Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
6, N
o
. 5
,
O
c
tob
e
r
201
6, p
p
. 2
362
~236
8
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
5.1
080
7
2
362
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
Feasibility and Efficacy of
BLE Beacon IoT Devices in
Inventory Management at the Shop Floor
Ravi
Ram
a
k
r
ishnan, L
o
veleen G
a
ur,
Guri
nder Sin
g
h
AIBS,
Amity
University
, India
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Apr 11, 2016
Rev
i
sed
Jun
1
,
2
016
Accepted
Jun 19, 2016
Inventor
y M
a
n
a
gem
e
nt
is
a
ke
y ar
ea f
o
r
cus
t
om
er s
e
rvic
e and
cos
t
optimization in
an
y
manufac
turing setup. As co
mpanies turn glo
b
al and h
a
v
e
thousands of co
mponents and h
undreds
of warehouses the inv
e
n
t
or
y
becomes
a nightmar
e
and
a lo
t of
time is
spend in
track
ing inventor
y
an
d ensuring
right shipm
e
nts. Trad
ition
a
l s
y
st
em
s of robotic arm
s
for inventor
y
p
i
ck
and
drop have been
based on premises of
marking areas of the warehouse and
track
ing it. However with the advent of
IoT
all this is set
to change
as
inventor
y
objects become more
self-awa
re
and self-broad
casting
.
This paper
techn
i
ca
ll
y s
ugg
es
ts
an appro
a
c
h
of
managing
inventor
y
using
low energ
y
blue too
t
h beaco
ns and also do
es a sta
tistical case research on
tw
o groups of
the sam
e
org
a
ni
zat
ion one
befo
re th
e pi
lot run
where
tradi
tion
a
l b
a
rcod
e
scanners ar
e used to tr
ack
inven
t
or
y
and other on
e where the pilo
t trial B
LE
beacon
te
chnolo
g
y
was used
. S
t
atist
i
c
a
ll
y
the I
o
T-bea
c
on users
are m
u
ch
m
o
re efficien
t a
nd accura
te and
save lot of time and costs in the short run
itself
.
Keyword:
An
dr
oi
d
B
l
uet
oot
h
In
tern
et o
f
th
ing
s
In
ve
nt
ory
Locat
i
o
n t
r
ac
ki
ng
Low e
n
ergy be
acons
Man
u
f
actur
ing w
a
r
e
hou
ses
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
:
R
a
vi
R
a
m
a
kri
s
hna
n,
Am
i
t
y Un
iv
ersity,
38
1
-
A,
R
e
ge
nt
,
Shi
p
ra
S
unci
t
y
,
I
n
d
r
ap
u
r
am
, G
h
azi
aba
d
20
10
1
4
,
Ut
t
a
r
Pra
d
es
h,
In
di
a.
Em
a
il: rav
i
.ramak
rishn
a
n@gmail.co
m
1.
INTRODUCTION
In
ve
nt
ory
C
o
nt
rol
i
n
sho
p
fl
o
o
rs i
s
necessa
r
y
i
n
pro
duct
i
o
n sy
st
em
s whi
c
h are com
p
l
e
x st
ruct
ural
l
y
and
p
r
oces
s w
i
se [1]
.
In
ve
nt
ory
c
ont
r
o
l
po
l
i
c
i
e
s need t
o
be a
d
o
p
t
e
d i
n
s
h
o
p
fl
o
o
r,
pr
o
duct
i
o
n
net
w
o
r
k
,
l
ogi
st
i
c
s and st
ores
or wa
reh
o
u
ses. I
n
vent
ory
m
a
nagem
e
nt
infl
uences
decis
i
on-m
aki
ng in alm
o
st all fir
m
s and
has
bee
n
e
x
tensively studie
d
i
n
the
academ
ic
and c
o
rporate
spheres
[2].
A c
once
p
t
u
al
f
r
am
ework
o
f
i
nve
nt
o
r
y
m
a
nagem
e
nt
foc
u
s
s
i
n
g
o
n
l
o
w
c
ons
um
pt
i
on a
n
d
pat
t
e
rn
o
f
d
e
m
a
n
d
h
a
s st
atistical
ly ex
ist
e
d
[3
]. How
e
ver
in
co
n
tinuou
s m
a
n
u
f
actur
i
n
g
fo
cu
ssing
on
expo
r
t
sales
it
m
a
y
becom
e
im
perat
i
v
e t
o
st
ock
fi
ni
she
d
g
o
o
d
s t
i
l
l
t
h
e t
i
m
e
of c
ont
ai
ne
r s
h
i
p
m
e
nt
ar
ri
ves
an
d
cann
o
t
be a
voi
ded
.
Trad
ition
a
lly in
v
e
n
t
ory con
t
ro
l syste
m
s are a co
m
b
in
atio
n
o
f
p
h
y
sical cycle co
un
tin
g
and
en
terprise
resource p
l
annin
g
au
t
o
m
a
tio
n
syste
m
s wh
ich
are IT
d
r
i
v
en
. Th
e trad
ition
a
l ap
pro
ach
t
o
tag
in
v
e
n
t
o
r
y ite
ms
h
a
s
b
e
en
eith
er as a
b
a
r
code stic
k
e
r or a
RFID tag
.
RFID
h
a
s
h
a
d
a
l
o
t of
po
sitiv
e
i
m
p
act o
n
bu
sin
e
ss
o
p
e
ration
s
an
d
is p
r
edo
m
in
an
t tech
no
log
y
i
n
th
e sh
op
floo
r
g
l
ob
al [4
].
Ho
we
ver
wi
t
h
t
h
e a
dve
nt
o
f
I
n
t
e
r
n
et
of
Thi
n
gs a
s
a
g
l
obal
net
w
or
k
al
l
o
wi
n
g
c
o
m
m
uni
cat
i
o
n
b
e
tween
o
b
j
ect
s-obj
ects an
d
o
b
j
ects-hu
m
a
n
s
wh
ich is an
yth
i
n
g
in th
e
wo
rl
d
b
y
prov
i
d
in
g
un
iqu
e
i
d
en
tity to
each a
n
d every
objects
ne
w te
chnology
horiz
ons
have
ope
n
ed
up which is
being
propose
d
in this
pa
per [5].
Bluetoot
h
bea
c
ons are
basic
a
lly
sensors e
m
bedded
with Bluetooth
tra
n
s-receive
rs
which ca
n s
e
nd
and recei
ve bi
-directional inform
ation
and a
r
e powere
d
by
sm
a
ll Li-on
ba
tteries and ha
ve with a
d
vance
m
ents
becom
e
m
o
re
an
d l
o
wer
en
ergy
c
o
n
s
um
ing
.
B
LE B
eac
ons
ha
ve
bec
o
m
e
t
h
e fr
ont
ru
n
n
ers
f
o
r c
r
eat
i
n
g
cont
e
x
t
u
al
an
d
l
o
cat
i
on base
d
expe
ri
ences f
o
r cust
om
ers as part
o
f
f
o
r
w
ar
d
t
h
i
nki
ng st
rat
e
gi
es t
a
i
l
o
r m
a
d
e
t
o
a cust
om
er [6].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Fea
s
ib
ility and Effica
cy o
f
BLE Bea
c
on
IoT
D
evices in In
ven
t
o
r
y Mana
g
e
m
e
n
t
.... (Ra
v
i
Ram
a
krishn
an)
2
363
2.
PROP
OSE
D
SOLUTI
ON
In t
h
e
pr
o
pos
e
d
sol
u
t
i
on
we
have
d
one a
pi
l
o
t
t
r
i
a
l
i
n
a process m
a
nufac
t
u
ri
n
g
com
p
an
y
wareh
o
u
se
sh
ed
spread
ov
er
36
00
sq
u
a
re m
e
ters (6
0
m
e
ters X 60
m
e
ters). Th
e so
lu
tion
co
n
s
ists o
f
th
e
fo
l
l
o
w
i
ng
su
bsystem
s
.
•
Sub
j
ect m
a
tter
to
b
e
track
ed co
n
s
ists of larg
e size po
lyester flex
ib
le
pack
ag
i
n
g
ro
lls
woun
d
o
n
a thick
pape
r
hol
l
o
w
core i
n
a cy
l
i
n
dri
cal
f
o
rm
at
whi
c
h
have
di
ffe
rent
c
o
m
b
i
n
at
i
on
of
di
m
e
nsi
ons:
Le
n
g
t
h
of
wo
u
nd m
a
t
e
ri
al
300
0-
4
5
00
m
e
t
e
rs , di
am
et
er up t
o
0
.
5
-1 m
e
t
e
r , wi
dt
h ran
g
i
n
g f
r
o
m
1-2 m
e
t
e
rs
an
d
m
i
crons
o
f
eac
h l
a
y
e
r
of
fi
l
m
fr
om
8
m
m
-
12
m
m
.
•
BLE b
eacon
s
with
in
tern
al
b
a
tteries and
su
ppo
rting
c
o
nfi
g
urable ra
nges (tap
m
ode
i.e. no
distance
betwee
n detect
or a
nd beacon, near
proxim
ity
m
ode around 1
m
e
ter between detector and beac
on a
nd
far
p
r
ox
imity
m
o
d
e
(1
m
e
ter to
2
5
m
e
ters). The BLE
b
eacons co
m
e
with
co
in
cells
wh
ich
h
a
v
e
a sh
elf
life
o
v
e
r
2
years
o
f
con
tinu
o
u
s
runn
ing
.
Th
e b
eaco
n
s
h
a
v
e
in
bu
ilt sen
s
o
r
s fo
r tem
p
eratu
r
e and
p
r
essu
re
m
oni
t
o
ri
ng
w
h
i
c
h h
o
w
eve
r
d
o
not
f
o
rm
part
o
f
t
h
e
cu
rre
nt
sol
u
t
i
o
n
(Est
i
m
ot
e B
LE)
.
•
Androi
d based
Bluetooth rea
d
er
tablets
placed ac
ros
s
the
wa
rehouse to detect inc
o
m
i
ng BLE
bea
c
on
signals a
nd
re
gister prese
n
ce
or a
b
sence or track
any m
ovem
e
nts of the
BLE beacon.
These tablets
are
placed at intervals
of 20 m
e
ters eac
h. Eac
h
of the
sca
nne
rs
has a
fi
xed
location and is
assigned a
fixe
d
coo
r
di
nat
e
.
•
An
dr
oi
d ap
pl
i
c
at
i
on t
o
t
r
an
sm
i
t
i
n
fo
rm
ati
on t
o
a
dat
a
base
f
o
r f
u
rt
her a
n
al
y
s
i
s
an
d re
p
o
rt
i
n
g
o
n
t
h
e
beac
on
and their m
ovement trajectory
.
The sol
u
tion s
t
arts at the
ma
nufacturing start wh
e
r
e the subject m
a
tter is produce
d
and wound on
cores usi
n
g wi
nde
rs. The
BLE beac
on is pl
aced insi
de
the
core
and m
a
pped to eac
h
role identity in the IT
sy
st
em
s. From
here as a
nd
w
h
en t
h
e r
o
l
l
s
and c
o
r
r
es
po
n
d
i
ng
beac
ons a
r
e
m
oved
by
cr
anes o
r
f
o
r
k
l
i
f
t
s
t
h
e
nearest
B
l
uet
o
ot
h
fi
xe
d sca
n
ners
cat
ch t
h
e
si
gnal
.
The
st
r
e
ngt
h
of t
h
e si
gnal
a
v
era
g
e
d
ove
r a t
i
m
e i
n
terval
of
5
second
s is u
s
ed
to
triang
u
l
at
e th
e p
o
s
ition
o
f
th
e ro
ll an
d
its d
i
stan
ce from th
e scan
n
e
rs. Th
is way th
e ro
ll is
m
a
pped
o
n
a
m
a
p
of
t
h
e
ware
ho
use
l
a
y
out
a
n
d
i
t
s
ent
i
r
e
m
ovem
e
nt
i
s
t
r
ac
ked
.
Ou
r si
m
u
l
a
ti
on has ascert
a
i
n
ed
pre
v
i
o
us r
e
search st
u
d
y
fi
n
d
i
n
gs t
h
at
sm
art
p
h
ones
rel
i
a
bl
y
repo
rt
BLE m
easurements with R
SS
values
as l
o
w as
-
100
dB
m
[
2
]. Si
n
c
e th
e
w
a
r
e
ho
u
s
e is op
en
s
w
e
d
i
d no
t
obs
erve a
n
y loss of si
gnal
due to
walls.
Also the
placem
ent
of t
h
e
roll a
n
d the
Bluetoot
h reade
r
at a
height
of
2 m
e
t
e
rs fu
rt
he
r a
voi
ded
any
s
i
gnal
c
h
an
ge
d
u
e t
o
h
u
m
a
n o
b
st
r
u
ct
i
o
n
.
3.
R
E
SEARC
H M
ETHOD
Lim
ited scholarly articles e
x
ist on the BLE beacon t
echnology and its application. It has bee
n
speci
fi
ed
B
l
uet
oot
h as
a set
of
speci
fi
cat
i
o
ns
fo
r c
o
m
m
on sh
ort
ran
g
e a
p
pl
i
cat
i
ons
, t
r
a
d
i
t
i
onal
l
y
B
l
uet
o
o
t
h i
s
co
nn
ection
o
r
i
e
n
t
ed
and
p
e
ak tran
sm
it cu
rren
t
is
2
5
m
A which
is
h
i
gh
er
for sm
all co
in
cells [7
].
B
l
uet
oot
h l
o
w
ener
gy
i
s
a new radi
o pr
ot
oc
ol
st
ack and e
n
abl
e
s t
h
e I
o
T
wi
t
h
feat
ures
l
o
w l
a
t
e
ncy
and
fast
t
r
ansa
ct
i
ons. T
h
e ra
n
g
e va
ri
es fr
om
15
0 m
e
t
e
rs t
o
a
m
a
x curre
nt
dra
w
n of
1
5
m
A
wi
t
h
a sl
eep
cur
r
ent
of
1
u
A
.
B
L
E
i
s
n
o
t
m
eant
fo
r st
ream
i
ng
of
dat
a
an
d
has
a
m
a
x rat
e
of
1
M
B
p
s a
n
d
can
sen
d
sm
al
l
l
o
cat
i
onal
dat
a
.
Earlier st
u
d
y
h
a
s exp
l
ain
e
d
th
e accu
r
acy o
f
Blu
e
too
t
h
low en
erg
y
fo
r i
n
doo
r
p
o
s
ition
i
ng
ap
p
lication
s
. BLE for p
r
ox
imity d
e
tect
io
n
also
prov
id
es
p
o
ssib
ilities o
f
p
o
s
itio
n
i
n
g
. As
p
e
r prev
iou
s
stu
d
i
es
while indoor
positioni
ng
using
W
i
Fi can
give accuracy up
to a few m
e
ters but it is powe
r
hungry while BLE
worki
n
g in the
sam
e
band 2.4 GHz is efficient as a m
achine t
o
m
achi
n
e
pr
ot
oc
ol
f
o
r s
h
ort
m
e
ssages.
Earl
i
e
r
study also explained the infl
uence
of hum
a
n body obstruc
tion on the
accuracy of positioni
ng
but conc
ludes
with establishi
ng the possi
bility of
having BLE accuracy
m
o
re than
W
i
Fi
whe
n
it comes to positioning by
havi
ng m
a
ny beacons [2].
C
ont
e
x
t
u
al
i
n
f
o
rm
at
i
on i
s
of great
rel
e
vance
i
n
In
vent
ory
t
r
acki
n
g usi
ng
p
o
si
t
i
oni
ng a
ppl
i
cat
i
ons [
8
]
.
In
trad
ition
a
l in
teractiv
e com
p
u
tin
g
,
users h
a
v
e
a poor m
ech
an
ism
for pro
v
i
d
i
ng in
pu
t to
com
p
u
t
ers.
C
onse
q
uent
l
y
,
com
put
ers a
r
e n
o
t
cu
rre
nt
l
y
enabl
e
d t
o
t
a
ke
ful
l
a
dva
n
t
age o
f
t
h
e c
o
nt
ext
of t
h
e
h
u
m
a
n-
com
puter dialogue
. Using BL
E beacons and IoT we ca
n im
prove the com
puter’s access to context making it
pos
si
bl
e t
o
p
r
o
duce
u
s
ef
ul
co
m
put
at
i
onal
servi
ces.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
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08
I
J
ECE
Vo
l. 6
,
N
o
. 5
,
O
c
tob
e
r
20
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:
236
2
–
23
68
2
364
Figure
1. Archi
t
ectural Dia
g
ra
m
for the BL
E
–Beacon
Im
plementation
4.
R
E
SU
LTS AN
D ANA
LY
SIS
The a
b
ove m
i
x of technology pr
ot
otyping and a statistical surv
ey for the accepta
nc
e of
suc
h
a
co
n
c
ep
t h
a
s resu
lted
in
estab
lish
i
ng
techn
i
cal feasib
ility to
estab
lish
an
i
n
v
e
n
t
o
r
y m
a
n
a
ge
m
e
n
t
syste
m
u
s
ing
t
h
e Io
T t
echn
o
l
ogy
w
h
i
c
h
ha
s t
h
e pot
e
n
t
i
a
l
t
o
fu
rt
her
be
scal
ed u
p
f
o
r
ot
he
r t
y
pes o
f
asset
m
oni
t
o
ri
ng a
n
d
l
ogi
st
i
c
s
m
oni
t
o
ri
ng
, vehi
cl
e m
oni
t
o
ri
ng , t
h
eft
det
ect
i
o
n , t
r
ans
p
o
r
t
m
a
nagem
e
nt
and m
a
ny ot
hers
. Th
e
st
at
i
s
t
i
cal
surv
ey
has i
ndi
cat
ed t
h
at
t
h
e
pi
l
o
t
r
u
n
has
s
h
ow
n a
dec
r
ea
se i
n
i
nve
nt
o
r
y
t
r
acki
n
g a
n
d al
s
o
im
proved efficiency.
4.
1.
Sample
Siz
e
, Surve
y
meth
o
ds and
Scales
To u
n
d
erst
a
nd
t
h
e effi
ci
ency
of t
h
e
pr
o
p
o
s
e
d
sol
u
t
i
on
usi
n
g o
b
se
rvat
i
o
n
m
e
t
hods
on l
o
cat
i
ons o
f
t
h
e
co
m
p
an
y w
a
s tak
e
n
on
e wh
er
e th
e so
lu
tion w
a
s p
ilo
ted
fo
r
two
m
o
n
t
h
s
an
d
w
e
m
a
d
e
n
o
t
e o
f
ar
ound
25
obs
er
vat
i
o
n
s
b
e
fo
re an
d aft
e
r t
h
e sol
u
t
i
on
was i
m
pl
em
ent
e
d of t
w
o
fo
r
k
l
i
f
t
ope
rat
o
rs
wh
o we
re di
r
ect
l
y
i
n
v
o
l
v
e
d
i
n
i
n
v
e
nt
o
r
y
an
d l
o
gi
st
i
c
s
m
a
nagem
e
nt
an
d m
ovem
e
nt
at
a speci
fi
c shi
f
t
t
i
m
e.
One
was t
o
det
ect
t
h
e
m
a
t
e
ri
al
usi
ng
t
h
e cu
rre
nt
bar
c
ode
sca
nne
r
p
r
o
g
ram
or
usi
n
g m
a
nual
g
u
i
d
ance
whi
l
e
t
h
e
ot
he
r
was e
q
u
i
ppe
d
with
a tab
l
et pro
v
i
d
i
ng
th
e exact lo
catio
n b
a
sed
on
th
e BLE b
eacon
.
Th
e fo
llowing
step
s were
tak
e
n
•
Th
e d
e
p
e
n
d
a
n
t
v
a
r
i
ab
les ar
e
o
n
a con
tinu
ous scale n
a
m
e
ly
ti
me to
d
e
tect
a r
o
ll (
t
i
m
e
in
m
i
n
u
t
es r
o
unded
of
f) an
d acc
ur
acy
of a rol
l
b
e
i
ng i
d
e
n
t
i
f
i
e
d
by
t
h
e for
k
l
i
f
t
operat
o
r e.
g.
i
s
he pi
cks u
p
a wr
on
g r
o
l
l
on
atte
m
p
t 1st and the
right roll
in attem
p
t 2nd it will be c
o
nsidere
d
.5
accura
cy
but
i
f
he picks up
the corre
ct
roll in 1st atte
m
p
t its accurac
y
is 1.
•
Ind
e
p
e
nd
en
t v
a
riab
le is a m
a
tch
e
d p
a
i
r
th
at is it rem
a
in
s sa
me fo
r
bo
th
b
e
fore an
d after scen
ari
o
.
•
No sign
ifican
t
o
u
tliers are
ob
serv
ed
.
•
App
r
ox
im
ate
l
y
no
rm
al v
a
lu
es
are
v
e
rified
u
s
i
n
g Sh
ap
i
r
o-W
i
lk
test of
n
o
rmality.
The f
o
l
l
o
wi
n
g
hy
pot
heses
w
e
re d
r
aw
n u
p
(gi
v
en t
h
e c
o
llected sam
p
le r
e
sults at 95%
confide
n
ce
lev
e
ls).
HA0
: Nu
ll Hy
p
o
t
h
e
sis” Th
ere is no
d
i
ffer
e
n
ce in efficiency in tim
e
of
det
ectin
g
an
inven
t
or
y b
y
i
n
tr
od
u
c
i
ng
the BLE
beac
on technology.
HA1
: Altern
at
e Hypo
th
esis”
Th
ere is sign
ifi
cant diffe
r
ence
in efficiency i
n
t
i
m
e of det
e
c
t
i
ng a
n
i
n
ve
nt
o
r
y
by
introducing the
BLE
beacon t
echnology.
HB0
: The
r
e is
no
differe
n
ce i
n
the acc
uracy
of inform
ation
i.e. retriev
i
n
g
t
h
e correct
ro
ll
b
y
fork
lift op
erato
r
b
y
in
trod
u
c
i
n
g
th
e BLE
b
eacon
techno
log
y
.
HB1
: T
h
ere is
a signi
ficant
diffe
rence i
n
the
accuracy
of in
form
ation i.e.
retrieving
the c
o
rrect
ro
ll by forklift
o
p
e
rator
b
y
in
t
r
odu
cing
th
e BLE b
eacon
tech
no
log
y
.
4.
2.
Statistica
l Tests
We fi
rst fin
d
o
u
t if the s
u
rve
y
resp
on
se dat
a
is
no
rm
al
ly
di
st
ri
b
u
t
e
d
fo
r
t
h
e ab
o
v
e t
w
o
poi
nt
s bei
n
g
co
nsid
ered
. For th
e first
qu
est
i
o
n
we
draw th
e test o
f
norm
a
lity o
f
th
e
d
a
ta
as b
e
l
o
w.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Fea
s
ib
ility and Effica
cy o
f
BLE Bea
c
on
IoT
D
evices in In
ven
t
o
r
y Mana
g
e
m
e
n
t
.... (Ra
v
i
Ram
a
krishn
an)
2
365
Tabl
e 1.
T
e
st
s of
N
o
rm
al
i
t
y
Bef
o
re Af
ter
Kolm
ogor
ov-
S
m
irnova
Shapiro-Wilk
Statistic
df
Sig.
Statistic
df
Sig.
Average Ti
m
e
Det
ect
AFTER
.
208
25
.
007
.
844
25
.
001
BEFORE
.
254
25
.
000
.
769
25
.
000
Average Ac
curac
y
Percent
AFTER
.
506
25
.
000
.
445
25
.
000
BEFORE
.
506
25
.
000
.
445
25
.
000
a. Lilli
efors Signifi
cance Correct
ion
The Sha
p
iro-W
i
l
k
Test is
m
o
re appropri
ate for sm
all s
a
m
p
le sizes (< 50
sam
p
les).
Sin
ce th
e
sig
v
a
lu
e is 0
h
e
n
c
e it is b
e
lo
w 0.0
5
, th
e
d
a
ta sig
n
i
fican
tly d
e
viate fro
m
a n
o
r
mal d
i
strib
u
tion
and
th
ere is lo
t of
skew
ness
i
n
b
o
t
h g
r
ou
ps
o
f
da
t
a
. Si
nce
t
h
e
d
a
t
a
i
s
n
o
t
n
o
r
m
a
l
l
y
di
st
ri
but
e
d
we ca
n
not
g
o
fo
r
param
e
t
r
i
c
t
e
st
s
(m
ean
s) and
h
e
n
ce
we
will g
o
for th
e non
-p
ara
m
etric tests (med
ian
s
)
[9
].
We will u
s
e t
h
e
W
ilcoxo
n
sig
n
e
d
-
rank
test
for two
related
sam
p
les sin
ce o
u
r d
a
ta is
n
o
t
n
o
rm
a
l
d
i
stribu
tio
n and
th
e sam
p
le co
llectio
n
if of sa
m
e
set
of
pe
o
p
l
e
be
f
o
re a
n
d
aft
e
r t
h
e
c
h
an
g
e
[
10]
.
4.
3.
Testing Hy
po
thesis HA
We
run
t
h
e tests in
SPSS fo
r th
e
ob
serv
ation
s
co
llected
b
e
fore an
d after
th
e im
p
l
e
m
en
t
a
tio
n
o
f
the
BLE Beacon te
chnology. T
h
e result show
s an im
provem
ent in the
m
ean scor
e a
nd a dec
r
e
a
se i
f
t
h
e
m
a
xi
m
u
m
t
i
m
e
t
a
ken wi
t
h
a
dec
r
ease i
n
t
h
e st
a
nda
rd
d
e
vi
at
i
on
bet
w
e
e
n t
i
m
es.
Tabl
e 2.
Descrip
tiv
e Statistics
Tabl
e 3.
R
a
n
k
s
N
Mean Rank
Su
m
of Ranks
After
T
i
m
e
Detect I
nventor
y
–
Befor
e
T
i
m
e
Detect I
nventor
y
Negative Ranks
14
a
9.
46
132.
50
Positive Ranks
3
b
6.
83
20.
50
Ties
8
c
Total
25
a.
AfterT
im
eDetectI
nventor
y
< BeforeT
i
m
e
DetectI
nventor
y
b.
After
T
i
m
eDetectI
nventor
y
> BeforeT
i
m
e
DetectI
nventor
y
c.
AfterT
im
eDetectI
nventor
y
= BeforeT
i
m
e
DetectI
nventor
y
The a
b
ove
ra
nks indicate that
fourt
een case
s
saw a
drop i
n
timings
while t
h
ree c
a
ses sa
w an i
n
crease
i
n
t
i
m
i
ng a
n
d
e
i
ght
had
nea
r
a
b
o
u
t
t
h
e
sam
e
tim
i
ngs.
Tab
l
e
4
.
Test Statistics
After
T
i
m
e
Detect I
nventor
y
–
Befor
e
T
i
m
e
Detect I
nventor
y
Z
-
2
.
656
b
A
s
ym
p.
S
i
g
.
(
2
-
t
a
i
le
d
)
.
008
a.
W
ilcoxon Signed Ranks T
e
st
b. Based on positive ranks.
Th
e test statis
tics tab
l
e sh
ows th
at Z h
a
s a v
a
lue < -1.96 for p=
.05 hence we reject the null
h
ypo
th
esis HA0
.
4.
4.
Testing Hy
po
thesis HB
We
run t
h
e test
s in SPSS using the
data
gat
h
ered for accura
cy of retrie
vals
.
N
Mean
Std.
Deviation
Min
i
m
u
m
Max
i
m
u
m
Percentiles
25th
50th (
M
edian)
75
th
Befor
e
T
i
m
e
Detect I
nventor
y
25
4.
1400
3.
9330
9
1.
00
15.
00
1.
5000
2.
5000
5.
5000
After
T
i
m
e
Detect I
nventor
y
25
1.
8080
.
82205
1.
00
4.
00
1.
0000
2.
0000
2.
0000
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE
Vo
l. 6
,
N
o
. 5
,
O
c
tob
e
r
20
16
:
236
2
–
23
68
2
366
Tab
l
e 5
.
Descrip
tiv
e
Statistics
N
Mean
Std.
Deviation
Min
Max
Percentiles
25th
50th (
M
edian)
75th
Bef
o
re Accurac
y
P
e
rcent
I
nventor
y
25
88.
333
2
24.
295
95
25.
00
100.
00
100.
00
00
100.
00
00
100.
00
00
Af
ter Accur
acy Pe
rcent
I
nventor
y
25
100.
00
00
.
00000
100.
0
100.
00
100.
00
00
100.
00
00
100.
00
00
There is a c
h
a
nge i
n
the m
e
an sc
ores a
n
d
the
m
i
nim
u
m
score
s
be
fo
re
and a
f
t
e
r t
h
e
B
LE beac
o
n
in
trodu
ctio
n
.
Tabl
e 6.
R
a
n
k
s
N
Mean Rank
Su
m
of Ranks
Af
ter Accur
acy Pe
rcent Inventory –
Befor
e
Accur
a
cy
Per
cent I
nventor
y
Negative Ranks
0
a
.
00
.
00
Positive Ranks
5
b
3.
00
15.
00
Ties
20
c
Total
25
a. Af
terA
ccurac
y
P
e
rcentInventory
< Bef
o
reAccuracyPe
rcentInventory
b. Af
terAccur
acyP
e
rcentInventory
> Bef
o
reAccuracyPe
rcentInventory
c. Af
terA
ccurac
y
P
e
rcentInventory
= Bef
o
reAccuracyPe
rcentInventory
The
positive ranks showe
d
five cases
whe
r
e accura
cy has increase
d
a
nd tw
enty cases
whe
r
e it is
sim
ilar and t
h
e
r
e are
no ca
ses
whe
r
e acc
ura
c
y
has
decrea
se
d.
Tab
l
e
7
.
Test Statistics
a
After
Accuracy Per
cent I
nventor
y
–
Befor
e
Accur
a
cy
Per
cent I
nventor
y
Z
-
2
.
060
b
A
s
ym
p.
S
i
g
.
(
2
-
t
a
i
le
d
)
.
039
a.
W
ilcoxon Signed Ranks T
e
st
b.
Based on negative r
a
nks.
The z
val
u
e i
s
< -
1
.
9
6
at
p
< .
0
5
he
nce
we
re
ject
t
h
e
n
u
l
l
hy
pot
hesi
s a
n
d
c
oncl
ude
an
i
m
pr
o
v
em
ent
i
n
accuracy
of tra
c
king inve
ntory.
4.
5.
An
al
ysi
s
Th
e literatu
re
research
, cu
rren
t stu
d
y
and
data g
a
th
ered
sh
ows th
at th
ere co
u
l
d
h
a
v
e
been
th
ree ap
pro
ach
es
fo
r i
n
d
o
o
r
pos
i
t
i
oni
ng
nam
e
ly
W
i
-Fi
,
B
l
u
e
t
oot
h
o
r
GPS
.
Whi
l
e
GPS
i
n
i
n
d
o
o
r
posi
t
i
oni
n
g
has
n
o
t
bee
n
success
f
ul t
h
e
cost of
Wi-Fi s
e
ns
ors t
o
be fit
t
ed at each
i
nventory ite
m
seem
s
to be
very
high.
Also the
powe
r
co
nsu
m
p
tio
n
of
W
i
-Fi d
e
v
i
ces m
a
k
e
it n
ece
ssary fo
r an
y
W
i
-Fi clien
t
to b
e
con
s
tan
tly p
o
wered
b
y
aux
iliary
sou
r
ces
w
h
i
c
h
i
s
very
di
f
f
i
c
ul
t
i
n
case
of
m
ovi
n
g
i
n
ve
nt
o
r
y
i
t
e
m
s
.
4.
6.
C
o
mpa
r
i
n
g Approa
ches
Th
ou
g
h
t
h
e u
s
e of si
m
i
l
a
r
app
r
oach
of
B
LE devi
ces
seem
s t
o
be very
rar
e
due
t
o
t
h
e adve
n
t
of t
h
i
s
tech
no
log
y
on
ly in
th
e last t
w
o
year
s, th
ere h
a
s b
e
en
some stu
d
y
an
d
i
m
p
l
e
m
en
tatio
n
of
BLE fo
r
in
door
positioning
[11]. T
h
e following
positioni
ng m
e
thods
ha
ve bee
n
consi
d
ered a
n
d c
o
m
p
are
d
in our research:
Ang
l
e of
Arriv
a
l (AOA), C
e
ll Id
en
tity (CI), and
Tim
e
o
f
Arriv
a
l (TOA), Tim
e
Differen
ce o
f
Arriv
a
l
(TDOA), and
RX po
wer level. Our stud
y v
a
lid
ates th
e
earlier find
ing th
at all th
e a
b
ov
e param
e
te
rs in
com
b
ination help pinpoi
nt the accuracy levels of th
e B
LE beacon. Si
milarly the use of BLE bea
c
ons in
m
a
nagi
n
g
m
o
v
i
ng i
n
v
e
nt
ory
i
s
a ve
ry
l
e
ss
researc
h
e
d
t
o
p
i
c an
d
one
o
f
t
h
e m
o
st
i
n
n
o
v
at
i
v
e
uses
o
f
B
L
E
beacons
. T
h
is
is highlighted
by the
above
findings
on
i
n
c
r
eased product
i
vity. No
c
o
mparative
with
earlier
work is
p
o
s
sib
l
e du
e to th
e less exp
l
ored n
a
t
u
re
o
f
t
h
is so
lu
tio
n.
5.
CO
NCL
USI
O
N
The a
b
ove m
i
x of technology pr
ot
otyping and a statistical surv
ey for the accepta
nc
e of
suc
h
a
co
n
c
ep
t h
a
s resu
lted
in
estab
lish
i
ng
techn
i
cal feasib
ility to
estab
lish
an
i
n
v
e
n
t
o
r
y m
a
n
a
ge
m
e
n
t
syste
m
u
s
ing
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Fea
s
ib
ility and Effica
cy o
f
BLE Bea
c
on
IoT
D
evices in In
ven
t
o
r
y Mana
g
e
m
e
n
t
.... (Ra
v
i
Ram
a
krishn
an)
2
367
t
h
e Io
T t
echn
o
l
ogy
w
h
i
c
h
ha
s t
h
e pot
e
n
t
i
a
l
t
o
fu
rt
her
be
scal
ed u
p
f
o
r
ot
he
r t
y
pes o
f
asset
m
oni
t
o
ri
ng a
n
d
l
ogi
st
i
c
s
m
oni
t
o
ri
ng
, vehi
cl
e m
oni
t
o
ri
ng , t
h
eft
det
ect
i
o
n , t
r
ans
p
o
r
t
m
a
nagem
e
nt
and m
a
ny ot
hers
. Th
e
st
at
i
s
t
i
cal
surv
ey
has i
ndi
cat
ed t
h
at
t
h
e
pi
l
o
t
r
u
n
has
s
h
ow
n a
dec
r
ea
se i
n
i
nve
nt
o
r
y
t
r
acki
n
g a
n
d al
s
o
im
proved efficiency.
Th
e
IoT co
n
c
ep
t aim
s
at
mak
i
ng
th
e in
tern
et ev
en m
o
re i
m
m
e
rsiv
e and
p
e
rv
asiv
e
bu
t it is v
e
ry
diffic
u
lt to build a gene
ral architecture
for the Io
T
because of large num
b
er of devices, link layer
t
echn
o
l
o
gi
es a
n
d
ser
v
i
ces
[1
2]
. Si
m
i
l
a
r fram
e
wor
k
st
udi
e
s
ha
ve s
u
gge
st
ed t
h
e
s
u
ccess
i
n
l
o
cat
i
o
n
bas
e
d
I
o
T
sens
ors i
n
case
s
l
i
k
e el
derl
y
li
fe st
y
l
e
m
a
nagem
e
nt
a
nd care which can
gather data for
furthe
r analysis and
pre
d
i
c
t
i
o
n
,
t
h
e
sam
e
seem
s to
be u
s
ef
ul
i
n
t
h
e co
nt
ext
of
i
nve
nt
o
r
y
m
a
nagem
e
nt
as we
l
l
[13]
.
T
h
e s
e
rve
r
an
alyzes and
rep
o
rts th
e d
a
il
y activ
ities an
d
activ
ity p
a
ttern
s of inv
e
n
t
ory
m
o
v
e
m
e
n
t
an
d fo
rk
lift
o
p
e
ratio
n
s
.
In
ad
d
ition
,
u
n
ex
p
ected
em
erg
e
n
c
y situ
ations can
b
e
es
ti
mated
and
prev
en
ted
thro
ugh
an
alysis o
f
th
e activ
ity
in
fo
rm
atio
n
.
W
h
ile th
e tech
no
log
y
above co
n
s
i
d
ers on
ly a li
mited
r
o
le of
d
e
tectio
n
b
y
BLE sen
s
o
r
s, a
com
b
i
n
at
i
on o
f
l
o
w p
o
w
er C
P
U m
odel
s
l
i
k
e C
o
rt
ex-M
0 whi
c
h i
s
l
o
w po
we
r hu
n
g
ry
and
uses sm
aller gat
e
cou
n
t
s
wi
t
h
t
h
e B
LE ca
n
hel
p
a
voi
d
ro
u
n
d
t
r
i
p
dat
a
t
o
t
h
e
ser
v
ers
an
d
re
duce
l
o
a
d
of
c
o
m
put
i
ng
o
n
c
e
nt
ral
reso
u
r
ces
whi
l
e m
a
nagi
ng
pe
er t
o
pee
r
ob
je
ct
pai
r
i
n
g
fo
r
f
a
st
er res
u
l
t
s
[
1
4]
.
Sm
art Manufa
c
turing ca
n help com
p
an
ies
gather a
nd c
o
ns
olidate data at
each step
of their operations
t
o
get
m
eani
ngf
ul
i
n
si
g
h
t
s
f
o
r
pr
oact
i
v
e d
eci
si
on m
a
ki
ng [
15]
. Sm
art
M
a
nu
fact
u
r
i
n
g
com
b
i
n
ed wi
t
h
Sm
art
Inv
e
n
t
o
r
y m
a
n
a
g
e
m
e
n
t
can
help
redu
ce co
st
s and
i
n
crease
cu
sto
m
er serv
ice qu
ality
m
a
n
y
fo
l
d
s.
The
pa
per l
e
a
v
es sco
p
e
f
o
r
f
u
rt
her
resea
r
c
h
i
n
bot
h t
h
e
t
ech
nol
ogy
fr
o
n
t
a
n
d
t
h
e m
a
nage
m
e
nt
area
of
statistical
ly finding accepta
bility
levels of the technical
m
odel. The technology m
odel needs to
be
tested
co
nsid
er
i
n
g d
i
f
f
e
r
e
n
t
w
a
r
e
hou
se layou
ts and
o
b
s
tacles
or
radi
o interfere
n
ces to BLE
devices. T
h
e s
a
m
p
le
s
coul
d
have
b
e
en
dra
w
n f
r
o
m
a skewed
d
i
st
ri
but
i
o
n
du
e
t
o
c
o
n
v
e
n
i
e
nc
e nat
u
re
of
sa
m
p
li
ng
hen
ce
a m
o
re
elab
orate stud
y
of a larg
er sized
sam
p
le with d
i
stribu
tio
n
will b
e
adv
i
sed
as th
e
n
e
x
t
step
fo
r fu
rt
h
e
r
research
.
ACKNOWLE
DGE
M
ENTS
Th
e in
itial id
ea for th
e research
was g
e
n
e
rated
po
st a stud
y o
f
a
rou
tin
e p
r
o
b
l
em
o
f
Ind
i
an
p
r
o
cess
m
a
nufact
uri
n
g
com
p
ani
e
s of
phy
si
cal
st
ock n
o
t
m
a
t
c
hi
ng wi
t
h
t
h
e sy
st
em
st
ock bal
a
nces an
d d
u
e t
o
t
h
e
cont
i
n
u
o
u
s
nat
u
re o
f
m
ovem
e
nt
of m
a
t
e
ri
al to an
d fr
om
i
nvent
o
r
y
due t
o
r
o
u
n
d
t
h
e cl
ock
ope
rat
i
ons
ph
y
s
i
cal
co
un
ting
requ
i
r
ed
do
wn
tim
e
lead
ing
to pro
d
u
c
tiv
ity lo
sses.
W
e
are also
stron
g
l
y indeb
t
ed
t
o
th
e In
d
i
an
co
m
p
an
y who h
e
lp
ed
u
s
imp
l
em
en
t th
e tr
ial ru
n
and
al
so
tak
e
statistical
m
easu
r
emen
ts to
en
su
re it was
success
f
ul.
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BIOGRAP
HI
ES OF
AUTH
ORS
Ravi Ram
a
kris
h
n
an is
an M
B
A
from
F
acult
y of
Management Studies Delh
i Unive
r
si
ty
a
n
d ha
s
further don
e
a P
o
st Graduate Diploma from AIMA
in IT s
y
stems,
DOEACC A level
certified
and a Post Graduate Diploma
in Operations
Management fr
om IGNOU after Bach
elors in
S
c
ienc
e. He is
a
P
r
ince 2 cert
i
fi
ed profes
s
i
ona
l
,
Microsoft Certif
ied
Professional and a Oracle
Certified profes
sional and has 19
y
ears of glo
b
al exper
i
ence and is an award
winning Global
CIO with a stro
ng techn
i
cal and
managerial
background and has
done numerous global ro
llouts
of Enterpr
i
se Inf
o
rm
ation S
y
ste
m
s –ERP/CRM
/
B
I and M2M/Mobilit
y
and IoT
solutions which
have been widely
acknowledg
ed
and awarded in
differen
t
forums. He is currently
pursuing his
Doctorate in Inf
o
rmation Techn
o
log
y
Man
a
gem
e
nt
with fo
cus on IoT strategies
and technologies
and has pub
lishe
d papers
on IoT
in S
p
ringer
and
I
EEEXplor
e.
Dr. Loveleen G
a
ur is PhD in
Computer Appli
cat
ions, M.Phil
.
in Com
puter
Applications
and
M
.
C.A. A exp
e
rienc
e
of
elev
en
years
in
a
cade
m
ics
and is
a
c
ti
vel
y
invo
lved
i
n
res
ear
ch and
acad
em
ic ac
tivi
t
i
es
. S
h
e has
att
e
nded m
a
n
y
int
e
r
n
ation
a
l conf
ere
n
ces
and has
num
erous
national
and in
ternation
a
l publications to
her
cred
it
and h
a
s published
her
resear
ch p
a
pers
with r
e
fer
eed
journals
lis
t
e
d in Cabel
l
’s
director
y l
i
ke “
J
ournal of S
t
rat
e
gic E- Com
m
e
rce” of All
i
ed
Academ
ies
Inc.
,
U.S
.
A and “
R
eview of Bus
i
nes
s
Res
earch”
of Internat
iona
l
Academ
y
of
Business and Economics. She is serving on the
edito
rial bo
ard f
o
r the Internatio
nal Journal of
“
G
lobal Digital .
B
usiness Association
”
. She is al
so serving as paper reviewer for
man
y
Nation
a
l
and Intern
ation
a
l Journals. She
Rece
ived ‘B
es
t paper Award’
b
y
Sm
t. Shei
l
a
Dixit (Ch
i
ef
Minister, Delhi)
for her research paper publishe
d in “
M
anagem
ent Review”,
J
ournal of Delhi
Management A
ssociation (DM
A
).She Received ‘Distinguished Resear
ch Award’ for her
research p
a
per
published in Interna
tiona
l “
J
ournal of Strat
e
gic E-Com
m
e
rce”
, of Allie
d
Academies Inc., U.S.A. She is al
so the author of two books in th
e
area of Computer Applications
and
has contr
i
bu
ted chapters
for
various manag
e
ment books.
Prof Gurinder Singh is PhD in I
n
ternational
Business and done
his Ma
sters from IIFT.He has
m
o
re than 20
ye
ars
of exp
e
rien
c
e
in S
t
r
a
t
e
gi
c
Management, teaching, c
onsultan
c
y
and
r
e
sear
ch.
He has launched
new organizatio
ns
in India,
UK,
USA,
France,
Ge
rman
y
,
Australia, and African
countries, Singapore, Dubai
and
Taiw
an. H
e
has been honour
ed
w
ith pr
estigiou
s
National and
Interna
tiona
l “
L
ife Tim
e
Ach
i
e
v
em
ent Award
& Gold Medal”
,”Rashtr
i
y
a Sa
m
m
a
n Puruskar
Award”, “
S
iks
h
a Bharti Award b
y
IEDRA”, “
A
m
b
as
s
a
dor of pe
ace Award”
,
“
G
em
of India”,
and “Arch of Ex
cellence Award”. He is th
e
y
oun
ge
s
t
P
r
ofes
s
o
r in the ar
ea of M
a
n
a
gem
e
nt and
is
known in the f
i
eld of
academ
i
c
s as an
institu
tion bui
lder
, wr
iter
,
professor,
distinguished
acad
em
ici
a
n, t
r
ainer
and int
e
r
n
ation
a
l busin
es
s m
a
nager. He
has written n
u
m
e
rous books,
research
papers
and presented in the Harv
ard
Business School, Thunde
rbird B
u
siness School
USA, New
Yo
rk University
,
Purdue Universi
ty
, St Cloud
University
, Ess
e
x University
,
Universit
y
of
L
e
eds, Universit
y
o
f
East
London
and m
a
n
y
oth
e
r p
r
estigious Univ
ersities.
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