Indonesi
an
Journa
l
of El
ect
ri
cal Engineer
ing
an
d
Comp
ut
er
Scie
nce
Vo
l.
1
3
,
No.
2
,
Febr
uar
y
201
9
, pp.
591
~
59
7
IS
S
N: 25
02
-
4752, DO
I: 10
.11
591/ijeecs
.v1
3
.i
2
.pp
591
-
59
7
591
Journ
al h
om
e
page
:
http:
//
ia
es
core.c
om/j
ourn
als/i
ndex.
ph
p/ij
eecs
Perform
ance ev
alu
atio
n
of arithm
etic codi
ng data
co
mp
re
s
sion
for inte
rn
et of thi
ngs app
lications
No
r
A
sil
ah
K
ha
ir
i
,
Asr
al Bahari J
am
bek
,
Riz
alafa
nde
Ch
e I
smail
School
of
Mi
cro
el
e
ct
roni
c Engi
n
ee
ring
,
Univ
ersiti
Mal
a
y
s
ia Perl
is
,
Mal
a
y
s
ia
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
Sep
3
, 2
018
Re
vised
N
ov
10
, 2
018
Accepte
d
Nov
2
4
, 201
8
W
ire
le
ss
Sensor
Network (W
SN
)
is
known
for its
aut
onom
ous
sensors
,
where
it
h
as
b
ee
n
foun
d
to
be
usefu
l
d
uring
the
dev
el
o
pm
ent
of
Int
ern
e
t
of
Th
ings
(IoT
)
d
evi
c
es.
H
oweve
r,
W
SN
is
al
so
known
for
its
li
m
it
ed
ene
rg
y
suppl
y
and
m
emory
spac
e, as
it
c
arr
i
es
sm
al
l
-
size
d
batter
ie
s
a
nd
m
emor
y
spac
e.
Henc
e, a
dat
a
compress
io
n
appr
oa
ch
h
as
b
ee
n
int
rodu
ce
d
i
n
thi
s
p
ape
r
with
the
purpose
of
solving
thi
s
p
art
i
cul
ar
issue.
T
his
pape
r
fo
cuse
d
on
the
p
erf
orm
anc
e
of
the
Arithmeti
c
Cod
ing
al
gor
it
hm
.
Te
m
per
at
ur
e
(T
emp),
Sea
-
L
evel
Press
ure
(Press
ure
),
strid
e
interva
l
(Strid
e),
and
h
ea
rt
ra
t
e
(BPM
)
were
c
hosen
as
th
e
dat
ase
t
in
thi
s
p
roje
c
t.
Based
on
th
e
r
esult
s,
the
compress
ion
rati
o
of
T
emp,
Press
ure
,
Stride,
and
BP
M
were
0.
428,
0
.
255,
0.
217,
and
0.
159
r
espe
ctive
l
y
.
From
thi
s
ana
l
y
s
is,
BP
M
produc
e
d
the
best
compress
ion
rat
io
.
Undenia
b
l
y
,
th
e
Arithmeti
c
Codi
ng
al
gor
it
hm
is
one
of
the
b
est
m
et
hods
to
com
pre
ss
rea
l
-
world
dataset
s.
Henc
e,
b
y
using
thi
s
appr
oa
ch,
it
ca
n
red
u
ce
th
e
usag
e
of
ene
rg
y
and
m
emor
y
spac
e
.
Ke
yw
or
d
s
:
Ar
it
hm
et
ic
Cod
in
g
Data com
pr
ess
ion
In
te
r
net
of Th
i
ng
s
(IoT
)
Re
al
-
world
dat
aset
s
W
i
reless
Senso
r
N
et
w
ork
(
WSN)
Copyright
©
201
9
Instit
ut
e
o
f Ad
vanc
ed
Engi
n
ee
r
ing
and
S
cienc
e
.
Al
l
rights re
serv
ed
.
Corres
pond
in
g
Aut
h
or
:
Nor Asil
ah K
ha
iri,
School
of Mi
cr
oelect
ronic E
nginee
rin
g,
Un
i
ver
sit
i M
al
ay
sia
Per
li
s,
Pauh
P
utra
Cam
pu
s,
02
600 A
rau, Pe
rlis, Ma
la
ysi
a.
Em
a
il
: asi
la
h.
kh
ai
ri9
1@gm
ai
l
.co
m
1.
INTROD
U
CTION
I
nter
net
of
T
hin
gs
(
I
oT)
can
be
de
fine
d
as
th
e
ne
xt
gen
e
rati
on
of
t
he
i
nternet
ev
olu
ti
on,
w
her
e
de
vice
s
and
obj
ect
s
c
onnecte
d
via
th
e
inter
net
al
lo
w
i
nfo
rm
ation
to
be
delive
re
d
th
rou
gh
a
dig
i
ta
l
sign
al
[
1].
By
the
ye
ar 2020,
t
he nu
m
ber
of
de
vices co
nn
ect
e
d via the I
oT
m
ay
b
e as h
i
gh
a
s
7
5 bil
li
on
[2
]
. Ho
we
ver, b
il
li
on
s
of
dev
ic
es
co
nnec
te
d
th
rou
gh
the
inter
net
pro
duce
a
la
r
ge
am
ou
nt
of
data
t
o
be
sto
red.
Vural
et
al
.
[
3]
sta
te
d
t
hat
bill
ion
s
of
IoT
de
vices
co
nnect
ed
via
the
inter
net
th
at
al
so
ge
ne
rate
lo
w
-
rate
tra
ffi
c
of
m
easur
e
m
ent,
m
on
it
or
ing,
an
d aut
om
at
ion
da
ta
are
now a
m
ajo
r c
halle
ng
e
for n
et
w
ork p
rovide
rs a
nd
t
he
internet
as
a
whole.
Accor
ding to
t
hem
, altho
ugh ea
ch
I
oT has a
low rate
o
f
t
raffic
, th
e e
ntire s
um
o
f
traff
ic
on the c
or
e
net
work is
exp
ect
e
d
to
be
la
rg
e,
w
hich
can
obviously
disru
pt
re
gu
la
r
data
tra
ff
ic
.
A
num
ber
of
researc
hers
ha
d
al
s
o
discusse
d
the
possibil
it
y
of
im
plem
enting
the
com
pr
essi
on
a
lgo
rithm
into
I
oT
de
vices,
w
he
re
it
ca
n
m
ini
m
ise
bo
t
h
st
or
a
ge re
qu
i
rem
ents an
d i
nput/o
utput
proces
ses
[4
]
.
The
W
irel
ess
Sensor
Netw
ork
(
WSN)
is
c
omm
on
ly
us
ed
in
the
m
i
li
ta
r
y,
in
du
st
rial
,
m
edical
,
a
nd
agr
ic
ultur
al
se
ct
or
s
due
to
it
s
wireless
ca
pa
bili
ti
es.
It
i
s
al
so
know
n
for
it
s
auto
nom
ou
s
sen
sors
t
hat
ha
ve
t
he
abili
ty
to
m
on
it
or
physi
cal
or
env
i
ronm
ental
conditi
ons.
Th
eref
or
e
,
WSN
i
s
use
f
ul
durin
g
the
de
velo
pme
nt
of
Io
T
de
vices.
H
ow
e
ve
r,
se
nsor
node
dev
ic
es
are
kn
own
to
ha
ve
a
lim
it
ed
energy
sup
ply,
as
they
c
arr
y
s
m
al
l
-
siz
ed
batte
ries.
Alth
ough
it
ha
s
li
m
it
ed
energy
to
f
unct
ion,
it
re
qu
i
res
a
l
ow
op
e
rati
ng
powe
r
i
n
orde
r
t
o
sa
ve
energy an
d
e
xtend
t
he
li
fetim
e o
f
t
he
sen
sor
node [4]. Besi
des
that, Jam
bek
, a
nd
K
hairi
[5
]
m
entioned
that t
he
transm
issi
on
m
odule
ha
s
t
he
l
arg
est
power
c
on
s
um
ption
co
m
par
ed
to
oth
e
r
c
om
po
ne
nts
of
the
se
nsor
node
.
It
is
due
to
the
la
rg
e
am
ou
nt
of
energy
require
d
t
o
operate
th
e
wi
reless
tra
nsm
it
te
r
to
tra
nsm
it
data.
T
her
e
fore,
a
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
3
, N
o.
2
,
Fe
bru
ary
201
9
:
591
–
59
7
592
data
com
pr
essi
on
m
et
ho
d
is
hi
gh
ly
rec
omm
e
nd
e
d
to
overc
om
e
t
hese
prob
l
e
m
s.
In
this
pa
per,
the
Ar
it
hm
et
ic
Cod
i
ng alg
or
it
hm
is p
rop
os
e
d t
o pro
ve
that i
t
can p
rod
uce a hig
her com
pr
e
ssion rati
o an
d bett
er
perform
ance.
1.
1.
Arithme
tic C
od
in
g
Ar
it
hm
et
ic
Cod
in
g
is
known
as
a
m
et
ho
d
for
re
placi
ng
e
ve
r
y
input
sym
bo
l
with
a
co
de
word.
A
stream
of
in
put
sym
bo
ls
a
re
re
placed
with
a
sin
gle
fl
oating
po
i
nt
num
ber
as
th
e
outp
ut
[
6].
Accor
ding
t
o
J
acob,
So
m
van
s
hi,
a
nd
To
r
nek
a
r
[
7],
the
Ar
it
hm
et
ic
al
go
rith
m
is
a
ver
y
eff
ect
ive
m
echan
ism
in
el
im
inati
ng
redu
nd
a
ncy
i
n
the
e
nc
od
i
ng of
data.
Ba
sed
on
t
he
ex
planati
on o
f
the
A
rith
m
et
ic
Cod
in
g
in p
a
per [6
]
,
the
m
ai
n
obj
ect
ive
of
t
he
A
rithm
etic
Cod
i
ng
is
to
al
lo
cat
e
an
i
nter
va
l
to
eac
h
po
te
nt
ia
l
sy
m
bo
l.
Th
e
decim
al
nu
m
ber
is
la
te
r
assigne
d
t
o
the
inte
rv
al
,
wh
e
re
the
rang
e
of
t
he
inter
va
l
is
0.
0
t
o
1.0
.
Af
te
r
t
he
pr
oce
ss
of
rea
ding
th
e
input
of
t
he
sym
bo
l
is
com
plete
d,
th
e
interval
is
s
ubdi
vid
e
d
int
o
s
m
al
le
r
intervals
in
pr
opor
ti
on
to
the
in
put
sym
bo
l’s
pro
bab
il
it
y.
T
hi
s
subinte
rv
al
i
s
div
ide
d
int
o
par
ts
acc
ordin
g
t
o
the
pr
ob
a
bili
ty
of
the
sy
m
bo
ls
from
th
e
in
put.
This
ste
p
is
re
peated
for
e
very
sing
le
in
put
sy
m
bo
l.
Finall
y,
any
fl
oating
po
i
nt
num
ber
f
ro
m
the
final
i
nter
val
un
i
qu
el
y
deter
m
ines the in
pu
t data.
1.
2.
Pre
vious
A
ri
t
hmetic
C
od
in
g Rese
arc
h R
e
ferences
Ar
it
hm
et
ic
Cod
in
g
is
recog
nised
as
one
of
th
e
best
data
c
ompressi
on
a
ppr
oa
ch.
T
he
re
fer
e
nces
f
or
t
he
Ar
it
hm
et
ic
al
go
rithm
are
li
m
i
te
d
an
d
dif
ficul
t
to
obta
in
beca
us
e
of
pate
nt
is
su
es.
He
nce,
only
4
su
it
able
jour
nals
wer
e
selec
te
d f
or r
e
fer
e
nces.
Sh
a
nm
ug
asu
nd
aram
,
and
L
ou
rdusam
y
[6
]
pro
p
os
ed
a
tra
di
ti
on
al
Ar
it
hm
et
ic
Cod
i
ng
m
et
hod.
T
his
pap
e
r
is
fo
c
us
e
d
on
surveyi
ng
the
di
ff
e
ren
t
ba
sic
lossless
da
ta
com
pr
essio
n
al
gorithm
s.
Ba
sed
on
t
his
pap
e
r
,
the
e
xperim
ental
resu
lt
s
an
d
com
par
iso
n
of
t
he
l
os
sle
ss
com
pr
essio
n
a
lgorit
hm
s
us
ed
the
Stat
ist
ic
al
-
a
nd
Dict
ion
a
ry
-
ba
s
ed
a
ppr
oac
hes.
H
oweve
r,
the
Stat
ist
ic
al
ap
proac
hes
we
re
ch
os
e
n
a
s
a
r
efere
nce
due
t
o
t
he
com
par
ison
bet
ween
the
Ar
it
hm
et
ic
app
r
oaches
to
oth
e
r
data
com
pr
essio
ns
,
wh
e
reas
t
he
Dict
ion
ary
a
ppr
oa
ches
on
ly
us
ed
the
Lem
pel
Ziv.
In
this
pro
j
ect
,
t
he
auth
ors
only
us
e
d
12
dif
fer
e
nt
ty
pes
of
te
xt
file
s
as
the
dat
aset
s.
Fr
om
the
obser
vation,
this
Ar
i
thm
e
ti
c
al
go
rithm
has
been
prov
e
n
to
be
on
e
of
t
he
best
perf
or
m
ers
am
on
g
oth
e
r
m
et
ho
ds
by
ac
hieving
t
he
range
of
0.5
7
to
0.
76
in
te
rm
s
of
c
om
pr
essio
n
a
nd
5.15
in
a
ver
a
ge
of
bits
per
c
har
act
e
r
(BPC).
Pape
r
[7
]
st
ud
i
ed
t
he
c
om
par
at
ive
analy
sis
i
n
te
rm
s
of
the
com
pr
essio
n
e
f
fici
ency.
T
he
a
uthors
de
al
t
with
lossless
c
om
pr
essio
n
a
ppr
oac
hes
su
c
h
as
the
Huff
m
an,
A
rithm
et
ic
,
LZ
-
78,
a
nd
G
ol
om
b
Cod
i
ng.
En
glish
t
ext
file
s,
Lo
g
file
s,
Sorte
d
w
ord
li
sts,
an
d
ge
om
et
rically
di
stribu
te
d
data
t
ext
file
s
were
us
e
d
in
the
pro
j
ect
as
dataset
s,
as
we
ll
as
the
M
AT
LAB
softwa
re
durin
g
the
im
p
lem
entat
ion
process.
Accor
din
g
to
th
e
a
utho
rs,
th
e
Ar
it
hm
et
ic
Co
ding
was
su
it
a
ble
f
or
m
od
er
at
e
and
high
f
reque
ncy
oper
at
ion
.
Howe
ve
r,
the
al
gorith
m
al
so
pro
du
ce
d hig
h com
pu
ta
ti
on
al
com
plexity
an
d
sl
ow
com
pr
e
ssion spe
ed
com
par
ed
to t
he Huffm
an
ap
pro
ach.
Ba
sed
on
pa
pe
r
[8
]
,
P
orwal,
Chau
dhary,
J
oshi,
an
d
Jai
n
f
ocused
on
the
lossless
data
c
om
p
ressio
n
m
et
ho
dolo
gies
an
d
c
om
par
is
on
of
their
pe
rfor
m
ances.
The
aut
hors
us
e
d
a
tradit
io
nal
Ar
i
thm
e
ti
c
al
go
rit
hm
in
their
pro
j
ect
,
wh
ic
h
was
sim
il
ar
with
pro
j
e
ct
s
[6
]
a
nd
[7
]
.
H
ow
e
ve
r,
t
he
y
on
ly
us
e
d
si
ng
le
-
data
c
ompressi
on
durin
g
the
c
om
par
at
ive
per
f
or
m
anc
e,
su
c
h
as
the
H
uffm
an
m
et
ho
d.
Te
xts,
vid
e
os
,
a
udios
,
an
d
im
ages
were
us
e
d
as
dataset
s.
Acc
ordin
g
t
o
the
res
earc
hers,
t
he
Ar
it
hm
et
ic
al
gorithm
produce
d
a
high
com
pr
essio
n
ra
ti
o
an
d
us
e
d
le
ss
m
e
mo
ry
sp
ace
.
Howev
e
r,
the
wea
kn
e
ss
of
this
al
gorithm
is
si
m
i
la
r
to
pre
vious
pro
j
ect
[
7],
w
hi
ch
ha
d
slow
com
pr
ess
ion
a
nd
deco
m
pr
essi
on
proce
sses.
Pr
oject
[
9]
was
qu
it
e
dif
fer
e
nt
from
the
pr
evi
ou
s
pro
j
ect
s
in
te
rm
of
it
s
us
ag
e
of
a
m
od
ifie
d
Ar
it
hm
et
ic
appr
oach.
T
he
researc
her
f
oc
us
e
d
on
pro
pos
ing
a
ne
w
al
go
rithm
cal
le
d
the
j
-
bit
enc
odin
g
(JB
E),
w
her
e
it
has
the
abili
ty
to
m
ini
m
ise
the
siz
e
of
data.
This
m
od
ifie
d
al
gor
it
h
m
is
a
com
b
inati
on
of
the
Ar
it
hm
et
ic
(A
RI)
with
the
Ru
n
-
Len
gt
h
E
nc
od
i
ng
(
RLE),
B
urr
ows
-
Wh
eel
er
Tra
ns
f
or
m
(B
W
T
)
,
Mo
ve
-
To
-
F
r
on
t
(MTF
),
an
d
JBE
.
Durin
g
the
co
m
par
at
ive
proc
ess,
the
resea
rc
her
us
e
d
f
our
a
ppr
oach
es:
a
c
om
bin
at
ion
of
t
he
AR
I
with
R
LE,
the
ARI
with
B
WT
,
a
nd
MTF
,
th
e
ARI
with
B
WT
,
a
nd
RLE
an
d
the
ARI
wit
h
R
LE,
B
WT,
MT
F,
a
nd
RLE
.
Im
ages,
te
xts,
bin
a
ries,
and
a
ud
i
os
we
r
e
us
e
d
as
datas
et
s.
Ba
sed
on
t
he
obser
vatio
n,
the
pro
pose
d
a
lgorit
hm
pr
od
uc
ed
a
high c
om
pr
ession rati
o i
n 5 da
ta
set
s.
1.
3.
Co
m
pari
so
n
of the Per
fo
rm
an
ce
of P
reviou
s
Arith
metic C
od
in
g Rese
arch Refe
rences
Seve
ral
data
c
om
pr
essio
n
proj
ect
s
in
t
he
pa
st
ha
ve
us
ed
the
A
rithm
et
ic
ap
proac
h,
t
hough
not
as
fr
e
qu
e
ntly
as
ot
her
ap
proac
he
s
su
c
h
as
the
H
uffm
an
and
Le
m
pel
-
Ziv
-
W
el
ch
(LZ
W).
Howev
e
r,
s
om
e
of
these
pro
j
ect
s
us
e
d
a
m
od
ifie
d
A
rithm
e
ti
c
app
r
oa
ch
in
orde
r
to
achieve
a
bett
er
pe
rfo
rm
ance.
Table
1
s
ho
ws
the
su
m
m
arisation
of the c
om
par
ison b
e
twee
n
th
e Ari
thm
etic al
gorithm
s:
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Perf
orma
nce e
valu
ation of
a
r
it
hm
et
ic
codin
g da
t
a
c
ompre
ssion f
or i
ntern
et
…
(
Nor Asila
h
K
ha
iri
)
593
Table
1.
C
om
par
iso
n of t
he A
rithm
etic Cod
i
ng A
l
gorithm
i
n
P
re
vious Pr
oject
s
Descripti
o
n
[
6
]
[
7
]
[
8
]
[
9
]
Year
2011
2012
2013
2012
Fo
cu
s
Su
rvey
o
f
dif
f
erent
bas
ic
lo
ss
less
data
co
m
p
ressio
n
algo
rith
m
s
Co
m
p
a
rative
an
al
y
sis
in
ter
m
s
of
their co
m
p
ressio
n
ef
f
icien
cy
and
sp
eed
Fo
cu
s o
n
los
sles
s
d
ata
co
m
p
ressio
n
m
e
th
o
d
o
lo
g
ies an
d
co
m
p
a
res thei
r
p
erfo
r
m
an
ce
Prop
o
sed
new algo
rith
m
th
at can
m
in
i
m
ise t
h
e
size o
f
data
Architectu
re/
Alg
o
rith
m
Alg
o
rith
m
Alg
o
rith
m
Alg
o
rith
m
Alg
o
rith
m
Ty
p
e of
Data
Co
m
p
r
ess
io
n
Arith
m
eti
c Co
d
in
g
Arith
m
eti
c Co
d
in
g
Arith
m
eti
c Co
d
in
g
RLE
+
BW
T
+
M
TF
+
JB
E
+ AR
I
Co
m
p
a
ri
-
so
n
of
Oth
er
Dat
a
Co
m
p
r
ess
io
n
Sh
an
n
o
n
-
Fan
o
Co
d
in
g
,
Hu
f
f
m
an
,
Ad
ap
tiv
e
Hu
f
f
m
an
,
Ru
n
-
Len
g
th
Enco
d
in
g
Hu
f
f
m
an
,
L
Z
-
8
,
G
o
lo
m
b
Co
d
in
g
Hu
f
f
m
an
RLE
+
ARI
,
B
W
T
+
MT
F
+ AR
I,
B
W
T
+
RLE
+
ARI
,
RL
E
+
BWT
+
MT
F
+
R
LE
+
ARI
Dataset
Text d
ata
Eng
lish
text f
iles, log
f
iles,
so
rted wo
rd lists
,
a
n
d
g
eo
m
et
ricall
y
dis
tr
ib
u
ted
d
ata text f
ile
Text, vid
eo
,
au
d
io
,
an
d
i
m
ag
e
I
m
ag
e,
text, bin
ar
y
,
an
d
au
d
io
Ad
v
an
tag
e
Bes
t perf
o
r
m
an
ce i
n
the
co
m
p
ressio
n
r
atio
an
d
lo
w bits
per chara
c
ter
Can
perf
o
r
m
well
f
o
r
tex
ts
th
at con
tain
hig
h
ly
sk
ewed
p
rob
ab
ility
sy
m
b
o
l
s
Hig
h
co
m
p
ressio
n
r
atio
,
less
m
e
m
o
r
y
sp
ace
Hig
h
co
m
p
ressio
n
r
atio
in
5 ty
p
es o
f
f
iles
Disad
v
an
tag
e
No
t
stated
Hig
h
co
m
p
u
tatio
n
al
co
m
p
lex
it
y
,
less sp
eed
Slo
w co
m
p
ressio
n
an
d
d
eco
m
p
r
ess
io
n
No
t stated
Ba
sed
on
the
data
colle
ct
ed
in
t
able
1
,
pro
j
ect
[
6]
wa
s
t
he
m
os
t
su
it
ab
le
pro
j
ect
to
be
us
e
d
as
a
ref
e
ren
ce
.
O
ne
of
the
sim
il
ariti
es
of
proj
ect
[
6]
with
the
oth
er
pr
oj
ect
s
is
in
the
im
ple
m
entation
of
the
Ar
it
hm
et
ic
al
gorithm
m
eth
od
in
t
he
pr
oject
.
Proj
ect
[6
]
us
e
d
the
tra
diti
on
al
A
rithm
etic
appro
ac
h,
w
hich
was
al
so
s
i
m
i
la
r
with
proj
ect
s
[
7]
an
d
[
8].
Eac
h
of
t
hese
project
s
us
e
d
the
t
ext
file
as
the
dataset
.
A
ut
ho
r
s
in
[
6]
use
d
m
ulti
ple
ty
pes
of
te
xt
file
s
in
orde
r
to
pro
duce
dive
rse
resu
lt
s.
Be
side
s
that,
the
ad
va
ntage
of
pro
j
ec
t
[6
]
is
alm
os
t
s
i
m
i
la
r
to o
t
her p
roject
s,
in
which
it
had
bette
r
c
om
pr
essio
n rati
o.
Ba
sed
on
the
obser
vatio
n,
pa
per
[6
]
was
s
el
ect
ed
as
a
ref
e
r
ence
not
only
f
or
it
s
sim
il
arit
i
es,
but
al
so
for
it
s
own
uniqu
e
ness
.
T
he
m
easur
em
ent
m
et
ho
d
i
n
pa
pe
r
[
6]
is
dif
fer
e
nt
to
ot
her
pa
pe
rs
because
it
us
e
d
the
com
pr
essio
n
ra
ti
o
an
d
BPC
m
easur
em
ent
to
m
easur
e
t
he
pe
rfor
m
ance
of
da
ta
com
pr
essio
n,
w
herea
s
m
os
t
of
the
pr
oj
ect
s
us
e
d
on
ly
t
he
c
ompressi
on
rati
o
t
o
m
easur
e
t
he
pe
rfor
m
ance
of
com
pr
essio
n.
T
her
e
fore,
it
pro
du
ce
d
a
wide
ra
nge
of
res
ults.
Howe
ve
r,
pro
j
ect
[
6]
pe
rfor
m
ed
the
e
xp
e
rim
ent
qu
it
e
dif
fer
e
ntly
from
the
oth
e
r
proj
ect
s
.
This
pa
per
divi
ded
the
data
c
om
pr
essio
n
i
nto
t
wo
cat
e
gories:
sta
ti
sti
cal
-
and
dicti
on
a
ry
-
base
d
com
pr
e
ssi
on
te
chn
iq
ues
.
Th
e
aut
hors
di
d
no
t
lum
p
them
to
gethe
r
i
n
on
e
gr
oup
due
to
their
a
bili
ti
es.
I
n
sp
it
e
of
t
his,
t
he
resu
lt
s
of
this
pap
e
r
had
m
ore
releva
nce,
as
it
did
not
m
ix
th
e
res
ults
of
s
ta
ti
sti
ca
l
data
com
pr
essio
n
wi
th
the
dicti
on
a
ry
-
ba
s
ed data
co
m
pr
e
ssion.
Pape
r
[6
]
wa
s
chosen
as
the
best
ref
e
re
nce
pap
e
r
beca
us
e
it
is
nea
rly
si
m
il
ar
with
t
he
pr
opos
e
d
pro
j
ect
.
Alth
ough
it
was
ch
os
e
n
as
a
refe
ren
ce
paper,
the
Ar
it
hm
et
ic
Cod
in
g
in
[6]
req
uire
d
a
m
inor
m
od
ific
at
ion
t
o
m
eet
the
re
qu
i
rem
ents.
O
ne
of
the
exa
m
ples
was
a
m
od
ifie
d
ve
rsi
on
of
t
he
Ar
it
hm
etic
al
gorithm
. T
he
Ar
it
hm
etic
al
go
rithm
require
d s
om
e m
od
ific
at
ion
to
s
uppo
rt
real
-
w
or
ld
da
ta
set
s. M
os
t
of
these
pr
e
vious
pro
j
e
ct
s
did
no
t
use
a
num
ber
ing
te
xt
file
dataset
,
hen
ce
re
quiri
ng
certai
n
al
te
rati
on
s
to
al
lo
w
t
he
com
piler
to
process
the
num
ber
i
ng
data.
T
his
pro
pose
d
pro
j
ect
is
f
ocu
s
ed
on
c
ollec
ti
ng
real
-
w
or
l
d
da
ta
set
s
.
Ther
e
f
or
e,
the
env
i
ronm
ental
an
d bi
om
edical d
at
aset
s
wer
e
s
el
ect
ed
f
or it
s
un
i
qu
e
p
at
te
r
ns.
2.
RESEA
R
CH MET
HO
D
The
proc
ess
of
t
he
Ar
it
hm
etic
Co
ding
al
gorithm
is
e
xp
l
ai
ned
in
this
s
ect
ion
us
i
ng
t
he
flo
wc
har
t
appr
oach.
T
his
al
gorithm
can
on
ly
sup
port
num
ber
ing
du
e
t
o
t
he
us
e
of
th
e
do
ub
le
da
ta
t
ype.
The
str
uct
ur
e
of
the
al
go
rithm
was
buil
t
in
th
e
C
pro
gr
am
m
i
ng
la
ng
uag
e
.
I
n
t
his
pro
j
ect
,
i
t
is
c
on
si
der
e
d
as
1
byte
f
or
a
sing
le
char
act
e
r,
incl
ud
i
ng
a
new
li
ne
of
c
ha
racter
s. T
he
ou
t
pu
t
of
the
c
om
pr
essi
on
was
pri
nte
d
in
the
te
xt
file
form
a
t
(.
txt)
by
ass
umi
ng
t
he
f
or
m
at
o
f
file
that
will
be
im
ple
m
ent
ed
in
t
he
WSN
durin
g
t
he
tra
ns
fe
rr
i
ng
of
th
e
data.
More
ov
e
r, t
he a
lgorit
hm
o
f
A
rithm
etic w
as
m
od
ifie
d
to s
ui
t wit
h
the
r
e
quirem
ent.
The
flo
wch
a
rt
of
the
A
rithm
etic
al
go
rit
hm
was
div
i
ded
into
tw
o
par
ts:
c
om
pr
essio
n
a
nd
deco
m
pr
es
si
on.
Fig
ure
1
il
lus
trat
es
the
proc
ess
of
data
c
om
pr
ession
for
the
A
rithm
et
ic
Co
ding
a
pproach.
I
n
com
pr
essio
n,
t
he
raw
data
f
rom
the
.txt
f
or
m
at
file
was
inse
rted i
nto
ar
ray1.
I
n t
he
nex
t
s
te
p,
a
rr
ay
2 c
opie
d t
he
data
from
arr
a
y1
to
pr
e
ser
ve
the
or
i
gin
al
it
y
of
the
a
rr
ay
1
s
equ
e
nce.
T
he
l
ist
s
of
data
i
nsi
de
a
rr
ay
1
were
the
n
div
ide
d i
nt
o
10
data
an
d st
ore
d col
um
n t
o co
lum
n.
The dat
a
inside
the
c
olum
ns
wer
e
arr
a
ng
e
d i
n a
n asc
e
ndi
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
3
, N
o.
2
,
Fe
bru
ary
201
9
:
591
–
59
7
594
order,
si
nce
it
was
ea
sie
r
for
the
c
om
piler
to
co
unt
the
re
pe
ti
ti
on
of
t
he
da
ta
.
The
fr
e
qu
e
ncy,
pro
bab
il
it
y,
an
d
range
of the
re
petit
ion
s
of the
d
at
a
wer
e
lat
er
coun
te
d
a
nd st
or
e
d
i
nto
a
rr
ay
3.
Fig
ur
e
1.
Fl
owcha
rt
of
A
rithm
etic Cod
in
g com
pr
essio
n
a
lgorit
hm
The
pro
ba
bili
t
y
an
d
range
of
the
rep
et
it
io
ns
we
re
cal
c
ulate
d
base
d
on
the
eq
uatio
ns
(
1)
a
nd
(
2)
.
Eq
uation
(
1)
was
div
i
ded
by
10
du
e
to
a
sing
le
c
olu
m
n
requirin
g
10
da
ta
.
O
n
t
he
ot
her
ha
nd,
eq
ua
ti
on
(2)
consi
sts o
f
tw
o eq
uations:
L
owRa
nge a
nd
H
igh
Ra
ng
e
. E
qu
at
ion
(2) was a
dap
te
d from
Salom
on
’s boo
k [
10
]
.
Pr
oba
bili
ty
= Fr
eq
ue
ncy ÷ 1
0
(1)
Lo
wRan
ge
=
P
rev
i
ou
s
HighRa
ng
e
;
HighRa
nge = L
ow
Ra
ng
e
+ Freq
ue
ncy
(2)
Fo
r
t
he
ne
xt
st
ep,
the
c
om
pil
er
l
oope
d
a
rr
a
y2
un
ti
l
t
he
i
ndex
becam
e
zero
value.
Fi
rst,
the
c
om
piler
chec
ke
d
the
e
xi
ste
nce
of
valu
e
0
in
the
i
nd
e
x
of
ar
ray2.
T
he
com
pr
essi
on
proc
ess
will
occur
if
the
va
lue
di
d
no
t
e
qual
to
0.
The
c
om
piler
us
ed
the
A
rithm
et
ic
equ
at
io
ns
and
t
he
data
in
side
ar
ray3
dur
ing
th
e
com
pr
e
ssion
process
.
Be
f
or
e
the
c
om
piler
us
e
d
the
e
qu
a
ti
on,
t
he
sim
il
ari
ti
es
of
the
val
ue
s
in
ar
ray2
a
nd
a
rr
ay
3
wer
e
r
equ
i
red.
The
c
om
piler
cou
l
d
the
n
exec
ute
the
proce
ss
by
obta
ini
ng
th
e
data
from
arra
y3
an
d
determ
ine
the
cu
rr
e
nt
ind
e
x
value
of
a
rr
ay
2
by
m
odulo
wi
th
2.
If
the
value
was
eq
ual
t
o
0,
it
will
us
e
eq
uatio
n
(3).
Othe
rw
ise
,
it
will
us
e
the m
od
ifie
d
e
qu
at
io
n (
4)
.
E
quat
ions (
3) a
nd (4)
we
re a
dap
t
ed fr
om
b
ook [
10
]
.
St
ar
t
In
p
u
t
da
t
a
i
n
t
o
array
1
.
Co
p
y
dat
a
i
n
t
o
array
2
.
D
i
v
i
d
e d
at
a
i
n
s
i
d
e array
1
into
1
0
da
t
a f
o
r
ev
ery
inde
x
i
n
array
1
and
a
rra
n
g
e
d
a
t
a
i
n
s
i
d
e array
1
i
n
a
s
ce
n
d
i
n
g
or
d
er
Co
u
n
t
fre
q
u
en
cy
,
p
ro
b
a
b
i
l
i
t
y
,
an
d
ra
n
g
e of
value a
n
d
s
t
o
r
e da
t
a
i
n
t
o
n
ew
array
,
array
3
Co
m
p
res
s
d
a
t
a by
us
e
A
ri
t
h
m
et
i
c eq
u
a
t
i
o
n
,
an
d
array
3
data
Is
i
n
d
ex
o
f arr
ay
2
%
2 = 0
?
N
ew
L
o
w
=
O
l
d
L
o
w
+ (O
l
d
H
i
g
h
-
O
l
d
L
o
w
) *
L
o
w
Ran
g
e
N
ew
H
i
g
h
=
O
l
d
L
o
w
+
(O
l
d
H
i
g
h
–
O
l
d
L
o
w
) *
N
ew
L
o
w
=
(O
l
d
L
o
w
*
1
0
) +
((O
l
d
H
i
g
h
*
1
0
)
–
(O
l
d
L
o
w
*
1
0
))
*
L
o
w
Ran
g
e
N
ew
H
i
g
h
=
(O
l
d
L
o
w
*
1
0
) +
((O
l
d
H
i
g
h
*
1
0
)
–
(O
l
d
L
o
w
*
1
0
))
*
H
i
g
h
R
an
g
e
D
ecrem
en
t
of i
n
d
e
x
array
2
Pri
n
t
o
u
t
p
u
t
of
d
a
t
a
c
o
m
p
res
s
i
o
n
i
n
t
o
.
t
x
t
fo
rm
at
L
o
o
p
array
2
u
n
t
i
l
en
d
Is
i
n
d
ex
o
f arr
ay
2
= 0
?
St
o
re o
u
t
p
u
t
o
f c
o
m
p
res
s
da
t
a
i
n
t
o
n
ew
array
E
n
d
Y
es
No
Y
es
No
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Perf
orma
nce e
valu
ation of
a
r
it
hm
et
ic
codin
g da
t
a
c
ompre
ssion f
or i
ntern
et
…
(
Nor Asila
h
K
ha
iri
)
595
NewLo
w
=
O
l
dL
ow + (Old
H
igh
-
OldL
ow)
*
L
owRa
nge
NewHig
h
=
O
l
dL
ow + (Old
H
igh
–
OldL
ow)
*
HighRa
ng
e
(3)
NewLo
w
=
(OldL
ow * 1
0) +
(
(O
l
dH
i
gh * 10
)
–
(
OldL
ow
*10)) * L
owRa
ng
e
NewHig
h
=
(O
ldLow
* 1
0)
+
((Old
High
* 1
0)
–
(
OldL
ow
* 10)) *
H
i
gh
R
ang
e
(4)
Af
te
r
the
cal
cul
at
ion
process
is
com
plete
d,
t
he
in
dex
in
ar
ray2
decr
em
ented
and
lo
oped
unti
l
it
becam
e
0.
The
n
it
st
or
e
d
th
e
ou
t
pu
t
of
the
c
om
pr
essed
data
int
o
a
ne
w
a
rr
ay
.
Finall
y,
it
pr
i
nted
the
outp
ut
of
com
pr
ess
e
d
data into
the
.t
xt for
m
at
.
The
pr
ocess
of
deco
m
pr
essi
ng
wa
s
dif
fer
e
nt
a
nd
qu
it
e
di
ff
ic
ul
t
due
t
o
it
s
re
quirem
ent
to
pro
cess
a
r
ow
of
se
par
at
e
dat
a
be
fore
com
bin
in
g
it
i
nto
a
si
ng
le
li
ne
of
data.
Fi
gure
2
sho
ws
t
he
process
of
data
dec
om
pr
essio
n
us
in
g
t
he Arith
m
et
ic
Cod
ing a
ppr
oach.
Figure
2
.
Flo
w
char
t
of the
A
rithm
e
ti
c Cod
in
g decom
pr
essi
on alg
or
it
hm
The
first
ste
p
of
deco
m
pr
essi
on
was
to
in
pu
t
the
c
om
pr
esse
d
data
into
ar
ray
4.
The
n
the
co
m
pi
le
r rea
d
the
data
insi
de
arr
ay
4.
For
t
he
nex
t
ste
p,
the
c
om
piler
loope
d
ar
ray4
r
ow
by
row
unti
l
the
e
nd.
First,
the
co
m
pi
le
r
identifie
d t
he e
xistence
of
val
ue 0
in
in
de
x
of
ar
ray4.
If
it
does
no
t
e
qu
al
t
o 0
, t
he c
om
pil
er
the
n set
the
i
ntege
r
of
m
yL
oop
as
10,
as
t
he
sys
tem
was
set
by
storing
10
da
ta
for
eac
h
c
olu
m
n.
The
de
com
pr
essio
n
proces
s
occurre
d
by
usi
ng
dec
om
pr
es
sed
A
rithm
etic
eq
uatio
ns
a
nd
arr
ay
3
data.
T
he
com
p
il
er
wa
s
re
qu
i
red
to
ide
ntify
St
ar
t
In
p
u
t
da
t
a
i
n
t
o
array
4
.
Read
c
o
m
p
res
s
i
o
n
d
at
a i
n
s
i
d
e ar
ray
4
D
eco
m
p
res
s
d
a
t
a by
us
e A
r
i
t
h
m
et
i
c e
q
u
at
i
o
n
,
a
n
d
array
3
da
t
a
Is
m
y
L
o
o
p
>
5
?
N
u
m
=
(Co
m
p
res
s
i
o
n
Co
d
e
-
(L
o
w
Ra
n
g
e *
1
0
0
0
0
0
))
/
Pro
b
ab
i
l
i
t
y
N
u
m
=
(Co
m
p
res
s
i
o
n
Co
d
e
-
L
o
w
Ran
g
e)
/
Pro
b
a
b
i
l
i
t
y
D
ecrem
en
t
of m
y
L
o
o
p
Pri
n
t
o
u
t
p
u
t
of
d
a
t
a d
eco
m
p
res
s
i
o
n
i
n
t
o
.
t
x
t
form
at
L
o
o
p
array
4
row
b
y
r
o
w
u
n
t
i
l
e
n
d
Is
m
y
L
o
o
p
=
0
?
E
n
d
Y
es
No
Y
es
No
m
y
L
o
o
p
=
1
0
St
o
re o
u
t
p
u
t
o
f
d
ec
o
m
p
res
s
da
t
a i
n
t
o
n
ew
array
D
ecrem
en
t
of arr
ay
4
Is
i
n
d
ex
o
f arr
ay
4
=
0
?
No
Y
es
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2502
-
4752
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci,
Vo
l.
1
3
, N
o.
2
,
Fe
bru
ary
201
9
:
591
–
59
7
596
the
values
of
m
yL
oop.
If
the
i
nd
e
x
of
m
yL
oo
p
is
bigger
t
han
5,
it
th
us
re
quires
t
he
use
of
t
he
m
od
ifie
d
eq
uatio
n
(5).
Othe
r
wise,
it
w
ould
pr
oce
ed
to
use e
qu
at
ion
(
6)
.
E
qu
at
i
on
s
(5
)
-
(
6)
were ada
pted fr
om
book
[
10
]
.
Nu
m
=
(Com
pr
essio
nCode
-
(
Lo
wRan
ge *
1000
00)) / P
rob
abili
ty
(5)
Nu
m
= (
Com
pr
essio
nCode
-
Lo
wRan
ge) / P
roba
bili
ty
(6)
Be
fore
us
in
g
th
is
eq
uatio
n,
t
he
com
piler
wa
s
r
equ
i
red
to
ide
nt
ify
the
sim
il
ari
ti
es
of
the
val
ue
s
in
ar
ray4
with
ar
ray3
.
H
ence,
t
he
com
piler
would
be
a
ble
to
e
xecu
te
t
he
pr
ocess
by
obta
inin
g
the
da
ta
fr
om
arr
ay
3.
Af
te
r
the cal
culat
io
n was c
om
plete
d,
it
sto
r
ed
the
outp
ut
of the
de
com
pr
essed
d
a
ta
into
a
new a
rr
ay
.
Th
e
n, the
inde
x
in
m
yL
oop
wa
s
decr
em
ented
and
l
oope
d
unt
il
it
beca
m
e
0.
If
the
i
ndex
of
m
yL
oo
p
was
equ
al
to
0,
it
woul
d
then
procee
d t
o t
he
ne
xt
ste
p
by
dec
rem
entin
g
ar
ray4.
T
he
n,
it
id
entifi
ed
the
i
ndex
of
a
rray
4.
If
it
is
e
qu
al
to
0,
it
w
il
l t
hen
pro
ceed
by printi
ng the
outp
ut
of
the d
ec
om
pr
es
sed data
int
o
.t
xt for
m
at
.
To
m
ake
the
re
su
lt
s
m
or
e
reali
sti
c,
real
-
w
or
l
d
dataset
s
f
r
om
var
i
ou
s
a
pp
li
ca
ti
on
dom
ai
ns
w
ere
us
e
d
in
this
pa
per
.
Th
e
dataset
s
we
re
te
m
per
at
ur
e
m
easur
em
ent
in
Alor
Seta
r
(Tem
p)
[
11]
,
sea
-
le
vel
pr
e
ssu
r
e
m
easur
em
ent
in
Alor
Seta
r
(Pressu
re
)
[
11]
,
s
tride
inte
rv
al
of
healt
hy
hu
m
an
in
fa
st
-
w
al
ki
ng
(
Stride
)
[12
]
,
an
d
hear
t
rate
of
a
n
el
it
e
at
hlete
(
BPM
)
[12].
Th
e
reas
on
Tem
p,
P
ress
ur
e
,
Stri
d
e,
an
d
BPM
data
was
use
d
in
this
exp
e
rim
ent is b
ecau
se they
ha
d
a
va
riet
y of
patte
rn
s
.
3.
RESU
LT
S
A
ND AN
ALYSIS
This
sect
ion
di
scusses
the
c
om
pr
essio
n
re
su
lt
s
ac
quired
from
the
f
our
dataset
s.
To
es
tim
a
ti
ng
t
he
perform
ance
of
the
c
om
pr
essi
on
al
gorithm
,
com
pr
essio
n
rat
io
cal
culat
io
n
was
us
e
d
in
t
hi
s
resea
rch.
Fi
gures
3
and fi
gure
4
il
lustrate
the
per
f
or
m
ance of the
fo
ur
dataset
s in
a
ba
r gr
a
ph for
m
.
Figure
3
.
Size
befor
e
and a
fte
r
c
om
pr
ess for
four
dataset
s
Figure
4
.
Com
pr
essi
on r
at
io
for f
our data
set
s
Ba
sed
on
the
f
igures,
al
l
data
set
s
decr
ease
d
after
bei
ng
c
om
pr
essed.
Ba
s
ed
on
these
da
ta
set
s,
BPM
pro
du
ce
d
the
l
ow
est
c
om
pr
es
sion
rati
o
of
a
bout
0.159.
This
is
fo
ll
owe
d
by
Stride
at
0.217
and
the
n
P
ress
ur
e
at
about
0.2
55.
T
e
m
p
dataset
pr
oduce
d
the
highest
com
pr
essi
on
rati
o,
w
hich
was
ab
out
0.4
28.
T
her
e
fore,
BPM
achieve
d
t
he
be
st
com
pr
essio
n
rati
o
am
ong
al
l
the
dataset
s.
The
resu
lt
i
n
fi
gures
3
s
hows
that
the
A
rithm
et
ic
is
on
e
of
t
he
best
appro
ac
hes
t
o
com
pr
ess
data
.
He
nce,
the
A
rithm
etic
app
r
oach
is
a
s
uita
ble
for
a
pp
li
ca
ti
on
on
real
-
w
orl
d
dat
aset
s.
Accor
din
g
to
th
e
obse
rv
at
io
n,
the
re
su
lt
s
of
the
c
om
pr
ession
rati
o
we
re
a
ff
ect
e
d
by
t
he
le
ng
th
of
num
ber
s
.
T
he
refore
,
the
al
gorith
m
pr
oduces
be
tt
er
res
ult
in
c
om
pr
essio
n
rati
o
w
he
n
t
he
le
ng
t
hs
of
nu
m
ber
s
a
re
long
e
r.
Ba
se
d
on
t
he
res
ults,
it
can
be
deduced
t
hat
A
rithm
etic
is
on
e
of
t
he
best
m
et
hods
to
com
pr
ess
real
-
world
dataset
s
by
achie
ving
be
tt
er
com
pr
ession
rati
o.
As a
r
esult,
it
has t
he
abili
ty
to
redu
ce
the
us
a
ge of
m
e
m
or
y an
d
al
s
o
c
onsu
m
e less ener
gy.
4.
CONCL
U
SI
O
N
WSN
is
know
n
f
or
it
s
a
uton
om
ou
s
se
nsor
s
an
d
use
f
uln
es
s
in
the
I
oT
w
or
l
d.
H
oweve
r,
WSN
is
al
so
known
f
or
it
s
li
m
it
ed e
nergy
s
upply
an
d
m
e
m
or
y
sp
ace
due
to
t
he sm
al
l
-
siz
ed b
at
te
ry
an
d m
e
m
or
y.
The
refor
e
,
the
Ar
it
hm
et
ic
al
gorithm
was
introd
uced
in
t
his
pr
oject
i
n
hope
s
of
s
olv
i
ng
just
t
his
pro
bl
e
m
.
Dif
fer
e
nt
ty
pes
of
dataset
s
we
re
use
d
i
n
this
pro
j
ect
,
s
uch
as
Tem
p,
Pr
es
su
re
,
Stri
de,
a
nd
B
PM.
Ba
se
d
on
t
he
re
su
lt
s
of
the
Evaluation Warning : The document was created with Spire.PDF for Python.
Ind
on
esi
a
n
J
E
le
c Eng &
Co
m
p
Sci
IS
S
N:
25
02
-
4752
Perf
orma
nce e
valu
ation of
a
r
it
hm
et
ic
codin
g da
t
a
c
ompre
ssion f
or i
ntern
et
…
(
Nor Asila
h
K
ha
iri
)
597
exp
e
rim
ent, the
co
m
pr
essi
on
rati
o of Tem
p,
Pr
ess
ur
e
, St
rid
e, a
nd BPM
w
ere at
0.4
28, 0.
2
55,
0.217, a
nd
0.1
59
resp
ect
ively
.
It
sh
owe
d
t
hat
B
PM
pr
oduce
d
be
tt
er
com
pr
essi
on
rati
o
t
han
th
e
ot
her
th
ree
da
ta
set
s.
It
al
so
s
howe
d
that
the
Ar
it
hm
et
ic
al
go
rith
m
is
on
e
of
the
be
st
m
et
ho
ds
t
o
c
om
pr
ess
rea
l
-
w
or
ld
dataset
s.
T
he
refor
e
,
by
usi
ng
this al
gorit
hm
, it should be
ab
l
e to m
ini
m
ise
t
he
c
onsu
m
ption
of ene
rg
y a
nd m
e
m
or
y spac
e.
REFERE
NCE
S
[1]
K.
D.
Chang
,
J.
L.
Ch
en,
H
.
C.
C
hao
and
C.
W
.
L
iu,
"Th
e
Poten
ti
a
l
Cloud
Appl
ic
a
t
ion
Model
for
In
te
rne
t
of
Thi
ngs
-
Case
Stud
y
of
Shopping
Mall
s,
"
in
2014
Tenth
Inte
rnat
ional
C
onfe
renc
e
on
Int
el
li
g
ent
Informa
ti
on
Hid
ing
an
d
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imedi
a
Sign
al
Proc
essing
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IIH
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MSP)
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k
yushu ,
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pp
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957
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[2]
F.
J.
Riggi
ns
an
d
S.F.
W
amba,
"
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rch
Dire
c
tions
on
the
Adop
ti
on,
Us
ag
e,
and
Im
pac
t
of
the
I
nte
rne
t
of
Th
i
ng
s
through
th
e
Us
e
of
Big
Data
Ana
l
y
t
ic
s,"
in
2015
4
8th
Hawaii
Int
ernati
onal
Confe
re
nce
on
S
yste
m
S
c
ie
nc
es
(
HICSS)
,
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i, 2015, p
p.
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–
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[3]
S.
Vural
,
P.
Nav
ara
tn
am,
N.
W
a
ng,
C.
W
ang,
L
.
Dong
and
R
.
T
af
az
ol
li
,
"In
-
net
wo
rk
cachi
ng
of
In
t
ern
et
-
of
-
Thi
n
gs
dat
a
,
" i
n
2014
I
E
EE
In
te
rnationa
l
Confe
ren
ce on Com
municat
ions (
ICC)
,
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dn
e
y
,
2014,
pp
.
3185
–
3190.
[4]
S.
Sheikh
and
H
.
Dakho
re,
"D
a
t
a
Com
pre
ss
ion
Te
chn
ique
s
for
W
ire
le
ss
Sensor
Network
,
"
(
IJC
SIT)
Inte
rnation
al
Journal
of
Computer
Sc
ie
nc
e
and
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Te
c
hnologi
es
,
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.
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,
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r
i
Jam
bek
and
N.
A.
Khair
i
,
"P
erf
or
m
anc
e
Com
par
i
son
of
Huffm
an
And
Le
m
pel
-
Zi
v
W
elch
Dat
a
Com
pre
ss
ion
for
W
ire
le
ss
Sensor
Node
Appli
ca
t
i
on,
"
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rican
J
ournal
of
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li
e
d
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es
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2014.
[6]
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ugasunda
ram
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Lo
urdusam
y
,
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om
par
at
ive
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y
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T
ext
Com
pre
ss
ion
Algorit
h
m
s,"
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rnation
al
Journal
of
Wisd
om B
ased
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uti
ng
,
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,
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.
3,
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c
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[7]
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Jac
ob
,
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m
vanshi
and
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.
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k
ar,
"Co
m
par
at
ive
Anal
y
sis
of
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Te
x
t
Com
pre
ss
ion
T
ec
hniqu
es,
"
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rnational
Jo
urnal
of
Comput
er
Applications
,
vol.
56
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no
.
3
,
pp
.
17
-
21
,
Oct
201
2.
[8]
S.
Porw
al
,
Y.
Ch
audha
r
y
,
J.
Jos
hi
and
M.
Jain,
"D
a
ta
Com
pre
ss
ion
Methodol
ogie
s
f
or
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Dat
a
and
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par
ison
bet
wee
n
Algor
i
t
hm
s,"
Inte
rnatio
nal
Journal o
f
E
ngine
ering
Sc
ie
n
ce
and
Inno
vativ
e T
e
chnol
ogy
(
IJ
ESIT)
,
vol.
2,
no
.
2,
pp
.
142
-
147
,
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2013.
[9]
I.
M.
A.
D.
Suar
jay
a
,
"A
New
Algorit
hm
for
Dat
a
Com
pre
ss
ion
Optimiza
ti
o
n,
"
(
IJA
CSA)
Inte
rna
ti
onal
Journal
o
f
Adv
anc
ed
Comp
ute
r Sc
ie
nc
e
and
Applications
,
vo
l.
3
,
no
.
8
,
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.
14
-
17,
Sept
2012.
[10]
D.
Salomon,
Da
ta
Compr
ession
The
Complete
R
ef
ere
n
ce
,
3rd
ed.
New
York,
NY
:
Springer
-
Verl
ag
New
York,
In
c
,
2004,
pp
.
110
-
1
13.
[11]
W
ea
the
r
Underg
round,
"https:
//
w
ww
.
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com"
.
[12]
Ph
y
sioNet, "ht
tp
s
:/
/ph
y
s
ione
t
.
org
".
BIOGR
AP
HI
ES OF
A
UTH
ORS
Nor
As
il
ah
Khai
ri
is
cur
r
ent
l
y
pu
rsuing
her
Maste
r
of
Scie
n
ce
(M
.
Sc)
in
School
of
Microe
l
ec
tron
ic
Engi
ne
eri
ng,
Un
ive
rsiti
Mal
a
y
sia
Perli
s
(UniMA
P)
since
Janua
r
y
2016.
She
completed
bo
th
h
er
Diploma
and
Ba
che
lor
of
Com
pute
r
Sci
ence
(H
ons)
at
Unive
rsit
i
Te
kno
logi
MA
RA
(UiTM)
in
2012
and
2015.
Her
rese
ar
ch
stu
d
y
is
Design
of
Eff
icient
Data
C
om
pre
ss
ion
Algorit
hm
and
Its
Im
ple
m
ent
at
ion
in
Portab
le
Elec
t
ronic
Dev
ice
for
Inte
rne
t
of
Thi
n
gs Appli
cations.
As
ral
Baha
ri
Ja
m
bek
is
an
As
s
oci
a
te
Profess
or
at
th
e
School
o
f
Microe
l
ec
t
roni
cs
Engi
ne
eri
ng
,
Univer
siti
Ma
lay
sia
Perli
s
(Uni
MA
P),
and
was
a
Program
m
e
Chai
rpe
rson
for
the
E
lectr
oni
cs
Engi
ne
eri
ng
De
gre
e
Program
m
e,
UniMA
P.
He
h
as
m
ore
tha
n
15
y
e
ars
expe
r
ie
nc
e
in
integra
t
ed
ci
rcu
it
and
s
y
st
e
m
design
in
bot
h
the
industr
y
a
nd
acade
m
ic
se
c
tors,
and
has
be
en
invol
v
ed
at
var
ious
l
eve
ls
of
VLSI
design s
u
ch as
tra
nsistor
m
odel
li
ng,
digit
al
ci
rcu
it
d
esign,
analogue
ci
r
cui
t
design,
logic
sy
nth
esis
and
p
h
y
sic
al
pl
ace
a
nd
rout
e,
arc
hi
te
c
ture
design
and
al
gori
thm
deve
lopment
.
Riz
alafa
nd
e
Ch
e
Ism
ai
l
is
an
A
ss
oci
at
e
Profess
or
of
Elec
tron
ic
Engi
n
ee
r
ing
at
the
School
of
Microe
l
ec
tron
ic
Engi
ne
eri
ng,
Un
ive
rsiti
Mal
a
y
s
ia
Perli
s
(UniMA
P
).
He
serve
s
as
D
ea
n
of
School
of
Microele
c
tro
nic
Eng
ineeri
ng
UniMA
P.
He
o
bta
in
ed
his
Ph.
D
from
Newca
stle
Univ
ersity
in
Microe
l
ec
tron
ic
s
S
y
s
te
m
Design
.
His
rese
arc
h
a
ctivit
i
es
inc
lud
e
hi
gh
spee
d
comput
er
arithm
et
ic,
deve
lopment
of
high
p
erf
orm
ance
loga
ri
th
m
ic
ba
sed
proc
essor
an
d
FP
GA
design
pla
tform,
high
ac
cur
acy
vide
o
g
rap
hic c
om
putati
on
and
b
iomedical appl
i
cations.
Evaluation Warning : The document was created with Spire.PDF for Python.