Edge-computing-enabled smart cities: A comprehensive survey
Recent years have disclosed a remarkable proliferation of compute-intensive applications in
smart cities. Such applications continuously generate enormous amounts of data which …
smart cities. Such applications continuously generate enormous amounts of data which …
[HTML][HTML] Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications
Recently, interest in Internet of Vehicles'(IoV) technologies has significantly emerged due to
the substantial development in the smart automobile industries. Internet of Vehicles' …
the substantial development in the smart automobile industries. Internet of Vehicles' …
SDN/NFV-empowered future IoV with enhanced communication, computing, and caching
Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the
Internet with advanced communication and networking technologies, which can enhance …
Internet with advanced communication and networking technologies, which can enhance …
A survey of 5G network systems: challenges and machine learning approaches
Abstract 5G cellular networks are expected to be the key infrastructure to deliver the
emerging services. These services bring new requirements and challenges that obstruct the …
emerging services. These services bring new requirements and challenges that obstruct the …
Fog computing for sustainable smart cities in the IoT era: Caching techniques and enabling technologies-an overview
H Zahmatkesh, F Al-Turjman - Sustainable cities and society, 2020 - Elsevier
In recent decade, the number of devices involved with the Internet of Things (IoT)
phenomena has increased dramatically. Parallel to this, fog computing paradigm has been …
phenomena has increased dramatically. Parallel to this, fog computing paradigm has been …
Deep-learning-based wireless resource allocation with application to vehicular networks
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …
interference, improving network efficiency, and ultimately optimizing wireless communication …
Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey
Edge networking is a complex and dynamic computing paradigm that aims to push cloud re-
sources closer to the end user improving responsiveness and reducing backhaul traffic …
sources closer to the end user improving responsiveness and reducing backhaul traffic …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Making knowledge tradable in edge-AI enabled IoT: A consortium blockchain-based efficient and incentive approach
X Lin, J Li, J Wu, H Liang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Nowadays, benefit from more powerful edge computing devices and edge artificial
intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge …
intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge …
Unfolding WMMSE using graph neural networks for efficient power allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
In solving this problem, we depart from classical purely model-based approaches and …
In solving this problem, we depart from classical purely model-based approaches and …