rethinking indoor localization solutions towards … · rethinking indoor localization solutions...

RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF

MOBILE LOCATION-BASED SERVICES

C. Guney

Istanbul Technical Univeristy, Department of Geomatic Engineering, 34469 Maslak Istanbul, Turkey - [email protected]

KEY WORDS: Localization, indoor positioning, indoor navigation, wireless sensor networks, SLAM, geolocation

ABSTRACT:

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users

travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First,

GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of

satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments,

existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in

wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging

needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the

development of context-aware applications that increases business efficiency. In this study, how can the current wireless location

sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in

GNSS-deprived areas.

1. MOTIVATION

The advances in localization based technologies and the

increasing importance of ubiquitous computing and context-

dependent information have led to a growing business interest in

location-based applications and services, such as locating or real-

time tracking of physical belongings inside buildings accurately

(Farid et al., 2013). Hence, the demand for indoor localization

services has become a key prerequisite in some markets.

Positioning systems are used for determining the position and

they can be categorized depending on the target environment as

either indoor, outdoor, or mixed type (Farid et al., 2013).

Global Navigation Satellite System (GNSS) is one of the most

successful positioning systems in outdoor environments.

However, GNSS satellite signals are obstructed by the presence

of obstacles in the line of sight (LoS) between the satellite and

the receiver in closed structures, which makes the GNSS

unsuitable for indoor location estimation and indoor navigation

due to the high attenuation of GNSS signals across enclosing

materials, resulting in non-negligible positioning errors.

Although, one manages receive the GNSS signal inside a closed

environment, like buildings, the signal strength is not sufficient

enough and remains a limiting factor for the performance of

GNSS within indoor environments.

In recent years, numerous different systems are developed for

indoor localization in many end-user applications by aiming to

provide the highest accuracy at least cost in order to eliminate

GNSS deficiencies in GNSS-deprived areas. These systems

differ in respect to accuracy, coverage, frequency of location

updates, and cost of installation and maintenance. Among these

solutions, radio-based localization systems surpass the others.

Radio Frequency (RF)-based technologies are commonly used in

location systems because of some advantages; for example, radio

waves can penetrate through obstacles like building walls and

human bodies easily. Due to this, the positioning system in RF

based has a larger coverage area and needs less hardware

comparing to other systems. (Farid et al., 2013)

In comparison with outdoor environments, sensing location

information within indoor environments requires a higher

precision and is a more challenging task in part (Alarifi et al.,

2016) because of severe multipath, low probability for

availability of LoS path, and specific site parameters such as floor

layout, moving objects, numerous reflecting surfaces, and

disperse signals (Liu et al., 2007).

Wireless location sensing systems are used in the numerous real-

world application areas since wireless information access is now

widely available. However, none of the existing technologies can

meet the challenging demands of indoor localization, tracking

targets of interest, mapping environment, navigation of the user

and location-aware applications since there is a great diversity in

the accuracy, range, availability and costs of indoor positioning

systems.

A central problem in location-aware computing is the

determination of physical location. Indoor localization typically

relies on measuring a collection of RF signals in conjunction with

positioning algorithms corresponding to different measuring

principle. Once the readers, such as Access Points (APs),

beacons, are positioned at fixed locations in a given environment,

the location of the receiver, such as nodes, tags, is determined by

analyzing the various aspects of the communications between

readers and mobile devices (receivers). There are many kinds of

indoor sensors and each sensor has its own advantages and

disadvantage. Table 1 briefly compares the current indoor

localization technologies. After determining the distance

between readers and mobile clients, the next step is to estimate

the position of the node within the given area. Several

computation methods are available and the choice of a method

depends on the application requirements. Table 2 compares the

positioning methods in general and the methods of determining

location of mobile devices are given as follow:

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W4, 2017 4th International GeoAdvances Workshop, 14–15 October 2017, Safranbolu, Karabuk, Turkey

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-4-W4-235-2017 | © Authors 2017. CC BY 4.0 License.

235

1. Model-based

1.1. Proximity Detection

1.2. Triangulation

1.2.1. Direction-based (Angulation)

1.2.1.1. Angle-based

1.2.1.1.1. Angle of Arrival (AoA)

1.2.2. Distance/Range-based (Lateration)

1.2.2.1. Time-based

1.2.2.1.1. Time of Arrival (ToA)

1.2.2.1.2. Time Difference of Arrival

(TDoA)

1.2.2.1.3. Round Trip Time (RTT)

1.2.2.2. Phase-based

1.2.2.3. Signal Attenuation-based

2. Scene Analysis

2.1. Fingerprinting

2.2. Map Matching

3. Dead Reckoning

At first glance, it is easy to observe that indoor localization and

mapping are very different from outdoor counterpart because of

the complex nature of indoor environments. For instance,

positioning systems used outdoor and indoor can vary,

positioning systems used in different buildings can vary,

beacons/access points used in different parts of the same building

can vary. Hence, it is needed to combine measurements from

different signal sources even in the same location and detect a

particular region of a building to trigger a particular positioning

service.

Indoor location applications have varying accuracy needs. The

type of venue determines the type of location applications and

hence the accuracy needs. The type of user (consumer, enterprise,

public) determines the type of location application and

deployment model even in the same venue.

Since the ecosystem of indoor positioning systems are very large

subject and indoor positioning opens up a market for many

players, the following research topics are worth considering:

• How to deploy sensors to improve the positioning accuracy

• How to extend the positioning range

• How to combine different wireless positioning systems, in

terms of positioning technologies and/or positioning

algorithms, with other technologies such as inertial,

ultrasound, vision sensors (as sensor fusion)

• How to integrate indoor and outdoor positioning system

• How to evaluate the proposed models by researchers and

companies

In the context of this study, while trying to answer the questions

above, how indoor location sensing technologies can be

integrated with smart cities, context-aware applications, IoT

applications that will be expressed. It is needed to emphasize that

what can be expected from indoor positioning in a highly

dynamic and diverse real-world environment? To handle all these

issues, indoor positioning and navigation applications need an

indoor localization framework and figure 1 displays the

components of proposed indoor localization framework in this

study.

2. POSTIONING ALGORITHMS

For indoor environments, it is difficult to find a LoS channel

between the transmitter and the receiver. Radio propagation in

such environments would suffer from multipath effect. The time

and angle of an arrival signal would be affected by the multipath

effect; thus, the accuracy of estimated location could be

decreased. An alternative approach is to estimate the distance of

the mobile unit from some set of measuring units, using the

attenuation of emitted signal strength. Signal attenuation-based

(or Received Signal Strength (RSS)-based) methods attempt to

calculate the signal path loss due to propagation. (Liu et al., 2007)

IEEE Standard 802.11 for Wireless Local Area Networks

(WLAN) defines Received Signal Strength Indication (RSSI) as

the determination of the propagation distance between the reader

and the unknown node by measuring the signal’s degree of

attenuation. Mobile device receives the RSSI from beacon nodes

on the WLAN. The RSSI is decreased exponentially as the

distance from AP increased, and this can be expressed by a path

loss model (Farid et al., 2013). Since the strength is inversely

correlated with the distance from the AP to the receiver, the

distance travelled by the packets can thus be estimated.

One solution is measuring the signal strength and estimating

position using trilateration, i.e., trilateration of RSS-based

ranges. To estimate the distance between two stations, first of all,

the RSSI is needed to be converted from the difference between

the transmitted signal strength and the received signal strength

into distance value through an appropriate indoor path loss

model, for instance free space path loss given in the following

formula (1 and 2). Thereafter, the distance estimation can be

achieved by generating circles around each non-collocated, non-

collinear transmitters and the receiver must be at the position

where the circles from each transmitter coincide or by using the

Extended Kalman Filter to compute the position of the mobile

device on the basis of distance estimates, and finally updates the

location estimation using least square estimation or residual

weighted least square algorithm.

PathLossdB = 10 log10(4π x Distance

Wavelength)2 (1)

PowerRECEIVER(PR)=PowerTRANSMITTER(PT)-PathLossdB

Distance (d) = 10

𝑃𝑇10

− 𝑃𝑅−10 𝑙𝑜𝑔10(4 𝜋

𝑊𝑎𝑣𝑒𝑙𝑒𝑛𝑔𝑡ℎ)

20 µ (2)

Due to severe multipath fading and shadowing present in the

indoor environment, RSS-based models do not always work. The

problem with such an approach is that the signal strength is a poor

indicator of a distance. If signal strength is low, either the mobile

device is far from the reader or there is an obstacle between the

reader and the node. Another solution to solve this problem is

fingerprinting matching. As displayed in figure 2, the location

method of fingerprinting first collects features (fingerprints) of a

scene and stores the set of signal strengths in a fingerprint

database. Then, if a device wants to determine its location, it can

compare the set of currently measurable signal strengths to the

fingerprint database (a priori location fingerprints), and the

closest match is assumed to be close to the current location.



236

Figure 1. Positioning and Indoor Localization Framework



237

Figure 2. Fingerprinting Matching Technique Workflow



238

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y ti

me

s m

ore

acc

ura

te

than

to

day

’s

po

siti

on

ing

tech

no

logi

es

(WiF

i, B

LE, R

FID

or

GP

S)

3.)

U

WB

lo

cati

on

ex

plo

its

the

ch

arac

teri

stic

s o

f ti

me

syn

chro

niz

atio

n

of

UW

B c

om

mu

nic

atio

n t

o a

chie

ve v

ery

hig

h in

do

or

loca

tio

n a

ccu

racy

4

.)

Sho

rt-p

uls

e w

avef

orm

s p

erm

it

an

accu

rate

det

erm

inat

ion

of

the

pre

cise

TO

A

5

.) h

igh

leve

l of

mu

ltip

ath

re

solu

tio

n

6.)

UW

B t

he

sign

al p

asse

s ea

sily

th

rou

gh

wal

ls, e

qu

ipm

ent

and

clo

thin

g

7

.) d

oes

no

t in

terf

ere

wit

h m

ost

of

the

exis

tin

g ra

dio

sys

tem

s

1.)

n

eed

s n

ew

infr

astr

uct

ure

2

.) U

WB

har

dw

are

is e

xpe

nsi

ve,

mak

ing

it c

ost

ly f

or

wid

e-sc

ale

use

3

.) m

etal

lic a

nd

liq

uid

mat

eria

ls

cau

se

UW

B

sign

al

inte

rfer

ence

4

.)

UW

B

tech

no

logy

m

ay

affe

ct

GP

S an

d a

ircr

aft

nav

igat

ion

rad

io

equ

ipm

ent

and

ca

n

also

ca

use

in

terf

ere

nce

to

th

e e

xist

ing

syst

ems

that

op

erat

es in

th

e u

ltra

w

ide

spec

tru

m

fe

w c

m

5

cm-2

0cm

AN

T/A

NT

+

(IEE

E 8

02

.15

.4)

1.)

ult

ra lo

w p

ow

er

2.)

sp

ecif

ical

ly

des

ign

ed

fo

r w

irel

ess

se

nso

r n

etw

ork

s

3.)

eas

y to

use

wit

h lo

w s

yste

m c

ost

AN

T+ is

a s

et o

f m

utu

ally

agr

eed

up

on

d

efin

itio

ns

for

wh

at

the

info

rmat

ion

sen

t o

ver

AN

T re

pre

sen

ts

"

Ne

ar F

ield

C

om

mu

ni-

cati

on

(N

FC)

(I

EEE

80

2.1

5.4

)

(t

ou

chab

le)

1.)

th

e h

igh

est

accu

racy

, bec

ause

wh

en

a u

ser

tou

ches

a t

ag, t

he

exac

t lo

cati

on

is

ret

riev

ed im

med

iate

ly

2

.) s

ecu

rity

an

d p

riva

cy

3

.) lo

w c

ost

4

.) N

FC t

ags

are

pas

sive

co

mp

on

en

ts

that

do

no

t co

nta

in a

ny

bat

teri

es h

ence

th

ese

do

no

t re

qu

ire

rep

lace

men

t w

ith

re

spec

t to

po

wer

fai

lure

1.)

no

t an

inh

eren

t ca

pab

ility

of

mo

st m

ob

ile d

evic

es

2.)

po

siti

on

is d

eter

min

ed

by

tou

chin

g th

e m

ob

ile d

evic

e to

th

e N

FC t

ags

3.)

co

mm

un

icat

ion

ove

r sh

ort

ra

nge

4.)

Rea

l tim

e p

osi

tio

nin

g ca

nn

ot

be

pro

vid

ed in

NFC

Inte

rnal

sy

stem

. Th

e D

R m

eth

od

is u

sed

b

y N

FC In

tern

al t

o c

alcu

late

an

ap

pro

xim

ate

loca

tio

n w

hen

th

e u

ser

is m

ovi

ng

bet

wee

n L

oca

tio

n

Tags

, an

d f

ixe

d p

osi

tio

nin

g w

ill b

e p

erfo

rmed

imm

ed

iate

ly a

fter

th

e

use

r to

uch

es t

o t

he

ne

xt t

ag.

1.)

th

e m

ajo

rity

of

smar

tph

on

e m

anu

fact

ure

rs h

ave

star

ted

to

ad

op

t N

FC

2.)

pay

men

t te

chn

olo

gy in

th

eir

pro

du

cts

as a

“d

e fa

cto

sta

nd

ard

”

If t

he

de

nsi

ty o

f p

lace

d t

ags

is lo

w w

ith

res

pec

t to

th

e u

ser

traf

fic,

qu

eues

may

o

ccu

r in

fro

nt

of

man

y ta

gs.

Ad

dit

ion

ally

, qu

eues

will

p

oss

ibly

occ

ur

in f

ron

t o

f Lo

cati

on

Tag

s lo

cate

d a

ce

rtai

n k

ey p

lace

s su

ch a

s el

evat

ors

. To

pre

ven

t q

ue

ues

in s

uch

pla

ces,

re

du

nd

ant

tags

can

be

dep

loye

d s

o t

hat

a v

isit

or

can

use

an

oth

er s

par

e ta

g if

an

oth

er u

ser

is a

lrea

dy

usi

ng

the

oth

er t

ag.

10

cm

or

less

(b

etw

een

0

and

a f

ew

cm)

Hig

hes

t A

ccu

racy



241

Ce

llula

r-b

ase

d

(GSM

/CD

MA

) 1

.) o

utd

oo

r an

d in

do

or

po

siti

on

ing

1.)

No

t ve

ry h

igh

acc

ura

cy

the

accu

racy

is h

igh

er in

den

sely

co

vere

d a

reas

(e.

g, u

rban

pla

ces)

an

d m

uch

low

er in

ru

ral

envi

ron

me

nts

2

.) a

ccu

racy

dep

end

s o

n t

he

cell

size

(ou

tdo

or)

5

0-2

00

m

(in

do

or)

<5

m

Fem

toce

ll

(4G

LTE

sm

all

cells

)

rich

sig

nal

str

engt

h in

form

atio

n

smal

l cel

ls h

ave

limit

ed r

ange

(c

om

par

able

to

th

e ty

pic

al W

iFi

AP

co

vera

ge)

they

co

uld

be

use

d, i

n

com

bin

atio

n w

ith

in

do

or

rad

io

tech

no

logi

es, a

s in

pu

ts t

o a

lo

caliz

atio

n s

yste

m

LPW

AN

Lo

Ra

/ Lo

RaW

AN

1.)

low

po

wer

, lo

ng

ran

ge w

irel

ess

com

mu

nic

atio

n t

ech

no

logy

2

.) lo

w c

ost

, lo

w p

ow

er In

tern

et o

f Th

ings

(Io

T) n

etw

ork

s

3.)

GP

S-fr

ee g

eolo

cati

on

tec

hn

olo

gy

(Net

wo

rk-b

ased

Geo

loca

tio

n)

4

.) w

ork

s b

oth

ou

tdo

ors

an

d in

do

ors

1. L

oR

a (L

oR

a A

llian

ce)

2

. NB

-Io

T (V

od

afo

ne

and

Hu

awei

)

3. S

igfo

x

(ou

tdo

or)

1

5km

, (i

nd

oo

r)

3km

10

m-

10

0m

ZigB

ee

1.)

It is

mai

nly

de

sign

ed f

or

app

licat

ion

s w

hic

h r

equ

ire

low

-po

wer

co

nsu

mp

tio

n

bu

t d

o n

ot

req

uir

e la

rge

dat

a th

rou

ghp

ut

2

.) L

ow

dat

a tr

ansm

issi

on

rat

e

3.)

No

des

are

mo

stly

asl

eep

op

en t

o in

terf

ere

nce

fro

m a

wid

e ra

nge

of

sign

al t

ypes

usi

ng

the

sam

e fr

equ

ency

wh

ich

can

d

isru

pt

rad

io c

om

mu

nic

atio

n

bec

ause

it o

per

ate

s in

th

e u

nlic

en

sed

ISM

ban

ds

20

-30

m

3m

-5m

Tab

le 1

: E

xis

tin

g i

nd

oo

r lo

cali

zati

on

tec

hn

olo

gie

s

Co

mp

aris

on

of

Ind

oo

r Lo

caliz

atio

n M

eth

od

s M

eth

od

s A

dva

nta

ges

Dis

adva

nta

ges

Pro

xim

ity

De

tect

ion

(C

on

ne

ctiv

ity

Bas

ed

Po

siti

on

ing)

re

lati

vely

sim

ple

to

imp

lem

ent

pro

vid

es s

ymb

olic

rel

ativ

e lo

cati

on

info

rmat

ion

An

gle

of

Arr

iva

l (A

oA

)

Dir

ect

ion

of

Arr

iva

l

1.)

no

tim

e sy

nch

ron

izat

ion

bet

wee

n m

eas

uri

ng

un

its

is

req

uir

ed

2.)

NLo

S p

rop

agat

ion

of

sign

als

1.)

aff

ecte

d b

y m

ult

ipat

h e

ffec

t, t

hu

s d

egra

de

the

accu

racy

2.)

req

uir

es a

dd

itio

nal

an

ten

nas

(d

irec

tio

nal

an

ten

nae

or

wit

h a

n a

rray

of

ante

nn

ae t

o m

easu

re t

he

angl

es w

hic

h

incr

ease

th

e co

st o

f th

e sy

stem

3

.) F

or

accu

rate

po

siti

on

ing,

th

e an

gle

mea

sure

men

ts n

eed

to

be

accu

rate

, bu

t n

ot

easy



242

Tim

e o

f A

rriv

al (

ToA

) 1

.) h

igh

acc

ura

te t

ech

niq

ue

use

d in

ind

oo

r en

viro

nm

ent

wh

ich

can

filt

er o

ut

mu

ltip

ath

eff

ects

2

.) t

he

on

e-w

ay p

rop

agat

ion

tim

e is

mea

sure

d

1.)

all

tran

smit

ters

an

d r

ecei

vers

in t

he

syst

em h

ave

to b

e p

reci

sely

syn

chro

niz

ed

2

.) a

ffec

ted

by

mu

ltip

ath

eff

ect,

th

us,

th

e ac

cura

cy o

f es

tim

ate

d lo

cati

on

co

uld

be

dec

reas

ed

3.)

Fo

r ti

me

del

ay m

easu

rem

ent,

an

ad

dit

ion

al s

erve

r w

ill b

e n

eed

ed w

hic

h w

ill in

crea

se t

he

cost

of

the

syst

em

4.)

incr

ease

d d

elay

can

als

o b

e p

rop

agat

ed b

y a

den

ser

envi

ron

me

nt,

in t

erm

s o

f m

ore

peo

ple

.

Tim

e D

iffe

ren

ce o

f A

rriv

al (

TDo

A)

1.)

wh

en im

ple

men

ted

co

rrec

tly

is a

n a

ccu

rate

met

ho

d

1.)

aff

ecte

d b

y m

ult

ipat

h e

ffec

t, t

hu

s, t

he

accu

racy

of

esti

mat

ed

loca

tio

n c

ou

ld b

e d

ecre

ased

2

.) le

ss u

sed

me

tho

d d

ue

to t

he

pre

cisi

on

re

qu

ired

wit

h

syn

chro

niz

atio

n o

f cl

ock

s o

f th

e d

evic

es w

ith

in t

he

syst

em

3

.) n

eed

s an

infr

astr

uct

ure

su

pp

ort

Ro

un

d T

rip

Tim

e (

RTT

)

Tim

e o

f Fl

igh

t (T

oF)

R

ou

nd

-Tri

p T

ime

of

Flig

ht

(RTo

F)

1.)

so

lves

th

e p

rob

lem

of

syn

chro

niz

atio

n t

o s

om

e ex

ten

t (a

cco

rdin

g to

To

A)

1.)

ran

ge m

easu

rem

en

ts t

o m

ult

iple

dev

ices

th

at n

eed

to

be

carr

ied

ou

t co

nse

cuti

vely

wh

ich

may

cau

se p

reca

rio

us

late

nci

es

for

app

licat

ion

s w

her

e d

evic

es m

ove

qu

ickl

y

2.)

aff

ecte

d b

y m

ult

ipat

h e

ffec

t, t

hu

s, t

he

accu

racy

of

esti

mat

ed

loca

tio

n c

ou

ld b

e d

ecre

ased

RSS

-bas

ed

Si

gnal

Att

en

uat

ion

-Bas

ed

1.)

On

e o

f th

e m

ost

co

mm

on

ly im

ple

me

nte

d t

ech

niq

ues

, d

ue

its

pra

ctic

alit

y, lo

w c

ost

an

d a

vaila

bili

ty

2.)

Use

s p

rop

erti

es

of

the

rece

ive

d s

ign

al, w

ith

RSS

I bei

ng

the

mo

st w

idel

y u

sed

sig

nal

-rel

ated

fea

ture

1.)

RSS

inve

rsel

y p

rop

ort

ion

al t

o s

qu

are

of

dis

tan

ce

2

.) n

eed

to

cal

cula

te t

he

sign

al p

ath

loss

du

e to

pro

pag

atio

n,

can

be

ener

gy in

effi

cie

nt

and

pat

h lo

ss m

od

els

vary

wid

ely

wit

h v

enu

e

3

.) T

his

met

ho

d c

an o

nly

be

po

ssib

le w

ith

rad

io s

ign

als

Re

ceiv

ed

Sig

nal

Ph

ase

Ph

ase

of

Arr

ival

N

eed

s an

Lo

S si

gnal

pat

h, o

ther

wis

e it

will

cau

se m

ore

er

rors

fo

r th

e in

do

or

envi

ron

me

nt

Am

big

uo

us

carr

ier

ph

ase

mea

sure

men

ts t

o o

verc

om

e

Fin

gerp

rin

tin

g 1

.) n

o t

ime

syn

chro

niz

atio

n n

ece

ssar

y b

etw

een

th

e st

atio

ns

2.)

use

of

map

s is

an

eff

icie

nt

alte

rnat

ive

to t

he

inst

alla

tio

n

of

add

itio

nal

har

dw

are

1.)

Lo

wer

leve

l of

accu

racy

2

.) F

inge

rpri

nti

ng

do

es n

ot

nee

d g

eom

etri

c su

rvey

s

3

.) S

ite

surv

eyin

g ti

me

con

sum

ing

and

lab

or

inte

nsi

ve

4

.) R

SS c

ou

ld b

e af

fect

ed

by

dif

frac

tio

n, r

efle

ctio

n, a

nd

sc

atte

rin

g in

th

e p

rop

agat

ion

ind

oo

r en

viro

nm

ents

5

.) R

SS c

han

ges

in v

aria

tio

ns

du

e to

tim

e

6.)

To

mai

nta

in t

he

po

siti

on

ing

accu

racy

, th

e ca

libra

tio

n

pro

cess

sh

ou

ld b

e p

eri

od

ical

ly r

ep

eate

d t

o a

rec

alcu

lati

on

of

the

pre

def

ine

d s

ign

al s

tren

gth

map

.

De

ad R

eck

on

ing

1.)

pro

vid

es v

ery

accu

rate

dir

ecti

on

al in

form

atio

n 2

.) v

ery

wid

ely

app

lied

1

.) t

he

inac

cura

cy o

f th

e p

roce

ss is

cu

mu

lati

ve

2.)

th

e d

evia

tio

n in

th

e p

osi

tio

n f

ix g

row

s w

ith

tim

e

Tab

le 2

: T

he

com

par

iso

n o

f sp

atia

l p

osi

tio

nin

g a

lgo

rith

ms



243

In order to estimate the location of a device, fingerprinting-based

positioning algorithms use pattern recognition techniques, which

are either deterministic or probabilistic. Some instances of

positioning algorithms are K-nearest-neighbor (KNN), weighted

K-nearest neighbors, Naïve Bayes, artificial neural networks,

Bayesian inference, Markov Chain Monte Carlo, support vector

machine, WASP, or their combinations. (Chapre et al., 2013)

Fingerprinting accuracy performance depends on the number of

base stations and the density of calibration points where the

fingerprints are taken (Farid et al., 2013).

As expressed in table 2, each location estimation algorithm has

unique advantages and disadvantages, hence, using more than

one type of positioning algorithms at the same time could get

better performance depends on the indoor environment and

application type. Hybrid methods seem promising as they are

more tolerant to external side effect of interference and reflection

(Alarifi et al., 2016). For instance, the combination of

fingerprinting matching and trilateration technique enhances the

accuracy of the user position in an indoor environment. For an

indoor positioning system, it is possible to use the signal phase

method together with ToA/TDoA or RSS method to fine-tune the

location positioning (Liu et al., 2007). Cook and his colleagues

suggest using the position estimate generated by the trilateration

algorithm as a characteristic of a location rather than an absolute

position. They hope to use this “expected” position to recognize

pre-known locations and are investigating the use of pattern

matching techniques, such as Fuzzy Logic, to allow the system

to associate live data with the stored results (Cook et al., 2005).

The pedestrian dead reckoning (motion data from the

smartphone) with position fixes (such as NFC tags) and the

Simultaneous Localization and Mapping (SLAM) algorithm

adapted for RF signal data (Mirowski et al., 2013). Google

announced a new service to offer detailed indoor location

positioning using its Tango 3D sensing computer vision

technology, called Vision Positioning Service (VPS). VPS makes

use of a Tango-enabled mapping system that uses augmented

reality on phones and tablets to help navigate indoor locations.

3. RELIABLE LOCATION IN REAL-WORLD

ENVIRONMENT

The majority of the existing research on indoor positioning

centers around the improvement of positioning accuracy.

However, currently there exists a limited understanding of how

positioning algorithms proposed in research behave in real world

settings, since many evaluations have been conducted on small

data sets over short periods of deployment only or in small

environments. This makes the related research strong but

somewhat narrow focus on improving positioning accuracy,

sometimes neglecting to evaluate other concerns which manifest

in real-world deployments. There is missing a unanimous

evaluation methodology appropriately reflecting criteria relevant

in real-world use scenarios and their diversity. This leads to

major challenges -and often disappointments- when

implementing positioning methods in the hope of the promised

accuracies. (Mathisen et al., 2016)

Radio signal propagation is highly dependent on a number of

factors, such as attenuation across materials (or human presence),

interference from other wireless devices, and various objects

reflect or round corners refract signals – known as multipath

signal propagation. This means that it is not easy to model the

radio propagation in the indoor environment and, thus, the

wireless indoor positioning technique is inherently inaccurate.

The prevalent limitation in using RSSI from a reader is the

deviation of attenuation in the signal, which renders signal

strength a measure hard to predict, as it changes chaotically and

significantly with only small changes in position and tends to

fluctuate over time (Mathisen et al., 2016). The RSS at a

particular location and instant of time is average of the signal

received through different paths (multipath effects) and it varies

due to various in-path interferences. Therefore, it becomes

crucial to determine the factors that affect RSS (Chapre et al.,

2013).

In the indoor positioning literature, accuracy and precision are

the most important concepts of the location. However, there is

serious concern on reliability of the RSSI in fingerprinting. So

far, many characteristic features of RSSI have not been

thoroughly investigated in the literature. Many researchers have

relied on the RSSI as signal information to determine objects

location while ignoring the characteristic of RSSI. For instance,

any physical barriers, such as materials or human presence, can

drastically affect the signal travel time, potentially decreasing the

accuracy and performance level of the systems.

The robustness of the indoor localization is related to the received

signal and its strength. The factors, which causes of RSS

variations, can be classified into the following areas (Chapre et

al., 2013):

• Hardware

o Orientation of the receiver, i.e. directionality of the

antenna (bidirectional, omni-directional etc.)

o Hardware quality: WLAN card, type of antenna

The RSSI fluctuations are quite significant even for a single

hardware. In the case of multiple devices from different vendors

are used, this variations will be amplified even further due to

different sampling rates and quantization bins.

• Temporal

o Need to analyze the time-variant nature of RSS in terms

of its variation, distribution, nature and number of

samples over time (time and duration of measurement).

Location signature captured at a location during the day time may

not be same during night time. This difference in location

signature may affect the localization accuracy, since it uses pre-

stored radio-map. Therefore, it is important to determine the

temporal variations of RSS.

• Interference

o RF interference due to nearby devices operating is

same or nearby radio channel radio channel (or other

radio devices)

o interference and noise originating from wired and

wireless networks within the structures

o Influenced by florescent lights, sunlight

• Human Presence

o Presence, orientation, movement of human body and

number of people around affects the RSSI

Human body that contains more than 50% water, absorbs the

radio signals.

• Environment

o Building types and materials

o obstacles (i.e., building geometry, walls, equipment),

which reduce the propagation capability of

electromagnetic waves

o Building environment: Sample data gathering at

different building complex parts (from a small office

environment, to a large dynamic building complex)

Several factors can contribute to the enhancement of positioning

performance. For example, the data is logged for four different

orientations of the mobile device (east, west, north and south)

(Chapre et al., 2013), the RSS values are to be averaged over a



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certain time interval up to several minutes at each fingerprint

location.

4. INDOOR MAPPING AND BUILDING RADIO MAPS

The main challenge to the techniques based on location

fingerprinting is sensitivity to environment changes such as

object moving into the building (e.g., people, furniture), and RSS

could be affected by diffraction, reflection, and scattering in the

propagation indoor environments, which result in changes in

signal propagation. To maintain the positioning accuracy, the

calibration process should be periodically repeated to a

recalculation of the predefined signal strength map. (Farid et al.,

2013) (Liu et al., 2007)

Site survey of the training phase is performed generally by

walking predefined routes at constant speed. The time and effort

to manually collect signal fingerprints are often considered too

costly in maintenance by stakeholders and vulnerable to

environmental dynamics.

The challenging methods for automatically generating and

updating signal coverage map within indoor environments are as

follow:

• Indoor robotics method: Autonomous RF signal and venue

mapping using indoor mobile robots

o Wheeled robots can be impractical in some buildings

or some locations in the building, such as staircases,

elevators.

o Unmanned Aerial Vehicles (UAVs) can be utilized

o Mobile robot requires an Inertial Measurement Unit

(IMU)

o Involves deployment costs

• Crowdsourcing method: RF signal mapping using crowd-

sourced pedestrian trajectories

o Users will collect time stamped RF signals (such as

WiFi, Bluetooth, small cell, UWB, NFC signals) based

on inertial data while walking freely through a building

at different speeds, taking care of their daily activities

o Basically using only motion sensors available on a

smartphone in the user’s pocket

o Wearable systems consisting of a color and depth

(RGBD) camera, laser rangefinder can be utilized

• A calibration-free location algorithm

5. INDOOR SPATIAL DATA MODELING

Because of the unpredictable and complex nature of real world

environments and depends on the application, accuracy

requirements can be vary, for instance, some applications can ask

this question any time “where is the user within a building?”

Other applications do not need to know the location of the user at

all times – it is only interested in when the user is in particular

locations (Cook et al., 2005). The application which not need

accurate position estimates, then, topological representation of

the study area (based on a priori knowledge on the environment)

may enhance the positioning/navigation/tracking performance.

The use of a topological model can dramatically reduce the time

required to train the localizer, while the resulting accuracy is still

sufficient for many location-aware applications (Liu et al., 2007).

A user specifies the name or description of the destination on the

graphical interface of the Location API, the location

application/service first calculates the optimal route to the

destination according to the user’s preferences, for instance using

elevators, stairs, and escalators to a different floor using shortest

path algorithm based on the topological representation of the

venue, then navigates the user to the destination point by giving

real-time instructions.

There are two key issues in the indoor positioning and navigation

modeling process: fast 2D/3D modeling and spatial model

validity and accuracy problems.

Simultaneous Localization and Mapping (SLAM) has recently

been extended to incorporate signal strength from WiFi in the so-

called WiFiSLAM algorithm (URL 1).Another approaches

WiFiGraphSLAM based on the extension to WiFi of the

GraphSLAM algorithm and RFSLAM (SignalSLAM) based on

the extension to RSS for WiFi and Bluetooth, and Reference

Signal Received Power (RSRP) for small cells of the

GraphSLAM algorithm (Mirowski et al., 2013).

Using a SLAM algorithm, like WiFiSLAM, SignalSLAM, floor

plans and RF signal maps can be generated at the same time.

Topologically-enhanced venue map encoded with thematic maps

of RF signal fingerprints can be used as an indoor spatial data

model for humans and robots.

6. INDOOR LOCALIZATION OF MOBILE ROBOTS

In recent years, there has been a rising demand in the use of

multiple Unmanned Aerial Vehicles (UAVs) in indoor

environments in various indoor applications such as exploration,

search and rescue, time-sensitive medication or supplies delivery,

manufacturing, indoor precision farming. For all these

applications an imperative need for UAV autonomy is the ability

to self-localization in the environment and to interaction with the

environment. The challenge is to use radio based localization

systems in GNSS-deprived environments for indoor localization

of UAVs.

If the different scale of control of UAVs within an enclosed

environment is categorized according to the required spatial

resolution, it can be broadly generalized into two: micro-control

and macro-control. In a closed environment, macro-control refers

to the control of UAVs from one 3D point to another (i.e., its

maneuverability across regions or chunks of space within the

entire environment). If so, micro-control focuses more on the

control around a point space of reference within a chunk of space

and its maneuverability in the space around that point. For the

macro-control a few meters of resolution is sufficient, on the

other hand, a spatial resolution of less than one meter error is

required for the micro-control. (Mui, 2014)

In order to improve the performance of indoor location and

navigation of UAVs on-board sensors data, such as IMU, vision

systems, laser rangefinders, can be fused data from radio-based

indoor localization techniques, in particular, for macro-control

considering its ease of integration across various environments.

However, only using radio based localization is insufficient for

micro-control of UAVs. The same approach is also valid for

mobile wheeled robots, for instance, A robot moving through a

doorway needs to be sure that it is going to make it through

without bumping and damaging the sides of the door. Hence,

meter-level location accuracy simply is not good enough for

moving through a doorway.

7. LOCATION AWARENESS & LOCATION-AWARE

APPLICATIONS

Advances in the smartphones and the availability of equipment

at affordable prices have increased in the popularity of Location

Based Services (LBSs) over recent years. LBS is also an



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important component for a smart city. Central to such service is

accurate positioning of users. While GNSS has been widely and

successfully used for outdoor services, deploying indoor

positioning technologies is still at its infancy. Indoor positioning

technologies can be divided into two parts, one on client-based

active localization and the other one on sensor-based passive

tracking. With the location-based data collected, one can then

conduct user analytics to under behaviors and offer timely and

novel services. The ability for accessing to a user’s location

indoors is increasingly important in the development of context-

aware applications that increase business efficiency (Cook et al.,

2005).

Positioning and context information is becoming more important.

In order to make user’s position meaningful to the user, context

information can be sent to the mobile user on the move at the

right time and the right location, such as new events, offers, alerts

in indoor environments are pushed to user. Location information

can be also used to improve the quality of users’ experience and

to add value to existing services offered by wireless providers

(Farid et al., 2013).

Innovative indoor location applications and services can be

developed on top of positioning, which allows combining indoor

positioning and navigation with the power of business analytics

and provides (Geo)Location Intelligence. Some of them can be

illustrated as follow:

• Zone-based Real-Time Location Services (RTLS) (Real time

location tracking service of users and devices on a map)

• Real-time navigation inside large buildings

• Orientation of a user (visitors, traveler, etc.) in a new indoor

environment and improvement of the quality of users’

experience in museums, malls, campus, office buildings and

transportation hubs (airport, train station, etc.)

• Automatic object location detection

• Event-based spatial monitoring service

• Tracking many user’s locations, property, and statistics of

traffic flow and detecting possible areas of higher revenue

stream

• Optimization of the buildings due to most searched/visited

destinations

• Producing temporal heat maps of the buildings which show

most taken walking routes

• Indoor discovery pushed to user, for instance, showing the

places on navigation path, personal recommendations can

be performed based on use schedule, loyalty program, etc.

• Generating a Geofence area restrictions around some entity

for push notification about the entrance and exit of an entity

of interest, for instance, push notification when child/visitor

moved zone

Internet of Things (IoT) is an emerging technology envisioned

for the development of smart environments and context aware

services. One of the enabling technologies is positioning, which

can be simply understood as the estimation problem of object and

user locations from radio signals. Especially, technologies like

IoT or Wireless Sensor Networks rely on communication of

devices over wireless networks. In these kinds of applications,

devices need measurement of relative state of other devices in the

network as well as environmental parameters. Relative distance

between the devices is one of the important parameters to be

measured. Machina Research estimates that 60% of IoT devices

will use geolocation data, one-third of which will critically

depend on it for their application.

Indoor positioning is not very useful without the availability of

indoor maps since indoor maps help the user conceptualize her

position. However, indoor maps are not standardized and each

provider has their own API, such as Google Maps API (Google

Indoor Maps), HERE Mobile SDK. Google Maps offers a floor

plan generator (URL 2).

8. CONCLUSION

Recently, indoor and outdoor location-aware applications has

been become increasingly widespread. Location information for

outdoor applications is obtained from satellite and cellular-based

technologies, such as GNSS, GSM. Although there are several

systems for indoor localization, there is not a consensus on them

like the outdoor. Besides there is a huge demand for low cost, low

power consumption, low effort, low computational complexity,

but efficient, more accurate and reliable indoor

positioning/navigation systems in a large and diverse real-world

environment.

As a result, this is study has proposed an indoor localization

framework with the components of sensor technology,

measurement techniques, positioning algorithms, location API,

location-based services to provide fine-grain spatial information

with low-latency and frequent updates.

The indoor localization framework allow users to locate

assets/sensors, track devices/user’s location, monitor sensors,

detect geofences, navigate people/robots and manage location

context.

Since the local positioning systems fail to work outdoors,

whereas the GNSS-based positioning systems do not work inside

buildings, the indoor localization framework supports seamless

indoor and outdoor navigation and wayfinding.

Different prerequisites may require for different indoor

applications/positioning problems. There is no ‘one size fits all’

solution since indoor location applications have varying accuracy

needs. Type of venue, type of users affect selecting the right

technology in a given use case. If the solution is leveraging

existing wireless network infrastructure (e.g. Wi-Fi, cell towers,

Zigbee) to locate objects, this solution avoids expensive and

time-consuming deployment of infrastructure. If the solution

requires new dedicated infrastructure deployment with a specific

wireless system, the density of the sensors can be adjusted to the

required positioning accuracy. Designing, implementing,

deploying and utilizing indoor positioning solutions in real world

environments and in different scenarios involve tradeoffs when

selecting a specific system in a given use case. A wide variety of

technology choices are possible for developing indoor

positioning with different tradeoffs. For example, the one

tradeoff is between complexity and accuracy, another one is that

as the coverage area increases, the complexity becomes an

important concern. In addition to those, there is a trade-off

between coverage area and accuracy. The last tradeoff but not the

end is between accuracy and cost.

The accuracy can be categorized into three different groups due

to used wireless technology. First one is sub one meter accuracy

(UWB), second is between 1 and 5 meters, in other word room-

level (WiFi, BLE), the last one is above 5 meter (GNSS).

According to the framework, GNSS and Long Range Wide Area

Network (LoRaWAN) can be used in smart city and smart grid

applications, UWB or BLE with NFC can be used in smart

buildings. For the smart cars, vehicle-to-vehicle and vehicle-to-



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infrastructure communication can be provided by means of IEEE

802.11p standard. The key difference between IEEE 802.11p and

cellular connectivity pipes to the car is that there is a direct

communication among 802.11p equipped devices, however,

cellular based services rely on the presence of the network.

Within indoor environments, available RF signals and motion

sensors (IMU) can be used thorough an adaptation of the

GraphSLAM technique.

REFERENCES

Alarifi, A., Al-Salman, A., Alsaleh, M., Alnafessah, A., Al-

Hadhrami, S., Al-Ammar M.A., Al-Khalifa, H., 2016, “Ultra

Wideband Indoor Positioning Technologies: Analysis and

Recent Advances”, Sensors (Basel) 2016 May; 16(5): 707.

Published online 2016 May 16. doi: 10.3390/s16050707

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“Received Signal Strength Indicator and Its Analysis in a Typical

WLAN System (Short Paper)”, 38th Annual IEEE Conference on

Local Computer Networks, 21-24 Oct. 2013

Cook, B., Buckberry, G., Scowcroft, I., Mitchell, J., Allen, T.,

2005, “Indoor Location Using Trilateration Characteristics” In:

Proceedings London Communications Symposium. ISBN

0953226352

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K., 2016, “A comparative analysis of Indoor WiFi Positioning at

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7 Oct. 2016

Mirowski, P., Ho, T.K., Yi, S., MacDonald, M., 2013,

“SignalSLAM: Simultaneous localization and mapping with

mixed WiFi, Bluetooth, LTE and magnetic signals”, 2013

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Zahid, F., Rosdiadee, N., and Mahamod, I., 2013, “Recent

Advances in Wireless Indoor Localization Techniques and

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URL 1, https://angel.co/wifislam

URL 2, https://maps.google.com/floorplans/find



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