ABSTRACT In federated learning (FL)‐assisted Internet of Things (IoT) systems, FL trains models using datasets on various client devices without sending the datasets to a centralized server. This approach enhances the accuracy and reliability of models while preserving the privacy of client devices. However, FL implementations face challenges, such as single point of server failure and lack of incentives. To address the server failure issue, a backup server can be added. Meanwhile, each FL client has varying data quality and motivations to participate, leading to differences in the quality of local models uploaded to the server. To motivate clients to contribute more, we designed a novel incentive mechanism based on the Stackelberg game. This mechanism allocates rewards based on the quality of the models each client uploads, rather than the amount of data trained. We separately modelled the utilities of the server and the clients, allowing the server to rationally allocate rewards based on each client's contribution to model training. After analysing the utilities, we transform the game into two optimization problems and develop an algorithm whose per‐round complexity scales linearly with the number of clients under fixed numerical tolerances. The obtained equilibrium matches exhaustive search within numerical precision while significantly reducing computation.

This paper considers an integrated sensing and communication (ISAC) system, where two dual-functional base stations (BSs) serve their users and detect multiple targets. To improve detection accuracy while meeting communication quality of service, this paper proposes a two-phase cooperative target detection algorithm that relies on Capon-based adaptive beamforming and maximum likelihood estimation (MLE)-based hypothesis testing. Specifically, based on Capon’s detection results, the two BSs first scan targets with an omnidirectional beam and then track targets with a directional beam. Subsequently, multiple hypotheses regarding the locations of targets are established based on the detection results of the Capon method, and the MLE is employed for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angles-of-arrival estimation of multiple targets than conventional single-BS sensing, and enables high-precision localization by eliminating ghost targets.

Radio Frequency Fingerprint Identification (RFFI) has emerged as a promising technique for enhancing wireless security by uniquely identifying individual devices through their inherent RF characteristics. However, the performance of conventional RFFI methods deteriorates significantly when training and testing involve different modulation schemes, primarily due to the resulting domain shift between modulation types. To address this challenge, this paper proposes Domain-Invariant Adaptive Mixup Enhancement (DIAME), a novel framework that integrates domain-invariant feature extraction with a similarity-aware adaptive mixup strategy to improve generalization across modulation domains. Specifically, DIAME dynamically adjusts the mixup intensity based on inter-feature similarity and incorporates domain alignment and feature matching with pretrained models to promote modulation-invariant representation learning. Extensive experiments on a synthetic RF dataset comprising four modulation types and five devices demonstrate that DIAME achieves an average cross-modulation identification accuracy of 86.18%, significantly outperforming state-of-the-art methods. These results confirm the effectiveness of DIAME in mitigating domain shift and highlight its suitability for robust RFFI in heterogeneous wireless communication environments.

Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems provide flexible and resilient computing capabilities for mobile users by leveraging UAVs as edge servers (ESs). However, in practical deployments, user devices typically exhibit spatially non-uniform distributions, which impose significant challenges for achieving optimal UAV placement. Conventional fixed or pre-determined deployment strategies cannot dynamically adapt to heterogeneous user distributions. To overcome these challenges, this article investigates an air–ground cooperative MEC architecture comprising multiple UAVs and terrestrial ESs. A multi-objective optimization problem incorporating UAV trajectory optimization is formulated to jointly minimize task offloading latency and total system energy consumption. As the problem is an NP-hard mixed-integer nonlinear programming model, a Joint Alternating Optimization framework for Task Offloading, Resource Allocation, and UAV Trajectory control (JAOTRU) is proposed. The JAOTRU framework adopts an iterative block coordinate descent structure to decouple the highly coupled optimization variables into two subproblems, which are efficiently solved via a differential evolution algorithm and a successive convex approximation technique, respectively. The simulation results show that the proposed JAOTRU method substantially surpasses several benchmark methods regarding total offloading latency and system energy efficiency, validating its effectiveness and robustness for MEC systems assisted by UAVs.

Specific Emitter Identification (SEI) leverages unique hardware-induced Radio Frequency Fingerprints (RFFs) for secure physical-layer authentication. However, under cross-receiver scenarios where training and testing data exhibit hardware-induced distribution shifts, deep learning models are prone to shortcut learning. In such cases, networks inadvertently exploit spurious, receiver-specific artifacts as ”shortcuts” for identification rather than extracting the genuine, intrinsic fingerprints of the transmitter. To overcome this challenge, we propose a robust multi-task learning framework, termed MTL-SEI. This framework synergizes spectrum-based feature extraction with receiver-invariant adversarial training and channel-aware auxiliary supervision. Specifically, a gradient reversal layer (GRL) is employed to suppress receiver-dependent features, while an equalization-state prediction task provides semantic guidance to disentangle channel-induced distortions. Furthermore, an uncertainty-guided task weighting mechanism is introduced to dynamically balance the multiple optimization objectives based on predictive variance. Evaluations conducted on the ManySig dataset under a rigorous receiver-disjoint protocol demonstrate the superior generalization capability of MTL-SEI. Notably, our method achieves a transmitter identification accuracy of 88.50% —representing a 37.7% improvement over the 1D-CNN baseline—and yields an average performance gain of over 6.92% compared to state-of-the-art domain generalization methods. These results validate the effectiveness of the proposed feature disentanglement mechanism in mitigating receiver-induced biases.

With the rapid advancement of wireless communication technologies, WiFi signals have become essential for modern connectivity across diverse applications. However, their widespread deployment introduces significant security vulnerabilities, including unauthorized access, data leakage, and interference. Accurate identification of WiFi transmitters is crucial for mitigating these threats. While existing methods perform well in ideal conditions, their effectiveness degrades in real-world scenarios, particularly in environments with low signal-to-noise ratios (SNRs). To address this limitation, we propose a novel transmitter identification framework that integrates blueprint separable convolution (BSC) and a squeeze-and-excitation (SE) module. The BSC extracts critical features efficiently, while the SE module dynamically enhances feature representations. Simulation results demonstrate that the proposed approach achieves competitive or superior accuracy compared to state-of-the-art models. Moreover, the framework exhibits strong robustness, maintaining high recognition performance even in challenging transmission conditions with low SNRs.

Federated Learning (FL) is a promising paradigm for privacy-preserving collaborative intelligence in Internet of Vehicles (IoV) systems. In this work, vehicles are used to cooperatively train neural network models for the object recognition task. However, the inherent data heterogeneity across vehicles severely compromises the effectiveness of conventional FL frameworks that employ a unified global model. To address this challenge, we propose PFedOR - a confidence-aware personalized FL framework for vehicular object recognition. Specifically, we introduce a confidence quantification mechanism that uses public datasets on the server side to estimate class-specific confidence levels and data distribution patterns. Then, a personalized update strategy is employed, where batch normalization (BN) layers are regularized within a clustered structure through hierarchical clustering. At the same time, Non-BN layers are updated using similarity-based weighting across all vehicles. We conduct simulations on the nuImages dataset and experiment results demonstrate our approach’s superior performance against baseline methods, particularly under increasing data heterogeneity scenarios.

Federated learning for electric vehicle suspension (FLEVS) is a key technology to address the limitations of battery capacity and the rising use of sensing cameras in electric vehicles. However, current FLEVS systems face challenges such as energy constraints, privacy risks, and copyright issues in car enterprise cloud (CEC) trained models. This paper proposes a frozen watermark-based federated learning incentive mechanism to tackle these issues. It incorporates a global frozen watermark to leverage continuous learning while minimizing training interference. Additionally, a Stackelberg game-based incentive mechanism between the CEC and mobile vehicles (MVs) optimizes information security strategies. The derived optimal reward function and iteration parameters are validated through numerical results, demonstrating the scheme's effectiveness.

Specific emitter identification (SEI) is crucial in the Internet of Everything (IoE). Over the past decade, deep learning (DL) and broad learning (BL)-enabled SEI technologies have emerged. Recently, many researchers have begun exploring semi-supervised learning techniques to address the semi-supervised SEI (SS-SEI) problem with limited labeled RF signals. However, existing SS-SEI solutions often prioritize identification performance, leading to high computational overheads and lacking iterability. To overcome these challenges, this paper proposes a novel SS-SEI solution based on a learnable broad learning network (LBL). Initially, a pretrained DL-based SEI model is downloaded to the edge device. Meanwhile, an updatable BL-based SEI method is deployed locally on the edge device to identify unlabelled signals. When the LBL solution is operational, edge devices capture real-time unlabelled RF signals. The pretrained DL-based SEI method and the locally BL-based SEI method jointly identify these RF signals. The identification results and the new real-time RF signals are then used to update the weights of the BL-based SEI method at the edge devices. The LBL SS-SEI solution is validated using an open-source, large-scale, real-world automatic dependent surveillance-broadcast (ADS-B) dataset. Experimental results demonstrate that the proposed LBL solution offers significant advantages regarding SS-SEI performance.

In this study, we present an innovative architecture for blind modulation-type identification in single-antenna channels, specifically engineered for scenarios lacking transmitter cooperation and featuring time-varying spectrum occupancy. The proposed method integrates a lightweight Transformer-based deep learning architecture with a higher-order statistics-driven noise reduction module, designed to enhance feature discrimination in environments with poor signal clarity. Comprehensive testing demonstrates that the proposed approach significantly improves classification accuracy from 52% to 75% at 0 dB. The model achieves this with a highly compact footprint of only 0.228 M parameters and minimal computational overhead (0.0095 GFLOPs), highlighting its excellent balance of robustness and efficiency. The model consistently performs well across a broad range of signal-to-noise ratio, validating its generalization capability in challenging environments. By addressing the critical challenges of automatic modulation classification in non-cooperative and spectrum-constrained contexts, this work offers a scalable and efficient solution that supports intelligent spectrum awareness and facilitates robust cognitive communication in next-generation wireless networks.

Radio Frequency Fingerprinting Identification (RFFI) leverages signal processing to extract unique characteristics from wireless signals for device identification. In recent years, deep learning (DL) has significantly advanced signal identification, catalyzing progress in RFFI research. This paper proposes an enhanced RFFI method to manage variable-length signal inputs, typically problematic for neural networks such as convolutional neural networks (CNNs) and multilayer perceptrons (MLPs), by treating these signals as images to solve data formatting problems. The robust representation of the variable-length signal ultimately achieves over 90% accuracy, meeting the expected results. Furthermore, conventional DL-based RFFI methods require a comprehensive analysis of the entire RF signal, consuming significant computational resources and vulnerable to environmental variations. We address these issues by proposing an incremental learning (IL)-based RFFI method that allows dynamic model updates and improves recognition and generalization performance. Our method’s efficacy, tested on the power amplifiers (PA) dataset, enables real-time data stream processing.

To enhance the reliability and stability of simultaneous localization and mapping (SLAM) in dynamic environments, we propose a novel SLAM system integrating an advanced real-time object detection algorithm, the real-time detection transformer (RT-DETR). Our approach combines RT-DETR’s object detection capabilities with an optical flow-based dynamic thresholding method, effectively filtering out feature points associated with dynamic objects and thereby improving SLAM performance in such environments. We have optimized RT-DETR by substituting its original network backbone with lightweight modules, which reduces the number of parameters by 45% while only incurring a 5% reduction in accuracy. This optimization significantly lowers computational costs, making it feasible for deployment on mobile devices. Experiments conducted on the TUM and BONN dynamic datasets demonstrate that our system reduces the root mean square error (RMSE) of absolute trajectory and relative pose error (RPE) by approximately 28.82% compared to oriented fast and rotated brief-SLAM3 (ORB-SLAM3). Furthermore, experiments conducted on both a high-performance device and an embedded device demonstrate that, compared to Crowd-SLAM, which employs you only look once (YOLO) for dynamic object removal, our approach achieves an 8.52% improvement in absolute trajectory error (ATE), while the average frame per second (FPS) only decreases by 3.07%.

In the 5G era, the demand for high-quality communication services has rendered spectrum resources increasingly scarce, particularly for remote users (UEs). Unmanned Aerial Vehicles (UAVs) provide a viable solution to this challenge by facilitating dynamic spectrum sharing. This paper proposes a hybrid two-stage Stackelberg game model that enhances UAV-assisted communications by integrating both static and dynamic spectrum-sharing strategies. In this model, UAVs serve as relays for base stations (BSs) to serve remote UEs. They can optimize spectrum allocation and negotiate pricing directly with UEs instead of BS, thereby reducing the burden on BS. The model operates in two stages: In the first stage, UAVs act as followers with BS as leaders; In the second stage, UAVs become leaders in interactions with remote UEs. Our analysis focuses on the effects of spectrum sharing and pricing strategies on system performance, emphasizing the enhanced utility for BS, UAVs, and UEs. We demonstrate that the proposed model significantly improves spectrum efficiency, particularly under high-demand conditions, and maintains stable and efficient operations within UAV-assisted communication systems, as supported by simulation results.

To address the low efficiency of traditional spectrum-sharing systems, UAVs can serve as airborne relays to enhance user communication services. However, the issues of spectrum scarcity and underutilized spectrum holes have not been fully resolved. Therefore, designing an effective spectrum resource reallocation mechanism is essential to improve spectrum utilization efficiency further. Moreover, existing research rarely explores co-channel interference arising from spectrum trading between UAVs and often overlooks queuing mechanisms in spectrum trading scenarios. This paper proposes a dynamic spectrum-sharing scheme (DSS) leveraging UAV-assisted communication to address these challenges. To enhance spectrum utilization, a two-level Stackelberg game-based incentive mechanism is developed for on-demand UAV spectrum trading. Additionally, a co-channel interference preference-based spectrum matching scheme (CIPS) is designed, which comprehensively considers co-channel interference resulting from spectrum sharing and prioritizes the importance of UAV users’ services. Finally, optimal pricing and trading volume strategies are efficiently determined using a gradient-based iterative search algorithm. Simulation results demonstrate that the proposed model achieves higher system revenue, which is 8.97% to 186.82% higher than other models, and ensures flexible spectrum allocation and effective co-channel interference mitigation.

Traditional cell-free massive multiple-input multiple-output (CF-mMIMO) systems face challenges of resource scarcity, cognitive limitations, and coverage blind spots, which primarily stem from the extensive deployment of long cables connecting each access point to the central processing unit in the system. To maximize the minimum achievable user rate and enhance the performance of a downlink CF-mMIMO system, we propose an innovative scheme that jointly integrates user association, unmanned aerial vehicle (UAV) placement, and transmission power allocation, with UAV-satellite assisted. The scheme also considers stringent constraints, including maximum power capacities, cross-layer interference limitations, and essential coverage demands. Confronting the complexity of the initial non-convex optimization challenge, we dissect it into more tractable sub-problems that encompass user association, UAV placement, and power allocation. Our approach employs an iterative algorithm, systematically resolving these sub-problems in sequence. Simulation results conclusively demonstrate the efficacy of the proposed scheme in optimizing system resource allocation and achieving comprehensive coverage.

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.

Unmanned aerial vehicles (UAVs) are increasingly pivotal as mobile access points in cell-free massive multiple-input multiple-output (CF-mMIMO) systems, supporting both communication and task execution. To extend UAV endurance during complex missions, this paper explores the energy transmission and trajectory design for UAVs utilizing wireless power transfer (WPT) in the CF-mMIMO systems. By optimizing UAV flight trajectory, charging/discharging slots, and beamforming, communication fairness among outage users (UEs) is enhanced while considering the energy consumption constraints imposed by energy replenishment from access points during mission execution. To address this complex problem, we propose an angle search-based communication-assisted deep Q-network (DQN) algorithm, facilitating targeted spatial exploration. Simulation results demonstrate that the proposed approach effectively balances energy efficiency and communication requirements, improving UAV resource utilization and ensuring communication fairness for interrupted UEs, ultimately achieving dynamic regional coverage.

Federated Learning (FL) is a new way of training models used in Internet of Things (IoT) systems. It is a method that maintains the privacy of client devices while improving model accuracy and reliability. However, there is a problem in FL applications due to the lack of incentives. Clients have different motivations and produce different quality datasets, which leads to a divergence in the quality of local models uploaded to the central server. To address this issue, we propose a new incentive model based on the Stackelberg game. The mechanism we suggest distributes rewards based on the quality of the models uploaded to the server by each client, rather than the amount of data trained. We transform the model into two optimization problems, and we propose a linear complexity algorithm to solve them. This algorithm can achieve the optimal solution and greatly reduce computational complexity, as shown in our experimental results.

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.

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-wayK-shot USTC-TFC2016 dataset and 10-wayK-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

Specific emitter identification (SEI) uses advanced techniques to identify radio equipment by analyzing unique characteristics in radio frequency signals. Recently, deep learning (DL) has been considered a promising tools for designing various intelligent SEI methods. This is primarily due to its ability to fully exploit hidden data features and make autonomous classification decisions, leading to effective performance. The existing DL-SEI methods are based on the availability of extensive labeled datasets, however, collecting and annotating such data is challenging and time-consuming in real-world scenarios. Furthermore, these datasets often contain both device-specific and irrelevant features, which limits the adaptability of models to fixed channels. To overcome these challenges, we propose a robust variable-channel SEI (VC-SEI) method. This method uses semantic consistency-powered semi-supervised domain adaptation (SSDA). We introduce domain adversarial training to ensure global semantic consistency (GSC), allowing the extraction of channel-irrelevant features. Additionally, we design two loss functions to maintain local semantic consistency (LSC) for extracting category-relevant features. This approach enables effective domain adaptation. Our SSDA-based VC-SEI method has been rigorously evaluated using the ORACLE RF fingerprinting datasets from 16 USRP X310 radios. When only 1% of training samples in the target domain are labeled, our method achieves 84.20% identification accuracy in the target domain and 92.00% identification accuracy in the source domain. These results surpass those of current state-of-the-art methods. Simulation results confirm the robust identification performance of our proposed VC-SEI method in both source and target domains across all scenarios. Our code can be downloaded fromhttps://github.com/frownean/VC-SEI-based-SSDA.

Against the background of 6G communication and big data, more and more attention have been paid to the communication reliability and resource efficiency. Reconfigurable intelligent surface (RIS) is envisioned as a potential technology to improve the communication environment by controlling the electromagnetic wave propagation. A majority of related researches use the central limit theorem (CLT) to implement the analytical evaluation, which results in inaccuracy for the case with small number of reflective elements. In this letter, we investigate the outage behavior of RIS-enabled downlink cellular networks where exist several device-to-device (D2D) pairs under the general fading channels, i.e., Nakagami-m fading channels. We propose a comprehensive solution to evaluate the outage probability for all the cases with different numbers of reflective elements. Our solution utilizes the multivariate Fox's H-function and proposes the outage expressions in closed form. Finally, The accuracy of the closed-form outage probability is verified in simulation section for different cases of system configurations.

Abstract Energy efficiency (EE) is an important indicator in ultra‐reliable and low‐latency communication (URLLC). Power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem is non‐convex, it is difficult to obtain the analytical solution efficiently. Moreover, to ensure reliable and low‐latency communication within a finite blocklength, the Shannon formula becomes impractical for URLLC. Therefore, finite blocklength coding theory is used to meet the requirements of URLLC. In this paper, the EE problem of URLLC is formulated and the power allocation function is parameterized to be optimized through a deep neural network (DNN). The DNN is trained through the primal‐dual iterative algorithm offline in the unsupervised manner, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

The ultra-reliable and low-latency communication (URLLC) is one of the critical scenarios in future communications. Energy efficiency (EE), as an important indicator in URLLC, has attracted more and more attention especially in the fields of industrial internet and automation control, etc. At present, power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem in URLLC is usually formulated in the form of fractions with several statistical constraints, it is difficult to obtain the real time analytical solution. Moreover, the traditional expression based on Shannon formula is no longer applicable. In this paper, we formulate the EE problem of URLLC and adopt an unsupervised learning method to parameterize the power allocation function to be optimized through a deep neural network (DNN). The DNN is trained through the primal-dual iterative algorithm offline, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and LF-norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

Utilizing the idle computing resources from the distributed Internet of Things devices can sustainably increase the computational capacity and thereby effectively alleviate the pressure on resource-constrained devices, which is referred to as collaborative computing. However, extra computing consumption potentially impacts the local computation tasks of collaborative computing devices. Hence, it is essential to design an efficient incentive mechanism for computational resources sharing. Specifically, we consider the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices (ICDs) by a centralized computing sharing platform (CSP). We first propose a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance; then, a game-theoretic computing task allocation approach is developed among the CSP and multiple ICDs to maximize all participants' profit. The CSP first determines the optimal task partition dynamically upon the tasks' arrival; then, the ICDs derive the optimal central processing unit-cycle frequency correspondingly. Simulation results demonstrate that the overall computational latency of our proposed mechanism is significantly decreased, and achieves by at least 13.5 percent improvement compared with the existing schemes. Meanwhile, the profit of all participants is maximum in collaborative computing, which is improved by 41.5 and 27.9 percent for the CSP and the ICDs, respectively.

Virtualization is the fundamental attribute of next generation network. Embedding customized virtual networks (VNs) onto the underlying physical network (PN) is the core issue in virtualization research. This issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the re-embedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be re-embedded. The dynamic embedding algorithm ensures flexible VN assignment and improves the physical resource utilization. Finally, simulation results are illustrated in order to validate the strength of our proposed dynamic algorithm. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Efficient use of available energy resources for real‐time multimedia communication with a quality guarantee is one of the main challenges for future mobile computing systems. To meet the high quality‐of‐experience (QoE) demand of multimedia services, we propose a novel quality‐driven joint source‐channel coding (JSCC) scheme with an unequal error protection (UEP) technique that allocates bits by optimising source intra refreshing rates and channel correction coding rates. The key contribution of this work is that the frame importance diversity at the application layer is jointly considered with the error correction techniques and resource constraints at lower layers. For any given bit budget, the JSCC model is capable of allocating adaptive bits between source coding and channel coding among media streams. The source‐aware UEP scheme is capable of dynamically allocating the error correction bits among frames, achieving the maximum overall QoE. Simulation results demonstrated that significant improvement in multimedia quality and remarkable energy/time conservation can be achieved by deploying the proposed group‐based JSCC strategy, although the complexity is limited.

With the popularity of multimedia sensing at cloud edges and the reducing cost of the Internet of Things (IoT) fog devices and systems, new challenges have been posed to efficiently deal with the big data multimedia traffic generated from IoT sensing units. Specifically, in this paper we introduce the concept of multimedia sensing as a service (MSaaS), and propose a generalized premium prioritization-based quality of experience paradigm for wireless big-volumes-of-data (BVDs) multimedia communications, with significant energy saving potentials for future multimedia IoT devices and systems. The key contribution of this new framework is its data diversity flexibility at the application layer, which could be flexibly adopted by future multimedia communication systems. Data dependencies in spatial, frequency and temporal domains are analyzed, and interaction with uplink resource allocation optimization are investigated with regards to wireless communication energy cost estimation. Extensive simulation results demonstrate that the proposed prioritization-based communication paradigm has significant energy saving potentials for BVD MSaaS wireless multimedia communications at cloud edges and fogs.

This study proposes a quality‐driven approach that jointly selects the frequency reuse mode of device‐to‐device (D2D) links at lower layers, the video coding mode of frames, and multiple paths at higher layers for multimedia transmission in a cellular network with multi‐antenna relays placed at the intersection of adjacent cells. Based on power control of the shared relay, the user‐centered mode selection problem is formulated by maximising the quality of service (QoS) with total energy consumption constrained. Underlay mode and overlay mode are both considered. For each frame, one coding mode (I, P, or B) and multiple paths (cellular links and/or D2D links with/without relays) are selected to optimise the received video quality expectation. Different retransmission polices are considered to give higher protection levels to paths with higher transmission rates. The authors’ evaluation shows that the proposed unified power allocation and mode control scheme could enhance the perceived QoS as changing the energy constraint and the distances of different paths. The underlay mode is optimal in most cases. It provides evidences that it is not always the best to code all frames as I‐frames, to initialise the relay transmission power as the maximum, or to select one single path for video transmission.

In this paper, the mode selection (underlay or overlay) for device-to-device (D2D) links at low layers is jointly analyzed with the video coding mode selection and multiple transmission path selection at high layers. For each packet, three video coding modes (I, P, and B) and three transmission paths (cellular link with/without the shared relay and D2D link) are considered to optimize the received video quality expectation. The cross-layer mode selection problem is formulated by maximizing the Quality of Service (QoS) with total energy consumption constrained. Our evaluation shows that the proposed joint mode selection strategy could enhance the perceived QoS with less energy consumption as changing the channel conditions and transmitting power of different paths in a single sector. The coding mode selection is mainly decided by the energy constraint and the path selection mostly depends on the channel conditions.

This paper addresses the problem that devices are reluctant to participate in local device-to-device (D2D) communications in the purpose of improving the data transmission quality. We propose a novel game-theoretic approach of joint source selection and power control, which enhances the multimedia transmission quality with latency constraint. The proposed approach first analyzes the interactions between the base station (BS) and the devices using a Stackelberg game model. The optimal transmission power and price are obtained for each source device through deriving Stackelberg equilibrium off-line, in which the BS and the device both achieve maximum utilities. Second, the BS schedules the devices according to its equilibrium of signal-to-interference-and-noise ratios in D2D links. Finally, the selected best source devices complete the multimedia transmission with their power controlled. Simulations demonstrate that our proposed scheme can significantly improve multimedia transmission quality with low complexity.

Providing end-to-end reliability for data transmission is still an intractable challenge for delay-sensitive wireless multimedia networks. This paper deals with an unequal error protection (UEP) scheme for scalable video delivery over packet-lossy networks using forward error correction (FEC). The proposed cross-layer approach allows the wireless multimedia networks to jointly optimize the application layer and the error protection strategies available at the lower layers. The redundancy is tuned in accordance with both the wireless channel condition (as indicated by symbol error rate) and the time limit (as indicated by parity budget). Simulations demonstrate that the proposed cross-layer grouping scheme can significantly improve video transmission quality by allocating more correction bits to the vital frames, while the time constraint is satisfied and the complexity associated with performing this group allocation algorithm is reduced.

Traditional tracing systems are often limited to recording a fixed set of basic program events. This limitation can frustrate an application or compiler developer who is trying to understand and characterize the complex behavior of software systems such as a Java program running on a Java Virtual Machine. In the past, many developers have resorted to specialized tracing systems that target a particular type of program event. This approach often results in an obscure and poorly documented encoding format which can limit the reuse and sharing of potentially valuable information. To address this problem, we present STEP, a system designed to provide profiler developers with a standard method for encoding general program trace data in a flexible and compact format. The system consists of a trace data definition language along with a compiler and an architecture that simplifies the client interface by encapsulating the details of encoding and interpretation.