Inter
national
J
our
nal
of
Inf
ormatics
and
Communication
T
echnology
(IJ-ICT)
V
ol.
15,
No.
2,
June
2026,
pp.
909
∼
924
ISSN:
2252-8776,
DOI:
10.11591/ijict.v15i2.pp909-924
❒
909
Semantic
inter
operability
in
IoT
f
or
Industry
4.0:
Re
view
,
taxonomy
,
challenges,
and
futur
e
r
esear
ch
De
v
amekalai
Nagasundaram
1
,
Erum
Ashraf
2
,
Selv
akumar
Manickam
1
,
Shams
Ul
Arfeen
Laghari
3
,
Shankar
Karuppayah
1
1
Cybersecurity
Research
Centre,
Uni
v
ersiti
Sains
Malaysia
(USM),
Gelugor
,
Malaysia
2
Department
of
Computer
Science,
Bahria
Uni
v
ersity
Islamabad,
Islamabad,
P
akistan
3
F
aculty
of
Engineering
Design
Information
and
Communication
(EDICT),
Bahrain
Polytechnic,
Isa
T
o
wn,
Bahrain
Article
Inf
o
Article
history:
Recei
v
ed
Aug
28,
2025
Re
vised
Apr
1,
2026
Accepted
Apr
15,
2026
K
eyw
ords:
Industry
4.0
Internet
of
things
Ontology
Re
vie
w
Semantic
interoperability
ABSTRA
CT
Semantic
interoperability
is
a
critical
enabl
er
for
achie
ving
the
Industry
4.0
vi-
sion,
ensuring
that
heterogeneous
IoT
de
vices,
systems,
and
applications
can
e
x-
change
and
interpret
data
consistently
.
Despite
its
importance,
achie
ving
seman-
tic
interoperability
continues
to
pose
signicant
challenges
due
to
the
di
v
ersity
of
data
formats,
standards,
and
ontol
ogies
used
across
industrial
IoT
en
viron-
ments.
This
paper
presents
a
comprehensi
v
e
re
vie
w
and
taxonomy
of
semantic
interoperability
within
Industry
4.0,
analyzing
e
xisting
frame
w
orks,
protocols,
and
ontological
models.
W
e
classify
current
approaches
based
on
their
architec-
tural
layers,
semantic
technologies,
and
application
domains.
Additionally
,
this
study
identies
the
limitations
of
pre
v
ailing
solutions,
highlights
open
research
challenges,
and
proposes
future
directions
for
enhancing
semantic
interoperabil-
ity
in
industrial
IoT
systems.
The
insights
pro
vided
aim
to
support
researchers
and
practitioners
in
de
v
eloping
scalable,
secure,
and
semantically
aligned
IoT
ecosystems
for
Industry
4.0.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Selv
akumar
Manickam
Cybersecurity
Research
Centre,
Uni
v
ersiti
Sains
Malaysia
(USM)
Gelugor
,
Pulau
Pinang,
Malaysia
Email:
selv
a@usm.my
1.
INTR
ODUCTION
The
IoT
refers
to
a
rapidly
e
xpanding
ecosystem
of
interconnected
ph
ysical
objects
ranging
from
v
ehicles
and
home
appliances
to
industrial
machinery
which
are
embedded
with
electronics,
s
o
f
tw
are,
sensors,
and
netw
ork
connecti
vity
,
enabling
autonomous
data
collection,
e
xchange,
and
processing
[1].
This
inte
gration
of
the
ph
ysical
and
digital
w
orlds
has
dri
v
en
transformati
v
e
changes
across
sectors
such
as
manuf
acturing,
healthcare,
transportation,
and
smart
cities,
impro
ving
operational
ef
cienc
y
,
decision-making
accurac
y
,
and
economic
producti
vity
.
The
emer
gence
of
Industry
4.0
has
further
accelerated
the
deplo
yment
of
IoT
,
by
mer
ging
c
yber
-
ph
ysical
systems
(CPS)
with
intelligent
industrial
i
nfrastructures
to
enable
autonomous,
real-time,
and
adapti
v
e
production
en
vironments
[2].
Central
to
the
success
of
Industry
4.0
is
the
seamless
interchange,
comprehen-
sion,
and
ut
ilization
of
data
generated
by
heterogeneous
IoT
de
vices,
platforms,
and
services.
A
major
barrier
to
achie
ving
this
vision
is
the
lack
of
semantic
interoperability
,
which
ensures
that
de
vices
and
systems
from
di
v
erse
manuf
acturers
interpret
and
process
e
xchanged
data
with
a
consistent,
shared
understanding
[3].
W
ith-
out
this
capability
,
Industry
4.0
infrastructures
struggle
to
inte
grate
ne
w
de
vices
and
services
ef
ciently
and
J
ournal
homepage:
http://ijict.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
910
❒
ISSN:
2252-8776
manage
data-dri
v
en
decision-making
w
orko
ws
reliably
.
The
global
IoT
landscape
continues
to
e
xpand
at
a
remarkable
pace,
with
recent
projections
esti
mating
that
o
v
er
75
billion
IoT
de
vices
will
be
operational
by
2026
[4].
This
e
xponential
gro
wth
is
fueled
by
the
gro
wing
demand
for
smart
homes,
industrial
automation,
connected
v
ehicles,
and
wearable
technology
[5],
[6].
Consequently
,
the
v
olume
of
dat
a
generated
by
IoT
systems
is
anticipated
to
surpass
175
zettabytes
annually
by
2025,
presenting
unprecedented
challenges
in
terms
of
data
storage,
real-ti
me
processing,
inte
gration,
and
analysis
[7],
[8].
A
primary
obst
acle
lies
in
the
signicant
heterogeneity
of
IoT
de
vices,
which
v
ary
widely
in
terms
of
their
hardw
are
capabilities,
communication
protocols,
data
formats,
ontological
models,
and
security
archi-
tectures
[9],
[10].
This
heterogeneity
contrib
utes
to
fragmented
IoT
ecosystems,
dat
a
silos,
and
interoperabil-
ity
bottlenecks,
undermining
the
scalability
,
adaptability
,
and
reliability
of
Industry
4.0
infras
tructures.
The
problem
is
further
compounded
by
the
accelerated
pace
of
digital
transformation
catalyzed
by
the
CO
VID-19
pandemic,
which
highlighted
the
ur
gent
need
for
interoperable,
resilient,
and
scalable
IoT
architectures
capable
of
supporting
autonomous
and
distrib
uted
industrial
operations
[11],
[12].
Recent
studies
ha
v
e
e
xplored
v
arious
semantic
interoperability
frame
w
orks,
ontologi
cal
models,
and
middle
w
are
solutions
that
aim
to
harmonize
data
semantics
across
heterogeneous
IoT
en
vironments
[10].
No-
tably
,
Multidisciplinary
Digital
Publishing
Institute
(MDPI)
research
has
proposed
a
metamodeling-based
inter
-
operability
and
inte
gration
testing
platform
that
formalizes
IoT
system
interactions
and
enables
cross-platform
v
alidation
across
di
v
erse
de
vices
and
data
o
ws
[13].
This
w
ork
demonstrates
the
feasibility
of
systematic
in-
teroperability
management
approaches
b
ut
highlights
ongoing
limitations
in
dynamic
semantic
alignment
and
real-time
inte
gration
for
lar
ge-scale
industrial
deplo
yments.
Moreo
v
er
,
research
in
applied
domains
has
emphasized
the
practical
adv
antages
of
semanti
c
interop-
erability
.
F
or
instance,
a
spatio-temporal
semantic
data
management
frame
w
ork
has
been
deplo
yed
in
precision
agriculture
to
enhance
interoperability
in
IoT
-dri
v
en
f
arming
en
vironments
[14].
Similarly
,
an
ontology-based
semantic
middle
w
are
for
smart
campus
infrastructures
has
demonstrated
the
ability
to
automate
de
vice
and
data
inte
gration
w
orko
ws
in
heterogeneous
IoT
systems
[15].
These
implementations
reinforce
the
importance
of
semantically
a
w
are
architectures,
though
uni
v
ersal,
scalable,
and
domain-independent
solutions
remain
elusi
v
e.
Despite
these
adv
ancements,
achie
ving
seamless,
scalable,
and
dynamic
semantic
interoperability
in
IoT
systems
for
Industry
4.0
re
mains
an
open
research
challenge.
The
absence
of
uni
v
ersally
adopted
seman-
tic
frame
w
orks
and
the
limited
maturity
of
real-ti
me
semantic
alignment
mechanisms
continue
to
hinder
the
inte
gration
of
heterogeneous
de
vices,
platforms,
and
services
[15].
T
o
address
these
challenges,
this
paper
presents
a
comprehensi
v
e
re
vie
w
and
taxonomy
of
semantic
in-
teroperability
frame
w
orks,
ontol
og
i
es,
and
enabling
technologies
for
IoT
in
Industry
4.0.
It
cate
gorizes
e
xisting
approaches
based
on
their
architectural
layers,
semantic
models,
and
application
domains;
identies
persistent
limitations
and
open
research
challenges;
and
proposes
future
research
directions
to
guide
the
de
v
elopment
of
scalable,
dynamic,
and
domain-independent
semantic
interoperability
solutions
for
industrial
IoT
ecosystems.
Semantic
interoperability
has
become
a
critical
research
topic
within
the
industrial
IoT
domain,
g
ain-
ing
increasing
attention
due
to
its
role
in
enabling
seamless
data
e
xchange
and
system
inte
gration
in
Industry
4.0
en
vironments.
Numerous
studies
ha
v
e
in
v
estig
ated
v
arious
approaches
to
achie
ving
semantic
interoperability
ho
we
v
er
,
se
v
eral
open
challenges
remain.
T
o
understa
n
d
the
current
state
of
the
art,
identify
e
xisting
g
aps,
and
propose
future
directions,
this
paper
conducts
a
comprehensi
v
e
re
vie
w
of
recent
research
on
semantic
interoperability
in
IoT
for
Industry
4.0.
In
particular
,
the
follo
wing
research
questions
(RQs)
guide
the
scope
and
objecti
v
es
of
this
study:
-
RQ1:
Ho
w
is
semantic
interoperability
dened
in
the
conte
xt
of
Industry
4.0
and
industrial
IoT?
-
RQ2:
What
approaches
and
strate
gies
ha
v
e
been
proposed
in
pre
vious
studies
to
address
semantic
interop-
erability
in
IoT
,
and
ho
w
ef
fecti
v
e
are
the
y?
-
RQ3:
What
are
the
primary
challenges
and
limitations
f
aced
by
IoT
systems
and
Industry
4.0
infrastructures
due
to
the
lack
of
semantic
interoperability?
-
RQ4:
What
future
research
directions
and
open
challenges
need
to
be
addressed
to
enhance
semantic
inter
-
operability
in
IoT
systems
for
Industry
4.0?
T
o
address
these
research
questions,
the
remainder
of
this
paper
is
or
g
anized
as
follo
ws:
Section
2
pro-
vides
an
o
v
ervie
w
of
interoperability
components
within
IoT
systems.
Section
3
and
sect
ion
4
discuss
semantic
interoperability
technologies
and
the
k
e
y
obstacles
to
achie
ving
semantic
interoperability
,
respecti
v
ely
.
Sec-
tion
5
outlines
the
operat
ional
and
inte
gration
challenges
caused
by
the
lack
of
semantic
interoperability
in
IoT
Int
J
Inf
&
Commun
T
echnol,
V
ol.
15,
No.
2,
June
2026:
909–924
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Inf
&
Commun
T
echnol
ISSN:
2252-8776
❒
911
en
vironments.
A
comprehensi
v
e
re
vie
w
of
related
w
orks
is
presented
in
section
6.
Finally
,
the
paper
concludes
with
a
summary
of
k
e
y
ndings,
current
limitations,
and
proposed
future
research
directions
in
section
7.
This
paper
aims
to
pro
vide
a
comprehensi
v
e
analysis
of
semantic
interoperability
in
IoT
syst
ems
within
the
conte
xt
of
Industry
4.0.
It
re
vie
ws
the
current
state
of
interoperability
technologies,
highlights
the
challenges
posed
by
semantic
heterogeneity
,
and
identies
open
research
problems
that
hinder
seamless
data
e
xchange
and
inte
gration
in
industrial
IoT
en
vironments.
The
main
contrib
utions
of
this
study
are
summarized
as
follo
ws,
-
T
o
dene
and
clarify
k
e
y
concepts
related
to
IoT
interoperability
,
including
semantic
interoperability
,
se-
mantic
technologies,
and
their
underlying
models
and
frame
w
orks.
-
T
o
systematically
re
vie
w
and
cate
gorize
e
xisting
semantic
interoper
ability
processing
strate
gies
based
on
recent
research
contrib
utions.
-
T
o
identify
and
analyze
the
major
challenges
and
limitations
f
aced
by
IoT
and
Industry
4.0
systems
due
to
insuf
cient
semantic
interoperability
.
-
T
o
discuss
open
research
challenges
and
future
research
directions
for
enhancing
semantic
interoperability
in
IoT
-based
Industry
4.0
ecosystems.
2.
RESEARCH
METHOD
This
study
emplo
ys
a
semi-systematic
lit
erature
re
vie
w
m
ethodology
to
in
v
estig
ate
semantic
int
er
-
operability
within
the
conte
xt
of
Industry
4.0
and
the
IoT
.
A
semi-systematic
re
vie
w
is
appropriate
for
con-
ceptually
broad
and
emer
ging
elds
where
research
outcomes
are
heterogeneous
and
quantitati
v
e
data
may
be
limited
[16].
This
approach
aims
to
identify
,
analyze,
and
synthes
ize
conceptually
signicant
patterns
within
the
e
xisting
literature
through
meta-narrati
v
e
synthesis.
It
w
as
chosen
for
this
research
as
it
enables
a
concise,
conte
xtually
rele
v
ant,
and
criti
cal
o
v
ervie
w
of
the
state
of
kno
wledge
on
semantic
interoperability
in
IoT
for
Industry
4.0.
The
re
vie
w
process
follo
ws
the
six-step
frame
w
ork
proposed
by
T
emplier
and
P
are,
which
includes
the
formulation
of
research
questions,
literature
search,
study
screening,
quality
appraisal,
data
e
xtraction,
and
syn-
thesis
as
illustrated
in
Figure
1
[17].
The
research
questions
(RQ1–RQ4)
were
designed
to
e
xplore
denitions,
e
xisting
approaches,
challenges,
and
future
directions
related
to
semantic
interoperability
.
In
the
second
phase,
a
comprehensi
v
e
literature
search
w
as
conducted
across
multiple
academic
databases
including
Scopus,
W
eb
of
Science,
ScienceDirect,
DO
AJ,
and
Google
Scholar
.
K
e
yw
ords
such
as
“semantic
interoperability
,
”
“IoT
,
”
“In-
dustry
4.0,
”
“ontology
,
”
and
“semantic
frame
w
orks”
were
used
in
v
arious
combinations
with
Boolean
operators
to
rene
the
search
results.
Bot
h
published
and
unpublished
articles
were
considered
to
capture
a
comprehen-
si
v
e
picture
of
the
current
research
landscape.
Additionally
,
interoperability-related
concepts
in
adjacent
areas,
such
as
fog
computing
and
industrial
automation,
were
also
e
xplored
to
conte
xtualize
the
ndings.
Figure
1.
Implemented
research
methodology
steps
During
the
third
phase,
a
screening
procedure
w
as
implemented
based
on
predened
inclusion
criter
ia,
such
as
selected
studies
be
published
between
2015
and
2024
and
rele
v
ance
to
interoperability
denitions,
conceptual
models,
technological
frame
w
orks,
and
application
conte
xts
within
industrial
IoT
systems.
Studies
were
e
xcluded
if
the
y
were
non-Engli
sh,
lack
ed
rele
v
ance
to
semantic
aspects,
or
were
duplicates.
Studies
were
initially
screened
by
title
and
abstract,
follo
wed
by
full-te
xt
screening.
The
fourth
phase
in
v
olv
ed
a
quality
appraisal
of
the
selected
studies,
assessing
thei
r
research
design,
methodology
,
and
ndings
for
academic
rigor
and
rele
v
ance.
Semantic
inter
oper
ability
in
IoT
for
Industry
4.0:
Re
vie
w
,
taxonomy
...
(De
vamekalai
Na
gasundar
am)
Evaluation Warning : The document was created with Spire.PDF for Python.
912
❒
ISSN:
2252-8776
In
the
fth
phase,
data
e
xtraction
w
as
conducted
on
the
nalized
set
of
studies.
K
e
y
information
such
as
denitions,
models,
interoperability
dimensions,
technologies,
and
challenges
w
as
systematically
recorded.
This
process
identied
the
foundational
elements
of
semantic
interoperability
rele
v
ant
to
IoT
and
Industry
4.0.
The
nal
phase
in
v
olv
ed
data
analysis
and
synthesis
through
content
analysis
techniques
commonly
applied
in
narrati
v
e
re
vie
ws
[16].
This
f
acilitated
the
identication
of
themes,
trends,
and
challenges
in
the
liter
-
ature,
forming
the
basis
for
the
taxonomy
,
challenges,
and
future
research
directions
proposed
in
this
paper
.The
nal
dataset
included
70
peer
-re
vie
wed
articles,
with
emphasis
on
recent
contrib
utions
from
2020
to
2024.
These
studies
span
multiple
domains
including
smart
manuf
acturing,
healthcare,
smart
cities,
and
industrial
automation,
ensuring
broad
co
v
erage
of
semantic
interoperability
challenges
and
solutions.
While
the
semi-systematic
approach
pro
vides
a
rich
conceptual
o
v
ervie
w
,
it
may
not
capture
all
quan-
titati
v
e
metrics
or
unpublished
industrial
implementations.
Additionally
,
the
r
eliance
on
English-language
sources
may
e
xclude
rele
v
ant
re
gional
studies.
Despite
these
limitations,
the
methodology
of
fers
a
rob
ust
foun-
dation
for
understanding
the
current
landscape
and
guiding
future
research
in
semantic
interoperability
for
Industry
4.0.
3.
RESUL
TS
AND
DISCUSSION
3.1.
IoT
inter
operability
This
section
addresses
the
rst
research
question
by
pro
viding
an
o
v
ervie
w
of
interoperability
within
the
IoT
ecosystem,
particularly
in
the
conte
xt
of
Industry
4.0.
In
IoT
systems,
interoperability
refers
to
the
ability
of
heterogeneous
de
vices,
platforms,
and
applications
de
v
eloped
by
dif
ferent
manuf
acturers
or
v
endors
to
seamlessly
communicate,
e
xchange,
and
utilize
data
wi
thin
a
unied
en
vironment
[18].
It
ensures
that
de
vices
operating
on
distinct
hardw
are
architectures,
softw
are
protocols,
and
communication
s
tandards
can
ef
fecti
v
ely
collaborate
and
deli
v
er
inte
grated
services
[19],
[20].
Achie
ving
interoperability
in
industrial
IoT
systems
requires
the
adoption
of
standardized
communica-
tion
protocols,
data
formats,
and
inte
grati
on
frame
w
orks
[21].
Common
lightweight
communication
protocols
such
as
mess
age
queuing
telemetry
transport
(MQTT),
constrained
application
protocol
(CoAP),
and
HTTP
are
widely
emplo
yed
to
enable
reliable
data
e
xchange
among
resource-constrained
IoT
de
vices
[22].
Simi-
larly
,
standardized
data
serial
ization
formats
lik
e
JSON,
XML,
and
Y
AML
f
acilitate
syntactic
compatibility
and
simplify
data
processing
across
disparate
systems
[23].
Be
yond
de
vice-to-de
vice
communication,
interoperability
also
e
xtends
to
the
inte
gration
of
di
v
erse
subsystems,
including
edge
de
vices,
cloud
platforms,
data
analytics
tools,
and
enterprise
applications
[10].
This
necessitates
the
use
of
inte
gration
frame
w
orks,
middle
w
are,
and
standardized
APIs
that
allo
w
seamless
data
e
xchange
and
operational
coordination
across
heterogeneous
infrastructures.
Interoperability
is
essential
for
realizing
the
full
potential
of
IoT
-based
Industry
4.0
en
vironments,
as
it
enables
di
v
erse
systems
to
cooper
-
ate
and
deli
v
er
cohesi
v
e,
scalable,
and
adapti
v
e
industrial
solutions.
Figure
2
illustrates
these
four
fundamen-
tal
dimensions
of
interoperability
in
IoT
systems,
highlighting
the
relationships
and
inte
gration
requirements
among
them.
Figure
2.
The
dimensions
of
interoperability
[24]
Interoperability
within
the
IoT
conte
xt
encompasses
multiple
dimensions,
each
addressing
a
di
stinct
aspect
of
inte
gration.
According
to
Santos
et
al.
[25],
these
include
technical,
syntactic,
semantic,
and
or
-
g
anizational
interoperability
.
A
clear
understanding
of
these
interoperability
types
is
crucial
for
the
ef
fecti
v
e
implementation
and
management
of
interoperable
IoT
systems.
The
four
primary
dimensions
are
summarized
as
follo
ws,
Int
J
Inf
&
Commun
T
echnol,
V
ol.
15,
No.
2,
June
2026:
909–924
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Inf
&
Commun
T
echnol
ISSN:
2252-8776
❒
913
-
T
echnical
Interoperability:
The
ability
of
de
vices,
systems,
and
applications
to
communicate
and
e
xchange
data
using
common
netw
orking
protocols,
communication
standards,
and
data
serialization
formats.
It
estab-
lishes
the
foundational
connecti
vity
necessary
for
de
vice-le
v
el
inte
gration
in
IoT
en
vironments
[26].
-
Syntactic
Interoperability:
The
capacity
of
disparate
systems
to
e
xchange
data
in
a
structured
and
recogniz-
able
format,
ensuring
that
the
transmitted
data
can
be
correctly
parsed
and
understood
by
recei
ving
systems.
This
is
typically
achie
v
ed
through
standardized
data
formats
such
as
JSON,
XML,
and
RDF
[27].
-
Semantic
Interoperability:
The
ability
of
systems
and
applications
to
interpret
the
meaning
of
e
xchanged
data
consistently
and
meaningfully
.
Semantic
interoperability
ensures
that
data
semantics
are
preserv
ed
across
heterogeneous
systems,
enabling
accurate
and
conte
xt-a
w
are
information
e
xchange
[28].
-
Or
g
anizational
Interoperability:
The
capability
of
dif
ferent
or
g
anizations,
b
usiness
processes,
and
go
v
er
-
nance
structures
to
ef
fecti
v
ely
collaborate
and
e
xchange
information
across
IoT
systems,
supported
by
com-
patible
policies,
standards,
and
b
usiness
objecti
v
es
[29].
In
summary
,
achie
ving
comprehensi
v
e
interoperability
across
these
dimensions
is
crucial
for
ensuring
the
seamless
inte
gration
and
ef
cient
operation
of
industrial
IoT
systems.
It
enables
di
v
erse
de
vices,
plat-
forms,
and
or
g
anizations
to
cooperate
in
real
time,
f
acilitating
scalable,
intelligent,
and
automated
Industry
4.0
en
vironments.
The
subsequent
section
will
specically
e
xamine
semantic
interoperability
,
its
technological
enablers,
and
its
pi
v
otal
role
in
o
v
ercoming
inte
gration
barriers
within
IoT
-based
industrial
systems.
3.2.
T
axonomy
of
semantic
inter
operability
in
IoT
f
or
Industry
4.0
Semantic
interoperability
is
a
crucial
component
in
Industry
4.0
IoT
ecosystems,
as
it
ensures
that
de
vices,
systems,
and
applications
can
e
xchange,
interpret,
and
process
data
with
a
shared,
unambiguous
un-
derstanding
of
its
meaning
[30].
It
enables
heterogeneous
de
vices
to
interact
autonomously
and
allo
ws
ap-
plications
to
le
v
erage
data
from
di
v
erse
sources
without
ambiguity
or
the
need
for
human
interv
ention
[28],
[31],
[32].
Importantly
,
semantic
interoperability
e
xtends
be
yond
merely
dening
information
models
or
align-
ing
data
transport
formats;
it
in
v
olv
es
the
consistent
and
meaningful
interpretation
of
e
xchanged
data
across
distrib
uted
systems
and
kno
wledge
frame
w
orks.
In
recent
years,
signicant
adv
ancements
ha
v
e
emer
ged
in
semantic
technologies
tailored
for
Industry
4.0
and
related
domains
such
as
healthcare
and
smart
cities
[33]-[35].
F
or
e
xample,
Elkhodr
et
al.
[36]
pro-
posed
a
blockchain-inte
grated
semantic
IoT
middle
w
are
that
le
v
erages
ontology-po
wered
conte
xt
a
w
areness
and
secure
data
e
xchange,
addressing
both
semantic
alignment
and
trust
in
healthcare
IoT
deplo
yments.
Ad-
ditionally
,
NGSI-LD
an
ETSI-standardized
information
model
and
API
has
been
widely
adopted
across
smart
industry
and
digital
twin
projects,
enabling
conte
xtualized
semantic
data
interchange
between
platforms
[37].
Among
widely
adopted
standards
is
the
semantic
sensor
netw
ork
(
SSN)
ontology
,
which
pro
vides
a
formal,
machine-interpretable
frame
w
ork
for
describing
sensors,
observ
ations,
and
related
metadata
[38].
Lik
e
wise,
the
open
platform
communications
unied
architecture
(OPC
U
A)
has
been
recognized
as
a
k
e
y
Industry
4.0
standard,
of
fering
a
unied
data
model
and
service
set
for
seamless,
platform-independent
data
e
xchange
across
industrial
systems
[39].
These
ef
forts
highlight
the
importance
of
semantic
ontologies
in
enabling
standardized
and
scalable
industrial
data
ecosystems.
Gi
v
en
the
comple
xity
and
di
v
ersity
of
industrial
en
vironments,
achie
ving
semantic
interoperability
requires
a
multidimensional
approach
that
considers
v
arious
architectural,
technological,
and
modeling
aspects
[40].
T
o
systematically
analyze
the
current
l
andscape
and
guide
future
research,
a
taxonomy
comprising
v
e
k
e
y
dimensions
proposed
in
this
section:
netw
ork
model,
ontology
,
middle
w
are,
data
model,
and
information
model.
These
dimensions,
as
illustrated
in
Figure
3,
were
deri
v
ed
from
a
synthesis
of
recent
literature
and
reect
the
most
inuential
f
actors
shaping
semantic
interoperability
frame
w
orks
in
Industry
4.0
[41].
The
netw
ork
model
dimension
cate
gorizes
semantic
interoperability
solutions
based
on
their
deplo
y-
ment
architecture.
F
og-based
models
perform
semantic
process
ing
at
the
edge
of
the
netw
ork,
closer
to
data
sources
[42].
These
models
are
particularly
suited
for
latenc
y-sensiti
v
e
applications,
such
as
real-time
mon-
itoring
and
control
in
industrial
automation,
as
the
y
of
fer
reduced
data
transmission
delays
and
impro
v
ed
re-
sponsi
v
eness
[43].
Cloud-based
models
centralize
semantic
operations
in
cloud
infrastructures,
beneting
from
scalable
computing
resources
and
supporting
lar
ge-scale
data
aggre
g
ation
and
reasoning
tasks
[43].
Ho
we
v
er
,
the
y
may
introduce
latenc
y
and
bandwidth
o
v
erhead,
making
them
less
suitable
for
time-critical
applications.
W
eb-based
models
le
v
erage
web
technologies
and
standards
to
enable
semantic
data
e
xchange
across
dis-
trib
uted
systems,
of
fering
adv
antages
for
interoperability
across
or
g
anizational
boundaries
and
inte
gration
with
e
xternal
services
[44].
Semantic
inter
oper
ability
in
IoT
for
Industry
4.0:
Re
vie
w
,
taxonomy
...
(De
vamekalai
Na
gasundar
am)
Evaluation Warning : The document was created with Spire.PDF for Python.
914
❒
ISSN:
2252-8776
Figure
3.
T
axonomy
of
semantic
interoperability
[24]
Ontologies
serv
e
as
the
backbone
of
semantic
interoperability
by
pro
viding
formal
repres
entations
of
domain
kno
wledge
[45].
The
taxonomy
distinguishes
between
lightweight,
hea
vyweight,
and
domain-
specic
ontologies.
Light
weight
ontologies
are
designed
for
simplicity
and
ef
cienc
y
,
making
them
suitable
for
resource-constrained
de
vices
such
as
sensors
and
embedded
systems
[45].
Hea
vyweight
ontologies
of
fer
rich
semantic
e
xpress
i
v
eness
and
support
comple
x
reasoning,
typically
used
in
centralized
systems
or
cloud
en
vironments
where
computational
resources
are
ab
undant
[46].
Domain-specic
ontologies
are
tailored
to
particular
industrial
sectors,
such
as
manuf
acturing
or
healthcare,
capturing
specialized
terminology
and
rela-
tionships
that
enhance
semantic
precision
and
conte
xtual
rele
v
ance.
This
classication
reects
the
trade-of
f
between
semantic
richness
and
system
performance,
where
lightweight
ontologies
enable
f
ast
processing
b
ut
may
lack
depth,
while
hea
vyweight
and
domain-specic
ontologies
pro
vide
detailed
semantic
co
v
erage
at
the
cost
of
increased
comple
xity
.
Middle
w
are
plays
a
pi
v
otal
role
in
managing
communication
and
semantic
inte
gration
between
di
v
erse
IoT
components
[47].
The
taxonomy
includes
distrib
uted
and
centralized
middle
w
are
architectures.
Distrib
uted
middle
w
are
decentralizes
semantic
services
across
multiple
nodes,
enhancing
scalability
,
f
ault
tolerance,
and
e
xibility
,
which
is
particularly
suitable
for
lar
ge
and
dynamic
industrial
en
vironments
[48].
Centralized
mid-
dle
w
are
consolidates
semantic
processing
in
a
single
location,
simplifying
management
and
deplo
yment
b
ut
potentially
limiting
scalability
and
resilience
[49].
The
distinction
between
these
architectures
is
based
on
design
choices
that
directly
impact
system
performance,
maintainability
,
and
adaptability
[48].
Distrib
uted
middle
w
are
is
increasingly
f
a
v
ored
in
Industry
4.0
due
to
its
alignment
with
decentralized
and
autonomous
system
requirements.
The
data
model
dime
n
s
ion
addresses
ho
w
semantic
data
is
structured
and
managed
within
IoT
s
ystems.
It
includes
dynamic,
static,
and
real-time
models.
Dynamic
models
support
schema
e
v
olution
and
accommodate
changes
in
data
structures
o
v
er
time,
essential
for
en
vironments
where
de
vices
and
services
are
frequently
updated
or
recongured
[50].
Static
models
rely
on
x
ed
schemas
and
predened
data
structures,
of
fering
simplicity
b
ut
limited
e
xibility
[15].
Real-time
models
enable
immediate
semantic
interpretation
of
streaming
data,
which
is
critical
for
time-sensiti
v
e
applications
such
as
predicti
v
e
maintenance
and
real-time
analytics
[15].
This
classication
is
justied
by
the
need
to
balance
e
xibility
,
performance,
and
comple
xity
in
data
handling,
with
dynamic
and
real-time
models
being
particularly
rele
v
ant
for
Industry
4.0
[51].
Information
models
dene
the
formal
languages
and
standards
used
to
represent
semantic
data.
The
taxonomy
includes
resource
description
frame
w
ork
(RDF),
RDF
schema
(RDF-S),
and
web
ontology
language
(O
WL)
[52].
RDF
pro
vides
a
foundational
model
for
representing
information
about
resources
in
the
semantic
web
.
RDF-S
e
xtends
RDF
by
of
fering
basic
constructs
for
describing
groups
of
related
resources
and
their
properties
[52].
O
WL
of
fers
adv
anced
capabilities
for
dening
and
reasoning
o
v
er
comple
x
ontologies,
sup-
porting
higher
le
v
els
of
semantic
e
xpressi
v
eness
[53].
These
s
tandards
are
widely
adopted
in
semantic
web
technologies
and
pro
vide
the
syntactic
and
semantic
foundation
for
interoperability
.
The
classication
reects
Int
J
Inf
&
Commun
T
echnol,
V
ol.
15,
No.
2,
June
2026:
909–924
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Inf
&
Commun
T
echnol
ISSN:
2252-8776
❒
915
the
progression
from
basic
data
repres
entation
to
more
adv
anced
semantic
modeling
and
reasoning
capabilities,
allo
wing
systems
to
choose
the
appropriate
le
v
el
of
e
xpressi
v
eness
based
on
their
requirements
[53].
The
taxonomy
presented
in
Figure
3
of
fe
rs
a
comprehensi
v
e
frame
w
ork
for
understanding
the
s
truc-
tural
and
functional
components
of
semantic
interoperability
in
Industry
4.0.
Each
c
lassication
dimension
w
as
selected
based
on
its
pre
v
alence
in
the
literature
and
its
practical
rele
v
ance
to
industrial
IoT
deplo
yments.
By
or
g
anizing
the
landscape
into
these
v
e
cate
gories,
the
taxonomy
f
acilitates
comparati
v
e
analysis,
high-
lights
e
xisting
g
aps,
and
supports
the
de
v
elopment
of
rob
ust,
scalable,
and
adapti
v
e
semantic
interoperability
solutions.
In
addition
to
these,
adv
anced
semantic
technologies,
such
as
articial
intelligence
(AI),
m
achine
learning,
and
natural
language
processing
(NLP)
ha
v
e
g
ained
traction
for
automating
data
mapping,
ontology
alignment,
and
semantic
annotation
processes
in
IoT
en
vironments
[54],
[55].
F
or
instance,
Linardatos
et
al.
[56]
applied
machine
learning
to
automate
the
classication
of
sensor
data
from
heterogeneous
sources,
impro
ving
semantic
alignment
and
enhancing
interoperability
in
Industry
4.0
conte
xts.
Another
recent
study
proposed
a
frame
w
ork
for
automatically
detecting
and
classifying
data
streams
within
manuf
acturing
processes
to
impro
v
e
operational
ef
cienc
y
and
semantic
inte
gration
in
production
systems
[57].
Cross-platform
and
cross-domain
interoperability
are
equally
vital
for
Industrial
IoT
applica
tions,
particularly
in
scenarios
requiring
data
e
xchange
between
i
ndependent
systems
or
or
g
anizations
[58].
Further
ef
forts
ha
v
e
introduced
technologies
and
tools
for
enhancing
cross-domain
semantic
interoperabili
ty
.
F
or
e
x-
ample,
Abb
uru
[59]
proposed
a
method
for
inte
grating
multi-source
IoT
data
by
combining
ontologies
with
machine
learning
techniques
for
automated
data
mapping
and
con
v
ersion.
Similarly
,
Da
vies
and
Fisher
[60]
demonstrated
ho
w
embedded
semantic
models
and
annotations
within
industrial
applications
impro
v
e
IoT
data
consistenc
y
,
scalability
,
and
operational
ef
cienc
y
.
Balakrishna
et
al.
[61]
highlighted
the
importance
of
se-
mantic
models
for
optimizing
industrial
IoT
applications’
scalability
and
sustainability
in
Industry
4.0.
Despite
these
adv
ancements,
seamless,
scalable,
and
dynamic
semantic
interoperability
remains
an
open
research
challenge.
As
recent
studies
emphasize,
the
lack
of
uni
v
ersally
adopted
semant
ic
frame
w
orks
and
the
limited
maturity
of
real-time
semantic
al
ignment
mechanisms
continue
to
hinder
the
full
inte
gration
of
heterogeneous
de
vices,
platforms,
and
s
ervices
in
high-frequenc
y
,
data-intensi
v
e
Industry
4.0
en
vironments
[62].
Ongoing
research
into
semantic
data
annotation,
reasoning,
disco
v
ery
,
and
visualization
is
critical
to
addressing
these
barriers
and
enabling
truly
autonomous,
interoperable
industrial
ecosystems.
3.3.
Obstacles
to
achie
v
e
semantic
inter
operability
in
Industry
4.0
Achie
ving
semantic
interoperability
in
Industry
4.0
IoT
en
vironments
presents
se
v
eral
persistent
chal-
lenges
due
to
the
sheer
di
v
ersity
of
de
vices,
communication
protocols,
data
formats,
and
operational
conte
xts
[59].
Numerous
studies
ha
v
e
identied
critical
obstacles
that
hinder
seamless
semantic
interoperability
in
het-
erogeneous
industrial
systems
[63]-[65].
One
of
the
primary
challenges
lies
in
the
incompatibility
of
communication
protocols
and
data
for
-
mats
across
de
vices
and
platforms.
Although
standardized
protocols
such
as
MQTT
,
CoAP
,
and
OPC
U
A
ha
v
e
g
ained
traction,
the
lack
of
uni
v
ersally
accepted
semantic
data
models
and
ontol
og
i
es
limits
interoperability
and
impedes
cross-platform
inte
gration
[41],
[66].
Conse
q
ue
n
t
ly
,
data
generated
by
di
v
erse
IoT
de
vices
of-
ten
remains
conned
within
isolated
silos,
complicating
data
aggre
g
ation,
semantic
alignment,
and
real-time
analytics
in
Industry
4.0
en
vironments
[67].
De
vice
heterogeneity
represents
another
signicant
barrier
,
as
industrial
IoT
systems
typically
com-
prise
de
vices
from
mult
iple
manuf
acturers,
each
emplo
ying
di
stinct
data
models,
terminologies,
and
conte
xtual
interpretations
[68].
This
inconsistenc
y
in
semantic
s
tructures
leads
to
dif
culties
in
achie
ving
a
common
un-
derstanding
of
e
xchanged
data,
thereby
af
fecting
system
interoperability
and
inte
gration
at
the
semantic
layer
.
The
scale
and
v
ariety
of
data
produced
by
IoT
de
vices
in
Industry
4.0
applications
further
e
xacerbate
interoperability
challenges.
The
enormous
v
olume
of
heterogeneous,
real-time,
and
unstructured
data
demands
ef
cient
semantic
data
modeling,
kno
wledge
management,
and
inte
gration
techniques
capable
of
supporting
dynamic,
scalable,
and
conte
xt-a
w
are
interoperability
solutions
[69],
[70].
Pri
v
ac
y
and
security
concerns
also
constitute
critical
obstacles
to
semantic
interoperability
.
Industri
al
IoT
systems
frequently
handle
sensiti
v
e
operational,
or
g
anizational,
and
personal
data,
necessitating
rob
ust
pri
v
ac
y-preserving
mechanisms
and
secure
semantic
data
e
xchange
protocols
[71].
W
ithout
adequate
security
models
and
access
control
mec
hanisms
inte
grated
into
semantic
interoperability
frame
w
o
r
ks,
data
condential-
ity
,
inte
grity
,
and
trust
cannot
be
ensured
in
interconnected
industrial
ecosystems.
Semantic
inter
oper
ability
in
IoT
for
Industry
4.0:
Re
vie
w
,
taxonomy
...
(De
vamekalai
Na
gasundar
am)
Evaluation Warning : The document was created with Spire.PDF for Python.
916
❒
ISSN:
2252-8776
T
o
address
these
challenges,
a
range
of
approaches
ha
v
e
been
proposed,
including
the
adoption
of
ontology-based
frame
w
orks,
semantic
web
technologies,
and
machine
learning-assisted
semantic
mapping
techniques
[32],
[69],
[72],
[73].
These
solutions
aim
to
enhance
semantic
compatibility
among
heterogeneous
platforms,
automate
data
mapping
and
translation
processes,
and
establish
common
kno
wledge
representation
frame
w
orks
to
f
acilitate
seamless
data
e
xchange,
inte
gration,
and
reasoning
within
Industry
4.0
en
vironments.
In
summary
,
while
notable
progress
has
been
made
in
de
v
eloping
semantic
interoperability
frame-
w
orks
and
tools,
achie
ving
scalable,
secure,
and
dynamic
semantic
inte
gration
across
heterogeneous
industrial
IoT
systems
remains
an
unresolv
ed
research
challenge.
Addressing
these
barriers
requires
the
continued
ad-
v
ancement
of
ontology
engineering,
real-time
semantic
annotation
techniques,
and
AI-dri
v
en
interoperability
solutions
tailored
to
the
demands
of
Industry
4.0.
3.4.
Challenges
caused
by
shortcomings
of
semantic
inter
operability
in
IoT
f
or
Industry
4.0
Se
v
eral
operati
o
na
l
and
strat
e
gic
challenges
within
Industry
4.0
IoT
ecosystems
ha
v
e
been
identied
as
direct
consequences
of
insuf
cient
semantic
interoperability
[74].
These
shortcomings
hinder
the
scalability
,
ef
cienc
y
,
accessibilit
y
,
and
economic
viability
of
industrial
IoT
deplo
yments.
The
principal
challenges
are
outlined
as
follo
ws:
-
Restricted
scalability
,
The
inte
gration
of
ne
w
IoT
syst
ems,
de
vices,
and
applications
at
scale
is
signicantly
constrained
when
semantic
interoperability
is
lacking
[75].
The
absence
of
standardized
semantic
frame-
w
orks
leads
to
compatibility
issues,
making
it
dif
cult
to
incorporate
adv
anced
or
heterogeneous
de
vices
into
e
xisting
systems
without
e
xtensi
v
e
custom
inte
gration
ef
forts
[68].
This
limitation
ulti
mately
restricts
the
scalability
and
e
xibility
of
Industry
4.0
infrastructures.
-
Inef
cient
data
storage
and
resource
utilization:
Industrial
IoT
systems
generate
v
ast
v
olumes
of
hetero-
geneous
data
from
distrib
uted
de
vices.
W
ithout
adequate
semantic
interoperability
,
ef
fecti
v
e
data
sharing
across
applications
and
platforms
becomes
dif
cult,
resulting
in
redundant
or
siloed
storage
of
o
v
erlapping
data
[66].
This
inef
cienc
y
increases
storage
costs
and
complicates
lar
ge-scale
data
management
in
Industry
4.0
en
vironments.
-
V
endor
lock-in:
A
lack
of
semantic
interoperabil
ity
forces
industries
to
adopt
proprietary
IoT
systems
and
de
vices
from
single
v
endors,
as
inte
gration
with
alternati
v
e
systems
is
often
comple
x
and
costly
[58].
This
v
endor
dependenc
y
restricts
system
e
xibility
,
hinders
the
adoption
of
competiti
v
e
technologies,
and
im-
pedes
long-term
inno
v
ation
by
creating
monopolistic
tendencies
within
the
industrial
IoT
mark
et.
-
Reduced
system
accessibility
and
Data
Sharing:
Interoperability
limitations
result
in
closed,
siloed
IoT
ecosystems
where
data
and
services
cannot
be
easily
accessed
or
shared
across
platforms
and
applications
[76].
This
restrict
ed
accessibility
diminishes
the
potential
for
inte
grated,
cross-or
g
anizational
collaboration,
limiting
the
operational
and
strate
gic
benets
of
Industry
4.0
architectures.
-
T
echnological
uncertainty
and
instability:
The
absence
of
unied
semantic
interoperability
frame
w
orks
increases
the
risk
of
technological
fragmentation,
where
v
endors
f
ail
to
deli
v
er
agreed-upon
services
or
maintain
consistent
functionality
across
de
vices
[77].
Industrial
operators
are
then
forced
to
adopt
unreliable
or
unstable
solutions
due
to
incompatibility
constraints,
undermining
operational
continuity
and
trust
in
IoT
systems.
-
Increased
operational
costs:
The
cost
of
deplo
ying
and
maintaining
Industry
4.0
IoT
ecosystems
escalates
in
the
absence
of
semantic
interoperability
[65].
Industries
are
frequently
unable
to
adopt
more
af
ford-
able,
adv
anced
IoT
solutions
without
fully
replacing
e
xisting
incompatible
systems.
This
lack
of
modular
upgradeability
dri
v
es
higher
operational
e
xpenses
and
reduces
the
economic
feasibility
of
long-term
IoT
deplo
yments.
In
summary
,
semantic
interoperability
decienci
es
introduce
substantial
barriers
to
the
scalability
,
ef
-
cienc
y
,
and
cost-ef
fecti
v
eness
of
Industry
4.0
IoT
systems.
Addressing
these
challenges
is
essential
for
enabling
e
xible,
scalable,
and
inte
grated
industrial
ecosystems
capable
of
supporting
dynamic,
data-dri
v
en
operations.
The
follo
wing
sections
re
vie
w
current
solutions
and
propose
future
research
directions
for
o
v
ercoming
these
limitations.
3.5.
Recent
r
esear
ch
eff
orts
to
ward
achie
ving
semantic
inter
operability
Semantic
interoperability
has
emer
ged
as
a
crucial
component
of
IoT
frame
w
orks
for
Industry
4.0
applications
[78].
V
arious
s
tudies
and
research
projects
ha
v
e
proposed
frame
w
orks,
ontologies,
and
distrib
uted
architectures
to
address
semantic
interoperability
challenges
within
heterogeneous
IoT
ecosystems
[20],
[61].
Int
J
Inf
&
Commun
T
echnol,
V
ol.
15,
No.
2,
June
2026:
909–924
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Inf
&
Commun
T
echnol
ISSN:
2252-8776
❒
917
Ontology-based
models,
fog
computing-assisted
semantic
architectures,
and
lightweight
semantic
frame
w
orks
are
among
the
widely
e
xplored
approaches.
Iong
and
Smys
[79]
proposed
a
fog-assisted
semantic
frame
w
ork
designed
to
enhance
interoperabil
ity
among
IoT
de
vices
by
inte
grating
fog
computing
principles
with
semantic
technologies.
The
frame
w
ork
in-
troduces
a
distrib
uted
computing
infrastructure
where
fog
nodes
perform
localized
data
aggre
g
ation,
ltering,
modeling,
and
semantic
annotation
before
forw
arding
processed
data
to
cloud
serv
ers
for
archi
v
al
and
adv
anced
analytics
in
Figure
4.
The
semantic
model
within
this
frame
w
ork
utilizes
standardized
representations
based
on
semantic
web
technologies
such
as
RDF
and
O
WL,
f
acilitating
seamless
data
e
xchange
and
interoperabil-
ity
across
heterogeneous
de
vices.
Additionally
,
Iong
and
Smys
proposed
data
prioritization
algorithms
within
the
fog
layer
to
optimize
data
transmission
ef
cienc
y
and
reduce
service
delays,
thereby
supporting
scalable,
interoperable
Industry
4.0
systems.
Figure
4.
F
og-assisted
semantic
frame
w
ork
Rahman
and
Hussain
[80]
introduced
a
lightweight
ontology
model
(LiO-IoT)
to
support
sema
ntic
interoperability
for
commonly
encountered
IoT
components
such
as
sensors,
actuators,
and
radio
frequenc
y
identication
(RFID)
systems.
The
ontology
focuses
on
minimizing
comple
xity
by
adopting
a
simplied
se-
mantic
representation,
impro
ving
processing
ef
cienc
y
in
constrained
IoT
en
vironments.
Ho
we
v
er
,
the
pro-
posed
model
lacks
dynamic
semantic
adaptability
,
a
limitation
subsequently
addressed
in
Rahman’
s
later
w
ork
[73],
which
introduced
a
li
ghtweight
dynamic
ontology
frame
w
ork.
This
dynamic
ontology
inte
grates
machine
learning
techniques
for
automatically
identifying
and
incorporating
ne
w
attrib
utes
and
concepts
into
the
ontol-
ogy
,
f
acilitati
n
g
real-time
semantic
adaptation.
The
frame
w
ork
emplo
ys
clustering
algorithms
to
detect
no
v
el
patterns
within
data
streams,
though
the
authors
ackno
wledged
that
clustering-induced
delays
may
impact
sys-
tem
response
times
in
time-sensiti
v
e
industrial
applications.
Further
e
xtending
semantic
interoperability
solutions,
Rahman
and
Hussain
[81]
proposed
a
fog-based
semantic
frame
w
ork
that
migrates
semantic
processing
tasks
traditionally
handled
at
the
cloud
le
v
el
to
dis-
trib
uted
fog
nodes.
This
hierarchical
fog
computing
architecture
consists
of
Le
v
el-2
(L2-F
og)
nodes
respon-
sible
for
initial
data
collection,
ltering,
and
aggre
g
ation,
and
Le
v
el-1
(L1-F
og)
nodes
task
ed
with
higher
-
le
v
el
semantic
modeling,
annotation,
and
decision-making
in
Figure
5.
Semantic
annotation
is
achie
v
ed
using
lightweight
O
WL-based
ontologies
managed
via
a
lightweight
middle
w
are
layer
.
By
shifting
semantic
reason-
ing
and
data
processing
closer
to
data
sources,
this
frame
w
ork
reduces
netw
ork
utilization,
ener
gy
consumption,
and
service
latenc
y
while
impro
ving
interoperability
across
heterogeneous
IoT
de
vices.
Ho
we
v
er
,
the
frame-
w
ork
relies
on
static
ontologies,
limiting
its
ability
to
dynamically
accommodate
emer
ging
de
vices
or
e
v
olving
semantic
conte
xts.
Semantic
inter
oper
ability
in
IoT
for
Industry
4.0:
Re
vie
w
,
taxonomy
...
(De
vamekalai
Na
gasundar
am)
Evaluation Warning : The document was created with Spire.PDF for Python.
918
❒
ISSN:
2252-8776
Figure
5.
The
frame
w
ork
of
the
suggested
model
Gyrard
and
Ser
rano
[82]
proposed
a
unied
semantic
engine
for
IoT
and
smart
city
applications
that
inte
grates
semantic
web
technologies,
big
data
analytics,
and
IoT
middle
w
are.
The
engine
comprises
three
layers:
a
data
layer
for
collecting
and
preprocessing
sensor
data,
a
semantic
layer
for
standardizing
and
an-
notating
data
using
ontologies,
and
an
application
layer
for
de
v
eloping
conte
xt-a
w
are
services.
The
authors
v
alidated
their
approach
through
a
smart
parking
use
case,
demonst
rating
the
engine’
s
ability
to
process
and
analyze
real-time
sensor
data,
enabling
intelligent
parking
management
decisions.
Although
focused
on
smart
city
deplo
yments,
the
engine’
s
scalable,
ontology-dri
v
en
architecture
of
fers
v
aluable
insights
for
Industry
4.0
semantic
interoperability
frame
w
orks.
Collecti
v
ely
,
thes
e
studies
highlight
the
di
v
erse
m
ethodologies
proposed
to
address
semantic
inter
-
operability
challenges
in
industrial
IoT
en
vironments.
T
able
1
sho
ws
the
capabilities
and
limitation
analysis
of
the
related
w
ork
discussed.
Although
ontology-based
models,
fog-assisted
frame
w
orks,
and
lightweight
dy-
namic
ontologies
ha
v
e
adv
anced
interoperability
capabilities,
limitations
persist
in
achie
ving
real-time
semantic
adaptability
,
dynamic
ontology
generation,
and
standardized
cross-
p
l
atform
inte
gration.
Continued
research
is
necessary
to
de
v
elop
scalable,
secure,
and
dynamic
s
emantic
interoperability
frame
w
orks
capable
of
supporting
the
comple
x,
data-intensi
v
e
requirements
of
Industry
4.0
applications.
T
able
1.
Analysis
of
related
w
ork
Related
w
ork
research
reference
Attrib
utes
[79]
[80]
[73]
[81]
[82]
Support
real
time
No
No
Y
es
No
No
Dynamic
interoperable
No
No
Y
es
No
No
Suitable
for
small
scale
Y
es
Y
es
No
Y
es
No
Suitable
for
lar
ge
scale
Y
es
No
Y
es
Y
es
Y
es
Latenc
y
Medium
High
High
Medium
High
Ener
gy
consumption
Medium
Medium
High
Medium
High
Netw
ork
usage
Medium
High
Medium
Medium
High
Deplo
yment
cost
High
Lo
w
High
High
Medium
F
og
based
Y
es
No
No
Y
es
No
3.6.
Comparati
v
e
analysis
and
discussion
Semantic
interoperability
within
IoT
ecosystems
is
a
cornerstone
for
achie
ving
seamles
s
data
e
x-
change
and
inte
gration
in
Indust
ry
4.0
en
vironments.
While
earlier
studies
ha
v
e
e
xpl
o
r
ed
the
impact
of
seman-
tic
technologies
such
as
ontologies,
middle
w
are,
and
semantic
web
services,
the
y
ha
v
e
not
e
xplicitly
addressed
the
inuence
of
dynamic
semantic
alignment
and
cross-domain
adaptability
in
real-time
industrial
conte
xts.
This
study
in
v
estig
ated
the
classication
and
ef
fecti
v
eness
of
semantic
interoperability
frame
w
orks,
re
v
ealing
that
the
lack
of
uni
v
ersally
accepted
semantic
models
and
real-time
adaptability
remains
a
signicant
g
ap
in
e
xisting
research.
Int
J
Inf
&
Commun
T
echnol,
V
ol.
15,
No.
2,
June
2026:
909–924
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