Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
11,
No.
4,
August
2021,
pp.
3470
3482
ISSN:
2088-8708,
DOI:
10.11591/ijece.v11i4.pp3470-3482
r
3470
IoT
-based
air
quality
monitoring
systems
f
or
smart
cities:
A
systematic
mapping
study
Danny
M
´
unera
1
,
Diana
P
.
T
ob
´
on
V
.
2
,
J
ohnny
Aguirr
e
3
,
Natalia
Ga
viria
G
´
omez
4
1,4
F
aculty
of
Engineering,
Uni
v
ersidad
de
Antioquia,
Colombia
2,3
F
aculty
of
Engineering,
Uni
v
ersidad
de
Medell
´
ın,
Colombia
Article
Inf
o
Article
history:
Recei
v
ed
Jul
13,
2020
Re
vised
Dec
15,
2020
Accepted
Jan
13,
2021
K
eyw
ords:
Air
quality
monitoring
Internet
of
things
Smart
cities
Systematic
mapping
study
ABSTRA
CT
The
increased
le
v
el
of
air
pollution
in
big
cities
has
become
a
major
concern
for
se
v
eral
or
g
anizations
and
authorities
because
of
the
risk
it
represents
to
human
health.
In
this
conte
xt,
the
technology
has
become
a
v
ery
useful
tool
in
the
contamination
monitoring
and
the
possible
mitig
ation
of
its
impact.
P
articularly
,
there
are
dif
ferent
proposals
using
the
internet
of
things
(IoT)
paradigm
that
use
interconnected
sensors
in
order
to
measure
dif
ferent
pollutants.
In
this
paper
,
we
de
v
elop
a
systematic
mapping
study
defined
by
a
fi
v
e-step
methodology
to
identify
and
analyze
the
research
status
in
terms
of
IoT
-based
air
pollution
monitoring
systems
for
smart
c
ities.
The
study
includes
55
proposals,
some
of
which
ha
v
e
been
implemented
in
a
real
en
vironment.
W
e
analyze
and
compare
these
proposals
in
terms
of
dif
ferent
parameters
defined
in
the
mapping
and
highlight
some
challenges
for
air
quality
monitoring
systems
implementation
into
the
smart
city
conte
xt.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Dann
y
M
´
unera
F
aculty
of
Engineering-Uni
v
ersidad
de
Antioquia
Calle
70
No.
52
-
21,
of
fice
21-428,
Medell
´
ın,
Colombia
Email:
dann
y
.munera@udea.edu.co
1.
INTR
ODUCTION
In
a
recent
report
about
air
quality
,
the
W
orld
Health
Or
g
anization
(WHO)
w
arns
that
air
pollution
sources
represent
the
greatest
en
vironmental
risk
to
human
health,
e
videnced
in
more
than
o
v
er
6
million
premature
deaths
caused
by
e
xposure
to
contaminated
air
sources
[1].
Se
v
eral
studies
([2,
3])
ha
v
e
sho
wn
that
e
xposure
to
air
pollution
at
an
early
age
can
impair
lung
function,
and
increase
the
risk
of
respirat
ory
diseases
as
well
as
the
probability
of
premature
mortality
.
Pollution
problems
are
more
pre
v
alent
in
lar
ge
cities
with
high
population
density
due
to
the
f
act
that
the
sources
of
pollution
are
more
ab
undant
(i.e.,
a
greate
r
number
of
cars
and
industries
b
urning
fossil
fuels,
which
are
a
major
source
of
pollution)
and
their
population
is
often
constantly
e
xposed
to
high
le
v
els
of
air
pollution.
Using
technology
to
measure
and
manage
air
pollution
in
cities
is
k
e
y
in
the
path
to
mitig
ate
the
prob-
lem,
and
hence,
it
has
been
a
topic
of
study
for
se
v
eral
researchers
w
orldwide.
In
particular
,
the
internet
of
things
(IoT)
has
been
deemed
as
one
of
the
most
promisi
ng
technologies
to
achie
v
e
these
tasks.
IoT
refers
to
the
netw
ork
of
e
v
eryday
objects
(also
called
“things”)
connecting
intelligent
sensors
that
e
xchange
information
about
themselv
es
and
their
surroundings.
There
are
man
y
systems
based
on
IoT
technologies
for
the
manage-
ment
of
en
vironmental
pollution
in
cities
to
de
v
elop
smart
solutions,
which
consti
tute
a
mandatory
component
of
smart
cities.
IoT
has
emer
ged
as
a
solution
for
the
pollution
challenges
imposed
by
increasing
population.
The
J
ournal
homepage:
http://ijece
.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3471
main
goal
is
to
fight
ag
ainst
the
climate
change
and
g
as
emissions,
as
well
as
to
impro
v
e
ener
gy
ef
ficienc
y
in
cities
[4].
In
this
conte
xt,
se
v
eral
proposals
ha
v
e
come
to
light
tar
geting
dif
ferent
aspects
of
the
problem.
F
or
instance,
air
pollution
monitoring
in
a
smart
city
helps
to
impro
v
e
health
in
citizens
when
alerts
are
created
if
the
contamination
o
v
erpasses
a
specific
threshold
[5].
Smart
de
vices
constitute
a
k
e
y
component
of
the
IoT
technology
,
thus
allo
wing
the
connection
of
objects
o
v
er
e
xisting
netw
orks
[6].
The
y
aim
at
enhancing
city
operations
(such
as
transport,
healthcare,
education,
w
ater
,
communication,
and
ener
gy)
and
competiti
v
eness,
in
order
to
impro
v
e
the
quality
of
life
and
wellbeing
of
citizens
[4].
Smart
cities
use
IoT
technology
to
connect
a
city
in
an
intelligible
w
ay
with
minimal
human
interv
ention,
while
assuring
that
present
and
future
generations
ha
v
e
resource
a
v
ailability
,
where
cities’
resources
ha
v
e
to
be
optimally
managed
[6].
In
this
cont
e
xt
,
it
is
important
to
study
the
dif
ferent
solutions
that
ha
v
e
been
proposed
to
monitor
and
mitig
ate
the
pollution
problem
in
lar
ge
cities.
Hence,
we
ha
v
e
de
v
eloped
a
systematic
mapping
study
based
on
the
guidelines
proposed
by
[7].
The
main
contrib
ution
o
f
this
paper
is
to
pro
vide
an
o
v
ervie
w
of
IoT
-based
air
quality
monitoring
systems
for
smart
cities,
by
addressing
a
visual
summary
of
the
research
status
in
this
area
w
orldwide,
as
well
as
to
identify
its
technology
trends.
W
e
ha
v
e
defined
a
fi
v
e-step
m
ethodology
to
identify
and
analyze
the
studies
about
IoT
-based
air
pollution
monitoring
systems
for
smart
cities.
By
considering
the
rele
v
ance
of
IoT
technologies
to
measure
pollution,
this
w
ork
summarizes
recent
publications
in
this
area.
The
remainder
of
thi
s
paper
is
or
g
anized
as
follo
ws:
Section
2
describes
background
informat
ion
about
air
pollution
measurement
and
IoT
technology
.
Section
3
presents
the
methodology
of
the
systematic
mapping
study
,
follo
wed
by
the
presentation
of
the
results
and
discussion
in
section
4.
Section
5
identifies
some
research
challenges
for
air
-quality
monitoring
systems.
Finally
,
section
6
discusses
the
main
conclusions
of
our
systematic
mapping.
2.
B
A
CKGR
OUND
2.1.
IoT
technology
Internet
of
things
(IoT)
refers
to
the
collection
of
perv
asi
v
e
“things”
or
objects,
t
hat
can
interact
by
e
xchanging
information
with
neighbors
to
reach
common
goals
[8].
IoT
can
be
defined
as
a
netw
orking
infras-
tructure
that
connects
uniquely
identifiable
objects
to
the
Internet.
These
objects
are
usually
sensors/actuators
with
smart
capabilities.
Information
about
these
objects
can
be
collected,
whereas
their
st
ate
can
be
changed
from
an
ywhere,
an
ytime,
by
an
ything
[9].
IoT
is
considered
as
the
i
nternet
of
the
future,
with
the
potential
to
communicate
billions
of
smart
de
vices
without
human
interv
ention.
IoT
paradigm
has
increased
the
interest
in
monitoring
and
study
of
air
pollution
and
its
consequences
on
human
health,
thus
e
v
aluating
its
impact
in
life
forms
as
well
as
en
vironmental
damage.
Modern
air
quality
monitoring
systems
are
based
on
electronic
sensors,
microprocessor/micro-controller
chips
for
signal
acquisiti
on
and
processing.
These
systems
usually
acquire
data
to
be
processed
on
cloud
platforms
and
presented
through
mobile
or
web
applications.
In
order
to
e
xtract
useful
information
from
the
ra
w
sensors’
measurements,
big
data
strate
gies
are
normally
utilized
in
the
analysis.
IoT
has
been
widely
used
in
dif
ferent
domains
such
as
transportation,
agriculture,
healthcare,
ener
gy
production
and
distrib
ution,
en
vironmental
or
infrastructure
monitoring,
to
name
a
fe
w
.
Ev
en
though
there
are
dif
ferent
approaches
to
the
architecture
of
IoT
systems,
the
most
commonly
used
is
a
four
-layer
architec-
ture
[10]
as
sho
wn
in
Figure
1.
The
lo
wer
layer
,
also
called
the
perception
layer
,
is
responsible
for
g
athering
the
data
through
a
set
of
sensors.
These
sensors
are
part
of
the
end-point
de
vices,
which
are
usually
based
on
embedded
systems
that
ha
v
e
pre-processing
capabilities
and
communicate
with
the
upper
layers.
The
netw
ork
layer
interconnects
the
de
vices,
and
hence
the
selection
of
the
communication
protocols
in
this
layer
will
ha
v
e
an
impact
on
the
o
v
erall
performance
of
the
system.
The
upper
layers
(namely
service
and
application
layers)
pro
vide
further
processing
of
the
data
and
end-user
interf
aces.
The
choice
of
technologies,
platforms
and
pro-
tocols
for
each
of
the
layers
highly
depends
on
the
application,
and
will
also
determine
the
cost,
comple
xity
and
performance
of
the
IoT
deplo
yment.
Figure
1.
IoT
multi-layer
architecture
IoT
-based
air
quality
monitoring
systems
for
smart
cities:
A
systematic
mapping
study
(D.
M
´
uner
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
3472
r
ISSN:
2088-8708
2.2.
Air
contaminants
Air
pollution
consists
in
the
introduction
of
particles
or
substances
in
the
air
that
can
cause
damage
to
human
health
and
other
life
forms
.
It
also
damages
ecological
systems
by
de
grading
atmospheric
conditions
[11].
There
are
a
lot
of
compounds
that
can
be
considered
as
pollution
in
the
air
.
Ho
we
v
er
,
according
to
[12],
the
main
air
contaminants
are:
i)
particulate
matter
(PM),
which
are
micro-metrical
solid
particles
in
the
air
due
to
human
acti
vity
(studies
ha
v
e
sho
wn
that
most
dangerous
PM
is
between
1
micrometers
(
m)
and
2.5
m
[12]),
ii)
carbon
monoxide
is
a
sub-product
of
incomplete
comb
ustion
and
is
v
ery
dangerous
for
li
ving
beings,
iii)
carbon
dioxide
is
a
product
of
fossil
fuels
comb
ustion,
i
v)
nitrogen
oxides
are
products
of
fossil
comb
ustion,
which
generate
acid
rain
that
causes
serious
ne
g
ati
v
e
ecological
damages,
v)
methane
is
a
“greenhouse”
g
as,
mainly
produced
in
the
decomposition
of
or
g
anic
matter
,
vi)
ozone
in
the
high
atmosphere
is
a
protection
ag
ainst
the
most
ener
getic
solar
radiation,
b
ut
in
the
lo
w
atmosphere,
it
is
considered
as
pollution
because
it
af
fects
human
health.
3.
METHOD
A
systematic
mapping
st
ud
y
is
a
well-or
g
anized
method
to
summarize
the
state-of-the-art
around
a
particular
research
area.
It
in
v
olv
es
a
classification
and
counting
process
for
the
contrib
utions
in
the
literature
in
order
to
analyze
the
topics
that
ha
v
e
been
co
v
ered
and
those
that
remain
as
open
issues
[7,
13].
In
this
study
,
we
ha
v
e
de
v
eloped
a
systematic
mapping
study
based
on
the
guidelines
proposed
by
[7].
Fi
v
e
steps
were
defined
to
identify
and
analyze
studies
related
to
IoT
-based
air
pollution
monitoring
systems
for
smart
cities.
The
first
step
is
to
plan
the
r
esear
c
h
questions
,
where
a
set
of
questions
are
defined
to
be
solv
ed
according
to
the
main
topic
of
research.
The
second
step
is
to
define
the
sear
c
h
str
ate
gy
,
which
specifies
the
used
methodology
to
g
ather
information.
In
this
step,
the
“search
query”
to
use
on
the
academic
databases
is
defined.
The
third
step
is
to
define
the
selection
criteria
,
which
consists
in
defining
a
set
of
rules
to
include/e
xclude
the
found
studies
on
the
search
process.
The
fourth
step
is
to
synthesize
data
,
where
we
deeply
analyze
the
included
articles
in
the
study
,
by
e
xtracting
data
to
answer
the
research
questions.
Finally
,
the
fifth
step
is
to
analyze
the
r
esults
by
presenting
figures
and
making
conclusions
about
the
obtained
information.
In
order
to
update
the
results
of
the
study
and
maintain
current
information,
the
search
process
and
the
corresponding
analysis
should
be
repeat
ed
with
an
annual
periodicity
.
A
detailed
description
of
the
defined
steps
is
present
ed
in
the
follo
wing
sections.
3.1.
Resear
ch
questions
The
primary
goal
of
this
study
is
to
understand
and
classify
the
related
research
in
IoT
-based
air
quality
monitoring
systems.
W
e
aim
to
surv
e
y
research
literature
re
g
arding
softw
are
and
hardw
are
architectures
in
air
quality
solutions,
the
most
commonly
used
en
vironmental
v
ariables
and
sensors,
communication
technologies,
data
processing
analysis,
and
interaction
with
other
applications
(e.g.,
smart
cities).
T
able
1
presents
the
defined
research
questions
for
this
study
.
T
o
simplify
the
analysis
of
this
kind
of
systems,
we
link
ed
each
research
question
to
the
corresponding
IoT
layer
in
the
general
IoT
architecture
mentioned
in
section
2.1.
T
able
1.
Research
questions
of
the
systematic
mapping
re
vie
w
RQ1:
(Application
layer)
What
are
the
monitored
en
vironmental
v
ariables?
RQ2:
(Application
layer)
Where
has
the
solution
been
deplo
yed?
RQ3:
(Service
Layer)
What
are
the
main
pro
vided
services
to
the
applications?
RQ4:
(Netw
ork
Layer)
Which
communication
protocols
and
netw
ork
infrastructure
are
used
to
transfer
messages?
RQ5:
(Perception
Layer)
If
the
objects
communicate
with
each
other
,
what
type
of
netw
ork
is
used?
RQ6:
(Perception
Layer)
What
are
the
hardw
are
platforms
used
to
implement
the
“things”
in
the
IoT
-based
air
quality
monitoring
solutions?
RQ7:
(Perception
Layer)
What
type
of
access
netw
orks
are
used
to
transfer
the
data
to
the
upper
layer?
RQ8:
(Conte
xt
Information)
Ho
w
is
the
en
vironmental
data
processed?
RQ9:
(Conte
xt
Information)
Ho
w
do
IoT
A
Q
systems
interact
with
other
applications
into
smart
cities?
3.2.
Sear
ch
strategy
Based
on
the
research
questions,
we
identified
the
follo
wing
four
main
k
e
yw
ords:
internet
of
things
,
air
poll
ution
,
monitoring
,
and
smart
cities
.
Then,
we
b
uilt
the
search
query
strings,
considering
the
v
ariations
of
these
terms,
i.e.,
singular/plural
forms
and
synon
yms.
T
able
2
presents
the
resulting
search
queries,
where
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
4,
August
2021
:
3470
–
3482
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3473
we
highlight
the
main
k
e
yw
ords
(in
bold)
and
connect
with
their
corresponding
v
ariations
by
using
the
OR
logical
operator
.
W
e
used
the
AND
logical
operator
to
connect
the
resulting
k
e
yw
ord
groups.
This
search
w
as
implemented
in
Ja
n
ua
ry
2020
in
fi
v
e
of
the
most
important
electronic
databases
such
as
IEEEXplore,
A
CM
Digital
Library
,
Science
Direct,
SCOPUS,
and
ISI
W
eb
of
Science.
Those
databases
were
selected
based
on
the
e
xperience
reported
by
Chen
et
al.
[14].
W
e
conducted
the
search
considering
the
concordance
of
search
query
in
the
title,
abstract
and
k
e
yw
ords
of
the
published
studies.
In
total,
152
studies
were
obtained
from
this
search.
T
able
2.
Search
query
used
in
the
systematic
mapping
study
K
e
yw
ords
group
1:
internet
of
things,
iot
K
e
yw
ords
group
2:
air
pollution,
air
quality
,
en
vironmental
v
ariables
K
e
yw
ords
group
3:
monitoring,
sensing,
detecting
K
e
yw
ords
group
4:
smart
cities,
smart
city
3.3.
Inclusion/exclusion
criteria
W
e
defined
selection
criteria
to
e
v
aluate
the
rele
v
ance
of
the
retrie
v
ed
papers
on
the
pre
vious
st
age.
The
idea
is
to
e
xclude
those
papers
that
comply
to
the
search
query
b
ut
do
not
contrib
ute
to
answer
the
research
questions.
In
the
same
w
ay
,
we
e
xpect
to
include
the
rele
v
ant
st
udies
to
answer
them.
At
this
stage,
each
re
vie
wer
inspects
the
title,
abstract,
introduction
and
conclusions
of
the
paper
,
thus
aiming
to
identify
if
the
paper
must
be
included
or
e
xcluded.
The
follo
wing
incl
usion
criterion
(IC1)
w
as
defined:
the
study
includes
publications
with
a
clear
de-
scription
of
IoT
-based
air
quality
monitoring
systems
and
their
application
in
smart
citie
s.
In
the
same
manner
,
the
follo
wing
e
xclusion
criteria
were
defined:
(EC1:)
The
study
e
xcludes
papers
that
are
not
solutions
for
IoT
-based
air
quality
monitoring
systems
such
as
those
oriented
to
signal
processing
instead
of
sensing;
(EC2:)
The
study
e
xcludes
papers
that
are
not
written
in
proper
English;
(EC3:)
The
study
e
xcludes
papers
that
are
duplicated
or
are
a
pre
vious
v
ersion
of
a
more
complete
study
about
the
same
research;
(EC4:)
The
study
e
x-
cludes
papers
such
as
systematic
re
vie
ws,
mapping
studies,
editorials,
pref
aces,
article
summaries,
intervie
ws,
ne
ws,
correspondence,
discussions,
comments,
readers’
letters,
tutorial
summaries,
panel
discussions,
poster
sessions,
abstracts,
and
Po
werPoint
presentations;
(EC5:)
The
study
e
xcludes
papers
that
do
not
specify
a
direct
relationship
between
the
IoT
system
and
smart
cities
applications.
Each
re
vie
wer
de
v
eloped
an
indi
vidual
selection
process
to
filter
studies
based
on
the
abo
v
e
sel
ection
criteria,
thus
using
the
web
application
Rayyan
[15].
Afterw
ards,
a
meeting
w
as
conducted
to
compare
the
results
and
solv
e
e
xisting
conflicts,
thus
resulting
in
a
consensual
preliminary
selection.
Thereby
,
a
total
of
55
papers
were
selected.
3.4.
Data
extraction
and
mapping
study
pr
ocess
During
this
step,
we
di
vided
the
selected
papers
in
four
subsets.
W
e
assigned
each
subset
to
a
r
e
vie
wer
in
order
to
e
xtract
the
information
for
answering
the
research
questions
related
to
this
w
ork.
T
o
reduce
the
bias,
we
used
a
technique
reported
in
[16],
where
each
re
vie
wer
assessed
all
e
xtracti
on
s
made
by
another
re
vie
wer
in
the
group.
W
e
carried
out
an
agreement
meeting
out
to
compare
results
and
solv
e
conflicts.
4.
RESUL
TS
AND
DISCUSSION
In
this
section,
we
present
the
results
of
the
systematic
mapping
study
,
considering
the
research
ques-
tions
and
the
e
xtracted
data.
First,
we
present
an
o
v
ervie
w
of
the
selected
studies.
Then,
we
answer
the
research
questions
re
g
arding
each
layer
of
the
reference
IoT
architecture,
namely
perception
layer
,
netw
ork
layer
,
service
layer
,
and
application
layer
.
Finally
,
we
present
an
analysis
of
the
research
questions
about
conte
xt
information.
4.1.
Ov
er
view
of
selected
studies
The
academic
int
erest
in
IoT
-based
air
quality
monitoring
systems
is
recent,
as
sho
wn
in
Figure
2.
T
w
o
w
orks
were
published
in
2014,
and
from
that
date,
the
number
of
publications
has
gro
wn
to
eighteen
papers
in
2019.
Figure
3
pro
vides
the
distrib
ution
of
the
articles
between
the
considered
v
enues
in
this
study
(i.e.,
journal
and
peer
-re
vie
wed
conferences).
Most
of
the
articles
were
published
in
conferences
(54.6%),
from
which
eight
articles
(14.5%)
were
published
in
conferences
inde
x
ed
by
SCImago.
Around
13%
of
pub
l
ications
correspond
to
Q3
and
other
j
ou
r
nals.
It
is
w
orth
to
mention
that
around
33%
of
the
articles
were
published
in
high
quality
journals
(Q1-Q2).
IoT
-based
air
quality
monitoring
systems
for
smart
cities:
A
systematic
mapping
study
(D.
M
´
uner
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
3474
r
ISSN:
2088-8708
2
7
9
19
18
0
4
8
12
16
20
2014
2015
2016
2017
2018
2019
Number of papers
Figure
2.
Histogram
of
paper
publications
per
year
54.6%
18.2%
14.5%
9.1%
3.6%
Conf
erence
Q2 Jour
nal
Q1 Jour
nal
Q3 Jour
nal
Other Jour
nal
Figure
3.
Pie
chart
of
v
enue
types
According
to
the
classification
proposed
by
[7],
we
analyzed
the
type
of
research
for
each
w
ork.
Most
of
the
articles
(27)
fit
on
the
e
valuation
r
esear
c
h
cate
gory
,
11
articles
were
classified
as
solution
pr
oposals
,
14
as
validation
r
esear
c
h
,
and
only
3
as
e
xperience
r
eports
.
These
results
are
consistent
with
the
type
of
de
v
eloped
search,
since
we
e
xcluded
papers
that
are
not
solutions
for
IoT
-based
air
quality
monitoring
systems
(see
e
xclusion
criteria
in
section
3.3).
4.2.
A
pplication
and
ser
vice
lay
ers
Re
g
arding
the
application
layer
,
we
posed
tw
o
research
questions
(RQ1
and
RQ2),
related
to
the
monitored
en
vironmental
v
ariables
and
the
location
of
the
solution
deplo
yment.
Figure
4
sho
ws
the
used
v
ariables
in
the
analyzed
monitoring
systems
in
this
study
.
P
articulate
matter
(PM)
(2.5
m
and
10
m),
nitrogen
oxides
(NOx),
carbon
monoxide
(CO),
ozone
(O3),
and
carbon
dioxide
(CO2)
are
the
most
commonly
measured
pollutants.
T
emperature
and
humi
dity
are
often
related
to
sensors
calibration,
which
could
be
the
reason
for
their
frequent
usage.
Ammonia,
h
ydrocarbons,
solar
radiation,
and
v
olatile
or
g
anic
compounds
are
useful
in
other
specific
applications.
Usually
,
the
decision
of
which
v
ariables
to
include
depends
on
the
particular
conditions
of
the
city
to
be
monitored,
i.e.,
the
main
air
pollutants
present
in
the
city
area.
The
service
layer
,
which
is
responsible
of
pro
viding
services
to
“things”
or
applications,
is
between
the
netw
ork
and
the
appli
cation
layer
.
The
implementation
of
the
service
layer
us
ually
in
v
olv
es
cloud
de
v
elopment.
The
systems
analyzed
in
this
study
describe
mainly
the
tw
o
first
layers
of
the
IoT
architecture
(i.e.,
perception
and
netw
ork
layers),
which
resulted
in
a
poor
description
of
the
service
layer
,
thus
not
pro
viding
enough
implementation
details
in
this
layer
.
Most
of
the
processing
is
carried
out
on
cloud
platforms,
where
fog/edge
computing
is
still
little
e
xplored
in
this
conte
xt.
W
e
did
not
find
a
detailed
description
of
it
s
implementation,
thus
complicati
ng
to
answer
the
research
question
RQ3
stated
as,
what
are
the
main
pro
vided
services
to
the
applications?
9
5
25
16
4
27
4
6
19
11
5
6
14
24
5
32
0
5
10
15
20
25
30
35
T
emper
ature
Humidity
CO
PM 2.5
NOx
CO2
PM 10
O3
Air pressure
Noise
PM 1
Ammonia
other
SO2
Dust
Methane
Number of systems
CO corresponds to carbon mono
xide
, PM to par
ticulate matter
, NOx to nitrogen o
xides
, CO2 to carbon dio
xide
, and SO2 to sulfur dio
xide
.
Figure
4.
Histogram
of
en
vironmental
measured
v
ariables
by
the
re
vie
wed
systems
As
can
be
seen
from
Figure
5,
an
increasing
number
of
IoT
-based
air
quality
monitoring
systems
for
smart
cities
ha
v
e
been
deplo
yed
around
the
w
orld.
The
figure
presents
systems’
location,
highlighting
in
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
4,
August
2021
:
3470
–
3482
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3475
red
the
countries
where
the
solutions
ha
v
e
been
deplo
yed.
F
or
this
study
,
solutions
were
implemented
on
22
dif
ferent
countries
where
India,
USA
and
Italy
,
are
the
countries
with
the
most
reported
solutions
(11,
6
and
5,
respecti
v
ely).
4.3.
Netw
ork
lay
er
In
this
section,
we
discuss
the
RQ4
related
to
the
netw
ork
laye
r
.
Figure
6
sho
ws
the
frequenc
y
of
the
used
netw
ork
infrastructure
in
the
screened
papers.
The
most
used
type
of
netw
ork
w
as
pri
v
ate
netw
orks
(twenty-three
papers)
and
the
least
used
w
as
v
ehicular
to
infrastructure
(V2I)
netw
orks
(tw
o
papers).
Fifteen
papers
used
cellular
netw
orks,
six
papers
public
infrastructure,
and
nine
do
not
specify
the
used
netw
ork.
Pri
v
ate
netw
orks
are
highly
used
since
the
e
xperiments
were
performed
by
using
a
sensor
netw
ork
with
dif
ferent
access
technologies.
Re
g
arding
netw
ork
protocols,
there
w
as
not
enough
details
in
the
screened
papers
to
depict
on
this
study
.
It
is
w
orth
to
note
that
the
inno
v
ati
v
e
V2I
infrastructure
has
been
little
used
for
air
monitoring,
although
it
is
an
interesting
option
in
a
smart
city
en
vironment.
Classical
communication
technologies
such
as
W
i-Fi
and
cellular
are
the
most
used
for
communicating
sensed
data.
Ho
we
v
er
,
the
relati
v
ely
ne
w
LoRaW
AN
emer
ges
as
an
alternati
v
e
wit
h
long-range
and
lo
w-cost
implementation
in
an
urban
en
vironment.
Most
of
the
papers
reported
prototype
w
orks,
where
around
55%
of
the
s
ystems
use
hardw
are
de
v
elopment
kits
(e.g.,
raspberry
Pi
or
Arduino).
Figure
5.
Countries
where
air
-quality
monitoring
solutions
ha
v
e
been
implemented
15
23
6
2
0
6
12
18
24
Pr
iv
ate
Cellular
Pub
lic
V2I
Number of systems
Figure
6.
Histogram
for
netw
ork
infrastructure
(nine
studies
pro
vide
no
information)
4.4.
P
er
ception
lay
er
In
the
perception
layer
,
we
analyzed
the
hardw
are
nat
ure
of
the
prototypes
implementing
air
quali
ty
monitoring
systems
(RQ6).
Figure
7
presents
a
b
ubble
plot
that
sho
ws
the
information
related
to
the
hardw
are
platform.
W
e
g
athered
information
related
to
the
portability
of
the
air
quality
monitoring
systems,
defining
us-
ing
three
cate
gories:
fixed
,
mobile
,
and
mobile+fixed
.
W
e
also
analyzed
the
type
of
hardw
are
used
to
implement
the
monitoring
system,
by
defining
tw
o
types
of
cate
gories:
DK-based
for
implemented
prototypes
with
hard-
w
are
de
v
elopment
kits
(e.g.,
arduino,
raspberry
,
and
similar),
and
specific
purpose
for
de
v
eloped
prototypes
that
use
a
hardw
are
platform
specifically
designed
for
the
proposed
application.
6
19
4
1
1
3
1
5
4
7
4
Fix
ed
Mobile
Mobile + fix
ed
N/A
DK−based
Specific−pur
pose
N/A
T
ype of Hardw
are
P
or
tability
Figure
7.
Bubble
plot
for
hardw
are
platforms
IoT
-based
air
quality
monitoring
systems
for
smart
cities:
A
systematic
mapping
study
(D.
M
´
uner
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
3476
r
ISSN:
2088-8708
As
sho
wn
in
Figure
7,
i
n
30
of
the
55
analyzed
w
orks,
researchers
used
DK-based
prototypes,
while
in
15
w
orks
a
specific-purpose
prototype
w
as
used.
This
may
suggest
researchers
report
results
using
early-stage
prototypes
instead
of
commercial
or
pre-commercial
systems.
Using
mobile
technologies
is
a
good
strate
gy
to
co
v
er
e
xtensi
v
e
areas
with
fe
w
acquisition
nodes,
which
may
e
xplain
that
mobile
systems
are
used
frequently
for
air
quality
monitoring.
Only
11
out
of
55
systems
use
the
traditional
fix
ed
systems
to
g
ather
air
quality
information.
Re
g
arding
the
RQ7,
“What
type
of
access
netw
orks
are
used
to
transfer
the
data
to
the
upper
layer?”,
Figure
8
sho
ws
the
most
frequently
used
technologies
in
the
implementation
of
the
monitoring
systems.
W
i-Fi
technology
and
cellular
netw
orks
are
the
most
commonly
emplo
yed,
together
with
LoRaW
AN.
Other
technolo-
gies
such
as
Bluetooth
and
ZigBee
are
less
used.
This
may
be
e
xplained
by
their
short-range,
which
mak
es
dif
ficult
to
implement
air
quality
monitoring
applications
due
to
the
y
are
usually
deplo
yed
in
big
urban
areas.
3
13
1
8
8
1
1
19
3
0
4
8
12
16
20
WiFi
Cellular
LoRaW
AN
N/A
Bluetooth
ZigBee
D
ASH7
NB
SigF
o
x
Number of systems
Figure
8.
Histogram
of
access
technologies
in
the
perception
layer
(notice
some
studies
implement
se
v
eral
access
technologies)
4.5.
Context
inf
ormation
About
the
conte
xt
information,
we
are
interested
in
ho
w
the
en
vironmental
data
is
processed
(RQ8),
in
terms
of
time
(i.e.,
real-time
or
of
f-line)
and
place
(i.e.,
node,
cloud,
and
edge).
Figure
9
suggests
that
real-time
systems
are
preferred
in
air
-quality
monitoring
applications.
Latest
updated
v
alues
of
en
vironmental
v
ariables
are
useful
to
tak
e
timely
decisions.
It
is
e
v
en
more
important
if
we
tak
e
into
account
the
interaction
of
these
platforms
with
users
e.g.,
through
mobile
applications.
The
place
where
processing
is
carried
out
for
these
systems
usually
depends
on
se
v
eral
f
actors,
such
as
processors
computing
capacity
,
ener
gy
source,
amount
of
data,
sensors
conditioning,
among
others.
Figure
10
presents
the
processing
location
of
the
analyzed
systems
in
this
study
.
Cloud
computing
is
remarkably
preferred
o
v
er
the
other
options,
such
as
node
and
edge.
Node
processing
is
challenging
because
the
processing
units
(i.e.,
typically
micro-controllers)
ha
v
e
v
ery
limited
processing
capacity
.
Edge
computing
is
a
relati
v
ely
ne
w
strate
gy
,
b
ut
it
has
been
little
e
xplored
on
this
kind
of
applications.
It
is
necessary
to
define
a
model
for
determining
which
components
are
processed
locally
and
which
ones
should
be
sent
to
the
cloud.
38
11
4
Real−time
Of
f−line
Figure
9.
V
enn
diagram
for
data
processing
timing
(tw
o
studies
pro
vide
no
information)
29
11
4
1
1
1
Cloud
Node
Edge
Figure
10.
V
enn
diagram
for
data
processing
location
(eight
studies
pro
vide
no
information)
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
4,
August
2021
:
3470
–
3482
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3477
Finally
,
we
analyzed
the
interaction
between
ai
r
quality
monitori
ng
systems
and
smart-city
appli
ca-
tions
(RQ9)
as
depicted
in
Figure
11.
Most
of
the
systems
interact
with
a
smart-city
w
arning
platform,
which
usually
sends
w
arning
messages
to
interested
or
vulnerable
users.
Other
systems
interact
with
traf
fic
monitor
-
ing
in
order
to
reduce
pollutant
emissions
in
automoti
v
e.
Other
interactions
are
presented
b
ut
little
e
xplored
(e.g.,
health
systems,
open-data
platforms,
ne
ws
services,
and
polic
y-making
platforms).
Since
most
of
the
w
orks
mention
an
interaction
with
smart-city
applications,
28
systems
present
a
proposal,
and
only
7
w
orks
really
implement
or
simulate
this
interaction
see
Figure
12.
3
1
3
1
1
6
19
0
4
8
12
16
20
W
ar
ning
T
r
affic
Health System
Open−data
Ne
ws
P
olicy−making
Pub
lic tr
anspor
t
Number of Systems
Figure
11.
Histogram
for
interaction
between
air
-quality
monitoring
systems
and
smart-city
applications
(twenty-one
studies
pro
vide
no
information)
6
28
1
0
10
20
30
Proposed
Implemented
Sim
ulated
Number of Systems
Figure
12.
Histogram
for
the
type
of
smart
city
interaction
(twenty
studies
pro
vide
no
information)
4.6.
Citations
The
follo
wing
w
orks
were
included
in
this
systematic
mapping
study:
[17–71].
5.
CHALLENGES
Throughout
the
article,
we
ha
v
e
presented
the
main
conclusions
related
to
the
dif
ferent
topics
i
ncluded
in
the
analysis.
W
e
ha
v
e,
ho
we
v
er
,
found
some
challenges
that
we
discuss
in
the
follo
wing
paragraphs.
Massive
depl
oyments
of
IoT
Air
Quality
monitoring
systems:
According
to
[72],
the
t
op
fi
v
e
countries
with
the
w
orst
air
quality
inde
x
are
Me
xico,
China,
India,
USA
and
Mongolia.
Most
of
the
IoT
-based
air
quality
systems
were
found
to
be
located
in
highly
polluted
countries
(e.g.,
India
and
USA),
as
an
action
to
control
and
pre
v
ent
pollution.
Ho
we
v
er
,
there
are
still
some
highly
polluted
countries
that
ha
v
e
not
deplo
yed
an
important
number
of
IoT
-based
air
quality
monitoring
systems.
W
e
e
xpect
these
systems
to
be
massi
v
ely
deplo
yed
in
the
near
future,
and
hence,
more
research
is
needed
to
decrease
the
cos
ts
for
granting
lo
w-income
countries
to
ha
v
e
an
easier
access
to
this
technology
.
IoT
Pr
otocols
in
air
-quality
monitoring
systems:
Ne
w
communication
technologies
ha
v
e
emer
ged
for
IoT
applications,
pro
viding
ne
w
interesting
features
(e.g.,
machine-to-machine
interaction).
Ef
ficient
net-
w
ork
protocols
such
as
MQTT
or
CoAP
,
and
ne
w
communication
technologies,
namely
LoRaW
AN,
Sigfox
or
Narro
w-Band,
are
no
w
a
v
ailable.
Ho
we
v
er
,
according
to
our
study
,
v
ery
fe
w
deplo
yments
mak
e
use
of
the
IoT
-
specific
technologies.
The
implementation
of
these
technologies
can
enhance
the
performance
of
the
IoT
-based
air
quality
monitoring
systems
while
decreasing
implementation
costs.
Mobile
networks
and
V2I
tec
hnolo
gy:
W
e
ha
v
e
identified
the
gro
wing
utilization
of
mobile
nodes
for
air
-quality
monitoring
in
smart
cities.
Ho
we
v
er
,
technologies
lik
e
W
i-Fi
or
Cellular
netw
orks
can
ha
v
e
IoT
-based
air
quality
monitoring
systems
for
smart
cities:
A
systematic
mapping
study
(D.
M
´
uner
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
3478
r
ISSN:
2088-8708
a
limited
utilization
due
to
co
v
erage
problems
or
deplo
yment
costs
in
mobile
nodes.
Emer
ging
technologies
lik
e
V2I
netw
orks,
or
more
widely
V2X
(i.e.,
V
ehicular
-to-Ev
erything)
systems,
can
impro
v
e
the
air
-quality
monitoring
systems
by
pro
viding
lar
ge-co
v
erage
infrastructure
and
ef
ficient
communications
for
acquiring
data
in
a
city
conte
xt
[73].
Ne
w
v
ehicular
applications
can
emer
ge
with
the
adv
ent
of
air
-quality
monitoring
to
v
ehicular
netw
orks.
Smart
cities
inter
action:
A
smart
city
can
be
achie
v
ed
through
the
inte
gration
of
information
com-
munication
technology
(ICT)
into
cities
to
de
v
elop
smart
solutions.
One
of
the
main
goals
of
this
study
w
as
to
identify
the
appl
ication
of
IoT
-based
air
quality
monitoring
systems
in
the
conte
xt
of
a
smart-city
.
Ev
en
though
most
of
the
w
orks
propose
some
interaction
with
smart
cities,
only
a
fe
w
of
them
actually
implement
it.
The
de
v
elopment
of
a
smart
city
goes
be
yond
the
implementation
of
a
specific
application
and
demands
the
support
of
the
local
go
v
ernment
through
the
definit
ion
of
policies
that
aim
at
the
inte
gration
of
multiple
systems.
Hence,
it
is
necessary
for
cities
to
pro
vide
plat
forms
to
access
non-critical
city
applications
(i.e.,
that
do
not
af
fect
citizens’
security),
where
researchers
can
de
v
elop
and
test
ne
w
smart
city
solutions.
6.
CONCLUSION
In
this
systematic
mapping
study
,
we
presented
results
from
mapping
55
IoT
-based
air
quality
mon-
itoring
systems
for
smart
cities.
Nine
research
questions
were
defined
to
characterize
these
systems
using
a
four
-tier
architecture
of
an
IoT
system.
W
e
g
athered
the
main
information
of
the
systems,
in
order
to
identify
technology
trends,
which
can
be
useful
in
the
design
of
ne
w
systems.
W
e
ha
v
e
identified
the
main
v
ariables
being
sensed
and
hardw
are
types
utilized,
as
well
as
other
rele
v
ant
information
of
each
layer
of
the
IoT
sys-
tem.
Re
g
arding
to
the
netw
ork
protocols,
only
fe
w
w
orks
discuss
their
details,
and
hence,
we
did
not
find
an
y
trends
in
this
topic.
W
e
highlighted
some
research
challenges,
by
analyzing
the
obtained
results,
and
identified
interesting
research
directions
for
future
w
ork.
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