Automatika,
Год журнала:
2024,
Номер
65(3), С. 973 - 982
Опубликована: Март 12, 2024
Large
amounts
of
processing
resources
are
required
for
the
sensed
raw
big
data
during
generation
process.
Furthermore,
as
typically
privacy
sensitive,
blockchain
technology
can
be
used
to
ensure
concerns.
This
study
examines
a
multiuser
mobile
offloading
network
that
consists
cloud
server
located
remotely
and
an
edge
node.
We
formulate
problem
joint
optimization
task
decision
making
all
users,
computation
resource
allocation
among
executing
applications,
radio
assignment
remote-processing
applications.
The
goal
is
minimize
maximum
weighted
cost
users.
When
compared
other
benchmark
approaches,
simulation
results
show
proposed
algorithm
achieves
optimal
in
terms
both
energy
consumption
delay
result
collaboration.
Finally
strategy
with
93%
efficiency
obtained.
Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102601 - 102601
Опубликована: Июль 22, 2024
The
evolution
of
science
has
given
rise
to
many
technologies
that
have
changed
the
world.
upcoming
Six-Generation
(6G)
mobile
network
indicates
a
fundamental
transformation
in
wireless
technologies,
enhancing
connectivity
and
data
transmission
rates.
In
this
circumstance,
Mobile
Edge
Computing
(MEC)
is
paradigm
technology
emerges
as
key
major
supporter
mobility
awareness.
computing
offers
improved
efficiency
for
service
migration
from
edge
node
user.
However,
management
MEC
complex
challenge
seamless
handovers
between
nodes
must
be
efficiently
executed
ensure
uninterrupted
devices,
demanding
intricate
coordination
low-latency
decision-making.
To
best
author's
knowledge,
there
been
no
comprehensive
work
on
most
recent
developments
awareness
using
6G.
paper
aims
present
general
overview
intersection
over
6G
networks.
concept
networks
comprehensively
introduced.
This
will
highlight
integration
bringing
more
efficient
edge,
reducing
latency,
user
experience.
Meanwhile,
survey
discusses
augmented
reality
with
applications.
applications
emphasizes
need
results
providing
communication
during
serving
base
station
target
station.
study
contributes
understanding
trends
enable
operation
communication.
Furthermore,
we
delve
into
challenges
future
research
directions
networks,
underlining
complexities
potentials
integrating
computing.
Applied Sciences,
Год журнала:
2022,
Номер
12(23), С. 12080 - 12080
Опубликована: Ноя. 25, 2022
Heart
disease
is
one
of
the
lethal
diseases
causing
millions
fatalities
every
year.
The
Internet
Medical
Things
(IoMT)
based
healthcare
effectively
enables
a
reduction
in
death
rate
by
early
diagnosis
and
detection
disease.
biomedical
data
collected
using
IoMT
contains
personalized
information
about
patient
this
has
serious
privacy
concerns.
To
overcome
issues,
several
protection
laws
are
proposed
internationally.
These
created
huge
problem
for
techniques
used
traditional
machine
learning.
We
propose
framework
on
federated
matched
averaging
with
modified
Artificial
Bee
Colony
(M-ABC)
optimization
algorithm
to
issues
improve
method
prediction
heart
paper.
technique
improves
accuracy,
classification
error,
communication
efficiency
as
compared
state-of-the-art
learning
algorithms
real-world
dataset.
IEEE Transactions on Vehicular Technology,
Год журнала:
2023,
Номер
72(12), С. 16917 - 16922
Опубликована: Июль 6, 2023
In
this
paper,
we
propose
a
unmanned
aerial
vehicle
(UAV)-assisted
multi-hop
edge
computing
(UAV-assisted
MEC)
system
in
which
UE
can
offload
its
task
to
multiple
UAVs
fashion.
particular,
the
offloads
nearby
UAV,
and
UAV
execute
part
of
received
remaining
neighboring
UAV.
The
offloading
process
continues
until
execution
is
finished.
benefit
multihop
that
be
finished
faster,
load
shared
among
UAVs,
thus
avoiding
overloading
congestion.
Each
node,
i.e.,
or
needs
determine
size
for
minimize
cumulative
energy
consumption
latency
over
nodes.
We
formulate
stochastic
optimization
problem
under
dynamics
uncertainty
UAV-assisted
MEC
system.
Then,
deep
reinforcement
learning
(DRL)
algorithm
solve
problem.
Simulation
results
are
provided
demonstrate
effectiveness
DRL
algorithm.
