New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …

The next generation (6G) wireless systems aim to cater to the Internet of Everything (IoE)
and revolutionize customer services and applications to a fully intelligent and autonomous …

The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm
for emerging applications owing to its huge potential in providing low-latency and ultra …

The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …

To meet next-generation Internet of Things (IoT) application demands, edge computing
moves processing power and storage closer to the network edge to minimize latency and …

In recent decades, deep-learning dependent fruit disease detection and classification
techniques have evinced outstanding results in technologically advanced horticulture …

With advancements of cloud technologies Multi-Access Edge Computing (MEC) emerged as
a remarkable edge-cloud technology to provide computing facilities to resource-restrained …

Edge devices can offload deep neural network (DNN) inference to the cloud to overcome
energy or processing constraints. Nevertheless, offloading adds communication delay …

Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction
for edge computing, we consider the problem of caching and pricing applications for edge …

With the increased penetration of cloud computing and virtualization, a plethora of internet of
things devices have been deployed globally. As a result, computationally intensive tasks are …