Published: Dec. 9, 2023
Language: Английский
Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2025, Volume and Issue: 14(1)
Published: Feb. 5, 2025
Language: Английский
Citations
1
Smart Cities, Journal Year: 2024, Volume and Issue: 7(1), P. 680 - 711
Published: Feb. 19, 2024
The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications services to improve resilience responsiveness ensure closer real-time control. However, the large-scale integration Internet Things (IoT) devices has led generation significant amounts data at edge grid, posing challenges for traditional cloud-based smart-grid architectures meet stringent latency response time requirements emerging applications. In this paper, we delve into grid computational distribution architectures, including edge–fog–cloud models, orchestration, frameworks support design offloading across continuum. Key factors influencing process, such as network performance, Artificial Intelligence (AI) processes, requirements, application-specific factors, efficiency, are analyzed considering operational requirements. We conduct a comprehensive overview current research landscape decision-making regarding strategies from cloud fog or edge. focus is on metaheuristics identifying near-optimal solutions reinforcement learning adaptively optimizing process. A macro perspective determining when what offload in provided next-generation AI applications, offering an features trade-offs selecting between federated solutions. Finally, work contributes understanding grids, providing Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis cost–benefit strategies.
Language: Английский
Citations
8
Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)
Published: Jan. 23, 2024
Abstract
The
emergence
of
mobile
edge
computing
(MEC)
has
brought
cloud
services
to
nearby
servers
facilitating
penetration
real-time
and
resource-consuming
applications
from
smart
devices
at
a
high
rate.
problem
task
offloading
the
been
addressed
in
state-of-the-art
works
by
introducing
collaboration
among
MEC
servers.
However,
their
contributions
are
either
limited
minimization
service
latency
or
cost
reduction.
In
this
paper,
we
address
developing
multi-objective
optimization
framework
that
jointly
optimizes
latency,
energy
consumption,
resource
usage
cost.
formulated
is
proven
be
an
NP-hard
one.
Thus,
develop
evolutionary
meta-heuristic
solution
for
problem,
namely
WOLVERINE,
based
on
Binary
Multi-objective
Grey
Wolf
Optimization
algorithm
achieves
feasible
within
polynomial
time
having
computational
complexity
$$O(M^3)$$
Language: Английский
Citations
4
The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)
Published: Jan. 3, 2025
Language: Английский
Citations
0
International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(7)
Published: March 31, 2025
ABSTRACT The Internet of Things (IoT) paradigm has recently opened up new research opportunities in many academic and industrial fields, particularly medicine. IoT‐enabled technology transformed healthcare from a centralized model to personalized system driven by ubiquitous wearable devices smartphones. implementation IoT faces critical challenges, including energy efficiency, network reliability, task response time, availability services. An Adaptive Fox Optimizer (AFO) is proposed as novel IoT‐supported method for providing zero‐orientation nature AFO mitigated quasi‐oppositional learning. A reinitialization plan also presented improve exploration skills. Furthermore, an additional stage implemented with two movement techniques optimize search capabilities. In addition, multi‐best methodology used deviate the local optimum manage population more efficiently. Ultimately, greedy selection accelerates convergence exploitability. was rigorously evaluated, demonstrating significant improvements across key performance metrics. Compared conventional approaches, enhances 83.33%, reliability 11.32%, reduces consumption 19.12%, decreases times 25.14%. These results highlight AFO's ability resource allocation, enhance fault tolerance, prolong lifespan environments. By addressing this contributes developing efficient, reliable, responsive systems, paving way advancements health monitoring, telemedicine, smart hospital management.
Language: Английский
Citations
0
Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 284, P. 127814 - 127814
Published: May 8, 2025
Language: Английский
Citations
0
Journal of Network and Computer Applications, Journal Year: 2025, Volume and Issue: unknown, P. 104211 - 104211
Published: May 1, 2025
Language: Английский
Citations
0
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125762 - 125762
Published: Nov. 1, 2024
Language: Английский
Citations
3
Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(18), P. 10733 - 10760
Published: March 27, 2024
Language: Английский
Citations
1
Cyber-Physical Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 58
Published: Oct. 18, 2024
In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced architecture that optimizes network infrastructure, focusing on the adoption of MQTT protocol and introducing Cognitive Smart Objects for managing applications. These objects use Neural Networks to predict optimal actions based user behavior patterns. A Continuous Learning mechanism enables real-time adaptation evolving interactions. The study highlights role Transformation in Personal IoT, driving intelligent automation enhancing experience.
Language: Английский
Citations
1