AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges and future perspectives
The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …
Scheduling IoT applications in edge and fog computing environments: A taxonomy and future directions
Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the
network with low latency and high-access bandwidth to support a diverse range of IoT …
network with low latency and high-access bandwidth to support a diverse range of IoT …
The metaverse: Survey, trends, novel pipeline ecosystem & future directions
The Metaverse offers a second world beyond reality, where boundaries are non-existent,
and possibilities are endless through engagement and immersive experiences using the …
and possibilities are endless through engagement and immersive experiences using the …
A reinforcement learning model for the reliability of blockchain oracles
Smart contracts struggle with the major limitation of operating on data that is solely residing
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
ON-DEMAND-FL: A dynamic and efficient multicriteria federated learning client deployment scheme
In this article, we increase the availability and integration of devices in the learning process
to enhance the convergence of federated learning (FL) models. To address the issue of …
to enhance the convergence of federated learning (FL) models. To address the issue of …
[HTML][HTML] Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm
The number of Internet of Things (IoT)-based applications is constantly increasing, and
transferring all their associated data to a remote centralized cloud requires more latency …
transferring all their associated data to a remote centralized cloud requires more latency …
Graph convolutional recurrent networks for reward shaping in reinforcement learning
In this paper, we consider the problem of low-speed convergence in Reinforcement
Learning (RL). As a solution, various potential-based reward shaping techniques were …
Learning (RL). As a solution, various potential-based reward shaping techniques were …
Optimal placement of applications in the fog environment: A systematic literature review
The fog-computing paradigm complements cloud computing to support the deployment and
execution of latency-sensitive applications at the network edge by offering enhanced …
execution of latency-sensitive applications at the network edge by offering enhanced …
A fuzzy approach for optimal placement of IoT applications in fog-cloud computing
F Tavousi, S Azizi, A Ghaderzadeh - Cluster Computing, 2022 - Springer
Fog-cloud computing is a promising distributed model for hosting ever-increasing Internet of
Things (IoT) applications. IoT applications should meet different characteristics such as …
Things (IoT) applications. IoT applications should meet different characteristics such as …
Dynamic IoT service placement based on shared parallel architecture in fog-cloud computing
M Qin, M Li, RO Yahya - Internet of Things, 2023 - Elsevier
Fog-cloud computing is a promising computing paradigm for processing and storing
massive data produced by Internet of Things (IoT) devices. Considering that the fog …
massive data produced by Internet of Things (IoT) devices. Considering that the fog …