Indonesi
an
Journa
l
of El
ect
ri
cal Engineer
ing
an
d
Comp
ut
er
Scie
nce
Vo
l.
12
,
No.
3
,
Decem
ber
201
8
, p
p.
1312
~
1319
IS
S
N: 25
02
-
4752, DO
I: 10
.11
591/ijeecs
.v1
2
.i
3
.pp
1312
-
1319
1312
Journ
al h
om
e
page
:
http:
//
ia
es
core.c
om/j
ourn
als/i
ndex.
ph
p/ij
eecs
An Ene
rgy
-
Awa
re
and Load
-
bal
ancing Rou
ting
S
ch
eme for
Wireles
s
Sensor
Network
s
Omar
Adi
l
M
ah
di
1
, Yu
so
r
Rafid B
ahar
Al
-
May
ou
f
2
, Ahmed
Basil
Gha
z
i
3
,
Maz
in Abed M
ohammed
4
,
Ainud
din W
ahi
d
A
bdul
Wahab
5
,
Moh
d
Y
am
an
i Id
na Bi
n Id
ri
s
6
1
,2
,3
Depa
rtment
o
f
Com
pute
r
Sci
e
nce
s,
Co
llege
o
f Education
for
Pu
re
Sci
ences
-
Ibn A
l
-
Ha
y
th
am,
Un
ive
rsit
y
of
B
agh
dad,
10071
Baghda
d
,
Ira
q
1,5,6
Depa
rtment
o
f
Com
pute
r
S
y
s
t
em &
T
ec
hno
log
y
,
Facu
lty
of
Co
m
pute
r
Scie
n
ce
&
Inform
at
ion
T
ec
hnolog
y
Buil
d
ing,
Univer
sit
y
of
Malay
a
,
50603
Ku
al
a
Lumpur,
M
ala
y
si
a
4
Biom
edi
cal
Co
m
puti
ng
and En
gine
er
ing
T
ec
hn
ologi
es
(BIOCO
RE)
Appli
ed
R
e
sea
rch
Group
,
F
ac
ul
t
y
of
Inform
at
ion
and
Com
m
unic
a
ti
on
Te
chno
log
y,
Univer
si
ti T
ek
n
ika
l
Malay
si
a
M
el
ak
a, Ma
lay
si
a
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
A
ug
2
8
, 201
8
Re
vised
Oct
15
, 2
018
Accepte
d
Oct
29
, 201
8
Ene
rg
y
and
m
e
m
ory
li
m
it
a
ti
ons
are
consid
era
b
l
e
constraints
of
sensor
nodes
in
wire
l
ess
sens
or
net
works
(W
SN
s).
The
li
m
it
e
d
ene
rg
y
suppli
e
d
to
net
work
nodes
ca
uses
W
SNs
to
fac
e
cru
cial
fun
ct
ion
al
li
m
itat
ions.
The
ref
or
e,
the
proble
m
of
li
m
ited
ene
rg
y
r
esourc
e
on
sensor nod
es
ca
n
onl
y
b
e
a
ddre
ss
ed
b
y
using
the
m
eff
i
ci
en
tly
.
In
thi
s
rese
arc
h
work,
an
en
erg
y
-
ba
la
n
ci
ng
rou
ti
ng
sche
m
e
for
i
n
-
ne
twork
dat
a
aggr
e
gat
ion
is
pr
ese
nt
ed.
Th
is
sche
m
e
is
ref
err
ed
to
as
Ene
rg
y
-
awa
re
and
load
-
Bal
an
ci
ng
R
outi
ng
sche
m
e
for
Data
Aggrega
ti
on
(he
rei
naf
te
r
ref
err
e
d
to
as
EBR
-
D
A).
The
EBR
-
DA
ai
m
s
to
provide
an
en
er
g
y
eff
i
ci
en
t
m
ul
ti
ple
-
hop
rout
in
g
to
th
e
d
esti
na
ti
on
on
the
basis
of
the
qu
alit
y
of
th
e
l
inks
bet
wee
n
the
sou
rce
and
desti
n
at
i
on.
In
vi
ew
of
thi
s
goal,
a
l
ink
cost
fun
ct
io
n
is
int
roduc
ed
to
assess
the
qu
al
ity
of
th
e
li
nks
b
y
consid
eri
ng
the
new
m
ult
i
-
cri
t
eria
node
weight
m
et
ri
c,
in
which
ene
rg
y
and
lo
ad
bal
an
ci
ng
a
re
c
onsidere
d.
The
node
weight
is
c
onsidere
d
i
n
construc
t
ing
and
updat
ing
the
ro
uti
ng
tr
ee
to
a
ch
ie
ve
d
y
n
amic
b
e
havi
or
for
eve
nt
-
dr
ive
n
W
SN
s.
The
propos
ed
EBR
-
DA
wa
s
eva
lu
at
ed
and
val
id
at
ed
b
y
sim
ula
ti
on,
and
t
he
resul
ts
were
compare
d
with
t
h
ose
of
InFRA
a
nd
DRIN
A
b
y
using pe
r
for
m
anc
e
m
et
r
ic
s f
or
dense
static
n
et
works
.
Ke
yw
or
d
s
:
Data ag
gregati
on
Energy e
ff
ic
ie
ncy
Loa
d balanci
ng
Rou
ti
ng
protoc
ol
W
i
reless se
nso
r netw
ork
Copyright
©
201
8
Instit
ut
e
o
f Ad
vanc
ed
Engi
n
ee
r
ing
and
S
cienc
e
.
Al
l
rights re
serv
ed
.
Corres
pond
in
g
Aut
h
or
:
Om
ar A
dil M
a
hd
i
,
D
epa
rtm
ent o
f C
om
pu
te
r
Scie
nces
,
Coll
ege
of
Ed
uc
at
ion
fo
r
P
ure
Sciences
-
I
bn
Al
-
H
ay
tham
,
Un
i
ver
sit
y o
f B
aghda
d
,
1007
1
Ba
ghda
d,
Ir
a
q
.
Em
a
il
:
Corr
esp
onding a
utho
r,
om
arak
ove@
gm
ai
l.co
m
1.
INT
ROD
U
CTIO
N
The
de
velo
pme
nt
of
m
ic
ro
-
el
ect
ro
-
m
echan
i
cal
syst
e
m
s
(M
EMS)
has
sig
ni
ficantl
y
con
tri
bu
te
d
to
the
adv
a
ncem
ent
of
c
os
t
-
ef
fecti
ve
an
d
sm
all
wireless
sen
sor
nodes
with
div
e
rse
f
un
ct
i
on
s
[
1
-
3
]
.
Mo
nitor
i
ng
ph
ysi
cal
co
nd
i
ti
on
s,
handlin
g
sensed
i
nform
at
ion
,
an
d
m
akin
g
ap
pro
pr
i
at
e
decisi
on
s
a
re
possible
with
the
help
of
these
nodes
.
Borde
r
su
r
veill
ance,
he
al
thcare
pro
vi
sion,
tracki
ng
op
e
rati
on,
inte
ll
igent
trans
portat
ion
syst
e
m
s,
ur
ba
n
traf
fic
m
onit
or
in
g,
a
nd
disaste
r
m
on
it
or
i
ng
a
re
s
om
e
of
the
c
r
it
ic
al
app
li
cat
i
on
s
of
WSNs
[
4
-
9
]
.
A
sens
or
netw
ork
c
om
pr
ise
s
s
m
al
l
wireless
s
ens
or
node
s
with
data
-
acq
uisi
ti
on
,
batte
ry,
stora
ge,
and
m
ote
(p
r
oc
esso
r/rad
i
o
boar
d)
m
od
ules
tha
t
colle
ct
iv
el
y
help
in
se
ns
in
g.
Se
ns
or
nodes
exec
ute
three
pr
im
ary
ta
sk
s:
(i)
ph
ysi
cal
quantit
y
sa
m
pling
f
or
s
pecific
s
urrou
nd
i
ng
co
nd
it
io
ns
,
(ii)
proces
sin
g
a
nd
stori
ng
sense
d
data,
a
nd
(iii
)
trans
fe
rr
in
g
se
ns
e
d
da
ta
fr
om
the
data
colle
ct
ion
po
i
nt
to
the
sink
node
or
th
e
bas
e
sta
ti
on
(BS)
[
10
]
.
The
ra
dio
s
are
us
e
d
for
th
e
com
m
un
ic
at
i
on
betwee
n
th
e
sensor
node
s
and
the
BS
so
that
data
can
be
e
xch
a
nged
with
app
li
cat
ion
s
f
or
f
ulfill
ing
t
he
desire
d
ta
s
ks
.
Mo
re
ov
e
r,
the
com
m
un
i
cat
ion
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
An
E
ner
gy
-
Aware
and L
oad
-
ba
l
an
ci
ng R
ou
t
ing
Sc
hem
e
for
…
(
Omar
A
dil
Ma
hd
i
)
1313
betwee
n
the
sens
or
no
des
and
the
BS
al
lows
f
or
the
sha
rin
g
of
in
for
m
at
ion
via
add
it
ion
al
netw
orks
,
li
ke
LAN,
WL
AN,
WPAN
, a
nd th
e I
ntern
et
,
w
it
h othe
r
c
om
pu
te
rs
[
11
-
13
]
.
In
e
ven
t
-
dri
ve
n netw
orks,
s
uc
h
as
WSN
s
, g
e
ner
at
e a substa
ntial
a
m
ou
nt of
d
at
a that sh
ou
l
d
be ro
uted
via
m
ulti
-
hops
to
the
sink
node.
T
her
e
fore
,
r
ou
ti
ng
pr
oto
c
ols
play
a
sign
ific
ant
pa
rt
in
gather
i
ng
a
nd
forw
a
r
ding
dat
a
in
W
S
Ns.
In
-
net
work
data
aggre
gation
is
a
strat
egy
for
op
ti
m
iz
ing
a
ro
utin
g
ta
sk
i
n
WSNs.
In
i
n
-
netw
ork
data
ag
gregati
on,
the
pr
oces
sing
capa
bili
ty
of
interm
ediat
e
sens
or
no
de
s
al
ong
the
routing
paths
is
util
iz
ed
(see
[
14
]
for
m
or
e
detai
ls
).
T
his
sc
hem
e
re
duces
a
sign
i
ficant
num
ber
of
byte
s
that
ar
e
transm
itted
du
rin
g
the
netw
ork
ope
rati
on
by
ag
gr
e
gating
data
at
th
e
interm
ediat
e
nodes
t
o
al
low
for
band
width
a
nd
energy
savi
ngs.
T
he
issue
s
of
re
dunda
nc
y
and
num
bers
of
tra
ns
m
iss
ion
s
a
re
re
du
c
ed
by
e
m
plo
yi
n
g
i
n
-
ne
twork
d
at
a a
ggre
gatio
n.
The
m
on
it
or
i
ng
ca
pab
il
it
y
in
eve
nt
-
dri
ve
n
WSNs
is
deterior
at
e
d
wh
e
n
the
ov
e
rlap
pin
g
pat
hs
of
un
c
orrelat
ed
e
ven
ts
perform
extensive
da
ta
agg
re
gatio
n.
Con
se
que
ntly
,
netw
ork
pe
rfor
m
ance
is
no
t
i
m
pr
oved
.
In
a
dd
it
io
n,
ine
ff
ic
ie
nt
in
-
net
wor
k
data
ag
gr
e
ga
ti
on
ne
glect
s
the
netw
ork
sta
te
and
causes
the
early
energy
de
pleti
on
of
m
ulti
-
hop
relay
(M
HR
)
no
des
a
nd
t
he
uneve
n
str
uc
ture
of
t
he
network
beca
us
e
of
t
he
excessive
am
ou
nt
of
de
ad
nodes.
T
her
e
f
or
e,
a
balance
bet
ween
optim
iz
i
ng
data
ag
gr
e
ga
ti
on
an
d
li
nk
cost
is
necessa
ry
[
15
]
.
In
t
his
re
searc
h
work,
a
n
e
ne
rg
y
-
e
ff
ic
ie
nt
and
loa
d
-
balan
ci
ng
r
ou
ti
ng
s
chem
e
fo
r
i
n
-
netw
or
k
dat
a
aggre
gation
(E
BR
-
DA)
is
pre
sented
.
T
his
r
outi
ng
sc
hem
e
i
s
a
m
od
ific
at
io
n
of
Data
R
ou
t
ing
f
or
I
n
-
Networ
k
Aggregati
on
prot
oco
l
(
DRI
N
A)
.
EBR
-
D
A
c
on
si
ders
ene
rgy
and
loa
d
bal
ancin
g
awa
reness
m
et
rics
to
reduce
the
ene
r
gy
co
nsum
ption
a
nd
balance
t
he
lo
ad
distrib
ution
am
on
g
se
nsor
nodes
,
as
well
as
hel
p
im
pr
ove
th
e
netw
ork
li
feti
m
e,
especial
ly
in
a
la
r
ge
-
scal
e
en
vir
on
m
ent.
Un
li
ke
the
DR
INA
sc
hem
e,
the
pro
po
se
d
E
BR
-
DA
schem
e
exp
loit
s
m
ulti
-
m
et
ric
s
relat
ed
to
en
erg
y
an
d
loa
d
balancin
g
to
help
sel
ect
the
set
o
f
MHR
node
s
betwee
n
sourc
e
-
destinat
i
on
pa
irs.
This
pr
opose
d
sc
hem
e
m
akes
us
e
of
m
ulti
-
crit
eria
node
weig
ht
m
et
ric
(
MCNW
)
m
et
r
ic
associat
ed
with
each
node
with
res
pect
to
resid
ual
en
erg
y
capaci
ty
and
a
vaila
ble
buffe
r
m
e
m
or
y
siz
e.
These
m
et
rics
wer
e
util
iz
ed
by
a
li
nk
c
os
t
functi
on
to
m
e
asur
e
the
qual
it
y
of
li
nks
in
rout
e
com
pu
ta
ti
on
.
2.
REL
ATED
WO
RKS
The
stu
dies
co
nducted
on
in
-
netw
ork
ag
gre
gation
ha
ve
ta
r
geted
the
iss
ue
s
of
pack
et
f
orwardin
g
to
facil
it
at
e
the
in
-
net
work
ag
gre
gation
of d
at
a.
Th
e
m
ai
n
obj
e
ct
ive
of
the
se
s
tud
ie
s
is
to
m
od
ify
e
xisti
ng
r
outi
ng
protoc
ols
to
pe
rfor
m
data
ag
gregati
on.
N
ume
rous
protoc
ol
s
us
in
g
hiera
rc
hical
struct
ur
es
have
bee
n
pro
po
s
ed
.
Exam
ples
include
tree
-
base
d
routin
g
proto
cols
in
w
hich
the
sin
k
node
is
the
root
[
16
]
.
H
ow
e
ve
r,
m
any
com
plex
tree
const
ru
ct
io
n
a
ppr
oach
es
have
al
so
prese
nted.
I
n
a
ddit
ion
t
o
tree
str
uc
ture
-
base
d
pro
tocols
,
cl
us
te
r
-
, c
hain
-
, and g
rid
-
base
d protoc
ols
ha
ve
al
s
o been
e
m
plo
ye
d
for
in
-
net
work d
at
a
aggre
gation
[
17
-
19
]
.
Tiny
AGg
reg
a
ti
on
Ser
vice
(
TAG)
se
rv
ic
e
was
intr
oduc
ed
in
the
st
udie
s
of
Ma
dd
en,
F
rankli
n,
Hell
erstei
n,
a
nd
H
ong
(20
02)
and
Ma
dden
,
Szewczy
k,
F
ra
nk
li
n,
an
d
C
uller
(20
02),
w
he
re
data
a
ggre
gation
was
im
ple
m
ented
on
a
real
-
world
te
stbed
.
TA
G
se
r
vice
fall
s
unde
r
t
he
cat
egory
of
tr
ee
-
base
d
a
ggre
gation
appr
oach
an
d
aggre
gates
data
by
us
in
g
pe
rio
dic
traf
fic
patte
rn
s
.
Dif
fe
ren
t
t
ree
le
vel
s
with
di
ff
e
re
nt
ti
m
e
intervals
are
use
d
in
node
as
sign
m
ent
fo
r
ti
m
ing
in
TAG
Ser
vice
to
al
low
the
bo
tt
om
nodes
of
the
tr
ee
to
ini
ti
at
e
data
transm
issi
on
.
H
ow
e
ve
r,
in
cas
e
of
li
nk
or
de
vice
fail
ures
or
dy
nam
ic
top
ologies,
T
A
G
Ser
vice
m
ay
ex
hib
it
in
eff
ic
ie
ncy sim
i
la
r
to
oth
e
r
tree
-
base
d
a
ppr
oac
hes
[
16
]
.
Nak
am
ur
a
et
al
.
(2006)
pro
pose
d
the
reacti
ve
al
gorithm
i
nfor
m
at
ion
fu
s
ion
-
base
d
r
ole
assignm
ent
(InFRA
).
In
th
is
pr
ot
oc
ol,
cl
us
te
rs
a
re
f
orm
ed
wh
e
n
sim
il
ar
even
ts
are
detect
ed
by
va
r
io
us
no
des.
In
F
R
A
gen
e
rates
the
SPT
li
nkin
g
a
ll
the
so
urce
nodes
to
t
he
s
ink
to
e
na
ble
inter
-
cl
ust
er
a
nd
i
ntra
-
cl
us
te
r
data
aggre
gation
sc
hem
es.
Each
ti
m
e
a
new
eve
nt
is
detect
ed,
th
e
inform
at
ion
of
the
e
ve
nt
is
broa
dcast
thr
ough
ou
t
the
net
work
to
no
ti
fy
o
the
r
no
des,
a
nd
the
pa
ths
f
ro
m
the
av
ai
la
ble
coor
dina
tors
t
o
the
si
nk
node
a
re
up
da
te
d.
These
proces
se
s ar
e c
os
tl
y, a
nd they li
m
it
n
etw
ork
scala
bili
ty
[
20
]
.
In
(Lea
ndr
o
A
par
eci
do
Vill
as
et
al
.,
2013)
the
data
routin
g
f
or
in
-
net
work
a
ggre
gatio
n
(D
R
INA)
i
s
pro
po
se
d
to
ov
erco
m
e
the
disadv
a
ntage
s
of
In
FR
A.
T
he
pr
oto
c
ol
was
des
ign
e
d
to
m
axi
m
iz
e
the
adv
a
ntage
s
of
in
-
netw
ork
aggre
gation.
I
n
DRI
NA,
the
hop
distances o
f
the
nodes
a
re u
pdat
ed
i
n
DR
INA
to d
et
erm
i
ne
the
sh
ort
est
distan
ce
betwee
n
th
e
even
t
no
des
and
a
node
in
the
est
ablishe
d
r
ou
te
.
A
gre
edy
increm
ental
tree
(GIT)
is
co
ns
tr
ucted
by
the
ho
p
distance
m
e
tric
in
the
network.
The
m
ain
goal
s
of
DRI
NA
a
re
to
m
ax
i
m
iz
e
ov
e
rlap
ping
r
oute
s.
H
oweve
r
,
DRI
NA
pr
es
ents
a
hea
vy
load
is
exe
rted
on
the
nodes
on
the
pr
e
vi
ou
sly
const
ru
ct
e
d pat
h,
a
nd this
lack
of loa
d balanc
e causes
s
uch nod
e
s to
expire
pr
em
at
u
rely
[
21
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
12
, N
o.
3
,
Dece
m
ber
2
01
8
:
1312
–
1319
1314
3.
M
CNW
M
ET
RIC
FO
R E
BR
-
DA
The
MC
N
W
pe
rfor
m
ance
m
e
tric
represe
nts
the
sta
tus
of
th
e
nodes
in
te
r
m
s
of
their
e
ne
rg
y
resou
rce
and
c
ongestio
n
le
vel.
For
est
im
at
ing
the
MC
N
W
for
eve
r
y
n
ode
i
,
two
i
nd
i
vidual
node
weig
hts
are
ne
eded.
These
wei
gh
ts
dep
e
nd
on
th
e
inv
ol
ved
node
-
relat
ed
m
etr
ic
s,
nam
el
y,
r
esi
du
al
ene
rg
y
-
base
d
no
de
weig
ht
(
NW_E
res
(
i
)) a
nd av
ai
la
ble
bu
ff
e
r
-
base
d nod
e w
ei
ght
(
NW_
Bav
a
(
i
))
.
The
E
res
of
a
node
i
in
dicat
es
resid
ual
en
er
gy
of
the
batte
ry
at
ta
ched
to
tha
t
node
at
a
sp
e
ci
fic
instant.
This
par
am
et
er
is
der
i
ved
f
rom
the
batte
ry
m
od
el
.
The
node
w
ei
ght
(
N
W_E
res
(
i
)
)
base
d
on
E
res
is
cal
culat
ed
us
in
g
E
quat
ion
(1).
The
no
de
whose
rem
ai
ni
ng
ene
rg
y
is
hi
gh
co
rr
es
ponds
to
li
ghtwei
ght
NW_E
res
(
i
)
,
w
hich
reduces t
he pr
obabili
ty
o
f
en
e
r
gy exha
us
ti
on.
_
(
)
=
(
1
−
(
)
(
)
)
2
(1)
Wh
e
re
E
res
(
i
)
is
the
residu
al
energy
of
node
i
at
an
instant
and
E
init
(
i
)
i
s
the
init
ia
l
batte
ry
ener
gy
le
vel
of
the
node
,
w
hich
re
fer
s
to
th
e
m
a
xim
u
m
batte
ry
capaci
ty
.
Equat
ion
(
1)
s
ugge
sts
that
the
resu
lt
appr
oach
es
1
wh
e
n
t
he
rem
ai
nin
g
ene
r
gy
of
no
de
i
de
creases.
Co
nversely
,
the
res
ulti
ng
node
w
ei
gh
t
appr
oach
es
0,
and
the
c
os
t
de
creases
w
he
n
the
rem
a
ining
energy
is
hig
h.
F
ur
t
her
m
or
e
,
if
the
node
e
nergy
do
e
s
no
t c
ha
nge (i.e.
, th
e
sam
e as the
init
ia
l ener
gy),
the
n 0
en
e
rg
y c
os
t i
s
ob
ta
ine
d.
The
acce
ssible
buf
fer
siz
e
r
e
pr
ese
nts
t
he
re
sidu
al
m
e
m
or
y
sp
ace
,
wh
ic
h
can
be
us
e
d
t
o
store
the
sense
d
data
durin
g
the
tim
e
a
node
is
wait
i
ng
to
get
ser
vi
ced.
Data
buf
f
erin
g
occurs
w
hen
a
node
rec
ei
ves
data
whose
am
ount
excee
ds
the
am
ou
nt
of
da
ta
it
can
fo
r
w
ard.
Howe
ver,
if
no
buf
fer
s
pa
ce
is
avail
able
at
the
node,
the
n
the
data
is
dro
ppe
d,
a
nd
co
ngest
ion
ens
ues.
Th
us,
each
no
de
s
hould
be
a
wa
r
e
of
t
he
buff
e
r
siz
e
of
it
s
neighbors
t
o
co
nduct
a
rel
ia
ble
pack
et
tr
ansm
issi
on
an
d
to
av
oid
c
on
gestio
n
am
on
g
the
nodes
.
Th
e
node
w
ei
ght
dep
e
nd
ing
on
the
avai
la
ble
buff
e
r
siz
e
is
den
oted
by
NW_B
ava
(
i
),
w
hich
is
cal
culat
ed
by
Eq
uation
(2
)
.
The
node
wit
h
a
high
buf
fer
siz
e
correspo
nds
to
li
ghtwei
ght
NW_B
ava
(
i
)
,
wh
ic
h
le
ads
t
o
m
ini
m
a
l
con
ge
sti
on
and p
ac
ket l
os
s
.
NW
_
B
a
v
a
(
i
)
=
(
1
−
B
ava
(
i
)
B
t
ota
l
(
i
)
)
2
(2)
Wh
e
re
B
ava
(
i
)
i
s
the
avail
able
buff
e
r
m
e
m
or
y
of
no
de
i
at
an
insta
nt
an
d
B
total
(
i
)
is
the
node
’s
total
buff
e
r
siz
e
,
w
hich
re
fer
s
to
the
m
axi
m
um
bu
ff
e
r
ca
pa
ci
ty
.
As
s
ugge
ste
d
by
E
quat
ion
(2),
the
cost
is
appr
ox
im
at
e
to
0
wh
e
n
the
av
ai
la
ble
bu
f
fer
m
e
m
or
y
is
la
rg
e,
w
her
e
as
th
e
cost
appro
ac
hes
1
w
he
n
the
bu
f
fer
siz
e is ex
haust
ed.
A
com
po
sit
e
MC
N
W
wei
gh
t
of
no
de
i
is
con
sta
ntly
and
i
nd
e
pe
nd
e
ntly
m
easur
ed
i
n
ac
corda
nce
with
a
norm
alized
weig
hted
ad
dit
ive
util
it
y
fu
nction
(
N
WAUF;
[
22
]
).
The
M
CN
W
w
ei
ght
dep
e
nds
on
the
values
sat
isfyi
ng
t
he
norm
al
iz
ing
crite
ria an
d weig
ht
s o
f
im
po
rtanc
e that ra
nge f
rom
0
to
1.
The
fi
nal MC
N
W
w
ei
ght
of no
de
i
is est
i
m
at
ed
by E
qu
a
ti
on
(3).
NW
(
i
)
=
W
1
×
N
W
E
r
es
(
i
)
+
W
2
×
N
W
B
a
v
a
(
i
)
(3)
Wh
e
re
NW_
E
res
(
i
)
and
NW_
B
ava
(
i
)
are
the
node
weig
hts
of
the
node
-
relat
ed
m
et
rics
of
MC
N
W
;
W
1
and
W
2
are
t
he
norm
al
iz
ed
weigh
t
fact
or
s
of
the
no
de.
In
t
his
stud
y,
the
no
rm
alized
weig
ht
facto
rs
of
E
r
es
and
B
ava
m
et
rics
are
set
to
0.6
an
d
0.4
res
pecti
ve
ly
.
The
su
m
of
the
no
rm
aliz
ed
wei
gh
t
f
act
or
s
,
w
hich
de
note
the
i
m
po
rtance
of t
he
c
om
po
ne
nts
of
the
MC
N
W
m
e
tric
, is equa
l t
o
1.
4.
DES
C
RIP
TION
OF EB
R
-
DA S
CHE
ME
The
de
velo
ped
EBR
-
D
A
r
ou
t
ing
a
ppr
oac
h
i
ntr
oduces
a
ppr
opriat
e
m
od
ific
at
ion
s
t
o
s
olve
the
iss
ue
s
relat
ed
to
ene
r
gy
an
d
l
oad
ba
la
ncing
in
DR
I
NA.
I
n
EBR
-
DA,
ce
rtai
n
f
unct
ions
of
DRI
NA
are
retai
ne
d
a
nd
le
ver
a
ged,
w
he
reas
ot
her
func
ti
on
s
are
sig
nif
ic
antly
m
od
ifie
d.
Li
ke
DR
I
NA,
EBR
-
DA
is
a
routing
pr
oto
c
ol
for
in
-
net
wor
k
data
ag
gregat
ion
.
It
use
s
M
HR
r
ou
ti
ng
t
o
sen
d
pac
kets
from
the
so
urc
e
to
the
destin
at
ion
.
It
al
so
m
ai
ntain
s
a
routin
g
tre
e
that
inco
r
por
at
es
the
ne
wly
est
ablished
r
oute
by
updatin
g
the
val
ue
of
th
e
ho
p
tree o
f
a
node
t
o
m
axi
m
iz
e ro
utes
ov
e
rlap
ping to
pr
om
ote in
-
net
work d
at
a
aggre
gation.
Both
e
nergy
ef
fici
ency
an
d
lo
ad
balancin
g
a
re
en
ha
nced
in
the
de
velo
pe
d
EBR
-
D
A
r
out
ing
sc
hem
e
by
intr
oducin
g
a
new
m
echani
s
m
s
to
m
od
ify
the
m
a
in
funct
ion
s
of
t
he
co
nventio
nal
DR
I
NA.
A
ne
w
MC
N
W
perform
ance
m
et
ric
co
m
pr
ise
s
t
wo
r
outi
ng
m
e
tric
s,
na
m
el
y,
residu
al
batte
ry
and
a
vaila
ble
buf
fer
m
e
m
or
y.
Fu
rt
her
m
or
e,
t
he
struct
ur
es
of
the
hop
co
nf
i
gurati
on
m
essage
(
HCM)
and
cl
ust
er
co
nf
i
gurati
on
m
essage
(CCM
)
wer
e
m
od
ifie
d
to
i
nc
lud
e
t
he
MC
N
W
m
et
ric
in
the
routin
g
tr
ee
f
or
m
at
ion
a
nd
cl
us
te
r
hea
d
(CH
)
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
An
E
ner
gy
-
Aware
and L
oad
-
ba
l
an
ci
ng R
ou
t
ing
Sc
hem
e
for
…
(
Omar
A
dil
Ma
hd
i
)
1315
sel
ect
ion
pro
ce
sses.
T
hen,
the
routin
g
al
gorit
hm
deter
m
ines
the
best
path
t
o
the
destinat
ion
w
hile
o
pti
m
iz
ing
the d
at
a
ag
gr
e
ga
ti
on
.
The
propose
d
schem
e
in
this
researc
h
wor
k
com
pr
ise
s
th
ree
phases
.
Th
e
first
phase
i
nvolv
e
s
the
broa
dcast
o
f
th
e
reg
i
on
disc
overy
by
the
nodes
a
nd
t
he
est
ablishm
ent
of
a
hop
tree
bet
ween
t
he
se
nso
r
no
des
and
sin
k
no
de
.
The
sec
ond
ph
a
se
be
gin
s
as
soon
a
s
a
node
se
ns
es
a
ny
eve
nt.
I
n
t
his
phase,
cl
ust
ers
are
form
ed,
and C
Hs
a
re s
el
ect
ed
. T
he
thir
d p
ha
se inclu
des r
oute
establi
sh
m
e
nt, data
agg
regat
ion
, a
nd
routing.
4.1.
Disc
ov
er
y o
f
Node Br
oadcast
Reg
i
on
a
n
d
Ho
p
Tree
Buil
ding (
Ph
as
e
I)
In
init
ia
li
zat
ion
phase
eac
h
node
ide
ntifie
s
it
s
neighbors,
wh
ic
h
are
possible
par
e
nts,
within
it
s
rad
i
o
fr
e
que
nc
y
(b
r
oa
dcast)
reg
i
on.
It
al
so
determ
ines
its
hop
distance
to
the
sin
k,
r
esi
du
al
e
nergy,
an
d
avail
able
buff
e
r
siz
e.
The
pro
cedure
res
ponsi
ble
fo
r
init
ia
li
zat
ion
be
gin
s
by
br
oa
dcasti
ng
a
ho
p
co
nf
i
gurati
on
m
essage
(H
C
M)
from
the
sink
to
al
l
the
sensors
wit
hin
it
s
com
m
un
ic
at
i
on
ran
ge.
I
n
ad
diti
on
to
the
c
omm
on
m
essage
fiel
ds,
the
HCM
co
ntains
f
our
ke
y
par
am
et
ers,
nam
ely
,
Nod
e
-
ID
,
HtS
,
E
res
,
and
B
ava
,
w
hic
h
are
def
i
ned in T
abl
e 1
.
Table
1
.
Hea
de
r of
t
he HCM
for EB
R
-
DA
No
.
Para
m
eter
Descripti
o
n
1
No
d
e
-
ID
ID
o
f
the n
o
d
e that
trans
m
itt
ed
/retr
an
s
m
itted
the HC
M
2
HtS
Distan
ce fro
m
the
n
o
d
e to th
e sin
k
node (in h
o
p
s)
4
E
r
es
Res
id
u
al energy
o
f
the n
o
d
e
5
B
a
va
Av
ailab
le bu
f
f
er
m
e
m
o
r
y
size
of
the n
o
d
e
The
HtS
init
ia
l
value
for
the
s
ink
node
is
0
a
nd
i
nf
i
nity
fo
r
the
oth
e
r
node
s
when
t
he
ho
p
tree
be
gins
to
f
or
m
.
A
n
a
ct
ual
val
ue
of
the
node
ene
r
gy
is
us
ed
,
a
nd
the
a
vaila
ble
buf
fer
m
e
mo
ry
siz
e
of
a
node
i
s
consi
der
e
d
m
axim
u
m
.
On
ce
the
nei
ghbori
ng
nodes
of
th
e
sin
k
recei
ve
the
HCM,
th
e
nod
e
pe
rfo
r
m
s
the
fo
ll
owin
g
ta
s
ks:
1)
Ver
ify
w
hethe
r
the
value
of
HtS
in
t
he
HCM
m
essage
is
le
sser
tha
n
it
s
Ht
S
value
t
o
gu
a
r
antee
that
eac
h
node rec
ords
t
he
m
ini
m
u
m
n
um
ber
of
hops
to the si
nk.
2)
Dep
e
ndin
g
on
the
va
li
dity
of
the
c
onditi
on
in
(i)
,
t
he
node
m
ai
ntains
the
inf
or
m
at
ion
of
it
s
nei
ghbor
s
whose HCM
s
are r
ecei
ve
d
in
it
s n
ei
ghbor
s ta
ble.
3)
The
node
al
so
ver
ifie
s
w
heth
er
the
value
of
First
_S
e
ndin
g
is
true.
If
t
he
value
of
First
_Sen
ding
is
tr
ue
,
then
se
ns
or
no
de
increase
s
th
e
values
of
Ht
S
by
one
in
a
sens
or
no
de.
T
he
n,
the
se
ns
or
node
com
pu
te
s
their
rem
ai
nin
g
ene
r
gy
afte
r
one
c
om
plete
transm
issi
on
and
upda
te
s
the
E
res
fiel
d.
More
over
,
it
com
pu
te
s
the
ob
ta
ina
ble
buf
fer
siz
e
a
nd
up
dates
the
B
ava
f
ie
ld
an
d
finall
y
ci
rcu
la
te
s
th
e
HCM
to
othe
r
neig
hbors.
Ot
he
rw
ise
,
if
the
c
onditi
on
of
Fir
st_S
e
ndin
g
is
false,
that
is,
the
HCM
has
al
read
y
been
se
nt
by the
node
.
4)
If
the
co
ndit
ion
in
Step
(iii
)
is
false,
the
n
t
he
HCM
m
essage
will
be
dr
oppe
d,
wh
ic
h
i
nd
ic
at
e
s
that
t
he
store
d
HtS
i
n
t
he node
pro
vide
s m
or
e accu
ra
te
infor
m
at
ion
to the si
nk.
5)
The
node
al
so
updates
the
routing
ta
ble
by
us
in
g
the
MC
N
W
m
et
rics
to
com
pu
te
the
weig
hts
of
thei
r
nex
t
-
ho
p neig
hbors a
nd to
sel
ect
the lig
htwe
igh
t
node
a
s it
s n
e
xt ho
p,
de
pe
n
di
ng on E
quat
ion
(
3)
.
This
pro
cess
c
on
ti
nues
unti
l
t
he
tree
to
polo
gy
is
fo
rm
ed
by
al
l
the
networ
k
no
des.
T
he
s
ink
node
is
the ro
ot no
de o
f
the
tree.
4.2.
Even
t
-
d
ri
ven
Clust
er
F
orm
at
i
on
an
d
CH
El
ection
(Phas
e II)
In
t
his
ph
a
se,
a
dynam
ic
cl
us
te
r
arc
hitec
ture
i
s
form
ed,
any
node
t
hat
ha
s
s
ense
d
the
eve
nt
ta
kes
th
e
ro
le
of
cl
us
te
r
head
C
H.
T
he
n,
al
l
the
eve
nt
nodes
pro
pa
gate
their
in
f
or
m
at
ion
by
a
cl
us
te
r
co
nf
i
gurati
on
m
essage
(CCM
).
If
a
no
de
r
ecei
ves
a
CC
M
that
pr
ovide
s
m
or
e
accurat
e
inform
ation
r
egardin
g
the
d
i
sta
nce
in
hops
t
o
the
sin
k
node
(for
the
first
e
ve
nt)
or
already
est
abli
sh
e
d
pat
h
(for
the
s
uccessi
ve
e
ven
ts
),
t
he
node
will
set
it
s
ro
le
to
cl
us
te
r
m
e
m
be
r
CM
an
d
retr
ansm
it
s
the
received
CC
M.
Othe
rw
ise
,
the
node
will
dis
card
t
he
receive
d
CC
M,
an
d
afte
r
a
s
pe
ci
fic
tim
e
inter
val,
t
he
no
de
broa
dcasts
a
D
ecl
arati
on
Me
s
sage
as
cl
us
te
r
hea
d
(CH
DM)
with
it
s
Nod
e
_ID
to
it
s
cl
us
te
r
m
e
m
ber
s.
Finall
y,
the
m
e
m
ber
no
de
s
rem
e
m
ber
their
C
H,
a
nd
al
l
the
even
t
detect
io
n re
ports are
d
i
r
ect
ly
sen
t t
o
th
e CH.
4.3.
R
ou
te
Est
ab
li
shment
and
D
ata
Tr
an
smiss
ion B
as
ed
o
n
Dev
el
op
ed
M
CNW (
Ph
as
e
III)
In
t
his
ph
as
e,
r
ou
ti
ng
t
ree
f
or
m
at
ion
is
base
d
on
the
sa
ve
d
MC
N
W
wei
ghts
in
t
he
neig
hbors
ta
ble,
wh
ic
h
was
c
re
at
ed
in
t
he
fi
rst
phase.
Each
node
is
well
a
w
are
of
al
l
it
s
ne
ighbors
a
nd
ca
n
us
e
the
in
for
m
at
ion
in
the
neig
hbors
ta
ble
to
sen
d
data
pac
kets
to
the
sin
k
node
.
First,
t
he
C
H
is
res
ponsi
bl
e
for
the
r
ou
ti
ng
t
ree
form
ation
an
d
routin
g
pac
kets
of
the
ne
w
ev
ent
to
the
sink
.
The
CH
will
c
heck
if
it
s
HtS
is
zero
,
that
is,
it
is
a
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
12
, N
o.
3
,
Dece
m
ber
2
01
8
:
1312
–
1319
1316
par
t
of
t
he
bac
kbone
of
the
hop
t
ree;
th
us
,
c
reati
ng
a
f
resh
route
as
the
ne
w
bac
kb
on
e
of
the
hop
tree
is
no
t
require
d.
EBR
-
D
A
keeps
track
of
t
he
rem
ai
nin
g
ene
r
gy
le
vel
a
nd
a
ccessi
ble
buff
e
r
m
e
m
or
y
of
t
he
nodes
i
n
the
bac
kbone
to
acq
uire
a
n
e
ven
e
nergy
dis
tribu
ti
on
a
n
d
t
o
av
oid
c
onges
ti
on
delay
,
wh
i
ch
is
cause
d
by
data
colli
sion
s.
If
both
wei
gh
ts
of
par
am
et
ers
exceed
the
set
weigh
t
lim
i
t,
then
a
new
r
outi
ng
path
f
orm
ati
on
is
init
ia
te
d.
D
ur
i
ng
t
he
re
f
or
m
a
ti
on
pr
ocess,
t
he
nei
ghbori
ng
node
t
hat
has
the
hi
gh
est
E
res
and
Ba
va
am
on
g
t
he
cand
i
date
no
de
s
is
sel
ect
ed
as
the
al
te
rn
at
i
ve
ne
xt
ho
p.
F
urt
her
m
or
e,
i
n
th
e
refor
m
at
ion
of
t
he
routin
g
path,
the
thres
hold
weig
ht
facto
rs
for
NW_E
res
an
d
NW_B
ava
in
e
ver
y
no
de
sli
ghtl
y
increase
if
no
s
uitable
no
de
can
be fo
und.
The
C
H
the
n
c
reates
a
r
oute
est
ablishm
ent
m
essage
(RE
M)
an
d
se
nds
i
t
to
it
s
ne
xt
ho
p.
If
the
REM
is
received
by
the
ne
xt
-
ho
p
node
,
the
n
the
ne
xt
-
hop
no
de
will
retran
sm
it
the
m
essage
and
i
niti
at
e
the
proces
s
of
updatin
g
the
hop
tree.
T
hes
e
ste
ps
are
r
ep
eat
ed
unti
l
the
sink
node
is
re
ached
or
t
he
node
t
hat
par
ti
ci
pate
d
in
a
pr
evi
ou
sly
con
str
ucted
r
oute
is
discov
e
r
ed.
The
r
oute
s
are
create
d
by
sel
ect
ing
the
be
st
neighbor
in
ever
y
hop.
The
ho
p
t
ree
s
hould
be
updated
so
that
al
l
so
urce
node
s
can
be
c
onne
ct
ed
via
t
he
li
gh
t
weig
ht
pat
hs,
the
data
aggre
gation
ca
n
be
opti
m
iz
ed,
and
th
e
ener
gy
loa
d
can
be
bala
nc
ed
in
the
su
cc
eedin
g
eve
nts.
In
the
pro
po
se
d
s
che
m
e, the
E
res
, a
nd
B
ava
values
ar
e upd
at
e
d
at
ea
ch node t
o fu
lfi
ll
these ob
j
ect
ives.
5.
PE
RFO
R
MAN
CE EV
A
LUATI
ON
The
pr
opos
e
d
EBR
-
D
A
was
evaluate
d
with
var
io
us
netw
ork
te
st
cases
in
MATL
AB
e
nv
i
ronm
ent.
The
res
ults
of
the
sim
ulati
on
exp
e
rim
ents
wer
e
al
so
analy
z
ed
us
i
ng
se
ve
r
al
per
f
or
m
ance
m
et
rics
to
assess
the
capab
il
it
y
and
the
ef
fici
ency
of
t
he
propose
d
sc
hem
e.
EBR
-
D
A
wa
s
co
m
par
ed
with
t
he
DR
INA
a
nd
I
nF
R
A
protoc
ols,
wh
i
ch
we
re
al
so
i
m
ple
m
ented
in
MATL
AB
to
ens
ure
that
al
l
schem
es
w
ere
r
un
on
the
sa
m
e
platfo
rm
and
unde
r
t
he
sam
e
co
nd
it
io
ns
a
nd
sim
ulati
on
pa
ram
et
ers.
Fur
therm
or
e,
EB
R
-
D
A
was
te
st
ed
a
nd
validat
ed
t
o
pr
ov
e
i
ts
ef
fecti
ve
ness
on
prom
oting
e
ne
rg
y
e
ff
ic
ie
ncy
an
d
l
oad
balanci
ng.
The
pa
ram
et
e
rs
are
the stan
dards
used i
n pr
act
ic
e.
How
e
ve
r,
t
he param
et
ers
ap
pl
ie
d
to th
e sim
ulati
on
a
re list
ed
in
Ta
ble 2.
Table
2
5
.
Sett
ing
s
of
Sim
ulati
on Pa
ram
et
ers
for
EBR
-
DA
Para
m
eter
Ty
p
e/Valu
e
Ch
an
n
el
W
ire
less
chan
n
el
An
ten
n
a
O
m
n
id
irection
al
Un
d
erly
in
g
M
AC
p
roto
co
l
IE
E
E
8
0
2
.15
.4
Sin
k
no
d
e
On
e with f
ix
ed
coo
rdin
ates
Sh
ap
e of
m
o
n
ito
ri
n
g
r
eg
io
n
Sq
u
are
Size of
m
o
n
ito
ring
r
eg
io
n
5
0
0
m
× 50
0
m
Nu
m
b
e
r
o
f
sen
so
r
n
o
d
es
1
0
0
,
1
5
0
,
2
0
0
,
2
5
0
,
an
d
30
0
Top
o
lo
g
y
Tr
ee
-
b
ased
dyna
m
ic clus
ter
Initial n
o
d
e energy
2
J
Nu
m
b
e
r
o
f
even
ts
3
Even
t r
ad
iu
s
8
0
m
Co
m
m
u
n
icatio
n
r
a
d
iu
s
8
0
m
Si
m
u
latio
n
ti
m
e
3
0
0
0
s
Data pack
et siz
e
1
0
2
4
bytes
Co
n
trol p
acket si
ze
5
6
bytes
Netwo
rk
th
resh
o
ld
0
.1 o
f
nodes
be aliv
e
The
t
otal
ene
r
gy
co
nsum
ption
a
ver
a
ges
of
al
l
the
te
ste
d
protoc
ols
a
re
dep
ic
te
d
in
Fi
gure
1.
The
energy
co
nsum
ption
ave
rage
f
ro
m
the
node
i
niti
al
energy
of
the
pro
posed
EBR
-
D
A
(
11.
65%)
was
l
ower
tha
n
tho
se o
f
DRI
N
A
(
21.
75
%
)
a
nd
I
nF
R
A
(
35.
71%)
.
T
he
EBR
-
D
A
c
on
s
um
ed
the
le
ast
energ
y
by
consi
de
ring
t
he
rem
ai
nin
g
ene
r
gy (
E
res
) of
t
he nodes
to
sta
bili
ze the e
ner
gy c
on
s
um
ption
a
m
on
g
the
node
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
An
E
ner
gy
-
Aware
and L
oad
-
ba
l
an
ci
ng R
ou
t
ing
Sc
hem
e
for
…
(
Omar
A
dil
Ma
hd
i
)
1317
Figure
1
.
Com
par
is
on of t
he Tota
l A
ver
a
ge
Energy C
on
s
um
pt
ion
Le
vels
a
m
on
g EB
R
-
D
A,
DRI
N
A,
a
nd
In
FR
A
Figure
2
s
how
s
the
eff
ic
ie
nc
y
of
EBR
-
DA,
wh
ic
h
effe
ct
ively
decr
ease
d
the
nu
m
ber
of
pack
et
s
pe
r
processe
d
data
and
outpe
rform
ed
bo
th
I
nFR
A
and
DRI
N
A
in
al
l
the
e
xp
e
rim
ents
reg
ar
dless
of
th
e
node
qu
a
ntit
y.
Com
par
e
d
with
DR
INA
(InFR
A)
,
EBR
-
D
A
ac
hieved
9.0
9%
(
37.50%)
ef
fici
en
cy
i
m
pr
ovem
e
nt
at
a
node
quantit
y
of
10
0
a
nd
10.
31%
(
56.
52%)
at
a
node
qua
ntit
y
of
300.
T
he
ou
tst
a
nd
i
ng
pe
rfor
m
ance
of
EBR
-
DA
is
due
to
the
fac
t
that
its
desig
n
re
qu
i
res
a
relat
ively
low
nu
m
ber
of
co
ntr
ol
pa
ckets
to
est
ablish
an
d
m
ai
ntain a r
ou
t
ing
tree. Mo
re
ov
e
r,
the
data aggre
gation qu
a
li
ty ach
ie
ved
by
the r
ou
ti
ng tr
ee bu
il
t by EBR
-
D
A
was hig
her tha
n
th
os
e
achie
ve
d by the
r
ou
ti
ng trees c
onstr
uc
te
d by
InFRA a
nd D
R
INA.
Figure
2
.
Com
par
is
on of t
he Effici
ency
Lev
el
s f
or
Dif
fer
e
nt No
de Qu
a
nt
it
ie
s
Anothe
r
si
gn
ifi
cant m
et
ric is pack
et
loss
. T
he
n
um
ber
s
of p
a
ckets lost
for d
iffer
e
nt num
ber
s of
nodes
wer
e
dete
rm
in
ed.
C
olli
sion
s
and
f
ull
que
ue
s
cau
se
pack
et
s
to
dro
p
at
t
he
destinat
io
n
node
.
T
he
rese
ndin
g
of
these
pac
kets
i
ncr
ease
s
ene
r
gy
con
s
um
ption,
re
du
ces
thr
ough
pu
t
(ch
a
nn
el
is
blo
cke
d
f
or
a
s
hort
ti
m
e
with
ever
y dro
pp
e
d
pack
et
),
a
nd
in
creases d
el
ay
(
a
pac
ket
ar
rive
s
at
a
la
te
r
ti
m
e
).
A
s
s
how
n
i
n
Figure 3
,
t
he
pa
cket
loss
inc
reased
with
inc
reasin
g
num
ber
of
node
s.
T
her
e
for
e,
pac
ket
loss
is
directl
y
relat
ed
to
the
num
ber
of
sens
or
no
des.
The
per
c
enta
ge
of
pack
et
l
osse
s
was
r
oughl
y
the
sam
e
in
case
of
EBR
-
DA
an
d
DRI
N
A.
The
m
ini
m
u
m
pack
et
loss
rates
(0.02%
–
0.0
5%
)
wer
e
gen
e
rate
d
by
EBR
-
D
A
fo
r
diff
e
re
nt
num
ber
s
of
node
s.
By
con
t
rast,
the
aver
a
ge
pac
ket
loss
rates
of
the
two
oth
e
r
protoc
ols
ra
ng
e
d
f
ro
m
0.
03%
to
0.68%.
The
perform
ance
of
I
nF
RA
deteri
or
at
e
d
as
the
nu
m
ber
of
se
nsor
nodes
i
ncrea
sed.
T
he
high
pac
ket
loss
of
this
protoc
ol is n
ot o
nly d
ue
to it
s
centrali
zed ope
rati
on
but also to
the b
r
oad
ca
s
ti
ng
o
f
the in
form
ation
o
f
a
n
event
al
l
ov
e
r
the
ne
twork
to
noti
fy
oth
e
r
nodes
and
the
updati
ng
of
t
he
path
s
from
the
ava
il
able
CHs
to
the
s
i
nk
node
e
ve
ry ti
m
e a n
e
w
e
ve
nt i
s sen
se
d. T
hes
e proces
ses a
re
co
stl
y an
d
li
m
it
n
et
wor
k
scal
abili
ty
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
12
, N
o.
3
,
Dece
m
ber
2
01
8
:
1312
–
1319
1318
Figure
3
.
Com
par
is
on of t
he Av
e
ra
ge
Pac
ke
t Loss
Rat
es fo
r
Di
ff
e
ren
t
N
ode
Qu
a
ntit
ie
s
6.
C
ONCLU
S
ION
A
ND F
U
TUR
E
REC
O
MM
E
N
DA
TI
ONS
EBR
-
D
A
is
an
in
-
netw
ork
data
ag
gr
e
gatio
n
m
ulti
-
hop
routin
g
schem
e
that
aim
s
to
enh
a
nce
the
energy
ef
fici
e
nc
y
and
loa
d
ba
la
ncing
in
WSNs.
The
pro
po
sed
MC
N
W
m
et
ric
was
us
e
d
to
est
im
a
te
the
node
weig
ht
on
t
he
basis
of
th
e
r
e
m
ai
nin
g
ene
r
gy
and
a
vaila
ble
buffer
m
e
m
or
y
siz
e.
The
MC
N
W
m
etr
ic
was
util
i
zed
durin
g
the
route
com
pu
ta
ti
on
in
the
li
nk
qu
al
it
y
m
easur
em
ent,
an
d
sim
ultaneou
s
ly
ensu
rin
g
the
data
transm
issi
on
acro
ss
a
li
ghtw
ei
gh
t
r
ou
te
in
WSNs.
T
he
sim
ula
ti
on
resu
l
ts
ob
ta
ine
d
from
the
per
f
or
m
ance
evaluati
on
of
the
pro
posed
EBR
-
D
A
we
r
e
com
par
ed
w
it
h
tho
se
of
DRI
NA
a
nd
I
nF
RA
prot
ocol
s.
Th
e
si
m
ulati
on
res
ults
sho
wed
th
at
the
pr
opos
e
d
EBR
-
D
A
ou
t
perform
ed
DR
INA
a
nd
I
nF
R
A
in
te
rm
s
of
aver
a
ge
energy
c
on
s
um
pt
ion
,
pac
ket
s
pe
r
processe
d
data,
a
nd
a
ve
rag
e
pac
ket
l
os
s,
pa
rtic
ularl
y
in
de
ns
e
net
works.
Seve
ral
p
aram
et
ers
wer
e
us
e
d
in
the
propo
sed
sc
hem
e.
F
ur
t
her
in
vestig
at
ion
to
fi
nd
t
he
opti
m
u
m
values
of
these
pa
ram
et
e
rs
us
i
ng
op
ti
m
i
zat
ion
te
ch
niques
ai
m
s
to
ai
d
in
decidin
g
th
e
best
weig
hta
ge
base
d
on
m
ulti
ple
obj
ect
ives
.
REFERE
NCE
S
[
1]
Idris
,
M.Y.I
.
,
et
al
.
,
Low
commu
nic
ati
on
cost
(
LC
C)
sche
me
for locali
zing mobi
le wi
rele
ss
sensor
net
works.
W
irel
es
s
Networks,
2017.
23(3):
p
.
737
-
74
7.
[2]
La
z
are
scu
,
M.
T.,
Wire
le
ss
Senso
r
Net
works
for
t
he
Int
erne
t
of
T
hings:
barr
ie
rs
and
syne
rgies
,
i
n
Components
a
nd
Serv
ices f
or IoT
Pl
atf
orm
s
.
2017
,
Springer. p. 155
-
186.
[3]
Al
-
Ma
y
ouf
,
Y.R
.
B.
,
O.A
.
Mahdi,
and
D.M.
Uli
y
a
n.
An
inters
ec
ti
o
n
-
based
segment
aware
algorit
hm
for
geographic
routing
in
VA
N
ETs
.
in
Information
and
Communi
cat
ion
S
ystem
s
(
ICICS
)
,
201
8
9th
Inte
rnatio
nal
Confe
renc
e
on
.
2018.
IE
EE.
[4]
Al
-
Ma
y
ouf
,
Y.R
.
B.
,
et
al.,
Ef
fici
ent
and
stable
routing
algorit
hm
based
on
user
mobili
ty
and
no
de
densit
y
in
ur
ban
ve
hi
cul
ar ne
twork.
PloS
on
e, 201
6.
11(11):
p.
e01
65966.
[5]
Jaigi
rda
r
,
F.T
.
and
M.M.
Is
lam
.
A
new
cost
-
ef
f
ec
t
iv
e
approa
ch
for
batt
lefi
eld
sur
ve
il
lance
i
n
wirel
ess
sensor
net
works
.
in
N
etwor
ki
ng
Syst
ems and
Se
curity
(
NSysS)
,
2016
Inter
nati
onal
Conf
ere
nce on
.
2016.
I
EE
E
.
[6]
Al
-
Ma
y
ouf
,
Y.R
.
B.
,
e
t
al
.
,
Ev
alu
ati
on
of
e
ff
i
ci
e
nt
ve
h
ic
ular
ad
ho
c
n
e
tworks
base
d
on
a
ma
xi
mum
distance
routin
g
algorit
hm.
EURAS
IP Journal
on
W
ire
le
ss
Com
m
unic
a
ti
ons a
nd
N
et
working,
2016.
2016(1):
p
.
265
.
[7]
Sabr
y
,
A.
,
et
al.,
W
ireless
Monit
oring
Proto
ty
p
e
for
Phot
ovo
ltaic
Paramete
rs
.
Indone
sian
Journal
of
Elec
tri
c
a
l
Engi
ne
eri
ng
and
Com
pute
r
Sci
en
ce
,
2018.
11(1):
p.
9
-
17
.
[8]
Al
-
Ma
y
ouf
,
Y.R
.
B.
,
et
al.,
Surv
e
y
on
Van
et
t
ec
hn
ologi
es
and
sim
ulat
ion
mode
ls.
ARP
N
Journal
of
Engi
ne
eri
ng
an
d
Applie
d
Sc
ie
nc
e
s,
2016.
11(15):
p.
9414
-
9427
.
[9]
Al
-
Ma
y
ouf
,
Y.R.
B.
,
e
t
al.,
A
cc
id
ent
Man
age
ment
Syste
m
B
ased
on
Ve
hicular
Net
work
for
an
Inte
ll
igent
Tr
anspo
rtati
on
S
yste
m i
n
Ur
ban
Env
ironments.
Journal
of
Advan
ce
d
Tra
nsport
at
i
on,
2018
.
2018
.
[10]
Ak
y
il
d
iz,
I
.
F., e
t
al.,
A
sur
ve
y
on
sensor
net
works.
IEEE
Com
m
unic
ations m
aga
z
in
e,
2002
.
40(
8):
p
.
102
-
114
.
[11]
Zna
id
,
A.
,
et
al.,
Seque
nt
ial
Mon
t
e
Carlo
loc
al
izat
ion
methods
in
mobile
w
irel
ess
sensor
net
works:
a
rev
ie
w
.
Journ
al
of
Sensors
,
2017
.
2017
.
[12]
Mahdi,
O.A.,
et
al
.
ESA
M
:
end
ocrine
inspired
sensor
act
iv
ati
o
n
mec
hanism
for
multi
-
target
trac
ki
ng
in
WSNs
.
in
Fourth
Int
ernational
Confe
ren
ce
on
Wireless
an
d
Optic
al
Comm
unic
ati
ons
.
2016
.
Int
ern
ationa
l
S
oci
e
t
y
for
Opt
ics
and
Photoni
cs.
[13]
Al
-
Ma
y
ouf
,
Y.R
.
B.
,
et
a
l.,
Re
a
l
-
Time
Inte
rs
ec
ti
o
n
-
Based
Segme
n
t
Aware
Rout
ing
Al
gorithm
for
Ur
ban
Ve
hic
ula
r
Net
works.
I
EEE
Tra
nsac
ti
ons on
Inte
lligen
t
Tr
ans
porta
ti
on
S
y
s
te
m
s,
2018.
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
An
E
ner
gy
-
Aware
and L
oad
-
ba
l
an
ci
ng R
ou
t
ing
Sc
hem
e
for
…
(
Omar
A
dil
Ma
hd
i
)
1319
[14]
Mahdi,
O.A.,
e
t
al
.
,
A
comparis
on
study
on
nod
e
cl
ustering
tech
nique
s
used
in
target
track
ing
WSNs
for
ef
fi
ci
e
nt
data
aggregat
io
n.
W
ireless Com
m
unic
at
ions
and
Mobile Com
puti
ng,
2
016
.
16(16
):
p.
2663
-
2676.
[15]
Adil
Mahdi,
O.
,
et
al.,
WDA
RS:
A
wei
ght
ed
dat
a
aggregati
on
routing
strategy
wit
h
minimum
link
cost
in
e
ve
n
t
-
drive
n
WSNs.
Jo
urna
l
of
Sensors
,
2016.
2016.
[16]
Madde
n,
S.
,
et
a
l.
,
TAG:
A
ti
ny
a
ggregati
on
servi
ce
for
ad
-
ho
c
se
n
sor
net
works.
ACM
SIG
O
PS
Opera
ti
ng
S
y
ste
m
s
Revi
ew, 2002. 3
6(SI):
p.
131
-
14
6.
[17]
Nae
imi,
S.
,
e
t
al.,
A
sur
ve
y
on
t
he
tax
onomy
of
cl
uster
-
based
ro
uti
ng
protocol
s
for
homogene
ou
s
wirel
ess
sensor
net
works.
Senso
rs,
2012.
12(6):
p.
7350
-
7409
.
[18]
W
ang,
N.
-
C.
,
e
t
al
.
,
Gr
id
-
based
data
aggregati
o
n
for
wirel
ess
se
nsor
net
works.
J
ourna
l
of
Advanc
es
in
Com
pute
r
Networks,
2013.
1(4).
[19]
Sujat
ha
,
V.
and
E.
M.
Anith
a,
Imm
ense
ly
Disc
riminate
Routin
g
in
Wireless
Net
works.
Indo
nesia
n
Journal
of
El
e
ct
ri
ca
l
Eng
in
ee
ring
an
d
Com
pute
r
Sc
ie
nc
e, 2
017.
8(3):
p.
712
-
714.
[20]
Naka
m
ura
,
E
.
F.,
et
al.
On
dem
and
role
ass
ignment
for
event
-
det
e
ct
ion
in
sen
sor
net
works
.
in
Computers
and
Comm
unic
ati
ons,
2006
.
ISCC'06.
Proceedi
ngs.
11
th
IE
EE Sy
mpos
i
um on
.
2006
.
I
E
EE
.
[21]
Vill
as,
L
.
A.,
e
t
al
.
,
DRINA:
A
l
ight
wei
gh
t
and
reli
able
routing
approach
for
i
n
-
net
work
aggre
gati
on
in
wirel
e
ss
sensor
net
works.
IEEE Transa
ct
i
ons on
Com
puters
,
2013.
62(4):
p.
676
-
689
.
[22]
Mala
kooti,
B
.
an
d
I.
Thomas.
A
d
istribut
ed
composite
mult
ipl
e
criteria
routing
usin
g
distanc
e
vect
o
r
.
in
Ne
tworki
ng
,
Sensing
and
Con
trol,
2006
.
ICNS
C'06.
Proc
ee
di
n
gs of
th
e
2006
I
EE
E
Inte
rnat
ion
al
Conf
ere
nce o
n
.
2006
.
IE
EE.
Evaluation Warning : The document was created with Spire.PDF for Python.