d
i
n
ef
f
icie
n
t
en
er
g
y
d
is
tr
ib
u
tio
n
am
o
n
g
n
o
d
es.
Ho
wev
er
,
AI
-
MG
-
L
E
AC
H
is
th
e
m
o
s
t
en
er
g
y
-
ef
f
icien
t
p
r
o
to
co
l,
with
th
e
h
ig
h
est
r
esid
u
al
en
e
r
g
y
a
f
t
er
5
0
0
r
o
u
n
d
s
.
T
h
e
s
m
o
o
th
er
cu
r
v
e
an
d
s
lo
wer
d
e
clin
e
in
r
esid
u
al
en
er
g
y
s
ee
n
with
AI
-
MG
-
L
E
AC
H
s
u
g
g
est
b
etter
en
er
g
y
lo
a
d
b
alan
cin
g
,
r
ed
u
ce
d
n
o
d
e
f
ailu
r
e
r
ates,
an
d
p
r
o
lo
n
g
ed
n
et
wo
r
k
life
tim
e.
T
h
is
r
esu
lts
f
r
o
m
its
ad
v
an
ce
d
AI
-
d
r
iv
en
o
p
tim
izatio
n
tech
n
i
q
u
es
th
at
p
r
ed
ict
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
p
atter
n
s
an
d
d
y
n
a
m
ically
ad
ju
s
t
n
o
d
e
b
eh
av
io
r
to
m
in
im
ize
e
n
er
g
y
waste,
wh
ich
im
p
r
o
v
es
en
e
r
g
y
ef
f
icien
cy
b
y
a
p
p
r
o
x
im
ately
4
0
-
5
0
%
co
m
p
ar
e
d
to
MG
-
L
E
AC
H.
AI
-
MG
-
L
E
AC
H
al
s
o
ex
ten
d
s
th
e
n
etwo
r
k
life
tim
e
b
y
a
s
im
ilar
p
er
ce
n
tag
e,
as
its
en
er
g
y
co
n
s
er
v
atio
n
k
ee
p
s
m
o
r
e
n
o
d
es
o
p
er
atio
n
al
f
o
r
lo
n
g
e
r
p
er
i
o
d
s
.
MG
-
L
E
AC
H,
wh
ile
p
er
f
o
r
m
in
g
b
etter
th
an
L
E
AC
H
b
y
r
o
u
g
h
ly
2
0
-
3
0
%,
s
till
lag
s
b
eh
in
d
AI
-
MG
-
L
E
AC
H
b
ec
au
s
e
i
t
lack
s
r
ea
l
-
ti
m
e
ad
ap
tab
ilit
y
an
d
p
r
ed
ictiv
e
ca
p
ab
ilit
ies.
T
h
e
s
ig
n
if
ican
t
im
p
r
o
v
em
en
t
in
en
er
g
y
u
s
ag
e
an
d
n
etwo
r
k
lo
n
g
ev
ity
p
r
o
v
id
ed
b
y
AI
-
MG
-
L
E
AC
H
en
ab
les
it
to
b
e
s
u
itab
ly
to
r
eso
u
r
ce
-
r
estricte
d
an
d
en
er
g
y
-
s
en
s
itiv
e
ap
p
licatio
n
s
,
wh
er
e
n
etwo
r
k
life
tim
e
is
cr
itical.
T
h
is
r
esu
lt
s
h
o
ws
h
o
w
th
e
i
n
teg
r
atio
n
o
f
ar
tific
ial
in
tellig
en
ce
d
o
es
not
o
n
ly
m
ax
im
ize
en
er
g
y
c
o
n
s
u
m
p
tio
n
;
y
et
it
also
s
tab
ilizes
th
e
n
et
wo
r
k
b
e
h
av
io
r
o
v
e
r
tim
e.
T
h
u
s
,
AI
-
MG
-
L
E
AC
H
s
tan
d
s
o
u
t
as
th
e
m
o
s
t
ef
f
ec
tiv
e
s
o
lu
tio
n
,
o
f
f
er
in
g
u
p
to
5
0
%
m
o
r
e
en
er
g
y
s
av
in
g
s
an
d
a
n
ex
ten
d
ed
n
etwo
r
k
life
s
p
an
,
m
ak
in
g
it id
ea
l f
o
r
w
ir
eless
s
en
s
o
r
n
etwo
r
k
s
.
Fig
u
r
e
7
s
h
o
ws
th
e
co
m
p
a
r
is
o
n
o
f
th
e
n
etwo
r
k
life
tim
e
b
etwe
en
L
E
AC
H,
MG
-
L
E
AC
H
an
d
AI
-
MG
-
L
E
AC
H,
with
th
e
cr
itical
th
r
esh
o
ld
r
ep
r
esen
tin
g
1
0
%
o
f
t
h
e
liv
e
n
o
d
es.
L
E
AC
H
is
th
e
f
astes
t
d
ec
lin
in
g
,
r
ea
ch
in
g
a
cr
itical
t
h
r
esh
o
ld
o
f
ab
o
u
t
6
0
0
r
ev
o
lu
t
io
n
s
.
T
h
is
r
a
p
id
d
eg
r
a
d
atio
n
i
s
p
r
im
ar
ily
d
u
e
to
th
e
r
an
d
o
m
clu
s
ter
h
ea
d
s
elec
tio
n
m
ec
h
an
is
m
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ich
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ails
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o
r
r
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r
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k
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o
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ie
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atu
r
ely
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e
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e
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g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
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p
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g
,
Vo
l.
15
,
No
.
6
,
Decem
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r
20
25
:
5
0
8
0
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5
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0
5086
co
n
s
u
m
p
tio
n
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d
ea
r
l
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etwo
r
k
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ailu
r
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MG
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im
p
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es
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ity
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tay
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e
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esh
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o
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8
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p
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g
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y
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en
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itiv
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is
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ib
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er
g
y
m
o
r
e
ev
en
ly
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etwe
en
n
o
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es.
AI
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MG
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E
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wo
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k
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est,
k
ee
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es
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r
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d
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tim
izatio
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y
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am
ically
ad
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u
s
ts
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e
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s
ter
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ea
d
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elec
tio
n
,
th
u
s
s
ig
n
if
ican
tly
ex
ten
d
in
g
th
e
n
etwo
r
k
life
.
T
h
is
f
in
d
in
g
in
d
icate
s
th
at
AI
-
MG
-
L
E
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H
o
u
tp
er
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o
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m
s
th
e
o
th
er
s
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ten
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g
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h
e
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o
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th
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k
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y
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m
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ar
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to
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E
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H
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d
2
0
% c
o
m
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ar
ed
t
o
MG
-
L
E
AC
H.
Fig
u
r
e
7
.
Netwo
r
k
life
tim
e
an
aly
s
is
s
h
o
win
g
n
o
d
e
s
u
r
v
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al
f
o
r
L
E
AC
H,
MG
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AI
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MG
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E
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H
p
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o
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ls
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ai
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t c
o
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m
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n
d
s
T
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e
“
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r
y
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atio
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m
p
ar
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E
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H,
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AI
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E
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ls
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r
5
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r
o
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n
d
s
as
s
h
o
wn
in
Fig
u
r
e
8
.
L
E
AC
H
s
ta
r
ts
with
a
PDR
o
f
0
.
9
b
u
t
d
r
o
p
s
to
0
.
4
,
r
ef
lectin
g
a
5
5
%
d
ec
r
ea
s
e
in
r
eliab
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y
.
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L
E
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p
er
f
o
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m
s
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ette
r
,
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eg
i
n
n
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g
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9
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i
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g
at
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6
5
,
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%
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ec
lin
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I
-
AI
-
MG
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E
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H
s
h
o
ws
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e
b
est
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er
f
o
r
m
an
ce
,
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tar
tin
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at
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.
9
4
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n
d
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ec
r
ea
s
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g
t
o
0
.
7
5
,
o
n
ly
a
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0
%
d
r
o
p
.
T
h
e
AI
-
d
r
i
v
en
AI
-
MG
-
L
E
AC
H
o
p
tim
izes
lo
ad
b
alan
cin
g
,
d
y
n
am
ic
C
H
s
elec
tio
n
,
an
d
r
e
d
u
ce
s
co
n
g
esti
o
n
,
r
esu
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g
in
a
1
5
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-
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0
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im
p
r
o
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em
e
n
t
o
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er
M
G
-
L
E
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H
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d
4
0
%
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4
5
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b
etter
p
er
f
o
r
m
an
ce
th
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n
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E
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in
p
ac
k
et
tr
an
s
m
is
s
io
n
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eliab
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.
T
h
e
r
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lts
in
Fig
u
r
e
9
s
h
o
w
a
co
m
p
ar
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o
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o
f
th
e
L
E
A
C
H,
MG
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L
E
AC
H,
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d
AI
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MG
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L
E
AC
H
p
r
o
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ls
i
n
ter
m
s
o
f
late
n
c
y
o
v
er
tim
e
an
d
ca
n
b
e
in
t
er
p
r
eted
f
r
o
m
v
a
r
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s
a
n
g
les,
in
clu
d
i
n
g
e
n
er
g
y
ef
f
icien
cy
,
co
m
m
u
n
icatio
n
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aten
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d
im
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em
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r
o
u
g
h
t
b
y
ar
tific
ial
in
tellig
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ce
.
T
h
e
L
E
AC
H
p
r
o
to
co
l,
r
ep
r
esen
ted
b
y
th
e
r
ed
cu
r
v
e,
s
h
o
ws
in
cr
ea
s
in
g
laten
cy
with
a
r
elativ
ely
s
teep
s
lo
p
e,
with
a
n
ap
p
r
o
x
im
ate
in
cr
ea
s
e
o
f
6
0
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o
v
er
th
e
5
0
0
r
o
u
n
d
s
.
Alth
o
u
g
h
L
E
AC
H
is
ef
f
ec
tiv
e
in
co
n
s
er
v
in
g
en
er
g
y
th
r
o
u
g
h
its
clu
s
ter
in
g
ap
p
r
o
ac
h
,
its
co
n
f
ig
u
r
atio
n
p
h
ases
s
ig
n
if
ican
tly
in
cr
ea
s
e
laten
cy
.
T
h
e
MG
-
L
E
AC
H
p
r
o
to
co
l,
r
ep
r
esen
ted
b
y
th
e
b
lu
e
cu
r
v
e,
s
h
o
ws
a
n
o
ticea
b
le
im
p
r
o
v
em
en
t
in
laten
cy
co
m
p
ar
e
d
to
L
E
AC
H,
with
ar
o
u
n
d
a
3
0
%
r
ed
u
ctio
n
af
ter
5
0
0
r
o
u
n
d
s
.
T
h
is
is
d
u
e
to
o
p
tim
izatio
n
s
in
clu
s
ter
m
an
ag
em
en
t
an
d
co
m
m
u
n
icatio
n
s
.
Ho
wev
er
,
th
e
s
lo
p
e
r
em
ain
s
u
p
war
d
,
in
d
icatin
g
th
at
d
esp
ite
th
e
im
p
r
o
v
em
e
n
ts
,
s
o
m
e
lim
itatio
n
s
p
er
s
is
t
in
h
an
d
lin
g
tr
a
n
s
m
is
s
io
n
s
as
r
o
u
n
d
s
in
cr
ea
s
e.
T
h
e
AI
-
MG
-
L
E
AC
H
p
r
o
to
co
l,
r
ep
r
esen
ted
b
y
t
h
e
g
r
ee
n
cu
r
v
e,
d
em
o
n
s
tr
ates
th
e
b
est
p
er
f
o
r
m
a
n
ce
,
with
a
laten
c
y
r
ed
u
ctio
n
o
f
5
0
%
co
m
p
ar
ed
to
MG
-
L
E
AC
H
an
d
n
ea
r
ly
7
0
% c
o
m
p
ar
ed
to
L
E
A
C
H
af
ter
5
0
0
r
o
u
n
d
s
.
T
h
is
s
u
g
g
ests
th
at
ar
tific
ial
in
tellig
en
ce
in
teg
r
ate
d
in
to
t
h
is
p
r
o
to
c
o
l
allo
ws
f
o
r
m
o
r
e
ef
f
icien
t
cl
u
s
ter
an
d
tr
an
s
m
i
s
s
io
n
m
an
ag
em
e
n
t,
th
er
eb
y
m
i
n
im
izin
g
d
elay
s
.
I
n
s
u
m
m
ar
y
,
th
e
co
m
p
a
r
is
o
n
s
h
o
ws
th
at
th
e
in
teg
r
atio
n
o
f
AI
tech
n
iq
u
es
in
AI
-
MG
-
L
E
AC
H
s
ig
n
if
ican
tly
r
ed
u
ce
s
laten
cy
.
On
av
er
ag
e,
laten
cy
is
r
ed
u
ce
d
b
y
4
0
%
co
m
p
ar
ed
to
MG
-
L
E
AC
H
an
d
b
y
6
0
%
co
m
p
ar
ed
to
L
E
AC
H,
h
ig
h
l
ig
h
tin
g
th
at
AI
ef
f
ec
tiv
ely
o
p
tim
izes
co
m
m
u
n
icatio
n
s
i
n
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
Alth
o
u
g
h
th
e
g
r
ap
h
f
o
cu
s
es
o
n
ly
o
n
laten
cy
,
it
is
lik
ely
th
at
th
is
r
ed
u
ctio
n
also
lead
s
to
an
en
er
g
y
s
av
in
g
o
f
a
b
o
u
t
1
0
%
to
2
0
%
d
u
e
to
f
aster
an
d
m
o
r
e
o
p
tim
ize
d
co
m
m
u
n
icatio
n
s
,
f
u
r
th
er
e
n
h
an
cin
g
th
e
o
v
e
r
all
ef
f
icien
cy
o
f
th
e
p
r
o
to
c
o
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
MG
-
LEA
C
H:
in
ve
s
tig
a
tio
n
o
f MG
-
LEA
C
H
in
w
ir
ele
s
s
…
(
Hich
a
m
Ou
ld
z
ir
a
)
5087
Fig
u
r
e
8
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
PDR
v
er
s
u
s
co
m
m
u
n
icati
o
n
r
o
u
n
d
s
f
o
r
L
E
AC
H,
MG
-
L
E
AC
H,
an
d
AI
-
MG
-
L
E
AC
H
p
r
o
to
co
ls
Fig
u
r
e
9
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
n
etwo
r
k
laten
cy
as a
f
u
n
ct
io
n
o
f
c
o
m
m
u
n
icatio
n
r
o
u
n
d
s
f
o
r
L
E
AC
H,
MG
-
L
E
AC
H,
an
d
AI
-
MG
-
L
E
AC
H
p
r
o
to
co
ls
T
h
ese
r
esu
lts
h
ig
h
lig
h
t
th
at
AI
-
MG
-
L
E
AC
H,
with
an
av
er
ag
e
laten
cy
r
ed
u
ctio
n
o
f
5
0
%
to
7
0
%,
is
a
p
r
o
m
is
in
g
s
o
lu
tio
n
f
o
r
ap
p
lica
tio
n
s
r
eq
u
ir
i
n
g
b
o
th
lo
w
laten
cy
an
d
o
p
tim
ized
e
n
er
g
y
m
a
n
ag
em
en
t
in
wir
eless
s
en
s
o
r
n
etwo
r
k
s
.
AI
-
MG
-
L
E
AC
H
en
h
an
ce
s
n
etwo
r
k
p
er
f
o
r
m
an
ce
th
r
o
u
g
h
AI
-
d
r
iv
e
n
p
r
ed
ictiv
e
m
o
d
els
th
at
ac
cu
r
ately
f
o
r
ec
ast
en
e
r
g
y
co
n
s
u
m
p
tio
n
,
o
p
tim
izin
g
n
o
d
e
b
e
h
av
io
r
f
o
r
g
r
ea
ter
e
n
er
g
y
ef
f
icien
cy
co
m
p
ar
e
d
to
s
tan
d
ar
d
MG
-
L
E
AC
H.
I
t
i
n
tellig
en
tly
s
elec
ts
C
Hs
an
d
b
alan
ce
s
en
er
g
y
d
is
tr
ib
u
t
io
n
ac
r
o
s
s
n
o
d
es,
s
ig
n
if
ican
tly
ex
ten
d
i
n
g
th
e
n
etwo
r
k
’
s
life
s
p
an
.
T
h
e
p
r
o
to
co
l
d
y
n
a
m
ically
ad
ap
ts
to
c
h
an
g
in
g
co
n
d
itio
n
s
,
m
ak
in
g
it
id
ea
l
f
o
r
u
n
p
r
ed
ict
ab
le
en
v
ir
o
n
m
en
ts
.
B
y
ev
en
l
y
d
is
tr
ib
u
tin
g
tr
af
f
ic,
AI
-
MG
-
L
E
AC
H
im
p
r
o
v
es
lo
ad
b
alan
cin
g
,
p
r
ev
en
tin
g
n
o
d
e
o
v
er
l
o
ad
a
n
d
r
e
d
u
cin
g
f
ailu
r
es.
I
ts
p
r
ed
ictiv
e
r
o
u
tin
g
m
ec
h
an
is
m
s
h
elp
av
o
i
d
co
n
g
ested
p
at
h
s
,
lead
in
g
to
f
aster
d
ata
tr
an
s
m
is
s
io
n
an
d
f
ewe
r
d
elay
s
,
t
h
u
s
im
p
r
o
v
i
n
g
o
v
er
all
n
etwo
r
k
ef
f
icien
cy
.
I
n
c
o
m
p
ar
ativ
e
s
tu
d
ies,
AI
-
MG
-
L
E
AC
H
h
as
b
ee
n
s
h
o
wn
t
o
o
u
t
p
er
f
o
r
m
MG
-
L
E
AC
H
in
ter
m
s
o
f
n
etwo
r
k
life
tim
e,
e
n
er
g
y
ef
f
icien
cy
a
n
d
C
H
s
elec
tio
n
.
T
h
e
f
o
llo
win
g
ta
b
le
p
r
o
v
id
es
a
h
ig
h
-
lev
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o
m
p
ar
is
o
n
o
f
th
eir
p
er
f
o
r
m
an
ce
as sh
o
wn
in
T
ab
le
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
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n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
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r
20
25
:
5
0
8
0
-
5
0
9
0
5088
AI
-
MG
-
L
E
AC
H
en
h
an
ce
s
th
e
ca
p
ab
ilit
ies
o
f
MG
-
L
E
AC
H
b
y
u
tili
zin
g
AI
to
o
p
tim
ize
cl
u
s
ter
h
ea
d
s
elec
tio
n
,
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
r
o
u
tin
g
,
lead
in
g
to
a
m
o
r
e
en
er
g
y
-
ef
f
icie
n
t
an
d
r
o
b
u
s
t
wir
eless
s
en
s
o
r
n
etwo
r
k
.
T
h
is
n
ew
ap
p
r
o
ac
h
m
ax
im
izes
th
e
n
etwo
r
k
’
s
life
s
p
an
an
d
c
an
d
y
n
am
ically
ad
ju
s
t
to
c
h
an
g
i
n
g
n
etwo
r
k
co
n
d
itio
n
s
,
m
ak
in
g
it
h
ig
h
ly
s
u
itab
le
f
o
r
m
o
d
er
n
,
lar
g
e
-
s
ca
le,
an
d
co
m
p
lex
W
SN
d
ep
lo
y
m
en
ts
.
Ho
wev
er
,
th
e
in
cr
ea
s
ed
c
o
m
p
u
tatio
n
al
an
d
co
m
m
u
n
icatio
n
o
v
er
h
ea
d
m
ay
r
eq
u
ir
e
m
o
r
e
p
o
wer
f
u
l
h
ar
d
war
e
f
o
r
ef
f
ec
tiv
e
im
p
lem
en
tatio
n
.
T
ab
le
2
.
C
o
m
p
a
r
is
o
n
b
etwe
en
MG
-
L
E
AC
H
an
d
AI
-
MG
-
L
E
AC
H
P
a
r
a
me
t
e
r
MG
-
LEA
C
H
AI
-
MG
-
LEA
C
H
En
e
r
g
y
e
f
f
i
c
i
e
n
c
y
M
o
r
e
e
n
e
r
g
y
-
e
f
f
i
c
i
e
n
t
t
h
a
n
L
EA
C
H
d
u
e
t
o
b
e
t
t
e
r
C
H
se
l
e
c
t
i
o
n
.
F
u
r
t
h
e
r
e
n
h
a
n
c
e
d
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
d
u
e
t
o
A
I
-
b
a
se
d
o
p
t
i
m
i
z
a
t
i
o
n
s
i
n
C
H
se
l
e
c
t
i
o
n
a
n
d
c
o
mm
u
n
i
c
a
t
i
o
n
st
r
a
t
e
g
i
e
s.
C
l
u
st
e
r
h
e
a
d
(
C
H
)
S
e
l
e
c
t
i
o
n
C
H
se
l
e
c
t
i
o
n
c
o
n
si
d
e
r
s
e
n
e
r
g
y
,
a
n
d
d
i
s
t
a
n
c
e
.
AI
-
b
a
se
d
C
H
se
l
e
c
t
i
o
n
,
w
h
i
c
h
c
a
n
d
y
n
a
m
i
c
a
l
l
y
a
d
j
u
st
b
a
se
d
o
n
n
e
t
w
o
r
k
c
o
n
d
i
t
i
o
n
s,
n
o
d
e
b
e
h
a
v
i
o
r
,
a
n
d
h
i
st
o
r
i
c
a
l
d
a
t
a
.
C
o
mm
u
n
i
c
a
t
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o
n
s
t
r
a
t
e
g
y
O
p
t
i
mi
z
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d
c
o
mm
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n
i
c
a
t
i
o
n
p
h
a
s
e
s
b
u
t
st
i
l
l
man
u
a
l
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n
d
e
si
g
n
.
A
I
o
p
t
i
m
i
z
e
s c
o
mm
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n
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c
a
t
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o
n
p
h
a
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e
s
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y
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a
mi
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t
a
b
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t
y
a
n
d
e
f
f
i
c
i
e
n
c
y
.
5.
CO
NCLU
SI
O
N
T
h
is
wo
r
k
p
r
esen
ts
AI
-
MG
-
L
E
AC
H,
an
en
h
an
ce
d
L
E
AC
H
-
b
ased
p
r
o
to
c
o
l
th
at
in
teg
r
ates
AI
-
d
r
iv
en
C
H
s
elec
t
io
n
to
o
p
tim
ize
W
SNs
.
B
y
p
r
io
r
itizin
g
n
o
d
es
with
h
ig
h
er
r
esid
u
al
en
er
g
y
an
d
b
e
tter
p
o
s
itio
n
in
g
,
AI
alg
o
r
ith
m
s
im
p
r
o
v
e
en
er
g
y
co
n
s
u
m
p
tio
n
,
n
etwo
r
k
life
tim
e,
lo
ad
b
alan
cin
g
,
an
d
f
a
u
lt
to
ler
an
ce
.
T
h
e
p
r
o
to
co
l
em
p
lo
y
s
p
r
ed
ictiv
e
m
ec
h
a
n
i
s
m
s
to
an
ticip
ate
en
er
g
y
d
ep
letio
n
an
d
tr
af
f
ic
lo
a
d
s
,
en
h
an
cin
g
r
eso
u
r
ce
ef
f
icien
cy
.
Ad
d
itio
n
ally
,
AI
en
ab
les
m
u
lti
-
h
o
p
co
m
m
u
n
icat
io
n
an
d
d
y
n
am
ic
r
o
u
tin
g
,
r
ed
u
cin
g
u
n
n
ec
ess
ar
y
en
er
g
y
u
s
e
an
d
av
o
id
in
g
co
n
g
esti
o
n
.
Deta
iled
s
im
u
latio
n
-
b
a
s
ed
co
m
p
ar
is
o
n
s
with
MG
-
L
E
AC
H
an
d
L
E
AC
H
d
em
o
n
s
tr
ate
th
at
AI
-
MG
-
L
E
AC
H
ac
h
iev
es
u
p
to
a
4
0
%
in
cr
ea
s
e
in
n
etwo
r
k
life
tim
e,
a
2
5
%
r
e
d
u
ctio
n
in
en
er
g
y
co
n
s
u
m
p
tio
n
,
an
d
o
v
e
r
9
5
%
p
ac
k
et
d
eliv
er
y
r
ate
s
t
ab
ilit
y
in
d
y
n
am
ic
en
v
ir
o
n
m
e
n
ts
.
T
h
is
p
r
e
d
ictiv
e
an
d
ad
a
p
tiv
e
f
r
am
ewo
r
k
ef
f
e
ctiv
ely
h
an
d
les
th
e
co
n
s
tr
ain
ts
o
f
class
ic
s
tatic
an
d
p
r
o
b
ab
ilis
tic
clu
s
ter
in
g
m
eth
o
d
s
,
o
f
f
er
i
n
g
a
r
o
b
u
s
t
s
o
lu
tio
n
f
o
r
W
SN
o
p
tim
izatio
n
.
No
n
eth
eless
,
th
is
in
ten
s
if
ie
d
co
m
p
licatio
n
ca
n
ca
u
s
e
p
r
o
b
lem
s
in
c
o
n
tex
ts
wh
er
e
r
eso
u
r
ce
s
ar
e
v
er
y
lim
ited
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
d
ec
lar
e
th
at
th
e
y
h
av
e
n
o
f
u
n
d
in
g
s
u
p
p
o
r
t f
o
r
th
e
wo
r
k
r
e
p
o
r
ted
in
th
is
p
ap
e
r
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
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to
r
ec
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g
n
ize
in
d
iv
id
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al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
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ce
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th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
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co
llab
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r
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n
.
Na
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Aut
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Hass
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
MG
-
LEA
C
H:
in
ve
s
tig
a
tio
n
o
f MG
-
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in
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(
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5089
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T
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CAL AP
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s
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DATA AV
AI
L
AB
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L
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T
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a
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a
v
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.
RE
F
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R
E
NC
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S
[
1
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N
.
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1
6
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