TELK
OMNIKA
Indonesian
Journal
of
Electrical
Engineering
V
ol.
16,
No
.
1,
October
2015,
pp
.
191
199
DOI:
10.11591/telk
omnika.v16.i1.8751
191
Optimal
Placement
of
Sensor
s
in
Mission-specific
Mobile
Sensor
Netw
orks
Hic
ham
Ouc
hitac
hen
1,*
,
Abdellatif
Hair
2
,
and
Najlae
Idrissi
3
Sultan
Moula
y
Slimane
Univ
ersity
,
F
aculty
of
Sciences
and
T
echniques
1,2
Applied
Mathematics
and
Scientific
Calculus
Labor
ator
y
3
Inf
or
mation
Processing
Decision
Suppor
t
Labor
ator
y
P
.B
.
523,
Beni-Mellal,
Morocco
.
*
h.ouchitachen@gmail.com
Abstract
Placement
of
nodes
has
been
the
major
challenge
in
wireless
sensor
netw
or
ks
.
The
data
repor
ted
from
a
sensor
is
only
useful
when
the
position
of
that
sensor
is
f
ound.
In
this
conte
xt,
se
v
er
al
techniques
ha
v
e
been
proposed
to
conser
v
e
po
w
er
consumption
and
to
prolong
the
lif
etime
of
the
wire
less
sensor
netw
or
ks
.
In
this
paper
,
w
e
propose
a
ne
w
algor
ithm
that
accur
ately
finds
the
best
locations
of
sensors
while
minimizing
the
a
v
er
age
energy
consumed
in
the
netw
or
k.
More
precisely
,
w
e
consider
a
cr
itical
netw
or
k
in
each
sensor
satisfying
its
o
wn
missions
and
depending
on
its
locations
.
In
addition
to
fulfill
their
mission,
the
sensor
tr
ies
to
maintain
a
good
neighbor
ing
nodes
quality
.
w
e
deter
mine
the
location
of
node
b
y
using
tw
o
cr
iter
ia:
the
cost
and
the
quality
of
comm
unication.
The
ai
m
of
this
w
or
k
is
to
de
v
elop
a
ne
w
algor
ithm
so
as
to
solv
e
the
complicated
optimization
pro
b
lem
posed
in
this
case
while
minimiz
e
the
total
energy
consumption.
Our
sim
ulation
results
demonstr
ate
that
our
algor
ithm
is
v
er
y
adv
antageous
in
ter
ms
of
con
v
ergence
to
the
appropr
iate
locations
.
K
e
yw
or
ds:
energy
,
node
placement,
sensor
netw
or
ks
,
comm
unication
quality
,
genetic
algor
ithms
Cop
yright
c
2015
Institute
of
Ad
v
anced
Engineering
and
Science
.
All
rights
reser
v
ed.
1.
Intr
oduction
Wireless
Sensor
Netw
or
ks
(WSNs)
[1,
2]
are
f
orecast
to
become
highly
integ
r
ated
to
our
daily
activities
[3].
The
WSNs
are
becoming
increasingly
popular
f
or
mo
nitor
ing
spatial
phenom-
ena.
Indeed,
the
y
are
deplo
y
ed
to
collect
data
from
the
en
vironment,
process
sensed
data
and
tak
e
action
accordingly
.
T
ypical
applications
of
the
WSNs
include
en
vironmental
control
such
as
fire
fighting
or
mar
ine
g
round
erosion,
b
ut
also
sensors
installation
on
br
idges
or
b
uildings
to
mon-
itor
ear
thquak
e
vibr
ation
patter
ns
and
v
ar
ious
sur
v
eillance
tasks
such
as
intr
uder
sur
v
eillance
on
premises
.
Y
et,
it
is
still
v
er
y
ear
ly
in
the
lif
etime
of
such
systems
and
man
y
research
challenges
e
xist,
f
or
instance
their
computational
capabilities
,
limited
stor
age
,
and
especially
their
depen-
dence
on
a
limited
po
w
er
supplied
b
y
a
batter
y
.
The
lo
w
po
w
er
consumption
is
one
of
the
major
challenges
that
f
ace
WSNs
toda
y
.
Nodes
of
both
sensor
netw
or
ks
and
con
v
entional
netw
or
ks
such
as
wireless
LANs
and
cellular
netw
or
ks
,
f
ocus
on
p
ro
vid
ing
a
good
comm
unication
quality
,
or
gett
ing
letter
quality
ser-
vices
,
e
xcept
that
the
nodes
of
the
sensor
netw
or
ks
are
,
moreo
v
er
,
interested
in
the
satisf
action
of
their
missions
.
Thus
w
e
call
those
kinds
of
netw
or
ks
,
cr
itical
netw
or
ks
,
where
the
quality
and
satisf
action
of
the
mission
of
each
node
is
v
er
y
impor
tant
to
impro
v
e
the
o
v
er
all
perf
or
mance
of
cr
itical
netw
or
ks
mission.
Monitor
ing
in
mission-cr
itical
en
vironment
with
the
deplo
yment
of
WSNs
is
one
of
the
most
widely-used
application
areas
.
Mission-cr
itical
en
vironment
implies
monitor
ing
tasks
that
happened
in
locations
which
are
difficult
to
get
access
to
or
easy
to
lead
destro
y
of
the
netw
or
k
deplo
yment:
scenar
ios
in
hazard
and
emergency
monitor
ing
situations
such
as
chemical
pollution,
en
vironment-or
iented
pollutions
and
land
secur
ity
.
In
the
near
future
it
can
be
e
xpected
that
sur
v
eillance
areas
will
be
equ
ipped
with
a
r
ange
of
smar
t
sensors
to
pro
vide
par
ts
of
o
v
er
all
e
v
ent
detection
and
ser
vice
.
Hence
,
in
this
class
of
netw
or
ks
,
it
is
impor
tant
to
control
the
mobility
of
a
node
b
y
jointly
consider
ing
it
s
mission
and
comm
unication
quality
,
so
as
to
imp
ro
v
e
the
o
v
er
all
perf
or
mance
,
b
y
controlling
the
mobility
prob
lem
which
is
m
uch
difficult
to
study
currently
.
Receiv
ed
J
uly
2,
2015;
Re
vised
A
ugust
18,
2015;
Accepted
September
2,
2015
Evaluation Warning : The document was created with Spire.PDF for Python.
192
ISSN:
2302-4046
Researchers
ha
v
e
conducted
in
v
estigations
on
WSNs
at
almost
e
v
er
y
la
y
er
of
the
com-
m
unication
protocol
stac
k.
There
e
xist
se
v
er
al
papers
on
efficient
routing
algor
ithms
and
data
agg
regation
techniques
[4,
5],
localization
techniques
[6,
7]
and
medium
access
control
(MA
C)
methods
[8,
9].
In
addition,
researchers
tak
e
adv
antage
of
the
fle
xibility
of
WSNs
b
y
design-
ing
protocols
that
positiv
ely
aff
ect
energy
consumption.
The
major
ity
of
researcher
papers
that
appeared
until
no
w
consider
sensor
netw
or
k
to
be
static.
Recently
,
mobility
control
or
node
placement
prob
lems
become
the
aim
of
researches
and
studies
in
mobile
sensor
a
nd
ad
hoc
netw
or
ks
.
In
[10]
and
[11],
the
modeling
tr
ansmission
po
w
er
consumption
w
as
considered
as
a
tool
of
studying
prob
lems
of
rela
y
node
placements
related
to
minimizing
po
w
er
consumption
or
maximizing
netw
or
k
lif
etime
,
via
a
function
of
the
distance
betw
een
tw
o
nodes
.
In
[12]
and
[13],
a
study
of
prob
lems
that
place
minim
um
n
umber
of
rela
y
nodes
as
long
as
the
constr
aints
on
node
connectivity
or
tr
affic
demands
are
satisfied.
In
[14],
a
mobility
control
prob
lem
in
ad
hoc
netw
or
ks
is
studied
to
maximiz
e
the
throughput
based
on
the
IEEE
802.11
throughput
analysis
in
[15]
without
consider
ing
the
v
ar
iation
of
the
link
capacity
according
the
distance
change
betw
een
tw
o
end
nodes
.
In
[16,
18],
prob
lems
that
place
rela
y
nodes
consider
ing
the
throughput
of
the
netw
or
k
are
stu
died.
In
[16],
consider
ing
the
probabilistic
model
f
or
the
positions
of
nodes
,
an
algor
ithm
f
or
the
rela
y
node
placement
that
maximiz
es
the
throughput
of
the
n
etw
or
k
is
proposed.
In
[17],
joint
rela
y
node
placement
and
assignment
prob
le
m
is
studie
d.
Once
the
rela
y
node
assignment
is
deter
mined,
the
prob
lem
in
[17]
is
reduced
to
the
prob
lem
of
finding
the
optimal
position
of
a
single
rela
y
node
while
other
nodes
that
comm
unicate
with
the
rela
y
node
are
assumed
to
be
fix
ed.
In
[18],
a
cascaded
netw
or
k
in
which
rela
y
nodes
f
or
m
a
single
chained
comm
unication
flo
w
is
considered
and
a
g
r
adient-
based
algor
ithm
is
proposed
to
control
the
position
of
rela
y
nodes
to
maximiz
e
the
minim
um
throughput
among
those
of
all
the
links
in
the
chain.
The
w
or
k
that
w
e
consider
in
this
paper
is
clear
ly
diff
erent
in
se
v
er
al
cr
itical
aspects
.
First,
w
e
consider
a
netw
or
k
with
m
ultiple
nodes
with
controllab
le
mobility
in
a
mesh
topology
in
a
tw
o-dimensional
space
.
In
f
act,
the
e
xtension
from
a
single
node
with
controllab
le
mobility
[16,
17,
19,
20]
or
from
the
chained
topology
[18,
21]
to
m
ultiple
nodes
with
controllab
le
mobility
in
a
mesh
topology
is
not
str
aightf
orw
ard.
The
rest
of
the
paper
is
organiz
ed
as
f
ollo
ws
.
Section
2
deals
with
mathematical
modeling
of
the
prob
lem.
Section
3
descr
ibes
n
umer
ical
results
.
Conlusion
is
presented
in
section
4.
2.
Resear
c
h
Method
2.1.
Model
netw
ork
In
this
section,
w
e
present
our
approache
to
solv
e
the
prob
lem
of
optimal
placement
of
sensors
in
mob
ile
wireless
sensors
netw
or
k.
W
e
consider
a
WSNs
(Fig.1)
containing
se
v
er
al
sensors
managed
b
y
a
base
station
(BS).
The
nodes
satisfy
their
missions
depending
on
their
locations
.
T
o
accomplish
their
mission,
the
sensor
nodes
ha
v
e
to
maintain
a
good
quality
of
comm
unication
with
their
neighbors
,
which
also
depends
on
their
locations
.
Our
objectiv
e
is
to
find
the
best
locations
of
sensor
nodes
b
y
de
v
eloping
Sensor’
s
Genetic
Algor
ithm
(SGA).
The
c
h
oice
of
Genetic
Algor
ithms
(GA)
in
this
w
or
k
is
motiv
ated
firstly
b
y
finding
an
algo-
r
ithm
that
per
mits
to
solv
e
the
non-con
v
e
x
optimization
prob
lems;
GA
impose
no
regular
ity
on
the
function
studied
(contin
uity
,
diff
erentiabilit
y
,
con
v
e
xity
...).
Secondly
b
y
getting
a
rob
ust
algor
ithm
f
or
looking
f
or
a
solution
v
er
y
close
to
optimal
or
near-optimalsolution.
Thus
,
in
order
to
solv
e
the
energetic
constr
aint
which
is
the
main
challenge
of
WSNs
,
the
optimization
prob
lem
to
be
solv
ed
is
f
or
m
ulated
as
f
ollo
ws
:
W
e
tak
e
n
sensors
car
acter
iz
ed
each
one
b
y
tw
o
cost
funct
ions:
Mission
and
comm
unication
costs
.
W
e
then
aim
to
minimiz
e
the
total
cost
b
y
choosing
the
best
location
of
each
sensor
calculated
via
SGA.
W
e
suppose
that
a
set
of
n
sensors
is
deplo
y
ed
in
a
geog
r
aphic
area
of
interest
to
su-
per
vise
a
giv
en
ph
ysical
phenomenon.
The
topology
of
a
WSNs
is
represented
b
y
the
g
r
aph
G
=
(
C
;
E
)
,
where
C
=
f
1
;
2
;
:::;
n
g
is
a
set
of
n
sensors
and
E
C
C
is
the
set
of
wireless
links
betw
een
the
v
ar
ious
sensors
.
W
e
denote
C
v
(
i
)
the
neighbor
set
of
the
sensor
i
.
In
the
belo
w
,
w
e
present
the
meanings
of
the
notations
used
in
our
modeling.
TELK
OMNIKA
V
ol.
16,
No
.
1,
October
2015
:
191
199
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
2302-4046
193
Figure
1.
WSN
architecture
Notations
Meaning
C
set
of
sensors
A
set
of
activ
e
sensors
C
v
(
i
)
neighbor
set
of
sensor
i
S
i
area
in
where
each
sensor
i
can
mo
v
e
freely
(
x
i
;
y
i
)
current
position
of
sensor
i
(
x
s
i
;
y
s
i
)
mission’
s
position
of
sensor
i
(
x
c
i
;
y
c
i
)
comm
unication’
s
position
of
sensor
i
(
x
op
i
;
y
op
i
)
optimal
location
of
sensor
i
(
x
op
bs
;
y
op
bs
)
optimal
location
of
base
station
d
ij
distance
betw
een
sensors
i
and
j
d
is
distance
betw
een
current
and
mission’
s
position
of
a
sensor
i
f
c
ij
(
d
ij
)
cost
of
comm
unication
betw
een
sensors
i
et
j
f
s
i
(
d
is
)
mission
cost
of
sensor
i
.
c
comm
unication
f
actor
s
sur
v
eillance
f
actor
2.2.
Optimal
placement
of
sensor
s
T
o
minimiz
e
the
energy
consumed
b
y
all
sensors
consider
ing
the
joint
comm
unication
and
mission
costs
,
w
e
propose
to
find
the
optimal
location
(
x
op
i
;
y
op
i
)
of
each
sensor
b
y
solving
the
f
ollo
wing
optimization
prob
lem
[22]:
min
f
(
x;
y
)
=
i
sf
s
i
(
d
is
)
+
i
j
2
C
v
(
i
)
cf
c
ij
(
d
ij
)
(1)
Optimal
placement
of
sensors
in
mission-specific
mobile
sensor
netw
or
ks
(Hicham
Ouchitachen)
Evaluation Warning : The document was created with Spire.PDF for Python.
194
ISSN:
2302-4046
subject
of
(
x
i
;
y
i
)
2
S
i
8
(
i;
j
)
2
C
C
v
(
i
)
W
e
put:
f
c
i
(
d
ij
)
=
j
2
C
v
(
i
)
f
c
ij
(
d
ij
)
(2)
V
=
(
x
1
;
y
1
;
x
2
;
y
2
;
::::;
x
n
;
y
n
)
(3)
F
s
(
x;
y
)
=
i
f
s
i
(
d
is
)
(4)
F
c
(
x;
y
)
=
i
f
c
i
(
d
ij
)
(5)
F
(
V
)
=
f
(
x;
y
)
(6)
So
(1)
becomes:
minF
(
V
)
=
sF
s
(
V
)
+
cF
c
(
V
)
(7)
subject
of
V
2
Q
n
i
=1
S
i
Pr
oposed
algorithm
T
o
solv
e
the
optimization
prob
lem
giv
en
b
y
(7),
w
e
implement
the
f
ollo
wing
algor
ithm:
Data:
(
x
m
i
;
y
m
i
)
,
(
x
c
i
;
y
c
i
)
,
i
2
C
Result:
(
x
op
i
;
y
op
i
)
,
i
2
C
initialization
of
population
P
;
while
No
con
v
ergence
do
P
0
:=Selection
of
parents
in
P
;
P
0
:=
Apply
the
crossing
oper
ator
on
P
0
;
P
0
:=
Apply
the
m
utation
oper
ator
on
P
0
;
P
0
:=
Replace
old
parents
b
y
their
descendants
P
0
;
Ev
aluate
P
0
;
end
Algorithm
1:
Algor
ithm
SGA
Our
algor
ithm
star
ts
b
y
gener
ating
an
initial
population
P
and
e
v
aluating
the
adaptation
of
all
individuals
in
initial
population.
Then
the
individuals
are
r
andomly
selected
f
or
reproduction
according
to
the
pr
inciple
of
sur
viv
al
of
the
fittest.
After
that
the
children
(or
descendants)
are
gener
ated
applying
the
f
ollo
wing
tw
o
genetic
oper
ators:
crosso
v
er
and
m
utation.
Those
children
are
mo
v
ed
to
a
ne
w
population
P
0
and
will
be
replaced,
in
whole
or
in
par
t,
b
y
the
children
of
pre
vious
gener
ations
.
The
ne
w
population
of
individuals
will
then
tak
e
o
v
er
from
one
gener
ation
to
the
ne
xt,
each
representing
a
gener
ation
iter
ation
until
reaching
the
stopping
cr
iter
ion.
2.3.
Optimal
placement
of
base
station
3.
Result
and
Anal
ysis
In
this
section,
w
e
pro
vide
n
umer
ical
results
giv
en
b
y
our
algor
ithm
SGA
used
t
o
find
the
best
locations
of
sensors
.
W
e
set
cost
functions
and
par
ameters
as
f
ollo
ws
[22]:
f
s
ij
(
d
is
)
=
5
exp(10
2
d
is
1)
,
f
c
ij
(
d
ij
)
=
100
exp(
10
12
log
2
(10
6
(
d
ij
)))
,
and
C
=
f
1
;
2
;
:::;
12
g
.
W
e
consider
a
netw
or
k
in
Fig.2,
which
consists
of
12
nodes
,
where
MP=
(
x
s
i
;
y
s
i
)
and
CP=
(
x
c
i
;
y
c
i
)
denotes
respectiv
ely
the
mission
and
comm
unication’
s
position
of
each
sensor
i
.
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.
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2015
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TELK
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195
Figure
2.
Sim
ulation
topology
Figure
3
and
Figure
4
illustr
ate
that
as
the
sur
v
eillance
f
actor
becomes
larger
,
as
each
node
gets
closer
to
its
target
location
in
order
to
reduce
the
cost
of
its
mission,
as
the
comm
uni-
cation
quality
cost
increases
.
On
the
other
hand,
as
the
comm
unication
f
actor
becomes
larger
,
as
each
node
gets
closer
to
each
other
to
reduce
the
comm
unication
cost,
proceeding
the
increased
mission
cost
(sur
v
eillance).
Figure
3.
V
ar
iation
of
comm
unication
cost
Figure
4.
V
ar
iation
of
mission
cost
Optimal
placement
of
sensors
in
mission-specific
mobile
sensor
netw
or
ks
(Hicham
Ouchitachen)
Evaluation Warning : The document was created with Spire.PDF for Python.
196
ISSN:
2302-4046
Figure
5.
P
erf
or
mances
of
our
algor
ithm
Figure
5
illustr
ate
the
compar
a
ison
of
tw
o
cases
with
our
proposed
algor
ithm
which
are
as
f
ollo
ws:
First
case:
each
node
updates
its
location
consider
ing
only
its
mission(sur
v
eillance).
Second
case:
each
node
updates
its
location
consider
ing
only
its
comm
unication
quality
.
The
best
locatio
ns
of
sensors
and
the
base
station
calculated
respectiv
ely
b
y
using
tw
o
algor
ithms
SGA
and
BGA
are
represented
b
y
the
Figure
6.
Figure
6.
Best
locations
of
sensors
giv
en
b
y
SGA
Figure
7
sho
ws
clear
ly
that,
compar
ing
to
the
Sim
ulated
Annealing
(SA),
our
node
place-
ment
algor
ithm
SGA
can
pro
vide
the
appropr
iate
location
of
each
node
according
to
the
v
ar
iation
of
w
eight
f
actors
while
minimizing
the
total
w
eighted
cost
f
or
missions
and
comm
unication
qual-
ities
of
all
nodes
.
Our
algor
ithm
SGA
is
v
er
y
adv
antageous
in
ter
ms
of
con
v
ergence
,
this
f
act
is
sho
wn
clear
ly
in
Figure
8.
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Figure
7.
Con
v
ergence
of
objectiv
e
function
giv
en
b
y
SGA
Figure
8.
Number
of
iter
ation
as
function
of
mission
f
actor
giv
en
b
y
SGA
4.
Conc
lusion
In
this
paper
,
w
e
proposed
a
no
v
el
algor
ithm
to
solv
e
the
optimization
prob
lem
f
or
m
ulated
in
order
to
find
the
optimal
locations
of
sensors
in
mobile
wireless
sensor
netw
or
ks
,
where
each
node
tr
ies
to
minimiz
e
the
w
eighted
sum
of
mission
and
comm
unication
cost
in
a
distr
ib
uted
w
a
y
.
Our
approach
is
based
on
genetic
algor
ithms
.
W
e
sho
w
that,
compar
ing
to
other
techniques
,
our
algor
ithm
is
v
er
y
adv
antageous
in
ter
ms
of
con
v
ergence
to
the
optimal
solution.
Numer
ical
results
pro
v
e
that
our
algor
ithm
pro
vides
the
appropr
iate
location
of
each
node
b
y
consider
ing
jointly
the
cost
of
mission
and
the
quality
of
comm
unication.
Ref
erences
[1]
I.
Akyildiz,
T
.
Melodia,
and
K.
Cho
wdhur
y
,
”A
sur
v
e
y
on
wireless
m
ultimedia
sensor
netw
or
ks
,
”
Computer
netw
or
ks
,
v
ol.
51,
no
.
4,
pp
.
921-960,
2007.
[2]
I.
Almalka
wi,
M.
Guerrero
Zapata,
J
.
Al-Kar
aki,
and
J
.
Mor
illo-P
oz
o
,
”Wireless
m
ultimedia
sensor
netw
or
ks:
Current
trends
and
future
directions
,
”
Sensors
,
v
ol.
10,
no
.
7,
pp
.
6662-6717,
2010.
[3]
K.
F
o
wler
,
”The
future
of
sensors
and
sensor
netw
or
ks
sur
v
e
y
results
projecting
the
ne
xt
5
y
ears
,
”
in
Proc.
Sensors
Applications
Symposium
,
2009
(SAS
2009),
Ne
w
Or
leans
,
LA,
USA,
F
eb
2009,
pp
.
1-6.
[4]
J
.N.
Al-Kar
aki,
R.
Ul-Mustaf
a,
A.E.
Kamal,
”Data
agg
regation
and
routing
in
Wireless
Sensor
Netw
or
ks:
Optimal
and
heur
istic
algor
ithms
,
”
Computer
Netw
or
ks
53
,
2009,
pp
.
945-960.
Optimal
placement
of
sensors
in
mission-specific
mobile
sensor
netw
or
ks
(Hicham
Ouchitachen)
Evaluation Warning : The document was created with Spire.PDF for Python.
198
ISSN:
2302-4046
[5]
S
.
Zar
ifzadeh,
A.
Na
yy
er
i,
N.
Y
azdani,
A.
Khonsar
i,
H.H.
Bazzaz,
”Joint
r
ange
assignment
and
routing
to
conser
v
e
energy
in
wireless
ad
hoc
netw
or
ks
,
”
Computer
Netw
or
ks
53
,2009,
pp
.
1812-1829.
[6]
G.
Mao
,
B
.
Fidan,
B
.D
.O
.
Anderson,
”Wireless
sensor
net
w
or
k
localization
techniques
,
”
Com-
puter
Netw
or
ks
51
,
2007,
pp
.
2529-2553.
[7]
K.
Y
eda
v
alli,
B
.
Kr
ishnamachar
i,
”Sequence-Based
Localization
in
Wireless
Sensor
Netw
or
ks
,
”
Mobile
Computing,
IEEE
T
r
ansactions
on
7
(2008)
,
pp
.
81-94.
[8]
I.
Demir
k
ol,
C
.
Erso
y
,
F
.
Alagz,
”MA
C
protocols
f
or
wireless
sensor
netw
or
ks:
A
sur
v
e
y
,
”
IEEE
Comm
unications
Magazine
44
,
2006
pp
.115-
121.
[9]
H.
Tseng,
A.
P
ang,
J
.
Chen,
C
.
K
uo
,
”An
adaptiv
e
contention
control
str
ategy
f
or
IEEE
802.15.4-based
wireless
sensor
netw
or
ks
,
”
IEEE
T
r
ansactions
on
V
ehicular
T
echnology
58
,
2009,
pp
.
5164-5173
[10]
J
.
P
an,
Y
.T
.
Hou,
L.
Cai,
Y
.
Shi,
S
.X.
Shen,
”T
opology
control
f
or
wireless
sensor
netw
or
ks
,
”
in
A
CM
Mobicom
,
2003,
pp
.
286-299.
[11]
E.R.
Chittimalla,
A.
V
enkates
w
ar
an,
V
.
Sar
angan,
R.
Achar
y
a,
”On
the
use
of
nodes
with
controllab
le
mobility
f
or
conser
ving
po
w
er
in
manets
,
”
in
IEEE
ICDCSW
,
2006,
pp
.
88-93.
[12]
S
.
Misr
a,
S
.D
.
Hong,
G.
Xue
,
J
.
T
ang,
”Constr
ained
rela
y
node
placement
in
wireless
sensor
netw
or
ks
to
meet
connectivity
and
sur
viv
ability
requirements
,
”
in
IEEE
INFOCOM
,
2008,
pp
.
281-285.
[13]
B
.
Lin,
P
.-H.
Ho
,
L.L.
Xie
,
X.
Shen,
”Rela
y
station
placement
in
IEEE
802.16
j
dual-rela
y
mmr
netw
or
ks
,
”
in
IEEE
ICC
,
2008,
pp
.
3437-
3441.
[14]
T
.
Nadeem,
S
.
P
ar
thasar
ath
y
,
”Mobility
control
f
or
throughput
maximization
in
ad
hoc
net-
w
or
ks
,
”
Wireless
Comm
unications
and
Mobile
Computing
6
(7)
,
2006,
pp
.
951-967.
[15]
G.
Bianchi,
”P
erf
or
mance
analysis
of
the
IEEE
802.11
distr
ib
uted
coordinated
function,
”
IEEE
Jour
nal
on
Selected
Areas
in
Comm
unications
18
(3)
,
2000,
pp
.
535-547.
[16]
A.
So
,
B
.
Liang,
”Enhancing
WLAN
capacity
b
y
str
ategic
placement
of
tether
less
rela
y
points
,
”
IEEE
T
r
ansactions
on
Mobile
Computing
6
(5)
,
2007,
pp
.
522-535.
[17]
A.
Sr
iniv
as
,
E.
Mod
iano
,
”Joint
node
placment
and
assignment
f
or
throughput
optimization
in
mobile
bac
kbone
netw
or
ks
,
”
in
IEEE
INFOCOM
,
2008,
pp
.
1804-1812.
[18]
C
.
Dixon,
E.W
.
F
re
w
,
”Maintaining
optimal
comm
unication
chains
in
robotic
sensor
netw
or
ks
using
mobility
control,
”
Mobile
Netw
or
ks
and
Applicaitons
14
(3)
,
2009,
pp
.
281-291.
[19]
H.-T
.
Roh,
J
.-W
.
Lee
,
”Optimal
placement
of
a
rela
y
node
with
controllab
le
mobility
in
wireless
netw
or
ks
consider
ing
f
air
ness
,
”
in
IEEE
CCNC
,
2010.
[20]
H.-T
.
Roh,
J
.-W
.
Lee
,
”Joint
node
scheduling
and
rela
y
nod
e
placement
in
wireless
netw
or
ks
with
a
rela
y
node
with
controllab
le
mobility
,
”
Wireless
Comm
unications
and
Mobile
Computing
12
(8)
,
2012,
pp
.
699-712.
[21]
H.-T
.
Roh,
J
.-W
.
Lee
,
”Comm
u
nication-a
w
are
position
control
f
or
mobile
nodes
in
v
ehicular
netw
or
ks
,
”
IEEE
Jour
nal
on
Selected
Areas
i
n
Comm
unications
29
(1)
,
2011,
pp
.
173-186.
[22]
H.-T
.
Roh,
J
.-W
.
Lee
,
”Joint
Mission
and
Comm
unication
A
w
are
Mobility
Control
in
Mobile
Ad-Hoc
Netw
or
ks
,
”
WiOpt’12:
Modeling
and
Optimization
in
Mobile
,
Ad
Hoc
,
and
Wireless
Netw
or
ks
,
Ma
y
2012,
P
aderbor
n,
Ger
man
y
.
pp
.124-129.
TELK
OMNIKA
V
ol.
16,
No
.
1,
October
2015
:
191
199
Evaluation Warning : The document was created with Spire.PDF for Python.