With the development of the Internet of energy, more and more participants share data by
different types of edge devices. However, such multi-source heterogenous data typically …

As a distributed computing paradigm, edge computing has become a key technology for
providing timely services to mobile devices by connecting Internet of Things (IoT), cloud …

A rapid-growing machine learning technique called federated edge learning has emerged to
allow a massive number of edge devices (eg smart phones) to collaboratively train globally …

Mobile-edge computing (MEC) is a in demand method for improving the quality of
computation experience on mobile devices (MD) since it helps MD's to offload computing …

Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a
promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the …

The rapid increase in the volume of data generated from connected devices in industrial
Internet of Things paradigm, opens up new possibilities for enhancing the quality of service …

In Internet of Things (IoT) employing centralized machine learning, security is a major
concern due to the heterogeneity of end devices. Decentralized machine learning (DML) …

Benefiting from the real-time processing ability of edge computing, computing tasks
requested by smart devices in the Internet of Things are offloaded to edge computing …

Blockchain technology with its secure, transparent and decentralized nature has been
recently employed in many mobile applications. However, the process of executing …

With the proliferation of compute-intensive and delay-sensitive mobile applications, large
amounts of computational resources with stringent latency requirements are required on …