Intern
ati
o
n
a
l
Journ
a
l of
Re
con
f
igur
able
and Embe
dded
Sys
t
ems
(I
JRES)
V
o
l.
3, N
o
. 3
,
N
o
v
e
m
b
er
2
014
, pp
. 11
9
~
13
2
I
S
SN
: 208
9-4
8
6
4
1
19
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
/
IJRES
Development of Wireless Se
nsor Network for Traffic
Monitoring Systems
Sanket Dess
ai
*, N.D.
Gang
adhar
*
, M.P. Bheema Rao*
* Departement o
f
Computer
Engin
eering
,
M.S. Ramaiah School of
Advanced Stud
ies, Bang
alor
e
Article Info
A
B
STRAC
T
Article histo
r
y:
Received
May 14, 2013
Rev
i
sed
Ju
l 25
,
20
13
Accepted Aug 18, 2013
Traffic cong
estion has been
a major
problem on roads around th
e world. I
n
addition
,
ther
e is increase in volu
m
e of
traffic veh
i
cl
e densit
y at a
stead
y
rat
e
.
Thus traff
i
c
on
m
a
jor roads h
a
s
to be
contro
ll
ed
to ke
ep th
e
traff
i
c flowing
a
t
an ac
cept
a
bl
e r
a
te
. S
e
ver
a
l s
c
h
e
m
e
s
for replacing the pr
edominantly
used
Round Robin (RR) scheme for r
e
ducing
c
ongestion at
traffic jun
c
tions hav
e
been proposed.
D
y
namic tr
affic control
schemes adapt to th
e changing traff
i
c
b
y
m
onitor
i
ng t
h
e s
t
at
e (s
uch
as
the
number
queued up on
each lane). Th
ese
need appropriate sensing and monitori
ng s
y
stems. In this paper a traffic
monitoring and
control s
y
s
t
em ba
sed on AMR (Anistro
pic Magneto
Re
sistive
)
ve
hic
l
e se
nsors,
wire
le
ss sensor network and
a proiritised
Weighted Roun
d Robin (WRR
) schedu
ling technique, is dev
e
loped.AMR
se
nsors insta
l
le
d in roa
d
pa
vement detect
the nu
mber
of veh
i
cles waiting in
a
traffi
c l
a
ne
.
The
AM
R s
e
ns
ors
are conn
ect
ed
to t
h
e m
a
s
t
er
contro
ller
to for
m
a Zigbee based
sensor networ
k. Th
e
master
node
consists of an ARM
proces
s
o
r int
e
gr
ated wi
th a
Zigb
ee m
a
s
t
ernod
e.
The tr
affi
c con
t
r
o
l algor
ithm
is implemented at master node whic
h is responsible for taking traffic
signaling d
e
cision. It r
e
ceiv
es sensor
dat
a
from
all
the
lan
e
s.
A two lev
e
l
priority
algor
ith
m with weighted r
ound robin
scheduling, wh
er
e first and
second maximu
m weighted lane are to pa
ss the signal is develop
e
d, To avoid
starving the leas
t loaded lanes, a cy
cle of normal round robin scheduling is
perform
ed af
ter
four rounds of pr
oiritised
weigh
t
ed round robin
schedule.
The proposed
algorithm is simulated
and
compar
ed with
the stan
dard round
robin algorithm. The developed
algorith
m
decr
eas
es
the aver
age w
a
iting
tim
e
for a commuter while maintain
ing the aver
age throughput up to averag
e
loads
.
The de
velopm
ent traf
fic m
onitoring
s
y
s
t
em
is
succes
s
f
ul
l
y
demonstrated
for
a fou
r
lane junction.
Keyword:
AM
R
ARM
RR
WRR
Zi
gbee
Copyright ©
201
4 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
:
Sanket De
ssai,
Depa
rt
em
ent
of C
o
m
put
er
En
gi
nee
r
i
n
g, M
.
S
.
R
a
m
a
i
a
h Sch
ool
o
f
Ad
va
nc
ed St
udi
es
,
#
470
-
P
,Peen
y
a In
du
str
i
al
A
r
ea,4
th
Phase
Bangal
o
re
560058,
Ka
rnata
k
a,India
Em
a
il: san
k
e
tdessai@g
m
ail.c
o
m
1.
INTRODUCTION
Th
e
g
a
th
ering o
f
traffic info
rm
atio
n
is a
b
a
se
for all k
i
n
d
s
of traffic
m
o
d
e
llin
g
,
si
m
u
la
tio
n
and
pre
d
i
c
t
i
on
f
o
r t
a
sks l
i
k
e em
i
ssi
on
red
u
ct
i
o
n,
effi
ci
ent
use
of
i
n
f
r
ast
r
uct
u
re
or e
x
t
e
n
s
i
o
n pl
anni
ng
o
f
t
h
e
r
o
ad
network as
we
ll as the intervention a
n
d res
o
urce pla
n
ni
ng. Auto accide
nts injure at least 10 m
i
llion people
each year, a
nd
two
or three m
i
llion of
them
s
e
riously. The hosp
ital
bill, da
maged
propert
y
, and
other costs are
expect
e
d
t
o
a
d
d u
p
t
o
1%
-
3
% of t
h
e
wo
rl
d’s
g
r
oss
d
o
m
e
st
i
c
pro
d
u
ct
.
W
i
t
h
t
h
e ai
m
of
red
u
ci
n
g
i
n
j
u
ry
a
n
d
accident se
veri
ty, pre-c
r
as
h se
nsing is bec
o
m
i
ng a
n
area
of
active researc
h
am
ong a
u
tom
o
tive m
a
nufact
ure
r
s,
suppliers and unive
r
sities. Ve
hicle accide
nt
statistics disclo
se that t
h
e m
a
i
n
t
h
reats a
driver is
facing a
r
e
from
ot
he
r ve
hi
cl
es.
In t
h
e
s
e sy
st
em
s, robust
a
n
d
rel
i
a
bl
e ve
hi
cl
e det
ect
i
on i
s
t
h
e fi
rst
st
e
p
.
A succe
ssf
ul
v
e
hi
cl
e
d
e
tectio
n
algorith
m
will p
a
ve th
e
way fo
r v
e
h
i
cle reco
gn
itio
n
,
v
e
h
i
cle track
i
n
g, an
d co
llisio
n avo
i
d
a
n
c
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
089
-48
64
I
J
RES Vo
l. 3
,
N
o
. 3
,
No
v
e
m
b
er
201
4
:
1
19
–
13
2
12
0
C
u
r
r
ent
hi
g
h
w
a
y
m
oni
t
o
ri
n
g
appl
i
cat
i
o
ns re
qui
re t
h
e i
n
st
al
l
a
t
i
on o
f
b
u
l
k
y
m
a
gnet
i
c
l
o
o
p
det
ect
or
s o
r
si
ngl
e
m
ount
hi
g
h
res
o
l
u
t
i
o
n
vi
de
o c
a
m
e
ras i
n
o
r
de
r t
o
det
ect
pass
i
ng
ve
hi
cl
es. T
h
ese
devi
ces a
r
e b
o
t
h
c
u
m
b
ersom
e
and expe
nsive
to install and
maintain. It is
propose
d
t
h
at s
e
ns
or
ne
tworks
be
use
d
as
a
replacem
ent for these
cu
rren
t techno
lo
g
i
es, sin
ce these are
m
u
ch
si
m
p
ler to
in
stall an
d
m
a
y b
e
less co
stly to
main
tain
in
th
e lo
n
g
run.
The t
r
affic param
e
ters on the
differe
n
t
sections
of the
roa
d
a
r
e not
uniform
,
becaus
e
the propa
gation
of
traffic fl
o
w
is v
u
l
n
e
rab
l
e to
th
e in
fl
u
e
n
ce
of d
r
i
v
er
s’ p
e
rso
n
a
lity an
d
skill, p
e
d
e
strian
s cro
ssi
n
g
th
e
ro
ads,
intersections of minor roa
d
s, accidents and so on.
The
precision and robustne
ss of tra
d
itional
traffic
forecasting m
e
thods ca
nnot m
eet the re
quire
m
ents of
de
vel
opi
ng traffic c
ont
rol
a
n
d gui
dance
tec
h
nologies.
Fig
u
re
1
.
Sen
s
o
r
s u
s
ed
fo
r Road
Traffic Monitirin
g
(Carlo
s
Sun
2
000
)
C
u
r
r
ent
l
y
, roa
d
-t
ra
ffi
c m
oni
t
o
ri
ng rel
i
e
s o
n
t
h
e t
echnol
og
y
of sens
ors
b
a
sed o
n
ra
dar,
m
i
crowa
v
es,
tu
b
e
s or
loop
detecto
r
s as show
n in
Figu
r
e
1.
Ra
dar
:
For ac
curately m
easuring
vehicle s
p
eed
Microwave Detectors:
Th
ese ar
e
u
s
u
a
lly mo
un
ted on
a br
i
d
g
e
or
g
a
n
t
ry su
ch th
at they po
in
t
v
e
rtically d
o
wn
ove
r a
lane
of t
r
affic. T
h
e
de
vice emits
m
i
cr
owa
v
es
wh
ich
are re
flected on the
roa
d
s
u
rface and
bounc
e
d
bac
k
to
ward
s th
e sen
s
or.
A
v
e
h
i
cle p
a
ssi
n
g
un
d
e
r th
e sen
s
o
r
will cau
se in
terferen
ce t
o
th
e reflected
m
i
cro
w
aves
whic
h e
n
ables
the ve
hicle to be detected.
Tubes:
A rubber t
ube
fixe
d
to the roa
d
s
u
rface across t
h
e
wi
dth of a la
ne of tra
ffic
form
s the basis of t
h
is
sens
or.
One e
n
d of the tube is closed and the othe
r is
connecte
d
to a pressure sensor. As each whe
e
l of a
vehi
cl
e
ru
ns
o
v
e
r t
h
e
t
u
be i
t
c
a
uses a
p
r
ess
u
r
e
.
W
i
rel
e
ss se
ns
o
r
n
e
t
w
or
ks
(
W
SN)
are
ne
w i
n
t
e
g
r
at
i
v
e t
ech
nol
ogi
es
ari
s
i
n
g f
r
o
m
t
h
e dev
e
l
opm
ent
of
w
i
rel
e
s
s
com
m
uni
cat
i
on an
d t
i
n
y
sens
ors
.
W
S
N i
s
a ki
n
d
o
f
m
oni
t
o
ri
n
g
net
w
or
ks
con
s
i
s
t
i
ng o
f
a l
a
rge n
u
m
b
er o
f
l
o
w
-
cost
,
po
we
r-sa
v
i
n
g,
hi
g
h
l
y
i
n
t
e
grat
i
v
e a
n
d s
e
l
f-o
r
g
ani
z
e
d
s
e
ns
or
n
odes
an
d
net
w
or
k
co
ord
i
n
a
tor
s
. W
S
Ns
own
br
oa
d a
n
d
va
l
u
abl
e
a
p
pl
i
cat
i
on
out
l
o
o
k
i
n
cl
u
d
i
n
g m
i
l
itary
,
ur
ban
m
a
nagem
e
nt
,
bi
om
edi
cal
t
r
eat
m
e
nt
,
envi
ro
nm
ent
a
l
m
oni
t
o
ri
ng a
n
d rem
o
t
e
m
oni
tori
ng o
f
da
n
g
e
r
o
u
s areas
.
W
S
Ns i
n
st
al
l
e
d o
n
road
s i
n
a swe
e
pi
n
g
manner can not only
obtain the traffic flow
param
e
ters of
the en
tries and
ex
its of in
tersectio
n
s
, bu
t also
o
f
t
h
e
forks
,
cross
w
al
ks,
bus stati
ons
and
othe
r s
p
ec
ial places. The
r
efore,
w
ith
WSNs cove
ring road net
w
orks over a
great a
r
ea, the
globe tra
ffic inform
ati
on ca
n
be o
b
se
r
v
ed i
n
det
a
i
l
s
i
n
t
h
e t
r
af
fi
c m
oni
t
o
ri
ng c
e
nt
re
. T
h
i
s
t
r
en
d
will certain
ly bring
g
r
eat
b
r
eak
t
hroug
h
s
in
t
h
e traffic m
o
n
ito
ri
n
g
techn
o
l
og
ies.
Ty
pi
cal
l
y
, sens
or
n
o
d
es a
r
e
o
r
gani
ze
d as
se
n
s
i
n
g
nod
es and agg
r
eg
ator
nod
es.
W
h
ile a
sen
s
ing
no
d
e
i
s
respo
n
si
bl
e fo
r sensi
ng
da
t
a
, an agg
r
e
g
a
t
or n
ode i
s
us
ed t
o
pr
ocess
dat
a
fr
om
sen
s
i
ng n
o
d
es an
d sen
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
RES I
S
SN
:
208
8-8
7
0
8
Developme
nt
of Wireless Se
nsor Netw
ork
for Traffic M
onitoring Syste
m
s
(
S
anket
Dess
ai
)
12
1
resu
lts d
a
ta
to a
b
a
se
statio
n. A b
a
se
station
is
u
s
ed
t
o
col
l
e
ct
dat
a
f
r
om
t
h
e ent
i
r
e se
ns
or
net
w
or
k a
n
d re
po
rt
s
th
e d
a
ta t
o
an
en
d u
s
er.
The sy
st
em
i
s
m
a
de up
of t
h
r
ee com
pone
nt
s for
det
ect
i
ng a
nd t
r
ac
ki
n
g
t
h
e
m
ovi
n
g
o
b
ject
s. The
fi
rs
t
com
pone
nt
co
nsi
s
t
s
o
f
i
n
e
x
p
e
nsi
v
e
of
f-t
he
shel
f
wi
rel
e
ss
sens
or
de
vi
ces, suc
h
as M
i
ca
Z m
o
t
e
s, capabl
e
o
f
m
easuri
n
g aco
ust
i
c
and m
a
gn
et
i
c
si
gnal
s
generat
e
d by
ve
h
i
cl
es. The seco
nd c
o
m
pone
nt
i
s
respo
n
si
bl
e for t
h
e
dat
a
ag
g
r
egat
i
o
n.
The
t
h
i
r
d c
o
m
ponent
of
t
h
e
sy
st
em
i
s
resp
onsi
b
l
e
f
o
r
dat
a
f
u
si
o
n
al
g
o
r
i
t
h
m
s
.
Fig
u
re 2
.
Th
e Arch
itectu
r
e
of
th
e Hybrid
Nod
e
(W
allin
20
04
)
Fi
gu
re
2 s
h
ow
s t
h
at
t
h
e
t
o
p
and
m
i
ddl
e l
a
y
e
rs o
f
the arc
h
itecture a
r
e c
onsiste
nt to
present tra
ffic
in
fo
rm
atio
n
network. Th
e
b
o
tto
m
layer
mak
e
s u
p
o
f
WSNs,
wh
ich are v
e
ry flex
ib
le.
W
i
t
h
the rap
i
d
d
e
v
e
l
o
p
i
n
g
WSN techn
o
l
o
g
i
es, all k
i
n
d
s
of in
form
at
io
n
of th
e p
h
y
sical wo
rl
d
aroun
d
u
s
will b
e
tran
sferred
to
th
e presen
t in
fo
rm
atio
n
system at a fin
e
level an
d
with
h
i
gh
sp
eed. As a resu
lt, th
e
u
r
b
a
n
traffic network
with
d
i
stribu
ted
p
a
ra
m
e
ters will b
e
co
m
e
m
o
re m
e
asu
r
ab
le an
d co
n
t
ro
llab
l
e.
The a
d
vant
a
g
e
of
usi
n
g
WSN
1
)
WSN
can mo
n
itor and
ev
alu
a
te th
e
ro
ad
s
au
to
m
a
tical
ly a
n
d con
tinu
o
u
s
l
y
, with
little human
effort
2)
WSN
can
w
o
r
k
i
n
ni
g
h
t
s
a
n
d
ab
om
i
n
abl
e
weat
he
r,
w
h
e
n
t
h
ere
i
s
f
o
g
or
dust
3)
WSN is abl
e
to accurately record
t
h
e tra
ffic fl
ow
data
of t
h
e road fo
r furthe
r analys
is whic
h is ha
rd for
vide
o cam
eras
4)
WSN is
be
com
i
ng chea
per and ca
n
be
depl
oyed i
n
a
fine-graine
d
mode
for
real time and “re
a
l space”
traffic m
o
n
itorin
g
2.
DESIG
N
Fu
nct
i
onal
bl
o
c
k di
a
g
ram
of
t
h
e Tra
ffi
c M
oni
t
o
ri
n
g
sy
st
e
m
i
s
show
n i
n
fi
g
u
re
3;
t
h
e
sy
st
em
has
m
a
i
n
l
y
t
w
o bl
o
c
ks o
f
m
a
st
er
no
de an
d sl
ave
no
de. M
a
st
er
no
de co
nsi
s
t
s
of
AR
M
based
pr
ocess
o
r ca
p
a
bl
e of
p
e
rform
i
n
g
m
u
ltitask
in
g
o
p
e
ratio
n
s
.
Sl
ave n
ode c
o
nsi
s
t
s
of v
e
hi
c
l
e det
ect
i
on senso
r
an
d zi
g
b
e
e
R
F
m
odul
e. Sens
or c
o
l
l
ect
s t
h
e vehi
cl
e
det
ect
ed
dat
a
and
sen
d
s t
o
m
a
st
er n
o
d
e t
h
r
o
ug
h R
F
m
o
dule. Based
on efficient al
g
o
rithm Master n
ode will
allow the
vehi
cle denser la
ne
to
be cleare
d
Traffic Mo
n
itoring
System
b
l
o
c
k con
s
ists
o
f
a Jun
c
tio
n with
4
Lanes
(N
ort
h
Lane,
So
ut
h
L
a
ne, Ea
st
Lane
,
W
e
st
La
ne)
.
M
a
gnet
i
c
Se
ns
ors a
r
e m
ount
ed o
n
eac
h La
ne i
n
reg
u
l
a
r
fashi
o
n
based
o
n
ef
fi
ci
ent
sensi
n
g
of t
h
e ve
hi
cl
e.
Sl
ave co
nt
r
o
l
l
e
r co
nsi
s
t
s
o
f
t
w
o z
o
nes zo
ne
-1
a
n
d
zone
-
2
,
Zo
ne-
1
i
s
co
n
n
ect
ed
t
o
No
rt
h
an
d
W
e
st
l
a
ne se
ns
or
s an
d
Zo
ne-
2
i
s
co
n
n
ect
ed t
o
So
ut
h a
n
d Ea
st
La
n
e
Sens
ors
.
Ve
hi
c
l
es passi
n
g
f
r
o
m
t
h
ese l
a
nes are det
ect
ed t
h
r
o
u
g
h
sen
s
o
r
s a
nd se
ns
or
dat
a
are sen
d
i
n
g t
o
zi
gbe
e
m
odules conne
c
ted at two zones. Master
node consists
of RF Transcei
ve
r and Tra
ffic s
i
gnal controlling is
do
ne
usi
n
g e
ffi
ci
ent
al
go
ri
t
h
m
.
R
F
dat
a
sent
f
r
om
sl
ave (z
on
e-1 a
n
d z
one
-
2
) i
s
col
l
ect
ed
b
y
m
a
st
er depe
n
d
i
n
g
on
t
h
e
sens
o
r
wei
g
ht
act
i
v
e
d
eci
si
on i
s
t
a
ke
n as
a re
su
lt the lan
e
with
h
i
gh
d
e
nsity is allo
wed
t
o
p
a
ss.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
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:
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64
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J
RES Vo
l. 3
,
N
o
. 3
,
No
v
e
m
b
er
201
4
:
1
19
–
13
2
12
2
Fi
gu
re
3.
B
l
oc
k
Di
ag
ram
of
Traf
fi
c M
o
ni
t
o
ri
n
g
Sy
st
em
s
a.
Hardw
a
re De
sign and
Impl
ementati
on
As s
h
ow
n i
n
Fi
gu
re
4
hi
g
h
l
e
v
e
l
desi
g
n
of
t
r
a
ffi
c m
oni
t
o
ri
ng
sy
st
em
consi
s
t
of
a
ju
nct
i
o
n
whe
r
e
fo
u
r
lan
e
s m
eets
an
d
it is rep
r
esen
ted
as
North
Lan
e
, Sou
t
h
Lan
e
, East lan
e
an
d
West lane. Each
lan
e
will b
e
havi
ng
vehi
cl
e
det
ect
i
on sens
or nam
e
d as NS (n
ort
h
sens
o
r), SS
(so
u
th s
e
ns
or
), ES (ea
s
t senso
r),
WS
(east
sen
s
o
r
) in
turn th
e co
rresp
ond
ing
lan
e
traffi
c lig
h
t
are re
present as TL-N, TL-S, T
L
-E
, TL-W. As algorithm
dem
a
nds i
t
ha
s bee
n
su
b
di
vi
de
d i
n
t
o
fo
u
r
l
a
ne
j
u
nct
i
o
n
wi
t
h
t
w
o
zo
nes as
z
one
-1
an
d z
o
ne-
2
,
zone
-
1
cor
r
es
po
n
d
t
o
no
rt
h
an
d
west
l
a
ne si
m
i
l
a
rl
y
zone
-
2
c
o
r
r
esp
o
n
d
s t
o
s
o
ut
h a
n
d
east
l
a
ne
.
This section consist
of im
ple
m
enting m
a
ste
r
node
which i
n
cludes ARM
process
o
r, Zigbee recei
ve
r
and T
r
af
fi
c co
nt
r
o
l
l
i
ng si
g
n
a
l
s as sho
w
n i
n
Fi
gu
re 5
.
Sa
m
s
ung S3C
2
4
4
0
A
(
A
R
M
9
2
0
T)
base
d AR
M
boar
d
m
i
ni
244
0 has
been
u
s
ed
w
h
i
c
h ru
ns
at
5
3
3
M
H
z, has 6
4
M
SDR
A
M
,
1
2
8
M
Na
nd
Fl
ash
,
2M
N
o
r Fl
ash
wi
t
h
B
I
OS
i
n
st
al
l
e
d
,
S
3
C
2
4
4
0
su
p
p
o
r
t
2
bo
ot
m
ode
Nan
d
Fl
ash
b
oot
a
n
d
No
r
Fl
ash
b
oot
.
M
e
m
o
ry
m
a
p and
ch
i
p
sel
ect
i
on i
s
di
ffe
rent
b
a
sed
on
di
f
f
ere
n
t
b
oot
m
ode. It
sup
p
o
rt
s
OS
p
o
rt
i
n
g com
p
at
i
b
l
e
wi
t
h
Li
n
u
x
2
.
6
,
An
dr
oi
d a
n
d
W
i
nC
E.
Fi
gu
re 4.
Hi
gh
Level
Desi
g
n
o
f
t
h
e
Tra
ffi
c
M
oni
t
o
ri
n
g
Sy
st
em
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
RES I
S
SN
:
208
8-8
7
0
8
Developme
nt
of Wireless Se
nsor Netw
ork
for Traffic Monitoring Syste
m
s
(
S
anket
Dess
ai
)
12
3
Fi
gu
re
5.
B
l
oc
k
Di
ag
ram
of t
h
e M
a
st
er
N
o
d
e
Fi
gu
re
6.a
.
M
a
st
er N
o
de Fl
o
w
C
h
art
Master no
d
e
fl
o
w
ch
art h
a
s
been
sh
own
in
fig
u
re 6.a to
6
.
d
.
Fi
rst all th
e
in
itializat
io
n
s
fo
r t
h
e port
pins
a
r
e done
a
nd othe
r neces
sary settings li
ke UART, sensor
port a
r
e don
e.
Next ste
p
i
s
to recei
ving
RF data
fr
om
zone-1 a
n
d zo
ne-
2
sl
ave
no
des
,
t
h
ere i
s
part
i
c
ul
a
r
fo
rm
at
fol
l
o
we
d i
n
reco
g
n
i
z
i
ng t
h
e exact
zone
d
a
t
a
, i
f
start o
f
p
a
ck
et
is ‘$
’ th
en
d
a
ta b
e
lo
ng
s to
zon
e
-1
and
if
start o
f
p
a
ck
et is ‘#
’ th
en
d
a
ta
b
e
lo
ng
s to
zon
e
-2
.Now
once
data is received, m
a
ster cont
roller
will
decode the
pac
k
et and calculate for fi
rst m
a
x
i
m
u
m
weighted lane
and t
h
en sec
o
n
d
m
a
xim
u
m
w
e
i
ght
ed l
a
ne.
As a res
u
l
t
t
h
e lan
e
p
a
rticu
l
ar to
first m
a
x
i
m
u
m lan
e
is a
llo
wed
first an
d th
en
seco
nd
m
a
x
i
m
u
m
lan
e
is allo
wed
to p
a
ss
n
e
x
t
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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S
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:
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64
I
J
RES Vo
l. 3
,
N
o
. 3
,
No
v
e
m
b
er
201
4
:
1
19
–
13
2
12
4
Fi
gu
re 6.
b.
B
l
o
c
k Di
ag
ram
of M
a
st
er
N
o
de
Fi
gu
re 6.c
.
B
l
o
c
k Di
ag
ram
of M
a
st
er
N
o
de
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
RES I
S
SN
:
208
8-8
7
0
8
Developme
nt
of Wireless Se
nsor Netw
ork
for Traffic Monitoring Syste
m
s
(
S
anket
Dess
ai
)
12
5
Fi
gu
re 6.
d.
B
l
o
c
k Di
ag
ram
of M
a
st
er
N
o
de
b.
Algorithmic I
m
plementati
on
Th
is section
ex
p
l
ains abou
t th
e algo
rith
m
i
m
p
l
e
m
en
ted
in
p
e
rform
i
n
g
efficien
t traffi
c
m
o
n
ito
ri
n
g
and signaling.
As explaine
d in earlie
r section the syste
m
c
onsist of thre
e
weighte
d
sens
ors locate
d
in each
lane and t
h
ere
is traffic signal
(Red
, Yello
w,
Gree
n) co
rre
s
p
o
n
d
ing
to
each
lan
e
. By d
e
fau
lt th
e traffic sig
n
a
l
p
a
ssing
sequ
en
ce
fo
llowed
at th
e jun
c
tion is No
rt
h
,
East
,
W
e
st and
So
u
t
h
lan
e
i.e
‘N’
‘E’
‘W’‘S’ th
is is
sho
w
e
d
i
n
Fi
g
u
re
8 an
d 9 re
prese
n
t
i
n
g R
o
u
nd
ro
bi
n se
que
nce an
d wei
g
h
t
ed ro
u
nd r
o
bi
n. Fi
g
u
r
e 7 sh
o
w
s t
h
e
zones and its cycles of tra
ffic
flow for sender and m
a
ster data r
ecepti
on with
round robin and
wei
ghted
round
r
o
b
i
n algo
r
ithm
.
Fi
gu
re
7.
R
o
un
d R
obi
n
Al
g
o
ri
t
h
m
and
W
e
i
g
ht
ed R
o
un
d R
o
bi
n
Al
g
o
r
i
t
h
m
Evaluation Warning : The document was created with Spire.PDF for Python.
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S
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64
I
J
RES Vo
l. 3
,
N
o
. 3
,
No
v
e
m
b
er
201
4
:
1
19
–
13
2
12
6
Fig
u
r
e
8
.
Flow D
i
agr
a
m
Rep
r
esen
ting
Round
R
o
b
i
n
A
l
go
r
i
th
m
Fi
gu
re
9.
Fl
o
w
Di
ag
ram
R
e
pr
esent
i
n
g
Wei
g
ht
ed R
o
un
d R
o
bi
n
Al
g
o
r
i
t
h
m
c.
Schem
a
tic Circuit Diagram
for
Mas
t
er Node
Al
gori
t
hmi
c
Implementation
The schem
a
t
i
c
ci
rcui
t
di
agra
m
of t
h
e Traff
i
c
m
oni
t
o
ri
n
g
sy
st
em
for
m
a
st
er no
de i
s
sh
ow
n
i
n
Fi
g
u
re
10 a
nd
1
1
. It
c
onsi
s
t
of c
o
n
n
ect
i
n
g zi
g
b
ee m
odu
l
e
t
h
ro
u
gh
UA
R
T
po
rt
fr
om
AR
M
b
o
a
r
d
and traffic signal lights are c
o
nnect
e
d
t
o
p
o
r
t
pi
n
s
of
AR
M
boa
r
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
RES I
S
SN
:
208
8-8
7
0
8
Developme
nt
of Wireless Se
nsor Netw
ork
for Traffic M
onitoring Syste
m
s
(
S
anket
Dess
ai
)
12
7
Figu
re
1
0
.
Sch
e
m
a
tic Diagra
m
for M
a
ster
No
de
Figu
re
1
1
.
Sch
e
m
a
tic Diagra
m
for M
a
ster
No
de
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
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-48
64
I
J
RES Vo
l. 3
,
N
o
. 3
,
No
v
e
m
b
er
201
4
:
1
19
–
13
2
12
8
3.
R
E
SU
LTS AN
D ANA
LY
SIS
Fi
gu
re
12
sh
o
w
s sl
a
v
e zo
ne
n
ode al
o
ng
w
i
t
h
sens
or
an
d
ad
ded
wei
ght
, t
h
ere
are t
h
r
ee m
a
gnet
i
c
sens
ors
whic
h are in yellow color as shown
whic
h is in
terfaced to zone-1
PIC micr
ocont
r
oller through
GPIO,
whe
n
any m
e
tal piece is detected it sends
the data. Fo
r e
xpe
rim
e
ntal process m
e
tal washer a
nd m
e
tal slab
piece is used.
Once the c
o
m
p
lete set-up is ready and
work
ing prope
rly, we can ensure
the wireless module to
b
e
w
o
rk
ing
in
g
ood
co
nd
ition b
y
ch
eck
i
ng
an
d d
i
ag
no
si
n
g
th
ro
ugh
H
y
p
e
rTer
m
i
n
a
l set-
up
.
Th
e m
a
ster
w
ill
sche
dul
e t
h
e si
gnal
i
n
g
pr
oces
s of al
l
o
wi
n
g
t
h
e ve
hi
cl
es ba
sed o
n
se
nso
r
i
n
p
u
t
bel
o
w fi
g
u
re
14
sho
w
s
N
o
r
t
h,
So
ut
h, Ea
st
,
West
W
e
i
g
ht
and
i
t
s
corres
p
on
d
i
ng t
r
a
ffi
c l
i
ght
si
gnal
s
s
h
o
w
i
ng i
n
Hy
pe
rTe
r
m
i
nal
and fi
g
u
re
1
5
shows the zone-1 one recei
ved data on
hyperterm
i
nal.
Vehicles are be
en sense
d
through sensor laid on
pavem
e
nt from
slave
node
, it
fram
e
s the coll
ected RF
data
and se
nds
to t
h
e m
a
ster
for
f
u
rther
p
r
ocessin
g
the
R
F
dat
a
bee
n
col
l
ect
ed i
s
s
h
ow
n i
n
Fi
gu
re
13
.
Based
o
n
th
e
weigh
t
s sh
own th
e Master con
t
ro
ller will allow
t
h
e si
gnal
fo
r
t
h
e l
a
ne i
n
se
que
nce
of Eas
t
l
a
ne t
h
en S
o
ut
h La
ne, t
h
e
n
No
rt
h La
ne, t
h
en
West
La
n
e
, t
h
i
s
sequence
is
fol
l
owe
d
for four cycles
t
h
en
o
n
e
R
o
un
d R
obi
n
i
s
exec
ut
ed
.
Fi
gu
re
1
2
.
Im
pl
em
ent
a
t
i
on o
f
t
h
e Tra
ffi
c
M
o
ni
t
o
ri
ng
Sy
st
em
Set
up
Fig
u
re
13
.
Im
p
l
e
m
en
tatio
n
of
th
e Slav
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