Collaborative sensing leverages the cooperation of a collection of sensors to complete a
large-scale sensing task in Internet of Things (IoT). Although some previous studies have …

Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of
vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep
learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile …

Satellite-ground integrated networks (SGINs) are regarded as promising architectures for
sensing heterogenous measurements, reducing network congestion and for providing …

Fog computing allows services to be deployed on computing resources at the edge of the
network to address the limitations of centralized cloud systems. However, the adoption of fog …

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL)
algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with …

In this paper, multiple machine learning-enabled solutions are adopted to tackle the
challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal …