Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
6,
No.
5,
October
2016,
pp.
2403
–
2414
ISSN:
2088-8708
2403
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
An
Unequal
Cluster
-based
Routing
Pr
otocol
Based
on
Data
Contr
olling
f
or
W
ir
eless
Sensor
Netw
ork
Slaheddine
Chelbi
*
,
Majed
Abdouli
***
,
Mourad
Kaddes
***
,
Claude
Duv
allet
**
,
and
Rafik
Bouaziz
*
*
MIRA
CL
Laboratory
,
Uni
v
ersity
of
Sf
ax,
T
unisia
**
LITIS,
Uni
v
ersity
of
Le
Ha
vre,
France
***
FCIT
,
Uni
v
ersity
of
Jeddah,
Saudi
Arabia
Article
Inf
o
Article
history:
Recei
v
ed
Apr
3,
2016
Re
vised
Aug
4,
2016
Accepted
Aug
21,
2016
K
eyw
ord:
W
ireless
Sensor
Netw
orks
Unequal
cluster
size
Sensiti
v
e
data
controlling
Routing
Protocol
Ener
gy
Sa
ving
ABSTRA
CT
W
ireless
Sensor
Netw
orks
(WSN)
dif
fer
from
traditional
wireless
communication
netw
orks
in
se
v
eral
characteristics.
One
of
these
charac
teristics
is
po
wer
a
w
arness
,
due
to
the
f
act
that
the
batteries
of
sensor
nodes
ha
v
e
a
res
tricted
lifeti
me
and
are
dif
ficult
to
be
replaced.
There-
fore,
all
designed
protocols
must
minimize
the
ener
gy
consumption
and
therefore
preserv
e
the
longe
vity
of
the
netw
ork.
In
this
paper
,
we
propose
(i)
to
f
a
irly
balance
the
load
among
nodes.
F
or
this,
we
generate
an
unequal
clusters
size
where
the
cluster
heads
(CH)
election
is
based
on
ener
gy
a
v
ail
ability
,
(ii)
to
reduce
the
ener
gy
consumption
due
to
the
transmission
by
using
multiple
metrics
in
the
CH
jointure
process
and
taking
into
account
the
link
cost,
residual
ener
gy
and
number
of
cluster
mem
bers
to
construct
the
routing
tree
and
(iii)
to
min-
imize
the
number
of
transmissions
by
a
v
oiding
the
unnecessary
updates
using
sensiti
v
e
data
controller
.
Simulation
results
sho
w
that
our
Adv
anced
Ener
gy-Ef
ficient
Unequal
Clustering
(AEEUC)
mechanism
impro
v
es
the
f
airness
ener
gy
consumption
among
all
sensor
nodes
and
achie
v
es
an
ob
vious
impro
v
ement
on
the
netw
ork
lifetime.
Copyright
c
2016
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Slaheddine
Chelbi
MIRA
CL
Laboratory
Uni
v
ersity
of
Sf
ax,
T
unisia
slaheddine.chelbi@gmail.com
1.
INTR
ODUCTION
In
the
last
decade,
with
the
de
v
elopment
and
adv
ancement
of
sensor
technologies
and
their
relati
v
e
cheapness,
WSN
ha
v
e
been
widely
deplo
yed
for
both
ci
vil
and
military
applications,
e.g.,
harbor
container
monitoring
system,
marine
monitoring
for
earthquak
e
and
tsunami,
and
military
surv
eillance
in
battlefields
[1][2].
A
WSN
consists
of
hundreds
to
thousands
of
sensor
nodes,
which
ha
v
e
the
ability
to
communicate
among
themselv
es
using
radio
antenna.
Since
sensor
nodes
are
battery
po
wered
and
may
be
applied
in
dangerous
or
inaccessible
en
vironments,
it
is
dif
ficult
and
e
v
en
impossible
to
replace
or
rechar
ge
the
po
wer
supplies
[3]
[4].
W
e
need
ener
gy-ef
ficient
mechanisms
to
reduce
ener
gy
consumption
of
nodes
and
maximize
netw
ork
lifeti
me.
Balancing
the
ener
gy
consumption
in
the
netw
ork
is
an
important
issue
for
prolonging
the
netw
ork
lifetime.
Therefore,
ef
ficient
routing
techniques
are
highly
required
to
balance
the
ener
gy
consumption
among
the
netw
ork
nodes
and
maximize
the
netw
ork
lifetime
[5][6].
Dif
ferent
routing
protocols
for
WSN
proposed
in
literature
are
cate
gorized
based
on
their
computational
comple
xity
,
netw
ork
structure,
ener
gy
ef
ficienc
y
and
path
establishment
[7][8]
[9].
One
of
the
principal
clas
ses
is
hierarchical
routing
protocols.
In
this
class,
the
main
idea
is
that
e
v
ery
sensor
node
within
a
WSN
is
grouped
along
with
some
other
of
its
neighboring
nodes
in
order
to
constitute
a
specific
clus
ter
.
Clustering
pro
vides
an
ef
fecti
v
e
method
for
prolonging
the
lifetime
of
a
WSN.
Data,
collected
by
the
sensor
nodes
belonging
to
a
cluster
,
are
not
directly
transmitted
to
the
Base
Station
(BS).
Instead,
a
node
of
the
cluster
,
called
CH,
collects
these
data
and
forw
ards
them
to
the
BS
after
possibly
ha
ving
performed
appropriate
data
aggre
g
ation.
In
this
w
ay
,
the
number
of
transmitted
messages
to
the
BS
is
reduced
and
considerable
po
wer
conserv
ation
is
achie
v
ed.
Ho
we
v
er
,
routing
protocols
rarely
consider
the
hot
spot
problem
in
multi-hop
sensor
netw
orks.
Indeed,
when
CH
cooperate
with
each
other
to
forw
ard
their
data
to
the
BS,
the
CH
closer
to
the
BS
are
b
urdened
with
hea
vier
relay
traf
fic
and
tend
to
die
much
f
aster
,
lea
ving
the
areas
of
the
net
w
ork
unco
v
ered
and
causing
netw
ork
partitions.
T
o
J
ournal
Homepage:
http://iaesjournal.com/online/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
Evaluation Warning : The document was created with Spire.PDF for Python.
2404
ISSN:
2088-8708
mitig
ate
the
hot
spot
problem,
an
Unequal
Cluster
-based
R
ou
t
ing
(UCR)
protocol
is
proposed
in
[10].
As
sho
wn
in
Figure
1,
nodes
are
grouped
into
unequal
sizes
clusters:
Clusters
closer
to
the
BS
ha
v
e
smaller
sizes
than
those
f
arther
a
w
ay
from
it.
Thus
CH
closer
to
the
BS
can
preserv
e
some
ener
gy
for
the
purpose
of
inter
-cluster
data
forw
arding.
cluster head
cluster member
base station
Figure
1.
Unequal
Cluster
size
with
the
UCR
mechanism
[10]
In
this
paper
,
we
propose
a
ne
w
mechanism
called
AEEUC
in
order
to
enhance
the
WSN
lifetime.
AEEUC,
as
well
as
UCR
[10],
use
an
unequal
clustering
and
multi-hop
routing
scheme
to
impro
v
e
the
netw
ork
lifetime.
Ho
we
v
er
,
the
proposed
mechanism
to
choose
CH
and
inter
-cluster
communication
in
AEEUC
is
dif
ferent
from
those
of
UCR.
Our
protocol
aims
to
balance
the
amount
of
the
ener
gy
consumption
among
CH
within
the
netw
ork.
Furthermore,
we
propose
to
reduce
t
he
consumed
ener
gy
by
minimizing
the
unnecessary
data
transmissions
and
hence
prolong
the
lifetime
of
the
system.
T
o
achie
v
e
this
goal,
we
propose
to
a
v
oid
the
transmission
of
ne
w
v
alue
of
data
if
it
does
not
de
viate
from
the
current
v
alue
more
than
Maximum
Data
Error
(MDE).
W
e
remind
that
the
MDE
indicates
the
maximum
de
viation
tolerated
between
the
current
v
alue
and
the
ne
w
one.
The
rest
of
this
paper
is
structured
as
follo
ws:
section
2
presents
a
brief
related
w
ork.
Section
3
outlines
the
design
of
our
proposed
routing
protocols
AEEUC.
Section
4
presents
simula
tions
and
results.
Finally
,
secti
on
5
dra
ws
the
conclusion
and
some
future
w
ork.
2.
RELA
TED
W
ORK
In
the
fe
w
recent
years,
a
WSN
ha
v
e
g
ained
the
attention
of
researchers
in
man
y
challenging
aspects.
The
most
important
challenge
in
these
netw
orks
is
ener
gy
conserv
ation.
P
articulary
,
t
w
o
major
issues
are
e
xplored
to
optimize
ener
gy
consumption:
routing
techniques
and
data
aggre
g
ation.
The
routing
techniques
are
classified
into
three
cate
gories
depending
on
the
netw
ork
structure:
flat,
hierar
-
chical,
and
location-based
routing
[11].
Hierarchical
clustering
techniques
can
help
to
reduce
useful
ener
gy
consumption.
Hierarchical
or
cluster
-based
routing
are
well-kno
wn
techniques
with
special
adv
antages
related
to
scalability
and
ef
ficient
communication
[2]
[12]
[13].
Data
aggre
g
ation
in
WSN
is
a
data
transfer
technique
by
which
se
v
eral
pack
ets
from
sensor
nodes
are
combined
into
one
[14]
[15].
This
technique
is
essential
because
the
reduction
of
data
pack
ets
reduces
ener
gy
consumption,
increases
netw
ork
lifetime,
and
therefore
impro
v
es
successful
data
transmission
ratio.
LEA
CH
(Lo
w
Ener
gy
Adapti
v
e
Clustering
Hierarch
y)
is
one
of
the
first
hierarchical
routing
protocol
pro-
posed
for
WSN
[3].
It
is
also
the
first
clustering
protocol
that
w
as
proposed
for
reducing
po
wer
consumpti
on.
LEA
CH
randomly
selects
a
fe
w
sensor
nodes
as
CH
and
rotates
this
role
to
e
v
enly
distrib
ute
the
ener
gy
load
among
the
sensors
in
the
netw
ork.
The
idea
is
to
form
clusters
of
the
sensor
nodes
based
on
the
recei
v
ed
signal
strength
and
use
local
CH
as
routers
to
the
BS.
The
operations
of
LEA
CH
are
done
into
tw
o
phases,
the
setup
phase
and
the
steady
state
phase
.
In
set
up
phase,
the
clust
ers
are
or
g
anized
and
CH
are
selected.
CH
change
randomly
o
v
er
time
in
order
to
balance
the
ener
gy
dissipation
of
nodes.
In
the
steady
state
phase,
the
current
data
is
transferred
to
the
BS.
Although
LEA
CH
is
able
to
increase
the
netw
ork
lifetime,
it
suf
fers
from
a
fe
w
limits:
(1)
routing
in
LEA
CH
protocol
is
based
on
the
assumption
that
each
node
can
transmit
directly
to
both
its
CH
and
the
BS.
Ho
we
v
er
,
such
a
single
hop
routing
is
impractical
in
netw
orks
ha
ving
their
sensors
deplo
yed
o
v
er
wide
re
gi
ons.
It
is
not
al
w
ays
a
realistic
assumption
for
single-hop
inter
-cluster
routing
with
long
communication
range.
Besides,
long-range
commu-
nications
directly
from
CH
to
the
BS
can
lead
to
high
ener
gy
consumption.
(2)
Despite
the
f
act
that
CH
rotati
on
is
IJECE
V
ol.
6,
No.
5,
October
2016:
2403
–
2414
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
2405
performed
at
each
round
to
achie
v
e
load
balancing,
LEA
CH
cannot
ensure
r
eal
load
balancing
because
CH
are
elected
in
terms
of
probabilities
without
ener
gy
considerati
ons.
(3)
LEA
CH
randomly
selects
a
list
of
CH
and
it
has
not
a
con-
straint
on
their
distrib
ution.
Hence,
probably
all
CH
will
be
concentrated
in
the
same
area.
Therefore,
some
nodes
will
not
ha
v
e
CH
in
their
neighborhoods.
T
o
o
v
ercome
these
dra
wbacks,
man
y
e
xtension
of
LEA
CH
are
proposed
in
liter
-
ature
[16].
In
[17],
the
authors
propose
a
central
control
algorithms
named
C-LEA
CH.
The
latter
propose
to
construct
the
clusters
in
such
a
w
ay
that
CH
are
scattered
throughout
the
netw
ork.
In
[18],
the
authors
propose
MODLEA
CH
protocol.
In
this
protocol,
the
CH
is
changed
until
and
unless
it
has
more
ener
gy
than
the
certain
required
threshold,
unlik
e
LEA
CH
where
the
CH
is
changed
after
e
v
ery
round.
Furthermore,
MODLEA
CH
cate
gorised
communication
into
dif
ferent
cate
gories
in
order
to
optimize
ener
gy
spend
for
communication.
In
[19],
a
multi-hop
protocol
entitled
MH-LEA
CH
is
proposed.
This
protocol
increases
netw
ork
life
time
by
using
neighbour
nodes
for
data
transmission
which
results
in
lesser
ener
gy
consumption.
Hybrid
Ener
gy
Ef
ficient
Distrib
uted
(HEED)
is
a
distrib
uted
clustering
algorithm
in
which
tw
o
parameters
are
used
to
determine
the
eligibility
of
a
node
to
become
a
CH
[20].
Since
prolonging
the
netw
ork
lifetime
is
the
main
goal,
residual
ener
gy
is
used
as
the
first
parameter
,
which
allo
ws
nodes
with
higher
residual
ener
gy
to
become
CH,
thus
balancing
the
o
v
erall
ener
gy
of
the
net
w
ork.
The
second
f
actor
intra
communication
cost,
which
can
be
cluster
density
,
allo
ws
a
node
to
join
a
CH
with
the
least
number
of
nodes
so
as
to
r
educe
the
load
of
the
int
ra-cluster
traf
fic
on
the
CH.
Ho
we
v
er
,
HEED
does
not
mak
e
an
y
assumption
about
the
netw
ork
such
as
density
or
size.
HEED
introduces
e
xtra
communication
o
v
erhead
because
it
needs
to
e
xchange
a
lar
ge
number
of
messages
in
order
to
compute
the
communication
cost
with
its
neighbors.
In
[21],
Y
u
et
al.
proposed
a
cluster
-based
routi
ng
protocol
for
WSN
with
non-uniform
node
distrib
ution
(EADC),
whose
cores
are
an
ener
gy-a
w
are
clustering
algorithm
and
a
cluster
-based
routing
algorithm.
EADC
is
a
competition
based
algorithm,
where
CH
is
selected
on
the
basis
of
the
ratio
between
its
residual
ener
gy
to
the
a
v
erage
residual
ener
gy
of
its
neighbors.
T
o
form
clusters,
each
node
chooses
t
he
nearest
CH
without
tak
en
into
account
its
residual
ener
gy
.
Each
CH
chooses
a
ne
xt
hop
CH
with
higher
residual
ener
gy
and
fe
wer
cluster
members
as
its
ne
xt
hop.
In
BCEE
[22],
CH
is
selected
by
using
K-means
algorithm.
T
o
form
clusters,
BCEE
does
not
require
position
of
each
node
b
ut
uses
the
idea
of
recei
v
e
signal
strength
indicator
(RSSI).
T
o
route
the
data
to
the
sink,
the
techniques
of
ant
colon
y
optimization
is
used
to
establish
an
optimal
multi-hop
route
from
CH
to
the
sink
using
rational
and
hop-selecting
technique.
Ho
we
v
er
,
BCEE
introduces
e
xtra
communication
o
v
erhead
and
delay
of
data
transmission
to
the
sink
node.
In
[23],
the
authors
ha
v
e
used
the
adv
antages
of
tw
o
approaches
i.e.
fuzzy
c-means
(FCM)
clustering
and
neural
netw
ork
to
mak
e
an
ener
gy
ef
ficient
netw
ork
by
prolonging
the
lifetime
of
netw
ork.
The
cluster
formation
is
done
using
FCM
to
form
equally
sized
clusters
in
netw
ork
and
the
decision
of
choosing
CH
is
done
using
neural
netw
ork
ha
ving
input
distance
from
BS,
heterogeneity
and
ener
gy
of
the
node.
The
main
focuses
of
these
algorithms
are
to
re
d
uc
e
and
balance
the
ener
gy
consumption
of
nodes
by
using
clustering
algorithms.
In
f
act,
clustering
(1)
enables
data
aggre
g
ation
at
CH,
hence
reduces
the
number
of
unnecessary
data
transmissions,
and
sa
v
es
ener
gy
of
the
sensor
nodes,
(2)
simplify
routing
management
because
only
CH
need
to
maintain
the
local
route
setup
of
other
CH
and
thus
require
small
routing
information,
(3)
conserv
es
communication
bandwidth
[24]
.
Ho
we
v
er
,
CH
bear
some
e
xtra
w
ork
load
contrib
uted
by
their
member
sensor
nodes.
In
the
other
hand,
when
the
multi-hop
inter
-cluster
communication
model
i
s
adopted,
the
CH
closer
to
the
BS
are
b
urdened
with
hea
vier
relay
traf
fic
and
will
depletes
ener
gy
much
f
aster
,
lea
ving
areas
of
the
netw
ork
unco
v
ered.
T
o
mitig
ate
the
hot
spot
problem,
Chen
etal
.
[10]
ha
v
e
proposed
UCR
protocol.
In
UCR,
CH
is
selected
based
on
local
information
i:e:
,
the
residual
ener
gy
of
neighboring
nodes.
The
node’
s
competition
range
decrea
ses
as
its
distance
to
the
BS
decreases.
The
result
is
that
clusters
closer
to
t
he
BS
are
e
xpected
to
ha
v
e
smaller
cluster
sizes,
thus
the
CH
will
consume
lo
wer
ener
gy
during
the
intra-cluster
data
processing,
and
can
preserv
e
more
ener
gy
for
the
inter
-cluster
relay
traf
fic.
The
UCR
protocol
produces
a
distrib
ution
of
CH
through
an
election
process
using
a
competition
radius.
The
remaining
sensor
nodes
recei
v
e
the
broadcast
from
one
or
more
CH
and
mak
e
their
association
decision
based
on
minimum
communication
cost.
The
inter
-cluster
multi-hop
routing
protocol
considers
the
tradeof
f
between
the
ener
gy
cost
of
relay
links
and
the
ener
gy
of
relay
nodes.
UCR
e
xtends
LEA
CH
and
HEED
by
choosing
CH
with
more
residual
ener
gy
.
In
UCR
protocol,
clusters
closer
to
the
BS
are
e
xpected
to
ha
v
e
smaller
cluster
sizes.
Thus
the
y
will
consume
lo
wer
ener
gy
during
the
intra-cluster
data
processing,
and
can
preserv
e
more
ener
gy
for
the
inter
-cluster
relay
traf
fic.
But,
in
netw
orks
with
non-uniform
node
distrib
ution,
this
mechanism
is
not
al
w
ays
ef
fecti
v
e.
Consequently
,
the
higher
ener
gy
consumption
still
e
xists
among
CH
closer
to
BS
due
to
the
non-uniform
node
distrib
ution.
Moreo
v
er
,
in
UCR
se
v
eral
tentati
v
e
CH
are
random
ly
selected
to
compete
for
final
CH.
Hence,
the
participation
of
some
tentati
v
e
CH
An
Unequal
Cluster
-based
Routing
Pr
otocol
Based
on
Data
Contr
olling
for
WSN
(S.
Chelbi)
Evaluation Warning : The document was created with Spire.PDF for Python.
2406
ISSN:
2088-8708
may
be
at
the
e
xpense
of
other
nodes
ha
ving
higher
residual
ener
gy
.
3.
THE
AEEUC
MECHANISM
In
this
section,
we
present
the
rele
v
ant
details
of
Adv
anced
Ener
gy
Ef
ficient
Unequal
Clustering
mechanism
in
WSN.
W
e
suppose
that
the
WSN
is
composed
by
a
BS
and
a
set
of
homogeneous
sensor
nodes
which
are
randomly
distrib
uted
o
v
er
a
bounded
area
of
interest.
The
sensors
nodes
and
BS
are
stationary
after
deplo
yment.
The
BS
is
a
node
with
high
capabilities
and
unlimited
po
wer
b
ut
the
sensor
nodes
are
ener
gy
constrained.
The
main
idea
of
AEEUC
is
based
on
the
creation
of
ener
gy-ef
ficient
clusters
for
a
gi
v
en
number
of
transmissions.
The
task
of
being
a
CH
is
rotated
among
sensors
in
each
round
(a
set
of
transmissions)
to
distrib
ute
the
ener
gy
con-
sumption
across
the
netw
ork.
AEEUC
is
a
distrib
uted
CH
competiti
v
e
algorithm
where
nodes
with
higher
ener
gy
will
be
elected
as
CH.
T
o
mitig
ate
the
hot
spot
problem,
we
use
in
the
AEEUC
protocol
unequal
sizes
clusters.
Thus,
clusters
closer
to
the
BS
ha
v
e
smaller
cluster
sizes,
and,
consequently
,
the
y
will
consume
less
ener
gy
during
the
intra-cluster
data
processing,
and
can
conserv
e
some
more
ener
gy
for
the
inter
-cluster
relay
traf
fic.
By
producing
cluster
with
unequal
sizes,
UCR
succeeds
in
making
the
ener
gy
consumption
of
CH
balanced.
Ho
we
v
er
,
in
some
cases
and
due
to
non-uniform
distrib
ution
of
nodes,
it
may
happen
that
CH
with
a
small
competition
range
will
ha
v
e
more
members’
nodes.
In
this
case,
the
y
ha
v
e
high
intra-cluster
ener
gy
consumption
[21]
.
F
or
this,
in
our
w
ork,
each
CH
will
select
t
he
neighbor
CH
with
higher
residual
ener
gy
,
minimum
link
cost
and
a
smaller
number
of
cluster
members
as
the
ne
xt
hop
to
balance
the
ener
gy
consumption
among
CH.
T
o
a
v
oid
the
unnecessary
updates
and
subsequently
optimize
the
use
of
resources,
in
[25]
data
is
allo
wed
to
de
viate,
with
a
certain
de
gree,
from
their
corresponding
v
alues
in
the
e
xternal
en
vironment.
The
y
introduce
the
notion
of
data
error
,
denoted
DE,
which
gi
v
es
an
indication
of
ho
w
much
the
v
alue
of
stored
data
de
viates
from
the
corresponding
real-w
orld
v
alue.
This
de
viation
has
a
threshold
named
MDE
(Maximum
Data
Error)
.
The
transmission
of
data
is
dis-
carded
if
the
de
viation
between
the
current
data
v
alue
and
the
stored
v
alue
is
less
or
equal
to
MDE
(if
D
E
M
D
E
).
In
the
same
w
ay
to
reduce
the
number
of
transmission,
we
allo
w
data
error
in
AEEUC.
The
whole
p
r
ocess
is
di
vided
into
three
phases:
CH
competition
phase;
cluster
formation
phase
and
data
transmission
phase.
3.1.
CH
competition
phase
AEEUC
is
a
distrib
uted
CH
competiti
v
e
algorithm,
where
the
CH
selection
is
primarily
based
on
the
residual
ener
gy
of
tentati
v
e
CH
and
rotating
CH
periodically
to
distrib
ute
the
ener
gy
consumption
among
nodes
in
each
cluster
.
Only
nodes
which
ha
v
e
not
been
CH
in
N
late
rounds
are
eligible
to
be
a
tentati
v
e
CH
for
the
current
round
to
ensure
that
only
nodes
with
suf
ficient
ener
gy
are
selected
as
CH.
Ordinary
nodes
which
ha
v
e
not
been
CH
in
N
late
round
become
tentati
v
e
CH
with
the
same
probability
T
which
is
a
predefined
threshold.
It
should
be
noted
that
T
is
used
to
reduce
the
message
o
v
erhead
on
the
dense
netw
ork.
S
tate
(
s
i
)
=
"
T
entativ
e
"
if
s
i
2
G
and
<
T
"
M
ember
"
O
ther
w
ise
(1)
Where
is
the
probability
of
being
selected
as
tentati
v
e
CH
and
G
is
the
group
of
the
nodes
which
ha
v
e
not
been
CH
in
N
late
rounds.
Each
tentati
v
e
CH
s
i
has
a
competition
range
R
i
.
Dif
ferent
competition
ranges
are
used
t
o
produce
clusters
of
unequal
sizes.
Only
one
final
CH
is
allo
wed
in
each
competition
range.
If
s
i
becomes
a
CH
at
the
end
of
the
competition,
there
will
not
be
another
CH
s
j
in
s
i
’
s
competition
range.
The
task
of
being
a
CH
is
rotated
among
sensors
in
each
round
to
distrib
ute
the
ener
gy
consumption
across
the
netw
ork.
Similar
to
UCR,
CH
closer
to
the
BS
should
support
smaller
cluster
sizes.
Thus,
more
clusters
need
to
be
produced
closer
to
the
BS.
That
i
s
to
say
,
the
tentati
v
e
CH’
s
competition
range
should
decrease
as
its
distance
to
the
BS
decreases.
W
e
need
to
select
a
proper
scope
of
competition
ranges
in
the
netw
ork.
R
is
the
maximum
competition
range
and
the
minimum
competition
range
is
set
to
(1
c
)
R
correspondingly
,
where
c
is
a
constant
coef
ficient
between
0
and
1.
Thus
the
tentati
v
e
CH
s
i
’
s
competition
range
R
i
can
be
e
xpressed
as
a
l
inear
function
of
its
distance
to
the
BS
(cf.
F
ormula
2):
R
i
=
(1
c
d
max
d
(
s
i
;
B
S
)
d
max
d
min
)
R
(2)
IJECE
V
ol.
6,
No.
5,
October
2016:
2403
–
2414
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
2407
Where
d
max
and
d
min
denote
respecti
v
ely
the
maximum
and
minimum
distance
between
sensor
nodes
in
the
netw
ork
and
the
BS
and
d
(
s
i
;
B
S
)
denotes
the
distance
between
s
i
and
the
BS.
Each
tentati
v
e
CH
maintains
a
set
S
C
H
of
its
adjacent
tentati
v
e
CH.
T
entati
v
e
CH
s
j
is
an
adjacent
node
of
s
i
if
s
j
is
in
s
i
’
s
competition
diameter
or
s
i
is
in
s
j
’
s
competition
diameter
.
In
the
CH
competition
phase,
the
broadcast
radius
of
e
v
ery
control
message
is
R
.
Each
tentati
v
e
CH
broadcasts
a
C
ompeteH
eadM
sg
which
contains
it
s
competition
radius
and
residual
ener
gy
.
After
the
construction
of
S
C
H
has
finished,
each
tentati
v
e
CH
checks
its
S
C
H
and
mak
es
a
decision
as
to
whether
it
can
act
as
a
CH.
If
the
node
s
i
has
the
lar
gest
residual
ener
gy
among
all
tentati
v
e
CH
in
its
S
C
H
,
it
is
elected
as
CH
and
broadcasts
a
F
inal
H
eadM
sg
message.
Otherwise,
i
t
gi
v
es
up
the
competition.
The
follo
wing
pseudo-code
gi
v
es
the
details
of
this
phase.
Algorithm
1
CH
competition
algorithm
if
s
i
2
G
then
R
AN
D
(0
;
1)
else
1
end
if
if
<
T
then
beT
entativ
e
=
T
r
ue
end
if
f
or
each
tentati
v
e
s
i
do
C
ompeteH
eadM
sg
(
id
;
R
comp
;
E
r
es
)
end
f
or
On
recei
ving
a
C
O
M
P
E
T
E
H
E
AD
M
S
G
form
node
s
j
if
d
(
s
i
;
s
j
)
<
s
j
:R
comp
or
d
(
s
i
;
s
j
)
<
s
i
:R
comp
then
Add
s
j
to
s
i
:S
C
H
end
if
while
S
tate
==
T
entativ
e
do
if
s
i
:E
r
es
>s
j
:E
r
es
;
8
s
j
2
s
i
:S
C
H
then
State
=
Head
F
inal
H
eadM
sg
(
id;
E
r
es
)
Exit
end
if
On
recei
ving
a
F
I
N
ALH
E
AD
M
S
G
form
node
s
j
if
s
j
2
s
i
:S
C
H
then
State
=
Member
QuitE
l
ectionM
sg
(
id
)
Exit
end
if
On
recei
ving
a
QU
I
T
E
LE
C
T
I
O
N
M
S
G
form
node
s
j
if
s
j
2
s
i
:S
C
H
then
Remo
v
e
s
j
from
s
i
:S
C
H
end
if
end
while
After
the
CH
has
been
selected,
sleeping
nodes
no
w
w
ak
e
up
and
each
CH
broadcasts
a
C
H
AD
V
M
S
G
across
the
netw
ork
field
which
contains
its
id
and
its
residual
ener
gy
.
3.2.
Cluster
f
ormation
phase
In
UCR,
each
ordinary
node
chooses
i
ts
closest
CH.
If
node
i
w
as
to
mak
e
a
decision
bas
ed
on
a
single
parameter
it
could
result
in
a
bad
choice
o
v
er
all.
F
or
e
xample,
selecting
the
closest
CH
will
lead
to
choosing
a
CH
which
is
at
a
smaller
residual
ener
gy
,
thus
resulting
in
more
CH
load.
Hence,
when
a
node
mak
es
a
decision
about
associating
with
the
CH,
it
is
necessary
that
man
y
parameters
should
be
considered.
In
the
proposed
technique,
the
first
focus
is
optimizing
the
ener
gy
usage
in
cluster
formation;
i.e.
t
he
decision
process
An
Unequal
Cluster
-based
Routing
Pr
otocol
Based
on
Data
Contr
olling
for
WSN
(S.
Chelbi)
Evaluation Warning : The document was created with Spire.PDF for Python.
2408
ISSN:
2088-8708
used
by
an
ordinary
sensor
node
to
associate
itself
with
a
CH
is
based
on
minimum
o
v
erall
communication
cost.
F
or
the
node
j
,
the
cost
of
joining
the
CH
k
is
computed
by
equation
(3):
C
ost
k
j
=
d
(
j
;
k
)
d
max
+
(1
)
E
max
E
k
R
es
E
max
(3)
d
max
=
max
f
d
(
j
;
k
)
g
;
K
2
S
(4)
E
max
=
max
f
E
R
es
g
;
K
2
S
(5)
Where
is
the
weighted
f
actor
for
the
trade-of
f
between
the
distance
to
the
CH
and
the
residual
ener
gy
of
the
CH,
and
S
is
a
candidate
CH
set
of
the
node
j
.
Each
ordinary
node
chooses
its
CH
and
then
informs
the
CH
by
sending
a
J
O
I
N
C
LU
S
T
E
R
M
S
G
which
contains
the
id
and
residual
ener
gy
of
this
node.
The
CH
sets
up
a
time
di
vision
multiple
access
(TDMA)
schedule
and
transmits
it
to
the
nodes
in
its
cluster
.
Each
CH
collects
the
messages
from
its
cluster
members
and
sa
v
e
them.
3.3.
Data
transmission
The
data
transmission
is
di
vided
in
tw
o
phases.
The
first
one
is
intra-cluster
where
each
non
CH
nodes
send
data
to
CH
and
the
second
is
inter
-cluster
where
the
CH
transmits
aggre
g
ated
data
to
BS.
3.3.1.
Intra-cluster
communication
In
our
protocol,
the
task
of
being
a
CH
is
rotated
among
sensors
in
each
round
to
balance
the
ener
gy
con-
sumption
across
the
netw
ork.
Each
round
is
composed
of
a
predefined
number
of
iterations
in
which
each
cluster
will
k
eep
its
structure.
But,
a
ne
w
clustering
will
be
triggered
only
when
one
CH
is
dead
or
the
end
of
a
round
is
reached.
This
mechanism
reduces
the
o
v
erhead
traf
fic
through
reducing
control
messages.
Thus,
the
ener
gy
will
be
sa
v
ed.
At
the
be
ginning
of
each
round
(first
iteration),
e
v
ery
non-CH
node
w
aits
for
their
TMD
A
slot
to
transmit
data.
When
the
time
slot
arri
v
es,
the
node
transmits
the
data
to
CH.
From
the
second
iteration,
each
node,
during
its
allocated
trans-
mission
time,
sends
to
the
CH
quantitati
v
e
data
concerning
the
sens
ed
e
v
ents.
Data
are
transmitted
by
a
node
to
its
CH
only
when
this
v
alue
changes
by
an
amount
equal
to
or
greater
than
the
MDE
(if
D
E
M
D
E
).
Hence,
only
parts
of
nodes
passing
data
v
erification
need
to
transmit
data
to
CH.
This
reduces
the
transmission
ener
gy
consumption.
In
the
second
phase,
each
CH
recei
v
es
the
data
from
its
cluster
nodes.
When,
all
the
data
are
recei
v
ed,
each
CH
aggre-
g
ate
the
data
it
has
recei
v
ed
along
with
its
o
wn
data
into
a
single
composite
message.
When
CH
recei
v
es
a
pack
et
of
an
y
type,
it
updates,
in
its
table
of
cluster
member
,
the
residual
ener
gy
field
and
the
data
v
alue
of
the
sender
node
to
be
a
w
are
of
the
death
of
a
node.
When
the
time
slot
of
one
node
arri
v
es
and
this
node
doesn’
t
transmit
the
data
to
CH,
this
later
will
use
the
v
alue
of
data
stored
and
the
de
viation
is
allo
wed.
3.3.2.
Inter
-cluster
communication
After
recei
ving
and
aggre
g
ating
data
from
dif
ferent
cluster
members,
the
CH
be
gin,
in
this
phase,
the
routing
of
data
to
the
ne
xt
hop
nodes.
As
the
ener
gy
dissipation
is
directly
proportional
to
transmission
distance
and
due
to
their
ener
gy
constraint,
WSN
usually
ha
v
e
a
limited
transmission
range
making
multi-hop
data
routing
to
w
ard
the
BS
more
ener
gy-ef
ficient
than
one-hop
transmissions.
The
CH
sent
a
ll
collected
data
of
the
cluster
members
to
BS
by
multi-hop
manner
,
which
sa
v
es
the
ener
gy
consumption
of
CH
when
BS
is
v
ery
f
ar
in
WSN.
Each
CH
s
i
selects
the
ne
xt
CH
s
j
.
If
a
s
i
’
s
distance
to
the
BS
is
smaller
than
TD-MAX
,
it
transmits
its
data
to
the
BS
directly;
otherwise
it
should
find
a
relay
node
which
can
forw
ard
its
data
to
the
BS.
Before
selecting
the
ne
xt
hop
node,
each
CH
broadcasts
a
beacon
message
across
the
netw
ork
at
a
fix
ed
po
wer
which
consists
of
its
node
id
,
residual
ener
gy
,
number
of
cluster
member
and
distance
to
the
BS.
The
multi-hop
forw
arding
algorithm
considers
nodes
on
the
CH
backbone
in
the
forw
ard
direction
(i.e.,
closer
to
the
BS)
only
.
The
neighboring
node
set
R
C
H
of
CH
s
i
is
defined
by
equation
6
where
x
is
the
minimum
inte
ger
that
lets
s
i
:R
C
H
contain
at
least
one
item.
s
i
:R
C
H
=
f
s
j
=d
(
s
i
;
s
j
)
xR
i
;
d
(
s
j
;
B
S
)
<
d
(
s
i
;
B
S
)
g
(6)
IJECE
V
ol.
6,
No.
5,
October
2016:
2403
–
2414
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
2409
In
UCR,
in
order
to
reduce
inef
ficiencies
of
ener
gy
consumption,
a
tradeof
f
is
made
between
the
tw
o
criteria
of
residual
ener
gy
and
link
cost
E
r
el
ay
.
E
r
el
ay
(
s
i
;
s
j
)
=
d
2
(
s
i
;
s
j
)
+
d
2
(
s
j
;
B
S
)
(7)
s
i
first
chooses
k
eligible
neighbor
nodes
from
s
i
:R
C
H
,
denoted
as
the
set
S
el
ig
ibl
e
:
s
i
:S
el
ig
ibl
e
=
f
s
j
=s
j
2
s
i
:R
C
H
;
E
r
el
ay
is
the
k
smal
l
est
g
(8)
In
this
mechanism,
s
i
chooses
as
its
relay
node
the
neighbor
in
s
i
:S
el
ig
ibl
e
that
has
the
biggest
residual
ener
gy
.
This
method
has
some
dra
wbacks:
In
UCR,
it
is
goi
ng
to
choose
k
nodes
with
the
smallest
E
r
el
ay
(
k
=
2
).
Then,
it
chooses
the
one
with
the
highest
res
idual
ener
gy
.
Practically
,
in
some
cases,
the
choice
based
on
residual
ener
gy
will
be
at
t
he
e
xpense
of
distance.
In
f
act,
the
choice
of
the
second
node
is
not
al
w
ays
v
alid
mainly
when
the
g
ap
between
the
first
E
r
el
a
y
and
the
second
one
is
lar
ge.
Thus,
it
can
results
in
more
ener
gy
e
xpenditure.
In
UCR,
if
CH
s
j
’
s
distance
to
the
BS
is
smaller
than
TD-MAX
,
and
CH
s
i
selects
s
j
as
its
relay
node
according
to
the
approach
described
before
and
if
the
residual
ener
gy
of
s
j
is
smaller
than
that
of
s
i
it
l
et
s
i
communicate
with
the
BS
directly
rather
than
aggra
v
ating
the
load
of
s
j
:
this
h
ypothesis,
in
some
cases,
is
not
the
best
choice
when
the
dif
ference
between
s
i
:E
r
es
and
s
j
:E
r
es
is
too
small
and
at
the
same
time
the
distance
between
s
i
and
BS
is
too
long.
In
our
solution,
if
a
node’
s
distance
to
the
BS
is
smaller
than
TD-MAX
,
it
transmits
its
data
to
the
BS
directly;
otherwise
it’
s
better
to
find
a
relay
node
which
can
forw
ard
its
data
to
the
BS.
Therefore,
a
tradeof
f
is
made
between
the
three
criteria
of
residual
ener
gy
,
number
of
cl
uster
members
and
link
cost
E
r
el
a
y
.
CH
selects
the
neighbor
CH
with
higher
residual
ener
gy
and
a
smaller
number
of
cluster
members
and
minimum
link
cost
E
r
el
ay
as
the
ne
xt
hop
in
order
to
balance
the
ener
gy
consumption
among
CH
and
reduce
the
ener
gy
cost
of
the
link
in
the
relay
process.
In
this
case,
the
CH
s
i
calculates
the
cost
function
to
choose
the
best
relay
node
among
all
candidates
S
.
The
node
that
has
the
minimum
v
alue
of
C
ost
j
will
be
selected
as
the
ne
xt
relay
.
The
cost
function
is
defined
by
equation
9:
C
os
t
j
=
E
r
el
ay
(
s
i
;
s
j
)
R
el
ay
max
+
E
cons
(
s
j
)
E
max
+
s
j
:d
N
(9)
R
el
ay
max
=
max
f
E
r
el
ay
(
s
i
;
s
j
)
g
;
s
j
2
S
(10)
Where
N
is
the
number
of
ali
v
e
node
and
s
j
:d
is
the
number
of
cluster
member
of
s
j
.
E
cons
(
s
j
)
represents
the
ener
gy
consumed
by
the
node
j
.
W
e
can
s
ee
from
formula
9,
the
CH
with
minimum
consumed
ener
gy
(i.e.,
higher
residual
ener
gy),
minimum
link
cost
and
fe
wer
cluster
members
will
ha
v
e
smaller
C
os
t
.
CH
s
i
chooses
the
neighbor
CH
with
the
smallest
C
ost
as
its
ne
xt
hop.
4.
SIMULA
TIONS
AND
RESUL
TS
In
order
to
e
v
aluate
the
proposed
mechanism,
we
use
our
simulator
and
we
ha
v
e
compare
results
to
the
UCR
mechanism
[10].
Our
simulations
in
v
olv
e
tw
o
parts.
In
the
first
part,
data
is
sent
to
CH
without
controls.
In
the
second
part,
we
control
data
before
sending
it
to
CH.
4.1.
Experiment
settings
and
metrics
Our
paper
adopts
the
same
radio
ener
gy
model
with
[17].
The
ener
gy
consumed
in
the
transmitter
node
(
E
tx
)
and
in
the
recei
v
er
node
(
E
r
x
)
with
distance
d
for
transmitting
a
b
-bit
data
pack
et
can
be
calculated
as
follo
ws:
E
tx
(
b;
d
)
=
b
E
e
+
b
E
f
d
2
if
d
d
c
b
E
e
+
b
E
tr
d
4
if
d
>
d
c
(11)
The
parameters
E
f
and
E
tr
are
the
amount
of
ener
gy
dissipates
per
bit
in
the
radio
frequenc
y
amplifier
according
to
the
distance
d
c
.
d
c
is
the
threshold
distance
that
depends
on
the
en
vironment.
In
our
w
ork
we
consider
,
both
the
free
space
(
d
2
po
wer
loss)
and
multi-path
f
ading
(
d
4
po
wer
loss)
channel
models
depending
on
the
distance
between
the
transmitter
and
the
recei
v
er
.
W
e
assume
free
space
model
if
(
d
<
d
c
),
otherwise
multi-path
f
ading
model
is
intended.
The
ener
gy
for
recei
ving
a
b
-bit
message
is
calculated
as
follo
ws:
E
r
x
(
b
)
=
b
E
e
(12)
Where
E
e
represents
the
electronics
ener
gy
.
It
depends
on
such
electronic
f
actors
as
digital
coding,
modulation,
filtering,
and
spreading
of
the
signal.
Simulation
parameters
are
gi
v
en
in
T
able
1.
F
or
simplicity
,
we
assume:
An
Unequal
Cluster
-based
Routing
Pr
otocol
Based
on
Data
Contr
olling
for
WSN
(S.
Chelbi)
Evaluation Warning : The document was created with Spire.PDF for Python.
2410
ISSN:
2088-8708
T
able
1.
Simulation
parameters.
P
arameters
V
alues
Netw
ork
field
400
x
200
BS
location
(500,
100)
E
e
50
nJ/bit
E
f
r
iss
amp
10
pJ
=bit=m
2
E
tw
o
r
ay
amp
0
;
0013
pJ
=bit=m
4
d
C
87
Data
pack
et
size
b
500
bytes
Initial
Ener
gy
of
sensor
E
0
1
J
Number
of
sensors
600
TD-MAX
150
An
ideal
MA
C
layer
and
error
-free
communication
links.
Nodes
are
GPS-enabled
and
each
node
is
a
w
are
of
its
geographic
location.
Each
node
is
assigned
a
unique
id
to
help
us
identifying
one
node
from
other
neighboring
nodes.
Sensors
can
use
po
wer
control
to
modify
the
amount
of
transmission
po
wer
according
to
the
distance
to
the
desired
recipient.
Each
node
can
communicate
directly
with
an
y
other
node
on
the
netw
ork.
Sensors
are
capable
of
operating
in
an
acti
v
e
mode
or
a
lo
w-po
wer
sleeping
mode.
In
this
paper
,
the
performance
will
be
e
v
aluated
via
simulations
with
respect
to
the
follo
wing
metrics:
First
Node
Dies
(FND)
and
Half
of
the
Nodes
Ali
v
e
(HN
A).
W
e
note
that
a
node
is
considered
”dead”
if
its
remaining
ener
gy
is
less
than
the
v
alue
for
the
transmission
task.
4.2.
AEEUC
without
data
contr
olling
Figure
2
sho
ws
that,
under
AEEUC,
the
le
v
el
of
residual
ener
gy
of
CH
is
lar
ger
than
under
UCR.
In
f
act,
in
UCR
protocol,
CH
are
randomly
selected
to
compete
for
final
CH.
Y
et
,
in
AEEUC
protocol,
only
nodes
which
ha
v
e
not
been
CH
in
N
late
round
are
el
igible
to
be
a
tentat
i
v
e
CH
for
the
current
round.
Thus,
only
nodes
with
suf
ficient
ener
gy
are
selected
as
CH.
This
e
xplains
which
leads
to
a
v
oiding
the
premature
death
of
CH.
Our
protocol
tends
to
minimize
the
unbalanced
communication
cost
which
is
e
v
aluated
for
each
CH
as
follo
ws:
Figure
2.
A
v
erage
of
residual
ener
gy
of
elected
CH.
C
unbal
anced
=
M
ax
(
C
i
)
M
in
(
C
i
)
(13)
IJECE
V
ol.
6,
No.
5,
October
2016:
2403
–
2414
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
2411
Where
C
i
is
the
communication
cost
of
CH
i
and
is
calculated
as
follo
ws:
C
i
=
E
cons
E
r
es
(14)
Where
E
cons
and
E
r
es
represent
the
consumed
ener
gy
and
the
residual
ener
gy
respecti
v
ely
.
The
higher
v
alue
of
C
unbal
anced
means
the
more
unbalanced
communication
cost.
Figure
3
plots
the
le
v
el
of
the
unbalanced
communication
cost
o
v
er
time
for
dif
ferent
algorithms.
Figure
4
sho
ws
the
a
v
erage
of
the
unbalanced
communication
cost.
Figure
3.
Unbalanced
communication
cost
As
sho
wn
in
Figure
3
and
4,
we
find
that
the
unbalanced
communica
tion
cost
in
the
proposed
algorithm
is
lo
wer
than
Figure
4.
A
v
erage
of
Unbalanced
communication
cost
the
other
algorithm.
Our
protocol
minimize
the
unbalanced
ener
gy
consumption
among
CH
within
the
netw
ork.
Figure
5
sho
ws
the
comparison
between
UCR
and
AEEUC
in
term
of
netw
ork
lifetime.
Under
AEEUC,
the
first
node
of
the
netw
ork
is
died
aft
er
425
rounds
while
under
UCR
the
first
node
die
much
before,
i.e.
380
rounds.
Moreo
v
er
,
HND
is
reached
later
under
AEEUC
(480
rounds)
than
under
UCR
(465
rounds).
AEEUC
gi
v
es
better
performances
than
UCR
in
prolonging
netw
ork
lifetime.
This
can
be
e
xplained
firstly
by
the
f
act
that
the
CH
elected
is
the
node
with
higher
ener
gy
.
Second,
when
nodes
join
clusters,
the
y
consider
both
the
distance
to
CH
and
the
residual
ener
gy
of
CH.
Third,
CH
choose
those
nodes
as
relay
node,
which
ha
v
e
minimum
ener
gy
consumption
for
forw
arding,
fe
wer
number
of
cluster
member
and
maximum
residual
ener
gy
to
a
v
oid
early
death.
An
Unequal
Cluster
-based
Routing
Pr
otocol
Based
on
Data
Contr
olling
for
WSN
(S.
Chelbi)
Evaluation Warning : The document was created with Spire.PDF for Python.
2412
ISSN:
2088-8708
Figure
5.
Netw
ork
lifetime
4.3.
AEEUC
with
data
contr
olling
Our
w
ork
aims
to
reduce
the
number
of
unnecessary
data
transmissions.
T
o
achie
v
e
this
goal,
a
Maximum
Data
Error
(MDE)
technique
is
used
to
reduce
the
number
of
transmissions
and
thus
considerable
ener
gy
conserv
ation
is
achie
v
ed.
Figure
6
sho
ws
the
change
in
the
number
of
messages
transmitted
by
the
nodes
to
CH
in
a
gi
v
en
period.
In
the
UCR
Figure
6.
V
ariance
of
number
of
sent
messages
to
CH
protocol,
e
v
ery
non-CH
node
must
transmit
its
sensed
data
to
CH
at
e
v
ery
iteration.
In
AEEUC
protocol,
before
each
transmission,
all
the
nodes
are
entitled
to
v
erify
their
data,
b
ut
only
parts
of
them
will
transmit
their
data
to
CH.
This
ob
viously
reduces
the
transmission
ener
gy
consumption.
As
sho
wn
in
Figure
7,
the
ener
gy
consumption
of
our
protocol
is
less
than
that
of
UCR.
Less
ener
gy
consumption
means
longer
lifetime
for
the
netw
ork.
Figure
8
sho
ws
the
v
ariation
in
the
number
of
all
ali
v
e
nodes
based
on
the
number
of
messages
recei
v
ed
by
the
BS.
As
sho
wn
in
the
figure,
the
number
of
dead
nodes
in
the
proposed
protocol
is
al
w
ays
less
than
that
of
UCR
protocol.
AEEUC
clearly
impro
v
es
the
netw
ork
lifetime
o
v
er
UCR.
Simulations
sho
wed
that
AEEUC
reduces
the
en-
er
gy
dissipation
and
thus
e
xtending
the
o
v
erall
netw
ork
lifetime.
IJECE
V
ol.
6,
No.
5,
October
2016:
2403
–
2414
Evaluation Warning : The document was created with Spire.PDF for Python.