Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the uncertainty of the arrival computing tasks, it is difficult for a single vehicle to determine the optimal offloading strategy. In this paper, we propose a Digital Twin (DT) empowered task offloading framework for Internet of Vehicles. As a software agent residing in the cloud, a DT can obtain both global network information by using communications among DTs, and historical information of a vehicle by using the communications within the twin. The global network information and historical vehicular information can significantly facilitate the offloading. In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT. Accordingly, we model the offloading scheduling process as a Markov Decision Process (MDP) to minimize the long-term cost in terms of a trade off between task latency, energy consumption, and renting cost of clouds. Simulation results demonstrate that our algorithm can effectively find the optimal offloading strategy, as well as achieve the fast convergence speed and high performance, compared with other existing approaches.
Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西大学智能组网与场景系统重点实验室（桂林电子科技大学信息与通信学院））；Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Architecture and Transportation Engineering, Guilin University of Electronic Technology（桂林电子科技大学建筑与交通工程学院）
University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula大都会数字工程中心）
The rapid development of Industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing eff…
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks
The Ohio State University（俄亥俄州立大学）；BTS Group（BTS集团）；Istanbul Technical University（伊斯坦布尔理工大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Edinburgh Napier University（爱丁堡龙比亚大学）
Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data …
Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing
Chonnam National University（全南大学）；Institute for Energy Technology（能源技术研究所）
Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model
Guilin University of Electronic Technology（桂林电子科技大学）；Guangxi University（广西大学）；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通大数据应用技术国家工程实验室（广西））；Guangdong University of Technology（广东工业大学）
University of Waterloo（滑铁卢大学）；Carleton University（卡尔顿大学）；Toronto Metropolitan University（多伦多都会大学）
In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation. T…
The envisioned 6G networks are expected to support extremely high data rates, low-latency, and radically new applications empowered by machine learning. The futuristic 6G networks require a novel framework that can be used to operate, manage, and optimize its underlying services such as ultra-reliable and low-latency communication, and Internet of everything. In recent years, artificial intelligence (AI) has demonstrated significant success in optimizing and designing networks. The AI-enabled traffic orchestration can dynamically organize different network architectures and slices to provide q…
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications（重庆邮电大学电子信息与网络研究院）；Concordia University（康考迪亚大学）
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanyang Technological University（南洋理工大学）；Guangdong University of Technology（广东工业大学）；Concordia University（康考迪亚大学）；University of Waterloo（滑铁卢大学）
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks
School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科学技术学院）；School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）；Zhejiang University/University of Illinois Urbana Champaign Institute, Zhejiang University（浙江大学浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究院）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
In this paper, we study the coexistence and synergy between edge and central cloud computing in a heterogeneous cellular network (HetNet), which contains a multi-antenna macro base station (MBS), multiple multi-antenna small base stations (SBSs) and multiple single-antenna user equipment (UEs). The SBSs are empowered by edge clouds offering limited computing services for UEs, whereas the MBS provides high-performance central cloud computing services to UEs via a restricted multiple-input multiple-output (MIMO) backhaul to their associated SBSs. With processing latency constraints at the centra…
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系，北京信息科学与技术国家研究中心）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信工程学院）；Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology (HKUST)（香港科学与技术大学电气与计算机工程系）
