Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
22,
No.
1,
April
2021,
pp.
315
325
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v22i1.pp315-325
r
315
Mobility-pr
ediction
and
ener
gy
optimization
f
or
multi-channel
multi-interface
ad
hoc
netw
orks
in
the
pr
esence
of
location
err
ors
Hassan
F
aouzi
1
,
Mohammed
Boutalline
2
1
Sultan
Moulay
Slimane
Uni
v
ersity
,
Morocco
2
National
Schools
of
Applied
Sciences
of
Beni
Mellal,
Sultan
Moulay
Slimane
Uni
v
ersity
,
Morocco
Article
Inf
o
Article
history:
Recei
v
ed
Oct
11,
2020
Re
vised
Jan
27,
2021
Accepted
Mar
2,
2021
K
eyw
ords:
A
OD
V
End
to
end
delay
Ener
gy
consumption
Kalman
filter
Mobile
ad-hoc
NS2
(Simulator)
Routing
protocols
ABSTRA
CT
W
e
present
a
mobility-prediction
and
ener
gy
optimization
solution
for
multi-channel
multi-interf
ace
(MCMI)
ad
hoc
netw
orks
in
the
presence
of
location
errors.
This
solu-
tion
incl
udes
routing
of
the
MCMI
communication
links
that
adapt
to
dynamic
chan-
nel,
traf
fic
conditions,
interference
and
mobility
of
nodes.
W
e
start
first
with
imple-
menting
a
no
v
el
cross-layer
routing
solution
in
order
to
share
information
between
netw
ork
and
MA
C
layer
,
the
benefit
of
t
his
technique
is
to
collect
information
about
the
channel
quality
and
residual
ener
gy
of
the
nodes
and
send
them
directly
to
the
netw
ork
layer
.
Ne
xt,
we
present
a
mobility-predi
ction
model
using
Kalman
filter
to
predict
accurate
locations
and
enhance
routing
performance,
through
estimating
link
duration
and
selecting
reliable
routes.
The
performance
of
proposed
mechanism
is
measured
using
NS2.35
simulations
with
dif
ferent
scenarios
and
v
arying
load
in
a
net-
w
ork.
Comparati
v
e
analysis
of
simulation
results
sho
ws
better
performance
of
our
protocol
(ME-MCMI
A
OD
V)
in
terms
of
reducing
end-to-end
delay
,
total
dropped
pack
ets
and
increasing
netw
ork
lifetime
and
pack
et
deli
v
ery
ratio
(PDR).
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Hassan
F
aouzi
Sultan
Moulay
Slimane
Uni
v
ersity
Beni
Mellal,
Morocco
Email:
f
aouzi.hassan.mi@gmail.com
1.
INTR
ODUCTION
W
ireless
netw
ork
w
orks
under
tw
o
modes,
infrastructure
and
the
other
without
the
aid
of
central
ized
administration.
Mobile
ad
hoc
netw
orks
(MANET)
does
not
ha
v
e
a
fix
ed
topology
and
include
a
set
of
wireless
mobile
nodes
which
transfer
data
dynamically
among
themselv
es.
Unlik
e
V
ANET
netw
orks
where
we
can
control
traf
fic
by
reducing
the
speed
of
mo
v
ement
of
v
ehicles
either
by
the
dri
v
er
or
by
utilizing
mechanisms
lik
e
the
ones
described
in
the
papers
[1,
2]
which
the
authors
proposed
a
ne
w
approach
to
estimate,
track,
and
control
users
mo
ving
abo
v
e
speed
limits
in
L
TE-Adv
anced
(L
TE-A)
netw
orks.
T
o
reach
these
objecti
v
es
the
y
use
mapping
of
the
uplink
CQI
inde
x
of
the
UE
since
the
CQI
range
can
pro
vide
an
indication
to
the
system
re
g
arding
the
mo
v
ement
and
the
speed
of
the
UE.
Ho
we
v
er
,
MANET
netw
orks
do
not
ha
v
e
this
specificity
because
nodes
can
mo
v
e
freely
in
space.
The
main
objecti
v
e
of
mobile
ad
hoc
netw
orks
(MANET)
is
to
e
xtend
the
concepts
of
mobility
to
enable
access
to
information
and
communication
“an
ywhere
and
an
ytime”
using
routing
protocols
as
ad-hoc
on-demand
distance
v
ector
(A
OD
V)
[3],
dynamic
source
routing
(DSR)
[4],
destination
sequenced
distance-
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
316
r
ISSN:
2502-4752
v
ector
(DSD
V)
[5],
and
optimized
link
state
routing
(OLSR)
[6]
and
temporally
ordered
routing
algorithm
(T
ORA)
[7]
to
transfer
data.
So,
it
is
important
to
increase
netw
ork
lifetime
by
minimizing
the
control
pack
ets,
because
the
mobile
nodes
are
po
wered
by
independent
po
wer
sources
such
as
batteries
or
other
consumable
ones.
In
addition,
since
nodes
ha
v
e
limited
battery
po
wer
and
mo
v
e
frequently
in
the
netw
ork,
the
links
between
them
are
frequently
brok
en
and
causes
a
loss
of
data
pack
ets.
Therefore,
the
node
should
send
only
necessary
pack
ets
and
select
the
path
with
lo
wer
link
f
ailure
based
on
the
localization
of
neighboring
nodes
to
sa
v
e
its
ener
gy
resource.
Ho
we
v
er
,
dynamic
topology
of
MANET
mak
es
it
harder
to
determine
the
location
of
nodes
in
re
al
time.
Hence,
our
proposed
method
uses
a
mobility
prediction
to
measure
the
link
duration
time,
which
can
reduce
o
v
erhead
messages
and
impro
v
e
routing
performance.
Most
of
the
e
xisting
researches
mainly
concentrate
on
the
influence
of
node
mobility
on
link
rel
iability
,
the
y
ha
v
e
ignored
the
residual
ener
gy
of
nodes
in
their
methods
and
instead
of
w
orking
on
multi-channel
multi-
interf
ace,
the
y
adapt
their
mechanisms
to
use
single
interf
ace
single
channel
(SISC)
en
vironment.
In
this
paper
,
ener
gy
and
mobility
are
introduced
as
a
ne
w
routing
metric
to
select
links
i
n
terms
of
reliability
in
a
multi-
channel
multi-interf
ace
ad
hoc
netw
orks.
Firstly
,
the
node
in
our
approach
uses
mobility
to
predict
the
link
duration
time.
Then,
it
combines
this
duration
with
the
residual
ener
gy
to
find
a
stable
route
that
has
a
long
lifetime.
Finally
,
it
searches
a
good
mapping
between
channels
and
interf
aces
to
send
data
to
destination
nodes.
The
remainder
of
this
paper
i
s
or
g
anized
as
follo
ws:
Section
2
introduces
the
related
w
ork
and
discuss
some
details
about
Mobility-prediction
and
ener
gy
opt
imization
in
MANET
.
Section
3
describes
functional
details
of
the
proposed
ME-MCMI
A
OD
V
to
impro
v
e
the
A
OD
V
protocol.
Section
4
presents
the
e
xperimental
modeling
and
results
of
our
proposed
protocol
using
netw
ork
simulator
NS2.
Finally
,
conclusions
and
some
plans
for
future
de
v
elopment
in
this
field
are
gi
v
en
in
Section
5.
2.
RELA
TED
W
ORK
In
literature,
a
lot
of
research
has
been
done
to
impro
v
e
the
performance
of
mobile
Ad
hoc
netw
orks.
These
searches
seek
to
find
routes
satisfying
certain
constraints.
Some
ha
v
e
used
probabilistic
approaches
to
limit
the
number
of
routing
pack
ets
while
others
ha
v
e
used
queue
length,
bandwidth,
mobility
,
ener
gy
and
hop
count
that
separates
the
source
and
the
destination.
Rare
researchers
who
ha
v
e
tak
en
into
account
the
location
errors
in
their
models.
In
[8],
the
authors
implemented
a
mobility-assisted
using
A
OD
V
protocol
and
taking
into
consideration
location
errors.
T
o
that
end,
the
y
implement
Kalman
filter
to
predict
accurate
locations
and
tak
e
for
granted
le
v
el
confidence
in
disco
v
ering
routes
to
choose
the
best
route.
The
authors
in
[9]
proposed
tw
o
e
xtensions
of
A
OD
V
protocol
[3]
to
find
routes
based
on
residual
ener
gy
and
hop-count,
the
first
uses
Flo
yd
W
arshall
and
the
other
Bellman-F
ord
algorithm.
These
protocols
implemented
in
the
netw
ork
in
which,
each
node
equipped
with
a
multiple
netw
ork
interf
ace
[10]
to
o
v
ercome
the
problems
of
SISC.
Their
idea
is
to
add
a
no
v
el
cross-layer
routing
solution
which
allo
ws
communication
between
ph
ysical
and
netw
ork
layers.
The
authors
of
[11]
ha
v
e
de
v
eloped
tw
o
topologies
namely
chain
and
grid,
in
which
the
y
further
w
ork
ed
on
directional
and
Omni-directional
antenna.
After
simulation,
the
results
sho
w
that
directional
antenna
is
more
ef
ficient
in
enhancing
the
spatial
di
v
ersity
and
reducing
collisions.
In
an
another
w
ork
[12],
the
authors
de
v
eloped
tw
o
e
xtension
of
A
OD
V
protocol,
the
first
is
A
OD
VEA
proto-
col,
which
incorporates
local
forw
arding
decision
based
on
max
min
ener
gy
of
nodes
in
order
to
increase
the
lifetime
of
the
netw
ork.
The
second
(A
OD
VM)
combines
the
same
local
forw
arding
decision
parameters
used
in
A
OD
VEA
protocol
and
shortest
distance.
Instead
of
using
hop
count
as
a
parameter
to
calculate
the
best
routes
from
source
to
destination,
the
authors
of
[13-15]
utilize
the
ener
gy
and
po
wer
le
v
el
of
the
nodes.
Simu-
lations
ha
v
e
sho
wn
that
these
impro
v
ements
gi
v
e
better
results
compared
to
other
algorithms
in
the
same
field
of
research.
In
[16]
recei
v
ed
signal
strength
from
the
(MA
C)
layer
is
used
to
estimate
the
stability
of
the
radio
connection.
Their
objecti
v
e
is
to
select
the
stable
route
to
a
v
oid
paths
which
ha
v
e
a
higher
probability
to
be
brok
en.
The
authors
propose
another
solution
to
impro
v
e
Quality
Of
Servic
e
by
incorporating
residual
ener
gies
of
source
and
destination
nodes
to
calculate
a
v
ailable
link
bandwidth.
A
recent
study
[17]
proposed
a
m
ethod
to
frame
up
a
stable
link
netw
ork
using
a
temporal
data
anal
ysis
model.
In
this
model,
the
authors
analyzed
the
mobi
lity
,
position
of
neighbor
nodes
and
used
the
statistical
model
auto
re
gressi
v
e
mo
ving
a
v
erage
(ARMA)
to
predict
the
stable
neighbors
of
each
node
in
a
future
time
frame.
The
y
applied
a
Biogeographic
-based
optimization
(BBO)
technique
to
estimate
rele
v
ant
parameters
in
the
ideal
path
from
source
to
destination
nodes.
According
to
them,
this
optimal
link
of
fers
a
stable
and
reliable
connection
for
the
remaining
lifetime
of
the
data
transfer
in
the
netw
ork.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
22,
No.
1,
April
2021
:
315
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
317
A
dynamic
po
wer
ad
hoc
on-demand
di
stance
v
ector
(DP-A
OD
V)
protocol
which
is
an
impro
v
em
ent
of
the
e
xisting
A
OD
V
routing
protocol
w
as
applied
in
[18].
In
this
e
xtension,
the
authors
modified
the
pack
ets
headers
in
the
routing
layer
to
include
the
distance
i
nformation
of
the
destination
and
neighbors
count.
The
y
also
modified
hello
pack
ets
of
A
OD
V
to
carry
the
“x-y
position”
coordinate
field
information
in
order
to
obtain
the
e
xact
location
of
a
node,
which
used
by
the
routing
protocol
to
determine
the
route.
At
the
wireless
ph
ysi-
cal
layer
,
the
algorithm
selects
the
po
wer
required
to
k
eep
the
connecti
vity
among
the
nodes
and
consequently
reduce
the
o
v
erall
po
wer
.
The
authors
of
[19]
use
a
routing
conditioned
upon
the
achie
v
ement
of
man
y
require-
ments.
Among
these,
is
the
intermediate
U
A
V
able
to
respond
with
a
throughput
requested
by
t
he
source
U
A
V?
Does
the
speed
not
e
xceed
a
predefined
threshold?
Simulation
results
sho
w
that
their
protocol
(MDRMA)
gi
v
es
better
results
in
terms
of
speed
of
establishment
and
stability
of
routes.
In
[20],
the
authors
proposed
impro
v
ements
of
ad
hoc
on
demand
distance
v
ector
.
These
i
mpro
v
em
ents
tak
e
into
account
a
metric
based
on
ener
gy
consumption
during
route
disco
v
ery
in
order
to
decrease
load
of
control
pack
ets
and
increase
both
the
netw
ork
lifetime
and
pack
et
deli
v
ery
ratio.
The
y
did
the
simulation
in
an
en
vironment
close
to
realit
y
by
using
the
Gilbert-
Elliot
model.
T
o
reduce
the
route-establishment
o
v
erhead
in
A
OD
V
,
the
authors
of
[21,
22]
attempt
to
minimize
the
number
of
intermediate
nodes
that
participate
in
the
route
disco
v
ery
process.
This
is
achie
v
ed
by
reducing
the
number
of
route
request
(RREQ)
depending
on
the
length
queue
and
ener
gy
of
nodes
or
count
of
RREQ
(nodes
stop
transfer
the
requests
if
the
count
of
RREQ
e
xceeds
a
threshold).
Additionally
,
[
23
]
presented
tw
o
techniques
for
computing
the
link
a
v
ailabil
ity
and
decreasing
the
broadcast
of
RREQ
pack
ets.
In
t
h
e
first
technique,
the
l
ink
a
v
ailability
ratio
(LAR)
for
all
neighboring
links
is
calculated
using
the
present
position
of
the
neighbor
and
its
angular
sector
in
the
transmission
range.
In
the
second
technique,
the
transmission
range
of
each
node
is
di
vided
into
the
outer
,
inner
and
middle
zone.
So,
based
on
the
recei
v
ed
signal
strength
and
tw
o
predefined
thresholds
only
the
nodes
in
the
middle
zone
participate
in
the
route
disco
v
ery
process.
The
Multi-path
routing
allo
ws
data
to
be
sent
o
v
er
a
set
of
paths
leading
from
source
to
destinat
ion.
This
is
wh
y
other
authors
choose
to
w
ork
on
this
component
[24-26]
so
as
to
limit
the
problem
of
road
disruption
and
dis
trib
ute
the
traf
fic
between
source
as
well
as
destination.
In
summary
,
the
techniques
used
to
decrease
the
dropped
pack
ets,
end-to-end
delay
and
increase
the
netw
ork
lifetime
and
pack
et
deli
v
ery
ratio
(PDR)
in
ad
hoc
netw
orks
focused
only
on
one
performance
parameter
or
based
on
a
single-interf
ace
single-channel
en
vironment,
and
in
order
to
b
uild
a
better
routing
protocol,
we
must
satisfy
all
quality
services.
This
is
what
we
tried
to
implement
in
this
paper
.
3.
PR
OPOSED
APPR
O
A
CH
AND
FUNCTION
AL
DET
AILS
In
this
section,
we
present
our
model
to
impro
v
e
A
OD
V
protocol
by
using
link
duration
time
and
residual
ener
gy
as
a
metric
of
routing
in
multi-channel
multi-interf
ace
communications
in
mobile
Ad
Hoc
netw
orks.
3.1.
Cr
oss-lay
er
T
o
tak
e
benefit
of
information
about
the
channel
quality
and
residual
ener
gy
of
the
nodes,
we
de-
v
eloped
a
cross-layer
to
share
this
information
between
netw
ork,
MA
C
and
ph
ysic
layer
.
Although
se
v
eral
methods
using
single
channel
single
interf
ace
schemes
tried
to
achie
v
e
a
high
quality
of
service
scheme,
most
of
them,
if
not
all,
were
not
successful
due
to
intra-flo
w
interference
and
inter
-flo
w
interference.
So,
in
our
w
ork
we
used
the
multichannel
en
vironment
to
solv
e
t
hese
problems
and
pro
viding
a
more
reliable
MA
C
protocol
for
the
users.
In
MANET
,
channels
are
separated
in
frequenc
y
,
so
to
use
the
dif
ferent
channels
of
fered
by
the
ad
hoc
netw
ork
we
need
to
de
v
elop
a
channel
assignment
approaches
which
allo
w
coordination
between
nodes
[27].
These
approaches
classified
into
three
cate
gories:
Static,
dynamic
and
h
ybrid
channel
assignment.
In
this
w
ork,
we
focus
on
h
ybrid
channel
strate
gy
to
benefit
from
the
adv
antages
of
static
and
dynamic
assignment.
In
this
strate
gy
,
each
node
has
a
multiple
interf
ace,
only
one
is
designed
to
be
fix
ed
and
the
others
become
switchable.
When
a
source
node
needs
to
communicate
with
a
destination,
it
will
switch
its
switchable
interf
ace
to
the
same
channel
as
pointed
by
fix
ed
interf
ace
of
the
tar
get
node.
Figure
1
illustrates
an
e
xample
of
communication
between
nodes
when
using
“
fixed
”
and
“
switc
hable
interfaces
”.
Assuming
that
node
X
has
a
data
to
be
sent
to
node
Y
.
The
fix
ed
interf
aces
of
nodes
X
and
Y
are
assigning
to
channels
3
and
1
respecti
v
ely
.
T
o
ensure
this
communication,
the
switchable
interf
ace
of
node
X
is
assigning
to
channel
1,
before
transmitting
the
pack
et,
because
channel
1
is
the
fix
ed
channel
of
node
Y
.
Mobility-pr
ediction
and
ener
gy
optimization
for
multi-c
hannel
multi-interface
...
(Hassan
F
aouzi)
Evaluation Warning : The document was created with Spire.PDF for Python.
318
r
ISSN:
2502-4752
So,
node
Y
c
an
recei
v
e
the
pack
et
since
its
fix
ed
interf
ace
is
listening
to
channel
1.
In
the
replay
step,
node
Y
switches
its
switchable
interf
ace
to
channel
3
and
send
a
replay
request,
which
is
recei
v
ed
by
node
X
using
its
fix
ed
interf
ace
on
channel
3.
N
o
d
e
X
F
i
xe
d
i
n
t
e
r
f
a
c
e
(
C
h
a
nne
l
3
)
S
w
i
t
c
h
a
bl
e
I
n
t
e
r
f
a
c
e
s
C
h
a
nne
l
C
h
a
nne
l
C
h
a
nne
l
N
o
d
e
Y
F
i
xe
d
i
n
t
e
r
f
a
c
e
(
C
h
a
nne
l
1)
S
w
i
t
c
h
a
bl
e
I
n
t
e
r
f
a
c
e
s
C
h
a
nne
l
C
h
a
nne
l
C
h
a
nne
l
Figure
1.
Communication
between
tw
o
nodes
using
fix
ed
and
switchable
interf
aces
3.2.
Residual
ener
gy
The
nodes
consume
the
ener
gy
during
transmission
and
reception
acti
vities
.
Therefore,
ener
gy
is
one
of
the
actual
considerable
constrained
in
MANET
.
When
a
node
participates
in
route
establishment
in
man
y
times,
it
may
run
of
f
its
po
wer
in
later
stages
resulting
in
the
breakdo
wn
of
the
link.
So,
our
approach
is
ener
gy
a
w
are
reacti
v
e
protocol
which
considers
the
nodes
residual
ener
gy
to
select
path
to
the
destination,
by
applying
this
method,
nodes
can
select
paths
with
maximum
lifetime,
thus
achie
ving
considerable
ener
gy
sa
vings.
T
o
attain
this
objecti
v
e,
we
modified
the
route
disco
v
ery
process
to
select
the
path
that
consists
of
nodes
with
higher
remaining
ener
gy
.
In
this
method,
when
a
RREQ
message
is
transmitted
in
the
netw
ork,
not
e
v
ery
node,
which
recei
v
es
the
message,
will
dif
fuse
it.
If
the
residual
ener
gy
of
intermediate
node
is
lo
wer
than
a
predefined
threshold
v
alue,
the
RREQ
is
dropped,
otherwise,
the
message
is
forw
arded
Figure
2.
S
E
r
=
8
j
2
E
r
=
1
j
5
E
r
=
1
0
j
4
E
r
=
1
0
j
6
E
r
=
6
j
D
E
r
=
8
j
3
E
r
=
7
j
Figure
2.
Route
disco
v
ery
process
in
ME-MCMI
A
OD
V
3.3.
Location
corr
ection
In
reality
,
the
location
measurement
tools
do
not
gi
v
e
the
e
xact
location
of
the
nodes.
So,
to
gi
v
e
credibility
to
our
approach,
we
ha
v
e
introduced
a
model
that
tak
es
into
account
the
presence
of
location
errors.
The
measurement
error
(also
called
Observ
ational
Error)
is
the
dif
ference
between
a
measured
quantity
and
its
true
v
alue.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
22,
No.
1,
April
2021
:
315
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
319
In
our
model,
we
used
the
Kalman
filter
to
estimate
the
internal
state
location
of
nodes
i
n
the
netw
ork.
Each
node
runs
a
Kalman
filter
predictor
,
which
is
em
plo
yed
to
predict
the
node’
s
o
wn
position
(x,
y)
and
v
elocity
(vx,
vy).
The
position
v
ector
at
moment
t
is
measured
by:
X
t
=
[
x
(
t
)
;
y
(
t
)
;
v
x
(
t
)
;
v
y
(
t
)]
T
(1)
T
o
find
the
best
estimate
of
the
current
state
in
re
gular
interv
als,
we
apply
the
time
and
measur
ement
update
mechanisms
of
Kalman
filter
as
sho
wn
in
(2)
and
(7).
The
steps
in
v
olv
ed
in
state
estimation
of
our
system
are
described
as
follo
ws:
T
ime
update
(Prediction)
Location
Prediction
:
X
0
t
=
AX
t
1
+
W
t
(2)
Error
Co
v
ariance
:
P
0
t
=
AP
t
1
A
T
+
Q
t
(3)
Measurement
update
(Correct)
Measurement
of
state
:
Y
t
=
C
X
0
t
(4)
Kalman
Gain
:
K
=
P
0
k
H
.
H
P
0
t
H
T
+
R
(5)
Update
Prediction
Measurement
:
X
t
=
X
0
t
+
K
Y
t
H
X
0
t
(6)
Update
Error
Co
v
ariance
:
P
t
=
(
I
K
H
)
P
0
t
(7)
In
abo
v
e
equations:
A
:
the
state
transition
matrix;
Q
:
the
noise
co
v
ariance
matrix;
H
:
the
observ
ation
matrix;
R
:
the
noise
co
v
ariance
matrix
of
the
observ
ation.
Y
t
:
the
observ
ation
v
ector
achie
v
ed
from
the
current
node,
namely
,
the
node’
s
current
position.
The
figure
as
sho
wn
in
Figure
3
the
general
flo
w
and
o
v
ervie
w
of
our
system
model.
In
itial
lo
catio
n
Pr
evio
u
s
lo
catio
n
New
lo
catio
n
(p
r
ed
icted
,
b
ased
o
n
p
h
ysical
m
o
d
el
an
d
p
r
evio
u
s
lo
catio
n
)
Resu
lt
of
u
p
d
ated
lo
catio
n
State
an
d
co
varian
ce
u
p
d
ate
Kalm
an
gain
Meas
u
r
em
en
t
from
sen
so
r
I
nit
ial
s
tat
e
be
c
om
e
s
pr
e
v
ious
X
=
S
t
at
e
m
at
r
ix
P
=
P
r
oc
e
s
s
c
ovar
ian
c
e
m
at
r
ix
(
r
e
p
r
e
s
e
n
t
s
e
r
r
or
in
t
he
e
s
t
im
at
e
)
I
=
I
dent
it
y
m
at
r
ix
K
=
K
al
m
an
gain
R
=
S
e
n
s
or
n
ois
e
/m
e
as
u
r
e
m
e
n
t
c
ovar
ian
c
e
m
at
r
ix
H
=
C
onve
r
s
ion
m
at
r
ix
(
t
o
m
ak
e
s
iz
e
s
c
ons
is
t
e
nt
)
Y
=
M
e
as
ur
e
m
e
nt
of
s
t
at
e
W
=
P
r
e
d
i
c
te
d
s
tate
n
oi
s
e
m
atr
i
x
Q
=
P
r
oc
e
s
s
n
oi
s
e
c
ova
r
i
an
c
e
m
atr
i
x.
K
e
e
p
s
th
e
s
tate
c
ovar
i
an
c
e
m
atr
i
x fr
om
b
e
c
om
i
n
g too
s
m
al
l
or
goi
n
g
to 0.
A
,
C
=
A
d
ap
tati
on
m
atr
i
c
e
s
, to c
on
ve
r
t
i
n
p
u
t
s
tate
to p
r
oc
e
s
s
s
tate
X
0
P
0
X
t
P
t
X
t
-
1
P
t
-
1
=
′
′
+
=
′
′
=
−
1
+
′
=
−
1
+
=
′
+
(
+
′
)
=
(
−
)
′
Figure
3.
Ov
ervie
w
of
our
system
model
Mobility-pr
ediction
and
ener
gy
optimization
for
multi-c
hannel
multi-interface
...
(Hassan
F
aouzi)
Evaluation Warning : The document was created with Spire.PDF for Python.
320
r
ISSN:
2502-4752
3.4.
Pr
edicting
link
expiration
time
W
e
shall
ensure
that
the
select
ed
route
wil
l
be
the
stable
one
by
calculati
ng
its
link
e
xpiration
time.
Only
if
this
time
is
greater
than
t
he
predefined
threshold
v
alue,
then
that
node
will
t
ak
e
part
in
route
disco
v
ery
.
The
link
e
xpiration
time
is
calc
ulated
at
each
hop
of
the
route
and
taking
into
consideration
the
mobility
of
the
nodes
in
v
olv
ed
in
a
netw
ork.
So,
the
node
computes
the
link
duration
time,
after
reception
the
RREQ,
between
itself
and
the
sender
which
implies
the
predicted
lifetime
of
the
link
using
the
real
location
calculated
in
the
pre
vious
section
as
sho
wn
in
(6)
instead
of
the
measured
location
information.
Let
us
assume
tw
o
nodes
i
and
j
are
within
the
transmission
range
r
of
each
other
.
Let:
(
x
i
,
y
i
):
The
coordinate
of
node
i
.
(
x
j
,
y
j
):
The
coordinate
of
node
j
.
V
i
and
V
j
be
the
speeds
of
nodes
i
and
j
respecti
v
ely
.
i
and
j
(
0
<
=
i
,
j
<
2
)
be
the
mo
ving
directions
of
nodes
i
and
j
respecti
v
ely
.
So,
the
link
e
xpiration
time
(length
of
the
longest
time
interv
al
during
which
the
tw
o
nodes
are
within
the
transmission
range
of
each
other)
D
t
,
of
the
link
between
the
tw
o
nodes,
as
defined
in
[28],
is
gi
v
en
as
sho
wn
in
(8)
:
D
t
=
(
ab
+
cd
)
+
q
(
a
2
+
c
2
)
r
2
(
ad
bc
)
2
a
2
+
c
2
(8)
Where
:
a
=
i
cos
i
j
cos
j
b
=
X
i
X
j
c
=
i
sin
i
j
sin
j
d
=
Y
i
Y
j
If
the
Link
Expiration
T
ime
v
alue
is
smaller
than
the
Link
Duration
T
ime
in
the
modified
RREQ,
the
recei
ving
node
replaces
the
Link
Duration
T
ime
v
alue
by
the
ne
w
one.
In
case
the
recei
v
er
is
not
the
destination
of
the
RREQ,
the
node
broadcasts
it
to
other
nodes.
4.
EXPERIMENT
AL
MODELING,
AN
AL
YSIS
AND
RESUL
TS
4.1.
Experimental
modeling
4.1.1.
Simulation
model
In
this
paper
,
the
simulation
of
our
proposed
protocol
(ME-MCMI
A
OD
V),
A
OD
V
and
A
OD
V
[8]
is
done
by
using
netw
ork
simulator
(NS-2)
softw
are
due
to
its
a
v
ailability
.
NS-2
is
a
discrete
e
v
ent
Simulator
written
in
C++
and
O
TCL,
C++
for
data
per
e
v
ent
pack
ets
and
O
TCL
are
used
for
periodic
and
triggered
e
v
ent.
NS-2
includes
a
netw
ork
animator
called
Nam
Animator
,
which
pro
vides
visual
vi
e
w
of
simulation.
A
WK
scripts
are
used
to
analyze
output
of
TCL
and
get
the
netw
ork
performance.
4.1.2.
Model
parameters
In
these
simulations,
we
used
a
wireless
netw
ork,
which
is
a
1km
x
1km
simulation
en
vironment.
W
e
emplo
yed
MA
C
802.11
protocol,
with
node
transmission
range
of
250m.The
constant
bit
rate
(CBR)
traf
fic
under
the
user
datagram
protocol
(UDP)
is
used
to
accurately
compare
dif
ferent
routing
protocols
with
a
send-
ing
rate
of
4
pack
ets
per
second,
512
bytes
of
pack
et
size
and
simulation
time
of
600s.
The
random
w
aypoint
mobility
(R
WP)
[29]
model
is
used
as
a
mobility
model
with
randomly
selected
speed
between
1
m/s
and
20
m/s.
The
performances
of
protocols
are
e
v
aluated
by
v
arying
both
the
netw
ork
size
(number
of
nodes)
and
the
pause
time.
W
e
consider
10
random
simulation
runs
and
the
performance
of
the
considered
f
actor
is
the
a
v
erage
of
these
outputs.
The
parameter
settings
are
listed
in
T
able
1.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
22,
No.
1,
April
2021
:
315
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
321
T
able
1.
The
parameter
settings
of
our
simulation
P
arameter
V
alues
Netw
ork
simulator
NS-2.35
Simulation
area
1
km
x
1
km
Number
of
mobile
nodes
10,
20,
30,
40,
50,
100,
150
Simulation
time
(s)
600
Mobility
model
R
andom
w
ay
point
P
ause
time
(s)
10,
20,
30,
40,
50,
100,
150
P
ack
et
generation
rate
4
pack
ets/s
P
ack
et
size
(bytes)
700,
800,
900,
1000,
1100,
1200
T
ransmission
range
(m)
250
Number
of
interf
aces
2,
3,
4
4.2.
Results
and
discussion
4.3.
Choosing
the
number
of
interfaces
In
our
model,
de
vices
can
communicate
by
means
of
multiple
interf
aces.
So,
we
ha
v
e
to
choice
the
optimal
number
of
interf
aces
to
implement
in
ph
ysical
layer
.
The
T
able
2
sho
ws
the
results
of
our
simulation
study
in
multi-channel
multi-interf
ace
en
vironment.
From
the
table
we
can
notice
that
be
yond
tw
o
interf
aces
the
performance
of
our
protocol
decreases.
This
is
due
to
se
v
eral
f
actors
such
as
RF
interference,
antenna
cor
-
relation.
In
addition,
each
antenna
in
the
MIMO
system
needs
a
radio-frequenc
y
(RF)
unit,
so
the
battery
gets
drain
f
aster
due
to
processing
of
comple
x
and
computationally
intensi
v
e
signal
processing
algorithms.That’
s
wh
y
we
opted
to
use
only
tw
o
interf
aces
per
node
in
the
rest
of
our
contrib
ution.
T
able
2.
Performance
analysis
vs
number
of
interf
aces
Number
of
interf
ace
PDR
%
End
to
end
delay
T
otal
Dropped
P
ack
ets
Lifetime
2
96.9411
338.87
364
105.949859
3
94.19
345.66
387
109.243
4
93.45
376.634
392
111.324
4.3.1.
P
ack
et
deli
v
ery
ratio
P
ack
et
deli
v
ery
ratio
(PDR)
is
calculated
by
di
viding
the
number
of
pack
ets
recei
v
ed
by
the
CBR
sink
at
the
final
destination
by
the
number
of
pack
ets
originated
by
the
“appl
ication
layer”
CBR
sources.
The
PDR
needs
to
be
high
for
ef
fecti
v
e
performance
of
routing.
Figure
4
sho
ws
deli
v
ery
rat
io
of
the
data
pack
ets
of
ME-MCMI
A
OD
V
,
A
OD
V
and
A
OD
V
[3]
in
terms
of
v
ariation
of
number
of
nodes,
pause
time
and
pack
et
size.
W
e
observ
ed
that
the
protocols
ha
v
e
higher
PDR
when
the
nodes
mo
v
e
at
lo
w
speeds.
When
the
speed
increases,
routing
protocols
suf
fer
a
decrease
in
PDR.
This
is
normal
because
higher
speeds
of
nodes
mak
e
routes
unstable
which
leads
to
an
increase
of
loss
data.
W
e
notice
also
that
our
proposed
protocol
sho
ws
higher
v
alues
re
g
ardless
of
v
ariation
of
parameters
(node
density
,
pause
time
and
pack
et
size)
as
compared
to
A
OD
V
and
A
OD
V
[8],
because
proposed
protocol
select
the
nodes
which
ha
v
e
suf
ficient
ener
gy
and
high
link
duration
time.
This
mechanism
reduces
the
chances
of
route
f
ailure
especially
in
case
of
high
mobility
(lo
w
pause
time),
which
result
in
impro
ving
the
pack
et
de-
li
v
ery
ef
ficienc
y
.
70
75
80
85
90
95
100
10
20
30
50
100
150
Pack
et
D
el
i
very
R
ati
o
(%)
40
N
um
ber
of
nodes
A
O
D
V
AODV[
8
]
O
ur
a
ppr
oa
ch
(a)
65
70
75
80
85
90
95
100
10
20
30
40
50
100
150
P
ack
et
D
el
i
very
R
ati
o
(%)
Pause
ti
m
e
(s
)
A
O
D
V
AODV[
8
]
O
ur a
ppr
oach
(b)
70
75
80
85
90
95
700
800
900
1000
1100
1200
Packet
Delivery
Ratio
(%)
Pack
et
s
i
ze
(Byte
)
A
O
D
V
A
O
D
V
[
8]
O
ur
a
ppr
oa
ch
(c)
Figure
4.
P
ack
et
Deli
v
ery
Ratio
by
changing
the:
(a)
number
of
nodes,
(b)
pause
time
and
(c)
pack
et
size
Mobility-pr
ediction
and
ener
gy
optimization
for
multi-c
hannel
multi-interface
...
(Hassan
F
aouzi)
Evaluation Warning : The document was created with Spire.PDF for Python.
322
r
ISSN:
2502-4752
4.3.2.
T
otal
dr
opped
pack
ets
The
T
otal
Dropped
P
ack
ets
is
the
number
of
pack
ets
that
is
not
recei
v
ed
by
the
destination.
The
pack-
ets
may
be
lost
due
to
man
y
f
actors
such
as
transmission
errors
and
congestion.
This
loss
may
tak
e
place
at
both,
netw
ork
and
MA
C
layer
.
The
result
of
these
f
actors
is
related
with
the
host
mobility
,
number
of
connections,
traf
fic
load
and
pack
et
size.
From
Figure
5,
the
v
ariation
in
number
of
mobile
nodes,
pause
time
and
pack
et
size
depicts
that
the
A
OD
V
and
A
OD
V
[8]
ha
v
e
more
dropped
pack
ets
than
ME-MCMI
A
OD
V
.
The
number
of
nodes
in
the
netw
ork,
pause
time
and
pack
et
size
will
af
fect
the
requirement
of
route
disco
v
ery
between
dif
ferent
pairs
in
the
netw
ork.
So,
it
can
be
seen
from
this
figure
that
the
number
of
dropped
pack
ets
increases
when
the
pause
time
decreases
and
pack
et
size
increases,
because
higher
mobility
leads
to
more
brok
en
links
and
higher
pack
et
size
mak
es
the
chances
of
loss
v
ery
significant
due
to
collisions
and
interf
ace
o
v
erflo
ws.
In
our
approach,
the
paths
of
the
high
residual
ener
gy
and
li
nk
duration
time
are
selected,
so
the
route
will
not
brok
e
quickly
,
thus
it
reduces
the
number
of
dropped
pack
ets.
500
1000
1500
2000
2500
3000
10
20
30
50
100
150
T
otal
D
ropp
ed
P
ack
ets
40
N
um
ber
of
nodes
A
O
D
V
AODV[
8
]
O
ur
a
ppr
oa
ch
(a)
200
400
600
800
100
0
120
0
140
0
160
0
10
20
30
40
50
100
150
Total
D
ropped
Pack
et
s
P
ause
ti
m
e
(s
)
A
O
D
V
AODV[
8
]
O
ur
app
r
oach
(b)
350
400
450
500
550
600
650
700
700
800
900
1000
1100
1200
T
otal
D
ropp
ed
P
ack
ets
Pack
et
s
i
ze
(Byte
)
A
O
D
V
A
O
D
V
[
8]
O
ur a
ppr
oac
h
(c)
Figure
5.
T
otal
Dropped
P
ack
ets
by
changing
the:
(a)
number
of
nodes,
(b)
pause
time
and
(c)
pack
et
size
4.3.3.
End
to
end
delay
The
end
to
end
delay
is
the
ratio
of
tim
e
dif
ference
between
numbers
of
pack
et
send
and
recei
v
ed
o
v
er
the
total
time
require
to
reach
the
destination.
It
is
a
significant
parameter
for
e
v
aluating
a
protocol,
the
more
delay
is
reduced,
the
performance
of
netw
ork
gi
v
es
better
output.
Figure
6
sho
ws
end
to
end
delay
for
number
of
nodes
from
10
to
150,
pause
time
from
10s
to
150s
and
pack
et
size
from
700
to
1200
bytes.
From
this
figure,
we
notice
that
this
parameter
is
decreased
as
the
pack
et
size,
density
of
the
netw
ork
and
mobility
of
nodes
increased.
Firstly
because
the
probability
of
success
in
accessing
the
medium
decreased
when
a
greater
number
of
nodes
contend
for
access
to
the
channel
in
stable
netw
ork,
b
ut
also
because
a
lar
ger
pack
et
needs
more
ti
me
to
reach
destination
than
smaller
pack
ets
due
to
more
pack
et
drops
and
pack
et
retransmi
ssions
are
needed.
W
e
observ
e
also
that
the
a
v
erage
end
to
end
delay
of
ME-MCMI
A
OD
V
is
smaller
than
both
A
OD
V
and
A
OD
V
[8]
in
all
simulation
scenarios
(number
of
nodes,
pause
time
and
pack
et
size).
The
reason
is
that
our
protocol
reduce
the
traf
fic
load
by
selecting
the
stable
paths
and
this
reduces
queuing
and
propag
ation
delays.
35
40
45
50
55
60
65
70
75
10
20
30
50
100
150
E
ndtoE
nd
de
l
ay
(m
s)
40
N
um
ber
of
node
s
A
O
D
V
AODV[
8
]
O
ur
a
ppr
oa
ch
(a)
0
50
100
150
200
250
300
350
400
450
500
10
20
30
40
50
100
150
E
ndtoE
nd
de
l
ay
(m
s)
P
ause
ti
m
e
(s
)
A
O
D
V
AODV[
8
]
our
a
pp
r
oa
c
h
(b)
300
350
400
450
500
550
600
700
800
900
1000
1100
1200
En
d
to
end
del
ay (m
s)
Pack
et
s
i
ze
(
Byte
)
A
O
D
V
A
O
D
V
[
8]
O
ur a
ppr
oac
h
(c)
Figure
6.
End
to
end
delay
by
changing
the:
(a)
number
of
nodes,
(b)
pause
time
and
(c)
pack
et
size
4.3.4.
Lifetime
From
Figure
7,
the
results
sho
w
that
the
netw
ork
lifetime
increases
as
the
node
density
or
pause
time
increases.
Because
if
the
number
of
nodes
in
the
netw
ork
is
too
small,
feasible
routes
between
sources
and
destinations
may
not
e
xist
in
this
netw
ork,
so
lar
ger
o
v
erhead
messages
need
to
k
eep
and
disco
v
er
routes,
which
lead
to
high
consumption
of
ener
gy
.
F
or
the
third
parameter
,
we
observ
e
that
as
pack
ets
size
increases,
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
22,
No.
1,
April
2021
:
315
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
323
more
ener
gy
will
be
required
to
transmit
data
pack
ets
from
one
end
to
the
other
,
hence
reducing
the
lifetime
of
the
netw
ork
nodes
and
the
o
v
erall
lifetime
of
the
netw
ork.
The
results
re
v
eal
t
hat
our
modified
algorithm
outperforms
both
A
OD
V
and
A
OD
V
[8]
by
achie
ving
long
duration
of
time
for
the
first
node
witch
e
xhausts
its
ener
gy
on
the
netw
ork.
The
impro
v
ement
in
netw
ork
lifetime
is
due
to
the
f
act
that
ME-MCMI
A
OD
V
pre
v
ents
small
residual
ener
gy
nodes
to
be
a
relay
node
or
selects
a
path
that
has
long
duration
time
than
shorter
path
between
source
and
destination.
50
100
150
200
250
10
20
30
50
100
150
L
ifetime
(s)
40
N
um
ber
of
nodes
A
O
D
V
AODV[
8
]
O
ur a
ppr
oach
(a)
90
110
130
150
170
190
210
230
250
270
290
10
20
30
40
50
100
150
L
ifetime
(s)
Paus
e
t
i
m
e
(
s
)
A
O
D
V
AODV[
8
]
O
ur
a
ppr
oa
ch
(b)
55
65
75
85
95
105
700
800
900
1000
1100
1200
L
i
f
eti
m
e (s)
P
ack
et
si
ze
(Byte)
A
O
D
V
A
O
D
V
[
8]
O
ur a
ppr
oac
h
(c)
Figure
7.
Lifetime
by
changing
the:
(a)
number
of
nodes,
(b)
pause
time
and
(c)
pack
et
size
4.3.5.
Reason
behind
the
r
esult
As
mentioned
before,
the
results
demonstrate
that
the
ME-MCMI
A
OD
V
generates
better
performance
results
as
compared
with
a
basic
A
OD
V
and
A
OD
V
[8].
The
impro
v
ed
performance
of
ME-MCMI
A
OD
V
compared
to
the
tw
o
others
protocols
can
be
attrib
uted
to
se
v
eral
design
f
actors.
One
of
the
major
f
actors
is
the
incorporation
of
the
enhanced
RREQ
mechanism,
which
lo
wers
the
rate
of
problems
in
our
approach.
In
ME-MCMI
A
OD
V
,
the
route
created
between
an
y
pair
of
nodes
consists
only
of
nodes
whose
ener
gy
le
v
el
is
higher
than
the
threshold,
so
our
protocol
ensures
a
more
stable
link,
without
unnecessary
link
breakages,
and
as
a
result
more
successful
pack
et
deli
v
ery
to
destination
nodes.
Consequently
,
ME-MCMI
A
OD
V
sends
out
less
number
of
control
pack
ets
that
can
reduce
the
o
v
erhead
and
increase
lifetime
of
the
netw
ork.
5.
CONCLUSION
The
stability
of
the
route
and
lifetime
of
the
netw
ork
are
considered
as
challenging
tasks
in
MANET
.
This
paper
proposed
a
multi-channel
multi-interf
ace
on-demand
routing
algorithm
(ME-MCMI
A
OD
V)
with
a
mobility
prediction
that
tak
es
into
account
the
location
errors
and
residual
ener
gy
of
nodes.
V
ia
simulations,
our
proposed
algorithm
sho
ws
significant
performance
impro
v
ements
in
terms
o
f
pack
et
deli
v
ery
ratio,
total
dropped
pack
ets,
end-to-end
delay
and
netw
ork
lifetime
compared
with
other
protocols
in
the
field,
especially
in
a
netw
ork
with
more
connections,
high
mobility
of
nodes
and
lar
ge
pack
et
size.
Because
ME-MCMI
A
OD
V
protocol
select
the
nodes
which
ha
v
e
suf
ficient
ener
gy
and
high
link
duration
time
so
the
route
will
not
brok
e
quickly
.
T
aking
for
granted
the
benefit
of
the
solution
proposed
in
this
paper
,
in
the
future
w
ork
we
will
try
to
e
xpand
the
solution
by
proposing
a
model
that
will
tak
e
into
account
other
parameters
in
the
process
of
establishing
routes
between
source
and
destination
nodes
such
as
channel
queue
length,
signal
to
noise
ratio
and
v
arying
parameters
of
simulations
(adding
some
results
v
ersus
the
maximum
speed
in
mobility
model,
generation
and
rate.
REFERENCES
[1]
A.
O.
al
Janaby
,
A.
Al-Omary
,
S.
Y
.
Ameen,
and
H.
M.
Al-Rizzo,
“T
racking
high-speed
users
using
snr
-cqi
mapping
in
lte-a
netw
orks,
”
2018
International
Conference
on
Inno
v
ation
and
Intelligence
for
Informatics,
Computing,
and
T
echnologies
(3ICT)
,
2018,
pp.
1–7,
doi:
10.1109/3ICT
.2018.8855771.
[2]
A.
O.
Al
Janaby
,
A.
Al-Omary
,
S.
Y
.
Ameen,
and
H.
Al-R
izzo,
“T
racking
and
controlling
high-speed
v
ehicles
via
cqi
in
lte-a
systems,
”
International
Journal
of
Computing
and
Digital
Systems
,
v
ol.
9,
pp.
1–10,
Jul.
2020.
[3]
C.
E.
Perkins,
E.
M.
Ro
yer
,
“
Ad
hoc
ondemand
dis
tance
v
ector
(aodv)
routing,
”
Proceedings
WM-
CSA
’99.
Second
IEEE
W
orkshop
on
Mobile
Computing
Systems
and
Applica
tions
,
v
ol.
3561,
2003,
doi:
10.1109/MCSA.1999.749281.
[4]
D.
B.
Johnson
and
D.
A.
Maltz,
“Dynamic
source
routing
in
ad
hoc
wireless
netw
orks,
”
Mobile
com-
puting
,
pp.
153–181,
1996.
Mobility-pr
ediction
and
ener
gy
optimization
for
multi-c
hannel
multi-interface
...
(Hassan
F
aouzi)
Evaluation Warning : The document was created with Spire.PDF for Python.
324
r
ISSN:
2502-4752
[5]
C.
E.
Perkins
and
P
.
Bhagw
at,
“Highly
dynamic
destinati
on-sequenced
distance-v
ector
routing
(dsdv)
for
mo-
bile
computers,
”
A
CM
SIGCOM
M
computer
communication
re
vie
w
,
v
ol.
24,
no.
4,
pp.
234–244,
1994,
doi:
10.1145/190314.190336.
[6]
Z.
Haas,
“The
zone
routing
protocol
(zrp)
for
ad
hoc
netw
orks,
”
IETF
Internet
draft,
draft-ietf-manet-
zone-zrp-01.
txt
,
1998.
[7]
V
.
P
ark,
“T
emporally-ordered
routing
algorithm
(tora)
v
ersion
1
functional
specification,
”
Internet
Draft,
draft-ietf-
manet-tora-spec-04.
txt
,
2001.
[8]
T
.
K.
V
u
and
S.
Kw
on,
“Mobility-assisted
on-demand
routing
algorithm
for
manets
in
the
presence
of
location
errors,
”
The
Scientific
W
orld
Journal
,
v
ol.
2014,
2014,
doi:
10.1155/2014/790103.
[9]
H.
F
aouzi,
H.
Mouncif,
and
M.
Lamsaadi,
“
Aodv
ener
gy
routing
mechanism
for
multi-channel
multi-interf
ace
ad
hoc
netw
orks
(emcmi-aodv)
using
a
dynamic
programming
algorithm,
”
International
Journal
of
Mobile
Computing
and
Multimedia
Communications
(IJMCMC)
,
v
ol.
7,
no.
4,
pp.
1–16,
2016,
doi:
10.4018/IJMCMC.2016100101.
[10]
R.
A.
Calv
o
and
J.
P
.
Campo,
“
Adding
multiple
i
nterf
ace
support
in
ns-2,
”
Uni
v
ersity
of
Cantabria
,
2007.
[11]
I.
L.
Cherif,
L.
Zitoune,
and
V
.
V
`
eque,
“Throughput
and
ener
gy
consumption
e
v
aluation
in
directional
antennas
mesh
netw
orks,
”
2016
IEEE
12th
International
Conference
on
W
ireless
a
nd
Mobile
Computing
,
2016,
pp.
1–8,
doi:
10.1109/W
iMOB.2016.7763211.
[12]
N.
K
et
and
S.
Hippar
gi,
“Modified
aodv
ener
gy
a
w
are
routing
for
optim
ized
performance
in
mobile
ad-hoc
netw
orks,
”
2016
International
Conference
on
W
ireless
Communications,
Signal
Processing
and
Netw
orking
(W
iSPNET)
,
2016,
pp.
1030–1034,
doi:
10.1109/W
iSPNET
.2016.7566293.
[13]
H.
Ashwini,
V
.
R.
KP
,
and
I.
Ginima
v
,
“Cm-aodv:
an
ef
ficient
usage
of
netw
ork
bandwidth
in
aodv
protocol,
”
2018
In-
ternational
Conference
on
Design
Inno
v
ations
for
3Cs
Compute
Communicate
Control
(ICDI3C)
,
2018,
pp.
111–114,
doi:
10.1109/ICDI3C.2018.00032.
[14]
A.
Ab
u-Ein
and
J.
Nader
,
“
An
enhanced
aodv
routing
protocol
for
manets,
”
International
Journal
of
Computer
Science
Issues
(IJCSI)
,
v
ol.
11,
no.
1,
pp.
54,
2014.
[15]
Z.
Zhaoxiao,
P
.
T
ingrui,
and
Z.
W
enli,
“Modified
ener
gy-a
w
are
aodv
routing
for
ad
hoc
netw
orks,
”
2009
WRI
Global
Congress
on
Intelligent
Systems
,
v
ol.
3,
pp.
338–342,
2009.
[16]
A.
Sharma,
A.
Bansal,
and
V
.
Rishiw
al,
“Sbadr:
stable
and
bandwidth
a
w
are
dynamic
routing
protocol
for
mobile
ad
hoc
netw
ork,
”
International
Journal
of
Perv
asi
v
e
Computing
and
Communications
,
2020.
[17]
A.
P
al,
P
.
Dutta,
A.
Chakrabarti,
J.
P
.
Singh,
and
S.
Sadhu,
“Biogeographic-based
temporal
prediction
of
link
stability
in
mobile
ad
hoc
netw
orks,
”
W
ireless
Personal
Communications
,
v
ol.
104,
no.
1,
pp.
217–233,
2019.
[18]
A.
M.
Bamhdi,
“Ef
ficient
dynamic-po
wer
aodv
routing
protocol
based
on
node
den-
sity
,
”
Computer
Standards
and
Interf
aces
,
v
ol.
70,
pp.
103406,
2020.
[Online].
A
v
ailable:
http://www
.sciencedirect.com/science/article/pii/S0920548919304453.
[19]
K.
A.
Darabkh,
M.
G.
Alf
a
w
ares,
and
S.
Althunibat,
“Mdrma:
Multi-data
rate
mobility-a
w
are
aodv-based
protocol
for
flying
ad-hoc
netw
orks,
”
V
ehicular
Communications
,
v
ol.
18,
pp.
100163,
2019,
doi:
10.1016/j.v
ehcom.2019.100163.
[20]
H.
F
aouzi,
M.
Er
-rouidi,
H.
Moudni,
H.
Mouncif,
and
M.
Lamsaadi,
“Impro
ving
netw
ork
lifetime
of
ad-hoc
netw
ork
using
ener
gy
A
OD
V
(e-aodv)
routing
protocol
in
real
radio
en
vironments,
”
International
Conference
on
Netw
ork
ed
Systems
,
2017,
pp.
27–39.
[21]
A.
K.
Dogra,
et
al
.,
“Q-A
OD
V
:
A
flood
control
ad-hoc
on
demand
distance
v
ector
routing
protocol,
”
2018
first
inter
-
national
conference
on
secure
c
yber
computing
and
communication
(ICSCCC)
,
2018,
pp.
294–299,
doi:
10.1109/IC-
SCCC.2018.8703220.
[22]
P
.
Rani
and
G.
Bisw
as,
“
A
OD
V
enhancement
based
on
the
minimization
of
route-request
pack
ets,
”
International
Conference
on
Computer
Science
and
Information
T
echnology
,
2012,
pp.
442–454.
[23]
S.
R.
Mal
we,
N.
T
aneja,
and
G.
Bisw
as,
“Enhancement
of
DSR
and
A
OD
V
protocols
using
link
a
v
ailability
predic-
tion,
”
W
ireless
Personal
Communications
,
v
ol.
97,
no.
3,
pp.
4451–4466,
2017,
doi:
10.1109/ICSCCC.2018.8703220.
[24]
S.
Ahn,
“
Aodv
e
xtensions
for
multipath
routing,
ie
tf
internet
draft,
”
Uni
v
ersity
of
Seoul
,
No
v
.
2017.
[25]
H.
Jhajj,
R.
Datla,
and
N.
W
ang,
“Design
and
implementation
of
an
ef
ficient
multipath
A
OD
V
routing
algorithm
for
manets,
”
2019
IEEE
9th
Annual
Computing
and
Communication
W
orkshop
and
Conference
(CCWC)
,
2019,
pp.
0527–0531,
doi:
10.1109/CCWC.2019.8666607.
[26]
Y
.
Mai,
F
.
M.
Rodriguez,
and
N.
W
ang,
“Cc-ado
v:
An
ef
fecti
v
e
multiple
paths
congestion
control
A
OD
V
,
”
2018
IEEE
8th
Annual
Computing
and
Communication
W
orkshop
and
Conference
(CCWC)
,
2018,
pp.
1000–1004,
doi:
10.1109/CCWC.2018.8301758.
[27]
P
.
K
yasa
nur
and
N.
H.
V
aidya,
“Routing
and
interf
ace
assignment
in
multi-channel
multi-interf
ace
wire-
less
netw
orks,
”
IEEE
W
ireless
Communications
and
Netw
orking
Conference
,
2005,
pp.
2051–2056,
doi:
10.1109/WCNC.2005.1424834.
[28]
W
.
Su,
S.-J.
Lee,
and
M.
Gerla,
“Mobility
prediction
and
routing
in
ad
hoc
wireless
netw
orks,
”
International
journal
of
netw
ork
management
,
v
ol.
11,
no.
1,
pp.
3–30,
Feb
.
2001,
doi:
10.1002/nem.386,
doi:
10.1002/nem.386.
[29]
E.
Hyyti
¨
a,
H.
K
oskinen,
P
.
Lassila,
A.
Penttinen,
J.
Roszik,
and
J.
V
irtamo,
“Random
w
aypoint
model
in
wireless
netw
orks,
”
Netw
orks
and
algorithms:
Comple
xity
in
ph
ysics
and
computer
science
,
v
ol.
590,
Jan.
2005.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
22,
No.
1,
April
2021
:
315
–
325
Evaluation Warning : The document was created with Spire.PDF for Python.