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With the increase of spacecrafts, data relay satellite network (DRSN) plays an essential role in the space task transmission and processing through broad coverage and efficient information distribution of data relay satellites. However, the growing demand of space tasks and the limited number of satellite antenna lead to the conflicts between high-priority unpredictable tasks and common tasks with timely processing requirements, which further result in the decreasing scheduling efficiency of space tasks. In this paper, a dynamic scheduling scheme with task laxity (DSTL) is proposed to solve scheduling conflicts and enhance scheduling efficiency. Firstly, a task model in visible time window is analyzed according to the processing time limit of tasks and the intermittent connection nature of satellites under a constructed DRSN model. After that, the task laxity is defined by the deadline and execution time of tasks to judge the urgency of tasks, and the optimization problem is formulated to maximize the number of tasks and prioritize high priority tasks by calculating task laxity. Then, the DSTL scheme is designed via four stages, which are task preprocessing, task-resource matching, task-conflict assessment, task-resource updating. In DSTL, a task sorting method with task laxity is raised to arrange the priority of tasks; an urgent task scheduling algorithm (UTS) is put forward to prioritize unpredictable tasks; a task-conflict resolution algorithm (TCR) is brought up to assess conflicting tasks and reschedule them. Following that, an adaptive large neighborhood search algorithm combined with deadline-aware scheduling (ALNS-DAS) is presented to obtain the optimal scheduling scheme, which has the maximum number of completed tasks. The simulation results show that the DSTL can maximize the guarantee ratio of completed tasks and ensure timely processing with the increase of tasks demand.

Efficient scheduling of vehicle resources is of great significance to guarantee vehicle safety and to achieve a higher level of automated driving. Considering the performance fluctuations in data transmission and processing during driving, this paper proposes an adaptive data transmission and computation optimization scheme, where the concept of the Internet-of-Intelligence is introduced to improve the resource decision-making efficiency through knowledge sharing instead of data sharing among vehicles. Specifically, the joint optimization problem is formulated to minimize the long-term energy consumption with the consideration of the average queuing latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem into a series of short-term deterministic optimization subproblems. Afterwards, both the optimization-based solution and the learning-based solution are presented to fully illustrate the performance advantages of Internet-of-Intelligence applied to vehicle networks. The former can output the global optimal solution by iteration, while the latter aims at accelerating the distributed optimization decision-making through building a fast deep reinforcement learning framework based on shared knowledge among vehicles. Simulation results show the advantages of the proposed scheme in stability, energy consumption, and latency, and it also verifies the convergence speed and training accuracy of the proposed fast deep reinforcement learning framework.

The Metaverse, an interconnected network of immersive digital realms, is poised to reshape the future by seamlessly merging physical reality with virtual environments. Its potential to revolutionize diverse aspects of human existence, from entertainment to commerce, underscores its significance. At the heart of this transformation lies Generative AI, a branch of artificial intelligence focused on creating novel content. Generative AI serves as a catalyst, propelling the Metaverse's evolution by enhancing it with immersive experiences. The Metaverse is comprised of three pivotal domains, namely, text, visual, and audio. The Metaverse's fabric intertwines with Generative AI models, ushering in innovative interactions. Within Visual, the triad of image, video, and 3D Object generation sets the stage for engaging virtual landscapes. Key to this evolution is five generative models: Transformers, Diffusion, Autoencoders, Autoregressive, and Generative Adversarial Networks (GANs). These models empower the Metaverse, enhancing it with dynamic and diverse content. Notably, technologies like BARD, Point-E, Stable Diffusion, DALL-E, GPT, and AIVA, among others, wield these models to enrich the Metaverse across domains. By discussing the technical issues and real-world applications, this study reveals the intricate tapestry of AI's role in the Metaverse. Anchoring these insights is a case study illuminating Stable Diffusion's role in metamorphosing the virtual realm. Collectively, this exploration illuminates the symbiotic relationship between Generative AI and the Metaverse, foreshadowing a future where immersive, interactive, and personalized experiences blackefine human engagement with digital landscapes.

A wireless federated learning system is investigated by allowing a server and multiple workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via band-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principal component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients to relieve the communication bottleneck. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov’s momentum. For the non-convex empirical risk, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.