Transactions on Emerging Telecommunications Technologies,
Год журнала:
2025,
Номер
36(2)
Опубликована: Фев. 1, 2025
ABSTRACT
The
rapid
evolution
of
the
Internet
Things
(IoT)
has
revolutionized
various
sectors,
fostering
seamless
intercommunication
and
real‐time
monitoring.
Central
to
this
transformation
is
integrating
blockchain
technology,
which
ensures
data
integrity
security
in
IoT
networks.
This
paper
provides
a
meticulous
exploration
aggregation
techniques
within
context
blockchain‐based
systems.
study
categorizes
algorithms
into
Privacy‐Preserving,
Machine
Learning‐Based,
Hierarchical,
Real‐Time,
Custom
Aggregation
Algorithms,
each
tailored
specific
requirements.
Privacy‐Preserving
Algorithms
focus
on
safeguarding
sensitive
through
encryption
secure
protocols.
Learning‐Based
adapts
dynamically
patterns,
offering
predictive
insights
adaptability.
Hierarchical
organizes
devices
structured
hierarchy,
optimizing
processing.
Real‐Time
processes
instantly,
ensuring
low
latency
for
time‐sensitive
applications.
are
bespoke
solutions
unique
application
demands,
emphasizing
efficiency
security.
Through
comparative
analysis
these
techniques,
explores
their
advantages,
disadvantages,
applicability,
addressing
challenges
suggesting
future
research
directions.
integration
not
only
enhances
network
but
also
longevity
modern
technological
infrastructures.
builds
upon
prior
field
technology
by
extending
implications
SLR
method
been
used
investigate
one
terms
influential
properties
such
as
main
idea,
strategies.
results
indicate
most
articles
were
published
2021
2022.
Moreover,
some
important
parameters
privacy
security,
latency,
processing,
energy
consumption,
complexity,
reliability
involved
investigations.
Sustainability,
Год журнала:
2023,
Номер
15(16), С. 12406 - 12406
Опубликована: Авг. 15, 2023
With
the
swift
pace
of
development
artificial
intelligence
(AI)
in
diverse
spheres,
medical
and
healthcare
fields
are
utilizing
machine
learning
(ML)
methodologies
numerous
inventive
ways.
ML
techniques
have
outstripped
formerly
state-of-the-art
practices,
yielding
faster
more
precise
outcomes.
Healthcare
practitioners
increasingly
drawn
to
this
technology
their
initiatives
relating
Internet
Behavior
(IoB).
This
area
research
scrutinizes
rationales,
approaches,
timing
human
adoption,
encompassing
domains
Things
(IoT),
behavioral
science,
edge
analytics.
The
significance
applications
based
on
IoB
stems
from
its
ability
analyze
interpret
copious
amounts
complex
data
instantly,
providing
innovative
perspectives
that
can
enhance
outcomes
boost
efficiency
IoB-based
procedures
thus
aid
diagnoses,
treatment
protocols,
clinical
decision
making.
As
a
result
inadequacy
thorough
inquiry
into
employment
ML-based
approaches
context
using
for
applications,
we
conducted
study
subject
matter,
introducing
novel
taxonomy
underscores
need
employ
each
method
distinctively.
objective
mind,
classified
cutting-edge
solutions
challenges
five
categories,
which
convolutional
neural
networks
(CNNs),
recurrent
(RNNs),
deep
(DNNs),
multilayer
perceptions
(MLPs),
hybrid
methods.
In
order
delve
deeper,
systematic
literature
review
(SLR)
examined
critical
factors,
such
as
primary
concept,
benefits,
drawbacks,
simulation
environment,
datasets.
Subsequently,
highlighted
pioneering
studies
issues.
Moreover,
several
related
implementation
medicine
been
tackled,
thereby
gradually
fostering
further
endeavors
health
studies.
Our
findings
indicated
Tensorflow
was
most
commonly
utilized
setting,
accounting
24%
proposed
by
researchers.
Additionally,
accuracy
deemed
be
crucial
parameter
majority
papers.
Cognitive Computation,
Год журнала:
2024,
Номер
16(3), С. 1073 - 1091
Опубликована: Янв. 26, 2024
Abstract
In
recent
years,
the
proliferation
of
deep
learning
(DL)
techniques
has
given
rise
to
a
significant
challenge
in
form
deepfake
videos,
posing
grave
threat
authenticity
media
content.
With
rapid
advancement
DL
technology,
creation
convincingly
realistic
videos
become
increasingly
prevalent,
raising
serious
concerns
about
potential
misuse
such
Deepfakes
have
undermine
trust
visual
media,
with
implications
for
fields
as
diverse
journalism,
entertainment,
and
security.
This
study
presents
an
innovative
solution
by
harnessing
blockchain-based
federated
(FL)
address
this
issue,
focusing
on
preserving
data
source
anonymity.
The
approach
combines
strengths
SegCaps
convolutional
neural
network
(CNN)
methods
improved
image
feature
extraction,
followed
capsule
(CN)
training
enhance
generalization.
A
novel
normalization
technique
is
introduced
tackle
heterogeneity
stemming
from
global
sources.
Moreover,
transfer
(TL)
preprocessing
are
deployed
elevate
performance.
These
efforts
culminate
collaborative
model
zfacilitated
blockchain
FL
while
maintaining
utmost
confidentiality
effectiveness
our
methodology
rigorously
tested
validated
through
extensive
experiments.
experiments
reveal
substantial
improvement
accuracy,
impressive
average
increase
6.6%
compared
six
benchmark
models.
Furthermore,
demonstrates
5.1%
enhancement
area
under
curve
(AUC)
metric,
underscoring
its
ability
outperform
existing
detection
methods.
results
substantiate
proposed
countering
conclusion,
represents
promising
avenue
advancing
detection.
By
leveraging
resources
power
we
critical
need
As
continues
grow,
comprehensive
provides
effective
means
protect
integrity
trustworthiness
far-reaching
both
industry
society.
work
stands
step
toward
menace
content
rapidly
evolving
digital
landscape.
International Journal of Communication Systems,
Год журнала:
2024,
Номер
37(15)
Опубликована: Июль 15, 2024
Summary
The
latest
developments
in
the
industrial
Internet
of
things
(IIoT)
have
opened
up
a
collection
possibilities
for
many
industries.
To
solve
massive
IIoT
data
security
and
efficiency
problems,
potential
approach
is
considered
to
satisfy
main
needs
IIoT,
such
as
high
throughput,
security,
efficiency,
which
named
blockchain.
blockchain
mechanism
significant
boosting
protection
performance.
In
quest
amplify
capabilities
blockchain‐based
pivotal
role
accorded
Glowworm
Swarm
Optimization
(GSO)
algorithm.
Inspired
by
collaborative
brilliance
glowworms
nature,
GSO
algorithm
offers
unique
harmonizing
these
conflicting
aims.
This
paper
proposes
new
improve
performance
optimization
using
due
blockchain's
contradictory
objectives.
proposed
system
addresses
scalability
challenges
typically
associated
with
technology
efficiently
managing
interactions
among
nodes
dynamically
adapting
network
demands.
optimizes
allocation
resources
decision‐making,
reducing
inefficiencies
bottlenecks.
method
demonstrates
considerable
improvements
through
extensive
simulations
compared
traditional
algorithms,
offering
more
scalable
efficient
solution
applications
context
IIoT.
simulation
computational
study
shown
that
considerably
improves
objective
function
systems'
algorithms.
It
provides
secure
systems
industries
corporations.
Sensors,
Год журнала:
2025,
Номер
25(4), С. 1258 - 1258
Опубликована: Фев. 19, 2025
The
exponential
growth
of
wireless
traffic
has
imposed
new
technical
challenges
on
the
Internet
and
defined
approaches
to
dealing
with
its
intensive
use.
Caching,
especially
cooperative
caching,
become
a
revolutionary
paradigm
shift
advance
environments
based
technologies
enable
efficient
data
distribution
support
mobility,
scalability,
manageability
networks.
Mobile
ad
hoc
networks
(MANETs),
mesh
(WMNs),
Wireless
Sensor
Networks
(WSNs),
Vehicular
(VANETs)
have
adopted
caching
practices
overcome
these
hurdles
progressively.
In
this
paper,
we
discuss
problems
issues
in
current
paradigms
as
well
spotlight
versatile
potential
solution
increasing
complications
We
classify
multiple
schemes
distinct
communication
contexts
highlight
advantages
applicability.
Moreover,
identify
research
directions
further
study
enhance
mechanisms
concerning
This
extensive
review
offers
useful
findings
design
sound
strategies
pursuit
enhancing
next-generation
