Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
Vol
.
3,
N
o
.
1
,
F
e
br
uary
2
0
1
3
,
pp
.
13
6~
14
4
I
S
SN
: 208
8-8
7
0
8
1
36
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
Improving Quality of Vehicle Trac
king Systems in Hill Stations
Using IEEE 802.16 Networks
Roop
Sin
g
h T
a
kur
,
E.
Ram
kumar
Department o
f
I
n
formation Tech
nolog
y
,
Cy
ry
x Co
l
l
e
ge
(
H
e
l
p Un
iv
e
r
si
ty
)
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Oct 18, 2012
Rev
i
sed
D
ec 25
, 20
12
Accepte
d
Ja
n 21, 2013
IEEE 802
.16 sta
ndard was desig
n
ed to support t
h
e vehi
cle
tra
c
k
i
ng s
y
stem
applications with quality
of s
e
r
v
ice (QOS
). Tr
acking s
y
s
t
em
i
s
us
ed for
track
ing the veh
i
cl
es in hill stati
ons w
ith qualit
y of service (QOS). W
ith the
help of s
ubs
crib
er s
t
at
ion (S
S
)
can t
r
ack
the
ve
hicl
es
. S
ubs
crib
er s
t
at
ion’s
will provid
e
sig
n
als to
the m
obiles and
vehi
cl
es
.In th
is paper, w
e
propose
a
scheme, named
vehicle tr
acking
sy
stem,
to tr
ack the vehicles
without an
y
interrup
t
in hi
ll
stations with
qualit
y
of service (QOS). Th
e idea of th
e
proposed schem
e
is to
track th
e vehi
cl
es in th
e roads of
the
hill st
ation
s
which is coming in opposite direction and
back of the vehicle. A
n
aly
s
is and
simulations are used to evalu
a
te
th
e proposed scheme. Simulation
and
analy
s
is r
e
sults confirm that
the p
r
oposed
can
track the v
e
hicles with the help
of subscriber s
t
ation
b
y
giv
e
n quality
of s
e
rvice (QOS).
Scheduling
algorithms ar
e p
r
oposed to impr
ove th
e over
a
ll
throughput. The simulation
results show that our proposed algor
ithm improves the overall thr
oughput b
y
40%
in a
stead
y
network.
Keyword:
IEEE 802.16
Vehicle trac
king
Wi
M
A
X
Copyright @
20
1x Insitu
te of
Ad
vanced
Engin
e
eering and Scien
c
e.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Ro
op
Si
ng
h Tak
u
r
,
Depa
rt
em
ent
of I
n
fo
rm
ati
on
Tech
nol
ogy
,
Cyryx
co
llege Help
un
iv
ersity,
Kot
h
am
agai, mavaa
m
agu, maafanu, m
a
le,
maldives.
Em
a
il: ro
op
sing
h.cyryx@g
m
ail.co
m
1
.
IN
TR
OD
UC
TI
ON
:
The Worldwide
Inte
rope
rabil
ity
for
Microwave Access
(
W
i
MAX), ba
se
d on
IE
EE 802.
16 standa
rd
stan
d
a
rds [1
][2
], is d
e
sign
ed
to
facilitate serv
ices
with h
i
gh
tran
sm
is
sio
n
rates
for
d
a
ta and
m
u
lti
m
e
d
i
a
applications inmetropolitan areas.
The physical (PHY) and
medium acce
ss control (MAC
) layers of
W
i
MAX
have
been s
p
e
c
ified in the IEEE 802.16 s
t
anda
rd.
Many adva
nced c
o
m
m
unicati
on technologies such as
Ort
h
ogonal Freque
ncy-Divisi
on Multip
le Access (OFDM
A
) a
nd m
u
ltipl
e
-in
put and m
u
ltiple-out
put (MIMO)
are em
braced in the standards
.
Supporte
d by
these
m
ode
rn technologies,
W
i
M
AX is abl
e
to provi
de a large
servi
ce c
ove
ra
ge,
hi
g
h
dat
a
rat
e
s an
d Q
o
S
gua
rant
ee
d s
e
rvi
ces. B
eca
u
s
e of t
h
e
s
e fe
at
ures,
Wi
M
A
X i
s
considere
d
as a
prom
ising alternative
for last
mile
broadba
n
d wi
reless acce
ss (B
WA).In
orde
r to
provide
QoS
gua
ra
nteed
se
rvices,
the subs
criber station (SS)
is
re
qui
re
d t
o
t
r
ac
k t
h
e
necessa
ry
ve
hi
cl
es fr
om
t
h
e base
st
at
i
on (B
S
)
be
fo
re any
t
r
ac
ki
ng t
r
ansm
i
ssi
ons t
o
t
h
e
ve
hi
c
l
es t
h
e SS t
e
n
d
s
t
o
kee
p
t
h
e t
r
ack
of t
h
e ve
hi
cl
es
wi
t
h
t
h
e
hel
p
of
ot
her s
u
bsc
r
i
b
er
st
at
i
o
n
(
n
ei
gh
bo
u
r
s)
an
d
base
st
at
i
on.
Sh
ow
t
h
at
veh
i
cl
es i
n
t
r
ac
ker
wi
t
h
q
u
a
lity o
f
service (QOS) to
t
h
e driv
er. Thus all th
e t
i
m
e
i
t
is d
i
fficu
lt to
track
th
e v
e
h
i
cles in
th
e h
ill areas.
Because of ba
d weathe
r,
bad signals, traffi
c j
a
m
s
in
hill
areas. To i
m
prove the
quality of vehicle tracking
syste
m
wh
ile
main
tain
in
g
the sa
m
e
q
u
a
lity
g
u
a
ran
t
eed
se
rv
ices,
ou
r research
o
b
j
ective is two
f
o
l
d
:
1
)
t
h
e
v
e
h
i
cle track
i
ng
is
d
o
n
e
with
th
e qu
ality o
f
serv
ice. 2)
o
u
r
research work fo
cuses
o
n
track
i
ng
th
e v
e
h
i
cl
es b
y
usi
n
g fa
st
est
al
go
ri
t
h
m
s
and
go
o
d
t
r
ac
ker s
y
st
em
s. W
e
pr
op
ose
d
a sc
he
m
e
, nam
e
d vehi
cl
e t
r
acki
n
g
sy
st
em
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Imp
r
o
v
ing
Quality o
f
Veh
i
cle
Tra
cki
n
g
S
y
stems in H
ill
S
t
a
tio
n
s
Using
IEEE 802
.1
6 … (Ro
o
p
s
i
n
gh
Ta
kur)
13
7
wh
ich
i
m
p
r
oves th
e q
u
a
lity o
f
track
i
ng
fo
r v
e
h
i
cles witho
u
t
an
y ex
t
r
a d
e
lays an
d
in
t
e
rru
p
t
s. Th
e
gen
e
ral
conce
p
t
behi
n
d
o
u
r
sch
e
m
e
is t
o
al
l
o
w
ot
h
e
r SSs t
o
t
r
ac
k
t
h
e ve
hi
cl
es l
e
ft
by
t
h
e c
u
r
r
e
n
t
t
r
acki
ng
SS.
Si
nce
th
e track
i
n
g veh
i
cles is no
t sup
p
o
r
ted
to o
c
cu
r
regu
la
r
l
y
,
ou
r sc
hem
e
al
l
o
ws S
S
s
wi
t
h
no
n-
rea
l
t
i
m
e
ap
p
lication
s
wh
ich
h
a
v
e
m
o
re flex
ib
ility o
f
d
e
lay req
u
i
remen
t
s, to
track
th
e
b
a
d
weath
e
r, surro
und
i
n
g
of
envi
ro
nm
ent
v
e
hi
cl
es. C
onse
que
nt
l
y
, t
h
e
u
n
t
r
ac
ked
ve
hi
c
l
es i
n
t
h
e
c
u
r
r
e
nt
l
o
cat
i
o
n ca
n
be i
d
ent
i
f
i
e
d
.
It
i
s
di
ffe
re
nt
fr
om
t
h
e ve
hi
cl
e t
r
a
c
ki
n
g
i
n
w
h
i
c
h t
h
e t
r
acke
d
vehicle is e
n
forced a
s
early a
s
in the
ne
xt vehicle
tracking. M
o
re
ove
r, t
h
e trac
ked
vehicle is likely to be
released
tem
p
o
r
ari
l
y (i.e.,
o
n
l
y in th
e curren
t
lo
catio
n)
and E
x
i
s
t
e
d t
r
a
c
ki
n
g
ve
hi
cl
e doe
s cha
n
g
e
i
n
l
o
cat
i
on. T
h
e
r
efo
r
e,
ou
r sc
he
m
e
im
proves t
h
e o
v
er
al
l
t
h
ro
ug
h
put
while
providing the
sam
e
QOS
guara
n
teed services.
Acc
o
rding t
o
the
I
EEE 802.16
st
anda
rd, SSs sc
hedule
d
o
n
the up
lin
k
(UL) m
a
p
sh
ou
ld
h
a
v
e
tran
smissio
n
op
po
rt
u
n
ities in
th
e
cu
rren
t lo
cation
.
Th
e SSs are called
Transm
issio
n
SSs
(TSs) i
n
this p
a
p
e
r. Th
e
main
id
ea
o
f
t
h
e propo
sed sche
m
e
is to
allo
w th
e
b
a
se
statio
n
(BS)
to
sch
e
du
le a back
up
SS fo
r each
TS.
Th
e b
a
ck
up
SS is
assi
g
n
e
d
t
o
stan
d
by for an
y
o
pportun
ities to
track
th
e
unt
racke
d
ve
hi
cles of its corresponding TS.
We call th
e backup SS as the
com
p
le
m
e
ntar
y station (CS).
In t
h
e
IEEE 802.16
st
anda
rd, Ve
hicle tracking syste
m
(VTS) are
made
in per ve
hicles to each
connection loc
a
lly
in
their areas
. T
h
ere fore
, untra
c
ked
ve
hicles is defi
ned a
s
t
h
e t
r
acki
n
g
vehi
cl
es by
su
bs
cri
b
er stations (SSs).
In
ou
r sc
hem
e
, w
h
en
a T
S
has
u
n
t
r
ac
ked
ve
hi
c
l
es, i
t
sh
oul
d t
r
ansm
i
t
a conne
ct
i
on
basi
s.
H
o
weve
r,
t
h
e B
S
t
r
acks
v
e
h
i
cles in
p
e
r SS b
a
sis.
It g
i
v
e
s th
e SS flex
ib
ility to
track
th
e av
ailab
l
e
m
e
ssag
e
, called
releasing
message
(R
M
)
, t
o
i
n
fo
r
m
i
t
s
corres
p
o
ndi
ng
C
S
t
o
t
r
ack t
h
e
u
n
t
r
ac
ked
ve
hi
cl
es.
Ho
we
ver
beca
use
of
t
h
e
vari
et
y
of
geographical distance betwee
n TS and
C
S
and t
h
e
vehi
cl
e, t
r
ansm
i
ssi
on po
wer o
f
t
h
e TS, t
h
e C
S
m
a
y
not
receive RM. In this case, t
h
e bene
fit of
our sc
hem
e
m
a
y be reduce
d.
In this
researc
,
we investiga
t
e the
probability that the CS receives a RM
successfully. Our
theoretical analys
is shows that this proba
b
ility is
l
east
42%,
w
h
i
c
h i
s
co
nfi
r
m
e
d by
ou
r s
i
m
u
l
a
t
i
on. B
y
furt
her i
nves
t
i
g
at
i
ng t
h
e fa
ct
ors, t
h
at
aff
ect
t
h
e
effective
n
ess
of our sc
hem
e
, two factors a
r
e
concl
ude
d:1)
t
h
e CS ca
nnot
receive the RM
2) the CS doe
s
not
have
non real ti
m
e
data
to
transm
it while receiving a
R
M
. To m
i
t
i
gate
those
factors, additional sc
heduling
algorithm
s
are
propose
d. Our anal
ysis sho
w
th
at th
e propo
sed
algo
ri
th
m fu
rth
e
r im
p
r
o
v
e
th
e av
erage
th
ro
ugh
pu
t
b
y
40
% i
n
a
stead
y n
e
t
w
ork
(i.e. 15
t
o
75
seco
nd
i
n
ou
r
an
alysis)
.
Th
e r
e
st
o
f
th
is
pap
e
r i
s
or
ga
ni
zed a
s
f
o
l
l
o
w
s
.
In
sect
i
on
2,
we
pr
o
v
i
de t
h
e
bac
k
gr
ou
n
d
i
n
f
o
rm
at
ion
o
f
IEE
E
80
2.
16
m
o
t
i
v
at
i
on a
n
d
rel
a
t
e
d wo
r
k
s are prese
n
t
e
d i
n
sect
i
on 3
.
Th
e pro
p
o
se
d sc
he
m
e
is
present
e
d in secti
on 4. The analysis of the
propose
d
sc
he
me is placed in section 5 a
n
d section 6.
The perform
a
nce analysis of t
h
e
schem
e
in section 7.
At
t
h
e en
d, t
h
e concl
u
si
o
n
i
s
gi
ve
n i
n
sect
i
on
8.
Vari
ou
s at
t
r
i
but
es o
f
w
e
b f
o
r
u
m
di
scussi
o
n
s a
nd t
h
e fi
rm
s
st
ock
be
ha
vi
or
.
2
.
BA
CK
GROUN
D
ANA
LY
SIS:
The IE
EE 802.16 sta
nda
rd specifies three types of
t
r
a
n
sm
issi
on m
e
di
um
s sup
p
o
rt
e
d
as t
h
e p
h
y
s
i
cal
l
a
y
e
r (PH
Y
):
si
ngl
e cha
n
nel
(SC
)
, O
r
t
h
og
onal
f
r
eq
ue
nc
y
-
di
vi
si
o
n
m
u
lt
i
p
l
e
xi
ng
(O
F
D
M
)
an
d O
r
t
h
og
o
n
al
Fre
que
ncy-Division M
u
ltiple
Access (OFDMA).
We assu
me OFDM
A a
s
the
PHY in
our a
n
alytical m
odel
sin
ce it is e
m
p
l
o
y
ed
t
o
supp
ort m
o
b
ility
i
n
IEEE 80
2.16
e stand
a
rd
and
th
e sch
e
m
e
work
i
n
g
in
OFDM
A
sho
u
l
d
al
so w
o
r
k
i
n
ot
hers
. There a
r
e f
o
u
r
t
y
pes of m
odul
at
i
ons s
u
pp
o
r
t
e
d by
O
F
DM
A:
B
PSK
, QP
SK,
16
-
QAM
a
n
d
64
-
QAM
.
T
h
i
s
pa
per i
s
f
o
cu
sed
o
n
t
h
e
p
o
i
n
t
-
t
o
-m
ul
t
i
poi
nt
(
P
M
P
) m
ode i
n
w
h
i
c
h t
h
e S
S
i
s
n
o
t
allowed t
o
communicate with any
othe
r
SSs bu
t th
e BS d
i
rectly. Based
on
th
e tran
smissio
n
d
i
recti
o
n, th
e
t
r
ansm
i
ssi
ons bet
w
ee
n B
S
and SS
s are cl
assi
fi
ed i
n
t
o
d
o
w
nl
i
n
k (
D
L) a
nd
upl
i
n
k (
U
L
)
t
r
ansm
i
ssi
ons. The
form
er are the
transm
issions from
the BS t
o
SSs. Conve
rs
el
y, th
e latter are th
e tran
sm
issi
o
n
s
in th
e
op
po
site
di
rect
i
o
n.
The
r
e are t
w
o
t
r
a
n
s
m
i
ssi
on m
odes
:
Tim
e
Di
vi
si
o
n
Du
pl
ex
(
T
D
D
)
an
d
Fre
q
ue
ncy
Di
vi
si
o
n
Du
pl
ex
(FD
D
)
s
u
p
p
o
rt
ed i
n
IEEE
8
0
2
.
1
6
.
B
o
t
h
U
L
and
DL
t
r
an
sm
i
ssi
ons ca
n
not
be
ope
rat
e
d si
m
u
lt
aneou
s
l
y
i
n
T
D
D
m
ode but
i
n
F
DD m
ode. I
n
t
h
i
s
pape
r,
ou
r
schem
e
i
s
focuse
d o
n
t
h
e T
DD m
ode. I
n
W
i
M
A
X
,
t
h
e
B
S
is
resp
o
n
si
bl
e
f
o
r
sche
d
u
l
i
n
g
bo
t
h
UL a
n
d
DL
t
r
an
sm
i
ssi
ons. Al
l
sc
he
dul
i
n
g
be
havi
or
i
s
e
x
p
r
esse
d i
n
a
M
A
C
fram
e
. The
st
r
u
ct
u
r
e
of
a M
A
C
f
r
am
e defi
ned
i
n
IEE
E
8
0
2
.
1
6
st
an
dar
d
co
nt
ai
ns t
w
o
part
s:
UL a
n
d
DL s
u
b
fram
e
. The UL
sub
fram
e
is for
UL tra
n
sm
issions
. Sim
i
la
rly
,
the DL s
u
b
fram
e
is for
DL tra
n
sm
issions
. I
n
IEEE 802.16
networks
, the S
S
is coordinate
d by the BS
. All coordinating inform
ati
on including burst profiles
and
o
f
f
s
et
s i
s
i
n
t
h
e
D
L
a
n
d
UL m
a
ps,
whi
c
h a
r
e
br
oa
dca
s
t
e
d at
t
h
e
beg
i
nni
n
g
o
f
a M
A
C
f
r
am
e.The
IEEE
80
2.
1
6
net
w
or
k i
s
con
n
ect
i
o
n
-
o
r
i
e
nt
ed
. It
gi
ves t
h
e ad
vant
age of
havi
ng
bet
t
e
r co
nt
rol
ove
r net
w
o
r
k r
e
so
urce
to
pro
v
i
d
e
Q
o
S gu
ar
an
teed
ser
v
ices.
I
n
ord
e
r to
supp
or
t
w
i
d
e
v
a
r
i
ety
o
f
app
licatio
n
s
, th
e I
E
EE
802
.1
6
stan
d
a
rd
classi
fies traffic in
to fiv
e
sch
e
du
ling
cla
sses: Un
so
licited
Gran
t Serv
ice (UGS), Real Ti
m
e
Po
llin
g
Serv
ice (rtPS), Non-real Time Po
llin
g
Service (nrtPS), B
e
st Effo
rt
(BE) and
Ex
tend
ed
Real Tim
e
Po
llin
g
Serv
ice (ertPS). Each
ap
p
licatio
n
is
classified int
o
one
of t
h
e sc
he
dulin
g
classes and est
a
blishes a
connecti
on
with
th
e BS
based
on
its sched
u
ling
class.
Th
e BS a
ssigns a co
nn
ection I
D
(
C
I
D
)
t
o
each
co
nn
ection. Th
e
vehicle tracki
n
g is m
a
de
base
d
on
the
CID
via se
ndi
ng a
VTR(ve
hicle tr
acki
n
g re
ques
t).
Whe
n
recei
ving a
VTR, the BS a
nd SS can either grant or re
je
ct the
VTR
de
pen
d
i
n
g
on i
t
s
avai
l
a
bl
e res
o
u
r
ces an
d sch
e
d
u
l
i
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l.
3
,
N
o
.
1
,
Feb
r
u
a
ry 2
013
:
13
6 – 14
4
13
8
policies. T
h
ere are two types of
VTR
(
ve
hicle tracki
ng r
e
qu
est)
d
e
f
i
n
e
d
i
n
th
e
I
E
EE 80
2.16
stan
d
a
rd
:
id
en
tifying
and
ov
erall track
i
ng
VTRs
. T
h
e Form
er allow the
SS to
indicate the next vehicle tracki
n
g
req
u
i
r
e
d
f
o
r t
r
a
c
ki
n
g
. T
hus
, t
h
e ove
ral
l
vehi
c
l
e t
r
acki
n
g
can also
b
e
id
en
tified
v
i
a o
v
e
rall track
ing
VTRs. Th
e
BS resets its perception
of t
h
at services
nee
d
s
upon
recei
ving t
h
e VTRs
.
Conse
q
uently the VTRs re
que
s
t will
decrease
.
3
.
M
O
TIVA
TION
S
AND
RELATED
WORK:
Vehicle tracki
n
g system
allo
ws
I
EEE 802.16
networks
to provide
QO
S guara
n
teed servi
ces. The SS
track
s th
e
requ
ired
v
e
h
i
cle b
e
fo
re an
y v
e
h
i
cles tran
sm
is
sio
n
. Du
e to
th
e n
a
t
u
re
o
f
ap
p
lication
s
, it is v
e
ry
d
i
fficu
lt for the SS to
m
a
k
e
th
e op
tim
al p
a
th
v
e
h
i
cle track
i
ng
system
.
It is po
ssib
l
e
th
at th
e am
o
u
n
t
of
req
u
est
e
d
ve
hi
cl
e t
r
acki
ng c
a
nn
ot
be
ful
l
y
t
r
acked
. A
ltho
ugh
th
e r
e
quested
v
e
h
i
cle is tracke
d
via
VTRs,
ho
we
ver
,
t
h
e u
pdat
e
d re
que
st
ed ve
hi
cl
e t
r
acked i
s
ap
pl
i
e
d
as early as to the ne
xt
ve
hicle tracking system and
there is
no
wa
y to track t
h
e untrac
k
v
e
h
i
cle in
th
e cu
rren
t
lo
catio
n
.
In
our sch
e
m
e
, th
e
SS track
its untrack
ed
v
e
h
i
cles in
the cu
rren
t lo
catio
n
and
ano
t
h
e
r SS pr
e assig
n
e
d
b
y
th
e BS h
a
s op
portun
ities to
track
th
e
unt
racke
d
ve
hi
cl
es. Thi
s
i
m
prove
s t
h
e
ve
hi
cl
e t
r
acki
n
g
syste
m
. More
over since t
h
e e
x
isted
vehicle trac
ki
ng
is no
t ch
ang
e
d, th
e
sam
e
QOS
gu
aran
teed services are
provide
d
without
an
y ex
tr
a
d
e
la
y.
Ma
n
y
re
s
e
a
r
ch
works related
t
o
v
e
h
i
cle trackin
g
system
i
m
p
r
ov
em
en
t h
a
ve
b
een
propo
sed
in
th
e literartu
r
e. In
[2
] th
e task
is
pr
o
pose
d
i
s
v
i
si
on base
d v
e
hi
cl
e det
ect
i
on has t
r
i
g
ge
re
d vast
i
m
pro
v
em
ent
of au
t
o
n
o
m
ous ve
h
i
cul
a
r
tech
no
log
y
in
o
r
d
e
r t
o
au
tomatical
ly d
e
te
ct
m
o
v
i
ng
v
e
hicles in com
p
lex traffic sc
ene.
In
[3
] th
e
pap
e
r
is
pr
o
pose
d
a
co
m
put
er vi
si
o
n
sy
st
em
for
da
y
t
im
e vehi
cl
e
det
ect
i
on a
l
o
cal
i
zat
i
on. A
s
essent
i
a
l
st
ep
i
n
t
h
e
devel
opm
ent
o
f
several
t
y
pe
s of ad
va
nce
d
dri
v
er assi
st
an
ce sy
st
em
s. In [4]
t
h
ey
pr
o
p
o
s
ed a t
a
sk t
o
p
r
o
v
i
d
e
s
a bet
t
e
r
pl
at
fo
rm
t
o
t
r
ack a
nd
di
sa
bl
e a v
e
hi
cl
e usi
n
g
wi
rel
e
ss t
ech
n
o
l
o
gy
. T
h
i
s
sy
st
em
sho
w
s e
m
bed a
m
i
crocom
put
er
whi
c
h m
oni
t
o
rs t
h
e seri
es
of
aut
o
m
o
t
i
v
e
syste
m
s like engine, fuel an
d b
r
aki
n
g sy
st
em
.
In [
5
]
t
h
ey
pr
o
pose
d
i
n
t
h
at
pa
per
t
h
eo
ret
i
cal
fo
un
dat
i
o
ns
a
n
d a practical re
alization of a
real-tim
e
traffic si
gn
det
ect
i
o
n
,
t
r
ac
ki
n
g
a
nd
rec
o
g
n
i
t
i
on
o
p
erat
i
n
g o
n
b
o
ar
d
of
a ve
hicle. The
authors
predict
the QOS
guarantee
d
base
d
on
t
h
e
i
n
fo
rm
ati
on
of
t
h
e bac
k
l
o
g
g
ed
wi
t
h
hea
v
y
t
r
af
fi
c jam
s
, ba
d
weat
he
r i
n
t
h
e
fut
u
re.
I
n
[
1
7
,
18
,1
9]
,
a dy
nam
i
c resou
r
ce re
ser
v
at
i
on m
echani
s
m
i
s
pr
op
ose
d
.
I
t
can dy
nam
i
cal
l
y
change t
h
e am
ount
of
re
serve
d
reso
u
r
ce depe
n
d
i
n
g on
t
h
e
a
c
t
u
al
num
b
er of active
connections
.
4. PROPOSE
D
S
C
HE
ME:
The objective
s
of our research
are t
w
o
fol
d
:1) the
vehicl
e tracking
syst
em
is done
with quality of
servi
ce.
2
)
o
u
r
researc
h
wo
rk
foc
u
ses
on t
r
a
c
ki
n
g
t
h
e
vehi
cl
es by
usi
n
g f
a
st
est
al
gori
t
h
m
s
and g
o
od t
r
ack
e
r
syste
m
s. To
ach
iev
e
th
ese
ob
j
ectives, our sch
e
m
e
n
a
m
e
d
i
m
p
r
o
v
i
n
g
q
u
ality o
f
v
e
h
i
cle track
ing
syste
m
s is
p
r
op
o
s
ed
.
Th
e
m
a
in
id
ea of
th
e propo
sed
sch
e
m
e
is to
allow t
h
e BS t
o
pre as
sign a C
S
for eac
h T
S
at the
b
e
g
i
n
n
i
n
g
o
f
a lo
catio
n
.
Th
e syste
m
h
a
s th
e ab
lity to
d
e
tect
th
e o
p
tim
a
l
p
a
th
b
e
tween
source an
d
d
e
stin
atio
n
,
depe
n
d
i
n
g o
n
m
a
ny
fact
ors s
u
ch as t
r
a
v
el
t
i
m
e
, t
r
affi
c jam
s
, t
o
p
o
g
r
a
phy
and
bad
weat
h
e
r. He
re i
n
t
h
i
s
pape
r
usi
n
g
gree
dy
t
echni
que
s (
G
T
)
s
u
ch a
s
Di
ji
st
ra’s a
n
d
kr
us
k
a
l
’
s al
g
o
ri
t
h
m
s
t
o
g
r
a
p
h
a w
e
i
ght
de
pen
d
i
n
g
on
t
h
e
pr
o
pose
d
c
o
st
fu
nct
i
o
n (C
F).
T
h
e
geo
f
e
n
ci
ng
t
ech
ni
q
u
e i
s
ap
pl
i
e
d t
o
t
h
e sy
st
em
base
d
on
real
c
o
or
di
nat
e
s
an
d
gran
ts secu
rity and
safet
y
o
f
veh
i
cles.
It h
a
s th
e
ab
ilit
y to
v
i
su
alize th
e real
p
o
s
ition
of veh
i
cles in
m
a
p
s
an
d
t
o
tak
e
d
e
cisio
n
s
acco
r
d
i
n
g
t
o
real-tim
e
in
fo
rm
a
tio
n
.
We will d
i
scu
s
s o
p
tim
al tran
sp
ortatio
n
m
o
ve
m
e
n
t
with
real ti
m
e
i
n
fo
rm
atio
n
.
Cos
t
fu
ncti
on
param
e
ters
:
Th
e propo
sed
CF (co
s
t fu
n
c
t
i
o
n
) will co
m
p
u
t
e th
e ti
m
e
req
u
i
red
to
m
o
ve fro
m
source
(ve
h
icles) t
o
destin
ation(SS or
BS).
The
propose
d
design
‘t
rac
k
ing syste
m
’
receives real tim
e
or
hi
st
ori
cal
i
n
fo
r
m
at
i
on fr
om
geo dat
a
base an
d t
h
e
n
i
t
com
put
es t
h
e o
p
t
i
m
al
pat
h
o
f
t
r
ac
k
i
ng
depe
n
d
i
n
g
on t
h
e
follo
win
g
para
m
e
ters:
1)
Time:
first
o
f
all, th
e
p
r
opo
sed
CF
will co
m
p
u
t
e th
e time d
e
p
e
n
d
i
n
g
on
th
e
d
i
stance b
e
tween
so
urce(v
eh
icles) an
d d
e
stin
atio
n(SSs
o
r
B
S
s) and
th
e av
erage sp
eed
on
th
e
h
ill statio
n
s
as
fo
llows:
T
1
=
Dist
ance
A
V
G s
p
eed
2)
T
r
avel
ti
me:
C
F
di
vi
des a
d
a
y
t
o
fo
u
r
i
n
t
e
r
v
al
s an
d t
h
e trav
ellin
g
tim
e wi
ll affect th
e time as sh
own
in
Tab
l
e 1.
3)
Bad W
e
ather
:
th
e tim
e b
e
tween
th
is factor is sho
w
n
in Table 2
.
4)
Traffic jam
factor
:
th
e tim
e
after th
is fact
o
r
is shown in
Tab
l
e 3.
5)
Hill statio
n conditio
n
:
t
h
e time after th
is
facto
r
is
shown in
Tab
l
e
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Imp
r
o
v
ing
Quality o
f
Veh
i
cle
Tra
cki
n
g
S
y
stems in H
ill
S
t
a
tio
n
s
Using
IEEE 802
.1
6 … (Ro
o
p
s
i
n
gh
Ta
kur)
13
9
Th
e
resu
lts o
f
CF will b
e
t
h
e
weigh
t
b
e
t
w
een
two
po
in
ts; th
e au
tho
r
s u
s
ed
g
r
ap
h th
eo
ry
an
d Di
j
k
st
ra’s
rou
ting
alg
o
rith
m
to
co
m
p
u
t
e th
e
op
ti
m
a
l p
a
th
b
e
t
w
een
sou
r
ce(v
e
h
i
c
l
e
s)
an
d dest
i
n
at
i
o
n
(
S
S
s or
B
S
s).
M
o
di
fi
c
a
t
i
ons
to
Di
j
i
sk
tra’s alg
o
rith
m
were
mad
e
as fo
llows:
The
net
w
or
k m
a
y
have
cy
cl
es,
b
u
t
al
l
arc l
e
n
g
t
h
s
m
u
st
be n
o
n
-
negat
i
v
e
.
Tabl
e
1. T
r
a
v
el
Ti
m
e
Travel Ti
m
e
Ef
f
ect
(
6
-
12)
AM T
2
=T
1*0.
20+T
1
(
12-
6)
PM T
2
=T
1*0.
05+T
1
(
6
-
12)
PM T
2
=T
1*0.
17+T
1
(
12-
6)
AM T
2
=T
1*0.
25+T
1
Tabl
e 2.
B
a
d w
eat
her
E
f
f
ect
Cli
m
ate Te
m
p
erat
ure
Ef
f
ect
Constant
T
3
=T
2
25
▫
T
3
=T
2+30
40
▫
T
3
=T
2+60
12
▫
T
3
=T
2+10
5
▫
Below
dr
iving
m
o
de
(30
,
60
,10
)
are
th
e sp
eed of the v
e
h
i
cles m
e
a
s
u
r
i
n
g throug
h
cli
m
ate te
m
p
er
atu
r
e i
n
h
ill statio
n
s
.
Tabl
e
3. R
e
si
d
e
nt
i
a
l
Agai
nst
Effect
Residential Effect
Dense T
4
=T
3*0.
13+T
3
Med
i
u
m
T
4
=T
3*0.
08+T
3
L
o
w T
4
=T
3*0.
01+T
3
Tab
l
e 4
.
Hill
Statio
n
Areas
T
opogr
aphy
E
ffect
Bad weather
T
5
=T
3+0.
6+T
4
T
r
affic Jam
T
5
=T
3+0.
1+T
4
Accidents T
5
=T
3+1.
0+T
4
Road Pr
oblem
T
5
=T
3+0.
12+T
4
Main
tatin
s a
partitio
n
o
f
N i
n
to
two
sub
s
ets:
Set p
:
Perm
an
en
tly lab
e
led
nod
es
Set
T:
Tem
por
ari
l
y
l
a
bel
e
d
n
ode
s
Mo
v
e
nod
es
fro
m
T in
to
S
on
e at a ti
m
e
in
an
non
d
ecrea
si
ng
o
r
de
r
by
t
h
e m
i
nim
u
m
pat
h
f
r
om
t
h
e s
o
u
r
ce
no
de.
B
e
gi
n
P:={}; T :=N;
d(i
)
=
∞
f
o
r eac
h
no
de
I i
n
N
d(s
)
=0
an
d
p
r
e
d
(S
):
=0;
Wh
ile P <
n
do
Beg
i
n
Pick
I in T
with
m
i
n
i
m
u
m
d
(
i)
v
a
lu
e;// th
e
valu
e will be taken
fro
m
o
u
r
CF.
M
ove
I
fr
om
T t
o
P;
Fo
r each
(I
,,
j)
in
A
d
o
I
f
d(
j
)
> d(
i)
+ cij
th
en
d
(
j
)
:= d(i)
+
ci
j
and
p
r
ed
(j):= i
End;
End;
An ex
am
p
l
e of Dij
i
k
s
tra’s algo
rith
m
an
d how to d
e
term
in
e
th
e m
i
n
i
m
u
m
co
st is sho
w
n
i
n
Fi
g
1
Th
e m
o
d
i
ficatio
n to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l.
3
,
N
o
.
1
,
Feb
r
u
a
ry 2
013
:
13
6 – 14
4
14
0
Fi
gu
re
1
.
E
x
a
m
pl
e of Di
ji
kst
r
a’s
al
g
o
ri
t
h
m
Dij
s
t
k
stra al
g
o
rith
m
was m
a
d
e
as fo
llows:
P
=
{},
T={1
,2
,3,
4
,5
,6
}
P
=
{1
,4
}, T={4,
5
,5
,6
}
P
=
{1
}, T={2
,3,
4
,5
,6
}
P
=
{1
,4
,5
},T={5
,6
}
P
=
{1
,4
,5
,2
},T={1
,2
,3
,4
,5
,6
}
P
=
{1
,4
,5
,2
,6
},T={}.
For
eac
h l
i
n
k,
t
h
ere a
r
e a
ssoc
i
at
ed wei
ght
g
r
ap
hs c
o
m
put
e
d
wi
t
h
pr
o
p
o
s
e
d
C
F
as s
h
ow
n i
n
Fi
g.
2.
C
o
m
put
e t
h
e
m
i
nim
u
m
cost
m
a
p t
r
ans
v
e
r
se
de
pen
d
i
n
g
o
n
t
h
e p
r
op
ose
d
C
F
:
In
t
h
i
s
sec
t
i
on, t
h
e a
u
t
h
o
r
s
use
d
k
r
u
s
k
a
l’s algorith
m
to
d
o
this task
with
propo
sed
CF
to co
m
p
u
t
e th
e Weigh
t
b
e
tween
two
po
in
ts. Th
e
al
go
ri
t
h
m
begi
ns by
so
rt
i
n
g t
h
e m
a
p st
reet
wei
g
ht
s i
n
n
o
n
–dec
r
easi
n
g o
r
de
r an
d t
h
e
n
s
t
art
i
ng wi
t
h
t
h
e em
pt
y
sub
g
r
ap
h.
It
s
cans t
h
e s
o
rt
ed
l
i
s
t
addi
n
g
t
h
e
next
e
dge
on
th
e list to
th
e cu
rren
t sub
graph
if su
ch
an
inclu
s
ion
doe
s
not
c
r
eat
e
a cy
cl
e;
i
t
sim
p
l
y
ski
p
s t
h
e
e
dge
ot
her
w
i
s
e.
Algo
rith
m
Mi
n
i
m
u
m co
st tran
sv
erse (m
ap
G) {//k
ru
sk
al
’s alg
o
r
ith
m
fo
r co
n
s
tru
c
ting
th
e min
i
m
u
m
spa
nni
ng t
r
ee/
/
I
n
p
u
t
:
a wei
g
ht
ed c
o
n
n
ect
e
d
g
r
ap
h
G=(
V
,E)/
/
O
ut
p
u
t
:
ET, t
h
e set
o
f
edge
s com
posi
ng t
h
e
m
i
nim
u
m
spanni
n
g
t
r
ee
of
G.
sort
E i
n
n
o
n
-
d
ecreasi
n
g o
r
der
of t
h
e ed
ge w
e
i
ght
s ET=
0;
co
unt
e
r
=0;
=
0
;
Whi
l
e
e co
unt
e
r
<|
v|
-
1
K=k+
1;
If
ET
U {ei
k
};
E c
o
u
n
t
e
r= e
co
u
n
t
e
r
+
1;
R
e
t
u
r
n
ET;
}
Th
e
w
e
igh
t
g
r
ap
h of
t
h
e
p
r
o
posed
CF is sh
own
in FI
G3
Fig 2 weigh
t
gr
aph of th
e Propos
ed CF
Fig 3 Weight graph of th
e Propos
ed CF
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Imp
r
o
v
ing
Quality o
f
Veh
i
cle
Tra
cki
n
g
S
y
stems in H
ill
S
t
a
tio
n
s
Using
IEEE 802
.1
6 … (Ro
o
p
s
i
n
gh
Ta
kur)
14
1
Geo
f
e
n
ci
n
g
:
t
h
e a
u
t
h
ors
de
vel
o
ped
t
h
i
s
p
r
oces
s t
o
a
p
pl
y
t
h
e ge
ofe
n
ci
ng
t
ech
ni
q
u
e t
o
gene
rat
e
a
bu
ffe
rz
one a
n
d
i
t
wi
l
l
hel
p
t
o
pr
ovi
de i
n
f
o
r
m
at
i
on fo
r
Su
b
s
cri
b
e
r
st
at
i
on(
SSs) a
nd B
a
se
st
at
i
on(B
S
s
)
as wel
l
as ve
hi
cl
es. T
h
e res
p
o
n
se
o
f
t
h
e
pr
o
pose
d
sy
st
em
i
s
sho
w
n
i
n
Fi
g
.
4
Fig 4 Response
of the Proposed
s
y
stem
M
a
nagem
e
nt
pha
se (
d
at
abas
e):
t
h
e m
a
nag
e
m
e
nt
phas
e
cont
ai
n
s
f
u
nct
i
ons
o
f
o
r
gani
zi
ng
dri
v
er
s
inform
ation, received data from
the Base station(BS)/
Subscri
b
er
sta
tion(SSs
)(trac
k
ing data
) and GIS
dat
a
(c
heck
-
poi
nt
s).
Th
is
p
h
a
se
was bu
ilt u
s
ing
m
i
cro
c
on
tro
ller datab
a
se
1)
User tab
:
t
h
e
user
t
a
b
co
nt
ai
ns dri
v
ers
(
vehi
cl
es)
i
n
fo
rm
at
ion
,
wi
t
h
f
unct
i
ons
l
i
k
e
‘a
dd
’,
‘edi
t
‘an
d
del
e
t
e
conce
r
ned
wi
t
h
t
h
e
dri
v
ers
i
n
f
o
rm
at
i
on. T
h
e
rep
o
rt
s
bu
tton
allo
w b
r
owsing
repo
rts
fo
r
availab
l
e
d
r
i
v
ers.
2)
Tra
c
king
ta
b
:
t
h
i
s
t
a
b c
o
nt
ai
ns t
h
e
co
re
of t
h
e sy
st
em
, whi
c
h i
s
di
vi
de
d i
n
t
o
‘o
nl
i
n
e t
r
ac
ki
n
g
’ a
s
s
h
o
w
n
in
Fig.
7
.
3)
Online trac
king
: on
lin
e track
i
ng
im
p
l
e
m
en
ts th
e in
terface
b
e
tween
bo
th co
nn
ection
ID(CID) su
ch
as
SS/
B
S
an
d
ve
h
i
cl
es. Thi
s
part
i
s
co
ncer
ne
d
wi
t
h
t
r
ac
ki
n
g
r
eal
t
i
m
e
dat
a
of ve
hi
cl
e p
o
si
t
i
ons
,
whe
r
e t
h
e
data receive
d from
the BSs/S
S
s are displaye
d directly
on t
h
e related m
a
p in the vehicle in front of the
dri
v
er seat.
Whe
n
cl
i
c
ki
ng
on
t
h
e st
a
r
t
t
r
a
c
k
but
t
o
n,
t
h
e
vehi
cl
e t
r
ac
ki
n
g
dat
a
base
st
ar
t
s
l
i
s
t
e
ni
ng
o
n
po
rt
n
u
m
b
er
‘655
’
for an
y
SSs/BSs
requ
est to
m
a
k
e
a conn
ec
tio
n with
t
h
e sat
e
llites. Wh
en th
e co
nn
ectio
n is
estab
lish
e
d(b
e
tw
een
B
S
/SS an
d
d
a
tab
a
se), th
e BS/SS star
ts send
ing
inf
o
r
m
at
io
n
abou
t
th
e v
e
h
i
cle th
rough
satellite
th
at d
a
ta co
n
t
ain
th
e lo
catio
n
,
sp
eed,ti
m
e
an
d
se
n
s
o
r
param
e
ters
to
th
e v
e
h
i
cle track
ing
d
a
tabase, as
sho
w
n Fi
g.
5.
Subscriber station
(
SS)
or
Ce
ll towe
r
Vehi
cl
e Tr
acki
n
g
S
y
ste
m
(
S
ta
tic
Tracki
n
g Ve
hi
cle
System
Tracki
n
g
s
y
st
em
database
Mana
g
e
ment D
a
tabase
G
I
S environment
BAS
E
S
T
AT
I
ON(
BS
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l.
3
,
N
o
.
1
,
Feb
r
u
a
ry 2
013
:
13
6 – 14
4
14
2
Figure 5
Trackin
g
paths of
veh
i
cles
Nex
t
fo
l
d
is to find
th
e QOS
b
e
tween
CSs an
d
TSs i
n
a M
A
C fram
e inform
at
io
n
(
e.g
bur
st
pr
of
ile)
resi
di
n
g
i
n
t
h
e
C
L
m
a
y
be red
u
ced t
o
t
h
e m
a
ppi
ng i
n
f
o
rm
at
i
on
bet
w
ee
n t
h
e C
S
an
d i
t
s
co
rres
p
on
di
n
g
T
S
. T
h
e
B
S
onl
y
speci
f
i
es t
h
e burst
p
r
ofi
l
e
s fo
r t
h
e S
S
s whi
c
h
are only scheduled
on the CL. For exam
ple as
shown
th
at CS
i
is sc
hedule
d
as
the
corres
ponding
CS of TS
i
, where 1<=j<=
k. whe
n
TS
i
has unt
racke
d
ve
hi
cl
e
,
i
t
per
f
o
r
m
s
our p
r
ot
ocol
s.
If C
S
i
receives the
message sent from
TS
i
, it star
ts to
tran
sm
it
d
a
ta ab
ou
t v
e
h
i
cle b
y
u
s
ing
th
e ag
reed
bu
rst p
r
o
f
il
e. Th
e
b
u
rst profile o
f
a CS
is resid
e
d
on
eith
er th
e UL
map
if th
e CS is also
sche
dul
e
d
on
C
L
. O
u
r P
r
o
p
o
se
d sc
hem
e
i
s
pre
s
ent
e
d i
n
t
o
:
t
h
e
sc
he
dul
i
n
g al
go
ri
t
h
m
.
The sc
he
dul
i
n
g
algorithm
helps the BS to
schedule a
CS
for
each T
S
.
5.
S
C
HED
U
L
I
NG ALG
O
R
I
THM
Ass
u
m
e
Q re
prese
n
t
s
t
h
e s
e
t
of
SSs
ser
v
i
n
g
no
n
-real
t
i
m
e
con
n
ect
i
ons
(i
.e.
,
nrt
PS
or B
E
connections) a
nd T is the set
of TSs. Due t
o
the feat
ur
e
of TDD that the
UL and DL operations ca
nnot be
p
e
rf
or
m
e
d
si
mu
ltan
e
ou
sly, we can
no
t sch
e
du
le th
e SS wh
ich
UL tran
sm
i
ssio
n
in
terv
al is o
v
e
rlapp
e
d
with
th
e
targ
et TS. Fo
r
an
y TS, St, let
Ot b
e
th
e set
of SSs wh
ich
UL tran
sm
issio
n
in
terval ov
erlap
s
with
th
at
o
f
St in
Q. Th
us
, t
h
e p
o
ssi
bl
e co
rres
p
on
di
n
g
C
S
of
St
m
u
st
be i
n
Q
−
Ot. All SSs
in
Q
−
Ot are conside
r
ed as candidates
o
f
t
h
e CS fo
r St.
A sch
e
dulin
g
algorith
m, called
Prio
rity-b
ased
Sch
e
du
lin
g Al
g
o
r
ithm
(
PSA
)
,
show
n in
Algo
rith
m
is u
s
ed
to
sch
e
d
u
le a SS with
t
h
e h
i
gh
est
prio
rity as th
e C
S
. Th
e priority o
f
each
cand
id
ate is
deci
de
d bas
e
d
on t
h
e sche
d
u
l
i
ng
fact
o
r
(S
F)
defi
ned as t
h
e rat
i
o
o
f
t
h
e cu
r
r
ent
ve
hi
cl
e t
r
a
c
ki
n
g
re
que
st
(
V
TR
)
to the current tracke
d
ve
hicl
e. Th
e SS with
h
i
gh
er SF
h
a
s m
o
re p
r
io
rity
to track that ve
hicle. Thus, we give
t
h
e hi
g
h
er
pri
o
ri
t
y
t
o
t
hose S
S
s ve
hi
cl
es. Th
e hi
ghe
st
pri
o
ri
t
y
i
s
gi
ven t
o
t
h
e SSs
vehi
cl
e
s
wi
t
h
zero C
G
. No
n
real
-t
i
m
e conn
ect
i
ons i
n
cl
u
d
e
nrt
PS c
o
n
n
ect
i
ons s
h
o
u
l
d
ha
ve hi
ghe
r p
r
i
o
r
i
t
y
t
h
e B
E
connect
i
o
n
s
beca
u
s
e of
th
e QOS requ
irem
en
ts. Th
e
p
r
i
o
rity of
v
e
hicles o
f
C
S
s is con
c
lud
e
d wi
th
h
i
g
h
to
l
o
w as:n
rtPS
with zero
CG,BE wit
h
Z
e
ro C
G
,
n
rt
ps
with n
o
n
-ze
r
o
CGan
d BE wit
h
Non zero C
G
. If the
r
e are
m
o
re than one
than SS
v
e
h
i
cle with
h
i
g
h
e
st p
r
i
o
rity, we select o
n
e
with
th
e
larg
est CR as th
e CS in
o
r
d
e
r to
d
e
crease th
e
p
r
o
b
ab
ility
o
f
ov
erf
l
ow
.
6. A
NAL
YSI
S
Th
e p
e
rcen
tage o
f
po
ten
tially track
in
g
u
n
t
r
ack
ed
v
e
hicles occupied in t
h
e tracke
d
ve
hicles SS is
cri
t
i
cal
for t
h
e
pot
e
n
t
i
a
l
per
f
o
r
m
a
nce gai
n
of
ou
r sc
hem
e
.
We inv
e
stig
ate th
is p
e
rcen
tage on
n
e
t
w
ork
traffics
whic
h is popularly used today. Additional
l
y In our sc
he
me each TS s
h
ould tra
n
sm
it a RM to inform its
corres
ponding
CS when it ha
s tracki
n
g untracked ve
hicles
at SS. Howe
ver, t
h
e tra
n
sm
ission ra
nge
of
the the
TS m
a
y not
be
abl
e
t
o
co
ver t
h
e co
rres
p
o
ndi
ng C
S
. It
d
e
pe
nds
o
n
t
h
e l
o
ca
t
i
on an
d t
h
e t
r
a
n
sm
i
ssi
on po
w
e
r o
f
the TS. It is possible that the
un trac
ke
d ve
hicles can
not be tracked
beca
use the
CS does n
o
t
no
t receiv
e th
e
RM. Th
erefo
r
e th
e
b
e
n
e
fit of
o
u
r sch
e
m
e
is red
u
c
ed
. In
t
h
is
sectio
n
,
we analze
m
a
th
e
m
ati
cally th
e p
r
ob
ab
ility
of a CS t
o
rece
ive a RM s
u
cc
essfully
obviously
Algortith
m
1
Priority-b
ase
Sch
e
du
ling
al
g
o
rith
m
Inpu
t: T is th
e
set o
f
TSs sch
e
d
u
l
ed
o
n
th
e
UL m
a
p
.
Q is th
e
set o
f
SSs sch
e
d
u
l
ed
o
n
th
e
no
n-realtim
e ap
p
licatio
n
s
.
Ou
t
p
u
t: Sch
e
du
le CSs
fo
r all
TSsin
T.
Fo
r i=1 to
║
T
║
do
a
.
S
t
TS
i
b.
Q
t
Q--
-
O
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Imp
r
o
v
ing
Quality o
f
Veh
i
cle
Tra
cki
n
g
S
y
stems in H
ill
S
t
a
tio
n
s
Using
IEEE 802
.1
6 … (Ro
o
p
s
i
n
gh
Ta
kur)
14
3
c.
Calculate the SF for ea
ch SS in Q
t
d.
IF
Any SS € Q
t
has
ze
ro
g
r
ant
e
d
ban
d
wi
dt
h,
IF
A
N
Y
SSs
ha
ve
nrtPS tra
ffics a
n
d
zero grante
d
Ba
ndw
i
dth
C
h
o
o
s
e on
e
runn
ing
n
r
tPS traffics
with
larg
estCR.
Else
Choose
one
with la
rgest
SF a
n
d C
R
.
e
.
Sc
he
dule the
SS a
d
t
h
e c
o
r
r
es
ponding CS
of
S
t
End
For
Th
is prob
ab
ility effects th
e veh
i
cle track
ing rate (VVR).
VVR stand
s
fo
r th
e
p
e
rcen
t
a
g
e
of th
e un
track
ed
vehi
cl
es w
h
i
c
h
i
s
not
t
r
acke
d
.
M
o
reo
v
er th
e
p
e
rform
a
n
ce an
alysis is p
r
esen
ted
in
term
s o
f
thro
ugh
pu
t g
a
in
(TG
)
.
7.
PERF
ORMANCE
ANAL
YSIS
OF
PROPOSE
D
S
C
HEME
The traffic load in a network
m
a
y
vary
dy
n
a
m
i
cal
ly
. Thus
, t
h
e net
w
or
k s
t
at
us can be cl
assi
fi
ed i
n
t
o
fo
ur
st
ages:
l
i
g
h
t
,
m
oderat
e
,
h
eavy
an
d
f
u
l
l
y
l
o
ade
d
.
The
pe
rf
orm
a
nce o
f
t
h
e
pr
o
pose
d
sc
hem
e
m
a
y
be v
a
ri
ant
in diffe
r
ent sta
g
es.
We i
nve
stigate the
perfor
m
a
nce
of our schem
e
in
each stage
.
Suppose B
all
r
e
p
r
e
s
en
ts
the
t
o
t
a
l
t
r
acke
d
v
e
hi
cl
es su
p
p
o
r
t
e
d
by
t
h
e B
S
.
Ass
u
m
e
repres
ent
s
t
h
e
ve
hi
cl
e t
r
acke
d
by
r
eal
t
i
m
e
conne
ct
i
ons
an
d
VTrt is the nu
m
b
er of add
itio
n
a
l
ve
hicles tracke
d
by t
h
em
via VTRs.
{i
−
1}
whe
r
e
m
a
x{0;
Q
nrt
i
−
1
−
W
nrt
i
−
1
}
(
1
)
i
s
t
h
e am
ount
of
q
u
e
u
ed
ve
hi
cl
es arri
vi
n
g
b
e
fo
re
fram
e
i
−
1.
Si
nce
Y
i
−
1
can
no
t b
e
n
e
g
a
tiv
e,
th
e prob
ab
ility
of
t
h
e C
S
,
den
o
t
e
d a
s
S
u
,
w
h
i
c
h
has
dat
a
t
o
cal
cul
a
t
e
t
h
e re
cal
cul
a
t
e
ban
d
wi
dt
h ca
n
be
obt
ai
ne
d a
s
:
P
u
(u
) =
∫
γ
nrt m
a
x
P
(
X
)
d
X
Y
i
−
1
(2
)
Whe
r
e _
n
r
t
m
a
x i
s
t
h
e m
a
xim
a
l
am
ount
of
no
n-
real
t
i
m
e
vehi
cl
es ar
ri
vi
ng i
n
a fram
e
and
ve
hi
cl
es
occupying.
A
CS whic
h ret
r
a
ces the untrac
ked ve
hicles
suc
cessfully while receiving a R
M
m
u
st be schedule
d
on t
h
e C
S
an
d ha
ve n
o
n
-
r
e
a
l
t
i
m
e
dat
a
to be t
r
a
n
sm
i
t
t
ed an
d ret
r
ace
d. F
r
om
equat
i
ons
(1
) an
d (
2
)
,
t
h
e
p
r
ob
ab
ility th
at a CS satisfies
th
ese two
con
d
itio
n
s
is
d
e
ri
v
e
d
as:
║
Q
n
║
Based
on
th
e
th
ree m
e
trics:
1
)
Th
ro
ugh
pu
t
g
a
in
(TG):
It represen
ts th
e p
e
rcen
tag
e
o
f
th
ro
ugh
pu
t which
is
i
m
p
r
ov
ed b
y
i
m
p
l
e
m
en
tin
g
ou
r sch
e
m
e
. Th
e fo
rm
al d
e
fin
itio
n can b
e
expressed
as:
TG =
T
Tracks
−
T
no
tracks
Whe
r
e T
retraces
and T
no
retraces
rep
r
esen
t t
h
e throug
hpu
t with
an
d withou
t i
m
p
l
e
m
en
tin
g
o
u
r sch
e
m
e
,
respect
i
v
el
y
.
T
h
e
hi
g
h
er
T
G
achi
e
ve
d s
h
ow
s t
h
e
hi
ghe
r
pe
rf
orm
a
nce t
h
at
o
u
r
sc
hem
e
can m
a
ke.
2)
Tra
c
ki
n
g
unt
racke
d
ve
hi
cl
e rat
e
(
V
VR
)
.
It
i
s
de
fi
ne
d
as t
h
e
pe
rcent
a
ge
of
t
h
e
u
n
t
r
a
c
ked
ve
hi
cl
es
occu
pi
ed
i
n
t
h
e t
o
t
a
l
id
en
tified v
e
h
i
cles in
th
e syste
m
with
ou
t usin
g v
e
h
i
cle ret
r
ack
ing
.
VVR =
V
tracks
V
untracks
1)
Thr
o
ug
h
put
ga
i
n
(T
G
)
:
TG=
pretracked VT
V
g
-V
T
Suppose
Vg is
the t
o
tal tracked
vehicles i
n
the syst
em
and the
un trac
ke
d ve
hicles of t
h
e syste
m
is
V
T.
b
y
eq
u
a
tion
,
t
h
e to
tal thro
ugh
pu
t
g
a
in
, is so
l
v
ed.
Dela
y is a critical factor a
ff
ecting th
e
QOS of serv
ices.
IN
o
u
r sc
hem
e
, we
prese
r
ve t
h
e exi
s
t
i
n
g
ve
hi
cl
e t
r
acki
n
g.
M
o
re
ove
r t
h
e
C
S
can
not
t
r
a
c
k t
h
e
ve
hi
cl
es unt
i
l
receiving t
h
e R
M
whic
h is
sent by TS.
8
.
CONC
LUSION
It is v
e
ry ch
allen
g
i
n
g
task
for SS t
o
pred
ict
th
e arri
v
i
ng
veh
i
cles p
r
ecisely. Alth
ou
gh
t
h
e ex
isting
syste
m
allo
ws th
e SS
v
e
h
i
cles to
ad
ju
st
th
e trac
k
e
d
v
e
h
i
cles
v
i
a risk
of
failin
g to
satisfy t
h
e QOS
r
e
qu
ir
em
en
ts. O
u
r
r
e
sear
ch
do
es f
o
cu
ses
on
p
r
op
osed
v
e
hi
cle tracking sy
ste
m
to track t
h
e
untracke
d
vehicles
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l.
3
,
N
o
.
1
,
Feb
r
u
a
ry 2
013
:
13
6 – 14
4
14
4
o
n
ce it o
c
curs with
im
p
r
o
v
i
n
g
qu
ality. It allo
ws th
e BS to
sch
e
d
u
l
e a co
m
p
le
m
e
n
t
ary statio
n
m
o
n
ito
rs th
e
en
tire U
L
t
r
ansmissio
n
s
in
terv
al
o
f
its correspon
d
i
n
g
TS an
d
stan
d
b
y
for an
y
o
pportun
ities to
track
th
e
unt
racke
d
ve
hi
cles. If we a
r
e observing
the tracki
ng system has the ability
to trace and c
o
-ordinate a fl
eet of
v
e
h
i
cles,
with in
teg
r
ation
of BS
(satellite)/SS (cell
to
wer) techn
o
l
o
g
y
. It
en
su
res that th
e track
i
ng pro
cess is
within an acc
urate and acceptable ra
nge
, since it a
llows m
a
nagers to supe
rvise vehicle status(i.e fuel,
te
m
p
eratu
r
e).
Th
is propo
sed
syste
m
can
b
e
u
s
ed
in
m
o
n
ito
ring
and
con
t
ro
llin
g
app
licatio
n
s
. Bu
t it is d
i
fficu
lt
task
to conn
ect in
h
ill statio
n
s
. Sign
al streng
th
will n
o
t
b
e
go
od
i
n
h
ill stati
o
n
s
.
REFERE
NC
ES
[1]
Real Time W
e
b
based Vehicle Tracking using G
P
S
.
Full
Text A
v
ailable
B
y
:
Mukesh, P. R.
World Academ
y
of
Science,
Engin
e
ering & Techno
log
y
.
Jan2010
, V
o
l. 61
, p91-99
. 9
p
. 4
Color Photo
g
raphs, 12
Diagr
a
ms.
[2]
Design of a stab
le controller for accurate path
tr
acking of autom
ated guided vehicles systems.
Full Text Avai
lab
l
e
B
y
:
Kuttol
a
m
a
d
o
m
,
Mathew; M
e
hrabi
,
Mostaf
a
G.; W
eav
er,
J. I
n
terna
tiona
l Jou
r
nal of
Advanc
e
d
Manufac
turing
Techno
log
y
. Oct2010, Vol. 50 Issue 9-12, p1183-1188. 6p.
3 Diagrams, 1 Chart, 5 Graphs. DOI:
10.1007/s00170-
010-2569-7.
[3]
Developmen
t of
a vehi
c
le
image-
t
r
a
cking s
y
s
t
em bas
ed on a long
-dis
tance d
e
te
ct
i
on algor
ithm.
F
u
ll T
e
xt Avai
lab
l
e
B
y
: Oh
, Jutaek
;
Min, Joon
y
oung
; Choi,
Eunsoo.
Canadian Journal of Civil
Engin
eering.
Nov2010
, Vol. 37 Issue 1
1
,
p1395-1405. 10
p. 5
Color Photo
g
raphs, 2
Black
a
nd White Photo
g
raphs, 3
Diagr
a
ms, 5 Charts.
[4]
Automatic v
e
hi
c
l
e locat
i
on trac
king
system based on GIS environment.
Full Text Availab
l
e B
y
: Aloquili, O.;
Elbann
a, A.; Al-
A
zizi
, A.
IET Software.
Aug2009
, Vol. 3 Issue 4, p255-263. 9p.
9 Black and White Photographs, 6
Diagrams, 5 Ch
arts, 1
Map.
DOI: 10.1049/iet-s
e
n.2008.0048.
[5]
Onboard vehicle detect
ion and
tracking
using bo
osted Gabor de
scriptor and sparse representatio
n
.
Ava
ilab
l
e B
y
:
Yang, S.; Wang, M.H.
Elec
tronics Letters.
8/2/2
012, Vol. 48 Issue 16, p995-997.
3p. 1 Color Photograph, 2 Char
ts,
2
Graphs.
DOI: 10.1049/el.2012
.1922.
[6]
Data communication and
autom
atic veh
ic
le
loca
tion syst
em “GPS-AVL”
, (Alsi-A
s
ia-page
Ltd
.
,
20
04).
[7]
IEEE 802.16W
G,
”IEEE standard for local and metropolitan area networks
part 16: Air interface for fix
ed
and
mobile Jianhua
broadband
wirel
e
ss
a
ccess syst
e
m
s, Amendment
2,”
IEEE 802
.16
Standard
, December 2005.
[8]
He, Kun Yang
and Ken Guild ”
A Dynamic Ba
ndwidth Reserv
ation Sc
heme fo
r Hybrid IEEE 802.16 Wireless
Networks”
Eun-
ICC’08 p.2571-
2575.
[9]
Chan Park, Hwangnam Kim, J
ae-Young Kim, Han-Seok Kim ”
Dynamic Bandw
idth Requ
est-Allocation Algorith
m
for Real-t
ime Se
rvices in I
EEE 8
02.16 Broadban
d Wireless Ac
ces
s Networks”
, INFOCOM 2008,p
.
852 - 86 Thomas
G. Robertazzi ”Computer Netw
orks
and
S
y
s
t
e
m
s
:
Theor
y
and
P
e
rform
ance
Evaluation.” Sp
ringer-Verlag 19
90
[10]
Eun-Chan Park
,
Hwangnam Kim, Jae-Y
oung
Kim, Han-Seok
Kim
”Dynamic Bandwidth
Request-Allo
cation
Algorithm for Real-tim
e Services in I
EEE 802.1
6
Broadband Wireless Access Networks”
,INFOC
OM 2008,p.852
–
860
[11]
Frank H.P. Fitz
e
k
, Martin R
e
issl
ein
, ”MPEG–4 H.263 Video Tr
aces for
Networ
k Performance
Evaluation”
, IE
EE
Network, Vol.15
, No. 6, p.40-
5
4
November/December 2001
[12]
Stereo Visual Tracking W
ithin
S
t
ructured
En
viro
nments for Mea
s
uring Vehicle S
p
eed.
Deta
il Onl
y
Ava
ilab
l
e
B
y
:
Zhu, Junda; Yuan, Liang
;
Zheng
,
Yuan F.; Ewing, Robert L.
IEEE Transactions on Circuits
&
Sy
s
t
e
m
s
for Video
Technology.
Oct2012, Vol. 22
Issue 10, p1471-1
484. 14p
. DOI: 1
0
.1109/TCSVT.2012.2202074.
BIBLIOGRAPHY OF
AUT
HORS:
ROOPSINGH
TAKUR, is a
Post
Graduate in Mast
er of Technolo
g
y
from J.N.T University
,
in
Computer Scien
ce and
Engin
eer
ing. hav
i
ng Teac
hing Exp
e
rience of 02
y
e
ars and at pr
esent
Working as Assistant
Professor, De
partment of
Information Technolog
y
C
y
r
y
xco
l
l
e
ge(Af
f
ilia
ted
to
Help
Universit
y
,
Mal
a
ysia)
,
Mal
e
,
Mal
d
ives
Email: roopsing
h
.w
its@gmail.co
m
Website: www.
cy
ry
xc
ollege.edu.mv
Mobile :+960
(7
758303)
E.Ramkumar, is
a Post Graduate
in Master of
Co
mputer Applications
from VL
B J
a
na
kia
mma
l
c
o
eng & techno
log
y
,
in Com
puter S
c
ienc
e and Eng
i
neer
ing. hav
i
ng
Teach
ing Exper
i
enc
e
of 04
y
at present Wo
rking as Assistant Professo
r, Department of In
formation Technolog
y
college(Affiliated to Help Un
iversity
,
Malay
s
ia),Ma
le, Maldives
Website
: www.
cy
ry
xcollege.ed
u
Email: er
ajesh27
7@
y
a
hoo
.com
Mobile :+960 (9
956821)
Evaluation Warning : The document was created with Spire.PDF for Python.