In this paper, we propose a novel generative artificial intelligence (GAI)-aided approach to address the quality of experience (QoE)-aware resource allocation for reconfigurable intelligent surface (RIS)-assisted digital twin (DT) interactions with uncertain evolutions. In the considered system, mobile users interact with a DT model, referring to the high-fidelity and interactive virtual counterpart of a physical entity, hosted by a DT server deployed on a wireless base station via the assistance of an RIS, for gaining DT services, such as real-time monitoring and predictive analytics. Noted that DT interactions involve round-trip communications with both uplink and downlink, and concern not only objective performance but also subjective experience. As such, we formulate an optimization problem for RIS-assisted DT interactions, aiming to maximize the sum of all mobile users' mixed objective and subjective QoE, by jointly determining the phase shift marix, receive/transmit beamforming matrices, feedback signal rendering resolution and computing resource configuration. Further taking into account the DT model's uncertain evolutions and the resulted variations of the DT scene that mobile users engage in, we extend the resource allocation problem to a series of scene-specific ones. To obtain a generalized approach with low complexity, avoiding to re-solve each scene-specific problem whenever the engaged DT scene changes, we develop a GAI-aided approach, called prompt-guided decision transformer integrated with zero-forcing optimization (PG-ZFO). Specifically, in PG-ZFO, we first reformulate each scene-specific problem into a Markov decision process (MDP). Then, we design a “decision-making trajectory” based prompt to capture the scene-specific information and extend the traditional decision transformer to a prompt-guided decision transformer with strong generalization. On top of that, a zero-forcing (ZF)-based optimization algorithm is integrated to help derive high-dimensional decisions, i.e., beamforming matrix, along with the offline training and online execution of PG-ZFO. Simulations show the effectiveness of the proposed approach, and demonstrate its superiority over counterparts, i.e., rigid optimization method and decision transformer without prompt.

This paper proposes a multi-objective Bayesian approach to optimize the end-to-end (E2E) performance of network function virtualization (NFV)-based tactile Internet (TI) by balancing subjective and objective performances. The system aims to deploy virtual network functions (VNFs) via middleware (e.g., servers, switches) to establish service function chains (SFCs), enabling bidirectional communication between tactile users and teleoperators, accelerating service deployment, and facilitating immersive tactile interaction requests (e.g., in the Metaverse). To meet the demands of an immersive user experience, we explore tailored E2E performance metrics and formulate a hybrid black-white box optimization problem. Addressing the uncertainty in subjective feedback (e.g., non-reproducible user ratings), the proposed approach jointly optimizes wireless resource allocation and SFC scheduling for bidirectional uplink/downlink communication in NFV-based TI, reducing E2E delay and enhancing user satisfaction. Simulations demonstrate the superiority of the proposed solution over existing approaches.

This paper proposes a novel multi-objective Bayesian optimization approach for tactile feedback transmission and reconstruction in network function virtualization-based Tactile Internet (NFV-based TI). For such a system, guaranteeing real-time and precise feedback transmission and reconstruction is crucial for achieving seamless remote interactions. By jointly optimizing virtual network function (VNF) placement, routing, service function chain (SFC) admission control and wireless resource allocation, this work addresses dynamic adjustment strategies under uncertain network conditions. We propose a multi-objective Bayesian approach tailored for hybrid black-white box optimization problems. A hybrid kernel surrogate model along with adaptive sampling strategies are designed to handle the uncertainties in NFV-based TI environments. This enables Pareto-optimal trade-offs between feedback delay and fidelity. Simulations confirm the superiority of our approach over existing solutions.

Multi-axis servo coordinated control enables multiple axes to track distinct target trajectories simultaneously. Through networked collaboration, these axes together can achieve complex tasks with enhanced adaptability and flexibility. In this paper, we introduce an edge-cloud collaborative multi-axis servo coordination control framework, exploiting both advantages of edge and cloud computing for optimizing the coordinated control performance of multi-axis servo system. Considering that axis states and control signal sequences are transmitted over a limited shared wireless channel, we formulate a long-term combinatorial decision problem under stringent communication resource constraints. A novel rein-forcement Q-knapsack approach is proposed, which solves a grouped knapsack problem at each time step concerning the number of consumed slots and action Q-values, while deep reinforcement learning is utilized to optimize the action Q-value estimation in the long run. Simulation experiments demonstrate that the proposed approach is not only effective but also superior compared to counterparts.

The integration of unmanned aerial vehicles (UAVs) and high-altitude platform (HAP) in hierarchical aerial computing offers mobile IoT devices enhanced computational services. However, three key challenges emerge. First, while UAVs provide faster responses than distant HAP due to mobility, their limited computing capacity and coverage require efficient task delegation. Second, UAVs’ energy constraints force periodic recharging, potentially causing service interruptions and demanding dynamic resource allocation. Third, IoT task dynamics require rapid task delegation (small-timescale), while UAV trajectory adjustments operate slowly (large-timescale), necessitating multi-timescale coordination. To address these, we propose a two-timescale optimization framework maximizing system energy efficiency. We formulate the problem as coupled multi-agent stochastic games, and then develop a deep reinforcement learning (DRL)-based algorithm, called UAV trajectory planning, replacement, task delegation and resource allocation (UTRTD). Simulations show that UTRTD is not only effective but also superior compared to counterparts.

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.

The Internet of Things (IoT) can significantly enhance the quality of human life, specifically in healthcare, attracting extensive attentions to IoT healthcare services. Meanwhile, the human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body in the digital world and reflect its physical status in real time. Naturally, HDT is envisioned to empower IoT healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed, simulating the outcomes and guiding the practical treatments. However, successfully establishing HDT requires high-fidelity virtual modeling and strong information interactions but possibly with scarce, biased, and noisy data. Fortunately, a recent popular technology called generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data. This survey particularly focuses on the implementation of GAI-driven HDT in IoT healthcare. We start by introducing the background of IoT healthcare and the potential of GAI-driven HDT. Then, we delve into the fundamental techniques and present the overall framework of GAI-driven HDT. After that, we explore the realization of GAI-driven HDT in detail, including GAI-enabled data acquisition, communication, data management, digital modeling, and data analysis. Besides, we discuss typical IoT healthcare applications that can be revolutionized by GAI-driven HDT, namely, personalized health monitoring and diagnosis, personalized prescription, and personalized rehabilitation. Finally, we conclude this survey by highlighting some future research directions.

Secure data sharing is imperative in human digital twin (HDT) systems due to the continuous communication requirements among physical and virtual twins, making data security and privacy essential concerns. Previous works have emphasized the significance of blockchain technology in mitigating security challenges within digital twin systems. Nevertheless, existing blockchain-based solutions often fall short of meeting the specific latency and throughput demands of HDT systems, primarily attributed to the complicated consensus process of conventional blockchain solutions. As a result, this paper introduces a novel reputation-enhanced shard-based Byzantine fault-tolerant scheme designed for zero-trust HDT systems. We propose a parallel validation-based reputation-enhanced practical Byzantine fault tolerance consensus framework to address the need for improved throughput and reduced latency during data-sharing processes. This framework incorporates a priority-based block-appending process to prevent forking attacks, ensuring that critical aspects of the blockchain-enabled framework, such as security and decentralization, remain uncompromised. Moreover, we formalize the communication process among validators and their computation resource allocation as a Markov decision process. We then adopt the branching duelling Q-network approach to address the challenge posed by the large dimensions of the action space in our formulated problem. The results demonstrate that the proposed framework significantly enhances authentication, authorization, and validation processes in HDT through increased throughput and reduced latency, providing a robust solution for secure and efficient data sharing in HDT systems.

In this paper, an energy efficient scheduling problem for multiple unmanned aerial vehicle (UAV) assisted mobile edge computing is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also decisions of whether returning to the depot for replenishing energies and updating application placements (due to limited batteries and storage capacities). Aiming to maximize the long-term energy efficiency of all UAVs, i.e., total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs, trajectory planning, energy renewal and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such problem as three coupled multi-agent stochastic games, and then propose a novel triple learner based reinforcement learning approach, integrating a trajectory learner, an energy learner and an application learner, for reaching equilibriums. Simulations evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

In this article, an energy-efficient scheduling problem for multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) is studied. In the considered model, UAVs act as mobile edge servers to provide computing services to end-users with task offloading requests. Unlike existing works, we allow UAVs to determine not only their trajectories but also the decisions of whether returning to the depot for replenishing energies and updating application placements (due to their limited batteries and storage capacities). With the aim of maximizing the long-term energy efficiency of all UAVs, i.e., the total amount of offloaded tasks computed by all UAVs over their total energy consumption, a joint optimization of UAVs’ trajectory planning, energy renewal, and application placement is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we reformulate such optimization problem as three coupled multiagent stochastic games. Since the prior environment information is unavailable to UAVs, we propose a novel triple-learner-based reinforcement learning (TLRL) approach, integrating a trajectory learner, an energy learner, and an application learner, for reaching equilibriums. Moreover, we analyze the convergence and the complexity of the proposed solution. Simulations are conducted to evaluate the performance of the proposed TLRL approach, and demonstrate its superiority over counterparts.

Ensuring reliable update and evolution of a virtual twin in human digital twin (HDT) systems depends on any connectivity scheme implemented between such a virtual twin and its physical counterpart. The adopted connectivity scheme must consider HDT-specific requirements including privacy, security, accuracy and the overall connectivity cost. This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain. Specifically, we adopt federated multi-task learning, a personalized learning method capable of providing higher accuracy, to capture the impact of heterogeneous environments. Next, we propose a new validation process based on the quality of trained models during the federated multi-task learning process to guarantee accurate and authorized model evolution in the virtual environment. The proposed framework accelerates the learning process without sacrificing accuracy, privacy and communication costs which, we believe, are non-negotiable requirements of HDT networks. Finally, we compare the proposed connectivity scheme with related solutions and show that the proposed scheme can enhance security, privacy and accuracy while reducing the overall connectivity cost.

In this paper, a joint optimization of sensor activation and mobile charging scheduling for industrial wireless rechargeable sensor networks (IWRSNs) is studied. In the considered model, an optimal sensor set is selected to collaboratively execute a bundle of heterogeneous tasks of production-line monitoring, meeting the quality-of-monitoring (QoM) of each individual task. There is a mobile charger vehicle (MCV) which is scheduled for recharging sensors before their charging deadlines (i.e., the time instant of running out of their energy). Our goal is to jointly optimize the sensor activation and MCV scheduling for minimizing the energy consumption of the entire IWRSN, subjected to tasks’ QoM requirements, sensor charging deadlines and the energy capacity of the MCV. Unfortunately, solving this problem is non-trivial, because it involves solving two tightly coupled NP-hard problems. To address this issue, we design an efficient algorithm integrating deep reinforcement learning and marginal product based approximation algorithm. Simulations are conducted to evaluate the performance of the proposed solution and demonstrate its superiority over counterparts.