With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

Space-air-ground integrated edge computing is expecting to provide pervasive computation services for Internet of Things, especially in remote areas. However, the offloading process of power-limited IoT devices is a challenge issue due to unreliable communications in aerial environment. In this paper, we propose an energy efficient space-air-ground integrated edge computing network architecture, in which the IoT devices choose the most appropriate LEO satellites or unmanned aerial vehicles (UAVs) for task offloading according to their energy level, communication conditions and computing capabilities. In order to providing efficient task offloading and energy saving policy under uncertainty aerial environment, a constrained Markov decision process is employed to formulate the task offloading decision problem and a deep reinforcement learning (DRL)-based algorithm is devised to solve the proposed problem. An adaptive federated DRL-based offloading method is further proposed to find sub-optimal offloading decisions by considering the privacy protection and communication failure in the proposed network. Numerical results confirm the effectiveness of the proposed schemes on energy saving and computation efficiency.

In 6G systems, space-air-ground integrated network (SAGIN), relying on space/aerial communications to complement terrestrial networks, is developed to achieve worldwide connectivity and multi-service access especially in remote areas. However, the heavy workload of the resource-limited satellites may raise an important issue about reducing service coverage and quality. In this paper, we propose a collaborative edge computing framework for SAGIN-aided 6G system, in which the LEO satellites are considered as both “servers” and “users”. Excepting provide services for ground users/devices, the LEO satellites is able to offload the tasks to nearby aircrafts via one-hop link or offload them to the cloud server along multi-hop satellite path. To minimize the long-term task completion delay of LEO satellites, a stochastic optimization problem is formulated by considering the variation of the space/aerial environment. The Lyapunovbased optimization method is developed to solve the problem and the delayed online learning technique is adapted to predict dynamic task arrival and queue length of satellites and aircrafts. Numerical results confirm the effectiveness of the proposed collaborative offloading scheme for reducing tasks completion delay of LEO satellites while guaranteeing computation efficiency.

In wireless-powered cognitive radio networks, secure information transmission is of paramount importance for the primary system. This letter considers cooperation between a primary system and a wireless-powered secondary system. In particular, we focus on secure information transmission for the primary system when the secondary users (SUs) are the potential eavesdroppers. We aim to jointly design power splitting and secure beamforming to maximize the secondary system data rate subject to the secrecy QoS requirement of the primary system and the secondary transmitter (ST) power constraint. To solve this nonconvex problem, we propose a two-stage optimization approach. Simulation results demonstrate that our proposed scheme achieves a significant transmission rate of the secondary system while provides a high secrecy rate for the primary system compared to the scheme without energy harvesting.

The development of industry 4.0 relies on emerging technologies of digital twin, machine learning, blockchain and Internet of Things (IoT) to build autonomous self-configuring systems that maximize manufactory efficiency, precision and accuracy. In this paper, we propose a new distributed and secure digital twin driven IIoT framework that integrates federated learning and Directed Acyclic Graph (DAG) blockchain with sharding. The proposed framework includes three planes: the data plane, the blockchain plane and the digital twin plane. Specifically, the data plane performs federated learning through a set of cluster heads to train models at network edges for twin model construction. The blockchain plane, which supports sharding, utilizes a hierarchical consensus scheme based on DAG blockchain to verify both local model updates and global model updates. The digital twin plane is responsible for constructing and maintaining twin model. Then, an efficient resource scheduling scheme is designed by considering performance of both federated learning and DAG blockchain with sharding. Accordingly, an optimization problem is formulated to maximize long-term utility of the digital twin driven IIoT. To cope with mapping error in the digital twin plane, a multi-agent Proximal Policy Optimization (MAPPO) approach is developed to solve the optimization problem. Numerical results illustrate that comparing with traditional approach, the proposed MAPPO improves utility by about 37 %, and reduces time latency by about 14%. Moreover, it also can well adapt to the mapping error.

The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the ‘Pareto optimal’ to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.