As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network,
enhancing the high-bandwidth and low-latency of wireless data transmission with the …

In the realm of next-generation mobile communication networks, characterized by dynamic
and evolving workloads, the efficient resource allocation becomes paramount for achieving …

Close computing paradigms such as fog and edge have become promising technologies for
mobile applications running on pervasive mobile equipment utilized by a wide range of …

The rapid development of the Internet of Vehicles (IoV) leads to various on-board
applications including delay-sensitive and compute-intensive applications. However …

Cloud-edge collaboration can overcome the shortcomings of high latency of the centralized
model of cloud computing and insufficient computing power of edge computing, and better …

The cloud-fog computing paradigm is a novel hybrid computing model that delivers
computational services to Fog nodes situated near data sources. This paradigm features a …

Computation offloading is considered as a promising method to improve computation
performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry …