6G wireless systems: A vision, architectural elements, and future directions
Internet of everything (IoE)-based smart services are expected to gain immense popularity in
the future, which raises the need for next-generation wireless networks. Although fifth …
the future, which raises the need for next-generation wireless networks. Although fifth …
Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks
Technological advancements of urban informatics and vehicular intelligence have enabled
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …
A survey on virtual network functions for media streaming: Solutions and future challenges
Media services must ensure an enhanced user's perceived quality during content playback
to attract and retain audiences, especially while the streams are distributed remotely via …
to attract and retain audiences, especially while the streams are distributed remotely via …
Matching-theory-based low-latency scheme for multitask federated learning in MEC networks
Nowadays, there is an ever-increasing interests in federated learning, which allows end
devices to collaboratively train a global machine learning model in a decentralized …
devices to collaboratively train a global machine learning model in a decentralized …
Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey
M Christopoulou, S Barmpounakis, H Koumaras… - Vehicular …, 2022 - Elsevier
The automotive industry is undergoing a profound digital transformation to create
autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning …
autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning …
Toward response time minimization considering energy consumption in caching-assisted vehicular edge computing
The advent of vehicular edge computing (VEC) has generated enormous attention in recent
years. It pushes the computational resources in close proximity to the data sources and thus …
years. It pushes the computational resources in close proximity to the data sources and thus …
Meta-hierarchical reinforcement learning (MHRL)-based dynamic resource allocation for dynamic vehicular networks
With the rapid development of vehicular networks, there is an increasing demand for
extensive networking, computting, and caching resources. How to allocate multiple …
extensive networking, computting, and caching resources. How to allocate multiple …
Joint road side units selection and resource allocation in vehicular edge computing
With powerful storage and computation capability, vehicular edge computing is considered
as a promising paradigm to enhance the safety and quality-of-service of vehicles in …
as a promising paradigm to enhance the safety and quality-of-service of vehicles in …
Distributed clustering-based cooperative vehicular edge computing for real-time offloading requests
Mobile vehicles have been considered as potential edge servers to provide computation
resources for the emerging Intelligent Transportation System (ITS) applications. However …
resources for the emerging Intelligent Transportation System (ITS) applications. However …
Meta-learning based dynamic computation task offloading for mobile edge computing networks
Deep learning-based algorithms provide a promising solution to efficiently generate
offloading decisions in mobile edge computing (MEC) networks. However, considering …
offloading decisions in mobile edge computing (MEC) networks. However, considering …