The metaverse services are promising to embrace multi-sensory experiences of human beings, which mainly include audio-visual and tactile senses. From the perspective of wireless transmission, tactile transmission requires ultra-reliable low-latency communications, while audio-visual transmission requires enhanced mobile broadband communications. Besides, the audio-visual segment can be divided into several correlated data packets, any loss of packets would result in failed decoding at users, thus degrading users’ immersive experiences. In multi-user wireless metaverse systems, the heterogeneous transmission characteristics of multi-modal streams and integrity requirements of audio-visual stream transmission pose a great challenge to the limited wireless resource scheduling. To this end, we design a multi-user resource schedule scheme for multi-modal stream transmission by jointly considering the integrity of audio-visual stream transmission and the puncturing-based tactile stream transmission. We model the multi-modal perception utility function based on the multi-attribute utility theory and wireless transmission performance of multi-modal streams. Then, we formulate the average multi-modal perception utility maximization problem, and we adopt the Lyapunov theory to decompose the original maximization problem. Furthermore, we integrate the matching-based two-timescale spectrum resource allocation algorithm and alternating direction method of multipliers-based power allocation algorithm to obtain the optimal spectrum and power allocation strategies. Simulation results show that, compared with the resource allocation scheme without considering the transmission integrity, the average multi-modal perception utility of the proposed scheme is maximumly improved by 25%.

Deep reinforcement learning (DRL) has emerged as a promising solution for autonomous operations of autonomous aerial vehicles (AAVs) in unknown environments. However, learning to navigate and avoid obstacles under sparse reward settings remains challenging. In this article, we propose an end-to-end learning approach that synthesizes imitation learning with DRL for AAV navigation and obstacle avoidance. Specifically, we formulate this problem as a partially observable Markov decision process with sparse rewards and learn an end-to-end policy that maps imperfect sensor data to control signals. To efficiently optimize the policy under the sparse reward setting, we propose the selective behavior cloning enhanced actor-critic (SBCAC) algorithm. By integrating an experience filter and a Q-value based action selector to selectively mimic an artificial potential field based non-expert policy, our approach significantly improves the learning performance and sample efficiency. Extensive simulations with fixed-wing and multi-rotor AAVs in different scenarios demonstrate that SBCAC achieves an average improvement of up to 16.07% in success rate, a 72.46% reduction in crash rate, and a 94.62% reduction in stray rate compared to the state-of-the-art selective imitation baseline. Furthermore, hardware-in-the-loop and physical experiments validate the effectiveness of our approach, showing its potential for practical applications in complex environments.

The dynamic spectrum sharing technology in cognitive radio can effectively improve the utilization rate of spectrum and relieve the current spectrum pressure. To realize spectrum sharing without trust between SUs and PUs belonging to various operators, a decentralized spectrum sharing scheme based on blockchain is proposed in this paper. A latency model and a decentralization degree model of blockchain-enabled spectrum sharing network are formulated. Network scale law in this paper is defined as the change law of structure and scale in a network. Based on the proposed decentralization degree and latency model, the scale law of blockchain networks under the constraints of latency and decentralization is studied. The simulation analyzes the change of the proportion of consensus nodes in the blockchain network with different requirements for latency and decentralization. The results show that when blockchain networks have both low latency and decentralization characteristics, the upper limit of the total number of nodes is 390, and the value range of the proportion of consensus nodes is between 0.13 and 0.56.

Users’ behaviors in wireless metaverse networks are usually affected by the surrounding people and limited network resources. How to allocate network resources in a human-centric way remains an open problem in wireless metaverse scenarios. To capture the psychological influence of bandwagon effects on users’ behaviors, we first propose bandwagon effect-based metaverse behavior metrics, including the metaverse bandwagon threshold and metaverse bandwagon probability, based on the multi-dimensional contract theory. The bandwagon effect-based metaverse behavior metrics are used to quantify the number of service adopters, which consider both users’ behaviors and resource allocation strategies. Moreover, the metaverse behavior utility is derived for wireless metaverse networks based on the multi-attribute utility theory. To solve the metaverse behavior utility maximization problem, a bandwagon effect optimal transport-based (BEOT) algorithm is proposed to optimize the resource allocation strategies considering users’ behavior characteristics. Compared with the maximum metaverse behavior utility of virtual reality tracking-based resource allocation (VRT), soft actor-critic with graph convolutional networks (SAC-GCN) and the deep Q-learning-based (DQL) algorithms, simulation results show that the maximum metaverse behavior utility of proposed BEOT algorithm is improved by 22.55%, 12.36% and 18.21%, respectively.

With the aging population, health and senior care are becoming to be crucial issues for the whole world. Because the number of healthcare professionals is far from fulfilling increasing patients’ and seniors’ needs, seeking services from non-professional healthcare staff, such as home caregivers, is indispensable. Methods that support locating video moments with natural language queries can improve the normalization of operations for the non-professional healthcare staff and reduce their time expenditure on specific action moment retrieval. Addressing this problem, we propose a cross-modal neural network model for effective health and senior care video localization. Our model learns procedures in the video reference and uses procedure knowledge to improve the model’s localization performance. We conduct experiments on a dataset for health and senior care video localization and an open-accessible dataset about medical instruction. Experiment results show procedure knowledge can remarkably improve the model’s capacity for video moment localization. We hope our dataset and method could promote the development of cross-modal research and application for health and senior care.

The development of Industrial Internet of Things (IIoT) has completely changed the traditional manufacturing industry. The data exchange between controllers and actuators needs to achieve extremely low delay in IIoT. Due to the limited communication resources, it is necessary to reasonably schedule data flow to reduce delay. Although the studies of data flow scheduling exist in IIoT, they have not considered the impact of time-varying environmental factors and most of them adopted centralized scheduling schemes, which increase computation and communication cost rapidly in large-scale network scenarios. In this article, the consensus-based distributed optimal transport (OT) algorithm is proposed to optimize data flow scheduling for IIoT networks. Specifically, a data flow scheduling optimization mechanism based on time-varying environmental factors is proposed and an online distributed data flow scheduling optimization algorithm is designed. Compared with the random data flow scheduling algorithm, numerical results show that the proposed algorithm can maximally reduce the average delay by 87%, increase the transmission rate and the spectral efficiency by 157% and 98%, respectively.

The seamless and ubiquitous wireless access is crucial to the immersive experiences in the metaverse. Considering the limited communication and computing resources, how to provide metaverse services with high Quality of Experience (QoE) for users is still challenging. In this paper, an innovative QoE model for metaverse services based on the virtual distance and network effect is proposed. Especially, we introduce a novel metric called “meta-distance” to measure virtual distance in the metaverse, which jointly considers the service delay and social distance among metaverse users. To solve the QoE utility maximization problem, we propose a Joint Resource Allocation and Metaverse service Selection (JRAMS) scheme, which is composed of a two-step mechanism. In the first step, referred to as the inner loop of JRAMS, a one-to-many matching game with externalities is used to match base stations and metaverse users with Non-Orthogonal Multiple Access (NOMA) based subchannel allocation. In the second step, referred to as the outer loop of JRAMS, a hedonic coalition formation game is used to solve the metaverse service selection problem. After finite iterations, JRAMS can converge to a stable solution. The simulation results show that compared with baselines, the average QoE utility of JRAMS can be significantly improved.

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities’ distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

The evolution of artificial intelligence and Internet of Things (IoT) envision a highly integrated artificial IoT (AIoT) network. Flocking and cooperation with multiple unmanned aerial vehicles (UAVs) are expected to play a vital role in industrial AIoT networks. In this article, we formulate the collision-free flocking problem of fixed-wing UAVs as a Markov decision process and solve it in the deep reinforcement learning (DRL) framework. Our method can deal with a variable number of followers by encoding the dynamic environmental state into a fixed-length embedding tensor. Specifically, each follower constructs a fixed-size local situation map that describes the collision risks with other followers nearby. The local situation maps are used by a proposed DRL algorithm to learn the collision-free flocking behavior. To further improve the learning efficiency, we design a reference-point-based action selection strategy and an adaptive mechanism. We compare the proposed MA2D3QN algorithm with several benchmark DRL algorithms through numerical simulation, and we verify its advantages in learning efficiency and performance. Finally, we demonstrate the scalability and adaptability of MA2D3QN in a semiphysical simulation experiment.

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.