In the past decade, we have witnessed a dramatically increasing volume of data collected
from various sources. To maximize utilization, various machine and deep learning models …

In this study, we focus on improving the energy efficiency of multiple mobile augmented
reality (MAR) devices (MDs) with different MAR applications, which are connected to a …

In mobile edge computing systems, a task offloading approach should balance efficiency,
adaptability, trust management, and reliability. This approach aims to maximise resource …

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings
for US public companies to disclose financial and other important business information. Due …

Federated learning (FL) is a distributed machine learning technique that enables model
development on user equipments (UEs) locally, without violating their data privacy …

Many IoT applications have the requirements of conducting complex IoT events processing
(eg, speech recognition) that are hardly supported by low-end IoT devices due to limited …

Edge computing offers a promising approach to building on-demand computing
infrastructure, leveraging clusters of small, commodity-scale, cost-effective devices called …

Businesses have made increasing adoption and incorporation of cloud technology into
internal processes in the last decade. The cloud-based deployment provides on-demand …

Computational offloading is used to augment the capabilities of edge devices by delegating
highly complex tasks (eg Deep Neural Network-DNN-inference) to remote devices. This …

Despite the emergence of edge computing as a key technology paradigm, there is a general
lack of auto-scaling techniques specifically designed for edge computing applications …