Journal of Cloud Computing Advances Systems and Applications,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: Nov. 14, 2024
Internet
of
Things
(IoT)
platforms
have
become
the
building
blocks
any
automated
system
but
they
are
more
important
in
case
industrial
systems
where
sensitive
data
captured
and
handled
by
information
system.
Therefore,
it
is
imperative
to
deploy
right
IoT
platform
perform
computational
operational
tasks
a
better
way.
During
last
few
years,
an
array
technologies/platforms
with
different
capabilities
features
were
introduced
markets.
This
abrupt
rise
created
selection
decision-making
issues
particularly
for
network
engineers,
designers,
managers
due
lack
technical
understanding
skill
this
area.
we
present
integrated
assessment
model
focusing
on
evaluating
ranking
environment.
It
encompasses
multiple
methods
such
as
proposed
leverages
well-known
collection
technique
Delphi
related
criteria
features.
adopts
Analytic
Hierarchy
Process
(AHP)
giving
weights
The
Order
Preference
Similarity
Ideal
Solution
(TOPSIS)
method
has
been
applied
evaluation
top
twenty
(20)
Industrial
IoT(IIoT)
alternatives
according
criteria.
selects
most
rational
choice
that
can
be
employed
Industry
4.0
setting.
produces
accurate
consistent
outcomes.
Hence,
believed
used
guideline
stakeholders
like
researchers,
developers,
policymakers
deployment
first
kind
multi-methods
mode
assessment,
decision-making,
prioritization
technologies
industry
domain.
Transactions on Emerging Telecommunications Technologies,
Journal Year:
2025,
Volume and Issue:
36(3)
Published: March 1, 2025
ABSTRACT
The
Internet
of
Things
(IoT)
has
accelerated
the
connectivity
between
physical
objects
and
Internet.
It
become
common
to
integrate
IoT
devices
into
our
lifestyles,
considering
fact
that
they
make
traditional
be
more
intelligent
self‐sufficient.
usage
5G‐enabled
can
one
such
improvement,
as
it
integrates
multiple
allows
for
effective
interaction
data
sharing.
However,
with
growing
extreme
increase
in
number
being
connected,
resource
utilization
efficiency
emerged
major
challenge.
Comparing
existing
management
strategies
current
environment
brought
by
even
complex
IoT,
former
have
consistently
failed,
leading
wastage
too
much
energy.
Resource
allocation
efficient
IoTs
encompass
processing
power,
bandwidth,
energy
appropriate
functioning
networks.
conventional
designs
are
inherently
inefficient
cannot
match
pace
nature
structures,
hence
making
difficult
achieve
any
meaningful
performance,
resources
also
wasted
process;
thus,
there
exists
necessity
energy‐efficient
approaches
adaptable
dynamic
workloads.
In
consideration
aforementioned
factors,
this
paper
proposes
an
entirely
new
approach
employing
a
Kohonen
neural
network
address
issue
focus
on
efficiency.
first
these
steps
is
collection
obtained
from
order
detect
important
features;
second
step
algorithm
produce
map
indicating
spatial
distribution
resources,
final
real‐time
modification
incoming
promote
allocation.
analysis
shows
when
using
method
provided,
energy,
costs,
delays
implementation
process
improved.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: April 20, 2025
Intrusion
Detection
Systems
(IDS)
play
a
crucial
role
in
ensuring
network
security
by
identifying
and
mitigating
cyber
threats.
This
study
introduces
hybrid
intrusion
detection
approach
that
integrates
Convolutional
Neural
Networks
(CNNs)
for
feature
extraction
the
Random
Forest
(RF)
algorithm
classification.
The
proposed
method
enhances
accuracy
leveraging
CNNs
to
automatically
extract
relevant
features,
reducing
data
dimensionality
noise.
Subsequently,
RF
classifier
processes
these
optimized
features
achieve
robust
precise
To
evaluate
effectiveness
of
approach,
experiments
were
conducted
on
KDD99
UNSW-NB15
datasets.
results
demonstrate
model
achieves
an
97%
precision
over
98%,
outperforming
traditional
machine
learning-based
IDS
solutions.
These
findings
highlight
potential
framework
as
scalable
efficient
cybersecurity
solution
real-world
environments.
