Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing collaborative task offloading schemes with DT empowerment are faced with challenges, such as the complex collaborative relationship between tasks and multiple Edge Servers (ESs), and spatio-temporal heterogeneity of ES resources. This paper investigates the issues of parallel collaborative offloading and resource allocation under the assistance of DT and Overlapping Coalition Formation (OCF) game. One novel scheme, abbreviated as OCF-based PCORA, is proposed. With comprehensive information within the digital space, the OCF-based PCORA scheme dynamically models collaborative relationships between tasks and ESs. Subsequently, each task is offloaded to its optimal ES coalition for efficient collaborative processing. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem is decomposed into three subproblems: ED association, bandwidth allocation, and collaborative task processing. The first subproblem is transformed through slack variable relaxation and solved using the interior-point method. The second subproblem, being naturally convex, is addressed via convex optimization method. For the third subproblem is modeled as an OCF game with transferable utility. On top of this, a bilevel iterative optimization algorithm is proposed to form overlapping task coalitions in a distributed manner. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 16.01%–44.97% and decreases the task offloading failure rate by 28.99%–75.41%, compared to state-of-the-art baselines.

WiFi-based human activity recognition (HAR) has emerged as a focal point within the Internet of Things landscape, owing to its non-intrusive sensing capabilities and inherent privacy-preserving advantages. In existing WiFi-based HAR research, channel state information (CSI) is primarily utilized to capture activity-related features and enable recognition. However, CSI-based cross-domain HAR remains challenged by issues such as redundant subcarriers, limited samples in the target domain, and high sensitivity of CSI to environmental variations. To address these challenges, this paper proposes Wi-DMAR, a WiFi-based cross-domain HAR framework that integrates three key modules. First, an adaptive subcarrier selection module computes the correlation between each subcarrier and the principal components, identifies subcarriers with high contribution, preserves essential activity-related features, reduces data dimensionality, and lowers computational overhead. Second, a conditional diffusion–based data augmentation module employs a Transformer-based feature extractor to capture domain-specific representations of target-domain data, and optimizes domain consistency loss and domain-guided diffusion loss to generate pseudo samples that resemble the target-domain distribution, thereby mitigating sample scarcity. Third, an activity recognition module based on sample similarity learning reformulates the traditional label classification problem into a sample comparison task, by quantifying similarity between samples, it performs activity recognition and enhances cross-domain generalization. Experimental results demonstrate that Wi-DMAR achieves superior recognition accuracy compared with state-of-the-art cross-domain HAR methods such as DiffAR and MetaAct. Ablation studies further confirm that each core component contributes positively to performance improvements.

Relay-based communication has become a key approach to meeting the growing demands for low latency and high reliability links in intelligent transportation and vehicle-to-everything (V2X) systems. In complex urban environments such as roads, tunnels, and dense high-rise building areas, traditional vehicle-to-vehicle (V2V) relay links are severely impeded by deep fading and multiple interference sources, which significantly degrade end-to-end performance. To enhance relay-based transmission under such harsh conditions, this paper investigates a dual reconfigurable intelligent surface (RIS)-assisted decode-and-forward (DF) relay V2V system as a representative relay-enhanced architecture. By combining two reconfigurable intelligent surfaces with a DF relay, the proposed scheme strengthens both hops of the relay link, effectively alleviating the performance bottlenecks commonly encountered in single-RIS or traditional relay schemes. The system adopts a Nakagami-mfading channel model and explicitly considers the aggregated interference at the relay and destination nodes. Based on this, we derive analytical expressions for the end-to-end outage probability and average channel capacity, employing Fox’s H function and the Gaussian-Laguerre quadrature method for precise evaluation. Additionally, an adaptive RIS reflection unit allocation algorithm is proposed to jointly optimize the total number of RIS units and their two-stage distribution under reliability constraints, thereby enhancing the efficiency of relay-based communication while reducing hardware deployment costs.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

Constrained by static databases, the traditional fingerprint positioning fails in dynamic indoor environments, and the most transfer learning methods neglect local features. To address these problems, a transfer learning based multi-source domain fingerprint database reconstruction method is proposed to improve indoor positioning performance. Firstly, the similarity between source domain and target domain samples is calculated using the repetition degree of access points (APs) and the proportion of effective values of source domain data, and high-quality source domain samples are extracted. Secondly, based on transfer learning, the unique features of the target domain are used to complete the knowledge transfer from common features to unique features in the source domain, thereby reconstructing a new fingerprint database. Then, position prediction is performed by combining the nearest neighbor reference points of multiple historical fingerprint databases. Experimental results show that the proposed method has a low reconstruction time cost, and its average positioning accuracy can reach 1.76m, which is 16.9%, 18.9%, and 28.7% higher than that of TransLoc, Transfer Component Analysis (TCA), and Balanced Distribution Adaptation (BDA) algorithms, respectively.

The proliferation of location-based services has accelerated the demand for indoor positioning technologies. Traditional Three-Dimensional (3D) localization methods remain costly due to their reliance on large-scale specialized infrastructure deployments. Achieving a favorable balance between positioning accuracy and infrastructure cost presents persistent challenges in developing practical 3D localization systems. To address the limitation, this paper proposes a low-cost 3D localization algorithm based on error optimal estimation. Firstly, a novel 3D error model is established which uses pedestrian dead reckoning (PDR) step-length drift and heading drift to characterize error sources. Secondly, a multi-data fusion adaptive extended Kalman filter (DA-EKF) is proposed which dynamically adjusts error parameters in real-time to enhance positioning accuracy. Additionally, an absolute initial position estimation technique combining PDR and Round-Trip-Time measurements is provided which enhances stability and convergence speed of the absolute initial position prediction. Finally, experiments show 0.081 m line-of-sight (LOS)/1.75 m non-line-of-sight (NLOS) accuracy with 75% fewer base stations (BSs), reducing errors by 77.35% versus baselines.

Constructing digital twin (DT) models of user equipments (UEs) efficiently is essential for enabling real-time monitoring of UEs, providing crucial support for optimizing the operation of Social Internet of Things (SIoT) systems. However, UE heterogeneity and UE mobility concerns impede the DT deployment in SIoT. In this article, we propose a real-time DT deployment (RDTD) scheme for SIoT systems, where the heterogeneous DT modeling and the DT migration are achieved based on personalized federated learning (PFL) ideas. Specifically, we decompose the DT model into the global generalization layers and the personalization layers, based on which we propose a hierarchical PFL (HPFL)-based DT model construction mechanism. The mechanism constructs customized DT models for heterogeneous UEs through a two-stage model parameter update process, involving end-edge-center collaboration training of all parameters and fine-tuning of the personalization layer parameters. Second, based on the above mechanism for DT model construction, a low-latency DT model parameter migration algorithm is proposed. This algorithm ensures real-time interaction by migrating only the personalized layer parameters of the DT model and reconstructing the DT model. Lastly, numerical experiments verify the effectiveness of the RDTD scheme, improving modeling accuracy by 13.91%, 41.06%, and 135.35% compared to the three baselines. Additionally, our proposed scheme significantly reduces interaction latency by 29.93% compared to the baseline.

In future 6G vehicular networks, precise positioning is essential for improving communication quality and efficiency. This paper proposes a novel TDOAIFDOA-based cooperative localization method among multiple vehicles. By selecting anchor vehicles within the range of the base station and utilizing their received echo information, this method enables efficient and low-latency positioning. First, based on a defined variable selection criterion, a subset of vehicles with optimal locations is chosen. Using the echo signals generated by inter-vehicle communication, the method jointly predicts the motion parameters of target vehicles. A time-delay Doppler approach based on matched filtering is employed to estimate the reflected echo information for dynamic vehicle cooperation, ultimately assisting the base station in achieving directional communication with the target vehicles. Results show that under specified noise conditions, the proposed V2V cooperative localization achieves performance close to the CRLB lower bound, with deviations between 0 and 0.8. This method offers a new approach to directional communication in vehicular networks, particularly suited for high-dynamic, high-concurrency large-scale vehicle communication in complex traffic environments.

With rapid adoption in vertical industries and further assistance of edge computing, Internet-of-Things (IoT) applications are experiencing phenomenal growth. However, the concurrence of heterogeneous IoT devices, limited system resources, and varying network conditions poses an ultimate challenge to resource scheduling for meeting the increasingly diverse requirements of IoT applications. Most existing resource scheduling techniques are achieved using common performance indicators for all devices as the optimization objective, which may lose effectiveness when dealing with the diverse requirements across heterogeneous IoT devices. Towards this end, we focus on enhancing IoT device-specific Quality of Experience (QoE) through jointly optimizing communication and computation resources. First, a three-layer QoE assessment model is constructed to characterize the general correlation between resource provisioning and device-specific QoE. Then, to maximize the overall QoE amongst IoT devices, a two-stage resource scheduling scheme is proposed to realize the simultaneous optimization of IoT devices and the edge system. Specifically, during stage I, a distributed resource scheduling algorithm with low complexity is designed for each IoT device to optimize the local computing rate by considering its resource-constrained nature. During stage II, a Proximal Policy Optimization (PPO)-based online learning approach is proposed on the edge system to schedule communication bandwidth and optimize computational rate. Finally, extensive experiments demonstrate that our proposal outperforms the existing works from the perspective of QoE performance.

The combination of air–ground integrated power Internet of Things (AGI-PIoT) and cloud-edge-end collaboration enables flexible coverage and real-time data processing. However, how to achieve intelligent cloud-edge-end collaboration in AGI-PIoT faces several challenges such as dynamics of aerial networks, coupling of resource allocation in multiple layers, timescales, and dimensions, incomplete information, and dimensionality curse. In this article, we propose a FEderated Deep rEinforcement leaRning-based multi-lAyer multi-Timescale multi-dImensional resOurce allocatioN algorithm (FEDERATION). The multilayer multitimescale multidimensional resource allocation problem is decomposed into three subproblems based on Lyapunov optimization. For the subproblem of joint task offloading and power control, a federated deep actor-critic-based semidistributed algorithm is developed. The subproblem of admission control is solved by quadratic programming. The third subproblem is addressed through smooth approximation and Lagrange dual decomposition. Simulation results indicate that FEDERATION outperforms existing algorithms in queuing delay, energy consumption, and convergence.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

The integration of air-ground electricity internet of things (AGE-IoT) and machine learning, enables flexible network coverage and intelligent task offloading. However, dynamics of AGE-IoT networks, incomplete information, and resource allocation coupling are still major challenges in achieving intelligent AGE-IoT. In this paper, we investigate a joint multi-timescale task offloading and power control optimization problem to minimize the queuing delay of all the EIoT devices under the long-term constraint of energy consumption. We firstly decompose the joint optimization problem and transform it to large-timescale task offloading optimization and small-timescale power control optimization. Then, we propose a fed-erated deep actor-critic-based task offloading algorithm (FDAC) with two actor-critic networks for multi-timescale optimization. Numerical results show that FDAC has excellent performances in queuing delay and energy consumption compared with existing algorithms.

Abstract Ultra‐dense networking is considered an effective solution to achieve high capacity in 5G networks. However, the densely distributed base stations (BSs) in ultra‐dense networks (UDNs) make the inter‐cell interference much more serious than that in traditional cellular networks. Therefore, it is important to mitigate inter‐cell interference in the UDNs to improve network performance. To tackle this problem, we propose a novel virtual resource mapping algorithm that includes a resource reservation (RR) algorithm and a real‐time resource embedding (RE) algorithm. Specifically, according to the number of services predicted by a dynamic service model, the RR algorithm is proposed to determine the sets of multiplexing BSs in the next time cycle and reserve channel resource required by each BS. Then, to further reduce inter‐cell interference, the real‐time RE algorithm is proposed to allocate the channel resource in real time. Finally, simulation results show that the proposed algorithm has better performance in terms of signal‐to‐interference‐plus‐noise ratio and acceptance ratio, compared to the existing algorithms, such as the frequency reuse channel allocation algorithm and inter‐cell interference coordination algorithm.

To analyze network QoS (Quality of Service) performance and better utilize network resources, this paper develops an analytical model of QoS class mapping for hybrid QoS domains based on network calculus theory. Based on this model, an elastic QoS Mapping scheme with Flow Aggregate (EQM-FA) is proposed to support end-to-end QoS of multimedia services over heterogeneous wireless networks. In EQM-FA, the QoS requirements of service flows are indicated by a unique flow aggregate identifier which can be described by the information describing QoS on a service flow map which is a multidimensional space of relevant QoS parameters. With flow aggregate identifier and mapping executors sitting at the border of different QoS domains, EQM-FA allows smooth QoS class mapping between different networks with different granularity of QoS class. Both numerical analysis and simulation studies are given to demonstrate the efficacy of the proposed method.

In ad hoc networks, stability is the priority factor in route selection. However, the traditional path stability computation methods are so idealized that their results do not accord with the reality. A novel path stability computation model is proposed in this letter, in which the correlation factor is introduced to describe the dependency degree between arbitrary adjacent links. Based on a series of correlation factors, a simple and universal expression for computing the stability probability of a path is derived. Simulation results show that our model, which can obtain more accurate results and select more stable routes, outperforms the traditional methods.