Int
ern
at
i
onal
Journ
al of Ele
ctrical
an
d
Co
mput
er
En
gin
eeri
ng
(IJ
E
C
E)
Vo
l.
11
,
No.
3
,
June
2021
,
pp. 2
432~
2442
IS
S
N: 20
88
-
8708
,
DOI: 10
.11
591/
ijece
.
v11
i
3
.
pp2432
-
244
2
2432
Journ
al h
om
e
page
:
http:
//
ij
ece.i
aesc
or
e.c
om
Sp
ider
m
onkey
o
ptim
iza
tion
r
out
ing
p
rot
ocol fo
r
w
ireles
s
s
ensor
n
etworks
Ali H.
Jabb
ar
1
, I
ma
d S.
Als
ha
w
i
2
1
Depa
rtment of
Com
pute
r
Scie
n
ce
,
Coll
ege of E
duca
t
ion
for
Pure
Sci
ence, Univers
ity
of Thi
-
Qar
,
Thi
-
Qar
,
I
raq
2
Depa
rtment of
Com
pute
r
Scie
n
ce
,
Coll
ege of
C
om
pute
r
Scie
n
ce a
nd
In
form
at
ion
Technol
og
y
,
Un
ive
rsit
y
of
B
asra
,
Basra
,
I
raq
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
Sep
3
, 2
0
20
Re
vised
Oct
2
,
20
20
Accepte
d
Oct
2
1
, 20
20
Uneve
n
energ
y
consum
pti
on
(UEC)
is
la
t
ent
troubl
e
in
wir
el
ess
sensor
net
works
(W
SNs)
tha
t
f
ea
tur
e
a
m
ult
iple
m
otion
pat
t
ern
and
a
m
ult
i
-
hop
routi
ng.
UEC
ofte
n
spl
it
s
th
e
net
work,
r
educe
s
net
work
l
ife,
and
l
ea
ds
to
per
form
anc
e
de
gra
dation.
Som
et
imes,
improvi
ng
ene
rg
y
consum
pti
on
is
m
ore
complic
a
ted
because
i
t
do
e
s
not
red
u
ce
energ
y
consum
pti
on
onl
y
,
but
it
al
so
extends
ne
t
work
li
fe
.
Th
is
m
ake
s
ene
rg
y
co
nsum
pti
on
bal
an
ci
ng
c
ritical
to
W
SN
design
calli
ng
for
en
e
rg
y
-
eff
icient
rou
ti
ng
proto
col
s
t
hat
in
crease
net
work
li
fe
.
Som
e
ene
rg
y
-
s
avi
n
g
protoc
ols
hav
e
bee
n
app
li
ed
t
o
m
ake
the
ene
rg
y
consum
pti
on
among
a
ll
nodes
inside
the
net
work
equ
il
i
bra
te
in
th
e
expe
c
ta
nc
y
and
end
power
in
alm
ost
al
l
nodes
si
m
ult
ane
ousl
y
.
T
his
work
has
suggested
a
pr
otoc
ol
of
ene
r
g
y
-
saving
routing
named
spider
m
onk
e
y
opti
m
iz
ation
rou
ti
ng
proto
col
(SM
ORP
),
which
ai
m
s
to
probe
t
he
issue
of
net
work
li
fe
in
W
S
Ns
.
The
proposed
protoc
ol
red
uce
s
excess
ive
routi
ng
m
essage
s
tha
t
m
a
y
lead
to
the
wasta
ge
of
signifi
c
ant
en
erg
y
b
y
re
c
y
clin
g
fre
quent
inform
at
ion
from
the
source
node
int
o
the
sink.
T
his
routi
ng
protoc
ol
ca
n
cho
ose
the
opt
imal
r
outi
ng
pa
th.
That
is
the
pr
efe
r
able
node
ca
n
be
chose
n
from
nodes
of
the
ca
n
dida
t
e
in
the
sen
ding
wa
y
s
b
y
pr
efe
rring
th
e
ene
rg
y
of
m
axim
um
residua
l,
th
e
m
ini
m
um
tra
ff
ic
lo
ad, a
nd
the
l
ea
st di
st
ance
to
the
sink.
Sim
ula
ti
on
resul
ts
h
ave
prov
ed
t
h
e
e
ffe
ctivene
ss
of
t
he
proposed
protoc
ol
in
t
e
rm
s
of
dec
reas
ing
end
-
to
-
en
d
del
a
y
,
r
educing
ene
r
g
y
consum
pti
on
co
m
par
ed
to
well
-
known rout
ing
p
rotoc
ols.
Ke
yw
or
d
s
:
Netw
ork
li
feti
m
e
Rou
ti
ng
Sp
ide
r
m
onkey
o
pti
m
iz
at
ion
WSNs
This
is an
open
acc
ess arti
cl
e
un
der
the
CC
B
Y
-
SA
l
ic
ense
.
Corres
pond
in
g
Aut
h
or
:
Im
ad
S. Alsha
wi
Dep
a
rtm
ent o
f C
om
pu
te
r
Scie
nce
Coll
ege
of
C
om
pu
te
r
Scie
nc
e an
d Inform
ation
Tech
nolo
gy
Un
i
ver
sit
y o
f B
asra,
Ba
s
ra, I
raq
Em
a
il
:
e
m
adalshaw
i
@g
m
ai
l.
com
,
e
m
ad.
al
sh
awi
@uo
basr
a
h.
e
du.iq
1.
INTROD
U
CTION
A
wi
reless
se
nsor
net
work
(
WSN)
is
a
bas
ic
structu
re
m
ade
up
of
se
nsor
node
s.
T
hes
e
nodes
ar
e
us
ua
ll
y
den
sel
y
sp
rea
d
to
se
rv
e
i
n
se
ns
at
io
n
a
nd
t
he
pro
cess
of
data,
c
apab
il
it
ie
s
of
com
m
un
ic
at
ion
,
a
nd
com
pu
ti
ng
.
W
SN
s
in
cl
ude
a
series
of
f
unc
ti
on
s,
a
ppli
cat
i
on
s
,
a
nd
ca
pa
bili
ti
es,
i.e.,
an
y
op
e
rati
on
d
e
m
and
s
inf
or
m
at
ion
co
m
m
un
ic
at
ing
a
nd
sen
sin
g,
li
ke
“vide
o
s
urvei
ll
ance”
an
d
“at
m
os
ph
eric
m
on
it
or
i
ng
”
.
WSNs
ca
n
be
in
sta
ll
ed
t
hro
ugh
open
areas,
su
c
h
a
s
pa
rks,
r
oads,
battl
egroun
ds
,
so
m
e
m
ac
hin
e
ry,
“c
omm
ercial
bu
il
di
ngs”, a
nd
ev
e
n on the
bo
dy of
hum
an
be
ing
s
[1].
In
ge
ner
al
, t
hes
e n
odes
of the
sens
or
within t
he
net
w
orks of “l
arg
e
-
scal
e”
processes
of g
a
therin
g data
are
supp
li
ed
w
it
h
power
by
c
heap
a
nd
sm
al
l
batte
ries.
Su
c
h
batte
ries
are
us
ua
ll
y
of
low
energy;
nev
e
rtheless,
they
are
ex
pec
te
d
to
w
ork
f
or
so
m
e
t
i
m
e
[
2].
W
S
Ns
have
a
deep
-
seat
ed
pro
blem
in
t
he
“u
ne
ven
e
ne
rg
y”
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
Sp
i
der m
on
key
opti
miza
ti
on r
ou
ti
ng
protoc
ol
for
wi
rel
ess s
ens
or
networks
(Al
i H.
J
abbar
)
2433
consum
ption
, w
hic
h
is
“t
he
m
ul
ti
-
hop
r
ou
ti
ng
a
nd
m
any
-
to
-
one
traf
fic
pa
tt
ern
”.
S
uc
h
a
n
une
ven
e
nerg
y
waste
m
ay
con
sidera
bly
sh
ort
en
t
he
per
i
od
of
net
work
li
fe
.
N
or
m
al
l
y,
in
the
r
ou
ti
ng
m
et
ho
ds
,
the
m
os
t
excell
ent
route
betwee
n t
he
s
ource a
nd
the d
e
sti
nation
is ch
os
en
to
t
ra
ns
m
it
the d
at
a [3,
4]
In
WSN,
each
sens
or
node
con
sist
s
of
m
any
par
ts;
na
m
el
y,
a
po
w
er
un
it
,
a
sen
sing
unit
,
a
transm
issi
on
unit
,
a
processi
ng
unit
.
The
re
are
al
so
opti
onal
p
arts
s
uc
h
as
a
po
sit
io
n
f
ind
in
g
syst
em
,
and
a
m
ob
il
iz
er.
Figu
re 1
cl
ari
fies
the
se
ns
or
str
uc
ture.
Th
e
se
nso
rs
w
ork
on
thei
r
ta
sk
s
,
the
c
urren
t
in
f
or
m
at
io
n,
t
he
com
pu
ta
ti
on
da
ta
, th
e c
omm
u
nicat
ion
,
a
nd the s
ources
of e
nergy [
5].
The
se
nsor
ca
n
perform
two
act
ion
s.
It
ei
t
her
sen
ds
it
s
s
ense
d
data
or
serv
e
s
just
li
ke
a
relay
to
transm
it
data
gathe
red
by
a
no
t
her
sens
or
in
a
netw
ork.
Con
se
quently
,
the
m
atter
of
energy
sa
ving
is
ver
y
essenti
al
to
s
ol
ve
the
pro
ble
m
of
the
se
nsor
netw
ork
s
w
it
h
lim
i
te
d
po
wer
t
hat
are
use
d
in
se
ns
in
g
data.
The
nce,
the
fa
ct
of
e
nergy
w
ast
e
has
to
be
highly
reg
a
rd
e
d
an
d
c
ounted
to
le
ng
t
hen
t
he
per
i
od
of
a
ne
twor
k
li
fetim
e
[2
]
.
So
m
e
al
go
rith
m
s
of
routin
g
hav
e
the
sa
m
e
beh
avio
r
to
chall
en
ge
m
ini
m
iz
ing
th
e
entir
e
consum
ption
of
e
n
e
r
g
y
i
n
t
h
e
n
e
t
w
o
r
k
c
o
n
c
e
r
n
i
n
g
t
h
e
d
r
a
i
n
a
g
e
o
f
t
h
e
n
o
r
m
a
l
e
n
e
r
g
y
o
f
t
h
e
n
e
t
w
o
r
k
s
[
6
]
.
S
u
c
h
b
e
h
a
v
i
o
r
s
r
e
s
u
l
t
i
n
t
h
e
m
a
t
t
e
r
o
f
n
e
t
w
o
r
k
p
a
r
t
i
t
i
o
n
;
b
e
c
a
u
s
e
t
h
e
n
o
d
e
s
t
h
a
t
a
r
e
c
o
n
n
e
c
t
e
d
t
o
m
o
r
e
t
h
a
n
o
n
e
n
e
t
w
o
r
k
p
a
r
t
s
c
o
n
s
u
m
e
b
a
t
t
e
r
y
e
n
e
r
g
y
q
u
i
c
k
l
y
w
h
e
n
c
o
m
p
a
r
e
d
t
o
t
h
e
c
a
s
e
o
f
t
h
e
o
n
e
-
p
a
r
t
-
c
o
n
n
e
c
t
i
o
n
no
des
[5,
7]
.
Ther
e
f
or
e,
the
delay
of
trans
m
issi
on
is
us
ua
ll
y
le
ssened
thr
ough
ch
oosing
the
sam
e
ro
ute
in
a
pr
ot
oc
ol
for
the
oth
e
r
c
om
ing
com
m
un
ic
at
ion
s.
The
n,
the
energy
o
f
t
h
e
n
o
d
e
s
i
n
t
h
i
s
r
o
u
t
e
i
s
d
r
a
i
n
e
d
q
u
i
c
k
l
y
[
8
-
1
0
]
.
T
h
e
s
e
a
l
g
o
r
i
t
h
m
s
o
f
t
e
n
m
a
k
e
d
i
f
f
e
r
e
n
t
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
o
f
W
S
N
s
a
s
t
h
e
y
r
e
d
u
c
e
t
h
e
a
g
g
r
e
g
a
t
e
o
f
t
h
e
e
n
e
r
g
y
dr
ai
ned.
That
is
w
hy
thi
s
k
i
n
d
o
f
a
l
g
o
r
i
t
h
m
h
a
s
t
h
e
c
a
s
e
o
f
a
n
e
t
w
o
r
k
p
a
r
t
i
t
i
o
n
t
h
a
t
c
o
r
r
u
p
t
s
t
h
e
b
e
n
e
f
i
t
s
o
f
t
h
e
W
S
N
[
5
,
8
]
.
T
h
e
n
e
t
w
o
r
k
p
a
r
t
i
t
i
o
n
p
r
o
b
l
e
m
i
s
c
l
a
r
i
f
i
e
d
i
n
F
i
g
u
r
e
2
w
h
e
n
s
o
m
e
s
e
n
s
o
r
n
o
d
e
s
a
r
e
i
m
p
o
s
s
i
b
l
e
t
o
reach
.
Figure
1. A
se
ns
or’
s c
om
po
ne
nts
Figure
2. Net
w
ork partit
io
n d
ue
to
the
deat
h of certa
in
nodes
As
a
res
ult,
th
e
sensi
ng
net
w
ork
li
feti
m
e
is
consum
ed
im
m
ediat
el
y
wh
en
the
ene
rg
y
of
the
“c
riti
cal
nodes”
batte
ries
is
con
s
um
ed.
Crit
ic
al
no
des
norm
ally
exist
on
se
ver
al
r
ou
te
s.
The
pe
rf
ec
t
beh
a
vio
r
of
r
ou
te
s
is
to
slow
dow
n
the
ene
rg
y
c
on
s
um
ption
an
d
to
d
ist
rib
ute
the
energy
ove
r
the
nodes
c
om
par
at
ively
so
that
all
the
nodes
in
a
net
work
w
oul
d
be
de
plete
d
si
m
ultaneou
sly
[
2].
As
s
oon
as
the
batte
ry
energy
of
the
nodes
wh
ic
h
relay
s
f
or
a
di
vision
of
a
W
SN
is
spe
nt,
the
li
fe
of
this
W
S
N
is
ov
er
.
So,
the
prob
l
em
is
to
propose
a
set
of
ste
ps
t
o
bu
il
d
a
path
for
eve
ry
node
s
o
that
it
ca
n
se
nd
the
si
gn
al
s
rely
ing
on
cert
ai
n
pa
ram
et
ers,
or
t
o
config
ur
e
a
good
pat
h
for
these
sig
nals
by
increasing
t
he
per
i
od
of
the
W
S
Ns
li
fe
[1
1,
12]
.
The
n,
the
“op
ti
m
iz
ation
pro
blem
”
is
t
o
le
ngthe
n
th
is
per
i
od,
an
d
r
oute
pa
ra
m
et
ers
are
th
e
var
ia
bles
in
su
c
h
op
ti
m
iz
ation
.
Ther
e
f
or
e,
the
sugg
est
e
d
m
e
thod
in
t
his
st
ud
y
e
ndeav
ors
to
so
l
ve
the
conu
ndr
um
of
energy
consum
ption
balance,
m
ini
m
iz
ing
the
“e
nd
-
to
-
en
d
dela
y”
wh
ic
h
is
r
esulte
d
from
routes
plan
ning,
a
nd
m
axi
m
iz
ing
t
he
netw
ork'
s
li
fetim
es
in
WSNs.
This
m
et
ho
d
util
iz
es
the
al
go
rith
m
of
sp
ider
m
on
key
op
ti
m
iz
ation
(
SMO)
to
ob
ta
in
the
best
route
betwee
n
the
source
a
nd
the
destinat
io
n.
T
his
is
done
via
choosi
ng
the
l
ongest
li
feti
m
e
of
ba
tt
ery
ene
rg
y
with
high
p
o
w
e
r
,
a
n
d
t
h
e
l
e
a
s
t
l
o
a
d
o
f
p
a
s
s
i
n
g
t
h
e
d
a
t
a
.
F
o
r
t
h
i
s
r
e
a
s
o
n
,
t
h
e
d
e
l
a
y
t
h
a
t
i
s
r
e
s
u
l
t
e
d
f
r
o
m
p
l
a
n
n
i
n
g
t
h
e
r
o
u
t
e
o
f
t
h
e
d
a
t
a
f
o
r
e
v
e
r
y
t
i
m
e
w
o
u
l
d
b
e
m
i
n
i
m
i
z
e
d
b
y
s
u
c
h
c
l
e
v
e
r
n
e
t
w
o
r
k
b
e
h
a
v
i
o
r
.
M
e
a
n
w
h
i
l
e
,
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
i
s
h
e
l
d
b
a
l
a
n
c
e
d
a
m
o
n
g
t
h
e
n
e
w
l
y
d
e
t
e
c
t
e
d
p
a
t
h
s
.
This
pap
e
r
ste
ps
on
to
or
gani
ze
it
s
con
te
nt
s
by
pr
e
sentin
g
Sect
io
n
2
ta
ckles
the
relat
ed
wor
ks
a
nd
relat
ed
c
on
ce
pt
s.
The
SMO
al
gorithm
is
s
umm
arized
in
sect
ion
3.
In
s
ect
ion
4,
t
he
r
esearche
rs
rais
e
the
po
te
ntial
util
i
t
y
of
i
m
ple
m
enting
the
sug
ge
ste
d
m
e
tho
d
of
the
route.
Se
ct
ion
5
off
ers
a
descr
ipti
on
of
the
si
m
ulati
on
r
es
ul
ts. A
s t
he
la
st
ste
p,
t
he
c
on
cl
us
io
ns
a
re list
ed
in
s
ect
io
n 6.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
11
, No
.
3
,
J
une
2021
:
24
32
-
2442
2434
2.
RELATE
D
W
ORKS
In
the
WSNs
fiel
d,
it
is
us
ual
to
dep
l
oy
a
lot
of
se
ns
or
no
ds
ov
e
r
wide
spots.
Su
c
h
node
s
are
usual
ly
su
ppli
ed
wit
h
powe
r
by
hav
i
ng
sm
al
l
and
cheap
batte
ries
that
are
no
t
inte
nd
e
d
to
be
re
pl
aced
or
rech
a
r
ged
in
the
f
uture,
due
to
the
diff
ic
ult
ci
rcu
m
sta
nces
in
w
hich
t
hese
netw
orks
a
re
pl
aced
in
t
he
co
m
m
on
ap
plica
ti
on
s
.
Be
side
s,
these
batte
ries
are
no
t
easy
to
ac
cess.
Th
us
,
t
he
sensor
no
de
s
stop
r
outi
ng
wh
e
n
their
ba
tt
eries’
energy
dies
out.
Co
ns
e
qu
e
nt
ly
,
this
kin
d
of
netw
ork
do
e
s
not
r
ou
t
e
any
longe
r
then,
or
at
le
ast
,
it
s
perform
ance
w
il
l
no
t
be
f
ull.
Ther
e
f
or
e,
t
he
factor
of
a
li
fetim
e
in
these
ne
tworks
is
of
hi
gh
c
onsiderati
on
t
o
count
thei
r
pe
r
form
ance
in
r
outi
ng
be
ha
vior
[5
]
.
N
ow
a
days
,
al
l
researc
he
r
s
are
c
oncentra
ti
ng
on
the
m
eth
ods
of
co
nsum
ption
reducti
on
in
network
e
nergy
by
su
ggest
ing
m
any
kin
ds
of
routin
g
protoc
ols
f
or
WSNs.
Hen
ce
these
protoc
ols
are
pro
po
s
ed
to
obta
in
the
best
rout
e
fo
r
recei
ving
and
f
orwardin
g
the
sense
d
da
ta
in
the
netw
ork.
T
sai
et
al.
[
8]
r
ecom
m
end
e
d
t
he
reducti
on
of
the
ho
p
dista
nce
of
a
ny
r
oute
.
By
doin
g
s
o,
t
he
pro
portion
of
this
ho
p
distanc
e
t
o
that
of
the
sh
ort
est
r
ou
te
has
re
duced
.
T
hen,
this
rati
o
reducti
on
hel
pe
d
t
o
reduce
the
e
ne
rg
y
wa
ste
d
in
data
receivi
ng
and
f
orwardin
g
withi
n
a
net
work.
Ra
na
et
al.
[
13]
su
gge
ste
d
a
transm
issi
on
te
chn
i
qu
e
t
o
ge
t
the
m
axi
m
um
li
fetim
e
of
the
ene
rg
y
in
a
W
S
N.
I
n
thi
s
te
chn
i
qu
e
,
WSN
is
pr
ea
rr
a
nged
in
pa
rin
g
of
ti
er
s
ha
ving
th
e
s
uppo
rt
of
a
n
A
-
sta
r
searc
h
al
gorithm
to
obta
in
a
n
op
ti
m
iz
ed
path
beg
i
nn
i
ng
at
the
res
ource
no
de
an
d
en
ding
at
the
destinat
ion
on
e
,
with
the
help
of
a
previo
us
ly
deter
m
ined
le
vel
of
t
he
le
ast
existi
ng
nodes
e
nergy,
so
that
they
w
ould
be
a
pa
rt
of
the
rese
nd
i
ng
r
ou
te
.
I
n
[
14
]
,
th
e
auth
or
s
go
t
th
e
ben
e
fit
of
a
“hig
h
-
weig
ht
gen
et
ic
al
gorit
hm
(G
A)
”
in
an
ap
proac
h
of
transm
issi
on
,
wh
e
re
sens
or
no
des
m
on
it
or
an
d
c
on
t
ro
l
t
he
m
agn
it
ud
e
of
pas
sin
g
da
ta
s
o
that
it
can
f
ollow
and
s
olv
e
the
netw
or
k
ov
e
rcro
wd
i
ng.
Alsh
awi
et
al.
[5
]
pr
ese
nte
d
a
com
plete
l
y
new
beh
a
vi
or
of
transm
issi
on.
This
be
hav
i
or
end
ea
vore
d
to
prolo
ng
the
e
ne
rg
y
li
feti
m
e
i
n
a
WSN
an
d
con
t
ro
l
the
e
ne
rg
y
c
on
s
um
ption
within
the
ne
twork
wh
e
n
c
onside
ring the t
raffic
hea
vin
ess
f
act
or.
Dep
e
ndin
g
on
the
m
od
el
of
“
bin
a
ry
detect
io
n”,
Wa
ng
et
al.
[
15]
at
tem
pted
to
deal
with
the
pr
ob
le
m
of
“
dynam
ic
dep
loym
ent”
thr
ough
the
t
wo
ty
pes
of
se
nsor
nodes
within
a WSN,
t
h
e
m
o
b
i
l
e
a
n
d
f
i
x
e
d
.
B
y
d
o
i
n
g
s
o
,
t
h
e
y
e
m
p
l
o
y
e
d
w
h
a
t
i
s
s
o
-
c
a
l
l
e
d
t
h
e
“
b
i
o
g
e
o
g
r
a
p
h
y
-
b
a
s
e
d
o
p
t
i
m
i
z
a
t
i
o
n
(
B
B
O
)
a
l
g
o
r
i
t
h
m
”.
Hu
a
ng
et
al.
[16]
pr
ese
nted
a
ne
w
“ene
rg
y
-
a
war
e
ge
ogra
phic
routin
g
protoc
ol”
in
the
fiel
d
of
WSN.
This
a
pproach
i
s
consi
der
e
d
as
an
en
dea
vor
to
reduce
the
c
o
n
s
um
p
t
i
o
n
o
f
e
n
e
r
g
y
d
u
r
i
n
g
t
h
e
“
e
n
d
-
to
-
e
n
d
”
r
o
u
t
i
n
g
.
T
h
i
s
p
r
o
t
o
c
o
l
a
d
a
p
t
s
i
t
s
e
l
f
w
i
t
h
a
n
o
t
h
e
r
g
e
o
g
r
a
p
h
i
c
p
r
o
t
o
c
o
l
t
o
m
e
e
t
a
n
a
n
c
h
o
r
l
i
s
t
w
h
i
c
h
d
e
p
e
n
d
s
o
n
t
h
e
n
o
d
e
s
'
p
r
o
j
e
c
t
i
o
n
d
i
s
t
a
n
c
e
t
o
s
t
e
e
r
t
h
e
r
e
s
e
n
d
i
n
g
o
f
d
a
t
a
.
E
v
e
r
y
n
o
d
e
t
h
a
t
s
e
n
d
s
t
h
e
m
e
s
s
a
g
e
d
e
p
e
n
d
s
o
n
t
h
r
e
e
t
h
i
n
g
s
t
o
d
e
c
i
d
e
t
h
e
r
o
u
t
i
n
g
:
“
g
e
o
g
r
a
p
h
i
c
i
n
f
o
r
m
a
t
i
o
n
”
,
t
h
e
f
e
a
t
u
r
e
s
o
f
h
o
w
t
h
e
e
n
e
r
g
y
i
s
s
p
e
n
t
,
a
n
d
t
h
e
m
e
a
s
u
r
e
m
e
nt
s
y
s
t
e
m
o
f
c
o
u
n
t
i
n
g
t
h
e
“
a
d
v
a
n
c
e
d
e
n
e
r
g
y
c
o
s
t
”
.
T
h
e
n
,
i
t
a
d
a
p
t
s
t
h
e
c
o
s
t
o
f
c
o
n
v
e
y
a
n
c
e
e
n
e
r
g
y
to
get the
dete
rm
ined
node
.
The
aut
hors
[
17]
pr
ese
nted
a
routin
g
m
e
thod
as
well
.
This
m
et
ho
d
is
consi
der
e
d
as
inco
rpor
at
io
n
bet
w
een
the
al
gorithm
“
art
ific
ia
l
bee
colon
y
(
ABC)”
of
op
ti
m
iz
a
ti
on
an
d
“F
uzzy
Log
ic
”.
T
hes
e
two
directi
ons
are
e
m
plo
ye
d
i
n
t
his
pr
oto
c
ol
to
cal
culat
e
the
best
r
oute
by
identify
in
g
t
he
op
ti
m
al
nex
t
node
to
m
ake
t
he
best
path
betwee
n
t
he
s
ourc
e
a
nd
the
sin
k.
I
n
[
18]
,
L.
Sh
i
et
al
.
sug
gested
an
eff
ect
ive
syst
em
ca
ll
ed
the
pr
oto
c
ol
of
data
-
dr
ive
n
routin
g
.
It
in
ve
sti
gated
the
pro
blem
s
of
m
o
b
i
l
i
t
y
i
n
t
h
e
n
e
t
w
o
r
k
s
o
f
m
o
b
i
l
e
s
i
n
k
s
.
D
a
t
a
-
d
r
i
v
e
n
R
o
u
t
i
n
g
s
t
r
a
t
e
g
y
e
n
d
e
a
v
o
r
e
d
t
o
l
e
s
s
e
n
t
h
e
o
v
e
r
h
e
a
d
s
o
f
t
h
e
p
a
t
h
p
l
a
n
n
i
n
g
,
w
h
i
c
h
h
a
s
r
e
s
u
l
t
e
d
f
r
o
m
t
h
e
s
i
n
k
m
o
b
i
l
i
t
y
.
T
h
e
n
i
t
k
e
e
p
s
t
h
e
h
i
g
h
p
e
r
f
o
r
m
a
n
c
e
o
f
d
e
l
i
v
e
r
i
n
g
t
h
e
p
a
c
k
e
t
.
C
.
Hs
u
e
t
al.
[19]
prese
nted
the
s
o
-
cal
le
d
AS
S
ORT,
w
hich
sta
nds
for
“
asy
nchron
ous
sle
ep
-
wa
ke
sc
hedulin
g
oppo
rtu
nisti
c
routin
g
te
ch
nolo
gy
”.
This
te
chnolo
gy
sugg
e
ste
d
a
cert
ai
n
ty
pe
of
de
sign,
w
hich
m
erg
ed
the
u
t
i
l
i
t
y
o
f
“
a
s
y
n
c
h
r
o
n
o
u
s
s
l
e
e
p
-
w
a
k
e
s
c
h
e
d
u
l
i
n
g
”
w
i
t
h
t
h
e
u
t
i
l
i
t
y
o
f
“
o
p
p
o
r
t
u
n
i
s
t
i
c
r
o
u
t
i
n
g
”
;
n
a
m
e
l
y
,
t
h
e
e
n
h
a
n
c
e
d
t
r
a
n
s
m
i
s
s
i
o
n
r
e
l
i
a
b
i
l
i
t
y
o
v
e
r
d
i
v
e
r
t
i
n
g
r
o
u
t
e
.
C
o
n
s
e
q
u
e
n
t
l
y
,
s
u
c
h
a
b
e
h
a
v
i
o
r
i
m
p
r
o
v
e
s
t
h
e
t
r
a
n
s
m
i
s
s
i
o
n
w
a
y
b
y
l
e
n
g
t
h
e
n
i
n
g
t
h
e
W
S
N
s
li
fetim
e.
In
[
6],
the
au
thors
dev
el
op
ed
a
r
ou
ti
ng
m
et
ho
d
cal
le
d
“Fuzzy
-
G
os
si
p”
wh
i
c
h
is
c
la
ssifie
d
as
“ener
gy
-
e
ff
ic
ie
nt”
in
us
in
g
th
e
fu
zzy
log
ic
t
o
m
od
ify
the
prot
oco
l
of
goss
ip.
I
n
this
prot
oco
l,
t
he
best
s
end
i
ng
path
from
the
source
into
the
sin
k
is
ve
rified
by
c
hoos
in
g
the
opti
m
al
no
de
s
wi
thin
a
path
.
This
i
s
accom
plished
wh
e
n
sel
ec
ti
ng
the
highest
energy
node
s
with
the
le
ast
distance,
to
ac
hieve
the
“m
i
nim
u
m
nu
m
ber
of
hops”
duri
ng
the
r
ou
ti
ng
pat
h
fro
m
the
so
urce
i
nto
the
sin
k.
T
he
a
uthor
s
[20]
prese
nted
t
he
routin
g
m
et
ho
d
a
nd
s
pe
ci
fical
ly
in
th
e
W
SNs
sprea
ding.
T
his
rout
ing
pr
oto
c
o
l
is
the
so
-
cal
le
d
“
fast
si
m
ple
flooding
strat
egy
”. I
t
c
oncent
rates on
the
e
ff
ic
ie
ncy of
e
nergy
as
a
ta
rg
et
t
o
t
he
“c
ruci
al
desig
n”
of the
r
outi
ng
m
eth
ods
work
i
ng
in
th
e
WSN
s
.
The
n,
it
m
us
t
giv
e
no
co
ns
i
derat
ion
s
to
the
oth
e
r
aspects
of
de
sig
nation.
It
al
s
o
decr
ease
s
the
“end
-
to
-
en
d
la
t
ency”.
F
ur
t
herm
or
e,
this
ne
w
appr
oach
is
cl
assifi
ed
as
very
si
m
ple
and
f
ast
in
routin
g
the
da
ta
into
the
sin
k.
Additi
on
al
l
y,
it
do
es
no
t
dem
and
m
or
e
too
ls
a
nd
ca
n
work
with
a
s
i
m
ple
m
at
he
m
at
ic
a
l
process
.
This
protoc
ol
occ
urs
within
the
li
st
of
flat
protoc
ols.
It
is
worth
y
to
m
ention
that
this
protoc
ol
deals
well
w
it
h
t
he p
rincipal
obsta
cl
es of tra
diti
on
a
l gossi
ping a
nd
f
lo
odin
g.
3.
SPID
E
R MO
NKEY OPT
I
MIZ
ATION (
SMO)
Nowa
days,
m
onkey
-
i
ns
pi
red
op
ti
m
iz
ation
(MO)
is
c
onsi
der
e
d
as
th
e
la
st
ver
si
on
of
the
Sw
a
rm
In
te
ll
igence
(SI).
The
re
are
m
or
e
t
han
t
wo
hund
red
a
nd
six
ty
(2
60)
ways
pro
po
se
d
to
de
fine
m
on
key.
On
e
of
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
Sp
i
der m
on
key
opti
miza
ti
on r
ou
ti
ng
protoc
ol
for
wi
rel
ess s
ens
or
networks
(Al
i H.
J
abbar
)
2435
them
is
that
th
ere
are
s
eve
ral
per
s
pecti
ves
of
t
he
hum
an
br
ai
n
go
vernin
g
the
process
e
s
of
thin
king
.
Thes
e
processes
a
re
con
si
der
e
d
near
to
tho
se
of
the
m
on
key
brai
n.
In
the
fiel
d
of
et
ho
lo
gy,
the
r
e
is
wh
at
is
cal
le
d
a
“
fissio
n
-
fu
si
on
so
ci
et
y
”.
I
n
t
hi
s
so
ci
et
y,
ani
m
al
s
are
put
to
li
ve
to
gethe
r
a
nd
are
c
ha
nged
with
ti
m
e.
They
are
div
ide
d
into
s
ub
-
groups
f
or
the
per
io
d
of
t
he
dayl
igh
t
t
o
get
the
sta
tus
of
fissi
on,
a
nd
they
a
re
re
-
m
ing
le
d
again
to
m
ake
the
sta
tus
of
f
us
io
n
duri
ng
the
per
i
od
of
the
night.
O
bs
e
rv
i
ng
s
uch
a
ki
nd
of
s
ociet
y,
sp
i
de
r
m
on
keys
are
noti
ceably
a
co
ns
ide
rab
le
sam
ple.
It
is
noti
ced
tha
t
t
he
m
al
e
m
on
keys
in
s
uch
a
kind
of
s
ociet
y
are
in
a
l
ow
e
r
rank,
i.e.,
not
c
on
t
ro
ll
in
g,
t
he
y
hav
e
m
or
e
a
ct
iveness
th
ough,
wh
e
reas
th
e
fem
al
e
m
on
ke
ys
are
ob
s
er
ved
t
o
be
at
a
hig
he
r
le
vel
of
le
a
der
s
hi
p
over
thei
r
pa
rtners
in
the
sam
e
so
ci
et
y
[21]
.
C
hand
B.
et
al.
su
ggest
e
d
s
pide
r
m
on
key
op
t
i
m
iz
ation
(
SMO)
a
s
a
s
ub
-
div
isi
on
of
S
I.
S
MO
is
co
ns
i
de
red
a
s
a
protoc
ol
that
dep
e
nds
upon
the
pr
i
nciple
so
-
cal
le
d
“
food
f
or
a
ging”;
for
the
nat
ur
e
of
s
pid
e
r
m
on
key
a
nd
th
ei
r
so
ci
al
beh
a
vioral
co
nv
e
ntio
ns
.
T
he
tradit
ion
s
of
this
fissio
n
-
fu
si
on
so
ci
et
y
are
gove
rn
e
d
as
the
so
ci
al
config
ur
at
io
nal
ly
of
a
sp
ider
m
on
key.
SM
O
m
e
tho
d
ha
s
been
sub
j
ect
to
sever
al
stu
dies.
Alth
ough
these
at
tem
pts
de
m
on
strat
e
that
SM
O
is
gr
eat
in
“e
xp
l
or
at
io
n
an
d
exp
l
oitat
ion
”,
s
ti
ll
there
are
as
pects
to
be
c
ounted
for
m
or
e
op
ti
m
iz
at
ion
.
Fi
gure
3
s
hows
the
flo
w
c
har
t
of the
SMO alg
ori
th
m
.
Den
s
el
y
popu
l
at
ed
fem
al
e
-
governe
d
m
on
ke
y
gr
ou
ps
are
s
ub
-
div
ide
d
int
o
m
ini
-
gr
ou
ps
of
forty
(40)
or
fifty
(50)
pa
rtners.
The
m
ai
n
ta
sk
of
t
he
se
sm
al
l
gr
oups
is
to
fi
nd
th
e
f
ood.
Wh
e
n
the
fem
al
e
do
e
s
not
su
ccee
d
in
it
s
aim
,
(i.e.,
f
ood
-
fin
ding
),
the
a
tt
e
m
pt
is
retried
by
a
f
ur
th
er
fem
al
e
-
go
ve
rned
s
ub
-
gr
oup,
and
s
o
forth
unti
l
the
ta
sk
is
acco
m
pl
ished.
N
onet
heless,
rece
ntly
,
dif
fer
e
nt
m
od
ific
at
ion
s
are
im
ple
m
e
nted
a
s
at
tem
pts
of
updates,
li
ke
exa
m
inati
on
of
se
arc
h
la
r
ge
spot
s
and
el
ect
ing
the
best
outc
om
es
[22]
.
Com
pr
isi
ng
seve
n
ph
ase
s,
t
he
SM
O
m
et
ho
d
reli
es
upon
“pop
ulati
on
re
petit
ive
m
et
ho
do
l
og
y”
.
T
hese
phases
a
re
e
xpla
ined
belo
w
[22
-
24]
:
a.
In
it
ia
li
zat
ion
phase
In
the
beg
i
nn
i
ng,
N
rand
om
so
luti
ons
ha
ve
to
be
ident
ifi
ed.
T
he
popul
at
ion
of
the
spi
der
m
on
key
then
is
to
be
di
vid
e
d
into
a
ce
rtai
n
num
ber
of
gro
ups,
a
nd
t
his
num
ber
is
ref
e
rr
e
d
to
as
n
.
Af
te
r
t
hat,
a
local
le
ader
to
each g
r
oup
is
deter
m
ined,
an
d
a
glo
bal
le
ader
th
at
go
ve
rns
al
l
t
he
gro
ups
is
determ
ined
as
we
ll
.
This
op
ti
m
iz
ation
al
gorithm
init
ia
lizes
f
our
pa
ram
et
ers
to
sta
rt:
l
ocal
-
le
ade
r
-
li
m
it
,
global
-
le
a
de
r
-
li
m
it
,
the
siz
e
of
m
axi
m
u
m
-
gro
up
(
MG
)
, a
nd
per
t
urbati
on
-
r
a
te
(
pr
).
=
min
+
(
0
,
1
)
×
(
max
−
min
)
(1)
wh
e
re,
min
and
max
are lim
it
s o
f
M
i
in
the j
th
vect
or
.
R (0,
1
)
is an a
rb
it
ra
ry num
ber
(0,
1).
b.
Local
le
ade
r p
hase
(LLP
)
In
this
ste
p,
th
e
SMs
will
hav
e
the
in
form
a
ti
on
f
ro
m
bo
th
their
local
le
ader
a
nd
t
heir
ne
ighbor.
B
y
do
i
ng s
o,
t
heir l
ocati
on
will
be u
pd
at
e
d.
This
pro
ce
dure is
clari
fied
a
s foll
ows:
=
+
(
0
,
1
)
×
(
−
)
+
(
−
1
,
1
)
×
(
−
)
(2)
wh
e
re,
an
d
is
the
updated
an
d
pr
e
vious
loc
at
ion
of
i
th
SM.
The
local
le
a
de
r
of
the
k
th
gr
oup
in
t
he
j
th
dim
ension
re
pr
ese
nts itse
lf
b
y
LL
kj
.
M
rj
sta
nds fo
r
the
n
e
ighbor
w
hich
i
s take
n
ar
bitra
r
il
y.
c.
Global
le
ade
r ph
a
se
(
GL
P)
In
this
ph
as
e,
a
no
t
her
opport
unit
y
is
avail
able
fo
r
t
he
SMs
to
upda
te
their
locat
ions
an
d
to
get
to
the
“glo
bal
opti
m
a
”
rely
ing
up
on
“fit
ness”
.
Havi
ng
t
heir
persi
ste
nce,
neig
hbors,
a
nd
global
le
ader
of
the
gro
up
,
the SMs ca
n be
ins
pired. T
he f
ollow
i
ng equa
t
ion
cla
rifies th
e b
e
hav
i
or of
updatin
g
t
he
l
oc
at
ion
in
this
ph
ase:
=
+
(
0
,
1
)
×
(
−
)
+
(
−
1
,
1
)
×
(
−
)
(3)
wh
e
re
GL
i
m
e
ans
t
he
l
ocati
on
of
the
gl
ob
a
l
le
ader
in
j
th
dim
ension
and
j=1,
2,3
,...,
D
identifie
s
the
inde
x
wh
ic
h
is
sel
ect
ed
ra
ndom
ly
.
M
i
m
od
ifie
s
it
s
po
sit
io
n
re
ga
r
ding
the
pro
ba
bili
ti
es.
Fit
ness
is
util
iz
ed
to
count
the li
kelih
ood of a certai
n
s
olu
ti
on, wit
h
m
any d
e
fer
e
nt
wa
ys, li
ke:
=
0
.
1
+
(
/
)
×
0
.
9
(4)
d.
Local
le
ade
r
le
arn
i
ng
(LL
L)
phase
As
the
global
op
ti
m
al
is
known,
t
he
al
go
rithm
un
co
vers
the
local
le
ader
of
t
he
s
ubgro
up
s
a
nd
identifie
s
the
local
opti
m
a.
T
hro
ugh
e
xam
in
ing
t
he
co
unte
r
of
t
he
th
res
hold,
w
het
her
or
no
t
t
he
local
le
aders
update
them
sel
ves
is
de
fine
d here
in
t
his pha
se.
e.
Global
le
ade
r
l
earn
i
ng
(
GLL
)
ph
a
se
Si
m
ply
from
t
he
nam
e
of
t
his
phase,
it
kn
ows
t
hat
the
gl
obal
le
ade
r
is
th
ere
in
the
be
vy
,
as
well
as
it
exam
ines w
het
her o
r no
t t
he
l
eader
up
dates i
ts l
ocati
on to
a
sp
eci
fic th
res
hold
f
or
m
or
e ac
ti
viti
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
11
, No
.
3
,
J
une
2021
:
24
32
-
2442
2436
f.
Local
le
a
der d
eci
sion
(LL
D)
ph
a
se
In
t
his
ph
ase
,
wh
e
n
t
he
local
le
ader
s
are
no
t
updated
to
a
sp
eci
fic
th
res
hold,
SMs
i
n
th
e
be
vy
will
entirel
y
update
the
po
sit
io
ns
by
the
order
of
the
glo
bal
le
ader
or
by
ar
bitr
ary
init
ia
li
z
ing
accor
ding
to
the
rate
of p
e
rtu
rb
at
io
n. H
oweve
r, t
he or
der o
f
t
he update
is cal
culat
ed by
(
5)
:
=
+
(
0
,
1
)
×
(
−
)
+
(
0
,
1
)
×
(
−
)
(5)
g.
Global
le
ade
r deci
sio
n
(
GL
D
)
ph
ase
In
this
ph
a
se,
the
fissio
n
an
d
fu
si
on
of
the
bev
y,
as
a
w
ho
le
,
occ
ur
;
in
the
case
of
“l
i
m
i
t
glo
bal
le
ader
”,
that is
to say,
global l
eaders
do n
ot upd
at
e t
hem
sel
ves
to a
certai
n t
hr
es
hold.
Figure
3. Flo
w
ch
a
rt of the
S
MO alg
or
it
hm
4.
SPID
E
R MO
NKEY OPT
I
MIZ
ATION
ROUTIN
G P
ROTO
COL
(
SMORP
)
F
O
R
W
SNS
The
WSN
to
po
log
y
was
desig
ned
as
a
G
gu
i
ded
grap
h
(
N,
A
)
in
t
his
pap
e
r,
wh
e
re
N
is
t
he
nodes
set
,
and
A
is
the
direct
li
nk
s
set
be
tween
nodes
.
The
node
of
t
he
sink
holds
t
he
respo
ns
ibil
it
y
fo
r
c
ollec
ti
ng
data
from
ever
y
othe
r
no
de
in
it
s
r
ang
e
of
tra
ns
m
issi
on
[5]
.
By
the
base
sta
ti
on,
t
he
ta
ble
of
routin
g
is
co
unte
d.
Broa
dcasts
a
nd
cal
c
ulate
s
th
e
opti
m
a
l
ta
ble
of
r
ou
ti
ng.
This
ta
ble
is
f
ollow
e
d
by
ea
ch
node
.
The
fin
ding
process
of
th
e
op
ti
m
al
path
is
fr
e
quent
broa
dcast
insi
de
t
he
netw
ork
,
a
nd
to
se
nd
data
f
r
om
ever
y
no
de
to
t
he
base
sta
ti
on
vi
a
f
ollow
i
ng
thi
s
ta
ble
of
r
outi
ng
in
eve
ry
round.
Dy
nam
icall
y,
the
ta
ble
of
r
outi
ng
is
c
ounte
d
ta
kin
g
into
c
onside
rati
on
the
le
vel
of
cu
rr
e
nt
f
or
s
om
e
par
am
et
ers
in
ev
ery
node
.
T
herefo
re,
t
he
no
de
s
are
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
Sp
i
der m
on
key
opti
miza
ti
on r
ou
ti
ng
protoc
ol
for
wi
rel
ess s
ens
or
networks
(Al
i H.
J
abbar
)
2437
require
d
to
s
ub
m
it
per
iod
ic
re
ports
on
thei
r
par
am
et
ers
to
the
bas
e
sta
ti
on.
The
base
sta
t
ion
c
ou
l
d
the
re
after
sel
ect
th
e sc
he
du
le
of ro
uting de
pendin
g u
pon t
his
update
d i
nfor
m
at
ion
.
The
li
fetim
e
o
f
the
netw
ork
is
a
sign
ific
ant
m
et
ric
fo
r
W
S
N.
Lin
ks
of
co
m
m
un
ic
at
ion
be
tween
th
e
var
ie
d
no
des
of
t
he
sen
sor
and
t
he
ba
se
s
ta
ti
on
will
br
e
ak
w
he
n
any
node
of
t
he
se
ns
or
dep
le
te
s
e
nergy
con
ce
r
ning
the
su
ggest
e
d
m
od
el
.
This
is
de
e
m
ed
to
the
ne
twork
’s
li
feti
m
e
end
.
As
th
e
li
fetim
e
of
ever
y
sens
or
no
de
is
dep
e
ndent
on
the
c
onsu
m
ptio
n
of
e
nergy
,
it
is
sig
nificant
t
o
c
onser
ve
t
he
rem
ai
nin
g
e
nergy
f
or
these n
odes in
a w
ay
that exte
nd
s the
total
li
fetim
e
o
f
the n
e
twork
. Th
e m
e
thod o
f
s
uggest
ed
ro
utin
g
sup
po
s
es
the
fo
ll
owin
g:
i)
al
l
no
des
of
the
sens
or
a
re
rand
om
l
y
div
ided
i
nto
the
a
r
ea
and
e
ver
y
node
of
t
he
sen
so
r
is
su
pp
os
e
d
to
de
fine
it
s
locat
io
n
in
ad
diti
on
t
o
the
sin
k
an
d
it
s
neighbors;
ii
)
al
l
no
des
of
the
sens
or
ha
ve
the
sam
e
ran
ge
of
m
axi
m
al
transm
issi
on
an
d
t
he
sam
e
init
ial
energy
am
ou
nt;
ii
i
)
ever
y
node
has
a
s
pe
ci
fi
c
a
m
ou
nt
of
tra
ff
ic
outst
and
i
ng
in
the
que
ue
of
the
node.
The
que
ue
of
the
no
de
c
onta
ins
t
he
tra
f
fic
of
app
li
cat
io
n
a
n
d t
he
tra
ff
ic
als
o t
hat a
node ha
s obli
ge
d
to
forwar
d
al
rea
dy.
The
m
ai
n
aim
of
this
pa
pe
r
is
to
create
a
pr
ot
oco
l
of
energy
-
suffici
ent
r
outi
ng
nam
ed
sp
i
der
m
on
key
op
ti
m
iz
ation
r
ou
ti
ng
pr
oto
c
ol
(S
MORP)
.
T
he
sug
gested
m
et
ho
d
ca
n
ex
te
nd
the
WSN
s
li
fetim
e
by
p
la
ci
ng
a
lim
it
on
the
c
ost
of
e
ne
rg
y
in
ad
diti
on
to
eq
uitable
distri
buti
on
f
or
the
c
onsu
m
ption
of
e
nergy.
SM
OR
P
us
e
s
three
routin
g
c
rite
ria
to
sel
ect
the
op
ti
m
u
m
path
from
the
node
of
the
source
to
t
he
sin
k.
N
od
e
crit
eri
a
are
t
he
highest
rem
ai
nin
g
e
nergy
(
R
E),
the
m
ini
m
al
nu
m
ber
of
hops
t
o
the
si
nk
(M
H)
,
an
d
t
he
lo
west
traf
f
ic
load
(TL).
The
s
uggested
m
et
ho
d
com
pu
te
s
the
op
ti
m
a
l
path
of
the
se
nsor
of
the
se
nd
e
r
to
be
us
e
d
f
or
data
transm
issi
on
t
ow
a
r
d
the
sin
k
unde
r
t
he
m
entione
d
crit
eri
a
(
RE,
MH
,
a
nd
TL
).
T
he
produce
d
routin
g
path
is
then
us
e
d
in
la
te
r
transm
issi
on
pr
oce
sses
f
or
sev
eral
tim
es
(r
ou
nd
s
)
chec
kin
g
the
sta
tus
of
each
node
involve
d
in that
path
aft
er eac
h
ti
m
e to
d
eci
de
wheth
e
r
to
u
se
the
sa
m
e p
at
h for t
he
n
e
xt ro
und or
no
t.
As
ass
um
ed
ea
rlie
r,
th
e
sin
k
ha
s
inform
at
ion
reg
a
rd
i
ng
t
he
p
r
e
s
e
n
t
s
t
a
t
u
s
o
f
e
v
e
r
y
n
o
d
e
c
o
n
c
e
r
n
i
n
g
t
h
e
l
e
v
e
l
o
f
b
a
t
t
e
r
y
e
n
e
r
g
y
,
c
o
o
r
d
i
n
a
t
e
s
o
f
t
h
e
l
o
c
a
t
i
o
n
,
a
n
d
t
r
a
f
f
i
c
l
o
a
d
.
C
o
n
s
e
q
u
e
n
t
l
y
,
t
h
e
m
e
t
h
o
d
o
f
p
r
o
p
o
s
e
d
r
o
u
t
i
n
g
l
o
c
a
t
e
s
t
h
e
r
o
u
t
i
n
g
p
a
t
h
o
f
t
h
e
n
o
d
e
o
f
t
h
e
s
e
n
s
o
r
w
h
i
c
h
h
a
s
t
h
e
d
a
t
a
t
o
b
e
s
e
n
t
t
o
t
h
e
s
i
n
k
as
foll
ows:
a.
Starti
ng
f
r
om
t
he
source
node
(the
current
node
)
as
the
loc
al
le
ader
SM
(LLSM)
to
be
e
xp
a
nded
,
fin
d
al
l
con
ti
gu
ous
no
des
wh
ic
h
ca
n
im
m
ediat
el
y
com
m
un
ic
at
e
with
LLSM
(
that
is
to
sa
y
,
their
ra
nge
of
transm
issi
on
c
an reach
LLS
M).
b.
Wh
e
ne
ver
t
he
sink
is
f
ound
a
s
a
neig
hbor
of
LLSM,
it
ca
n
sen
d
it
s
data
wh
ic
h
is
colle
ct
ed
i
m
m
ediately
without a
ny m
edium
h
op.
c.
On
th
e
oth
e
r
ha
nd,
cal
culat
in
g
the
val
ues
of
fitness
f
or
al
l
disco
ver
e
d
nodes
an
d
deter
m
ining
the
gl
obal
le
ader
SM
(G
L
SM))
is
the
be
st
LLSM’s
neighb
or
no
de
am
ong
al
l.
Ca
lc
ulati
ng
the
fitnes
s
values
rea
ds
as
fo
ll
ows:
-
Kno
wing
co
or
din
at
es
(
x
,
y
)
f
or
e
ver
y node
i
ns
ide
t
he
net
w
ork,
d
ist
ance (
d
)
of
e
ve
ry
no
de
(
n
)
t
o
sin
k
c
ou
l
d
be
c
ounted
as
f
ollows:
(
)
=
√
(
−
)
2
+
(
−
)
2
(6)
wh
e
re
(
x
s
, y
s
)
a
nd (
x
n
,y
n
)
a
re t
he
c
oor
din
at
es
(
x, y
) for
nodes
n
& the
sin
k
s
.
-
The val
ue
of
fitness
of the c
onti
guous
node
(
n
)
is
obta
ined
by u
si
ng the
(7)
:
(
)
=
∗
(
)
+
∗
1
/
(
)
+
∗
1
/
(
)
(7)
wh
e
re
RE(
n)
is
the
resid
ual
energy
of
node
n
;
TL(n)
is
the
current
tra
ff
ic
load
f
or
node
n
;
and
α,
β,
a
nd
γ
are inte
ger coe
ff
ic
ie
nts s
pecif
ie
d
by t
he user
to contr
ol the
e
ff
ect
ive
ness o
f e
a
ch varia
ble (m
et
ric).
d.
GLS
M,
t
hen,
e
valuates
the
i
nfo
rm
ation
ta
ke
n
f
ro
m
al
l
LLSM’s
nei
ghbor
nodes
a
nd
sel
e
ct
s
the
best
node
with the
h
i
ghes
t pro
bab
il
it
y
P
relat
ed
to
it
s pr
ob
a
bili
ty
v
al
ue
g
ive
n by:
(
)
=
(
)
∑
(
)
(8)
wh
e
re
P(
s
i
)
is
t
he
val
ue
of
the
prob
a
bili
ty
of
node
n
i
,
fi
tness
(
s
i
)
is
the
valu
e
of
fitne
ss
of
the
node
n
i
,
an
d
N
is t
he nu
m
ber
of
t
he
neig
hbor no
des.
e.
Wh
e
ne
ver
a
s
et
of
nodes
ar
e
disc
ov
e
re
d
i
n
t
he
sam
e
process
of
e
xpa
ns
io
n,
they
a
r
e
su
cce
ssors
t
o
t
he
exten
ded
node
and
substi
tute
s
to
each
oth
e
r
.
The
pointer
of
the
pack
f
or
each
no
de
de
te
ct
ed
durin
g
the
process
of e
xtension is
set
to
t
he
e
xten
ded no
de.
f.
A
ll
proces
ses
from
1
to
4
a
r
e
re
peated
unt
il
the
sin
k
is
detect
ed
t
hen
al
l
pack
et
s
are
sent
t
hro
ugh
the
op
ti
m
al
p
at
h
to
ward t
he
sin
k.
The flo
wch
a
rt
of the
SMORP
in WS
N
is s
ho
wn in Fi
gure
4.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
11
, No
.
3
,
J
une
2021
:
24
32
-
2442
2438
Figure
4. Flo
w
ch
a
rt of the
S
MORP f
or
WSNs
5.
PERFO
R
MANC
E
E
V
ALU
ATIO
N
Fo
r
eval
uatin
g
the
ef
fectuali
ty
of
the
SMOR
P
bala
ncin
g
c
onsu
m
ption
of
e
nergy
an
d
m
axi
m
izing
the
li
fetim
e
of
the
netw
ork
a
re
ta
ken
i
nto
c
onsiderati
on
a
nd
resu
lt
s
of
t
he
si
m
ulati
on
f
or
the
s
uggeste
d
ar
e
com
par
ed
with
these
of
the
two
well
-
kn
own
ap
proac
hes,
nam
ely
LEACH
[25]
an
d
PE
GASIS
[26].
T
he
three
m
et
ho
ds
inclu
de
the
sam
e
m
et
rics
of
r
outi
ng
nam
ely,
the
sh
ort
est
hop,
t
he
r
esi
dual
en
erg
y,
an
d
t
he
t
raffic
load
thr
ough
a
searc
h
of
the
op
ti
m
al
path
f
r
om
the
node
of
the
s
ource
t
o
the
node
of
th
e
sin
k
.
The
sy
stems
si
m
ulati
on
pr
oc
esses are
car
ried ou
t i
n
M
AT
LAB sim
ulator
softwa
re.
5.1.
Simul
at
i
on
set
up
The
sim
ulati
ons
are
ca
rr
ie
d
out
by
us
i
ng
MATL
AB.
A
hu
ndre
d
nodes
of
the
se
nsor
are
diffuse
d
i
n
a
topogra
ph
ic
al
area
that
has
of
(
10
0
m
×10
0
m
)
di
m
ension
.
Dep
loym
ents
of
nodes
are
done
rand
om
l
y.
The
topogra
ph
ic
al
area
has
the
se
ns
e
d
transm
issi
on
lim
it
ed
to
(
20
m
).
The
perform
ance
te
sts
of
al
l
appro
ac
he
s
are
done
in
this
ki
nd
of
to
pograp
hical
area.
T
he
sink
of
data
is
base
d
at
(
90
m
,
90
m
).
The
ini
ti
al
ener
gy
i
s
(
0.5
J
)
for
al
l n
odes
of the se
nsor
in
t
he netw
ork.
The
m
od
el
of
t
he
first
-
or
der
r
adio
wh
ic
h
is
la
rg
el
y
ap
plied
to
the
r
ou
ti
ng
protoc
ol
eval
ua
ti
on
area
i
n
WSNs
[25]
is
al
so
f
ollo
wed
in
al
l
ap
proac
he
s.
A
s
fa
r
as
the
m
od
el
of
t
hi
s
stud
y
is
c
on
cern
e
d,
recei
vin
g
an
d
transm
issi
on
costs
are
cha
r
act
erized
by
the
ex
pr
essi
ons
E
n
T(k)
=E
elec
k+E
amp
k.
d
2
and
E
n
R(
k)
=E
elec
k
,
resp
ect
ively
,
w
her
e
k
is
t
he
num
ber
of
bit per
pac
ket,
d
is
th
e
distance
f
rom
the
no
de
of
the
se
nder
to
t
he
no
de
of
the
receive
r
,
E
elec
a
nd
E
amp
are
pe
r
bit
en
erg
y
dissipati
on
in
recei
ving
or
tran
sm
i
tt
ing
ci
rcu
it
ry
a
nd
energ
y
necessit
at
ed
pe
r
bit
pe
r
squ
are
m
et
er
fo
r
the
am
plifie
r
t
o
yi
el
d
reas
onable
signa
l
to
no
ise
r
at
io
(
SN
R
)
resp
ect
ively
.
S
i
m
ulati
on
s
are
done
ap
plyi
ng
the
val
ues
50
nJ
/
bit
an
d
1
00
pJ
/
bit/m
2
f
or
E
elec
a
nd
E
am
p
,
resp
ect
ively
.
T
he
traff
ic
loa
d
is
su
ppos
e
d
to
be
pro
du
ce
d
in
a
ran
dom
way
hav
in
g
[
0.
.
.10
]
ran
ge
of
valu
es
in
ever
y
node
. Ta
ble 1 il
lustrate
s
the syste
m
p
ar
a
m
et
ers
in det
ai
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
Sp
i
der m
on
key
opti
miza
ti
on r
ou
ti
ng
protoc
ol
for
wi
rel
ess s
ens
or
networks
(Al
i H.
J
abbar
)
2439
Table
1
.
Sim
ul
at
ion
p
aram
et
e
rs
Para
m
eter
Valu
e
Top
o
g
raph
ical Ar
e
a (
m
eters
)
1
0
0
m
x 1
0
0
m
Sin
k
locatio
n
(
m
et
ers)
(90
,
9
0
)
Nu
m
b
e
r
o
f
no
d
es
100
Li
m
it
of
tr
an
s
m
iss
i
o
n
dis
tan
ce (m
ete
r
s)
2
0
m
Initial en
ergy
of
no
d
e
0
.5 J
Eelec
5
0
nJ
/b
it
Ea
m
p
1
0
0
pJ
/b
it/m
2
Pack
et data size
2
k
bit
Nu
m
b
e
r
o
f
trans
m
i
ss
io
n
pack
ets
2
x 1
0
3
Maxi
m
u
m
tr
af
f
ics
in
no
d
e’s qu
eu
e
10
5.2.
Simul
at
i
on
re
sults
The
li
ve
node
nu
m
ber
for
e
ver
y
tran
sm
iss
ion
r
ound
ap
pl
yi
ng
three
different
m
et
ho
ds
is
sta
te
d
in
Figure
5.
T
he
su
ggest
e
d
SM
ORP
kee
ps
m
or
e
li
ve
node
s
than
t
he
ones
ke
pt
by
both
P
E
GASIS
a
nd
L
EACH
appr
oach
es
a
fter
the
sam
e
nu
m
ber
of
a
pac
ket
is
transm
itt
ed.
Wh
e
ne
ver
al
l
the
20
00
pa
ckets
are
se
nt
inside
the
area,
the
li
f
et
i
m
e
of
network
ac
hieve
d
by
the
sugg
est
e
d
m
et
ho
d
is
ab
out
50%
m
or
e
than
t
hat
acqu
i
r
ed
by
PEGAS
IS
a
nd
near
ly
60
%
m
or
e tha
n
t
hat obt
ai
ned
by
LE
A
CH
.
Also
,
i
n
Fig
ur
e
5,
one
can
no
t
ic
e
that
the
su
gg
e
ste
d
m
et
ho
d
kee
ps
the
nu
m
ber
of
li
ve
node
s
al
ways
higher
t
han
t
ha
t
in
both
P
EG
AS
I
S
a
nd
L
EA
CH
ap
proac
he
s.
The
dif
fer
e
nc
e
in
th
e
durati
on
of
tim
e
relevan
t
t
o
the
fi
rst
dea
d
node
c
om
pu
te
d
a
pp
ly
in
g
t
he
three
di
ff
e
ren
t
ap
proac
hes
is
sta
te
d
in
Table
2.
It
rem
ark
ab
le
that
the o
cc
urre
nce
of the
first
node
d
eat
h i
n
t
he
s
uggeste
d
m
et
ho
d i
s m
uch
lat
er th
a
n
t
hat in ot
her ap
proac
hes
.
Figure
5. The
ra
ti
o
of se
nsors
sti
ll
ali
ve
in all
appr
oac
hes
Table
2
.
First
de
ad
se
nsor
b
as
ed on al
l ap
pro
aches
Ap
p
roach
es
LE
ACH
PEGAS
IS
SMORP
Lif
eti
m
e of
the f
irs
t dead
sen
so
r
(Ro
u
n
d
s)
78
246
1820
Give
n
Fi
gure
5
a
nd
Ta
ble
2,
one
ca
n
dedu
ce
that
the
sug
gested
m
et
ho
d
is
m
or
e
effe
ct
ive
tha
n
bo
t
h
appr
oach
es
of
PEG
A
SI
S
&
LEACH
in
ba
la
ncing
the
c
on
s
um
ption
of
ener
gy
an
d
e
xten
ding
of
ne
twork
li
fetim
e.
A
W
S
N
ave
rag
e
r
esi
du
al
ene
r
gy
de
creases
with
th
e
transm
issi
on
rou
nd
s r
ise
num
ber
.
As
the
num
ber
of
delive
re
d
pa
ckets
inc
reas
es,
the
res
ults
of
the
sug
gested
m
et
ho
d
in
values
tu
rn
to
be
of
hi
gher
a
ver
a
ge
resid
ual
ene
rgy
than
that
of
bo
t
h
PE
G
AS
I
S
an
d
LE
ACH
appr
oach
e
s.
F
igure
6
i
nd
ic
at
es
that
the
balance
of
bette
r
e
nergy i
n
a
WSN is
ac
hieve
d by the
s
uggeste
d
m
et
ho
d.
The
delay
res
ulted
f
ro
m
the
transm
issi
on
of
data
pac
ket
s
is
a
sign
ific
ant
pa
ram
et
er
for
spe
ci
fic
app
li
cat
io
ns
w
her
e
data
se
nse
d
is
required
t
o
be
colle
ct
ed
in
a
sh
ort
tim
e.
The
three
dif
f
eren
t
ap
proac
he
s
are
com
par
ed
in
Fi
gure
7.
Also,
t
he
s
uggeste
d
S
MO
RP
ha
s
the
shortest
delay
wh
e
n
c
om
par
ed
to
t
he
delay
in
the
oth
e
r
a
pproach
es
as
s
how
n
i
n
Fi
g
ure
8.
T
he
shorte
r
delay
i
m
plici
tly
indi
cat
es
energy
-
s
avin
g
a
nd
ef
fe
ct
ive
transm
issi
on
(
e
sp
eci
al
ly
f
or si
gn
i
ficant a
nd
s
ecur
e i
nfor
m
at
i
on).
I
n
pa
rtic
ul
ar, packets
o
f d
at
a are se
nt throug
h
paths
of
diff
e
r
ent
node
-
dis
joint
routin
g
wit
h
m
ulti
-
path
r
ou
ti
ng
to
a
vo
i
d
net
work
ove
rcrowdin
g
a
nd
exten
d
the li
fetim
e o
f t
he
net
wor
k.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
11
, No
.
3
,
J
une
2021
:
24
32
-
2442
244
0
Figure
6. Ra
ti
o
rem
ai
nin
g
e
ne
rg
y
of se
ns
ors i
n
al
l ap
proac
he
s
Figure
7. Sim
ulati
on
ti
m
e
in all
ap
proac
hes
Figure
8. En
d
-
to
-
e
nd
delay
in a
ll
ap
proac
hes
6.
CONCL
US
I
O
N
In
WSNs,
no
de
s
are
power
e
d
by
lim
it
ed
batte
ry
energy.
H
ence,
it
is
sig
ni
ficant
to
c
hoose
strat
egies
that
ef
fecti
vely
us
e
the
existi
ng
e
nergy.
Me
t
hods
of
r
ou
ti
ng
path
fin
dings
ha
ve
a
hi
gh
ef
fe
ct
on
the
li
feti
m
e
of
the
net
wor
k
a
nd
this
is
one o
f
the p
rim
e
fe
at
ur
es
of WSNs.
Un
e
ve
n
dr
ai
na
ge
of
e
ne
rg
y
is
an
i
nh
e
re
nt
pr
ob
le
m
inside
a
WSN.
For
achie
ving
tran
sm
issi
on
of
ef
fecti
ve
da
ta
thr
ough
t
he
pat
h
of
r
outi
ng
c
ho
se
n
to
be
a
n
op
ti
m
u
m
path
to
m
axi
m
iz
e
the
total
li
feti
m
e
of
the
net
work
with
dec
reasin
g
t
he
de
la
y
res
ulted
f
r
om
the
process
of
pat
h
fi
nd
i
ng,
a
m
od
e
r
n
m
et
ho
d
cal
le
d
sp
ide
r
m
on
key
op
ti
m
iz
at
ion
r
ou
ti
ng
protoc
ol
(
SMORP)
i
s
su
ggest
e
d
i
n
th
is
stud
y.
T
his
m
od
ern
m
et
hod
is
ca
pa
ble
of
fin
ding
a
path
of
op
ti
m
al
ro
ut
ing
t
o
be
us
e
d
in
t
he
transm
issi
on
of
data
from
th
e
node
of
s
ourc
e
towa
rd
the
si
nk
inclu
ding
a
m
edium
no
de
or
node
s
by
c
hoos
i
ng
on
e
s
with
t
he
highest
rem
ai
nin
g
e
nergy,
m
i
nim
a
l
com
bin
ed
ho
ps
,
a
nd
le
ast
pendin
g
tra
ff
ic
.
C
om
par
in
g
the
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
Sp
i
der m
on
key
opti
miza
ti
on r
ou
ti
ng
protoc
ol
for
wi
rel
ess s
ens
or
networks
(Al
i H.
J
abbar
)
2441
su
ggest
e
d
m
eth
od w
it
h
the o
t
her
t
wo
m
et
hods
,
t
he
res
ults
s
how
th
at
the per
f
or
m
ance
of the
pro
pose
d
m
et
ho
d,
accor
ding
t
o
th
e
sam
e
crit
eria
,
is
m
uch
be
tt
er
tha
n
t
hat
of
t
he
tw
o
m
et
ho
ds
re
gardin
g
t
he
li
fetim
e
of
net
wor
k
and tra
ns
m
issio
n delay
.
REFERE
NCE
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ss
Sensor
Networks
Us
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ta
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Li
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Algorit
hm
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arche
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ene
rgy
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eff
ic
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ero
gene
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le
ss
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”
Indone
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Journal
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ec
tric
al
Engi
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Li
fet
ime
Enha
nce
m
ent
in
W
ire
le
ss
Sensor
Networks
Us
ing
Fuzzy
Approac
h
and
A
-
Star
Algorit
hm
,
”
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I.
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AlShawi,
L.
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W
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Pan,
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B.
Luo,
“
A
Fuzzy
-
Gos
sip
routi
ng
protoc
ol
for
an
ene
rgy
eff
ic
ie
nt
wire
le
ss
sensor ne
tworks,
”
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ipe
i,
Ta
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pp.
1
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sw
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“
Ene
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ic
ie
nt
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ng
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ss
sensor
net
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base
d
o
n
m
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guide
d
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stic
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l
cl
imbing,
”
Inte
r
nati
onal
Journal
of
El
ec
tric
al
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Computer
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Q.
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dina
te
assignm
ent
protoc
ol
and
del
ive
ry
-
guar
ant
ee
d
routi
ng
protoc
ol
in
wire
le
ss
sensor
net
works
,
”
in
IEE
E
INFOCOM
2007
-
26th
IEE
E
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rnational
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ati
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“
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m
axi
m
um
li
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ime
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ng
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ss
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”
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ime
m
axi
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iz
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protoc
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”
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ic
ie
nt
cl
usteri
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ng
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iz
at
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m
odel
for
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axi
m
iz
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li
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ime
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le
ss
sensor
net
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”
Inte
rnational
Journal
of
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ec
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Computer
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le
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”
Inte
r
nati
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ere
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i
r
e
l
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n
s
o
r
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e
t
w
o
r
k
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b
y
b
i
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g
e
o
g
r
a
p
h
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b
a
s
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d
o
p
t
i
m
i
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a
t
i
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a
l
g
o
r
i
t
h
m
,
”
J
o
u
r
n
a
l
o
f
S
e
n
s
o
r
a
n
d
A
c
t
u
a
t
o
r
N
e
t
w
o
r
k
s
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v
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,
p
p
.
8
6
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96
,
2012.
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G.
Hu,
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F.
Yu,
“
Ene
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‐awa
re
geogr
aphi
c
routi
ng
in
wire
le
ss
sensor
net
works
with
anc
hor
nodes,
”
Inte
rnational
Journal
of
Comm
unic
ati
on
Syste
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,
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100
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Alshawi,
“
Bal
anc
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Ene
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Consum
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on
in
W
i
r
e
l
e
s
s
S
e
n
s
o
r
N
e
t
w
o
r
k
s
U
s
i
n
g
F
u
z
z
y
A
r
t
i
f
i
c
i
a
l
B
e
e
C
o
l
o
n
y
R
o
u
t
i
n
g
P
r
o
t
o
c
o
l
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
M
a
n
a
g
e
m
e
n
t
&
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