TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 12, No. 8, August 201
4, pp. 6267 ~ 6273
DOI: 10.115
9
1
/telkomni
ka.
v
12i8.553
2
6267
Re
cei
v
ed
De
cem
ber 3
0
, 2013; Re
vi
sed
April 5, 2014;
Accept
ed Ap
ril 20, 2014
A Novel Clustering Routing Protocol in Wireless
Sensor Network
Wu Rui, Xia Ke
w
e
n*, Bai
Jianchu
a
n, Zhang Zhi
w
e
i
Schoo
l of Information En
gi
ne
erin
g, Heib
ei U
n
iversit
y
of T
e
chno
log
y
,
Beich
en Distrct
,
T
i
anjin, Ch
ina
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: k
w
xi
a@h
e
b
u
t.edu.cn
A
b
st
r
a
ct
T
he phe
no
me
non of n
e
tw
ork resource-c
o
n
strain
ed ofte
n app
ears i
n
w
i
reless sens
o
r
netw
o
rk
(W
SN) becaus
e of nod
es w
i
th energy-
l
i
m
ite
d
,
so it bec
o
m
es
a researc
h
top
i
c to study on h
i
gh-
perfor
m
a
n
c
e
routin
g protoc
ol in w
i
re
less
sensor n
e
tw
ork. In a
ccorda
n
c
e w
i
th the ch
aracteristic of
qua
ntu
m
parti
cle
sw
arm opti
m
i
z
ation (QPSO),
a nov
el cl
uster
i
ng ro
utin
g pro
t
ocol w
i
th
QPSO
algorit
hm is prop
osed bas
e
d
on the
low
e
nergy
ada
ptiv
e clusteri
ng
h
i
erarchy (
L
EA
CH), which includes sever
a
l steps, such
as
deter
mi
nin
g
th
e n
u
mber
of
cluster-h
ead,
preli
m
in
ary
cl
u
s
tering,
opti
m
i
z
a
t
i
o
n
on
te
mporary c
l
uster
by
QPSO algorith
m
, clusteri
ng af
ter optimi
z
a
t
i
o
n
,
and so
on. C
o
mpar
ative an
alysis on si
mul
a
tion ex
peri
m
e
n
ts
show
that the
prop
osed
ro
uti
ng
prot
oco
l
is
super
ior to
tha
t
of LEA
CH,
a
nd s
a
ves
ener
gy co
nsu
m
pti
o
n of
netw
o
rk excell
ently an
d ba
lan
c
es ener
gy of nod
es so as to prol
ong th
e life
of the netw
o
rk.
Ke
y
w
ords
: w
i
reless se
nsor n
e
tw
ork, clustering routi
ng
pr
otocol, qu
antu
m
particl
e sw
arm
opti
m
i
z
at
ion
Copy
right
©
2014 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
The
advan
ce
s in
wirel
e
ss co
mmuni
cat
i
on te
chn
o
lo
gy, elect
r
oni
c te
chn
o
logy
, sen
s
o
r
techn
o
logy a
nd micro-ele
c
trom
echani
cal syst
em
s (MEMS) has
prom
oted the
developme
n
t
o
f
w
i
r
e
le
ss se
ns
o
r
ne
tw
or
k
(
W
S
N
)
a
p
p
l
ic
a
t
io
ns
a
n
d
r
e
se
ar
ch
. W
i
r
e
les
s
s
e
ns
or
ne
tw
or
k is a
netwo
rk com
posed of
n
u
merou
s
se
nso
r
n
ode
s whi
c
h
hav
e the p
o
we
rs
of pe
rce
p
tion
,
comm
uni
cati
on and comp
uting, it is conne
cted by
wirele
ss m
ode,
and it has a wide ap
plication
pro
s
pe
ct i
n
many field
s
[
1
, 2]. As the
importa
nt
pa
rt of the
network laye
r, the
network
rout
ing
deliver th
e inf
o
rmatio
n fro
m
the
sou
r
ce
nod
e to
n
e
twork, a
nd it’
s
the efficie
n
t
comm
uni
cati
on of
the network. Due to the li
mitations of t
he ha
rd
ware
whi
c
h ha
s th
e wide
dist
rib
u
tion of nod
e
s
,
limited en
erg
y
[3], the small mem
o
ry
, the ba
d
co
mputing
po
wer a
nd
na
rro
w b
and
width
in
wirel
e
ss se
n
s
or n
e
two
r
ks,
the
routin
g
algorith
m
s u
s
ed in
ma
ny tradition
al fixe
d-net
wo
rk an
d
mobile self-o
rgani
zing
n
e
twork ca
n
not
b
e
effectively use
d
in wi
rel
e
ss sensor n
e
twork [4]. In this
ca
se, it ha
s
extremely im
portant
signifi
can
c
e to
th
e
prop
osed
ro
uting pr
otoco
l
of low e
nergy
adaptive
clu
s
terin
g
hie
r
a
r
chy
(LEACH) [5], which define
s
th
e "rou
nd"
co
nce
p
t and
al
lows
different nod
es be
com
e
the clu
s
ter-he
ad with
the random p
r
ob
a
b
ility P in different time, the
relay com
m
u
n
icatio
n busi
ness is avera
ge sha
r
e
d
in WSN. Co
mp
ared to the same plan
e ro
uting
proto
c
ol
s, LE
ACH protoco
l
im
proves the ove
r
all
p
e
rform
a
n
c
e
of the n
e
two
r
k and
bal
an
ce
s
energy co
nsumption of n
ode
s.
Base
d
on LEACH, this pap
er p
r
opo
se
s the i
m
provem
ents to
solve ina
deq
uaci
e
s of LE
ACH.
Sun Ju
n et
al [6] prop
oses a
kin
d
of
quantum pa
rticle swarm optimizatio
n (QPSO)
algorith
m
based on qua
ntu
m
comp
uting,
due to the
particle can ap
p
ear any po
siti
on in the entire
feasibl
e
search
spa
c
e
with a certain
p
r
oba
bilit
y, and ha
s a bett
e
r fitness val
ue, so th
e gl
obal
sea
r
ch pe
rformance for
Q
PSO algo
rith
m is
sup
e
ri
o
r
to that of PSO algo
rithm
.
The state
of
particl
e i
s
o
n
l
y descri
bed
with the
po
si
tion vecto
r
,
and
only o
n
e
pa
ram
e
ter
need
s t
o
be
determi
ned in
QPSO algori
t
hm, so the Q
PSO algorith
m
has mo
re a
d
vantage
s:
(1) T
he few p
a
ram
e
ters, si
mple progra
mming an
d e
a
sy to imple
m
ent;
(2) T
he ra
pid
conve
r
ge
nce, and qui
ckly find t
he optima
l
solution in th
e global
scop
e.
Therefore,
a
c
cordi
ng to t
he adva
n
tag
e
s o
r
cha
r
a
c
teristics of
the
QPSO al
gorithm,
literature is replete with
appli
c
ation
s
of QPSO
in WSNs [7]. Low en
ergy awa
r
e cl
uste
ring
hiera
r
chy (LE
A
CH) i
s
a
si
mple
and
efficient
algo
rith
m. Clu
s
teri
ng
is an
NP
-ha
r
d
optimizatio
n
probl
em[8], whi
c
h QPSO
can
ha
ndle
efficiently. Cl
usteri
ng
or
cl
uster-h
ead
selectio
n is n
o
t a
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ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 626
7 –
6273
6268
one-tim
e
acti
vity;
therefore, the simpl
e
r the
o
p
timization
algo
rithm, the better the n
e
two
r
k
efficien
cy is.
This i
s
a
n
re
aso
n
why QP
SO is
a effici
ent ch
oice fo
r WS
N
clu
s
te
ring. Thi
s
pa
per
prop
oses
a novel WS
N clu
s
terin
g
ro
uting protoco
l
combi
n
ing
wi
th LEACH, which is u
s
ed i
n
actual a
ppli
c
a
t
ion. The mai
n
work o
r
inn
o
vation is a
s
follows.
(1) T
he n
u
m
ber of fixed cluster-h
ead i
s
at 5% of the total numbe
r of sen
s
o
r
no
des in
LEACH [6], which i
s
only e
x
perime
n
tal d
a
ta without a
n
y basi
s
. Thi
s
pap
er o
p
tim
i
ze
s the num
ber
of clu
s
ter-he
ad ba
sed
on
the mathem
atical mo
del
of the physi
cal image
sen
s
or
network, and
dynamically adjust
s
the pro
portion
so a
s
to
decrea
s
e the network e
nergy con
s
u
m
ption.
(2) Th
e ene
rgy con
s
umpt
ion betwe
en
the nodes i
s
only balan
ced by clu
s
t
e
ring in
LEACH. On the basi
s
of L
EACH,
the propo
sed routin
g proto
c
ol in this pape
r ca
n rebal
an
ce the
energy of ea
ch n
ode in
cl
uster, a
nd eff
e
ctively
avoid
the eme
r
ge
n
c
e of a l
o
t of blind no
de
s a
nd
further p
r
olo
n
g
the netwo
rk lifetime.
2. Principle
of QPSO Alg
o
rithm
2.1. Algorith
m
O
v
er
v
i
e
w
Quantum p
a
rticle swarm optimizatio
n (QPSO
) al
go
rithm is the
improvem
ent
of the
cla
ssi
cal PS
O algo
rithm
[5], the velocity and
po
si
tion of pa
rticle are cl
osel
y related
wit
h
a
para
m
eter
in the QPSO
algorith
m
. To
ensure the
c
onve
r
ge
nce
of the algo
rit
h
m, a me
dia
n
optimal p
o
siti
on i
s
a
dopte
d
to
cal
c
ulate
the n
e
xt
iterative variabl
e
for p
a
rti
c
le,
whi
c
h i
s
defi
ned
as th
e ave
r
a
ge value
of
person
a
l b
e
st positio
ns fo
r all
parti
cle
s
(
mbes
t
). The fo
rmula fo
r
mbes
t
is
as
follows
:
1
11
1
11
1
,,
MM
M
ii
i
d
ii
i
mb
es
t
P
P
P
MM
M
（）
(1)
Her
e
,
M
is the
numbe
r of
pa
rticle
s,
i
P
is the
perso
nal b
e
st position
of the i-th
pa
rticl
e
in
d
-dime
n
si
on
spa
c
e.
And the parti
cle evolutio
n equatio
n ca
n be obtain
ed a
s
follows:
12
1
2
()
/
(
)
id
i
d
g
d
PP
P
(2)
(1
(
)
l
n
(
1
/
)
id
id
X
tP
m
b
e
s
t
X
t
u
）
(3)
Her
e
,
id
P
is d-th dimensi
on coordi
nate of i-th particl
e,
g
d
P
is the global b
e
st positio
n in
all parti
cle
s
,
id
X
is d-th di
men
s
ion
coo
r
di
na
te of i-th
parti
cle in p
a
rticl
e
quantum p
a
rticle swa
r
m
spa
c
e;
12
,,
u
are
the
rand
om
numb
e
r bet
wee
n
0
an
d
1;
is
cont
ractio
n-expan
sion
coeffici
ent, whose role is t
o
control the spe
ed of con
v
ergen
ce, wh
en
0.5
u
, the formula (3)
t
a
ke
s “
”, otherwi
se takes
“
”.
The algo
rithm
steps fo
r QPSO are a
s
follows:
Step 1: Initialize e
a
ch qua
ntum pa
rticle
positio
n
ii
1
i
2
i
d
,,
,
X
xx
x
in
d
-
d
i
me
ns
ion
spa
c
e.
Step 2: Evalu
a
te the fitne
s
s valu
e of p
a
r
ticle. If the fit
ness
i
f
x
in
cu
rrent po
sition i
s
bigge
r than t
he fitness
i
f
pbe
st
of the pa
st best
position, the
n
ii
p
be
st
x
; then co
m
pare
the fitness v
a
lue
s
of all
curre
n
t pa
rticles, if
i
f
x
is bi
g
ger th
an th
e
fitness of gl
obal b
e
st
positio
n(
f
gbe
s
t
), the global optim
al solutio
n
is
i
gbe
s
t
x
.
Step 3: Updat
e the quantu
m
particl
e swarm in
a
c
cord
ance with formula (2
) and
(3).
Step 4: Che
c
k whethe
r t
he e
nd
con
d
i
tion is satisf
ied o
r
n
o
t. If it is m
e
t, Stop the
iteration; othe
rwi
s
e, retu
rn t
o
Step 2 to continue the it
eration.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Novel
Clu
s
tering
Routin
g
Protocol in
Wirel
e
ss Sen
s
or
Network (Wu Rui)
6269
2.2. Application and Anal
y
s
is on QPSO Algorithm
In orde
r to verify the validity of QPSO algorithm,
we com
pare th
e QPSO, Basic PSO
(BPSO) and Genetic
Algorithm
(GA)
by c
o
mput
er s
i
mulation.
The following c
o
mmon tes
t
function i
s
sel
e
cted fo
r com
pari
s
on.
Sphere functi
on:
2
1
()
n
i
i
f
xx
, find a minimum value
in the rang
e [-5,5].
The maximu
m numbe
r of iteration
ma
x
ite
r
is set to 100 times for three al
gorithm
s, the
siz
e
M
of the
p
a
rticle
swarm
is 10, th
e n
u
m
ber of
parti
cle
dimen
s
io
n (
d
) i
s
2,
is
0.7
5
.
T
h
e
cro
s
sove
r pro
bability is 0.9
5
and the mut
a
tion pro
babil
i
ty is 0.05 in GA.
For the
sa
ke
of compa
r
i
s
on, the perfo
rman
ce in
dicator for eval
u
a
tion is
set a
s
mea
n
s
q
uare error,
whic
h is
as
follows
:
2
1
1
ˆ
()
n
ii
i
ex
x
n
(4)
Her
e
,
i
x
is the actual o
u
tput value,
ˆ
i
x
is the desi
r
ed o
u
tpu
t
value.
The erro
r e repre
s
e
n
ts th
e mean squa
re erro
r between the iterat
ion re
sult in functio
n
optimizatio
n
and the
true
value, which displays
t
h
e optimi
z
atio
n preci
s
ion
of the alg
o
rit
h
m.
Figure 1 sh
o
w
s the ite
r
ation er
ror
cu
rve of Sphere f
unctio
n
.
I
t
er
at
i
o
n
er
r
o
r
I
t
e
r
a
t
ion times
Figure 1. The
Iteration Erro
r
Cu
rve for Sphere Fun
c
tio
n
From Fi
gure
1, the GA and BPSO algorithm
still maintain a big
er
ror value after 70
iteration time
s, but the e
r
ror
curve
of QPSO
algo
rit
h
m de
crea
se
s rapidly with
the numb
e
r
of
iteration time
s in
crea
sing.
After
42th ite
r
ation time
s, t
he e
rro
r valu
e be
come
s
small and
sta
b
l
e.
Among them,
erro
r e
=
2.73
85×10
-2
wh
en
iteration time
s at 73 for G
A
, error e
=
1.
6582
×1
0
-3
wh
en
iteration times at 53 for BPSO algo
rithm, and the error e=1.8578×10
-7
whe
n
itera
t
ion times at
4
5
for QPSO algorithm. Obviously, QPSO algorithm
is far superior to
GA and BPSO algorithm
in
the conve
r
ge
nce
spe
ed an
d optimizatio
n pre
c
isi
on.
3. Cluste
ring
Routing Pro
t
ocol Based
on QPSO
3.1. Net
w
o
r
k
Modeling
We
adopt th
e wi
rele
ss communi
catio
n
sy
stem mo
del in
refe
re
nce
[10] to
calcul
ate,
whi
c
h is
con
s
tituted by the transmissio
n circuit,
po
wer amplifier, an
d the re
ceivin
g circuit. Wh
e
n
the b
bit data
in tra
n
smissi
on p
o
rt i
s
tra
n
smitted to
th
e re
ceivin
g p
o
rt throug
h th
e di
stan
ce d,
the
energy con
s
u
m
ption by tra
n
smitting is a
s
follows:
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TELKOM
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Vol. 12, No. 8, August 2014: 626
7 –
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6270
2
10
4
20
Tx
e
l
e
c
Tx
e
l
e
c
E
bE
bu
d
d
d
E
bE
bu
d
d
d
(5)
The ene
rgy consumption b
y
receiving i
s
as follo
ws:
Rx
elec
Eb
E
(6)
Her
e
,
2
01
2
/
du
u
is th
e
critical
value
of dista
n
ce. T
he e
nergy co
nsum
ption f
r
o
m
the
transmitting circuit to receiving circuit
is
5
0
n
J
/b
it
ele
c
E
, the en
ergy co
nsum
ption of the
amplifier ci
rcuit
is
2
1
10pJ/b/m
u
and
4
2
0.0013pJ/b/m
u
, the en
ergy co
nsum
ption
of data
fusion i
s
5nJ/
b
it
F
E
.
Assu
me th
at
there i
s
N
no
des which a
r
e di
st
ribute
d
i
n
the
sq
uare
area
of
side
l
ength A
with Poi
ssi
on
distri
bution
o
f
distrib
u
tion
den
sity
2
/
NA
, aggregation
nod
e
(Sink) i
s
lo
cated
outsid
e
the
di
stributio
n a
r
e
a
, radi
us
of p
r
opa
gation
ra
nge
of nod
es is
r
, clusteri
ng probability is
P
, final cluste
ri
ng num
ber i
s
KN
P
. Distrib
u
tion
den
sity of cluster-h
ead i
s
1
P
, and
distrib
u
tion d
ensity of ordi
nary no
de is
0
1-
P
（）
. If
0
and
1
are
each inde
pen
dent, we
can
get th
e
numbe
r
[]
1
/
c
E
nP
P
of
membe
r
n
o
d
e
s i
n
e
a
ch
cl
uster,
and
th
e average
distan
ce fro
m
cluste
r mem
bers to clu
s
te
r-h
ead i
s
1
1/
to
C
H
d
.
Above mathe
m
atical mo
de
l is establi
s
h
ed,
we can g
e
t the total energy
con
s
u
m
ption of
WSN [9]:
2
4
1
2s
i
n
2(
1
)
(
)
to
ta
l
e
l
e
c
F
to
k
e
le
c
ub
A
N
Eb
N
E
b
E
N
K
b
u
d
E
K
(7)
Whe
r
e
sin
to
k
d
is the distan
ce bet
wee
n
clu
s
ter-head a
nd ag
g
r
egatio
n nod
e
(
Sink).
Formul
a
(7) i
s
the
fun
c
tion
with
an in
de
pend
ent vari
able
K
, then u
s
ing
the m
e
thod
of
Lapla
c
e extre
m
um, let
0
total
E
K
, and we can get
as follo
ws:
1
4
2s
i
n
()
to
k
e
l
e
c
u
K
AN
ud
E
(8)
From fo
rmula
(8), it is o
b
vious th
at the ideal cl
uste
rin
g
numb
e
r
K
is
related to
si
ze of a
given are
a
an
d the numbe
r
of the sensor node
s, not
N
*5% in LEACH protocol.
3.2. The Specific Steps o
f
Clus
tering
Rou
t
ing Protocol
(1)
Determina
t
ion of the number of cl
ust
e
r-hea
d
To dete
r
mine
the clu
s
terin
g
numb
e
r
K
base
d
on the
mathemati
c
al model a
n
d
the
formula (8), then get cl
ust
e
ring p
r
o
babil
i
ty
/
PK
N
.
(2) Pr
elimina
r
y
cluste
ring
Firstly, the netwo
rk
sho
u
ld be initial
i
z
ed an
d prelimina
r
y clu
s
tere
d with
LEACH,
determi
ning t
he temp
ora
r
y
clu
s
ters
and
clu
s
ter- he
ad.
At the sa
me
time, the tem
pora
r
y cl
uste
r-
head
colle
cts the state inf
o
rmatio
n of each mem
b
e
r
in cl
uste
r, inclu
d
ing lo
ca
tion inform
atio
n
(sp
a
tial co
ord
i
nates) and e
nergy info
rma
t
ion.
(3) Optimiz
e
t
he temporary
c
l
us
ter with t
he QPSO algorithm
The po
pulatio
n size of qua
ntum parti
cle
s
are initialize
d
with clu
s
te
r numbe
r
K
, and the
spatial
coo
r
d
i
nates of the
sen
s
o
r
nod
es corre
s
po
n
d
to the po
sition of qu
a
n
tum parti
cle
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Novel
Clu
s
tering
Routin
g
Protocol in
Wirel
e
ss Sen
s
or
Network (Wu Rui)
6271
Becau
s
e the
spatial co
ordinate
s
of the sen
s
o
r
no
des a
r
e ra
n
dom and di
screte, qu
ant
um
particl
e'
s p
o
si
tion shoul
d b
e
adj
uste
d af
ter pa
rtic
l
e
u
pdating
with
Step 3, which is adj
uste
d
by
the minimum
Euclide
an
distan
ce m
e
thod in th
i
s
pape
r, that mean
solvin
g the Eucli
d
ean
distan
ce
s bet
wee
n
up
date
d
qu
antum p
a
rticle
s and
all sen
s
or no
des,
and
po
sition of qu
ant
um
particl
e i
s
a
d
j
usted
to sp
atial co
ordin
a
te valu
e
s
of the nea
re
st
se
nsor
nod
e.Then th
e t
o
tal
energy con
s
u
m
ption of WS
N ca
n
be calculated on Eq
uation (7
).
To d
e
termi
n
e
the fitne
s
s fu
nction
is the
most im
po
rta
n
t thing
in th
e
stag
e. Ba
se
d on
the
mathemati
c
al
model and t
he equ
alization req
u
irem
e
n
ts, the fitness valu
e of cluster-h
ead n
o
t
only relate
s
to own
state
information,
but al
so
sh
ould be
affected by stat
e informatio
n
of
membe
r
no
d
e
s in
cluste
r.
Becau
s
e the
routing p
r
ot
ocol
with a single-hop tra
n
smi
ssi
on m
ode
(i.e., tran
smit
inform
ation
dire
ctly bet
ween
memb
er
node
in
clu
s
t
e
r
and
cl
uste
r-h
ead
no
de),
the
farther
dista
n
c
e the
clu
s
ter-hea
d no
de, the mo
re
relat
i
ve energy the membe
r
n
o
de in cl
uste
r, on
the co
ntra
ry, the less en
ergy the nea
r
membe
r
n
o
d
e
in clu
s
te
r. So the fitness fun
c
tion
ca
n be
establi
s
h
ed a
s
follows:
1
1
()
l
n
1
n
ji
i
i
ij
fj
e
e
nr
(9)
Her
e
,
,
0,
1
and
1
, represent the energy imp
a
ct facto
r
of the nod
e
j
and the
re
m
a
ining
nod
e i
n
clu
s
te
r respectively,
j
e
re
p
r
esents the
energy of th
e nod
e
j
,
i
r
rep
r
e
s
ent
s the distan
ce b
e
twee
n nod
e
i
and node
j
,
n
repre
s
e
n
ts
the numbe
r o
f
nodes in
clu
s
ter. The
more
suitabl
e
the cluste
r-h
ead no
de
s, the bigge
r the fitness functio
n
value.
The p
o
sitio
n
s of qu
antum
particl
es a
r
e
updat
e
d
with the
step
s of
QPSO. Th
e
pbe
sti
and
gbe
sti of
parti
cle i
s
co
nfirmed
a
c
cording to
the va
lue of fitne
s
s. The te
mpo
r
a
r
y clu
s
te
r-h
ea
d
can
be
optimi
z
ed
in
acco
rd
ance, an
d th
e form
al
clu
s
ter-hea
d in
thi
s
rou
nd i
s
de
termine
d
so
a
s
to achieve to
rebal
an
ce the
energy of no
des in
clu
s
ter.
(4)
Clu
s
terin
g
after optimiz
ation
The tempo
r
a
r
y cluste
r-he
ad bro
a
d
c
a
s
ts the inform
ation of formal clu
s
ter-he
ad to the
node
s i
n
clu
s
ter, th
en th
e cl
uste
ring
i
n
this
roun
d
and th
e d
e
te
rminatio
n of
clu
s
ter-he
ad
are
finis
h
ed.
4. Simulation and Analy
s
is
4.1. Simulation En
v
i
ronment
In the si
mulat
i
on expe
rime
nt, the wi
rele
ss se
n
s
or net
work
co
nsi
s
ts of 100
sen
s
o
r
no
de
s
with GPS po
sitioning fun
c
tion in the a
r
e
a
of 150m
×1
50m. The
dist
ance bet
wee
n
the Sink
(b
ase
station) n
ode
and the cen
t
er of the area is 150m.
, the contra
ction-expa
nsi
o
n coefficie
n
t,
values 0.75.
and
in the fitness fun
c
tion
value 0.6 a
n
d
0.4, re
spe
c
tively. The
numbe
r of
iteration
s
is 1
00.The
simul
a
tion paramet
ers fo
r
ra
dio model an
d ne
twork are given in Table 1.
Table 1. The
Simulation Param
e
ters
Parameter Value
Net
w
ork size
150m×150m
Base station loca
tion
X=75m,
y
=225m
Simulation round
450
Number of
node,
n
100
Cluster head
p
r
o
babilit
y
of LEA
C
H,
P
0.05
Initial energ
y
,
E
o
0.1J
Packet size
5000bit
Transceiver ener
g
y
,
E
el
e
d
50nJ/bit
Aggregation en
er
g
y
pe
r bit,
E
F
5nJ/bit
Multipath amplifier ener
g
y
,
u
2
0.0013pJ/bit/m
4
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 8, August 2014: 626
7 –
6273
6272
4.2. Simulation Res
u
lts a
nd Analy
s
is
First of all,
the simul
a
tion experim
ent
s a
nd compa
r
ative analysi
s
on
energy
con
s
um
ption
of network a
r
e carried
out
in a
c
cord
an
ce with th
e fo
rmula
(7),
Fig
u
re
2
sho
w
s t
h
e
relation
shi
p
b
e
twee
n network e
n
e
r
gy co
nsum
ption an
d the numbe
r of cluste
r-h
e
ad.
E
n
e
r
gy cons
um
p
t
i
o
n
(
J
)
N
u
mber
of clust
e
r
-
he
ad
Figure 2. The
Relation
ship
betwe
en the
Ener
gy Con
s
umption an
d the Num
b
e
r
of Node
s
From
Figu
re
2, there
is
a minimu
m i
n
the n
e
two
r
k en
ergy co
nsum
ption
when the
numbe
r of clu
s
ter-he
ad
K
=3, 4, and the theoretical result
K
=3.4318
cal
c
ulate
d
by the formula (8)
is ba
si
cally consi
s
tent
with the a
c
tual
value in
Fi
gu
re 2
.
It is cl
ear th
at the
minimization
on
netwo
rk e
n
e
r
gy con
s
umpti
on ca
n be a
c
hieved by
opt
imization of the numb
e
r of
cluste
r-hea
d.
En
er
gy
cons
um
pt
ion
(J)
Side
le
ng
t
h
of
a
r
ea (
m
)
N
u
m
b
e
r
of
s
u
r
v
i
v
i
ng nod
e
s
Ro
un
d
Figure 3. The
Relation
ship
betwe
en Network
Energy Con
s
umption an
d Side Length
of Area
Figure 4. Co
mpari
s
o
n
on
Network Lifetime of
Two Protocol
s
Figure 3 sh
o
w
s the
relatio
n
shi
p
betwee
n
netwo
rk e
n
e
rgy co
nsum
ption and
sid
e
length
of area.F
r
om
Figure 3, with
the
increa
sin
g
of the side
length A
of the netwo
rk area, the ene
rg
y
con
s
um
ption
of two
p
r
ot
ocol
in
cre
a
ses. T
here i
s
a little diffe
ren
c
e
in
ene
rgy
con
s
um
p
t
ion
betwe
en th
e
two p
r
oto
c
ol
s wh
en th
e si
de le
ngth of
area
is small
,
but when
th
e A>3
00m, t
he
energy con
s
u
m
ption valu
e
of LEACH is
highe
r tha
n
t
hat of p
r
op
osed p
r
oto
c
ol i
n
this p
ape
r, with
faster in
crea
sing rate almo
st in exponen
tial form
. This is be
cau
s
e
the clu
s
terin
g
numbe
r of the
prop
osed
pro
t
ocol i
s
a
dynamic variati
on, whi
c
h
is i
n
fluen
ced
by the facto
r
s such
as the
si
d
e
length A an
d
so o
n
, but the
clu
s
terin
g
proportio
n
of
L
EACH i
s
fixed at 5%. Clea
rly, the pro
p
o
s
ed
proto
c
ol is
su
perio
r to LEACH p
r
oto
c
ol.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
A Novel
Clu
s
tering
Routin
g
Protocol in
Wirel
e
ss Sen
s
or
Network (Wu Rui)
6273
In
o
r
d
e
r
to
an
a
l
yz
e th
e pe
r
f
o
r
ma
nc
e of th
e
e
n
tire
WSN,
we
ad
opt the
net
work lifetime
para
m
eter to
evaluate pe
rf
orma
nc
e of WSN. Figu
re
4 sho
w
s the
simulatio
n
curves
of network
lifetime from two routin
g protocols. Here, the netwo
rk lifetime is the time of all surviving no
des
from the begi
nning of the simulation to the death.
5. Conclusio
n
In this pap
er,
the clu
s
terin
g
routin
g protoc
ol of
wirel
e
ss
sen
s
o
r
net
works ba
se
d on the
QPSO ca
n ef
fectively overcome th
e sh
ortco
m
ing
s
,
such
as the l
o
w en
ergy of
wirel
e
ss
sen
s
or,
the poor
utilization, an
d so on. By use
of clus
te
ring
optimization
and en
ergy e
quali
z
ation, the
netwo
rk lifeti
m
e is prolon
g
ed and n
e
two
r
k resource i
s
better utilize
d
.
In addition, for the si
ngle
-
hop
comm
u
n
icatio
n may result in en
ergy loss in
the long-
distan
ce t
r
an
smissio
n
[11],
we
can
ado
p
t
the mu
lti-ho
p co
mmuni
ca
tions o
r
multi
-
layer
cluste
ri
ng
s
t
rategy to res
o
lve.
Ackn
o
w
l
e
dg
ements
This
work was supp
orted
by the Natio
nal Natu
ral S
c
ien
c
e F
oun
dation of Chi
na (No.
5120
8168
), T
i
anjin
Natu
ral
Scien
c
e
Fou
ndation
(No. 11JCYBJC0
0
900, No.
13
JCYBJC37
700
),
Heb
e
i Provin
ce Natu
ral S
c
ien
c
e Fo
und
ation (No. F20132
0225
4, No. F201
320
2102
) and Hebei
Province Fou
ndation for
Returne
d
Sch
o
l
ars
(No. C20
1200
3038
).
Referen
ces
[1]
L Hua
w
e
i
. AC
O-Based Ro
uti
ng Alg
o
rithm f
o
r W
i
reless Se
nsor Net
w
o
r
k.
Chin
ese Jo
urn
a
l of Sens
ors
and Actuat
ors.
200
7; 20(1
1
): 2450-
245
1.
[2]
Z
e
y
u
Su
n, Z
henp
ing
LI. W
i
reless S
ens
or
Net
w
or
k
P
a
th Optimizatio
n
B
a
sed on H
y
bri
d
Alg
o
rithm
.
T
E
LKOMNIKA Indon
esi
a
Jour
nal of Electric
al
Engin
eeri
n
g
. 2
013; 11(
9): 535
2-53
58.
[3]
L Guo
hua, T
Hui. A
Nove
l
Routi
ng A
l
gor
ithm Bas
ed on Energ
y
Pol
i
c
y
for
W
i
reless
S
ensor Net
w
o
r
k.
Journ
a
l of Elec
tronics & Informati
on T
e
ch
no
logy
. 20
06; 28(
1): 167-1
71.
[4]
T
Yong. T
he Researc
h
on R
o
uting a
nd Bro
a
d
castin
g Alg
o
ri
thms for W
i
reless Sensor N
e
r
w
o
r
ks. PhD
T
hesis. Cheng
Du: Doctora
l
D
i
ssertatio
n, Uni
v
ersit
y
of Electronic Sci
enc
e and T
e
chno
lo
g
y
of Ch
in
a.
200
7.
[5]
Heinz
e
lm
an W
,
Chandr
akas
a
n
A. Energ
y
-
efficient Com
m
unic
a
tion Pr
otocol for W
i
r
e
less Se
nsor
Net
w
orks.
IEEE Computer S
o
ciety
. 200
0; 1
75-1
87.
[6]
J Sun, B F
eng. Particle s
w
arm optimiz
ati
on
w
i
th p
a
rticl
e
s havi
ng q
u
a
n
tum beh
avi
o
r. Congr
ess o
n
Evoluti
onar
y C
o
mputati
on.
IEEE
. 2004: 32
5-
331.
[7]
H Yo
urui,
T
Yiming.
S
i
mul
a
ti
on
Rese
arch
of the
QoS M
u
lticast
Routi
n
g i
n
W
S
N
Bas
ed
on
QPSO
Algorit
h
m
.
T
h
ird Intern
atio
nal
S
y
mp
osi
u
m o
n
Intelli
ge
nt Info
rmation T
e
chn
o
lo
g
y
Ap
plic
ati
on. 2
0
0
9
; (3):
39-4
3
[8]
H Don
g
x
u
e
, Z
Ruih
ua. PSO-base
d
Do
ubl
e
Cluster-h
ea
ds
Clusteri
ng Al
g
o
rithm for W
i
reless Se
nsor
Net
w
ork
. Co
mputer Eng
i
n
eeri
n
g
. 201
0; 36(1
0
): 100-1
03.
[9]
Handy
M, Haase M.
Low
Energy A
dapti
v
e Cluster
in
g Hierarc
hy w
i
th Deter
m
in
istic
Cluster-H
ea
d
Selecti
on. F
o
u
r
th
IEEE Conferenc
e on Mo
bile a
nd Wirel
e
ss Commun
i
c
a
tions N
e
t
w
ork
s
. 2002; 368-
372.
[10]
Heinz
e
l
an W
,
Cha
ndrak
asa
n
A. An app
licat
ion s
pec
ific
pro
t
ocol arc
h
itectu
re for
w
i
re
less
microsens
or
net
w
o
rks.On
W
i
reless Com
m
unic
a
tions, C
o
mmunic
a
tio
n
societ
y.20
02; 1
(
4): 660-6
70.
[11]
He Nin
gh
ui, Li
Hongs
hen
g, Gao Jing. Ene
r
g
y
-s
avin
g Ro
uting Al
gorithm
Based on C
l
u
s
ter in W
S
N.
T
E
LKOMNIKA Indon
esi
a
Jour
nal of Electric
al
Engin
eeri
ng.
2
013; 11(
2): 835
-847.
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