Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
6, N
o
. 1
,
Febr
u
a
r
y
201
6,
pp
. 22
3
~
23
4
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
1.7
573
2
23
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Evalu
a
ti
on of Vi
deo Quality in
Wireless Multimedia Sensor
Network
s
Mus
t
afa Sh
ak
ir, Ob
aid
Ur
Rehm
an,
Z
eeshan
Abb
a
s,
Abdullah
Mas
o
od,
W
a
jee
h
a S
h
ahid
Departm
e
nt o
f
E
l
ec
tric
al
Engin
e
e
r
ing, COMSATS Institu
te
of
Inf
o
rm
ation T
echn
o
log
y
, Isl
a
m
a
ba
d, Pakist
an
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Feb 18, 2015
Rev
i
sed
No
v 9, 201
5
Accepted Nov 22, 2015
Simulating wireless sensor networks,
their im
plem
enta
tion an
d evalu
a
tion
,
requires th
e use
of a discrete ev
ent si
m
u
lator. O
m
net++ is quit
e
a powerful
simulator which
supports concise and
eas
y
modeling of wir
e
d
as well as
wireless sensors environm
ent. S
cenar
ios involvi
ng m
u
ltim
edia tr
ansm
issions
with characteris
tics of video
qualit
y
control and evalu
a
tio
n must be
computed on th
e basis of Quality of E
xperien
ce
(
Q
oE), which
relies on user’s
percep
tion to m
a
int
a
in the v
i
de
o qualit
y.
For the m
u
ltim
edia
growth and
awareness of fu
t
u
re W
i
rel
e
ss Multim
edia
Sensor Networks (W
MSNs), it is
quite n
e
cessar
y
that
the performance s
hould be tested for
different ty
pes o
f
radio models.
So var
y
ing th
e radi
o
param
e
ters may
allo
w for the
optimization
an
d improvement
of th
e v
i
deo
qu
ality
.
In
this
paper
,
we hav
e
worked on
Omnet++ fr
amework for
th
e
evalu
a
ti
on and
opt
im
iza
tion o
f
th
e
performance of
WMSN b
y
usin
g dif
f
er
ent r
a
dio
models.
Th
e performance is
evalu
a
ted
b
a
sed
on th
e
QoE
m
e
t
r
ics;
i.
e.
Pe
ak
Si
gnal-to-Noise
r
a
tio
(PSNR)
and Mean Opin
ion Score (Mo
S
), which dep
e
nd on user
’
s
p
e
rcep
tion
to
m
a
intain
the
vid
e
o qua
lit
y
.
Keyword:
M
ean op
ni
o
n
s
c
ore
M
u
l
t
i
m
e
di
a
senso
r
net
w
o
r
k
Peak signal
to noise
ratio
Qu
ality of ex
perien
ce
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Obai
d Ur
Reh
m
an,
Depa
rt
m
e
nt
of
El
ect
ri
cal
Engi
neeri
n
g
,
CO
MSA
T
S Institu
te
o
f
In
fo
rmatio
n
Technolo
g
y
,
Park Roa
d
, Chak She
h
zad
, Isl
a
m
a
bad, Paki
st
an.
Em
a
il: o
b
a
id
_
i
iec@yah
o
o
.
com
1.
INTRODUCTION
O
v
er t
h
e
past
few y
e
a
r
s
wi
re
l
e
ss sens
or
net
w
o
r
k
s
have
be
com
e
an ext
r
e
m
el
y
im
port
a
nt
area i
n
t
h
e
researc
h
com
m
uni
t
y
. A sensi
ng
net
w
or
k of
no
des co
nst
i
t
u
t
e
s t
o
a po
wer
f
ul
m
e
l
d
of sensi
n
g
,
pr
oc
essi
ng as
well as co
mmu
n
i
cating
techn
o
l
o
g
y
.
Th
e
versatility th
at
wireless sen
s
or n
e
t
w
orks offer h
a
s
g
i
ven
rise to
a
m
u
lt
i
p
l
e
num
ber
of a
ppl
i
c
at
i
ons. T
h
ese
appl
i
cat
i
o
n
s
i
n
cl
ude m
oni
t
o
ri
ng a
nd c
o
nt
r
o
l
of e
n
vi
r
onm
ent
s
specifically indust
r
ial proce
sses as
well as health
ca
re, wa
rfa
re, s
u
rveillance, tra
ffic m
onitori
ng a
n
d
enforcem
ent, ga
m
i
ng as
well
as agriculture [1,
2]. T
r
en
ds
h
a
ve
been
s
h
i
f
t
e
d f
r
o
m
cust
om
ary
W
i
rel
e
ss
S
e
ns
o
r
Net
w
or
ks
w
h
i
c
h
were
capa
b
l
e
o
f
ca
pt
uri
n
g
s
cal
ar dat
a
o
n
l
y
t
o
WM
S
N
s. T
h
ese net
w
or
ks
have
g
r
eat
rel
e
vance
t
o
Int
e
rnet
of
Thi
n
gs (
I
O
T)
whi
c
h i
n
vol
ve
s audi
o an
d
vi
deo i
n
f
o
rm
at
i
on suc
h
as m
u
l
t
i
m
e
di
a, t
r
affi
c and
envi
ro
nm
ent
a
l
m
oni
t
o
ri
ng t
o
fol
l
o
w t
h
e e
v
ent
s
an
d c
h
an
ges i
n
t
h
e en
v
i
ro
nm
ent
a
l
being m
oni
t
o
red
.
Thes
e
n
e
two
r
k
s
are
ou
tfitted
with
dev
i
ces w
ith
some sen
s
in
g
cap
a
b
ility th
at retriev
e
s d
a
ta from th
e en
v
i
ro
nmen
t fo
r
e.g. cam
era.
Th
e m
u
lti
med
i
a co
n
t
en
t retriev
e
d
i
n
su
ch
syste
m
s allo
ws the users t
o
perc
eive vis
u
ally the im
pact of
t
h
e scene
bei
n
g ca
pt
ure
d
a
n
d
bei
n
g awa
r
e
of
t
h
e e
n
vi
ro
nm
ent
.
So t
o
com
p
rehe
nd
t
h
e per
f
o
rm
ance of
t
h
e
sy
st
em
based on
use
r’s
perc
ept
i
o
n
,
Q
o
E
m
e
t
r
i
c
s are t
o
be use
d
[3
, 4]
. B
y
usi
n
g
t
h
e Q
o
E m
e
t
r
ics t
h
e
per
f
o
r
m
a
nce, i
n
v
o
l
v
i
n
g
m
u
l
t
i
m
e
di
a
m
a
nage
m
e
nt
and
t
r
a
n
s
m
i
ssi
on, m
u
st
be e
v
al
uat
e
d
b
y
keepi
n
g
i
n
vi
ew t
h
e
u
s
ers
p
e
rcep
tion
.
Th
ese m
e
tri
c
s in
clud
e obj
ectiv
e as well
as su
bj
ectiv
e
qu
ality
measu
r
es
wh
ich
in
o
u
r case are
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 1, Feb
r
uar
y
20
1
6
:
22
3 – 23
4
22
4
PSNR and
MoS resp
ectiv
ely. Th
e
Qo
S is
no
t ab
le t
o
in
terp
ret
u
s
er’s
p
e
rcep
tio
n as effi
cien
tly as QoE sin
c
e
Qo
E
ca
n
m
a
nage vi
de
o fl
o
w
s wi
t
h
di
ffe
re
nt
pr
o
p
ert
i
e
s
a
n
d
i
n
t
e
r
f
r
am
e
dep
e
ndei
e
s
.
The key
i
ssues
i
n
choo
si
n
g
and
devel
opi
ng
pr
o
p
er r
out
i
n
g
pr
ot
oc
ol
s i
n
WM
SNs al
so i
n
c
l
ude ene
r
gy
li
mitatio
n
s
,
m
a
in
tain
in
g
req
u
i
red
QoS
lev
e
l, b
a
ndwid
th
or
i
e
nt
ed
dem
a
nd f
o
r
part
i
c
ul
a
r
ap
pl
i
cat
i
ons al
o
n
g
wi
t
h
lo
w d
e
lay
fo
r selectio
n
b
e
tween
m
u
ltip
le
av
ailab
l
e p
a
th
s
[5
].
For t
h
e mu
lti
m
e
d
i
a g
r
owth
and
awaren
ess
of
fu
t
u
re
WMSNs, it is qu
ite
necessary th
at t
h
e
p
e
rform
a
nce sh
o
u
l
d
be t
e
st
ed f
r
o
m
di
ff
erent
t
y
pes
o
f
ra
di
o
m
odel
s
. So va
ry
i
ng t
h
e
radi
o pa
ram
e
t
e
rs
m
a
y
all
o
w f
o
r
t
h
e opt
i
m
i
zati
on a
nd i
m
pro
v
em
ent
of t
h
e
vi
de
o
quality. For the evaluation of
diffe
rent
para
meters firstly
an eve
n
t dr
ive
n
sim
u
lation is necessary
before the
act
ual
de
pl
oy
m
e
nt
t
o
hel
p
opt
i
m
i
ze t
i
me, cost as well as human res
o
urces.
One
o
f
t
h
e m
o
st
i
m
port
a
nt
chal
l
e
ng
es
fac
e
d
by
re
searc
h
ers
is the
de
velopm
ent of
ef
ficient a
n
d
fl
exi
b
l
e
sy
st
em
s
o
ft
wa
re
t
o
m
a
ke
f
unct
i
o
nal
abst
ract
i
o
ns
an
d i
n
f
o
rm
at
i
on
gat
h
e
r
i
n
g
fr
om
m
u
l
t
i
m
e
di
a se
nso
r
s
.
Si
m
u
latin
g
WMSNs requ
ires th
e
u
s
e
of
a
d
i
screte ev
en
t si
m
u
la
to
r
.
Distrib
u
t
ed
im
ag
e com
p
ressio
n
and
p
r
op
er
tran
sm
issio
n
sch
e
m
e
in
in
trod
u
c
ed
i
n
[6
],
wh
ich
lev
e
r
a
ges to
ov
er
co
me scar
ce
r
e
so
urces pr
ob
lem
in
sen
s
or
no
des
an
d
u
n
e
v
en
e
n
er
gy
c
o
nsum
pt
i
o
n
.
The
si
m
u
l
a
t
o
r t
h
at
we
use
d
i
n
o
u
r
p
r
o
j
ect
i
s
Om
net
++.
The
basi
c
n
o
v
e
lity is frame lo
ss,
PSNR and
M
O
S fo
r d
i
f
f
eren
t
fra
m
e
s to
ev
aluate v
i
d
e
o qu
al
ity
m
easu
r
em
e
n
ts an
d
perform
a
nce evaluation i
n
a
transm
ission.
OMNET
++ is
q
u
ite a powerfu
l sim
u
lato
r
wh
ich
supp
orts co
n
c
ise
and
easy
m
o
d
e
l
i
n
g
o
f
wi
re
d
as
wel
l
as
wi
rel
e
ss s
e
ns
o
r
s
envi
ro
nm
ent
.
Ho
we
ver
,
si
m
p
l
e
Om
net
++ doe
s
n
o
t
sup
p
o
rt
vi
de
o
t
r
ansm
i
ssi
on so we
use
d
a se
parat
e
f
r
am
ewor
k k
n
o
w
n
as
M
3
W
S
N.
Thi
s
fram
e
wor
k
n
o
t
onl
y
sup
p
o
rt
s
vi
deo
t
r
ansm
i
ssi
on b
u
t
al
so
t
h
e e
v
al
uat
i
o
n
an
d c
o
n
t
rol
of
vi
de
o
co
nt
ent
.
2.
OMNET++ F
R
AMEW
ORK FO
R
MUL
T
IMEDI
A
T
R
ANSMI
SSI
O
N
There ar
e num
ber o
f
fram
e
w
o
r
k
s t
h
at
can be use
d
wi
t
h
om
net
pp fo
r t
h
e creat
i
o
n of
t
h
e requi
red
netw
or
k.
Di
ffe
rent resea
r
che
r
s
w
o
r
k
e
d
o
n
diffe
rent
f
r
a
m
e
w
o
rk
s
fo
r th
e pur
po
se
of
m
u
lti
med
i
a str
eam
i
n
g and
th
e in
teresting th
in
g
is th
at
ev
ery n
e
x
t
framewo
r
k
us
es
the pre
v
ious
one. T
h
e
fram
e
w
orks that ha
ve the
capability to transm
it and receive the m
u
lti
media cont
e
n
t within the
network are C
a
stalia, W
i
se
-MNet,
WV
SN
an
d M
3
WSN
.
Castalia is a fram
ewo
r
k th
at
is m
a
d
e
to
m
o
d
e
l alg
o
r
ithm
s
fo
r
u
s
u
a
l
o
r
trad
ition
a
l
WSNs
un
d
e
r
p
r
actical and
realistic co
mm
u
n
i
catio
n
con
d
ition
s
[7,
8]. Th
e ov
erall
n
e
twork arch
itectu
r
e
of
Castalia
fram
e
wo
rk
consists o
f
a
wirel
e
ss ch
ann
e
l,
p
hysical
proce
ss,
and
n
o
d
es as
s
h
o
w
n i
n
t
h
e
Fi
gu
re
1.
Fi
gu
re
1.
C
a
st
al
i
a
Net
w
o
r
k
Ar
chi
t
ect
ure
Th
e
w
i
r
e
less ch
ann
e
l co
nn
ect
s d
i
ff
er
en
t nodes w
ith
each
o
t
h
e
r
and
also sim
u
la
tes th
e b
e
h
a
v
i
o
r
of
th
e
wireless link.
The
physical proces
s al
so con
n
ects th
e
no
des w
ith
i
n
th
e
netw
or
k
and
f
e
ed
th
e sensor
man
a
g
e
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Eva
l
ua
tion
o
f
Vid
e
o
Qua
lity
in
Wireless Mu
l
timed
ia
S
e
n
s
o
r
Netwo
r
ks
(Oba
id
U
r
Rehman
)
22
5
of di
f
f
ere
n
t
no
des wi
t
h
dat
a
. Di
ffe
re
nt
m
odul
es
c
o
m
pose a
n
ode
,
i
.
e.
,
c
o
m
m
uni
cat
i
on, appl
i
cat
i
o
n
a
n
d
se
ns
or
m
a
nager
.
W
i
se-M
NET
pr
o
v
i
d
es a
n
e
x
t
e
nsi
on t
o
s
o
m
e
of C
a
st
al
i
a
’s fu
nct
i
o
ns
or f
eat
ure
s
t
o
ge
ner
a
t
e
sim
u
l
a
t
i
on env
i
ro
nm
ent
for WM
SN
[9]
.
W
i
SE-M
Net
p
r
o
v
i
d
es a
pl
at
f
o
rm
for t
h
e
de
si
gni
ng
of t
h
e
net
w
or
k
pr
ot
ot
c
o
l
s
.
W
i
s
e-M
n
et
i
s
desi
gne
d f
o
r eval
u
a
t
i
ng
W
M
S
N
but
d
o
es
n
’
t
pr
o
v
i
d
e Q
O
E s
u
p
p
o
r
t
an
d vi
deo
cont
rol
,
by
w
h
i
c
h
we
m
a
y
eval
uat
e
t
h
e m
u
l
t
i
m
e
di
a cont
e
n
t
f
r
om
u
s
er’s
pe
rs
pect
i
v
e. M
o
re
o
v
er,
i
t
does
n
’t
c
o
ns
i
d
er t
h
e
real
i
s
t
i
c
com
m
uni
cat
i
o
n a
p
p
r
oach
w
h
i
c
h
sh
oul
d
be t
a
ke
n i
n
t
o
acc
o
unt
t
h
at
pract
i
cal
l
y
t
h
e
wi
rel
e
ss m
e
di
um
i
s
u
n
reliab
l
e and h
a
s to
b
e
con
s
id
ered
for realistic
co
mmunication approach. Als
o
,
W
i
SE-M
Net does not
sup
p
o
rt
n
ode
m
obi
l
i
t
y
.
The
WV
SN
m
odel
e
x
t
e
n
d
s t
h
e
ol
der
f
r
am
ewo
r
k
W
i
SE-M
Net. In
WVSN we
m
easure the se
nsing
ran
g
e
of
t
h
e
n
ode
s
usi
n
g t
h
e
fi
el
d
o
f
vi
ew
(F
o
V
).
I
n
pr
e
v
i
o
us case
s
a
u
t
h
ers m
easure
t
h
e se
nsi
n
g
ra
nge
s
assu
m
i
n
g
j
u
st lik
e a d
i
sk
th
at is an
o
m
n
i
d
i
rectio
n
a
l b
u
t
in
this th
e ran
g
e
is
d
e
fi
n
e
d
as a trian
g
l
e with
respect to
t
h
e di
rect
i
o
n
o
f
t
h
e cam
era [10]
. T
h
e se
nsi
ng
ra
nge
de
pe
nds
o
n
s
o
m
e
of t
h
e fa
ct
or
s l
i
k
e t
h
e
di
rect
i
o
n o
f
t
h
e
cam
e
ra (V
),
an
gl
e o
f
vi
ew
(al
pha
) a
n
d
dept
h
o
f
vi
ew
(d
).
In
t
h
i
s
pa
per
,
we
use
d
Om
net
++ fram
e
wo
rk
f
o
r
t
h
e
eval
ua
t
i
on a
n
d
o
p
t
i
m
i
zat
i
on
of
t
h
e
per
f
o
r
m
a
nce
usi
n
g
di
f
f
e
r
e
n
t
radi
o m
odel
s
.
The
per
f
o
rm
ance i
s
c
o
m
p
rehen
d
e
d
based
on
t
h
e
Qo
E m
e
t
r
i
c
s;
i
.
e. PS
NR
an
d
Mo
S wh
ich
d
e
p
e
nd
on
u
s
er
’
s
p
e
rcep
tion
to main
tain
th
e v
i
d
e
o
qu
ality
.
M
3
W
S
N i
s
ne
w f
r
am
ewor
k,
whi
c
h i
s
bas
e
d
on
Om
net
++ and C
a
st
al
i
a
wi
t
h
t
h
e i
n
t
e
grat
e
d
p
r
ope
rt
i
e
s
o
f
WiSE-MNet an
d
WVSN
alo
n
g
with its new fun
c
ti
o
n
a
lities for m
u
lti
med
i
a transm
issi
o
n
and
co
n
t
ro
l
[11
]
.
Th
is fram
e
wo
rk
i
n
clud
es ob
ject
d
e
tectio
n, m
o
b
ili
ty,
fi
eld
o
f
v
i
ew
FoV, co
v
e
r-set
and ap
p
lication
criticality.
The a
r
chitecture of M3WSN
fram
ew
ork
i
s
s
h
ow
n i
n
Fi
gu
re
2.
In
W
M
S
N
s,
w
e
nee
d
t
o
cal
cu
l
a
t
e
/
obser
ve t
h
e be
havi
or
o
f
m
o
ti
on
of
t
h
e
ob
ject
fo
r i
t
s
d
e
t
ect
i
on.
So
,
for th
is
pu
rpo
s
e till n
o
w
d
e
tectio
n
o
f
ob
j
e
cts is do
ne b
y
defin
i
ng
a
ran
g
e o
f
th
e
no
d
e
. An
y of t
h
e
n
o
d
e
s
occu
rs i
n
i
t
s
ra
nge c
a
n
det
ect
t
h
at
n
ode a
n
d t
h
e m
o
t
i
on o
f
t
h
e n
o
d
es m
a
y
be o
f
a
n
y
ki
nd
i
.
e. l
i
n
ear,
ci
rc
ul
ar
or
rando
m
.
In
th
i
s
m
o
d
e
l th
e scalar nod
es can
d
e
tect ob
j
e
cts in
its rang
e
u
s
in
g th
e
o
m
n
i
d
i
rectio
n
a
l way an
d the
ca
m
e
ras are usin
g
th
e FoV
co
n
c
ep
t fo
r the realistic v
i
e
w
. Th
e m
u
lti
med
i
a tran
sm
i
ssio
n
ap
p
licatio
n
s
m
u
st
h
a
v
e
to
calcu
late th
e v
i
d
e
o
q
u
a
lity n
o
t
ju
st in
ter
m
s o
f
QoS bu
t also
in
term
s o
f
QoE. Besid
e
s th
at if we
pr
o
v
i
d
e a ve
ry
go
o
d
ser
v
i
ce
of m
u
l
t
i
m
e
di
a
but
i
f
i
t
i
s
of
h
i
gh c
o
st
suc
h
t
h
at
a n
o
rm
al
perso
n
ca
nn
ot
a
f
f
o
r
d
t
h
en
i
t
has ba
d Qo
E.
Figu
re.
2
M
3
WS
N
No
de
Ar
chitecture
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 1, Feb
r
uar
y
20
1
6
:
22
3 – 23
4
22
6
3.
MULTI
MEDI
A MA
N
A
GE
MENT
The size
of multim
e
dia packets is
larg
e eno
ugh
su
ch
th
at
its tran
sm
issi
o
n
is
n
o
t
easy
as it req
u
i
res
hi
g
h
ba
nd
wi
dt
h an
d po
we
r. M
u
l
t
i
m
e
di
a t
r
ansm
i
ssi
on req
u
i
res hi
g
h
dat
a
r
a
t
e
s. No
rm
all
y
i
n
vi
deos t
h
e f
r
am
e
rate is 30
frames p
e
r
second
. In
t
h
ese frames d
i
ffer
en
t prio
rities are assi
g
n
e
d
to
d
i
fferen
t fram
e
s [12
]
. The
three
diffe
re
nt pri
o
rity
fram
e
s are, I
-
f
r
am
es (Intra
-
Co
de
d f
r
a
m
e
s), P-
fram
e
s (Pr
e
dictive
fr
am
es) and
B-f
r
a
m
e
s
(Bi-pred
ictiv
e fram
e
s).
Th
e lo
ss of
h
i
g
h
prio
rity frame can
affect t
h
e res
u
lt of vi
deo. On the
de
code
r side
the
I-fram
e
s are
use
d
as the ref
e
rence
fram
e
so the loss
of I
-
fram
e
m
a
y
affect the whole vide
o or th
e Group of
Picture
(GOP).
Si
m
ilarly, fo
r t
h
e loss
o
f
Predictiv
e fram
e
it will affect t
h
e
rem
a
in
in
g
frames in
th
e GoP and
t
h
e l
o
ss
o
f
Bi-
pre
d
ictive
frame can a
ffe
ct onl
y
the res
p
ective fram
e
.
Castalia an
d
the ex
ten
s
ion
s
(W
i
S
E-M
N
et &
W
V
SN)
no
t prov
id
ed
th
e con
t
ro
l an
d
ev
aluatio
n
of real
vi
de
o seq
u
e
n
c
e
s. The
r
ef
or
e,
M
3
W
S
N
po
rt
ed Eval
vi
d
w
h
i
c
h
pr
ovi
des
vi
de
o rel
a
t
e
d i
n
f
o
rm
at
i
on, s
u
ch as
delay, j
itter, fra
m
e
type, recei
ved
/lost and
de
codi
ng
errors e
t
c.
EVALID as sho
w
n
in
Fi
g
u
re
3
is a p
a
rt of
M3W
S
N
wh
ich
is u
s
ed
to
evalu
a
te th
e v
i
d
e
o
qu
ality at
the receive
rs e
n
d. To underst
and t
h
e
worki
n
g of EVAL
VID is esse
ntial so that sc
ripts
are written acc
ording
to
wh
at to
requ
ire an
d th
en
t
h
e
q
u
a
lity p
a
rameters co
u
l
d
be ob
serv
ed
. EVALVID
b
a
si
cally wo
rk
o
n
b
o
t
h
the
ends of the si
m
u
la
tion (i.e.
sender and rec
e
iver end)
s
o
that the diffe
re
nce betwee
n the sent and re
ceived
traces can
be
seen. E
v
al
vid
uses FFMPE
G
libraries
whic
h are a lea
d
ing m
u
ltimedia fram
e
work, a
b
le to
en
cod
e
,
d
e
co
de, tran
scod
e,
de-m
u
ltip
lex
e
r,
m
u
l
tip
lex
e
r, st
ream
, filter an
d
p
l
ay m
o
st st
u
f
f th
at hu
m
a
n
s
an
d
machines have
created.
The vi
deo trac
e contains all the in
form
at
io
n ab
ou
t th
e fram
es that build the vide
o and it is created
onl
y
fo
r si
n
g
l
e
t
i
m
e
. The i
n
for
m
at
i
on
m
a
y i
n
cl
ude f
r
am
e num
ber, fram
e
t
ype, f
r
am
e si
ze
and t
i
m
e t
o
t
r
ansm
it
each of the fra
m
es. Sim
i
larly, base
d
on the
inform
ation of the vide
o trac
e file, for eve
r
y video tra
n
smission
the source node also has to create
the sender trace file. The
video trace
file contains
inform
ation about the
packet size, pa
cket id and the
time
sta
m
p. T
h
ese two trace files contain al
l the inform
ation
for transm
ission at
sender side a
n
d for
furthe
r e
v
aluatio
n.
On t
h
e ot
her
hand t
h
e sink node
c
r
eates a receiver tr
ace file for every
receive
d vide
o. Like the sender trace file, the receive
r tr
a
ce file also contains inform
a
tion like pac
k
e
t
size,
packet i
d
a
n
d time sta
m
p.
As, t
h
e se
nso
r
m
a
nager m
odu
l
e
sup
p
o
r
t
s
a cam
e
ra i
n
ret
r
i
e
vi
n
g
a vi
de
o, t
h
ere
f
o
r
e t
h
e cr
eat
i
on o
f
t
h
e
sender t
r
aces a
r
e im
plem
ented
on this
m
odule.
On the
ot
her ha
nd,
due
to the
reas
on t
h
at a
pplication laye
r
receives m
u
ltim
edia pac
k
ets
and rec
o
nstruct it,
the
receive
r trace is c
r
eated at this
m
odul
e.
Figure 3.
Eval
vid Architecture
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Eva
l
ua
tion
o
f
Vid
e
o
Qua
lity
in
Wireless Mu
l
timed
ia
S
e
n
s
o
r
Netwo
r
ks
(Oba
id
U
r
Rehman
)
22
7
4.
PERFO
R
MA
NCE METR
I
C
S
Th
e two
b
a
sic
m
easu
r
es
o
f
q
u
a
lity th
at we are used
i
n
th
is p
a
p
e
r are
PSNR and
on
th
e b
a
sis
of
PSNR, we als
o
m
easure M
O
S. PSNR is
a term
used
fo
r th
e
ratio
of m
a
x
i
m
u
m
p
o
w
er of a sign
al to
th
e
max
i
m
u
m
n
o
i
se p
o
wer
wh
ich can
d
e
g
r
ad
e th
e qu
ality o
f
th
e sign
al and
affect its fid
e
lity. PSNR is
mo
stly
use
d
to m
easu
r
e the quality
of rec
o
nstructions
during
com
p
ressions [13]. In
our
case
the input signal is the
ori
g
inal vi
deo and the e
r
rors ar
e introduc
ed by t
h
e com
p
ression th
rough codecs
.
PSNR is the
hum
an
p
e
rcep
tion
app
r
ox
im
a
tio
n
ab
ou
t th
e recon
s
tru
c
ted
v
i
d
e
o
.
Gen
e
rally h
i
gh
er PSNR
mean
s th
e quality o
f
reco
nst
r
uct
i
o
n
i
s
hi
g
h
a
n
d
vi
ce ve
rsa.
1
0
l
og
Tab
l
e 1
d
e
fi
n
e
s
th
e rang
e of PSNR v
a
l
u
es, sh
ows
th
e qu
ality
lev
e
l.
Tab
l
e
1
.
Relatio
n of PSNR
and
v
i
d
e
o
q
u
a
lity
PSNR
QUAL
ITY
>37 E
x
cellent
31-
37
Good
25-
31
Fair
20-
25
Poor
<20 Bad
Vid
e
o
qu
ality
measu
r
em
en
ts
m
u
st
b
e
b
a
sed u
p
o
n
th
e hu
m
a
n
p
e
rcep
tion
i.e. th
e u
s
ers, by watch
i
ng
th
e v
i
d
e
o
co
m
m
en
ts ab
ou
t it
as g
o
o
d
o
r
bad
.
Th
is typ
e
o
f
v
i
d
e
o
qu
ality
measu
r
em
e
n
ts is also
called
as
su
bj
ectiv
e im
p
r
ession
o
f
th
e u
s
er and
p
r
ov
id
es m
o
re inform
at
io
n
.
Howev
e
r, it is
very costly an
d ti
m
e
con
s
um
i
ng t
h
a
t
hum
ans st
art
t
o
wat
c
h t
h
e
vi
deo
s
f
r
o
m
st
art
to
th
e en
d and sh
are th
ei
r commen
t
s ab
ou
t
th
at, it
req
u
i
r
es
hi
g
h
m
a
npo
we
r. S
o
,
t
h
ese t
y
pes
of
su
bject
i
v
e
m
e
t
h
o
d
s ar
e de
fi
n
e
d by
IT
U i
n
det
a
i
l
.
Tabl
e
2
sh
o
w
th
e MOS wh
ich
is
on
e
o
f
t
h
e
su
bj
ectiv
e qu
al
ity
m
e
trics d
e
scrib
i
ng
t
h
e
h
u
man
p
e
rcep
tion
[14
]
.
Tab
l
e
2
.
Relatio
n
s
of M
O
S and
v
i
d
e
o
q
u
a
lity
MOS
QUAL
ITY
5 E
x
cellent
4 Good
3 Fair
2
Poor
1 Bad
1
,
,
MSE is th
e m
e
an
squ
a
red
erro
r, I is th
e origin
al i
m
ag
e,
K i
s
the c
o
m
p
ress
ed im
age and
MxN is
the
di
mension
o
f
bo
th im
ag
es
.
5.
RA
DIO
M
O
D
ELS
The C
C
24
2
0
and
C
C
1
00
0
are t
w
o c
h
i
p
s
use
d
fo
r
di
ff
erent
p
u
r
p
o
s
es
l
i
k
e ve
ry
l
o
w
po
wer
dat
a
transm
itters and receive
rs,
hom
e
auto
m
a
tio
n, wi
reless
alarm
and security, gam
e
controllers and aut
o
m
a
tic
m
e
t
e
r readi
n
g
et
c. The
pa
ra
m
e
t
e
rs of
b
o
t
h
ra
di
os
di
f
f
er
a lo
t wh
ich
in tu
rn
s affects the resu
lts as
well. Th
e
param
e
t
e
rs are
sho
w
n i
n
Ta
bl
e 3.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 1, Feb
r
uar
y
20
1
6
:
22
3 – 23
4
22
8
Tabl
e 3. Param
e
t
e
rs
o
f
radi
o m
odel
s
Par
a
m
e
ter
s
CC2420
CC1000
Data rate
(
kbps)
250
19.
2
M
odulation
PSK
FSK
Bits/sym
bol
4 1
Bandwidth (MHz)
20
10
Noise Bandwidth
(MHz
)
194
30
Noise f
l
oor (dB
m
)
-
100
-
105
Sensitivity (dB
m
)
-95
-98
Power consu
m
ed (
m
W)
62
22.
2
Co
m
p
ariso
n
of d
a
ta
rate and
t
h
e
b
a
ndwid
th is ev
al
u
a
ted
t
h
ro
ugh
Sh
an
non
cap
acity form
u
l
a:
l
o
g
1
Th
e b
a
nd
wi
d
t
h is d
i
rectly p
r
op
ortion
a
l to
th
e cap
acity. So
, in
CC2
4
2
0
as th
e d
a
ta rate is 2
5
0
kbp
s th
erefor its
b
a
ndwid
th
is also
h
i
g
h
i.e. 20MHz wh
ile in
CC1
00
0
t
h
e
d
a
ta rate is 1
9
.2kb
p
s
wh
ich
is l
o
w co
m
p
ared
t
o
th
e
radi
o C
C
2
4
2
0
t
h
eref
o
r
t
h
e
re
qui
red
ba
n
d
wi
dt
h i
s
al
so l
o
w
i
.
e. 1
0
M
H
z.
6.
SIMULATION RESULTS
We anal
y
ze t
h
e fram
e
l
o
ss perce
n
t
a
ge a
nd
QoE m
e
t
r
ics l
i
k
e PSNR
and M
O
S
of
bot
h r
a
di
os.
An
alysis o
f
resu
lts rev
e
ils th
at th
e p
e
rfo
r
m
a
n
ce o
f
ra
di
o CC1000 is alm
o
st double as com
p
ared to the radi
o
CC2
42
0.
The Fi
g
u
r
e 4 a
nd
Fi
g
u
re
5 sh
ow t
h
e f
r
am
e loss
perce
n
t
a
ge
of
radi
o m
odel
C
C
2
4
20 a
n
d C
C
1
0
00
wi
t
h
r
e
sp
ect to
d
i
ff
er
en
t pow
er
s i
n
d
B
m
.
Fig
u
r
e
4
show
s th
at in
r
a
d
i
o
CC2420
th
e p
e
r
f
or
m
a
n
ce star
ts to
deg
r
ad
e
fr
om
-4dB
m
howe
v
e
r
, Fi
gu
re
5 p
o
r
t
r
ay
t
h
e
per
f
o
r
m
a
nce i
s
deg
r
a
d
i
n
g aft
e
r -
8dB
m
for
ra
di
o C
C
1
0
0
0
.
I
n
t
h
i
s
sense, we
are
able to fi
nd the thres
h
old power le
vel
fo
r
a rel
i
a
bl
e m
u
l
t
i
m
e
di
a t
r
ansm
i
ssi
on
f
o
r
di
f
f
e
rent
rad
i
o
s
.
Vid
e
o
s
co
nsists of frames with
d
i
fferen
t
p
r
i
o
r
ities lik
e I, P and
B
an
d th
e l
o
ss
o
f
h
i
gh
p
r
i
o
rity fram
es
distorts t
h
e vi
deo m
u
ch m
o
re com
p
ared to t
h
e low
pri
o
rity
fram
e
s. The Fi
gu
re
4 an
d Fi
g
u
re
5 also
sh
o
w
the
i
ndi
vi
dual
fra
m
e
l
o
sses o
f
I
,
P an
d B
a
nd a
l
so t
h
ei
r a
v
e
r
a
g
e.
I f
r
am
e l
o
ss pe
rcent
a
ge i
s
hi
g
h
as c
o
m
p
ared t
o
othe
rs beca
use
I fram
e is used as the
refe
re
nce fram
e fo
r t
h
e ot
hers a
n
d
h
a
s hi
g
h
pri
o
ri
t
y
. If a
n
y
o
f
t
h
e
P o
r
B
fram
e
s are lo
st
th
ese
fram
e
s can
b
e
recon
s
tructed
b
y
u
s
i
n
g th
eir
referen
c
e fram
e th
erefore, th
eir
p
r
ob
ab
ility of
lo
ss is lo
w. These fram
e
s h
a
v
e
n
o
t
to
o
m
u
ch effect o
n
th
e
qu
ality Ho
wev
e
r, on
th
e o
t
h
e
r
sid
e
if I fram
e
is lo
st
it can
no
t be reco
n
s
t
r
u
c
ted
in an
y case.
Fi
gu
re
4.
F
r
a
m
e l
o
ss f
o
r
C
C
2
4
2
0
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISS
N
:
2088-8708
Eva
l
ua
tion
o
f
Vid
e
o
Qua
lity
in
Wireless Mu
l
timed
ia
S
e
n
s
o
r
Netwo
r
ks
(Oba
id
U
r
Rehman
)
22
9
Fi
gu
re
5.
Fram
e l
o
ss
f
o
r C
C
1
0
0
0
PSNR
is m
o
stly u
s
ed to
m
easu
r
e th
e quality o
f
reconstru
c
tion
s
du
ri
n
g
co
m
p
ression
s. As
we
men
tio
n
e
d
ear
l
ier
th
at th
e p
e
rf
or
m
a
n
ce o
f
CC1
000
is d
ouble as co
m
p
ar
ed to
CC2
42
0, it can
b
e
o
b
s
erved
in
Fig
u
r
e
6
and
Fig
u
r
e
7
t
h
at th
e PSN
R
at
-
3
d
B
in
r
a
d
i
o
CC
2
4
2
0
is sam
e
as th
e PSN
R
at -6d
B
in r
a
d
i
o CC1
000.
Al
so
t
h
e
pe
rf
o
r
m
a
nce at
-3
d
B
an
d
-6
dB
i
n
ra
di
o C
C
1
0
0
0
is sim
i
lar therefor t
h
eir
gra
p
hs a
r
e
overla
pping i
n
Fi
gu
re
6. Si
m
i
l
a
rl
y
t
h
e per
f
o
r
m
a
nce at
-9d
B
and
-1
5
d
B
i
n
ra
di
o C
C
2
4
20 i
s
si
m
i
l
a
r
and t
h
ei
r
gra
p
hs ar
e
ove
rl
ap
pi
n
g
i
n
Fi
gu
re 7.
Fi
gu
re
6.
Pea
k
Si
gnal
t
o
Noi
s
e R
a
t
i
o
(C
C
2
4
2
0
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 1, Feb
r
uar
y
20
1
6
:
22
3 – 23
4
23
0
Fi
gu
re
7.
Pea
k
Si
gnal
t
o
Noi
s
e R
a
t
i
o
(C
C
1
0
0
0
)
MOS is on
e of th
e sub
j
ective q
u
a
lity
m
e
trics
wh
ic
h
d
e
scribes th
e hu
m
a
n
p
e
rcep
tion
ab
out th
e v
i
deo
wh
et
h
e
r it is
Ex
cellen
t
, Goo
d
, Fair, Po
or, o
r
Bad.
The
MOS gra
p
hs
are direc
tly
reflecting
th
e ab
ov
e
men
tio
n
e
d
g
r
ap
h
s
o
f
fram
e
l
o
ss and
th
e PSNR. Fram
e lo
ss in
rad
i
o
CC
24
20
starts from -4
d
B
till th
i
s
p
o
i
n
t
th
ere was
n
o
fram
e
lo
ss
m
e
an
s ex
cellen
t
q
u
a
lity o
f
th
e v
i
d
e
o. So
th
i
s
can
b
e
d
i
rectly o
b
s
erv
e
d
here in
Fig
u
re
8
.
Th
e
MOS till -4d
B
th
at sh
ow t
h
e
h
u
m
an
p
e
rcep
tio
n
of ex
cellent v
i
d
e
o
q
u
a
lity is alm
o
st 5
.
On
the
o
t
h
e
r h
a
nd
, Fi
g
u
re 9
shows th
e MOS is al
m
o
st eq
u
a
l to
5
till -8
d
B
refl
ectin
g
th
e
p
o
i
n
t
th
at th
e frame lo
ss
st
art
i
ng fr
om
t
h
i
s
poi
nt
.
Last
l
y
, Fi
gures
10-
1
3
, p
o
rt
ra
y
t
h
e screensh
ot
s of t
h
e vi
de
o at
a fi
xed t
i
m
e
(2 secon
d
s
)
at
di
ffere
nt
powe
rs for both
radi
os C
C
2420
and C
C
1000 showing t
h
e
quality of the vi
de
o recei
ved. Since the
per
f
o
r
m
a
nce of C
C
1
0
0
0
i
s
bet
t
e
r at
-6dB
and
-9
dB
res
p
e
c
t
i
v
el
y
t
h
an C
C
2
4
2
0
. The
be
st
resul
t
i
s
at
-
6dB
o
f
radi
o C
C
1
0
0
0
as at
t
h
i
s
poi
nt
we ca
n see
t
h
e
M
O
S i
s
al
m
o
st
5 a
n
d t
h
e
PS
N
R
i
s
al
so
great
e
r
t
h
a
n
3
7
.
Fi
gu
re
8.
M
ean
O
pni
on
Sc
ore
(C
C
2
42
0)
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISS
N
:
2088-8708
Eva
l
ua
tion
o
f
Vid
e
o
Qua
lity
in
Wireless Mu
l
timed
ia
S
e
n
s
o
r
Netwo
r
ks
(Oba
id
U
r
Rehman
)
23
1
Figure
9. Mean Openi
o
n Sc
ore (CC1000)
Fig
u
r
e
10
. V
i
deo
Sn
ap
at -
6dB
(
CC24
2
0
Fig
u
r
e
11
.
V
i
deo
sn
ap
at -6dB (
CC10
00)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
6, No
. 1, Feb
r
uar
y
20
1
6
:
22
3 – 23
4
23
2
Fig
u
r
e
12
.
V
i
deo
sn
ap
at -9dB (
CC24
20)
Figure 13.
V
i
deo
sn
ap
at -9dB (
CC10
00)
7.
CO
NC
CLU
S
I
O
NS
In
ou
r w
o
rk
, we eval
uated
m
u
ltim
edi
a
co
nt
ent
f
r
om
user’s
pers
pect
i
v
e
and m
a
nage
d t
o
m
a
i
n
t
a
i
n
a
fair QoE of video at the receiver end. Om
net
++ fram
e
work
analysis reveiled the vide
o quality based on QoE
m
e
t
r
i
c
s i
.
e PSNR
an
d M
o
S.
The re
sul
t
s
cl
e
a
rl
y
pr
ove
t
h
at
t
h
e ra
di
o m
o
d
e
l
C
C
100
0
pr
o
v
i
d
e a
bet
t
e
r
v
i
deo
q
u
a
lity as co
m
p
ared
to
CC2
420
. Th
e th
resho
l
d
po
wer
at wh
ich
th
e fram
e
lo
ss p
e
rcen
tag
e
is suffi
cien
tly
o
p
tim
ized
is also
at a le
ss
value in case
of C
C
1000.
REFERE
NC
ES
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Sharif, Atif
, Vi
d
y
asag
ar Potdar
, and E
l
i
zabe
t
h
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ess m
u
ltim
edia
sensor network techno
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:
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survey
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009. INDIN 200
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7th
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ence
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Ak
y
ild
iz, Ian
F., Tommaso Melodi
a, and
Kaushik R. Ch
owdhur
y
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uan
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l
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i
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Evaluation Warning : The document was created with Spire.PDF for Python.