As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

An essential consideration in cyber-physical systems (CPS) is the ability to support secure
communication services, such as points of interest (POI) microservices. Existing approaches …

A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to
maintain and update the global model. However, in many cases orchestrating a centralized …

Over the past decades, unmanned aerial vehicles (UAVs) have been widely used in both
military and civilian fields. In these applications, flocking motion is a fundamental but crucial …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

The advances in the Internet of Things (IoT) and increased availability of the road sensors
allow for fine-grained traffic forecasting, which is of particular importance toward building an …

With the prosperity of artificial intelligence, neural networks have been increasingly applied
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …

Synthetic data generation based on state-of-the-art deep learning methods has recently
emerged as a promising solution to replace the expensive and laborious collection of real …

Cooperative multiaccess edge computing (MEC) is a promising paradigm for the next-
generation mobile networks. However, when the number of users explodes, the …