The increasing need for communication capabilities in mobile devices has led to the
recognition of mobile edge computing (MEC) as a critical solution for addressing …

Driven by the Quality of Experience (QoE) demands for video analysis applications within
contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles …

Current connected vehicle applications, such as platooning require heavy‐load computing
capability. Although mobile edge computing (MEC) servers connected to the roadside …

Reinforcement learning (RL) has demonstrated its advantages in the intelligent control of
many vehicle systems. However, controlling the exploration-to-exploitation (E2E) ratio of RL …

Vehicular Edge Computing (VEC) has emerged as a promising paradigm to enable low-
latency Vehicle-to-Everything (V2X) services by bringing computing resources closer to …

This paper presents an innovative large model framework for optimizing the task offloading
efficiency in vehicular edge networks, with a focus on ultra-reliable lowlatency …

Vehicular service providers bear a heavy burden of scalability and service load balancing
problems in traditional edge computing systems. By abstracting the service computing …

Collaborative learning is widely applied in practical education due to its high efficiency and
positive effectiveness. Virtual reality (VR) has driven the development of collaborative …

Federated learning is a distributed machine learning method for learning consumer data
generated by consumer electronic devices. It provides personalized intelligent services for …

Stockpiling and scheduling plans for medical supplies represent essential preventive and
control measures in major public health events. In the face of major infectious diseases …