In this paper, we study a multi-objective Bayesian approach to optimize end-to-end (E2E) performance for network function virtualization-based Tactile Internet, called NFV-based TI, with joint subjective and objective evaluation. In the considered NFV-based TI system, the aim is to deploy virtual network functions (VNFs) through middleware (e.g., servers, switches, etc.) in providing service function chains (SFCs), establishing bidirectional communication links between tactile user-teleoperator pairs, accelerating the deployment of new services, and facilitating the completion of relevant tactile interaction requests. To meet the needs of an immersive user experience, we explore the joint subjective-objective evaluation of E2E performance characterization tailored for NFV-based TI and formulate a hybrid black-white box optimization problem. Due to the uncertainty of subjective evaluation in tactile interaction feedback (e.g., irreplicable subjective user ratings), E2E performance characterization is inaccurate, and addressing this problem is nontrivial. Particularly, to reduce the E2E delay and improve E2E user satisfaction, a multi-objective Bayesian approach is proposed involving joint wireless resource allocation and SFC scheduling applying to the uplink/downlink bidirectional communication for the NFV-based TI. Simulations evaluate the proposed solution and demonstrate its superiority over its counterparts.

The ability to ensure covert unmanned aerial vehicle (UAV) communications is imperative in critical missions such as military surveillance and emergency response. In this paper, a multi-UAV covert communication system with informed jammers is investigated. To increase ground wardens’ detection uncertainty, we propose a joint dynamic scheduling and sensing jamming (DSSJ) scheme. Unlike existing approaches with fixed UAV roles and non-informed jamming, DSSJ dynamically schedules UAVs across adjacent time slots (TSs), while the jamming UAV performs sensing-based informed jamming per TS. Closed-form expressions are derived for the covert rate and minimum detection error probability (MDEP) under the worst-case scenario with optimal warden detection. An optimization problem is formulated to maximize the normalized weighted sum of covert rate and MDEP, subject to multiple constraints, including scheduling, sensing ratio, and other key factors. To solve this mixed-integer non-convex problem, we design a double deep Q-network (DDQN)-DSSJ algorithm, integrating DSSJ within a deep reinforcement learning framework, accelerated by experience replay and dynamic exploration, achieving real-time covert decision-making with polynomial complexity. Simulations demonstrate that DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization and deep Q-network. Additionally, DDQN-DSSJ improves the covert rate by over 4× and MDEP by up to 28.3%, outperforming state-of-the-art schemes.

Intelligent air-ground integration communication is an emerging technology. Uncrewed aerial vehicles (UAVs) serve as mobile edge computing (MEC) servers in large-scale Internet of Things (IoT) applications, alleviating the computational load on ground users. Existing multi-UAV MEC approaches struggle with the complex computation and large data sizes of deep neural network tasks. To address these challenges, we propose a Deep Reinforcement Learning (DRL)-based DNN Partitioning and Dynamic Trajectory Selection (DPDTS) method, which reduces end-to-end latency and system energy consumption through task offloading and collaborative inference. Specifically, we propose an Optimal Partition Point Selection (OPPS) algorithm to minimize transmission overhead by selecting optimal partition points for DNN tasks. Then, we design a fairness-based matching algorithm to optimize user offloading and resource allocation. Finally, OPPS and matching algorithms are integrated to optimize UAV flight trajectories and user transmission power via DRL. The simulation results show that DPDTS outperforms existing benchmark methods in terms of delay and energy efficiency.

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.

Aiming to provide high-fidelity and real-time virtual replicas, a digital twin (DT) model must be dynamically updated to precisely characterize the evolution of physical objects. Unlike the existing work, this paper studies a novel edge-cloud collaborative DT update framework with adaptive model splitting and reliable crowdsourcing under uncertain data distortions. Specifically, we consider that a global DT model can be split into arbitrary subsets of its elementary components (DT units), re-forming disjoint partial-DTs. Each partial-DT is constructed on distributed edge servers (ESs) by model training using the locally collected feature data. To enhance the system reliability, being more robust against uncertain data distortions that widely occur in practice, we further improve partial-DT constructions via crowdsourcing. In other words, each partial-DT is simultaneously trained by multiple ESs, i.e., an ES crowd, with one coordinator ES intermediately aggregating all models from participating ESs into a unified one. Then, the cloud collects and integrates partial-DTs from ES crowds to update the global DT. We formulate an online joint optimization problem to adaptively determine partial-DT splitting and ES crowdsourcing across different DT evolution periods or frames, with the objective of maximizing the long-term physical-virtual mapping accuracy. To this end, we first study a simplified short-term problem in each frame, modeled as a Bayesian coalition formation game (BCFG). We then develop an uncertainty-aware crowd formation algorithm based on a particularly established believe function to solve the BCFG for short-term optimal partial-DT assignment and coordinator ES selection, given any partial-DT splitting decisions. Moreover, we modify the BCFG to accommodate dynamic settings and design a deep reinforcement learning-based algorithm integrated with this modified BCFG, called DBC. The DBC algorithm extends the short-term solution to a long-term one, which jointly and dynamically optimizes partial-DT splitting and ES crowdsourcing, thereby addressing the original problem. Simulations show the effectiveness of the introduced dynamic DT update framework, and demonstrate the superiority of the proposed DBC algorithm over counterparts in terms of increasing the average DT update accuracy while reducing the associated costs.

Collaborative mobile edge computing (MEC) has emerged as a promising paradigm to enable low-capability edge nodes to cooperatively execute computation-intensive tasks. However, straggling edge nodes (stragglers) significantly degrade the performance of MEC systems by prolonging computation latency. While coded distributed computing (CDC) as an effective technique is widely adopted to mitigate straggler effects, existing CDC schemes exhibit two critical limitations: (i) They cannot successfully decode the final result unless the number of received results reaches a fixed recovery threshold, which seriously restricts their flexibility; (ii) They suffer from inherent poles in their encoding/decoding functions, leading to decoding inaccuracies and numerical instability in the computational results. To address these limitations, this paper proposes an approximated CDC scheme based on barycentric rational interpolation. The proposed CDC scheme offers several outstanding advantages. Firstly, it can decode the final result leveraging any returned results from workers. Secondly, it supports computations over both finite and real fields while ensuring numerical stability. Thirdly, its encoding/decoding functions are free of poles, which not only enhances approximation accuracy but also achieves flexible accuracy tuning. Fourthly, it integrates a novel BRI-based gradient coding algorithm accelerating the training process while providing robustness against stragglers. Finally, experimental results reveal that the proposed scheme is superior to existing CDC schemes in both waiting time and approximate accuracy.

The statistical distribution of user geographic location data is widely used in various mobile applications. Although geo-indistinguishability (GI) has emerged as an effective privacy-preserving framework for processing location data, due to the lack of robust perturbation probability calculation and post-processing for eliminating statistical errors caused by random perturbation on user-side data, GI exhibits low accuracy when directly applied to two-dimensional continuous location data distribution estimation. To overcome this, we propose a novel and efficient location data distribution estimation mechanism by improving GI, termed gamma-based circle and uniform grids (GBC-UG). The GBC-UG mechanism consists of two key algorithms: i) the gamma-based circle (GBC) algorithm, which perturbs users' location data and ensures the calculability of perturbation probabilities on the server side, and ii) the uniform grids (UG) algorithm, which post-processes the perturbed data to accurately estimate the original distribution. We provide a theoretical analyses of the upper and lower bounds of the statistical error in distribution estimation and identify optimal parameter values to minimize this error. Experimental results on multiple real-world datasets demonstrate that the proposed GBC-UG mechanism can significantly improve the accuracy of distribution estimation, as well as the prediction accuracy of both the top-k and popularity ranking while guaranteeing user privacy, outperforming existing GI-based approaches.

Digital twin (DT) is envisioned not only to perform the high-fidelity virtual representation of its corresponding physical entity (PE), but also to serve as an active agent delivering diverse types of sophisticated services. This paper studies an end-edge-cloud collaborative DT placement and update framework. Specifically, we consider that DTs are dynamically placed across edge servers (ESs) via migration following their paired PEs' potential mobility, while being supported by real-time data fetched from the cloud center and user ends. On top of this, we emphasize a unique feature that DTs should also be continually updated capturing the uncertain evolutions for both personalized service ability improvement and versatile service ability maintenance, where the personalization is improved by utilizing the experiential knowledge from the cloud center and their corresponding PEs, and the versatility is maintained by integrating pre-stored profiles. To maximize the long-term system-wide average weighted quality-of-service (QoS) in handling all types of PEs' service requests under the stringent system cost constraint, we formulate an online problem to jointly optimize DT migrations, service priorities towards various request types, and all related DT updating strategies. To address underlying difficulties, we propose a novel adversarial bandit learning assisted online optimization approach, called ARBOK. We first leverage the Lyapunov decomposition method to transform the long-term problem into multiple instant ones, each of which is further decoupled into two correlated subproblems. For solving one subproblem with a bilinear structure, we develop a McCormick envelopes based algorithm (MO-EL). Besides, we design an extended adversarial combinatorial multi-armed bandit algorithm (AC-BL) to tackle the other subproblem, which constructs a super arm set to resolve the issue of excessively large decision space and employs a robust scheme to handle the inherent uncertainty and non-stationarity in each super arm's loss function. We integrate both algorithms seamlessly into ARBOK and alternately execute them till the convergence. Theoretical analysis and extensive simulations show the effectiveness of the introduced dynamic DT placement and continual update framework, demonstrating that ARBOK can converge to the asymptotic optimum within a polynomial-time complexity while outperforming counterparts.

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, we study a mobility-aware two-timescale online optimization for constructing a digital twin (DT) -assisted task execution system under end-edge-cloud collaboration. DTs are deployed on edge servers deployed on roadside units (RSUs), and needs to be proactively migrated based on the future location of its corresponding vehicle. To minimize the task response latency executed by DT with stringent energy consumption constraint, we jointly optimize the uploading frequency of vehicle’s status data for adaptive mobility prediction, the DT migration decision along with the communication and computation resource allocations. Considering that decision variables are triggered asynchronously, we propose a novel two-timescale mobility-aware online optimization approach (TMO), which first employs an extended two-timescale Lyapunov method to decompose the problem into a series of instant subproblems and then integrates a multi-armed bandit (MAB) algorithm to dynamically determine uploading frequency of vehicle’s status data as the length of the large-timescale. After that, for each small-timescale problem, we develop a GRU-based vehicle trajectory preidiction method to predict the location of vehicles, followed by an alternate minimization (AM) based algorithm to decide remaining decision variables based on the predicted trajectory. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum and demonstrate its superiority over counterparts.

With the rapid development of autonomous driving, how to enable unmanned vehicles (UVs) to efficiently avoid multiple dynamically moving obstacles, especially obstacle vehicles (OVs), has become a vital issue in the context of the Internet of Vehicles (IoV). This requires not only high-level adaptability to dynamic and complex traffic environments, but also extraordinarily agility in reacting to possible collision hazards with safer and proactive collision avoidance. Conventional methods, e.g., artificial potential field (APF), may overreact to distant targets which have no risk in collision, generating a false evasion direction when facing multiple OVs. To this end, we propose a novel improved APF-based algorithm along with the trajectory prediction. Specifically, to measure the safety distance for vehicle maneuvering, a trajectory prediction method integrated with unscented kalman filter (UKF) is developed. Then, an obstacle filtering method utilizing sensor information and trajectory prediction results is applied for wiping off collision-free targets. Afterwards, by employing APF method combining with avoidance strategies based on virtual forces and window-based collision detection, the potential pushing effect caused by multiple OVs is mitigated. Experimental results show that, given the scenario of collision avoidance with multiple OVs, the proposed solution can achieve an obstacle avoidance success rate of around 90%, which is about 20% higher than the best benchmark algorithms, simultaneously demonstrating advantages in efficiency and safety.

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT's model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts.

By synergistically integrating mobile networks and embodied artificial intelligence (AI),mobileembodiedAInetworks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of messages or vulnerability to malicious tampering, potentially causing severe accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the validator trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs.

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs’ trajectory planning and operating modes, the ARIS’s passive beamforming, and the GUs’ transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs’ trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.

In this paper, we introduce third-party intelligent reflecting surfaces (TIRSs) into the physical layer security aware wireless communication system, where a central legitimate transmitter is designed to transmit secret signals to a group of legitimate receivers in the presence of the threat from an active eavesdropper (EV). Due to the channel reshaping ability of TIRSs, they are able to not only help legitimate pairs (LPs) enhance the secure transmission rate but also assist EV in improving the eavesdropping performance. Furthermore, with the potential selfishness, TIRSs may dynamically choose to ally with LPs or EV in exchange for potential benefits (e.g., payoffs). This leads to complex dynamic ally-adversary relationships among LPs, EV, and TIRSs under unpredictable wireless channel conditions. To address this issue, we formulate a repeated coalition formation game (RCFG) with dynamic decision-making to model the long-term strategic interactions among LPs, EV, and TIRSs. In particular, we theoretically analyze the existence of Nash equilibrium in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based approach for obtaining such an equilibrium. Simulations examine the feasibility of the proposed approach and show its superiority over counterparts.

Human-machine interaction has sparked significant interest, leading to the rise of Channel State Information (CSI)-based gesture recognition systems. However, these systems often struggle with accuracy due to high noise levels and limited cross-domain performance. This paper presents a robust approach that enhances CSI-based gesture recognition by addressing these challenges. We introduce key concepts such as the CSI ratio and phase matrix and develop a robust data preprocessing method that reduces environmental noise while retaining dynamic components crucial for gesture recognition. Our method, WiMT, leverages a robust multi-features fusion transformer with spatiotemporal partitioning and a multiscale spatiotemporal self-attention mechanism to effectively capture both local spatial and global temporal features of gestures. Evaluations on the Widar3 dataset demonstrate that our model surpasses existing methods in in-domain and cross-domain gesture recognition tasks.

Nowadays, Federated learning (FL) is popular as it achieves distributed model training while allowing data to stay locally. It trains a global model by aggregating a selected set of local models from participants' local data. However, the global model may not perform well for all participants, especially when participants' data distributions are non-IID. Participants actually care more about the Personal Model Performance (PMP), i.e., the model performance on their own data distribution, instead of the model performance on all data. In this paper, we design a model selection method to assign a personalized set of models for each participant to maximize PMP. We first propose a model selection metric, that is model similarity. We prove theoretically that selecting models similar to a participant's own local model can make the aggregated model closer to the ideal one. Then we design a DRL-based model selection method to maximize PMP for each participant. By careful design and dimension reduction of actions and states, our TD3-based model selection method achieves the highest PMP compared with baselines. Moreover, it has a transfer ability, which means a model selection agent trained on a dataset, e.g., MNIST, works well on another similar dataset, e.g., FMNIST.

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.

In this paper, a repeated coalition formation game (RCFG) with dynamic decision-making for physical layer security (PLS) in wireless communications with intelligent reflecting surfaces (IRSs) has been investigated. In the considered system, one central legitimate transmitter (LT) aims to transmit secret signals to a group of legitimate receivers (LRs) under the threat of a proactive eavesdropper (EV), while there exist a number of third-party IRSs (TIRSs) which can choose to form a coalition with either legitimate pairs (LPs) or the EV to improve their respective performances in exchange for potential benefits (e.g., payments). Unlike existing works that commonly restricted to friendly IRSs or malicious IRSs only, we study the complicated dynamic ally-adversary relationships among LPs, EV and TIRSs, under unpre-dictable wireless channel conditions, and introduce a RCFG to model their long-term strategic interactions. Particularly, we first analyze the existence of Nash equilibrium (NE) in the formulated RCFG, and then propose a switch operations-based coalition selection along with a deep reinforcement learning (DRL)-based algorithm for obtaining such equilibrium. Simulations examine the feasibility of the proposed algorithm and show 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.

Container–based microservice provisioning, with its elasticity in terms of the layered structure, enables the sharing of common layers among different edge computing tasks, both within and across edge servers (ES). However, due to the potential hardware breakdowns, each ES may prone to failures, affecting its lifetime (i.e., the time-length that an ES works continuously without interruptions), and in turn leading to the collapse of their hosted/provided microservices or the other ESs’ microservices requesting common layers from it. To address such an issue, in this paper, we study the microservice deployment optimization with layer sharing for maximizing the system-wide reliability while satisfying all tasks’ delay requirements. Considering dynamic task generations and the asynchronization of various decision variables with different triggers, we design an online optimization algorithm by leveraging an improved Lyapunov technique integrating randomized rounding, Lagrangian method and convex optimization, which iteratively solves the problem over different timescales. Theoretical analyses and simulations evaluate the performance of the proposed solution, showing that it can achieve an increase of 12.4% in reliability and a reduction of 28.57% in total delay, compared to the counterparts.

The fusion of the Internet of Things (IoT) with sixth-generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultrareliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial intelligence-generated content (AIGC) harnesses advanced artificial intelligence (AI) algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. However, the current practice of edge devices as AIGC service providers (ASPs) lacks incentives, hindering the sustainable provision of high-quality edge AIGC services amidst information asymmetry. In this article, we develop a user-centric incentive mechanism framework for edge AIGC services in 6G-IoT networks. Specifically, we first propose a contract theory model for incentivizing ASPs to provide AIGC services to clients. Recognizing the irrationality of clients toward personalized AIGC services, we utilize prospect theory (PT) to capture their subjective utility better. Furthermore, we adopt the diffusion-based soft actor-critic algorithm to generate the optimal contract design under PT, outperforming traditional deep reinforcement learning algorithms. Our numerical results demonstrate the effectiveness of the proposed scheme.

Due to the potential of revolutionizing a variety of human-centric services, human digital twin (HDT) is envisioned to become an important part of our daily life. The HDT applications need to frequently collect and process data obtained from individuals and their environment, analyzing each physical twin while updating its corresponding virtual twin, which will consume a large amount of computing, storage and sensing resources cumulatively. Meanwhile, running HDT applications, such as emotion recognition, naturally contains the executions of several dependent tasks. Considering the resource limitations of mobile terminals, we enable dependent task offloading to mitigate terminal load and reduce the latency of HDT applications. Specifically, this paper proposes an energy and cost-aware offloading algorithm for dependent tasks with edge-cloud collaboration to empower HDT applications. We show that the problem of dependent task offloading under constraints of service cost and terminal energy consumption is NP-hard. The complexity of task interdependency makes the offloading decision under dual constraints even more challenging. The proposed offloading algorithm firstly generates task paths based on task interdependency and computation load, deriving the initial solution. Then, task reassignment and CPU frequency scaling methods are utilized to further optimize the obtained solution. Simulation results illustrate that our approach can achieve better performance in terms of makespan and service success ratio compared to the existing approaches.

The introduction of network function virtualization (NFV) leads to service function chain (SFC) deployment problems, promoting the idea of composing network services as virtualized network functions (VNFs). Meanwhile, the rapid development of edge computing, artificial intelligence and big data has led to a surge in data volume and explosive growth in computing and forwarding demands. As such, a traditional central processing unit (CPU)-based data forwarding mode in the NFV network appears to be a bottleneck, and a CPU-only computing framework can no longer meet the forwarding needs of diverse business scenarios and services. The data processing unit (DPU)-based architecture allows better forwarding performance to be achieved more cost-effectively, largely alleviating the computing pressure of the CPU and reducing the node forwarding delay. Therefore, in this paper, a heterogeneous CPU+DPU architecture is investigated to solve the SFC deployment problem. To handle diverse service needs, we establish a multi-objective SFC deployment scheme to optimize the service latency, deployment cost and service acceptance rate. Because extreme services require better real-time performance, DPUs are adopted for fast processing according to the requirement of service requests. To address the unacceptable delay in sequential mode, a parallel strategy is proposed to process SFCs. To solve the multi-objective SFC deployment problem, a deep reinforcement learning (DRL)-based heterogeneous algorithm that includes multiple subalgorithms is designed, named parallelizable, shared and horizontally scaled service function chain deployment (PSHD), which uses diverse processing algorithms to deploy SFCs and break the delay bottleneck in NFV-based networks.The performance of PSHD is evaluated through extensive experiments. PSHD is found to be time-efficient, and it achieves a higher request acceptance rate and 37.73% and 34.26% lower latencies than state-of-the-art methods.

Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining: 1) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame; and 2) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. For providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs' task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining$i$) large-timescale decisions, including access selection and service migration or task rerouting selection for each MD, and ii) small-time scale decisions, including computing and communication resource allocations. Then, we propose a two-timescale low-complexity algorithm based on the improved Lyapunov method, which solves the problem to asymptotic optimum in terms of the long-term system-wide average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution, and show its superiority over counterparts.

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 paper, a novel hierarchical game framework for physical layer security (PLS) with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under the uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model the strategic interactions among all three parties, i.e., LUs, JAs and EVs, in PLS. Particularly, we first analyze stability conditions of the trilateral coalitions. On top of this, we further propose a deep reinforcement learning (DRL) based approach for reaching the equilibrium with long-term performance guarantees for the hierarchical game. Simulations evaluate the proposed solution and show its superiority over counterparts.

In recent years, COVID-19 has spread rapidly around the world, leading to a global pandemic, which has become an unprecedented crisis for almost every country in the world. In this paper, we propose a novel federated learning (FL) algorithm to train a sensitivity-specificity-variable COVID-19 diagnosis model. By FL, patients' data stays at each hospital locally, and thus the privacy of patients is reserved. However, the commonly used FL algorithms, such as FedAvg cannot perform COVID-19 diagnosis efficiently because they did not consider the impact of noise and heterogeneity in the chest X-ray (CXR) data of different hospitals. Moreover, they commonly assumed that hospitals would voluntarily participate in FL without payments. To this end, our FL algorithm integrates a novel data selection module to distinguish participants having data with low noise, high representative distribution, and a payment scheme to incentivize each participant according to their contributions. Our contribution evaluation method is based on the Shapley value method widely applied in coalitional games. Compared to the existing works, our solution does not need to train models repeatedly, which significantly reduces the time and computation resource consumption, while achieving a competitive performance as shown in experiments.

This paper studies the dynamic application placement for edge computing with a variety of random task arrivals (or task offloading requests). Since the storage capacity of the edge server is inherently limited, for better serving mobile devices with heterogeneous computation demands, the edge server is required to update its application placement, leading to a potential energy-latency paradox (i.e., frequent updates may introduce a high energy consumption while infrequent updates may result in the growth of latency). To this end, we propose a novel application placement policy, consisting of a response threshold and a waiting duration for installing and uninstalling the application for each type of task, respectively. Furthermore, we leverage the vacation queue for analyzing the performance of such system, and formulate a joint optimization problem for deriving the optimal configurations of application placement, along with the computation resource allocations, in minimizing the average service latency with a desired energy constraint. A branch-and-bound method integrating an inner convex approximation approach is proposed, and then evaluated with numerical simulations which demonstrate its superiority over counterparts.

Hybrid circuit and packet switching networks combine optical circuit switching with electrical packet switching technologies. It can provide higher bandwidth at a lower cost than pure optical or electrical networks, which can meet different performance goals. Coflow is a superior traffic abstraction that captures applications' networking semantics. To improve the transmission performance at the application level, we focus on reducing coflow completion time (CCT). Unlike the problem of minimizing CCT in traditional networks, hybrid network provides larger coflow scheduling space about through which network the internal flows are transmitted, in addition to priorities and bandwidth allocations of coflows. To solve this problem, this paper proposes an Online Network State based coflow scheduling algorithm (ONS) in hybrid switching networks, which minimizes CCT by combining coflow priorities and internal flow path planning, fully considering different switches' characteristic. Extensive simulations prove that ONS has smaller CCT and higher throughput under different load levels.

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 long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users’ computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

For practical industrial wireless sensor networks (IWSNs), the system freshness of a specific task is usually related to multiple and multitype sensing data. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. To fill this gap, we propose the Age of Task-oriented Information (AoTI) for measuring the freshness of industrial tasks in IWSNs. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to one certain task. Furthermore, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access modes and sampling frequencies for all sensors. By first formulating the problem as a Mixed Integer Nonlinear Program-ming problem, we then transform it to a constrained Markov Decision Process (CMDP) and relax it as an un-constrained MDP using Lagrangian method. Finally, we develop a Learning-based Access mode selection and Sampling frequency Control (LASC) algorithm and verify its superiority through simulations.

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.

In this paper, an energy-efficient resource management framework for industrial Internet of Things (IIoT) with closed-loop control on end devices, edge servers (ESs) and cloud center (CC) is studied. In the considered model, each ES aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of i) sensors’ sampling rate adaption, ii) ESs’ preprocessing mode selection and iii) edge-cloud communication and computing resource allocation, is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Performance analyses and simulation results show that the proposed algorithm is superior compared to counterparts in terms of energy efficiency and delay performance under service satisfaction constraints.

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers and communication load in distributed computing. We propose a novel learning auction to allocate computing resource efficiently in a CDC scenario. The user demand types are usually het-erogeneous according to different variation trends of the value with finish time and workload, which can be modeled by deep learning. As the goal of social welfare maximizationthe platform would allocate computing resources according to inferred value functions of users. Due to the uncertain finish time and nonlinear structures of deep learning models, the considered optimization problem is non-convex. We then reformulate the non-convex optimization problem into a mixed integer program(MIP). After analyzing the inference error caused by deep learning, a payment rule referred to VCG is designed to achieve incentive alignment and individual rationality. Besides, experiments have been performed to show the superiority of our mechanism.

In this paper, a multi-layer edge computing frame-work for the virtual machine (VM) placement and computation offloading in industrial Internet of Things (IIoT) is proposed. Unlike most existing works, we focus on addressing the temporal dependency among tasks in an IIoT task flow, and consider that there is a stringent requirement on its completion time (including the transmission time, computation time and waiting time). For striking a balance between the system completion time and the energy consumption while satisfying the storage capacity of edge servers (ESs), completion deadline of time-sequential task flows, and placement requirements of VMs, we design a many-to-one matching game (MGVDA) to jointly determine the optimal VM placement and task offloading decisions. Finally, we prove that the resulted matching game solution is effective and stable. Simulation results examine the efficiency of the proposed MGVDA and show its superiority over the counterparts.

Edge–cloud collaboration is critical in the Industrial Internet of Things (IIoT) for serving computation-intensive tasks (e.g., bearing fault monitoring) that require low-response delay, low energy consumption, and high processing accuracy. In this article, an energy-efficient resource management framework for IIoT with closed-loop control on end devices, edge servers, and cloud center is studied. In the considered model, each edge server aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of: 1) sensors’ sampling rate adaption; 2) edge servers’ preprocessing mode selection; and 3) edge–cloud communication and computing resource allocation is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Particularly, the Lyapunov optimization method is first utilized to decompose the long-term problem into a series of instant ones [mixed-integer nonlinear programming (MINLP) problems], and then a Markov approximation algorithm is applied to solve such instant problems to near optimum with the consideration of future impacts. Performance analyses and simulation results show that the proposed algorithm is feasible under long-term service satisfaction constraints, and its energy consumption and service delay are approximately 20% and 28% lower than those of the benchmark schemes, respectively.

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

In this paper, the computation resource configuration for vehicular edge computing is investigated in this study. Dissimilar to a large portion of the current literature, we center around the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long-term management profit of the network operator (i.e., the road-side unit of the vehicular network) under the randomness of vehicular traffics and task processing. A multi-type management framework is used to characterize the heterogeneities among different vehicles in terms of their edge computing quality-of-service (QoS) requirements. A novel fluid model is proposed that facilitates the formulation of the corresponding resource optimization problem by taking into account the system’s steady state characteristics with dynamic evolutions. In addition, rather than considering fixed QoS requirements in long-run, we explore the impact of the resulted service quality on the QoS requirements determined by vehicles. The QoS requirements of each vehicle is allowed to change adaptively according to the service quality fed back by the system. Based on this, we propose a simple but efficient approach, called threshold-based computation resource configuration scheme (TCRCS). The proposed solution’s performance is assessed by theoretical analysis and simulations, which show that it outperforms competitors.

User association is a crucial aspect which greatly affects the performance of wireless networks. In this work, we investigate the user association problem in full-duplex cellular networks, wherein base stations (BSs) are densely deployed with highly variable transmit powers and topologies (e.g., heterogeneous networks). To enhance the system performance, decoupled UL-DL (DUDe) association is considered, which enables each user equipment (UE) to associate with different BSs in uplink (UL) and downlink (DL), respectively. Considering the challenges raised by asymmetric information (e.g., channel gains and intercell interferences) between UEs and BSs, we propose a contract-theory based distributed user association approach. Specifically, the association process is modeled as a labor market, where the BSs act as employers and offer two-dimensional contracts to employees (i.e., UEs) for maximizing the utility of the BS. Theoretical proof for contract feasibility is presented by providing sufficient and necessary conditions. To reach the optimality, a contract-theoretic decoupled user association algorithm is developed, in which a BS broadcasts the drafted contracts, and each UE self-selects the optimal contract by considering her own demands. Numerical results are presented to demonstrate the performance of the proposed approach in terms of node utilities and social surplus. Impacts of system settings on the network performance are also investigated.

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs’ strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts.

Mobile charging services (MCSs) have been developed as a supplement charging method for electric vehicles (EVs), wherein energy replenishment is provided by mobile charging vehicles (MCVs). An MCV has an internal storage system employed to replenish the energy of a certain number of EVs. Charging scheduling of MCV is one of the key issues on the Internet of EVs for providing efficient and convenient charging services, which requires determining the charging sequence and the amount of energy when serving multiple EVs by one MCV. In this paper, a multi-objective MCV scheduling problem is investigated. By optimizing the charging sequence and the actual amount of energy being charged, the proposed framework aims to minimize the EV waiting time while simultaneously to maximize the charging benefits of all EVs. To solve the multi-objective optimization problem (MOP), a deep reinforcement learning (DRL) based framework is further explored. The MOP is first decomposed into a set of subproblems. Each subproblem is modelled as a neural network, wherein an actor-critic algorithm and a modified pointer network are adopted to solve each subproblem. Pareto optimal solutions can be directly obtained through the trained models. The experimental results demonstrate that the proposed method can efficiently and effectively solve the MCV scheduling problem and outperform NSGA-II and MOEA/D in terms of solution convergence, solution diversity, and computing time. In addition, the trained model can be applied to newly encountered problems without retraining.

Radio environment monitoring is crucial for many network engineering applications. Integrated with mobile crowdsourcing (MCS), radio map can be updated by mobile users in a low-cost manner. However, the crowdsourced measurement data may get quite sparse, and contain noises and errors. Therefore, how to efficiently infer missing data under low-quality measurements is critical in crowdsourced radio map construction. Existing inference methods like matrix completion require certain strict conditions, e.g. missing at completely random (MACR), which is impractical in the city-scale sensing. To address these issues, we propose a deep learning scheme based on adversarial auto-encoder (AAE) to handle measurements with large missing regions and complicated loss patterns. Specifically, this scheme applies variational auto-encoder (VAE) to infer missing data, and further utilizes the adversarial nets to play a min-max game with the VAE to improve recovery quality. Comprehensive experiments on three real datasets show that the proposed scheme can outperform state-of-the-art methods under large missing rates and low-quality measurements.

Unmanned aerial vehicles (UAVs) have been introduced into wireless communication systems to provide high-quality services and enhanced coverage due to their high mobility. In this article, we study a UAV-assisted mobile-edge computing (MEC) system in which a moving UAV equipped with computing resources is employed to help user devices (UDs) compute their tasks. The computing tasks of each UD can be divided into two parts: one portion is processed locally and the remaining portion is offloaded to the UAV for computing. Offloading is enabled by uplink and downlink communications between UDs and the UAV. On this basis, two types of access modes are considered, namely, nonorthogonal and orthogonal multiple access. For both access modes, we formulate new optimization problems to minimize the weighted-sum energy consumption of the UAV and UDs by jointly optimizing the UAV trajectory and computation resource allocation, under the constraint on the number of computation bits. These problems are nonconvex optimization problems that are difficult to solve directly. Accordingly, we develop alternating iterative algorithms to solve them based on the block alternating descent method. Specifically, the UAV trajectory and computation resource allocation are alteratively optimized in each iteration. Extensive simulation results demonstrate the significant energy savings of our proposed joint design over the benchmarks.

In this paper, joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing is studied. In the considered system model, each subscribed mobile end user can choose to offload its computation task to either an edge server deployed at the base station via the cellular connection or one nearby third-party fog node via the direct D2D connection. After receiving offloading requests from all end users, the network operator determines the optimal management of the fog computing system, including both computation and communication resource allocations, according to its service agreements with end users, energy cost of edge-server processing and total expense in renting third-party fog nodes. With the objective of maximizing the network management profit, a joint multi-dimensional resource optimization problem, integrating link scheduling, channel assignment and power control, is formulated. An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. To facilitate the practical implementation in large-scale systems, a suboptimal greedy algorithm with significantly reduced computational complexity is also developed. Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the proposed resource allocation algorithm over the counterparts.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is an appealing concept, where a fixed-wing UAV equipped with computing resources is used to help local resource-limited user devices (UDs) compute their tasks. In this paper, each UD has separable computing tasks to complete, which can be divided into two parts: one portion is processed locally and the other part is offloaded to the UAV. The UAV moves around above UDs and provides computing service in an orthogonal frequency division multiple access (OFDMA) manner. This paper aims to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing resource allocation and UAV trajectory. The resulted optimization problem is nonconvex and challenging to solve directly. With that in mind, we develop an iterative algorithm for solving this problem based on the block coordinate descent method, which iteratively optimizes resource allocation variables and UAV trajectory variables till convergence. Simulation results show significant energy saving of our proposed solution compared to the benchmarks.

With the increasing popularity of car sharing, a large amount of vehicle data has been generated which has great potential values for various applications (e.g., analyzing user habits for more economic benefits). These valuable data can be traded among owners and buyers on a data trading platform. Traditionally, data is traded in a centralized market which requires data exchange by trustworthy authorities. In this work, to address the potential unreliable issues (e.g., data loss and leakage), we design a consortium blockchain-based data trading framework to create a P2P trading market and enhance the security of data trading. We classify the data into five types to distinguish data with different values. Specifically, we investigate the pricing issue in the proposed car-sharing data market, which consists of data owner, service provider and data buyer. The data owner gives the pricing strategy of original data, and then the service provider processes the raw data and provides hierarchical quality of data with different data accuracy and privacy levels to the buyer who determines the data purchase strategy. Based on the interactions among these three parties, we formulate the problem as a three-layer Stackelberg game. Backward induction is applied to analyze the solution of the problem, and we conduct theoretical analysis to show the existence of Stackelberg game equilibrium. Numerical results evaluate the performance of our system under different settings.

In this paper, a novel computation offloading framework for edge computing is proposed. Unlike existing studies, this work considers that for offloading those computation-intensive tasks, mobile users are allowed to intentionally defer the declarations of their offloading requests and reserve some time for local pre-processing. By doing so, the offloading cost (including edge service charge and transmission cost) may be reduced because of less edge service demand, while the delay cost may increase due to later report. To strike the balance, each mobile user can strategically and selfishly determine a best timing of when to declare its offloading request (or the time-length of its local preprocessing). To characterize the resulted strategic interactions, a computation offloading game built upon a queueing model with strategic queue timing is formulated. Theoretical analyses and simulations evaluate the performance of the proposed equilibrium solution and demonstrate its superiority over counterparts.

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme, a transmission scheduling discipline, and a pricing rule. A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

In this paper, a novel queueing game framework for computational workload assignment and strategic computing speed control in fog computing is proposed. Unlike most existing studies in the literature, our work jointly addresses two practical issues related to fog computing, i.e., i) computation tasks offloaded by mobile users are generated dynamically over the time; and ii) each third-party fog node can strategically allocate its computing resource (or control its computing speed) for balancing the tradeoff between the reward gained from executing offloaded computation tasks and the cost incurred by the dissatisfied service to its own subscribed tasks. To describe the long-term performance of the computation task distribution/assignment and inherent strategic interactions among fog nodes, a noncooperative game upon a queueing model is formulated. Based on this, an adaptive algorithm is designed to jointly determine the optimal task distribution and the equilibrium computing speed of each fog node. Theoretical analyses and simulation results examine the performance of the proposed approach and demonstrate its superiority over counterparts.

In this paper, the scheduling management of delay-constrained medical packet transmissions in Internet of Things (IoT)-based healthcare networks is studied. Unlike most existing works in the literature, we focus on beyond wireless body area network (beyond-WBAN) communications, i.e., data transmissions between smart WBAN-gateways (e.g., smartphones) and the base station (BS) of remote medical centers. In our model, various medical packets are randomly aggregated at each gateway (which ordinarily stands for one patient), and their delay-constrained beyond-WBAN transmission requests are immediately reported to the network controller (i.e., BS) with different priority levels reflecting their medical importance. The BS schedules the uplink beyond-WBAN transmissions by forming a queueing system which addresses specific medical-grade quality of service requirements, including the priority awareness and the delay constraints of medical packet transmissions. By taking into account the natural device intelligence of smart gateways in IoT-based networks, we design a truthful and efficient mechanism which can prevent gateways from strategically misreporting the priority levels of medical packets, while incentivizing the BS to manage the transmission scheduling according to the desired manner. Both theoretical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over the counterparts.

In this paper, the management of medical packet transmissions in electronic health (e-health) networks is studied. Unlike most existing works, we focus on beyond wireless body area network (beyond-WBANs) communications, i.e., data transmissions between WBAN-gateways (e.g., smart phones) and the base station of remote medical centers, and consider a delay-dependent prioritized transmission scheduling which jointly takes into account both the criticality of medical packets and their starving time (i.e., experienced delays). In our model, medical packets are randomly aggregated at WBAN-gateways, and their transmission requests are reported to the base station with different priority class information. The base station manages the beyond-WBAN transmissions following a constructed queueing system with a delay-dependent priority discipline. For maximizing the network social welfare while preventing unexpected strategic behaviors from smart gateways, we design a truthful and efficient mechanism, called DPMT. Theoretical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over counterparts.

In this paper, the management of delay-sensitive medical packet transmissions in beyond wireless body area networks (beyond-WBANs) is studied. The considered system addresses the random arrival of sensed medical packets at each WBAN-gateway, which are categorized into different classes (one class of emergent alarms and multiple classes of non-emergent routines). Upon receiving a medical packet, the associated gateway immediately declares a beyond-WBAN transmission request to the base station (BS). With the consideration of medical-grade quality of service (mQoS) requirements, the beyond-WBAN transmissions of heterogeneous packets are scheduled by following the constructed queueing models with specifically designed priority disciplines. By further considering the potential strategic behaviors of smart gateways, a non-cooperative delay-dependent prioritized queueing game for the beyond-WBAN transmission management is formulated. After that, we propose a novel analytical framework to jointly characterize the queueing performance and the properties of the game equilibrium. Theoretical and simulation results justify the feasibility and applicability of our designed transmission management system in beyond-WBANs.

In this paper, a downlink cellular traffic offloading framework with social-aware device-to-device (D2D) content sharing and proactive caching is studied. In the considered system model, each user equipment (UE) is intelligent to determine which content(s) to request/cache and to share according to its own preference. As the central controller, the base station (BS) can establish cellular transmissions and/or incentivize D2D communications for content dissemination (including proactive caching). By taking into account wireless features, social characteristics, and device intelligence, we formulate a welfare maximization problem integrating power control, channel allocation, link scheduling, and reward design. To solve this complicated problem, we propose a novel mechanism which consists of a newly developed optimization approach, called basis transformation method, for the joint resource management, and a specially devised pricing scheme for the reward determination. Theoretical and simulation results examine the desired properties of our proposed mechanism, and demonstrate its superiority in improving social welfare, network capacity, and utility of the BS.

In this paper, the design of priority-aware truthful mechanisms for multi-class delay-sensitive medical packet transmissions in electronic health (e-health) networks is studied. Unlike most of existing works, we focus on beyond wireless body area network (beyond-WBAN) communications, and consider the absolutely prioritized transmission scheduling as the realization of medical-grade quality of service, i.e., more critical medical packets have to always be transmitted prior to the ones with less emergency. In our model, medical packets arrive randomly at each WBAN-gateway (which ordinarily stands for one patient), and their beyond-WBAN transmission requests are reported to the network regulator (i.e., the base station) with different packet priorities which reflect their medical importance. The base station then dynamically manages the beyond-WBAN transmission service by formulating a multi-class multi-server priority queueing system. Taking into account the potential strategic behaviors from smart gateways, we design a truthful mechanism which can guarantee that all gateways will honestly report the actual priorities of their medical packets, while at the same time incentivize the base station to provide channel usages for e-health services. Theoretical analyses and simulation results examine the desired properties of our proposed mechanism, and demonstrate its feasibility and superiority compared to counterparts.

In this paper, we propose a spectrum auction mechanism for secondary spectrum access in cognitive radio networks. Different from existing works in the literature, the time-dependent buyer valuation information is employed in the proposed mechanism so that the primary spectrum owner (PO) can determine more favorable spectrum allocations and pricing functions in order to maximize the expected auction revenue. In addition, to exploit the temporal spectrum reusability, the proposed mechanism allows each secondary wireless user to declare its specific time preferences, including service starting time, delay tolerance, and service length. By further considering the heterogeneities in secondary wireless service provisioning, the proposed mechanism is able to support heterogeneous forms (continuous or disjointed spectrum usages) of secondary spectrum requests. Specifically, at the beginning of the auction frame, secondary wireless users report their different spectrum usage requests along with the bidding prices, while the PO decides a single-step spectrum allocation and calculates the payment for each winner based on not only the received bids but also the known time-dependent valuation information. Theoretical analyses and simulation results show that the proposed auction mechanism can satisfy all desired economic properties, and can improve the spectrum allocation efficiency and auction revenue compared with counterparts.

In this paper, the incentive mechanism design issue for device-to-device (D2D) content-sharing communications is discussed. In literature, most of works are based on auction/game theory, where all content owners first report their ask prices/costs towards the base station (BS) which finally decides only one winner from them to transmit data towards the content requester. One disadvantage of these works is that content owners may be frequently activated to provide auction/game information (such as prices/costs), leading to high energy consumption, but finally may not win to gain benefit. To address this, we propose a sequential posted price mechanism where the BS sends offers with posted prices to content owners in sequence and activates only one owner each time. The BS stops sending new offers as long as there is already an owner accepting an offer or when the BS finds the expected cost of sending a new offer is larger than the cost of direct transmission. The optimal posted prices and offer- stopping rule of the BS are derived by the backward principle of dynamic programming. Simulation results show that the proposed mechanism can effectively limit the proportion of content owners being activated while the BS maintains an acceptable expected cost.

In this paper, the design of a truthful mechanism for prioritized medical packet transmissions in e-health networks is studied. Unlike most of existing works, we focus on beyond wireless body area networks (beyond-WBANs), and consider the absolutely prioritized scheduling, i.e., emergent medical signals have to always be transmitted prior to normal ones. In our model, medical packets arrive randomly at each WBAN-gateway, and their transmission requests are reported to the base station (BS) with different packet priorities. The BS then dynamically manages the beyond-WBAN transmission service by following the proposed mechanism constructed on a multi-class multi-server queueing system. Theoretical and simulation results demonstrate that the proposed mechanism can guarantee all gateways to truthfully report their packet priorities, and can incentivize the BS to provide exclusive channel usages for e-health services.

In this paper, an incentive-compatible mechanism for transmission scheduling in electronic health (e-health) networks with delay-sensitive medical packets is studied. Unlike existing works in the literature, we focus on the beyond wireless body area network (beyond-WBAN) communications. In the considered system, medical packets arrive randomly at each gateway (which ordinarily stands for one patient), and their transmission requests are reported to the network regulator (i.e., the base station) with specific delay sensitivities that reflect their medical signal severities. The base station then determines the order of transmission by formulating a priority queue. With the construction of the packets' utility and the base station's profit functions, we analyze the characteristics of the service system and design an incentive-compatible mechanism such that all gateways will be forced to report the actual delay sensitivities of their medical packets. Theoretical analyses show that our proposed mechanism can maximize the profit of the base station (i.e., minimize the total waiting cost from all medical packet transmissions) while guaranteeing higher service priorities to more emergent medical packets. Numerical results examine the properties of the proposed mechanism, and demonstrate its feasibility in providing economic incentives for all individuals.

In this paper, we investigate an online spectrum auction problem in cognitive radio networks with uncertain activities of primary users (PUs). In our framework, a primary base station (PBS), acted as the spectrum auctioneer, leases its under-utilized channels to secondary users (SUs) who request and access spectrum on the fly. Different from most of existing works in online spectrum allocation, we focus on a more practical situation that the auctioneer (or the PBS) has no prior knowledge of PUs' activities so that its channel states are not static. In order to balance the auction profits from granted SUs' spectrum requests and the potential penalties caused by incomplete services to PUs, we introduce the idea of virtual spectrum sellers and formulate the problem as an online double spectrum auction. We then propose a novel online admission and pricing mechanism which also considers the reusability of wireless spectrum. Theoretical analyses are provided to prove that our auction algorithm satisfies all desired economic properties in terms of budget-balance, individual rationality and truthfulness. Simulation results show that our proposed auction algorithm can increase the utility of the PBS, enhance spectrum utilization and achieve better satisfaction for SUs compared to counterparts.

Spectrum auction has been considered as an economically incentive way to motivate both primary spectrum owners (POs) and secondary users (SUs) to participate in dynamic spectrum access (DSA). In this paper, we propose a new combinatorial spectrum auction framework for the scenarios that each PO has multiple channels to sell and each SU demands multiple channels. Moreover, the heterogeneity in terms of POs' channel bandwidths and SUs' demands is also considered. The winner determination problem (WDP) in the proposed auction framework can be formulated as a multiple multidimensional knapsack problem (MMKP) and both upper bound and an approximation algorithm with polynomial time are developed. A tailored pricing mechanism is adopted in the payment design to ensure truthfulness and individual rationality. Numerical results show that our proposed auction algorithm can improve the spectrum allocation efficiency compared to counterparts.

The dynamic spectrum access (DSA) among multiple heterogeneous primary spectrum owners (POs) and secondary users (SUs) in recall-based cognitive radio networks is investigated in this paper. In our framework, SUs demand a different amount of spectrum for their transmissions. Each PO provides a portion of radio resources for leasing and also offers its own primary users (PUs) a certain degree of quality of service (QoS). Furthermore, POs are allowed to have different spectrum trading areas and as well as heterogeneous activities between POs' users. We propose a Two-stage resource allocation scheme with combinatorial Auction and Stackelberg Game in spectrum Sharing (TAGS) to deal with the allocation problem in such a complicated system. In the first stage, a spectrum allocation is decided by running a geographically restricted combinatorial auction without the consideration of spectrum recall. In the second stage, a Stackelberg game is formulated for all users to determine their best strategies with respect to the potential spectrum recall. Both theoretical and simulation results prove that TAGS provides a feasible solution for the problem and ensures the desired economic properties for all individuals.