Sharing is undoubtedly one of the most important features of 6G networks, and blockchain can provide an extended trust-as-a-service (TAAS) distributed sharing solution for 6G networks. However, as a special distributed technology system, blockchain naturally needs to face the “impossible triangle” problem: in the case of ensuring security and decentralization, scalability will inevitably become the Achilles heel of the blockchain system. This article will design a high-throughput blockchain system for 6G networks to achieve trusted sharing and efficient scheduling of network infrastructure, assist future networks in integrating an open Internet architecture, and realize the combination of openness and distributed control. We adopt a shard blockchain design and incorporate digital twins and federated learning; formulate an optimization model for maximizing the throughput of the blockchain network by getting the optimal number of shards, also analyzing other factors affecting the throughput such as service distribution; propose a security performance analysis model for the blockchain network to describe the measures taken against the Byzantine attacks on the network. Analysis and simulation results show that the transaction throughput of the proposed method can reach more than 30 times larger than that of a nonsharding scheme. They also show that when one-third of the nodes in the system are attacked, the consensus of the system is hardly affected; even if the number of nodes being attacked at the same time reaches half of the total number of nodes, the probability of the occurrence of failed shards is still less than $10^{-4}$ , and the system still has good survivability.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Multiple Automated Guided Vehicles promise to be an effective solution for executing tasks such as material transportation and inspection in industrial parks. In particular, system stability is the key to maintaining connectivity among multiple agents. In this paper, we build the stability model of multi-agent system (MAS) under the background of swarm robots transporting materials in an industrial park, and propose a network topology optimization method to improve the stability of MAS. In concrete, we first analyze the effect of channel environment on network topology, and the communication delay is analyzed. Then considering the communication delay and artificial potential field, we establish the stability analysis model of MAS and obtain the stability condition of the MAS by using Lyapunov correlation theorem. Finally, we formulate the network topology optimization problem of MAS by maximizing the second smallest eigenvalue of the Laplacian and get the optimal solutions. Analysis and simulation results show the effectiveness of the proposed model and algorithm.

Immersive services are the typical emerging services in current IMT-2020 network. With the development of network evolution, real-time interactive applications emerge one after another. This article provides an overview on immersive services which focus on real-time interaction. The scenarios, framework, requirements, key technologies, and issues of interactive immersive service are presented.