Federated learning (FL) is a privacy-preserving distributed machine learning framework. However, recent studies have shown that implementing gradient-based data reconstruction attacks (DRA) can still lead to the leakage of user privacy through frequently uploaded model parameters in FL. Existing works leverage differential privacy (DP) to prevent privacy leakage, but the lack of effective scheduling of the privacy budget results in significant accuracy loss in the trained models. In this paper, we propose a novel dynamic privacy preserving federated learning framework, named NDPP-FL, capable of delivering robust defenses against DRA while significantly mitigating performance loss. Our key insight is to regard the privacy budget as a non-replenishable resource and dynamically schedule it based on privacy leakage risks to provide self-adaptive privacy protection for clients across varying communication rounds. Specifically, based on the amount of information between the local dataset and the transmitted parameters, we first design a parameter channel information leakage model. Then, during each update iteration, we introduce saliency perturbations based on the Hessian matrix to enhance defensive capabilities. Meanwhile, to improve the performance of NDPP-FL, sample-adaptive clipping and decaying noise perturbations are adopted in the construction. Furthermore, extensive experiments demonstrate that our framework performs excellently in terms of model accuracy and resilience against DRA.

The rapid evolution of unmanned aerial vehicles (UAVs) has positioned air-ground networks as vital infrastructures for diverse applications. However, the open channels of air-ground networks remain inherently vulnerable to persistent eavesdropping threats. While physical-layer key generation (PLKG) offers a lightweight security mechanism by leveraging channel reciprocity to extract shared secrets, the inherent mobility of UAVs introduces a paradoxical tradeoff. Increased channel randomness from dynamic flight patterns enhances security through entropy amplification but simultaneously disrupts channel reciprocity, leading to key mismatch between legitimate parties. Existing PLKG schemes struggle to maintain reliability in key generation due to static channel characteristics and synchronization overhead, limiting their practical deployment in air-ground networks. To resolve this conflict, we propose a Doppler shift key generation (DSKG) scheme that systematically regulates Doppler shifts through UAV trajectory design to derive secure keys. By formulating the problem as a Markov decision process, we develop a proximal policy optimization (PPO)-clip-based reinforcement learning algorithm to dynamically control UAV speed and steering angle, ensuring robust Doppler shift reciprocity while maximizing both key entropy and generation rate. Experimental results quantify the improvements of our scheme over benchmarks in maintaining high key unpredictability and generation efficiency. Furthermore, the analysis provides valuable insights into parameter impacts, confirming the practical viability of the DSKG scheme for securing air-ground communications.

Edge Intelligence (EI) combines edge computing and artificial intelligence to deliver low-latency and resource-efficient services. Integrated Sensing and Communication (ISAC) further empowers EI by enhancing edge perception and accelerating intelligent model training. However, integrating ISAC into EI complicates the coordination of dynamically varying sensing, communication, and computation resources, especially under device mobility and unpredictable network conditions, leading to degraded service performance. To address these coordination challenges and sustain high-quality service under mobility and dynamics, we aim to design an adaptive service provision framework that tightly couples real-time perception with intelligent decision-making at the edge. Specifically, we propose an adaptive service provision architecture for ISAC-assisted EI, where Digital Twins (DTs) hosted on edge servers represent edge devices and their contexts to enable accurate perception and intelligent decision-making, thereby enhancing the efficiency of ISAC-enabled services. By dynamically migrating DTs across edge servers based on device mobility and resource availability, the system supports continuous decision-making and seamless service delivery. We further integrate convex optimization for efficient multi-resource coordination and a Time-Varying Contextual Bandit (TVCB) algorithm to enable adaptive, context-aware DT migration in dynamic environments. Extensive simulations demonstrate that our approach significantly improves service quality, reliability, and adaptability in ISAC-assisted EI systems, reducing migration oscillations and overhead while achieving lower latency and higher utility compared with representative baselines.

Edge intelligence deploys artificial intelligence models on edge nodes proximal to data sources, and delivers real-time inference support for resource-constrained devices. To realize this vision, inference offloading differs from conventional computation offloading by tailoring offloading strategies to the intrinsic characteristics of AI inference tasks. In this field, existing researchs generally lack fine-grained model partitioning capabilities and long-term resource adaptability, failing to optimize resource utilization and sustain stable performance in mobile environments. To address these issues, we propose an adaptive inference acceleration framework that dynamically partitions inference models into hierarchical subtasks and offloads these subtasks to heterogeneous edge servers. We formulate a joint optimization problem for task partitioning, offloading and resource allocation, which takes queue stability as the constraint and aims to minimize the long-term average task completion time. To realize the optimal trade-off between latency and stability without future state prediction, we adopt Lyapunov optimization to decompose the long-term stochastic optimization into slot-by-slot solvable deterministic subproblems. For these slot-by-slot subproblems, we design a Q-network Mixing (QMIX)-based multi-agent reinforcement learning method to enable collaborative strategy selection across edge servers. Experimental simulations show that, compared with baseline algorithms including the greedy, genetic and MAD2RL methods, our proposed framework achieves a substantial reduction in task completion time while preserving inference accuracy and queue stability.

Edge intelligence enhances the computational capabilities of resource-limited devices by offloading inference tasks to edge servers. Traditional methods either execute the entire model on the device, resulting in slow inference, or fully offload it to the server, incurring communication delays and privacy risks due to raw data transmission. Model partitioning addresses these challenges by splitting the model for execution on both the device and edge server, transmitting only intermediate inference results. However, current model partitioning methods lack consideration of device mobility, resulting in reduced inference efficiency and task interruptions. To address these limitations, we introduce MobiSplit, a novel mobility-aware framework that dynamically partitions inference models between resource-constrained devices and edge servers. MobiSplit adapts to real-time device mobility, fluctuating network conditions, and computational constraints to minimize inference latency and energy consumption while ensuring robust task execution. Additionally, we propose a distributed auction-based algorithm that empowers edge devices to autonomously determine optimal partitioning and offloading strategies in a scalable and adaptive manner. Extensive simulations demonstrate that MobiSplit enhances inference efficiency, achieving a 60% latency reduction and a 20% energy consumption decrease compared to the best-performing baseline across diverse edge scenarios.

Recently, deploying static directional chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Extensive simulations and field experiments are conducted to evaluate the performance of our solution. The results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 132.09% on average.

Unmanned Aerial Vehicles (UAVs) with high mobility and flexibility have emerged as key enablers of computer vision (CV) applications. In the field of image recognition, federated learning can be integrated into UAV swarms, enabling distributed computing and efficient data sharing to train and deploy high-performance real-time image recognition models, while preserving the privacy of the UAV local data. However, despite its potential, federated learning in UAV swarms for real-time image recognition faces significant challenges of low convergence speed and insufficient model recognition accuracy posed by volatile environments. On the one hand, unstable UAV communication channels increase model upload latency. On the other hand, dynamic UAV states lead to fluctuations in local update quality. To address these challenges, we propose an accelerated asynchronous federated learning framework for UAV swarms to support real-time image recognition. Our framework introduces a Shapley-based asynchronous update mechanism, which enhances model accuracy by quantifying UAV update contributions and mitigating the effects of model staleness. Furthermore, we propose a fine-grained client selection strategy that accelerates convergence by selecting UAVs with low latency and high contributions to model recognition accuracy. A time-varying multi-armed bandit (MAB) model is employed to capture dynamic UAV states, optimizing client selection and further improving convergence. Numerical results in the simulated volatile environment show that our scheme outperforms benchmark methods in accuracy and convergence speed of the image recognition model.

The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.

Vehicular edge computing (VEC) is critical for the safe and efficient driving of intelligent vehicles, by which they can offload computation-intensive tasks (such as driving environment perception) to edge servers to overcome the limitations of onboard computational resources and cooperate with others. One of the major challenges faced by VEC is that the offloaded intelligent driving tasks generally generate large amounts of data, which can easily stretch and congest the vehicle communication channels. To address the above challenges, we first propose a novel semantic VEC (SVEC) architecture, which can extract the semantic information of tasks and offload them to edge servers, thereby achieving reliable and efficient offloaded task communication and computation adaptively. Considering the scarce channel resources of vehicles and the intelligent tasks with different priorities and modalities, we define a novel user utility model for SVEC and transform the problem of maximizing user utility into a joint optimization problem of semantic feature extraction, task offloading and resource allocation. Furthermore, to cope with the complexity of the solution space of the optimization problem, we propose a diffusion-based multi-agent reinforcement learning algorithm, which improves the ability of agents to explore the solution space through the diffusion process, thereby achieving optimal decisions for semantic feature extraction, task offloading and resource allocation. Simulation results show that the proposed scheme improves the overall performance of SVEC while reducing offload latency and average system cost.

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

Unpredictable network dynamics, resource heterogeneity, and user mobility pose challenges to efficient resource allocation in mobile networks. Digital twins (DTs), providing timely expression of features and accurate digital representations, offers new possibilities to enhance the performance of mobile networks. However, the DT deployment and allocation of computing power during the construction of DT models may have detrimental effects on service quality. Wireless computing power networks (WCPNs), an emerging computing network architecture, can efficiently orchestrate the computing and networking resources of heterogeneous computing nodes, thereby providing efficient computing services. Based on this, we propose an architecture of WCPN-empowered DT systems and investigate the adaptive placement and transfer scheme for DTs. Considering the correlation among DTs of cooperating and computing entities (e.g., edge servers), we exploit the Shapley value of the cooperative game theory that fairly quantifies the contributions of the cooperating computing entities. To further cope with the time-varying characteristics of computing resource demands in mobile networks, with the powerful computational support of WCPN, we propose a DT transfer scheme based on Shapley value and double auction scheme. Numerical results show that the proposed scheme in this article outperforms benchmarks in terms of average latency, DT error, and resource utilization.

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.

Federated Learning (FL) in mobile edge computing (MEC) systems has recently been studied extensively. In ubiquitous environments, there are usually cross-edge devices that learn a series of tasks across multipleindependentedge FL systems. Due to the differences in the scenarios and tasks of different FL systems, cross-edge devices will forget past tasks after learning new tasks, which is unacceptable for devices that pay system costs to participate in FL. Continual learning (CL) is a viable solution to this problem, which aims to train a model to learn a series of tasks without forgetting old knowledge. Currently, there is no work to investigate the problem of CL in a cross-edge FL scenario. In this paper, we proposeCross-FCL, aCross-edgeFederatedContinualLearning framework. Specifically, it enables devices to retain the knowledge learned in the past when participating in new task training through a parameter decomposition based FCL model. Then various cross-edge strategies are introduced, including biased global aggregation and local optimization, to trade off memory and adaptation. We conducted experiments on a real-world dataset and other public datasets. Extensive experiments demonstrate that Cross-FCL achieves best accuracy on IID and highly non-IID tasks with a low storage cost compared to other baselines.

Digital twins (DTs) have emerged as a promising enabler for representing the real-time states of physical worlds and realizing self-sustaining systems. In practice, DTs of physical devices, such as mobile users (MUs), are commonly deployed in multi-access edge computing (MEC) networks for the sake of reducing latency. To ensure the accuracy and fidelity of DTs, it is essential for MUs to regularly synchronize their status with their DTs. However, MU mobility introduces significant challenges to DT synchronization. Firstly, MU mobility triggers DT migration which could cause synchronization failures. Secondly, MUs require frequent synchronization with their DTs to ensure DT fidelity. Nonetheless, DT migration among MEC servers, caused by MU mobility, may occur infrequently. Accordingly, we propose a two-timescale DT synchronization and migration framework with reliability consideration by establishing a non-convex stochastic problem to minimize the long-term average energy consumption of MUs. We use Lyapunov theory to convert the reliability constraints and reformulate the new problem as a partially observable Markov decision-making process (POMDP). Furthermore, we develop a heterogeneous agent proximal policy optimization with Beta distribution (Beta-HAPPO) method to solve it. Numerical results show that our proposed Beta-HAPPO method achieves significant improvements in energy savings when compared with other benchmarks.

As one of the key enabling technologies, mobile edge computing can considerably reduce system latency and realize ubiquitous computing. The digital twin can constantly learn and update from entities to characterize the working conditions of physical entities. The integration of digital twins with mobile edge computing provides possibility for efficient resource allocation issues in the networks. However, the vast number of connected devices, resource heterogeneity, and dynamic network states are still challenging for the application of digital twins in mobile edge computing. In this paper, we propose a digital twin-empowered mobile edge computing architecture and investigate the energy-efficient digital twin placement. To adapt to the service demands of mobile edge computing, we exploit the Shapley value of the cooperative game theory and develop a Shapley value-based digital twin placement scheme. Numerical results show the efficiency of the proposed scheme in terms of average latency, average communication consumption, and digital twin error.

In the 6G era, the proliferation of data and data-intensive applications poses unprecedented challenges on the current communication and computing networks. The collaboration among cloud computing, edge computing, and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this article, we propose a wireless computing power network (WCPN) by orchestrating the computing and networking resources of heterogeneous nodes toward specific computing tasks. To enable intelligent service in WCPN, we design a task and resource-aware federated learning model, coined FedTAR, which minimizes the sum energy consumption of all computing nodes by the joint optimization of the computing strategies of individual computing nodes and their collaborative learning strategy. Based on the solution of the optimization problem, the neural network depth of computing nodes and the collaboration frequency among nodes are adjustable according to specific computing task requirements and resource constraints. To further adapt to heterogeneous computing nodes, we then propose an energy-efficient asynchronous aggregation algorithm for FedTAR, which accelerates the convergence speed of federated learning in WCPN. Numerical results show that the proposed scheme outperforms the existing studies in terms of learning accuracy, convergence rate, and energy saving.

The papers in this special section focus on digital twins for the Industrial Internet of Things.

Terrestrial-satellite networks (TSNs) can provide worldwide users with ubiquitous and seamless network services. Meanwhile, malicious eavesdropping is posing tremendous challenges on secure transmissions of TSNs due to their widescale wireless coverage. In this paper, we propose an aerial bridge scheme to establish secure tunnels for legitimate transmissions in TSNs. With the assistance of unmanned aerial vehicles (UAVs), massive transmission links in TSNs can be secured without impacts on legitimate communications. Owing to the stereo position of UAVs and the directivity of directional antennas, the constructed secure tunnel can significantly relieve confidential information leakage, resulting in the precaution of wiretapping. Moreover, we establish a theoretical model to evaluate the effectiveness of the aerial bridge scheme compared with the ground relay, non-protection, and UAV jammer schemes. Furthermore, we conduct extensive simulations to verify the accuracy of theoretical analysis and present useful insights into the practical deployment by revealing the relationship between the performance and other parameters, such as the antenna beamwidth, flight height and density of UAVs.

The ground-to-satellite communication network (G2SN) has highlighted the significance of constructing ubiquitous and seamless networks for the next-generation communication system. However, in the presence of secret eavesdroppers, securing massive transmission links is posing tremendous challenges for G2SNs. In this paper, we propose an aerial assistant scheme to safeguard legitimate transmissions in G2SNs, where multiple unmanned aerial vehicles (UAVs) are deployed between the ground users and the satellite. With the assistance of flexible UAVs and the directivity of directional antennas, the constructed link can significantly reduce the risk of wiretapping, resulting in the improvement of security. Furthermore, to evaluate the performance of G2SNs, we introduce the eavesdropping probability and link connectivity as metrics. With the comparison of the non-protection scheme, we validate the effectiveness of our aerial assistant scheme. Finally, we present useful insights into practical deployment by revealing the relationship between the performance and other parameters, such as antenna beamwidth, deployment height and density of UAVs.

As an enabler of sixth-generation communication technology (6G), Federated Learning (FL) triggers a paradigm shift from "connected things" to "connected intelligence". FL implements on-device learning, where massive end devices jointly and locally train a model without private data leakage. However, FL suffers from problems of low accuracy and convergence rate when no data is shared to the central server and the data distribution is non-IID. In recent years, attempts have been made on hybrid FL, where very small amounts of data (e.g., less than 1%) is shared from the participants. With the opportunities brought by shared data, we notice that the server is capable of receiving the data in order to assist the FL process and mitigate the challenge of non-IID. Notably, existing hybrid FL only applies the model-level technologies belonging to the traditional FL and does not make full use of the characteristics of shared data to make targeted improvements. In this paper, we propose FedAux, a novel hybrid FL method at knowledge-level, which utilizes shared data to construct an auxiliary model and then transfer general knowledge to traditional aggregated model or client model for enhancing the accuracy of global model and speeding up the convergence of global model. We also propose two specific knowledge transfer strategies named c-transfer and i-transfer. We conduct extensive analysis and evaluation of our methods against the well-known FL methods, FedAvg and Hybrid-FL protocol. The results indicate that FedAux shows higher accuracy (10.89%) and faster convergence rate compared with other methods.

In the 6G era, the proliferation of data poses unprecedented challenges on the current computing networks. The collaboration among cloud computing, edge computing and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this paper, we propose a Wireless Computing Power Networks (WCPN) by orchestrating the computing and networking resources of heterogeneous nodes towards specific computing tasks. To enable collaborative intelligence in WCPN, we design an energy-efficient federated learning model, which minimizies the sum energy consumption of all nodes by the joint optimization of the computing capability and the collaborative learning strategy. Based on the solution of the optimization problem, the neural network depth of computing nodes and the collaboration frequency among nodes are adjustable according to specific computing task requirements and resource constraints. Numerical results show that the proposed scheme outperforms the existing work in terms of convergence rate, learning accuracy, and energy saving.

The sixth-generation (6G) wireless network is conceptualized to provide ubiquitous and reliable network access through effective inter-networking among space, air, and terrestrial networks, while posing considerable pressure on dynamic network orchestration. Digital twin (DT) provides an alternative approach to proactively make real-time resource allocation by mapping and learning the complex network topology. However, the dual challenges of limited energy capacity and insufficient computing power of unmanned aerial vehicles make it difficult to establish digital twin on aerial networks. In light of this, in this paper, we propose a lightweight DT empowered air-ground network architecture, where the DT modelling task is distributed to diverse ground devices based on federated learning. To improve the efficiency of DT modelling, we design a distributed incentive mechanism that incentivizes high-performance ground devices to take part in federated learning. Considering the computing burden and possible private disclosure caused by the incentive, we further solve the incentive scheme with a new distributed algorithm. Simulation results show the effectiveness of the proposed lightweight DT scheme in energy consumption and model accuracy.

Internet of Vehicles (IoV), when empowered by aerial communications, provides vehicles with seamless connections and proximate computing services. The unpredictable network dynamics of aerial-assisted IoV pose challenges to the resource allocation. In this article, dynamic digital twin (DT) of aerial-assisted IoV is established to capture the time-varying resource supply and demands, so that unified resource scheduling and allocation can be performed. We design a two-stage incentive mechanism for resource allocation based on Stackelberg game where DT of vehicles or road side units (RSUs) is deemed as the leader, and the RSUs who provide computing services are as the follower. In the first stage incentive, we determine the computing resources that RSUs are willing to offer according to vehicles’ preferences. To further maximize the satisfaction of vehicles and the overall energy efficiency, a distributed incentive mechanism based on alternating direction method of multipliers (ADMMs) is then designed, in which the resource allocation policy for each vehicle is optimized. Thanks to ADMM, the incentive mechanism can be run at multiple RSUs in parallel to reduce delay and relieve the computational burden of UAVs. Simulation results show that the proposed scheme can improve the satisfaction of vehicles and the energy efficiency at the same time.

The Internet of Things (IoT), while providing comprehensive interconnection and ubiquitous services, poses security issues by enabling resources sharing among various devices from different untrusted authorities. Blockchain, as a distributed ledger, provides a traceable and verifiable platform to ensure the secure access control in IoT. The existing works based on blockchain may bring up intolerable computing overhead and delay to the lightweight IoT devices. In this article, we propose a dynamic and lightweight attribute-based access control framework for blockchain-empowered IoT, to achieve secure and fine-grained authorization. The proposed scheme allows access to resources by evaluating attributes, operations, and the environment relevant to a request. The access policy is executed through smart contract in blockchain for security and flexibility. To further adapt to IoT device constraints, we design a access control framework based on decentralized application (DApp), which can maintain tamper proof in a timely manner and be adapt to the delay-intolerant application. When delay-intolerant access is required, access can be allowed according to local replica of the blockchain, without a consensus of blockchain network. Considering the time-varying attributes of IoT devices, a trust management scheme is proposed based on the Markov chain to resist the security fluctuation caused by the vulnerability of IoT devices. In the experiments, we deploy our system prototype on Ethereum to evaluate the feasibility and effectiveness of the scheme. The results show the proposed scheme can achieve secure, high throughput, and flexible access control in IoT.

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the complex industrial environment to achieve the benefits of Industry 4.0. In this paper, we consider a new architecture of digital twin empowered Industrial IoT, in which digital twins capture characteristics of industrial devices to assist federated learning tasks of industrial scenarios. A trust-based aggregation is proposed in federated learning to alleviate the effects of digital twins deviation and emphasize the contribution of high-performance clients. Based on Lyapunov dynamic deficit queue and deep reinforcement learning, we propose a federated learning framework that adaptively adjusts the aggregation frequency to improve the learning performance under resource constraints. Numerical results show that the proposed framework outperforms the benchmark in terms of learning accuracy, convergence, and energy saving.

Internet of Vehicles (IoV) can realize seamless communication connection and computing offloading services with the assistance of air communication. Limited by the high network dynamics of the air-assisted IoV, resource allocation faces great challenges. In this paper, dynamic digital twin of air-assisted IoV is established to capture the time-varying resource supply and demands, so that unified resource scheduling and allocation can be performed. We designed an incentive mechanism for resource allocation based on Stackelberg games to maximize vehicle satisfaction and overall energy efficiency. In the game, the digital twin of air-assisted IoV within the coverage of unmanned aerial vehicles (UAV) are regarded as leaders, while RSUs that provide computing services are followers. At the same time, in order to reduce the delay and reduce the computational burden of the UAV, a distributed incentive mechanism based on the Alternating Direction Multiplier Method (ADMM) was designed to optimize the resource allocation strategy of each RSU. Simulation results show that the proposed scheme can improve the satisfaction of vehicles and the energy efficiency at the same time.

The densification of base station (BS) deployments is driving the evolution of network structures towards heterogeneous ultra-dense networks (UDN), making coordinated multipoint (CoMP) a viable and promising transmission solution. However, the BS cooperation regions formed by applying CoMP in the UDN are small and irregular, which causes frequent handover for mobile users. Different from most existing work that focus on the trigger time of handover, we explore how to choose the appropriate BS cooperation set to reduce handover rate. In this paper, we consider movement aware CoMP handover (MACH). By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. To enhance reliability, we further proposed improved MACH (iMACH) to achieve a trade-off between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. Using stochastic geometry method, expressions of coverage probability, handover probability and throughput that characterize performance of the proposed schemes are derived. The numerical results indicate that the theoretical analyses fit the simulation results well and the proposed schemes surpass the existing schemes in terms of the aforementioned metrics, and more intelligent and suitable for ultra-dense scenarios.

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the location of warden is uncertain. In this paper, we propose a covert communication scheme against a warden with uncertain locations, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the limit of covert outage probability (the probability of the transmission found by the warden). First, we analyze the monotonicity of the covert outage probability to obtain the optimal location for the warden. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. First, we derive the transmit-to-jamming power ratio limit from the maximum allowed covert outage probability. With this constraint, the connection probability is maximized over the transmit-to-jamming power ratio for a fixed transmission rate. Since the connection probability and the transmission rate are coupled, the bisection method is applied to maximize the connectivity throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Ultra-dense edge computing is expected to provide delay-sensitive and computational-intensive services for mobile devices. Due to the complexity and unpredictability of the network environment, it is challenging to ensure the continuity and security of computing offloading services in the process of user movement. Most existing works consider the decisions of communication handover and computational offloading simultaneously while ignoring the security on offloading tasks. In light of this, we propose a secure mobility management framework for blockchain-based ultra-dense edge computing, where blockchain reduces duplicate authentication between edge servers. We jointly optimize the wireless handover and service migration decisions between base stations, which is translated into a multi-objective dynamic optimization problem using the Lyapunov optimization. The optimization problem is solved by deep reinforcement learning approach based on theActor–Criticmethod. Finally, we use simulation studies to evaluate the performance of the proposed scheme. The results show that, compared with other existing schemes, the proposed scheme can reduce the average delay of computing tasks, the rate of tasks failure and the rate of handover.

The sixth generation (6G) of wireless cellular networks is envisioned to provide connected intelligence for mobile devices through ambient computing and caching services. Edge caching is an efficient solution to reduce transmission delay and offload traffic from backhaul network by caching frequently requested content on the edge servers. However, the security problems of edge caching such as eavesdropping are seldom considered in the literature. In this paper, we propose a two-hop edge caching scheme where physical layer security (PLS) and probabilistic caching scheme are adopted to prevent data from being eavesdropped. We jointly optimize content caching probability and redundancy rate to maximize the secure transmission probability. Extensive simulation results show that the proposed scheme can significantly improve the secure transmission probability of edge cache network facing the threat of eavesdropping.

The industrial Internet of Things (IIoT), as one of the indispensable paradigms of the future network, challenges existing computing network architectures by supporting computational-intensive applications. In the IIoT, resource-rich industrial devices may share idle computing resource to lightweight nodes through device-to-device (D2D) technology, whereas such resource sharing is under social and locality constraints. When industrial devices are carried by human or installed on manned machines, resource sharing more likely occurs among social-trustworthy and locality-adjacent devices. In this article, we propose two social-aware incentive mechanisms for D2D resource sharing in the IIoT, namely one-hop-based social-aware incentive mechanism (OSIM) and relay-based social-aware incentive mechanism (RSIM). In the OSIM, resource-constrained devices bid for offloading tasks using a Vickrey-Clarke-Groves auction, while the RSIM relaxes the locality constraint to two hops to achieve a higher resource utilization ratio. Extensive simulation results show that the performance of the proposed mechanisms can significantly improve the system efficiency while maintaining truthfulness.

Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

The densification of small cells, ultimately towards ultra-dense networks (UDN), makes coordinated multipoint (CoMP) a feasible transmission solution for mobile users. However, CoMP may increase the handover rate, as users move across small and irregular BS cooperation regions. In this paper, we consider movement aware CoMP handover (MACH) in heterogeneous UDNs. Unlike most prior works, which focus on the handover trigger time, we explore the appropriate selection of BS cooperation set to reduce handover rate. By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. Moreover, we achieve a balance between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. The performance of the proposed MACH is analyzed in terms of coverage probability and handover probability using stochastic geometry. Through extensive simulations, we show that the analytical results fit well with simulations, and the proposed MACH outperforms the existing works in both handover probability and coverage probability.

Mobile edge computing (MEC) enables a distributed computing environment closer to mobile devices (MDs) and substantially reduces the response time for a MD computing task. However, lightweight servers may be incapable of keeping up with all the tasks from MDs due to the limited resources. Therefore, how to effectively allocate resources of edge servers for profit-driven multi-task users is a key issue in MEC. In this paper, we study the cross-server resource allocation scheme in MEC from the perspective of network economics. Because of the supply-demand relationship between the edge servers providing services and the MDs requesting the services, we regard the resource allocation as an auction problem in the network economics. In particular, we propose a multi-task resource allocation algorithm based on double auction (MADA) to maximize the system efficiency. The simulation results indicate that MADA can efficiently allocate resources while maintaining the economic properties of individual rationality, truthfulness and weakly balanced budget.

Heterogeneous caching network consisting of edge servers and caching helpers is proposed to meet the huge amount of mobile traffic and provide low-latency caching services for users. The problem of caching redundancy caused by the overlapping of edge servers and caching helpers is neglected by most researchers. In this paper, we propose a spatially cooperative caching strategy to avoid the caching redundancy and improve the hit probability of the heterogeneous caching network. We analyze the performance of this caching strategy by using stochastic geometry, derive the optimal caching probability and make a comparison with available caching strategies. The numerical results show that, the proposed caching strategy has a better performance under the normal parameter settings.

Crowdsourcing, as an essential part in Internet of Vehicles (IoV), can provide vehicles with various functions such as road condition monitoring and path planning. The prevalence and heterogeneity of crowdsourcing devices, although enabling various emerging applications in IoV, makes it challenging to yield intelligent and flexible incentive and management framework, while ensuring optimal choice for all entities. Note that artificial intelligence (AI) algorithms could automatically select the significant features in the underlying data and globally find optimal solutions even for non-convex object functions. In this paper, we propose an AI-driven incentive scheme using a deep learning based reverse auction scheme, in order to achieve revenue-optimal, dominant-strategy incentive compatible objectives. The effectiveness of the proposed framework has been verified through extensive simulations.

In IoT, a flexible and trustworthy access control framework is of significance to ensure the security of lightweight IoT devices. The conventional centralized access control framework is no longer fit for the open and large-scale IoT environments. In this paper, we propose an attribute-based distributed access control framework (ADAC) for IoT using blockchain technology. The attributes, such as manufacturer and object-specified attribute, are considered in the proposed ADAC for more fine-grained access control in the open and lightweight IoT devices. Particularly, we design a smart contract system, which includes a subject contract (SC), an object contract (OC), an access control contract (ACC) and multiple policy contracts (PCs), to manage and access attributes of IoT devices for distributed and trustworthy access control (DTAC). SC and OC are responsible for managing subject attribute and object attribute information, respectively. PCs are used to manage access control policies. ACC performs authorization judgment by accessing attributes and policies. Finally, a case study is performed to demonstrate the workflow and show that ADAC could achieve fine-grained and flexible access control for IoT.

Due to the advantages of large area coverage, low capital cost and fast deployment, unmanned aerial vehicles (UAVs) are believed to play a key role in the emerging Internet of Things (IoT). In this paper, we first develop an effective analytical approach to characterize the properties of UAV-assisted communications over a large number of IoT devices by introducing average channel access delay for packets that can be successfully transmitted. Specifically, an IoT device is said to establish a full transmission to a UAV, only if its time duration covered by the UAV is greater than the specified average channel access delay. Then, we present a stochastic geometry based mathematical framework to analyze the coverage probability and average achievable rate for a multi-UAV assisted downlink network. Different from previous works: 1) we consider a flexible multi-UAV deployment strategy connecting IoT devices to the Internet via sky-haul links to the satellite, where the altitudes of the UAVs can be adjusted to fulfill the requirements of various IoT applications and 2) we derive analytical expressions, in particular integral form, for the coverage probability and average achievable rate. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum IoT system performances can be achieved.

In order to meet the ever-increasing demands on computational and spectrum resources in the era of 5G and Internet of Things (IoT), mobile-edge computing (MEC) and ultra-dense heterogeneous network (UDN) have been envisioned as two promising technologies, which gives rise to the so-called ultra-dense edge computing. Note that existing works on task offloading for ultra-dense edge computing mostly considered simple task offloading scenarios, ignoring the random request for types of computation tasks from the mobile devices (MDs) and the random arrival of the tasks at the edge servers. Toward this end, we provide this paper to study the multi-user task offloading problem in ultra-dense edge computing with multiple types of tasks requested by the MDs. To minimize the MDs' energy consumption and thus prolong their battery lifetime, task offloading, computation frequency scaling, and transmit power allocation are jointly optimized in this paper. After that, the problem is divided into two subproblems, i.e., local energy minimization, and joint task offloading and transmit power allocation. A game-theoretical joint offloading scheme is proposed as our solution. Extensive numerical results corroborate the superior performance of our proposed scheme rather than those with single edge server, fixed computation frequency and transmit power at the MDs.

The general tendency that cellular networks evolve toward small cells and ultimately ultra-dense networks (UDNs) will bring paradigm shift in network design. Cooperating multiple BSs for the service of a user, referred to as coordinated multipoint (CoMP), becomes feasible in UDNs to provide high quality of experience (QoE) for mobile users. To the best of our knowledge, we are the first to consider CoMP-based uplink transmission in UDNs. A simple CoMP uplink transmission scheme is firstly proposed to enable each user to transmit to multiple cooperating BSs with power control. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of outage probability and ergodic capacity, considering the effect of BS intensity, power control, and the number of cooperating nodes. Extensive simulation has been done and it is found that the proposed scheme could significantly improve outage probability and ergodic capacity of mobile users, as compared with non-CoMP scheme, especially in UDNs. Indications are also provided on network settings and parameter selection in CoMP-based uplink transmission in UDNs.

Mobile edge computing (MEC), as an emerging technique, brings computing resource near to the user end, and thus offers prompt and high-bandwidth service. In MEC, edge servers typically provide their limited computing resource to an appropriate set of users and expect to be rewarded for that, while users pay for the service. In this paper, We utilize the network economics to solve the problem of resource allocation in MEC to maximize system efficiency. We consider a multi-user and multi-server scenario with locality-awareness, i.e., an edge server can only serve multiple MDs in the vicinity, and propose a single-round double auction scheme based on breakeven in MEC (SDAB), which is proved to be individual rationality and truthful. The simulation results indicate that SDAB can significantly improve the system efficiency of MEC as compared with the existing works, while maintaining the economic property of budget balance.

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.

Energy harvesting techniques extend the lifetime of Internet of Things (IoT), whereas yield an unprecedented paradigm shift in network design. Base stations (BSs), being powered by self-contained energy harvesting modules, may keep OFF during recharging, leading to super-frequent handovers for nodes and high network dynamics. Under such dynamics, multiple cooperating BSs for the service of a “smart thing,” referred to as coordinated multipoint (CoMP), become a feasible solution by effectively improving communication reliability. To the best of knowledge, this is the first work to consider CoMP uplink transmission to alleviate outage caused by energy harvesting of BSs in IoT. A simple CoMP uplink transmission scheme is proposed for each node of IoT to transmit to two cooperating BSs in a K-tier heterogeneous network. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of availability and average rate, considering the effect of energy capacity, energy charging rate, in a K-tier heterogeneous network. Performance evaluation through extensive simulations are conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve availability and average rate.

Given the highly dynamic traffic loads of mobile Internet of Things (IoT) devices and their stringent quality-ofservice requirements, i.e., access delay particularly, as well as the heterogeneous infrastructures among IoT networks, it is a nontrivial task to efficiently deploy cloudlets among large number of access points (APs) in IoT networks, especially for the access delay and network reliability, since different placement schemes would produce various network performances. To combat this issue, we are motivated to investigate in details the optimal placement of cloudlets to minimize the average access delay by applying software-defined networking (SDN) techniques to provide flexible and programmable management for cloudlets deployment in IoT networks with considering the complicated queuing process at numerous SDN-based APs. An enumerationbased optimal placement algorithm (EOPA) is first proposed as benchmark. Then we propose a ranking-based near-optimal placement algorithm (RNOPA) which is able to dynamically adapt to mobile IoT devices and their traffic loads, by treating each AP as a single server queue and adopting an efficient ranking mechanism. As corroborated by extensive simulation results, RNOPA reports access delay very close to that of EOPA. Note that RNOPA outperforms the famous K-medians clustering algorithm (KMCA) in both of average cloudlet access delay and reliability, while at the cost of a much lower computational complexity than KMCA.

Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.

Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains, etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack of appropriate sensing triggers. TRANSense demonstrates how a judicious fusion of such disparate data sources can overcome these challenges and offer novel insights. We detail two examples: (a) Taxi Service Analyzer that provides accurate detection of commuter queuing for taxis and estimates their wait time, by using taxi trip records to identify potential taxi locations with high demand and subsequently selectively triggering mobile sensing-based queuing analytics on nearby commuters; and (b) Subway Boarding Analyzer that identifies instances when passengers fail to board arriving trains, by first estimating train arrivals from temporal patterns of passenger egress at station gantries, and then using mobile sensing-based analysis of commuter movement behavior on platforms. Experiments with real-world datasets (from over 20,000 taxis and 1.7 million commuters in Singapore) show the power of this approach: the taxi service analyzer detects commuter queuing with over 90 percent accuracy with negligible energy overhead and estimates wait times with error margins below 15 percent, whereas the subway boarding analyzer can detect failed boarding events with a precision of over 90 percent (more than thrice what is achievable through purely mobile sensing).

The ongoing densification of small cells, i.e., ultimately evolving toward ultra-dense networks (UDNs), yields an unprecedented paradigm shift in network design by bringing the base stations (BSs) and users to approximate the spatial scale and number magnitude. To address the super-frequent handovers of mobile users in such UDNs, the coordinated multipoint (CoMP) then becomes a feasible solution by effectively improving communication reliability. In this paper, we consider CoMP-based uplink transmission in heterogeneous UDNs. As our first step, a CoMP uplink transmission scheme is proposed for each user to transmit to multiple (2 to M) cooperating BSs with channel inversion power control in a multi-channel scenario. The performance of the proposed uplink CoMP is analyzed in the context of UDNs using stochastic geometry, in terms of outage probability and ergodic capacity, considering the effects of BS intensity, power control, number of cooperating nodes, and network tiers, first in a single-tier network, and later extended to a K-tier heterogeneous network. Performance evaluation through extensive simulations is conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve outage probability and ergodic capacity of mobile users.

Mobile edge computing (MEC), as an extension of the cloud computing paradigm to the edge network, is a promising solution to provide resource-intensive and time-critical applications to mobile users. It overcomes some obstacles of traditional mobile cloud computing by offering ultra-short latency and less core network traffic. This paper proposes a new framework based on the architecture of MEC to deliver cloud services to the edge. We introduce enumeration based optimal placement algorithm (EOPA) and divide-and- conquer based near-optimal placement algorithm (DCNOPA) to attain minimal data traffic by distributing virtual machine replica copies (VRCs) of applications to the edge network. Simulation results show that compared to the famous K-medians clustering algorithm (KMCA), the performance of DCNOPA is much closer to that of EOPA with lower computational complexity. Furthermore, we investigate the optimal number of VRCs within a given limitation of benefit-to-cost ratio.

This paper analyzes the information theoretic secrecy performance in finite-area wireless networks based on a stochastic geometry framework. Unlike most prior works, which explored the physical layer security with a large number of transmitters, legitimate receivers and eavesdroppers in infinite regions, we consider a finite downlink wireless network composing of a transmitter, a legitimate receiver and several eavesdroppers. The legitimate receiver attempts to receive confidential data from the transmitter in the presence of the eavesdroppers. We present the probabilistic characteristics of the achievable secrecy rates and average secrecy rates in both disk regions and regular L-sided convex polygon regions. As shown by extensive numerical results, the proposed framework could be leveraged to efficiently analyze the secrecy performance of finite-area networks, and give insights for network designers on how to achieve good secrecy performance in finite-area networks.