Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.
Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task sche…
Xidian University（西安电子科技大学）；Deakin University（迪肯大学）；University of Macau（澳门大学）
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 histo…
Reinforcement-learning robotic sailboats: simulator and preliminary results
Fluminense Federal University（弗卢米嫩塞联邦大学）；Center Inria de l’Université de Lille（里尔大学Inria中心）；Federal University of Rio Grande do Norte（北里奥格兰德联邦大学）；Center Inria Chile（智利Inria中心）
Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-…
Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University（江西省先进信号处理与智能通信重点实验室信息工程学院）；Department of Electronic Engineering, State Key Laboratory of Space Network and Communications, and the Beijing National Research Center for Information Science and Technology, Tsinghua University（电子工程系、空间网络与通信国家重点实验室及北京信息科学与技术国家研究中心、清华大学）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（信息与通信工程学院、西安电子科技大学信息与通信系统国家重点实验室）；Department of Electronic Engineering, Shanghai JiaoTong University（电子工程系、上海交通大学）；School of Information Science and Engineering, Southeast University（信息科学与工程学院、东南大学）
Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
Department of Electrical and Computer Engineering, McMaster University, CANADA（电气与计算机工程系，麦斯特大学）；Department of Computing and Software, McMaster University, CANADA（计算与软件系，麦斯特大学）
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Institute for the Wireless Internet of Things, Northeastern University（东北大学无线互联网研究所）
Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments
North Carolina State University（北卡罗来纳州立大学）；Southern Methodist University（南方卫理公会大学）；National University of Sciences and Technology（国立科学与技术大学）；University of Utah（犹他大学）；Florida International University（佛罗里达国际大学）；Columbia University（哥伦比亚大学）；The University of Georgia（佐治亚大学）；Florida Atlantic University（佛罗里达大西洋大学）；The University of North Texas（北德克萨斯大学）；University of Minnesota（明尼苏达大学）
University of Padova（帕多瓦大学）；University of Bologna（博洛尼亚大学）；Huawei Technologies（华为技术有限公司）
This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as the Radio Access Network (RAN) is concerned, centralized pre-configured resource allocation requires scheduling grants to be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their upl…
The emerging autonomous vehicles (AVs) will inevitably revolutionize the transportation systems. This is because of a key feature of AVs; instead of being managed by human drivers as the conventional vehicles, AVs are of the complete capability to manage the driving by themselves. As a result, the futuristic intelligent transportation system (FITS) can be a centrally managed and optimized system with the fully coordinated driving of vehicles, which is impossible by the current transportation systems controlled by humans. In this article, we envision the operation of such FITS when AVs, advance…
