Comprehensive systematic review of information fusion methods in smart cities and urban environments
Information Fusion,
Год журнала:
2024,
Номер
107, С. 102317 - 102317
Опубликована: Фев. 21, 2024
Smart
cities
result
from
integrating
advanced
technologies
and
intelligent
sensors
into
modern
urban
infrastructure.
The
Internet
of
Things
(IoT)
data
integration
are
pivotal
in
creating
interconnected
spaces.
In
this
literature
review,
we
explore
the
different
methods
information
fusion
used
smart
cities,
along
with
their
advantages
challenges.
However,
there
notable
challenges
managing
diverse
sources,
handling
large
volumes,
meeting
near-real-time
demands
various
city
applications.
review
aims
to
examine
applications
detail,
incorporating
quality
evaluation
techniques
identifying
critical
issues
while
outlining
promising
research
directions.
order
accomplish
our
goal,
conducted
a
comprehensive
search
applied
selective
criteria.
We
identified
59
recent
studies
addressing
machine
learning
(ML)
deep
(DL)
These
were
obtained
databases
such
as
ScienceDirect
(SD),
Scopus,
Web
Science
(WoS),
IEEE
Xplore.
main
objective
study
is
provide
more
detailed
insights
by
supplementing
existing
research.
word
cloud
visualisation
learning/deep
papers
shows
landscape,
covering
both
technical
aspects
artificial
intelligence
practical
settings.
Apart
exploration,
also
delves
ethical
privacy
implications
arising
cities.
Moreover,
it
thoroughly
examines
that
must
be
addressed
realise
revolution's
potential
fully.
Язык: Английский
B2RAM: Design and practical implementation of a secured information management framework for dynamic resource allocation using a novel 2-Tier blockchain model
Simulation Modelling Practice and Theory,
Год журнала:
2025,
Номер
unknown, С. 103096 - 103096
Опубликована: Март 1, 2025
Язык: Английский
Energy‐efficient resource allocation over wireless communication systems through deep reinforcement learning
International Journal of Communication Systems,
Год журнала:
2023,
Номер
38(1)
Опубликована: Авг. 21, 2023
Summary
As
the
popularity
of
Internet
Things
(IoT)
increases,
so
do
energy
requirements
IoT
terminal
equipment.
To
address
shortage
problem
equipment
and
ensure
continuous
stable
operation
in
light
renewable
an
uncertain
environment,
a
rational
efficient
allocation
strategy
is
required.
This
paper
proposes
deep
reinforcement
learning
that
uses
DQN
algorithm
to
directly
interact
with
unknown
environment.
The
best
method
independent
environmental
knowledge,
pretraining
proposed
maximise
initialization
state
strategy.
Experiments
comparison
simulation
are
conducted
under
various
channel
data
circumstances.
Results
indicate
outperforms
current
multiple
conditions
has
high
capacity
for
adaptation
changing
conditions.
Язык: Английский
Empowering Principals for Lifelong Learning: Self-directed Approaches in Digitalized Information Systems
Journal of Information Systems Engineering & Management,
Год журнала:
2024,
Номер
9(4), С. 27098 - 27098
Опубликована: Сен. 29, 2024
The
study
delves
into
the
dynamic
interplay
between
digitalized
information
systems,
competencies,
self-directed
learning,
and
lifelong
learning
in
context
of
contemporary
educational
landscape.
With
integration
Artificial
Intelligence
(AI)
evolving
competencies
becoming
integral
to
education,
understanding
their
combined
impact
on
individuals'
attitudes
toward
is
paramount.
Past
research
has
explored
these
elements
individually,
but
a
comprehensive
examination
interconnected
relationships
remains
scarce.
primary
purpose
investigate
how
AI
integration,
collectively
influence
attitudes.
aims
uncover
intricate
dynamics
by
exploring
systems
mediating
role
overall
implications
for
behaviors.
Utilizing
quantitative
approach,
focuses
teachers
China,
distributing
500
questionnaires
receiving
340
responses.
design
incorporates
cross-sectional
survey
methodology,
employing
structured
questionnaire
gather
data
Preliminary
findings
reveal
significant
correlations
observes
highlighting
its
importance
shaping
relationship
inclination
learning.
This
contributes
theoretical
complex
education.
Its
originality
lies
integrating
framework.
Язык: Английский
Multi-UAVs task allocation method based on MPSO-SA-DQN
Measurement and Control,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 13, 2024
Multi-UAVs
play
an
important
role
in
the
battlefield.
Although
many
methods
are
proposed
to
solve
Multi-UAV
task
allocation,
there
still
existing
problems
of
complex
time
constraints
and
uncertain
solution
space.
The
reason
is
that
multi-UAVs
usually
face
changing
environmental
factors.
Aiming
at
solving
such
problem,
this
paper
proposes
a
multi-UAV
assignment
method
based
on
Deep
Q-based
evolutionary
reinforcement
learning
algorithms
(MPSO-SA-DQN).
Specifically,
builds
multi-agent
training
framework
deep
mechanism
SA-DQN.
Its
aim
improve
global
exploration
optimization
capabilities
multi-agents.
At
same
time,
multi-dimensional
particle
swarm
algorithm
introduced
optimize
state
Based
priority
mapping,
MPSO-SA-DQN
proposed.
As
result,
multi-agents
can
execution
real
environment
interaction.
Besides,
it
also
has
ability
reach
optimal
maximum
reward.
According
characteristics
assignment,
designs
space
autoencoder
strategy
feature.
A
tasks
allocation
iterative
proposed,
so
as
continuously
scheme.
simulation
results
show
effectively
problem
uncertainty
allocation.
achieves
faster
convergence
good
prospect
promotion
field
UAV
cooperative
planning.
Язык: Английский
COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories
Journal of Cloud Computing Advances Systems and Applications,
Год журнала:
2024,
Номер
13(1)
Опубликована: Ноя. 5, 2024
With
the
dawn
of
Industry
5.0
upon
us,
smart
factory
emerges
as
a
pivotal
element,
playing
crucial
role
in
realm
intelligent
manufacturing.
Meanwhile,
mobile
edge
computing
is
proposed
to
alleviate
computational
burden
presented
by
substantial
workloads
factories.
Nonetheless,
it
very
challenging
effectively
incorporate
resources
improve
efficiency
resource
deployment
Accordingly,
we
devise
novel
approach
based
on
Proximal
Policy
Optimization
algorithm
with
Self-Attention
Mechanism
implement
allocation
MEC-Empowered
Smart
Factories.
More
specifically,
self-attention
mechanism
incorporated
enable
dynamic
focus
state
information,
accelerates
convergence
and
facilitates
global
control.
A
great
number
experiments
conducted
both
simulated
real
datasets
have
verified
superiority
our
compared
state-of-the-art
baselines.
Язык: Английский