With the increasing growth of information through smart devices, enhancing the quality of
human life necessitates the adoption of various computational paradigms including, cloud …

Federated Learning (FL) is an emerging paradigm for training machine learning models
across decentralized edge devices, ensuring data privacy and reducing computational tasks …

The existing research on dependent task offloading and resource allocation assumes that
edge servers can provide computational and communication resources free of charge. This …

Wireless powered mobile edge computing (MEC) has become a vital component of future
6G networks, offering efficient computational capabilities to internet of things (IoT) devices …

The rapid expansion of mobile broadband networks and the proliferation of Internet of
Things (IoT) applications have substantially increased data transmission and processing …

Smart city applications are revolutionizing the way people interact with diverse systems in
city-wide applications. Internet of Things (IoT) and machine learning are two enabling …

Modern cloud/edge architectures need to orchestrate multiple layers of heterogeneous
computing nodes, including pervasive sensors/actuators, distributed edge/fog nodes …

Modern Cloud/Edge architectures need to orchestrate multiple layers of heterogeneous
computing nodes, including pervasive sensors/actuators, distributed Edge/Fog nodes …

To deal with the diverse computing tasks generated by Internet of Things devices (IoTDs),
unmanned aerial vehicles (UAVs)-assisted edge offloading technology has emerged …

The rapid evolution of mobile networks presents challenges for devices with limited
computing power. Mobile or multi-access edge computing (MEC) addresses this by …