Real-time communication (RTC) applications demand not only low average latency but also tight tail-delay bounds to ensure smooth user experience. However, sudden fluctuations in wireless networks can cause in-flight packets to accumulate in bottleneck queues, delaying or invalidating subsequent frames. Traditional mechanisms focus on rate adaptation but largely overlook managing already enqueued packets that contribute to tail delay. We present Gecko, a lightweight end-to-network coordination mechanism that enables frame-aware queue flushing without requiring any in-network packet modification or protocol negotiation. Gecko-enabled routers monitor queuing delay and implicitly signal the sender, which then makes frame-skipping decisions and conveys flushing intent through minimal in-band RTP markings. This approach preserves end-to-end integrity and is broadly compatible with existing RTC applications. We evaluate Gecko via trace-driven simulations and real-world experiments. Results show that Gecko reduces average frame delay by 21.8% and cuts tail-delay frame ratios by 25% to 91%, demonstrating both its effectiveness and deployability in wireless RTC environments.

Integrating communication, sensing, and power transfer (ICSPT) is an emerging network paradigm for the sixth-generation (6G) systems, which is able to provide concurrent communication and sensing functions while simultaneously wirelessly powering low-power Internet of Things (IoT) devices with shared spectrum and hardware resources. To enhance the performance of ICSPT in fading channels, the outage probability (OP)constrained robust transmit beamforming design (OP-RTBD) is proposed, and a transmit power minimization problem is formulated with imperfect channel state information (CSI) by jointly optimizing information, sensing, and energy beam vectors at the base station (BS), subject to OP constraints on the communication rate, sensing Cramér-Rao bound, and energy transfer. To solve the non-convex problem, we propose a Bernstein-type inequality (BTI)-based method to conservatively approximate the probabilistic constraints to handle the CSI uncertainty. Then, a semi-positive definite relaxation-based method is proposed to solve the approximated problem. Simulation results show that the proposed OP-RTBD achieves near-optimal performance compared to the exhaustive search method with only less than 4% deviation, and it also significantly reduces the transmit power compared to baselines. Moreover, OP-RTBD exhibits strong robustness, achieving performance very close to that in perfect CSI scenarios, with a deviation of only less than 10%. Besides, the simulation results indicate that the BS’s transmit power should be allocated with priority to communication requirements over sensing and power transfer demands. Additionally, they further demonstrate that to simultaneously meet communication, sensing, and power transfer requirements, our proposed OP-RTBD in ICSPT is more energy-efficient, reducing energy consumption by approximately 10% and 20% compared to SWIPT and ISAC, respectively.

Leveraging their mobility and feasibility, Unmanned Aerial Vehicles (UAVs) present a promising solution for assisting covert communications to mitigate the risk of eavesdropping. However, existing studies mainly rely on passive optimization, where the UAV adjusts its transmit parameters according to the channel state, without actively balancing covertness constraints and average system throughput. To solve the above challenge, we propose for the first time a UAV relay-assisted covert communication framework with a buffer. Specifically, we derive the optimal detection threshold for the eavesdropper with mobility and uncertain locations, and obtain a closed-form solution for the lowest detection error probability. To solve the formulated average system throughput maximization problem, we transform the covertness constraint into a tractable analytical form, and obtain the optimal transmit power for both the UAV relay and the friendly UAV jammer. Then, through a rigorous theoretical analysis of upper and lower bounds on average system throughput, we prove the existence of optimal UAV trajectories. Finally, optimal transmission and reception decisions of the UAV relay are derived under covertness and buffer size constraints. Numerical results and theoretical analysis demonstrate the effectiveness of the proposed scheme in terms of average system throughput and covert performance.

Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through fullspace signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.

Underwater images typically suffer from two main types of degradation: reduced visibility caused by scattering and color distortion due to color cast. Most existing deep learning-based enhancement methods adopt end-to-end architectures to address both issues simultaneously. However, this design not only limits the model’s generalization capability but also hinders practical deployment due to excessive computational overhead. To this end, this paper proposes a binarized Decoupled Synergistic Optimization Strategy (DSOS), which explicitly decouples scattering and color cast degradations and performs collaborative optimization through specialized subtask modules. Each subtask learns purer features under the guidance of independent supervised signals, while a cascaded architecture ensures effective global restoration. Furthermore, cross-module collaborative optimization effectively mitigates the performance degradation caused by binarization, achieving a favorable balance between efficiency and high accuracy. Experimental results on multiple publicly available underwater image datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches in both restoration quality and computational efficiency.

Proactive detection with distributed flexible AC transmission system (D-FACTS) devices has been extensively studied for identifying false data injection (FDI) attacks in smart grids, while their potential for localizing remains largely unexplored. To meet this gap, this paper systematically explores the feasibility of localizing FDI attacks with D-FACTS devices. Specifically, we first thoroughly study the rationale underlying FDI localization with D-FACTS devices. We prove that an activated D-FACTS device is capable of localizing FDI attacks targeted on its connected end buses once a bad data detection (BDD) alarm is triggered. In addition, we elaborately analyze the inherent localization limitations: (i) the unlocalizable adversary cases targeting one-degree buses or super-buses; (ii) the localization uncertainty introduced by the defender's blind spots, resulting in huge operational costs and insufficient precision. Following this, a data-prompting framework is designed to over-come the above limitations. This framework integrates a data driven injected error identifier for precise localization and cost reduction, followed by a perturbation strategy with D-FACTS devices that significantly lowers false positive rates. Extensive simulations validate our theoretical findings on the rationale and limitations, while also demonstrating the effectiveness of the proposed framework in addressing limitations and enhancing localization accuracy.

Privacy-preserving federated learning (PPFL) is a strong-privacy distributed learning paradigm, which typically employs secure aggregation (SA) protocols to protect the aggregation results of federated learning. However, the high computational cost of existing SA protocols is difficult to generalize to decentralized federated learning (DFL) with the large number of clients and fails to defend against model poisoning attacks launched by Byzantine adversaries. In this paper, we propose an efficient computational SA protocol compatible with DFL, referred to as PriDFL, and address the issue of Byzantine-robust aggregation. Specifically, we design an advanced secret-sharing protocol based on number-theoretic transformations to reduce the computational complexity from$O(n^{2})$to$\mathcal {O}(n\log n)$during data sharing. We employ a single-mask approach to provide lightweight gradient privacy protection for DFL. To mitigate the impact of poisoned gradients on model convergence, we develop a Byzantine resilience criterion grounded in model cross-updating. The proposed criterion efficiently detects poisoned gradients and non-independent identically distributed (non-IID) data with local computation. Security analysis shows that PriDFL satisfies the security requirements in an honest but curious setting. We evaluate PriDFL on typical datasets (e.g., MNIST and CIFAR-10) and the results show that PriDFL is computation-communication efficient and Byzantine resilient. In particular, PriDFL optimizes the computational efficiency by 6-10× compared to the well-utilized SA protocols while supporting Byzantine robustness.

How to preserve the data privacy during the training of deep neural network (DNN) is a key security concern in the artificial intelligence era. However, most existing solutions based on homomorphic encryption and Trusted Execution Environment (TEE) are incompatible with heterogeneous Neural Network Accelerators (NNAs), leading to significant performance loss. We propose a novel method based on vector decomposition to allocate operators across different NNAs, ensuring both throughput and privacy simultaneously. Furthermore, based on this approach, we have designed a compiler that automatically converts front-end model descriptions into backend encrypted computation graphs, which is running securely over trusted and untrusted hardware. This compiler heuristically determines the allocation scheme based on hardware affinity and cross-hardware communication costs, significantly reducing additional overhead. Experimental results demonstrate that our method does not incur extra accuracy costs and achieves a throughput significantly higher than existing methods. Deploying our approach at scale on a platform with a billion users, we have verified its negligible impact on real-world operations while ensuring the privacy protection capability for cross-domain data.

Due to the complexity and diversity of practical environments, real-world image dehazing remains an unresolved problem, with one of the key challenges being how to bridge the distribution gap between synthetic and real domains. This paper proposes a Prompt-driven Domain Adaptation (PDDA) framework within the bi-level optimization perspective. Specifically, we introduce hyperparameter optimization-based bi-level modeling: the lower-level optimization emphasizes prior learning within the synthetic domain to stabilize dehazing performance, while the upper-level optimization focuses on enhancing cross-domain adaptability to ensure that the model can generalize across different domains. Given the scarcity of paired real haze images, we train learnable haze prompts by jointly optimizing the text-image similarity between positive/negative prompts and corresponding clear/haze images in the CLIP latent space to more effectively capture real-world haze characteristics. Based on the learned haze prompts, we construct an unsupervised cross-domain loss function that enhances the adaptability to complex real-world scenarios by integrating prompt learning with bi-level optimization strategy. Furthermore, we conduct a comprehensive exploration to uncover the inherent properties of PDDA, including architecture-irrelevant flexibility and domain-agnostic robustness. Extensive experiments across a wide range of benchmark datasets demonstrate that our method achieves both quantitative and qualitative improvements across diverse scenarios, showing robust performance not only in real-world daytime conditions but also exhibiting superior cross-domain adaptation capabilities in nighttime scenarios. Codes are available at https://github.com/YanZhang-zy/PDDA.git.

Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge computing (MEC), where image data collected by the sensors is offloaded from cellular vehicles to the MEC server using vehicle-to-infrastructure (V2I) links. Sensory data can also be shared among surrounding vehicles via vehicle-to-vehicle (V2V) communication links. To improve spectrum utilization, the V2V links may reuse the same frequency spectrum as the V2I links, which may cause severe interference. To tackle this issue, we leverage reconfigurable intelligent computational surfaces (RICSs) to jointly enable V2I reflective links and mitigate interference appearing at the V2V links. Considering the limitations of traditional algorithms in addressing this problem, such as the assumption of quasi-static channel state information, which restricts their ability to adapt to dynamic environmental changes and leads to poor performance under frequently varying channel conditions, in this paper, we formulate the problem at hand as a Markov game. Our novel formulation is applied to time-varying channels subject to multi-user interference and introduces a collaborative learning mechanism among users. The considered optimization problem is solved via a driving safety-enabled multi-agent deep reinforcement learning (DS-MADRL) approach that capitalizes on the RICS presence. Our extensive numerical investigations showcase that the proposed reinforcement learning approach achieves faster convergence and significant enhancements in both data rate and driving safety, as compared to various state-of-the-art benchmarks.

In this paper, we propose a dynamically segmented Intelligent Reflecting Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV) Computing Power Networks (CPNs) with tightly integrated communication and computing power resources. The IRS can be dynamically segmented and allocated to users, with computing resources allocated accordingly to satisfy their delay constraints. Considering the energy limitations of UAVs, we formulate a multi-objective optimization problem to minimize user delay and UAV energy consumption. To solve the problem, we propose a new scheme jointly considering UAVtrajectory,computing power allocation, reflecting element allocation,phase shift, and UAV-userassociation (TCPA) scheme. The phase alignment theory is utilized to determine the IRS phase shift control and decompose the problem into three subproblems based on the coupling of variables. Specifically, we use channel optimal matching to solve the first subproblem to obtain user association decisions. Then, we formulate a computing power communication matching subproblem, and propose a successive convex approximation scheme to solve it. The trajectory subproblem is optimized by a multi-agent deep reinforcement learning-based method. The evaluation results demonstrate that our proposed TCPA achieves high performance in terms of reward and system delay. Additionally, it demonstrates that integrating IRS and CPNs can effectively reduce the total system delay with only a marginal increase in energy consumption.

With the significant increase of communication network scales, intelligent Network Operation and Maintenance (NOM) becomes essential. Knowledge Graphs (KGs) are a key enabler for intelligent NOM, and Relational Triple Extraction (RTE) plays a critical role in KG construction. However, most existing RTE researches rely on general-domain corpora, with limited exploration into the specialized domain. In this paper, we identify a novel challenge in Chinese NOM corpus —Segmented Entity, which has garnered little attention in prior works. To address it, this paper proposes an Entity-level Autoregressive RTE (EARTE) method, which incorporates an innovative Segmented-BIO (Begin, Inside, Outside) tagging scheme. Furthermore, we construct the CMIM23-NOM1-RA, the first high-quality restricted domain RTE dataset for NOM. Throughout the experimentation, we meticulously reproduce all baselines and provide a comprehensive analysis. The results show that EARTE achieves the best performance on CMIM23-NOM1-RA. EARTE’s F1 scores surpass those of the best-performing baselines by 0.4%, 2.7%, and 0.8% under the strict criterion, the lenient criterion, and the setting focusing only on segmented entities, respectively. Finally, our codes, dataset, and reproduction guidelines are publicly available at: https://github.com/JYzzzzzz/PEAR-RTE.

The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.

As the sixth generation wireless technology evolves, applications such as, holography, autonomous driving, and telemedicine require enhanced data rates, reliability, and spectral efficiency. Unmanned Aerial Vehicles (UAVs) have gained attention due to their flexible deployment, line-of-sight transmission, and dynamic adaptability. However, UAV-assisted communication encounters challenges stemming from UAV position deviations caused by environmental factors such as wind and turbulence, which degrade transmission reliability. To address these problems, we propose a Non-Orthogonal Multiple Access-based full-duplex UAV relay protocol to improve the system transmission rate. The protocol utilizes successive interference cancellation for signal separation and maximal ratio combining for signal enhancement. Considering UAV position uncertainty, we formulate a robust optimization problem for joint UAV position optimization and power allocation. By employing the Bernstein-type inequality, we transform the probabilistic constraints into the deterministic constraints and solve the problem using a block coordinate descent-based algorithm. Simulation results demonstrate that, compared to the benchmark schemes, the proposed strategy improves system throughput and exhibits enhanced robustness, particularly under significant UAV position deviations.

The integration of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs) is promising for providing flexible and intelligent communications to users in urban areas. Existing studies are founded either on the complete Line of Sight (LoS) or complete Non-LoS (NLoS) communication scenarios, while ignoring their coexistence. To solve the above challenge in complicated and dynamic communication scenarios, we formulate an average system sum rate maximization problem with the optimization of joint IRS-user association, multi-UAV trajectory optimization, IRS phase shifts and transmit power allocation. Since the highly complex and coupled variables, we propose a Multi-Agent Deep Reinforcement Learning (MADRL)-based scheme to maximize the average system sum rate. First, we derive two composite channel power gains for different communication conditions. Then, phase alignment theory is utilized to obtain optimal phase control. To guarantee long-term optimization, we propose a scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and Successive Convex Approximation (SCA) method to jointly optimize multi-UAV trajectories, multi-IRS association and transmit power allocation. Finally, experimental results reveal that the proposed MGBA shows considerable advantages in both the convergence speed and the average system sum rate.

Integrated Sensing and Communication (ISAC) can assist Uncrewed Aerial Vehicle (UAV) secure communications by acquiring information about eavesdroppers. However, existing studies have not systematically investigated ISAC beamforming for simultaneously sensing the channel information of ground eavesdroppers, jamming eavesdropping links, and communicating with users, which poses significant challenges in ensuring both sensing accuracy and communication confidentiality. To address this issue, we propose a UAV-ISAC secure communication algorithm to maximize average secrecy rate by jointly optimizing communication and sensing beamforming, user scheduling, sensing time allocation, and UAV trajectory. We address the formulated NP-hard problem by decomposed it into three subproblems. We first relax binary user scheduling and sensing time allocation by a penalty-based successive convex approximation approach. The UAV trajectory is then iteratively optimized while beamforming is designed using semidefinite relaxation, with matrix lifting applied to handle the rank-one constraint. A triple-layer iterative algorithm is constructed by integrating these steps to achieve a suboptimal solution. Numerical experiment results and theoretical analysis validate the superiority of the proposed algorithm in terms of average secrecy rate, convergence and computational complexity.

Over-the-Air Federated Edge Learning (OTA-FEEL) has emerged as a promising paradigm for collaborative AI model training across heterogeneous edge devices. Despite its advantages in communication efficiency and privacy preservation, OTA-FEEL faces critical challenges, including channel fading, energy constraints of edge devices, and non-i.i.d data distributions. This paper is the first to investigate a joint impact of local data distribution heterogeneity and transmission distortion on model convergence of OTA-FEEL. Accordingly, we analyze the gap between global expected and optimal losses, and formulate the gap minimization problem under long-term energy consumption constraints. To solve this problem, we propose an energy-aware alternating resource allocation algorithm based on Lyapunov optimization framework, jointly addressing transmit power control and device sampling rate selection. Specifically, we transform the non-convex problem based on inverse convex optimization. Then, we employ first-order Taylor expansion to linearize the non-convex constraint, and also develop an iterative framework based on block coordinate descent and successive convex approximation to enable rapid convergence. Extensive simulations under three types of non-i.i.d data distributions validate the effectiveness of the proposed EARA algorithm, which consistently outperforms representative algorithms by achieving test accuracy approaching the theoretical upper bound, while maintaining significantly low energy consumption.

The energy-efficient transmission schemes are crucial to realize the Energy Harvesting (EH)-based Device-to-Device (D2D) communications. Multicast, one of the D2D modes, can serve as an effective approach to address the unreliable energy supply of EH-D2D communications and can further improve energy efficiency through cooperation among multiple users, but it has been rarely explored. To achieve the robust and energy-efficient performance for EH-D2D Multicast communications (EH-D2MD), we first design two cooperative transmission schemes: multi-cluster head content cooperation and single-cluster head energy cooperation by integrating the features of D2MD mode, efficient energy management method and wireless power transfer technology. To investigate the effectiveness and adaptability of the two cooperative schemes, we formulate a long-term average energy-efficient utility problem, which allocate the cluster heads, cooperative time and transmission power simultaneously and adaptively. We then propose an Online Convex Approximation (OCA) algorithm that combines the Lyapunov and convex approximation methods to address the non-convex Mixed Integer NonLinear Programming (MINLP) property of the modeled problem. With OCA, we can convert the long-term non-convex MINLP problem into a real-time convex MINLP problem, and obtain an optimal solution for this problem. Results reveal that the achieved energy efficiency of two proposed schemes is at least 10 times higher than that of no cooperation method, and improves at least 50% and up to 4 times compared to the single-slot cooperative algorithms.

Learning-based image dehazing algorithms have shown remarkable success in synthetic domains. However, real image dehazing is still in suspense due to computational resource constraints and the diversity of real-world scenes. Therefore, there is an urgent need for an algorithm that excels in both efficiency and adaptability to address real image dehazing effectively. This work proposes a Compression-and-Adaptation (CoA) computational flow to tackle these challenges from a divide-and-conquer perspective. First, model compression is performed in the synthetic domain to develop a compact dehazing parameter space, satisfying efficiency demands. Then, a bilevel adaptation in the real domain is introduced to be fearless in unknown real environments by aggregating the synthetic dehazing capabilities during the learning process. Leveraging a succinct design free from additional constraints, our CoA exhibits domain-irrelevant stability and model-agnostic flexibility, effectively bridging the model chasm between synthetic and real domains to further improve its practical utility. Extensive evaluations and analyses underscore the approach's superiority and effectiveness. The code is publicly available at https://github.com/fyxnl/COA.

The rapid proliferation of cellular vehicle-to-everything (C-V2X) communications calls for efficient radio resource management (RRM). Effective C-V2X systems require both ultra-low latency for V2V safety communications and high throughput for V2I services. Current systems struggle with coordinating heterogeneous requirements under rapidly changing channel conditions. To address this challenge, we propose a new framework that integrates Graph Neural Networks (GNNs) with decentralized federated learning(DFL). Our approach embeds a dynamic graph representation into the vehicular network, where communication links are modeled as graph nodes. To adapt to rapidly changing topologies, we adopt the enhanced Graph Sample and Aggregation (GraphSAGE) for scalable neighborhood aggregation. Additionally, we apply unsupervised primal-dual learning to parameterize RRM policies based on instantaneous channel conditions. To improve generalization across network, we introduce a federated model aggregation strategy that employs channel-state-aligned isomorphic GNNs. Experimental results demonstrate that, compared to traditional GNN-based benchmark solutions, our framework achieves near-optimal system throughput while maintaining low computational complexity.

The deep integration of the vehicular computing power network (VCPN) and artificial intelligence offers the potential to meet the computation-intensive and low-latency demands of emerging vehicular applications. However, due to the dynamic VCPN scenarios, task heterogeneity gives rise to differentiated and time-varying task requirements, while node mobility and wireless channel fluctuations further exacerbate scheduling complexity, which jointly hinder efficient and on-demand task scheduling. In this paper, we propose a metric termed the age of task (AoT), which characterizes the differentiated service requirements of tasks in VCPN. A utility-driven scheduling model is developed that jointly considers AoT reduction and computing cost of concurrent tasks, and a task utility maximization problem is formulated. Due to the complexity of directly solving the problem, the original problem is decomposed into a joint multi-task scheduling and matching subproblem and a resource allocation subproblem. To address the dynamic scheduling challenges in VCPN, including task heterogeneity, vehicle mobility, and fluctuating wireless channels, we model the joint task scheduling and matching subproblem as a Markov decision process. An age-aware, multi-dimensional double-deep Q-learning algorithm is designed to handle discrete action spaces and mitigate the overestimation bias in traditional DQN methods. Additionally, we develop an improved interior point-based resource allocation algorithm to obtain the optimal solution. Numerical results show that the proposed algorithm effectively adjusts learning strategies to maximize the task utility.

Mobile crowdsensing (MCS) networks enable largescale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cacheenabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach.

Using Clustered Vehicular Federated Learning (CVFL) in vehicular networks can enhance model intelligence through distributed collaboration while preserving data privacy. CVFL groups clients with similar data distributions to reduce the impact of non-independent and identically distributed (non-iid) data on federated learning efficiency, thereby supporting realtime and dynamic traffic decision-making. However, in practical applications, the high mobility of vehicles and the intermittent connectivity of communication links make it challenging to flexibly determine the number of clusters and the vehicles within each cluster. To address these issues, we propose a Graph Neural Network(GNN)-based clustering scheme called GNN-CVFL. Our proposed scheme includes two modules: clustering and resource allocation. In the clustering module, we treat the clustering problem of vehicle clients as a node classification problem in GNN, using a GNN-based method to cluster clients accurately based on their data distribution. The proposed method can automatically determine the number of clusters and the vehicles within each cluster in real-time. Moreover, based on the clustering results, We use the Lagrangian relaxation method to dynamically allocate available bandwidth resources and CPU frequencies to minimize system latency, ensuring efficient and flexible service for all vehicles. Numerical results demonstrate the feasibility and efficiency of our proposed scheme.

Existing learning-based dehazing methods perform well on synthetic data but struggle in real scenarios due to the domain gap, causing residual haze and detail loss. To address this, we propose a Multilevel Subspace Distribution Adapter (MSDA) to progressively reduce the feature distribution gap through hierarchical subspace modeling. We also introduce a Dual-Domain Synchronous Optimization (DDSO) strategy that jointly leverages synthetic supervision and adaptation to the real domain in a unified training scheme. Extensive experiments underscore the superiority of our approach and its excellence on no-reference image quality metrics.

Today's real-time communication (RTC) application requires consistent low latency to ensure the user experience. Many end-to-end rate control, as well as in-network active queue management (AQM) methods, have been designed to improve transport latency. However, most of previous work can only address the network issues in a reactive way - packets during the reaction time are still stuck on the way. No matter how fast the sender reacts to network changes in existing schemes, there will still be packets jammed in the network and increases the latency. To enhance the transport performance in wireless network and improve the user experience of RTC application, we propose Gecko, a practical application-oriented end-to-network collaboration scheme. Gecko can efficiently detect and process the congestion signal, early and proactively draining the jammed packets in the bottleneck queue. We conduct both trace-driven simulations and real-world experiments to evaluate the performance of our scheme. Gecko can reduce the overall frame delay by 21.8 % and reduce tail-delay frame ratio by 25% to 91% in our experiments.

Location-based vehicular traffic management faces significant challenges in protecting sensitive geographical data while maintaining utility for traffic management and fairness across regions. Existing state-of-the-art solutions often fail to meet the required level of protection against linkage attacks and demographic biases, leading to privacy leakage and inequity in data analysis. In this paper, we propose a novel algorithm designed to address the challenges regarding the balance of privacy, utility, and fairness in location-based vehicular traffic management systems. In this context, utility means providing reliable and meaningful traffic information, while fairness ensures that all regions and individuals are treated equitably in data use and decision-making. Employing differential privacy techniques, we enhance data security by integrating query-based data access with iterative shuffling and calibrated noise injection, ensuring that sensitive geographical data remains protected. We ensure adherence to epsilon-differential privacy standards by implementing the Laplace mechanism. We implemented our algorithm on vehicular location-based data from Norway, demonstrating its ability to maintain data utility for traffic management and urban planning while ensuring fair representation of all geographical areas without being overrepresented or underrepresented. Additionally, we have created a heatmap of Norway based on our model, illustrating the privatized and fair representation of the traffic conditions across various cities. Our algorithm provides privacy in vehicular traffic management by effectively balancing fairness and utility.

As a novel distributed learning paradigm, federated learning (FL) allows clients to train global models collaboratively without exchanging private data. However, recent research not only demonstrates the vulnerability of FL against privacy attacks where adversaries try to recover private data by intercepting local gradients/models but also its inadequacy in defending against poisoning attacks launched by malicious adversaries, who modify local datasets to disrupt the global training process. Even though many solutions have been proposed to defend against these attacks, there is still a gap in mitigating the risks in more complex nonindependent and identically distributed (Non-IID) scenarios that are prevalent in Internet of Things (IoT) systems. To fill this gap, this article proposes a data poisoning resistible and privacy protection FL mechanism (DPR-PPFL) for ubiquitous IoT. Based on representational similarity analysis, DPR-PPFL allows clients to construct asymmetric local models in defending against data inversion attacks, and also the server to detect and aggregate benign local models uploaded by the clients to correctly train the global model in the face of data poisoning attacks. By comparing the performance of DPR-PPFL with state-of-the-art baselines, its merits in securing the learning process under IID and Non-IID scenes of IoT are demonstrated.

Blockchain technology presents significant security challenges despite its transformative impact on digital transactions and decentralized data management. Key vulnerabilities include insecure smart contract execution, data privacy risks on transparent ledgers, and susceptibility of certain consensus mechanisms to attacks. Trusted Execution Environments (TEEs) offer a robust hardware-based solution to these critical issues. By providing isolated execution spaces, TEEs safeguard code and data confidentiality and integrity, thereby fundamentally strengthening blockchain security. This paper presents a comprehensive analysis of TEEs in blockchain technology. First, we analyze the challenges inherent in blockchain systems and demonstrate the advantages of TEEs over current methods. A detailed analysis of TEE properties, variants, and evolution in the blockchain field is provided. Additionally, we explore innovative TEE-based solutions across three key application domains: consensus mechanism optimization, confidential computation and execution, and payment networks and financial applications. Furthermore, we propose a research agenda addressing current challenges such as vulnerabilities to side-channel attacks and dependencies on hardware trust assumptions. Finally, we propose five critical directions for future TEE-blockchain integration: enhancement of security and privacy protection with particular attention to the Trusted Computing Base (TCB) minimization, performance optimization through hardware architecture advancement, trust model refinement to reduce centralization, expansion of application scenarios through interdisciplinary collaboration, and development of cross-chain interoperability standards. Our work contributes to blockchain security knowledge and provides a roadmap for researchers and practitioners in this rapidly evolving field.

Network optimization is a fundamental challenge in the Internet of Things (IoT) network, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. We provide code and data athttps://github.com/qiyu3816/DiffSG.

The deep integration of IoT and vehicular networks demands ultra-reliable, low-latency computing paradigms to support emerging applications like autonomous driving and smart traffic management. Existing Mobile Edge Computing (MEC) frameworks, however, struggle with dynamic resource heterogeneity, intermittent connectivity, and inefficient coordination among distributed nodes. To address these challenges, this paper proposes Vehicular Computing Power Networks (VCPN), an IoT-driven edge intelligence framework that orchestrates computational resources from mobile user equipments (MUEs), connected vehicles, and edge servers. We formulate a joint optimization problem to minimize end-to-end task latency by finding optimal task offloading decisions and resource allocation (e.g., CPU, bandwidth) policies under time-varying IoT channel conditions and node mobility. To enable decentralized coordination in IoT environment, we model the problem as a multi-agent Markov decision process (MDP) and propose a multi-agent deep deterministic policy gradient (MA-DDPG) algorithm in which agents (MUEs, vehicles, servers) collaboratively learn policies to optimize task scheduling and resource sharing. Furthermore, we design a robust MA-DDPG variant with error-resilient experience replay and channel-adaptive reward mechanisms to ensure reliable training under packet loss and unstable connectivity. Numerical results demonstrate that VCPN reduces average task latency and improves energy efficiency compared to federated MEC baselines. The proposed MA-DDPG algorithm achieves convergence stability in high-mobility scenarios, outperforming conventional deep reinforcement learning methods.

The novel notion of Edge Computing Power Networks (ECPN) has recently been proposed to provide highly flexible matching strategies among edge servers and user devices to facilitate seamless computing power and network architectures. However, recent work about ECPN is still in its infancy, and almost all work has ignored the tidal phenomenon of computing power requests from mobile user devices, including temporal characteristic of the quantity and spatial characteristic of the distribution in different periods of one day, which leads to the energy waste of idle edge servers for always keeping active. To deal with this problem, we propose the ECPN model in this article, taking into account the tidal phenomenon of mobile user devices to develop energy-efficient computing power matching strategies. Specifically, we formulate the optimization problem that encompasses computing power allocation and task matching for user devices as well as dynamic on-off for edge servers. Our main objective is to maximize the quality of service (QoS) for user devices while minimizing the energy consumption for task execution with respect to resource limitations and task requirements. To solve the formulated problem efficiently, we propose a distributed algorithm based on matching theory to determine the optimal computing power allocation, task matching and on-off strategies. Extensive numerical results show that the proposed scheme can reduce energy consumption while ensuring the QoS.

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 sixth-generation (6 G) networks aim to achieve ubiquitous intelligent connectivity while ensuring extremely low latency, reducing energy consumption, and enhancing privacy protection. Mobile edge computing (MEC) offers an effective solution to reduce latency and energy consumption by leveraging resources near end devices for task offloading. However, MEC faces significant challenges in meeting the requirements of 6 G networks, including limited computational resources, high mobility, and strict data privacy demands. Efficiently allocating edge resources while preserving privacy has become a critical issue for realizing the objectives of 6 G networks. In this paper, we propose a privacy-preserving edge computing power network (EdgeCPN) model that jointly leverages the computing resources of edge computing nodes and protects sensitive computing power information through differential privacy methods. In addition, we propose a task matching problem that aims to minimize the privacy-budget-weighted energy consumption while ensuring privacy protection and meeting task requirements. We propose a dynamic graph-based multiagent reinforcement learning (MADRL) algorithm to find the optimal strategy for task matching and computing resource allocation with privacy protection. The results show that our proposed task matching model with energy and privacy tradeoffs can minimize the energy consumption in the matching process while ensuring privacy, and the algorithm can find the optimal strategy for task matching efficiently.

The development of intelligent transportation has generated many ultra reliable low latency communication (URLLC) tasks, which require sufficient communication and computation resources for task offloading and processing. Although mobile edge computing (MEC) provides a promising solution, its efficiency is subject to the limited knowledge and analysis capability on the physical networks. Therefore, in this paper, we propose a digital twin (DT) empowered MEC framework to strengthen the MEC task offloading efficiency in cellular vehicle-to-everything (C-V2X) networks. Our proposed DT is constructed through a hybrid data-driven and model-driven approach to capture the realistic transportation network features. Then, DT leverages the metric of time to collision to predict vehicular safety levels and estimates the corresponding URLLC task requirements of future time slots. The prediction results are further utilized to make decisions on the URLLC resource reservation. Different from conventional studies, we consider the influence of DT’s inaccurate predictions (i.e., the prediction with error) on the resource allocations. Specifically, the inaccurate DT prediction results are considered as uncertain constraints of the resource reservation problem. A robust parameter from the robust optimization is adopted to adjust the tradeoff between the problem uncertainty and solution optimality degree. Further, we leverage the optimized resource reservation results to construct the task offloading problem. The problem is decoupled into two sub-problems of channel resource allocation and computation resource allocation, respectively. And a two-stage matching algorithm is developed to solve each sub-problem based on the resource reservation constraints. Finally, realistic road information is mapped into DT for simulations. Simulation results validate the advantages of our proposed approach by comparing with existing schemes.

UAV swarms offer substantial opportunities for Search and Rescue (SAR) applications. Confronted with numerous concurrent sensing tasks in complicated environment, resource-scarce UAV networks need a dynamic, task-driven deployment and resource configuration strategy for multi-UAV swarm coordination to ensure the efficient execution of sensing tasks. This paper introduces a Digital Twin (DT)-based collaboration architecture for resource management in UAV swarms, connecting realistic task crowdsourcing and virtual traffic flow scheduling to achieve a complementary multi-UAV swarm allocation. We propose an intelligent dynamic task crowdsourcing scheme that manages the swarm scale and membership configuration of multiple UAV swarms based on theoretical evaluation results. The architecture constructs DTs of UAV swarms and shifts the scheduling of traffic flow paths to the virtual world, thereby sidestepping the overhead of routing configuration and network reorganisation. With the aid of a traffic flow allocation algorithm based on Stochastic Network Calculus (SNC), the virtual swarm pre-schedules traffic flows and assesses end-to-end delay theoretically, so as to achieve a collaborative deployment of sensing, computational, and communication resources within the swarm. The simulation results substantiate that our architecture can uphold a 90% achievement ratio for task requirements while keeping UAV costs comparable to other algorithms.

This paper focuses on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to a multi-access edge computing (MEC) server. Considering that the V2I links sometimes can be reused by vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of the V2I link may suffer from severe interference, causing outages during the task offloading. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Simulation results show that the proposed RICS-assisted offloading framework significantly improves the safety of the autonomous driving network, in which the safety coefficient of the CVs is improved by nearly 34%. The V2V data rate is improved by around 60%, which indicates that the RICS’s adjustment of the signals can effectively mitigate the interference of the V2V link.

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we proposeSR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposedSR-CL. Compared to benchmark methods, theSR-CLachieves superior convergence and delay performance under various scenarios.

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×.

With the rise of new applications such as AR/VR, cloud gaming, and vehicular networks, traditional network management solutions are no longer cost-effective. Digital Twin Network (DTN) creates a real-time virtual twin of the physical network, which improves the network's stability, security, and operational efficiency. AI models have been used to model complex network environments in DTN, whose quality mainly depends on the model architecture and data. This paper proposes an automatic data generation and optimization method for DTN called AutoOPT, which focuses on generating and optimizing data for data-driven DTN AI modeling through data-centric AI. The data generation stage generates data in small networks based on scale-independent indicators, which helps DTN AI models generalize to large networks. The data optimization stage automatically filters out high-quality data through seed sample selection and incremental optimization, which helps enhance the accuracy and generalization of DTN AI models. We apply AutoOPT to the DTN performance modeling scenario and evaluate it on simulated and real network data. The experimental results show that AutoOPT is more cost-efficient than state-of-the-art solutions while achieving similar results, and it can automatically select high-quality data for scenarios that require data quality improvement.

One of the key challenges of current online social platforms is predicting the size of information cascades, also known as popularity prediction or cascade prediction. Accurate popularity prediction can benefit various fields, including news distribution, market decisions, and rumor detection. However, existing popularity prediction approaches concentrate more on the historical sequences of single messages, overlooking the interactions between message diffusion and the dynamic nature of social networks, which limits the timeliness and accuracy of predictions. To address this, we propose an online popularity prediction service based on minimal substitution reinforcement learning calledMSRL. Specifically, we explore a substitution theory and design a minimal substitution reinforcement learning method that models diffusion as message substitution and considers mutual information diffusion. That helps the model gain a broader perspective, allowing it to fully exploit the cooperative, competitive, or dependent relationships between information diffusions. Furthermore, the reinforcement learning scheme enables the service to dynamically adjust its parameters to respond to the dynamic social network environment in real-time. Finally, extensive experiments on real-world datasets show that the MSRL outperforms state-of-the-art methods regarding accuracy and service agility.

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.

The development of automated home energy management (HEM) and peer-to-peer energy trading mechanisms encourages a greater number of energy consumers to switch roles and become providers. To sustain this trend and maintain their long-term commitment to energy platforms, we face the challenge of aligning the primary psychological motivators of prosumers with our developed energy services. Most existing approaches target maximizing prosumer utility based on extrinsic benefits, such as economic rewards. However, the intrinsic motivations which are inherently satisfying for prosumers, have not been thoroughly analyzed. This article explores both extrinsic and intrinsic motivations of prosumers from a psychological perspective and addresses these within the technological field. Self-determination theory is adopted as a psychological framework to analyze prosumer behavior in energy systems. The study quantifies prosumers’ motivations and proposes a quality-of-energy-service measure to reflect individual preferences. Additionally, a leader-follower-based optimization framework is introduced, enabling individual prosumers to make optimal decisions regarding their energy management and trading strategies in a P2P energy market. The proposed system features a deep reinforcement learning agent as the leader, targeting optimal HEM solutions, while the follower aims to find the optimal trading strategy for prosumers in an auction-based P2P trading environment. Numerical results demonstrate that our proposed model outperforms baseline models.

We propose Lodge, a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture, and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion, which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast, the second-stage is the local diffusion, which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition, we propose a Foot Refine Block to optimize the contact between the feet and the ground, enhancing the physical realism of the motion. Our approach can parallelly generate dance sequences of extremely long length, striking a balance between global choreographic patterns and local motion quality and expressiveness. Extensive experiments validate the efficacy of our method. Code, models, and demonstrative video results are available at: https://li-ronghui.github.io/lodge

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.

Vehicle edge computing (VEC) leverages the computational and communication resources available from vehicles and mobile edge computing (MEC) servers to provide computing services for mobile vehicles. However, traditional task offloading and resource allocation methods based on deep reinforcement learning (DRL) often ignore the latent relationships between vehicles and MEC servers. This oversight results in a lack of robustness in highly mobile and complex environments. Therefore, this paper proposes a distributed dynamic task offloading and resource allocation (DDTORA) strategy in the context of vehicle-assisted multi-vehicle VEC scenarios. DDTORA aims to utilize idle computational resources of vehicles and find optimal task offloading and resource allocation schemes to minimize the weighted summation of latency and energy consumption for all tasks. To find the optimal solution, we propose a graph attention network based multi-agent deep reinforcement learning (GAMDRL) algorithm for distributed task offloading and resource allocation. Numerical simulations demonstrate that DDTORA converges faster than the benchmark algorithms, significantly reducing latency and energy consumption by 25.44% to 34.19%.

Many video streaming applications will simultaneously transfer data in both directions, from the user to the Internet (uplink) and from the Internet to users (downlink). However, for wireless local area networks (WLANs), the dominant scenarios, the uplink and downlink flows share the same half-duplex physical channel and compete for bandwidth resources. Their bandwidths would be fairly apportioned under the existing link layer access method, but a fair share might be suboptimal for applications. For better application performance, we propose Plum, to coordinate the bitrate of uplink and downlink flows, and allocate the bandwidth in both directions to cater to the application's demands. To make the deployment of Plum practical, we aim at not modifying the link layer but optimizing the transport layers and above. We evaluate our mechanisms with simulations based on real-world traces and testbed experiments, and results show that Plum could improve the video bitrate of streaming applications by up to 48-59%.

In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision-making stage and the request scheduling stage. To prevent the terminal from going out of the service area during offloading, we consider the mobility parameter of the terminal according to the human behaviour model when making the offloading decision, and then introduce a server evaluation mechanism based on both the mobility parameter and the server load to select the optimal offloading server. In order to fully utilise the server resources, we design a double deep Q-network (DDQN)-based reward evaluation algorithm that considers the priority of tasks when scheduling offload requests. Finally, numerical simulations are conducted to verify that our proposed method outperforms traditional mathematical computation methods as well as the DQN algorithm.

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

In this article, we introduce the concept of edge computing power network (EdgeCPN) as a new paradigm to facilitate elastic integration and flexible scheduling of computing resources for task offloading in computing power networks (CPNs). Previous studies mainly focused on scheduling computing resources in the vertical dimension and may not effectively consider the computing resources selection in CPNs with increasingly diverse computing resources, which results in inefficient and unstable computing resource scheduling performance for task offloading. In this article, we design an on-demand computing resource scheduling model to enable efficient task offloading in EdgeCPNs. To improve the search efficiency and stability, we decouple the search for task offloading problems in EdgeCPNs into two stages and present a two-stage evolutionary search scheme (TESA). In stage-1, TESA first optimizes computing resources selection by searching a computing resources subset depending on the user budget, with the objective of maximizing the total gain. In stage-2, TESA jointly optimizes task offloading decisions and computing resources allocations based on the subset found in stage-1, with the objective of minimizing total delay. Numerical results confirm that the proposed scheme significantly enhances the efficiency and stability of the computing resources scheduling performance for task offloading in EdgeCPNs.

Federated learning (FL) is an efficient decentralized machine learning methodology for processing non-independent and identically distributed (non-IID) data due to geographical and temporal distribution differences. Non-IID data generally indicates substantial disparities in data distribution and features among clients. This assumption is completely different from the conventional assumption of independent and identically distributed (IID) data in which all clients’ data originates from the same distribution. There are many factors that affect the features of non-IID data, such as user preferences, data collection methods, and client characteristics. The factors of data distribution, category proportions, and feature representation also affect the statistical properties of non-IID data. This paper conducts an in-depth exploration of FL with the consideration of diverse features and statistical properties of non-IID data. Specifically, we first discuss the impact of non-IID data on communication efficiency, model convergence, and FL accuracy. The presence of non-IID data leads to increased communication overhead, imbalanced class distribution, and uneven local model updates. All of these affect FL convergence and performance. Then, we present the latest advanced techniques, such as data partitioning/sharing, client selection, differential privacy, and secure aggregation [1], which are used to address the challenges posed by non-IID data in terms of communication efficiency and privacy protection. Furthermore, we show the emerging applications and use cases of FL with non-IID data in various domains, such as healthcare, IoT, and edge computing. Overall, this survey provides a comprehensive understanding of FL with non-IID data, including the challenges, advancements, and practical applications in different areas.

Digital twin (DT) is a technology that precisely replicates physical entities and seamlessly connects physical entities with virtual counterparts, which facilitates precise understanding, optimization, and decision-making. DT network (DTN) can be regarded as an information-sharing network, comprising a constellation of interconnected DT nodes. This survey provides an in-depth exploration of the concepts and potential of DTN, with a particular focus on the role of machine and deep learning in improving the efficiency of DTN systems, including anomaly monitoring, system state estimation, resource allocation, task offloading, model optimization, and security and privacy protection. Incorporating machine and deep learning into DTN stands to revolutionize industries by enabling the extraction of critical insights, enhancing anomaly detection capabilities, refining the accuracy of predictive models, and optimizing the allocation of resources. Finally, we discuss the challenges and future research directions in the application of machine and deep learning in DTN.

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.

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network (NN) models, the complexity of multiple access (MA) for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in sixth-generation (6G) systems. Traditional MA design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high quality-of-service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning (ML) techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state of the art and further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.

Well-trained neural network models are deployed on edge servers to provide valuable inference services for clients. To protect data privacy, a promising way is to exploit various types of secret sharing to implement privacy-preserving neural network inference. However, existing schemes suffer high communication rounds and overhead, making them hardly practical. In this paper, we propose Cenia, a new communication-efficient privacy-preserving neural network inference model. Specifically, we exploit arithmetic secret sharing to develop low-interaction secure comparison protocols, that can be used to realize secure activation layers (e.g., ReLU) and secure pooling layers (e.g., max pooling) without expensive garbled circuit and oblivious transfer primitives. Besides, we also design secure exponent and division protocols to realize secure normalization layers (e.g., Sigmoid). Theoretical analysis demonstrates the security and low complexity of Cenia. Extensive experiments have also been conducted on benchmark datasets and classical models, and experimental results show that Cenia achieves privacy-preserving, accurate, and efficient neural network inference. Particularly, Cenia can achieve 37.5% and 60.76% of Sonic’s communication rounds and overhead, respectively, compared to Sonic (i.e., the state-of-the-art scheme).

Efficient data processing and computation are essential for the Industrial Internet of Things (IIoT) to empower various applications, which can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this article, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

The development of automatic driving and assisted driving breeds the problem of proximity detection in road networks, which plays a significant role in ensuring safe driving. Due to the fact that it is a time-sensitive task, the problem of proximity detection requires to judge whether two vehicles are close to each other in a very short time. However, the battery life and computation capacity of vehicles are limited in the actual scenario. Therefore, how to solve this problem with low latency and energy consumption is an important issue. In this paper, we investigate the Joint Optimization of the Latency and Energy consumption problem in the scenario of Proximity Detection, namely, JOLE-PD, which is formulated into a constrained multiobjective optimization problem. The DDPG-CMOA method is proposed to find a tradeoff between latency and energy consumption, achieving the Pareto optimal solutions. Besides, NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and MOEA-D (Multi-objective Evolutionary Algorithm Based on Decomposition), as the typical algorithms to solve multiobjective optimization problem, are used as the baseline methods to compare the performance of DDPG-CMOA method under different parameters. The experimental results show the proposed DDPG-CMOA method requires much lower running time and has strong generalization ability. Moreover, the solutions obtained from the DDPG-CMOA method have a slightly better ability of convergence and diversity.

Given the frequently changing and potentially unreliable environment, the seamless handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network with dramatically enhanced communication and transportation safety. The traditional centralized handover authentication schemes may suffer from the excessive latency and situation agnostic limitation, leading to potential interruption of critical services for fast moving vehicles. To overcome the above challenges, this paper proposes a novel decentralized edge collaboration-based handover authentication scheme with the assistance of blockchain for providing continuous protections in zero-trust IoV. A distributed learning process is designed by involving multiple authentication cooperators (ACs) to collect device/location-related features of vehicles at network edge and then to verify their identities. During the movement of vehicles, the access point (AP) could select new ACs by transferring the security information from existing ACs to the new members for seamless handover authentication. A situation-aware AC selection and update algorithm is proposed for maximizing handover authentication accuracy. Moreover, a hierarchical blockchain-assisted security information transfer and reputation management mechanism is designed for reliable collaboration and efficient management in zero-trust IoV. Compared with the existing schemes, our results characterize the outperformance of the proposed scheme in authentication accuracy and time cost of handover.

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.

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, successive convex approximation (SCA) and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

Traditional network management solutions can not easily meet the requirements of new applications (such as AR/VR, cloud gaming, and vehicular networks) at a reasonable cost. Digital Twin Network (DTN) builds real-time mirrors of physical networks, which can enhance the simulation, optimization, verification, and control capabilities that physical networks lack. AI models have been used to model complex network environments, which helps build real-time, lightweight, and high-precision DTN. This paper proposes a data generation and optimization method for data-driven DTN AI modeling through Data-Centric AI called AutoOPT. First, AutoOPT generates data in small networks based on scale-independent indicators, which helps the model generalize to large networks. Then, AutoOPT automatically filters out high-quality data through seed sample selection and incremental optimization, which helps the model be effectively trained to enhance accuracy and generalization. We apply AutoOPT to the DTN performance modeling scenario and test on GNNet Challenge datasets. The experimental results show that AutoOPT is more cost-efficient than the winning solutions of the challenge but can obtain similar results.

Federated Learning (FL) is an emerging distributed learning architecture that allows multiple agents to share knowledge in machine learning. However, in the scenario with mobile agents, the mobility of agents significantly affects the learning performance. Besides, frequent exchange of local models could result in high communication overhead. In this paper, we propose a Mobility-aware Federated Reinforcement Learning (MFRL) framework. In MFRL, we model the influences of agent mobility on communication quality and data correlation, and devise a mobility-aware node selection algorithm, so as to accelerate the training procedure and improve the learning performance, taking learning quality, wireless channel quality, and data correlation of agents into consideration. A knowledge distillation (KD) based model compression method is integrated into the MFRL to reduce the communication overhead as well as accelerate the inference process. Finally, taking deep reinforcement learning (DRL) based collision avoidance of intelligent vehicles as a study case, the effectiveness of MFRL is verified. Numerical results demonstrate that the proposed MFRL can accelerate the training process and improve the learning performance.

In mobile edge computing (MEC)-assisted federated learning (FL), the MEC users can train data locally and send the results to the MEC server to update the global model. However, the implementation of FL may be prevented by the selfish nature of MEC users, as they need to contribute considerable data and computing resources while scarifying certain data privacy for the FL process. Therefore, it is of great importance to design an efficient incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission and data privacy, we design an energy-efficient and privacy-preserved incentive scheme to facilitate the FL process by investigating interactions between the MEC server and MEC users in a MEC-assisted FL system. Using a Stackelberg game model, we explore the transmit power allocation and privacy budget determination of MEC users and reward strategy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results demonstrate the effectiveness of our proposed scheme.

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.

The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. However, the configuration of MIMO also brings high hardware cost, high power consumption, and high signal processing complexity. How to efficiently apply MIMO to achieve balanced communication and sensing performance is still open. In this survey, we discuss joint communication and sensing (JCAS) in the context of MIMO. We first outline the roles of MIMO in the process of wireless communication and radar sensing. Then, we present current advances in both communication and sensing coexistence and integration in detail. Three novel JCAS MIMO models are subsequently discussed by combining cutting-edge technologies, i.e., cloud radio access networks (C-RANs), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs). Examined from the practical perspective, the potential and challenges of MIMO in JCAS are summarized, and promising solutions are provided. Motivated by the great potential of the Internet of Things (IoT), we also specify JCAS in IoT scenarios and discuss the uniqueness of applying JCAS to IoT. In the end, open issues are outlined to envisage a ubiquitous, intelligent, and secure JCAS network in the near future.

The sixth generation (6G) wireless communication aims to enable ubiquitous intelligent connectivity in future space–air–ground–ocean-integrated networks, with extremely low latency and enhanced global coverage. However, the explosive growth in Internet of Things devices poses new challenges for smart devices to process the generated tremendous data with limited resources. In 6G networks, conventional mobile edge computing (MEC) systems encounter serious problems to satisfy the requirements of ubiquitous computing and intelligence, with extremely high mobility, resource limitation, and time variability. In this article, we propose the model of wireless computing power networks (WCPNs), by jointly unifying the computing resources from both end devices and MEC servers. Furthermore, we formulate the new problem of task transfer, to optimize the allocation of computation and communication resources in WCPN. The main objective of task transfer is to minimize the execution latency and energy consumption with respect to resource limitations and task requirements. To solve the formulated problem, we propose a multiagent deep reinforcement learning (DRL) algorithm to find the optimal task transfer and resource allocation strategies. The DRL agents collaborate with others to train a global strategy model through the proposed asynchronous federated aggregation scheme. Numerical results show that the proposed scheme can improve computation efficiency, speed up convergence rate, and enhance utility performance.

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.

Sixth-Generation (6G) technologies will revolutionize the wireless ecosystem by enabling the delivery of futuristic services through satellite-terrestrial integrated networks (STINs). As the number of subscribers connected to STINs increases, it becomes necessary to investigate whether the edge computing paradigm may be applied to low Earth orbit satellite (LEOS) networks for supporting computation-intensive and delay-sensitive services for anyone, anywhere, and at any time. Inspired by this research dilemma, we investigate a LEOS edge-assisted multi-layer multi-access edge computing (MEC) system. In this system, the MEC philosophy will be extended to LEOS, for defining the LEOS edge, in order to enhance the coverage of the multi-layer MEC system and address the users’ computing problems both in congested and isolated areas. We then design its operating offloading framework and explore its feasible implementation methodologies. In this context, we formulate a joint optimization problem for the associated communication and computation resource allocation for minimizing the overall energy dissipation of our LEOS edge-assisted multi-layer MEC system while maintaining a low computing latency. To solve the optimization problem effectively, we adopt the classic alternating optimization (AO) method for decomposing the original problem and then solve each sub-problem using low-complexity iterative algorithms. Finally, our numerical results show that the offloading scheme conceived achieves low computing latency and energy dissipation compared to the state-of-the-art solutions, a single layer MEC supported by LEOS or base stations (BS).

Smart grid integrates the distributed energy resources such as renewable energy with massive information to facilitate the flow of energy in the industries. The renewable energy accommodation is one of the key issues to achieve the energy efficiency in smart grid, which is difficult to obtain dynamic optimal policies due to the intermittency of renewables. To capture statuses of renewable energy for decision-making, large amounts of information in heterogeneous forms are collected by massive end devices deployed in smart grid. Such information not only provides fruitful features for existing learning based algorithms but also incurs high computation complexity. Besides, such heterogeneous data may also contain missing values, which may result in wrong policies by existing algorithms. In this article, a novel cloud-edge-end orchestrated computing scheme is proposed to efficiently repair missing values and obtain optimal policies in two separate layers. In the first layer, deep learning based algorithms deployed can perceive the characteristics and repair the missing values. In the second layer, deep reinforcement learning based algorithms are employed to obtain optimal policies. Simulations on the real power grid dataset illustrate the effectiveness of proposed fault-tolerant renewable energy accommodation algorithm.

This article proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics, and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics, and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named$\text{C}^{3}$-FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that$\text{C}^{3}$-FLOW performs superior in loss function, communication efficiency, and carbon emission reduction.

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

The dramatically growing trend of vehicles equipped with driving camera recorders has allowed realizing real-time crowdsourced video sharing in vehicular edge computing (VEC). Such cameras can assist in monitoring objects directly in front of and behind the vehicles, enabling them to provide important visual information through real-time video streaming in case of possible accidents. Exploiting the on-board units (OBUs) for VEC can allow drivers and passengers to share and access on-road video surveillance services. However, data security and privacy concerns of video generators (owners) are two key challenges that can severely limit video sharing in a VEC environment. In this article, we propose a blockchain empowered publish/subscribe (P/S) scheme to enable one-to-many secure video sharing in the VEC scenario. Then, we design an attribute-based encryption algorithm with static and dynamic attributes (ABE-SD) to achieve fine-grained access control in a mobile environment. Finally, We utilize permissioned blockchain and smart contracts to record access policy and publish and subscribe events, thus resulting in user self-certification and event traceability. The numerical results indicate that our proposed scheme ABE-SD outperforms traditional centralized CP-ABE methods in terms of encryption and decryption performance. The simulation experiments demonstrated that the proposed video-sharing scheme is secure and efficient.

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem for the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. We present three system models for two-tiered social crowdsourcing system based on the arrival modes of registered users and social neighbors: offline model, semi-online model, and full-online model. We consider the tasks are associated with different end times. We present an incentive mechanism for each of three system models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness. We further show that our incentive mechanisms for semi-online model and full-online model can obtain averagely 51.1$\%$and 39.7$\%$value of approximate optimal untruthful offline algorithm, respectively.

Forwarding path selection for data traffic is one of the most fundamental operations in computer networks, whose performance drastically impacts both transmission efficiency and reliability in network domains. Although deep reinforcement learning (DRL) has attracted considerable attention for path selection instead of hand-tuned heuristics, few works have considered how to exploit graph-structured information in networks to improve routing and forwarding efficiency. In fact, generating routes is essentially a process for finding a subgraph in a graph-structured network. To this end, this paper proposes an effective and novel graph embedding-based DRL framework for adaptive path selection (termed GRL-PS), aiming at reducing end-to-end (E2E) latency and promoting network throughput while maintaining stability in dynamically changing environments. Specifically, graph representation learning (GRL) is deployed as an effective enabler for the DRL agent to learn the relational knowledge of interacting entities for route decisions in networks. However, training such an agent in a dynamically changing environment encounters a knowledge acquisition bottleneck, since the DRL agent is always forced to learn every task from scratch. To improve the adaptation of behaviors and acquire skills beyond what the source policy can teach, we introduce potential-based reward shaping as a means of knowledge transfer to guide the agent in unfamiliar conditions with sparse rewards. Experimental results show that compared with baseline methods, our solution can achieve nearly-optimal performance with both latency and throughput, especially in large-scale dynamic networks.

In this paper, we investigate energy-efficient computation offloading and trajectory planning for aerial edge networks, wherein multiple Unmanned Aerial Vehicles (UAVs) cooperate with each other to provide computing service to the ground users. In particular, we formulate the joint optimization of computation offloading, channel allocation, power control, resource allocation, and trajectory planning as a multi-agent deep reinforcement learning (MA-DRL) problem to minimize the system energy consumption while guaranteeing the latency re-quirements of computation tasks. Then, we propose a distributed algorithm to enable UAV s to independently make action decisions based on their local observations, still collaboratively learn their policies for system performance. Numerical results demonstrate that our proposed algorithm can effectively plan the trajectory, reduce the system energy consumption, and improve the latency requirements satisfaction ratio.

Aerial edge networks that leverage Unmanned Aerial Vehicles (UAVs) to provide task processing and data caching in infrastructure-less areas have emerged as a prominent paradigm in the upcoming 6G era. However, the endurance of UAV flight generally suffers from constrained onboard battery capacity. Thus, energy efficiency turns out to be a key issue for implementing aerial edge networks. In this paper, we focus on green mobile edge computing of rechargeable UAVs, and propose an aerial edge network aided by distributed charging services. In this network, an energy minimization problem that jointly considers aerial edge resource allocation, UAV trajectory planning and battery charging scheduling as well as task delay constraint is formulated. Due to the non-convexity and the coupled optimization variables of the problem, we adopt a deep reinforcement learning approach to design an intelligent iterative algorithm to obtain energy efficient aerial edge strategies, and demonstrate its convergence. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to two benchmark schemes.

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.

With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

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.

Transient stability assessment (TSA) plays an important role to ensure the safe operation of the power system in Internet of Energy (IoE). Many time-domain simulation (TDS)-based and transient energy function (TEF)-based methods have been proposed to assess the transient stability of the power system. With the wide area measurement system (WAMS) and the phasor measure units (PMUs) applied to observe the real-time data, methods of TSA based on the machine learning and data-driven are continuously studied. These kinds of methods can only assess the transient stability of the power system when subjected to large disturbances. However, these kinds of methods cannot infer the type of event which leads to the collapse of the power system. In this article, the gated graph neural network (GGNN) is applied to assess the power system transient stability and infer the type of event leading the instability of the power system. First, conditional generative adversarial network (CGAN) is applied to generate unstable samples making the training data more balanced. With the balanced data graph-structured and used to train the GGNN-based TSA model, the GGNN-based TSA model achieves better performances. Finally, the real-time data is input into the trained TSA model and the transient stability of the power system is assessed. When the power system is considered unstable, the proposed TSA model can also infer the type of event leading the instability of the power system, classifying the unstable state to the corresponding event. Simulations performed on the New England 39-bus system verify the effectiveness of the proposed method.

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit-sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated the performance superiority of our proposal as compared with the baselines.

The increasing penetration of renewable energy sources and electric vehicles (EVs) poses a significant challenge for the power grid operator in terms of increasing peak load and power quality reduction. Moreover, there is a growing demand for fast charging services in smart grids. Addressing the growing demand from fast charging services is challenging. To overcome this challenge, in this article, we propose a new computational architecture combining energy trading and demand responses based on cloud computing for managing virtual power plants (VPPs) in smart grids. In the proposed system, EVs can be charged at high charging rates without affecting the operation of the power grid by purchasing energy through the energy trading platform in the cloud. In addition, users with storage devices can sell energy surplus to the market. On the one hand, the energy trading platform can be regarded as an internal market of the VPP that aims to maximize its revenue. The interest of the EV owners, on the other hand, is to minimize the cost for charging. Therefore, we model the interactions between the EV owners and the VPP as a non-cooperative game. To search for the Nash equilibrium (NE) of the game, we design an algorithm and then analyze its computational complexity and communication overhead. We utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of the proposed algorithm. Our results illustrate that the users with only storage devices can obtain nearly$200\%$200%higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly$50\%$50%in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately$120\%$120%where the users get profit by utilizing the internal market.

The rapid proliferation of smart vehicles along with the advent of powerful applications bring stringent requirements on massive content delivery. Although vehicular edge caching can facilitate delay-bounded content transmission, constrained storage capacity and limited serving range of an individual cache server as well as highly dynamic topology of vehicular networks may degrade the efficiency of content delivery. To address the problem, in this article, we propose a social-aware vehicular edge caching mechanism that dynamically orchestrates the cache capability of roadside units (RSUs) and smart vehicles according to user preference similarity and service availability. Furthermore, catering to the complexity and variability of vehicular social characteristics, we leverage the digital twin technology to map the edge caching system into virtual space, which facilitates constructing the social relation model. Based on the social model, a new concept of vehicular cache cloud is developed to incorporate the correlation of content storing between multiple cache-enabled vehicles in diverse traffic environments. Then, we propose deep learning empowered optimal caching schemes, jointly considering the social model construction, cache cloud formation, and cache resource allocation. We evaluate the proposed schemes based on real traffic data. Numerical results demonstrate that our edge caching schemes have great advantages in optimizing caching utility.

In peer-to-peer (P2P) energy trading, the incorporation of distributed energy resources with unprotected data, originating from sources such as home energy management systems that are connected through the Internet, provokes vulnerabilities that can manifest security breaches. In this article, two threat scenarios based on a novel false data injection attack (FDIA) model in a local P2P energy trading system are explored. In these scenarios, an attacker gains free energy by manipulating prosumers’ consumption and demand. Precise and fast attack detection is needed to guarantee suitable countermeasures to prevent potential risks. We propose a novel instance-based machine learning (ML) classifier for detecting FDIAs. In contrast to black-box ML models, our algorithm provides a transparent decision-making procedure with significant predictive performance. We apply our detection model to a real-world dataset from Austin, Texas. Our experimental results show superior performance as compared to several popular interpretable and non-interpretable ML methods. On average, we achieve a 96.10 percent detection rate, a 96.18 percent accuracy rate, and a false negative rate of 1.97 percent with our approach.

In this article, we propose a transfer learning (TL) enabled edge convolutional neural network (CNN) framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder.

Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly,mixing servicesin Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.

In smart grids, the large-scale integration of distributed renewable energy resources has enabled the provisioning of alternative sources of supply. Peer-to-peer (P2P) energy trading among local households is becoming an emerging technique that benefits both energy prosumers and operators. Since conventional energy supply is still needed to help fill the gap between local demand and supply when the local solar generation is not sufficient, demand–response management will keep playing an important role in the future P2P energy market. Blockchain and smart contract technology has gained increasing attention in P2P trading for its secure operation. The performance of blockchain-based P2P energy trading still remains to be improved, in terms of latency and cost of computation resources. This article studies the challenges of demand–response management in P2P energy trading and proposes a blockchain-empowered energy trading system for a community-based P2P market. The proposed demand–response mechanism is developed using two noncooperative games, in which dynamic pricing is applied for suppliers. The proposed energy trading system is prototyped on a cluster network, with a coordinator running as a smart contract in a Hyperledger blockchain. We implemented both on-chain and off-chain processing modes to study the system performance. The results from experiments with our prototype indicate that our proposed demand–response games have a great effect on reducing the net peak load, and at the same time, the off-chain processing mode provides lower latency and overhead compared to the on-chain mode while still keeping the same system integrity as the on-chain mode.

Energy saving for mobile devices is considered to be one of prospective benefits of mobile edge computing (MEC) networks, where computation-intensive tasks can be offloaded from the mobile devices to their associated MEC servers for execution. Extra energy consumption for data migration should therefore be less than the energy consumption for local execution. However, in multi-cell MEC-assisted networks, due to both the presence of co-channel interference and the latency requirement of each offloading task, power control is tightly coupled with computation offloading, which becomes an obstacle to achieve the aim of energy saving. In this paper, we develop an analytic model to decouple power control and computation resource allocation from each other, in which the transmission power can be considered as a solution to a set of linear equations with a coefficient matrix depending on the computation resource budget. Based on this analytic foundation, we show that with a fixed offloading decision, the joint power control and computation resource allocation problem is invex, which ensures that every KKT (Karush–Kuhn–Tucker) stationary point of the problem must be a global minimizer. Moreover, we deduce a criterion for energy-efficient offloading decision making from the partial derivative of the total energy consumption of mobile devices with respect to the computation resource budget. Finally, we propose a heuristic algorithms to jointly optimizing power and computation resource allocation, and offloading decision. The numerical results demonstrate the optimality and efficiency of our proposed algorithm.

Connected Automated Vehicle (CAV) is a promising paradigm for achieving safe and intelligent transportation systems. In CAV scenario, massive raw sensor data needs to be shared among vehicles under strict latency and high data rate constraints, which poses critical challenges on existing low data rate vehicular communication on 5.9 GHz band. To address these challenges, we propose a millimeter wave (mmWave) enabled CAV network with high capacity to support the raw sensor data sharing among vehicles. A joint vehicle association and power allocation (JVAPA) algorithm is proposed to maximize date rate while minimize energy consumption for CAVs. The one-to-many vehicle association problem is formulated as a swap matching model, and the stable matching states for CAVs and BSs association are achieved. The non-convex power allocation problem is transformed into a convex problem using the first order Taylor expansion, and the optimal power allocation results are achieved. Numerical results verify that the proposed JVAPA algorithm can significantly improve the energy efficiency compared with the benchmark algorithms.

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.

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.

Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.

Sixth-generation (6G) is envisioned to be characterized by ubiquitous connectivity, extremely low latency, and enhanced edge intelligence. However, enriching 6G with these features requires addressing new, unique, and complex challenges specifically at the edge of the network. In this article, we propose a wireless digital twin edge network model by integrating digital twin with edge networks to enable new functionalities, such as hyper-connected experience and low-latency edge computing. To efficiently construct and maintain digital twins in the wireless digital twin network, we formulate the edge association problem with respect to the dynamic network states and varying network topology. Furthermore, according to the different running stages, we decompose the problem into two subproblems, including digital twin placement and digital twin migration. Moreover, we develop a deep reinforcement learning (DRL)-based algorithm to find the optimal solution to the digital twin placement problem, and then use transfer learning to solve the digital twin migration problem. Numerical results show that the proposed scheme provides reduced system cost and enhanced convergence rate for dynamic network states.

Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.

Blockchain is a promising emerging technology that is envisioned to play a key role in establishing secure and reliable Internet-of-Things (IoT) ecosystems without the involvement of any third party. Hyperledger Fabric, a permissioned blockchain system that can yield high throughput and low consensus delay, has shown its capability in enhancing security and privacy protection for delay-sensitive IoT services. The literature, however, has not considered the conflicting transaction problem which may substantially limit the system performance and degrade QoS for the end users. In this article, we propose CATP-Fabric, a new blockchain system to address the conflicting transaction problem by reducing the number of potentially conflicting transactions with less overhead. First, the transactions within a block are divided into different groups to facilitate parallel transaction processing. Then, CATP-Fabric filters stale transactions and prioritizes the read-only transactions in each group to eliminate unnecessary overhead. Finally, we formulate the selection of aborting transactions in CATP-Fabric as a binary integer-programming problem and develop a low-complexity optimization algorithm to minimize the number of aborted transactions. Illustrative results show that our proposed CATP-Fabric blockchain system achieves high throughput of successful transactions while maintaining a lower aborting transaction rate compared to the benchmark blockchain systems.

To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.

With the fast growing demands for the big data, we need to manage and store the big data in the cloud. Since the cloud is not fully trusted and it can be accessed by any users, the data in the cloud may face threats. In this paper, we propose a secure authentication protocol for cloud big data with a hierarchical attribute authorization structure. Our proposed protocol resorts to the tree-based signature to significantly improve the security of attribute authorization. To satisfy the big data requirements, we extend the proposed authentication protocol to support multiple levels in the hierarchical attribute authorization structure. Security analysis shows that our protocol can resist the forgery attack and replay attack. In addition, our protocol can preserve the entities privacy. Comparing with the previous studies, we can show that our protocol has lower computational and communication overhead.

The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity bills, and the stress to the power grid during peak hours can be reduced. However, implementing such a method is challenging due to the existence of randomness in the electricity price and the consumption of the appliances. To address this challenge, in this article, we employ a model-free method for the households, which works with limited information about the uncertain factors. More specifically, the interactions between households and the power grid can be modeled as a noncooperative stochastic game, where the electricity price is viewed as a stochastic variable. To search for the Nash equilibrium (NE) of the game, we adopt a method based on distributed deep reinforcement learning. Also, the proposed method can preserve the privacy of the households. We then utilize real-world data from Pecan Street Inc., which contains the power consumption profile of more than 1000 households, to evaluate the performance of the proposed method. In average, the results reveal that we can achieve around 12% reduction on peak-to-average ratio and 11% reduction on load variance. With this approach, the operation cost of the power grid and the electricity cost of the households can be reduced.

Wind power is a clean and widely deployed alternative to reducing our dependence on fossil fuel power generation. Under this trend, more turbines will be installed in wind farms. However, the inspection of the turbines in an offshore wind farm is a challenging task because of the harsh environment (e.g., rough sea, strong wind, and so on) that leads to high risk for workers who need to work at considerable height. Also, inspecting increasing number of turbines requires long man hours. In this regard, unmanned aerial vehicles (UAVs) can play an important role for automated inspection of the turbines for the operator, thus reducing the inspection time, man hours, and correspondingly the risk for the workers. In this case, the optimal number of UAVs enough to inspect all turbines in the wind farm is a crucial parameter. In addition, finding the optimal path for the UAVs' routes for inspection is also important and is equally challenging. In this article, we formulate a placement optimization problem to minimize the number of UAVs in the wind farm and a routing optimization problem to minimize the inspection time. Wind has an impact on the flying range and the flying speed of UAVs, which is taken into account for both problems. The formulated problems are NP-hard. We therefore design heuristic algorithms to find solutions to both problems, and then analyze the complexity of the proposed algorithms. The data of the Walney wind farm are then utilized to evaluate the performance of the proposed algorithms. Simulation results clearly show that the proposed methods can obtain the optimal routing path for UAVs during the inspection.

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

Power equipment is one kind of basic element in smart grid, and how to design an efficient detection and analysis scheme of electric signature (ES) for power equipment failure (PEF) monitoring is a key and challenging issue. This article proposes an ES detection and analysis method which can monitor multiple kinds of PEF in smart substation. The bottleneck of ES analysis is explored in the view of Heisenberg uncertainty, and an optimal time–frequency analysis method is designed to solve the problems. The proposed method (PM) is based on union of time and frequency bases whose decomposition is realized by Bayesian compressive sensing using Laplace prior. Simulated and field ESs are employed to test PM with comparisons of existing methods. Also, PM is applied in a smart substation of China. Several typical PEFs and measurement soft failures caused by electromagnetic interference are discussed. The results indicate that the PM can accurately monitor PEFs whose mechanism can be revealed by time–frequency features of ESs, if the required sampling rate and sampling time are satisfied because of its immunity of the uncertainty principle restriction. The robustness in noise environment and optimal time–frequency representation of ESs make the PM an efficient general-purpose PEF monitoring in smart grid by time–frequency analysis.

Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

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.

The papers in this special section explores the state-of-the-art advances in adopting blockchain technologies for the Industrial Internet of Things (IIoT). There is a growing trend of adopting blockchain technologies to the IIoT due to the traceability, nonrepudiation, and immutability of blockchain systems. The proliferation of IIoT to industrial systems is fostering the fourth industrial revolution (aka Industry 4.0) while IIoT also confronts several challenges exhibiting in the following two perspectives: 1) security and privacy protection of IIoT data; 2) interoperability absence across IIoT systems. Blockchain and blockchain-enabled smart contracts can essentially offer solutions to address the emerging challenges in IIoT.

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes' behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes' computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.

Empowered with advanced computation units, autonomous sensing platforms and various wireless access capabilities, connected and autonomous vehicles evolve over time and become tightly coupled and closely cooperative. Being one of the most active research fields in both academic and industry, the Internet of Vehicles (IoV) enables various types of vehicular applications, such as autonomous driving, precise fleet management, and real-time video analytics, which contribute significantly to bring us traffic efficiency, driving safety, and ride comfort. However, these powerful applications always require intensive computation and very large size caching services under ultra-low latency constraints, and thus pose significant challenges on resource-constrained vehicles.

Uncoordinated charging of a rapidly growing number of electric vehicles (EVs) and the uncertainty associated with renewable energy resources may constitute a critical issue for the electric mobility (E-Mobility) in the transportation system especially during peak hours. To overcome this dire scenario, we introduce a stochastic game to study the complex interactions between the power grid and charging stations. In this context, existing studies have not taken into account the dynamics of customers’ preference on charging parameters. In reality, however, the choice of the charging parameters may vary over time, as the customers may change their charging preferences. We model this behavior of customers with another stochastic game. Moreover, we define a quality of service (QoS) index to reflect how the charging process influences customers’ choices on charging parameters. We also develop an online algorithm to reach the Nash equilibria for both stochastic games. Then, we utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of our proposed algorithms. The results reveal that the electricity cost with the proposed method can result in a saving of about 20% compared to the benchmark method, while also yielding a higher QoS in terms of charging and waiting time. Our results can be employed as guidelines for charging service providers to make efficient decisions under uncertainty relative to power generation of renewable energy.

Parked Vehicle (PV) assistance in vehicular edge computing and networks is proposed to exploit underutilized computing resources from PVs for enhancing the resource capacity at the edge vehicular network. Containerization is used to improve task execution of PVs with fast start-up time, less hardware overheads and safe resource isolation. To this end, we introduce a task-container matching market to provide on-demand offloading services. For network implementation, the related entities including requesters, PVs with containers as performers and a service provider are described. Considering parking behaviors and resource availability, we measure the serviceabilities of PVs to select appropriate PVs for reliable and efficient task processing. According to utility functions, preference profiles of requesters and performers in the task-container matching market are modeled through the best response analysis. Finally, we apply matching game approach to cope with associations between tasks and containers deployed inside PVs. Numerical results demonstrate that compared with baseline schemes, our scheme accomplishes more tasks and acquires a higher overall utility in computation offloading.

With the exploitation of wind power, more turbines will be deployed at remote areas possibly with harsh working conditions (e.g., offshore wind farm). The adverse working environment may lead to massive operating and maintenance costs of turbines. Deploying unmanned aerial vehicles (UAVs) for turbine inspection is considered as a viable alternative to manual inspections. An important objective of automated UAV inspection is to minimize the flight time of the UAVs to inspect all the turbines. A first contribution of this paper is thus formulating an optimization problem to compute the optimal routes for turbine inspection satisfying the above goal. On the other hand, the limited computational capability on UAVs can be used to increase the power generation of wind turbine. Power generation from the turbines can be optimized by controlling the yaw angle of the turbines. Forecasting wind conditions such as wind speed and wind direction is crucial for solving both optimization problems. Therefore, UAVs can utilize their limited computational capability to perform wind forecasting. In this way, UAVs form edge intelligence in offshore wind farm. With the forecasted wind conditions, we design two algorithms to solve the formulated problems, and then evaluate the proposed methods with real-world data. The results reveal that the proposed methods offer an improvement of 44% of the power generation from the turbine compared to hour-ahead forecasting and 25% reduction of the flight time of the UAVs compared to the chosen baseline method.

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.

Nowadays we are witnessing the formation of a massive Internet-of-Things (IoT) ecosystem that integrates a variety of wireless-enabled devices ranging from smartphones, wearables, and virtual reality facilities to sensors, drones, and connected vehicles. As IoT is penetrating every aspect of people’s life, work, and entertainment, an increasing number of IoT devices and the emerging IoT applications are driving exponential growth in wireless traffic in the foreseeable future. As a result, current IoT system architectures are facing significant challenges to handle millions of devices; thousands of servers; the transmission and processing of large volume of data, etc.

Research and processing development on satellite communications has strongly re-emerged in recent years. Following the prosperity of various wireless services provided by satellite communications, the security issue has raised growing concerns since the space information network is susceptible to be eavesdropped by illegal adversaries in such a large-scale wireless network. Recently, the physical-layer security (PLS) has emerged as an alternative security paradigm that explores the randomness of the wireless channel to achieve confidentiality and authentication. The success story of the PLS technique now spans a decade and thrives to provide a layer of defense in satellite communications. With this position, a comprehensive survey of satellite communications is conducted in this article with an emphasis on PLS. We first briefly introduce essential background and the view of the satellite Internet of Things (IoT), as well as discuss related research challenges faced by the emerging integrated network architecture. Then, we revisit the most popular satellite channel model influenced by many factors and list the commonly used secrecy performance metrics. Also, we provide an exhaustive review of state-of-the-art research activity on PLS in satellite communications, which we categorize by different architectures including land mobile satellite communication networks, hybrid satellite-terrestrial relay networks, and satellite-terrestrial integrated networks. In addition, a number of open research problems are identified as possible future research directions.

In recent years, we have witnessed the compelling application of the Internet of Things (IoT) in our daily life, ranging from daily living to industrial production. On account of the computation and power constraints, the IoT devices have to offload their tasks to the remote cloud services. However, the long-distance transmission poses significant challenges for latency-sensitive businesses, such as autonomous driving and industrial control. As a remedy, mobile edge computing (MEC) is deployed at the edge of the network to reduce the transmission delay. With the MEC joining in, how to allocate the limited computing resource of MEC is a critical problem to guarantee efficient working of the whole IoT system. In this article, we formulate the resource management among MEC and IoT devices as a double auction game. Also, for searching the Nash equilibrium, we introduce the experience-weighted attraction (EWA) algorithm performing behind each participant. With this AI method, auction participants acquire and accumulate experience by observing others' behavior and doing introspection, which accelerates the trading policy's learning process of each agent in such an opaque environment. Some simulation results are presented to evaluate the convergence and correctness of our architecture and algorithm.

In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.

With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve ε-differential privacy. In particular, for the first time, we consider the existence of unreliable participants, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

The papers in this special section focuses on security and privacy in industry. Industries, governments, and scientific communities are increasingly drawing special attention to competitive advantages that Industry 4.0 can bring to business sustainability and economy of a country. The tendency to couple information technologies (ITs) with the existing operational technologies (OTs) adds new opportunities to improve and optimize operational processes, products, and services in which multiple stakeholders (e.g., end-users) can interact with the new industrial ecosystems to speed up and customize processes. In this sense, Industry 4.0 constitutes a relevant investment source composed of a complex technological showcase in which multiple connections and accesses can arise, seriously impacting on the good performance of the different production and distribution chains associated with smart factories and manufacturing, smart grid systems, smart transportation, or smart health environments.

Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.

The rapid increase in the volume of data generated from connected devices in industrial Internet of Things paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g., data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first design a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we formulate the data sharing problem into a machine-learning problem by incorporating privacy-preserved federated learning. The privacy of data is well-maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain, so that the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency, and enhanced security.

Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles, as well as unpredictable vehicle routes, make efficient content distribution a challenge. To cope with this challenge, in this article we develop a social-aware nobile edge computing and caching mechanism by exploiting the relation between vehicles and roadside units. By leveraging a deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

The maturity of network storage technology drives users to outsource local data to remote servers. Since these servers are not reliable enough for keeping users' data, remote data auditing mechanisms are studied for mitigating the threat to data integrity. However, many traditional schemes achieve verifiable data integrity for users only without resolutions to data possession disputes, while others depend on centralized third-party auditors (TPAs) for credible arbitrations. Recently, the emergence of blockchain technology promotes inspiring countermeasures. In this article, we propose a decentralized arbitrable remote data auditing scheme for network storage service based on blockchain techniques. We use a smart contract to notarize integrity metadata of outsourced data recognized by users and servers on the blockchain, and also utilize the blockchain network as the self-recording channel for achieving non-repudiation verification interactions. We also propose a fairly arbitrable data auditing protocol with the support of the commutative hash technique, defending against dishonest provers and verifiers. Additionally, a decentralized adjudication mechanism is implemented by using the smart contract technique for creditably resolving data possession disputes without TPAs. The theoretical analysis and experimental evaluation reveal its effectiveness in undisputable data auditing and the limited requirement of costs.

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, the road side unit observes the global network state, allocates channels and schedules data packets for all vehicle user equipment-pairs (VUE-pairs). The decision-making procedure is modelled as a discrete-time Markov decision process (MDP). The technical challenges in solving an optimal control policy originate from highly spatial mobility of vehicles and temporal variations in data traffic. To simplify the decision-making process, we first decompose the MDP into a series of per-VUE-pair MDPs. We then propose an online long short-term memory based deep reinforcement learning algorithm to break the curse of high dimensionality in state space faced by each per-VUE-pair MDP. With the proposed algorithm, the optimal channel allocation and packet scheduling decision at each epoch can be made in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.

Mobile-edge computing (MEC) and non-orthogonal multiple access (NOMA) has been envisioned as two promising technologies in the future wireless networks. In this paper, we concentrate on the joint computation offloading and result downloading strategy for a NOMA-assisted MEC system, where uplink/downlink NOMA is used for computation task offloading or result downloading, respectively. The energy consumption minimization problem is formulated with joint optimization of time assignment, power control, CPU frequency, and computation offloading scheme. By exploiting block coordinate descent (BCD) method, we develop a joint communication and computation resource allocation algorithm to address the original nonconvex problem. Specifically, the optimal solution is obtained in closed form. Furthermore, extensive numerical results are provided to demonstrate the effectiveness of the NOMA-assisted MEC system, when compared to the OMA-based scheme.

This paper proposes a new device-to-device edge computing and networks (D2D-ECN) framework which facilitates low-latency execution of real-time Internet-of Things applications through computation offloading with minimal overhead. Our framework accounts for key challenges of D2D-ECN in terms of the efficiency of the resource management and the resulting security concerns caused by lacking trustworthy between task owners and resource providers. In particular, we propose to use a blockchain-empowered framework for implementing resource trading and task assigment as the smart contracts. However, the existing Proof-of-Work (PoW) is impractical for the resource-constrained IoT devices due to high computational complexity of the mining process. Thus, we present a reputation-based consensus mechanism called proof-of-reputation (PoR), where the device with the highest reputation score is responsible for packaging the resource transactions and reputation records in the blockchain. Furthermore, we evaluate the reputation score of each device according to the current computation performance and history reputation. Security, feasibility analysis and numerical results show that our proposed computation offloading scheme can be deployed in the decentralized D2D-ECN system safely and effectively.

This paper studies a new model of energy storage (ES) sharing in a residential community in which some homes have physical ESs (PESs) but some do not. The non-PES homes can buy ES capacity from PES homes, creating virtual ESs (VESs). Based on the transaction results between PESs and VESs, an online algorithm is developed for real-time energy management of ES sharing among the homes. During online control, non-negative long-term utilities of homes and practical charging/discharging constraints of PESs and VESs are considered. The advantage of the proposed algorithm is that system state forecasting, such as home load, renewable generation, and grid price, is not required. The algorithm only needs current system states to make a control decision. Theoretic analysis shows that the worst-case system cost under the algorithm is upper bounded, guaranteeing the online solution is near-optimal. In the simulation, real-time data of grid price and home power use is employed, and the proposed algorithm is benchmarked against a greedy algorithm and a theoretic lower bound.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.

With emerging requirement of local low-latency services, Mobile Edge Computing (MEC) is a promising solution to tackle the challenge between urgent demands for computation capability and limited battery energy of mobile devices. Moreover, the sharing property of applications costs waste as for the processing of redundant data, which derives an imperative need for the collaboration among users. In this paper, by leveraging these features, we design a D2D-assisted MEC system for energy efficiency of devices with the consideration of task delay. For sake of energy minimization, a strategy that jointly optimizes resource allocation and tasks offloading assignment is proposed. Further, a low-complexity algorithm is developed to decompose the original problem into two subproblems and get the sub-optimal solution efficiently. Simulation results present the efficient and effective performance of the proposed algorithm with different application parameters. Particularly, it is shown that our proposed algorithm gets 48.41%~90.58% and 37.33%~96.63% improvement of energy consumption than those of non-collaboratively offloading scheme and randomly offloading scheme, respectively.

Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.

The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions.

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for smart grid network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users' validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehicular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such integrated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation-based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level of security for data sharing in VECONs.

The expected influx of Internet of Things (IoT) in 5G will provide new opportunities for uplink traffic offloading. In general, base stations with proximity require lower transmission power of the IoT device (IoTD), thus saving energy consumption as spectral efficiency (SE) of the transmissions increase. By letting IoTDs send to base stations with better link conditions the IoTDs' battery lifetime is prolonged. In this paper, we present a many-to-many offloading scheme for uplink traffic. The scheme works when link conditions are private information and gives incentives to all involved players to participate. We believe this approach is better suited for the expected complex ecosystem of 5G base station cells. The sensitivity analyses show that there is a limited gain by requiring that the link conditions are public knowledge. Further, the suggested market optimizes the SE for all involved players. Numerical results show that the IoTDs can on average increase their SE with 25% and their spectral energy efficiency with 40%. The networks which are offloaded to and from can both expect an increase in the SE of 1%-6%. Sensitivity analyses show that the market equilibrium's benefits are robust as they stay positive for a range of different network configurations. Also, the work proves that market equilibrium is stable and unique. To derive the equilibrium, two approaches are presented, a closed form solution and a distributed algorithm, that both are solvable in polynomial time.

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication toward 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains, including physical layer, network layer, and joint communication, computing, and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

The Internet of Things (IoT) platform has played a significant role in improving road transport safety and efficiency by ubiquitously connecting intelligent vehicles through wireless communications. Such an IoT paradigm however, brings in considerable strain on limited spectrum resources due to the need of continuous communication and monitoring. Cognitive radio (CR) is a potential approach to alleviate the spectrum scarcity problem through opportunistic exploitation of the underutilized spectrum. However, highly dynamic topology and time-varying spectrum states in CR-based vehicular networks introduce quite a few challenges to be addressed. Moreover, a variety of vehicular communication modes, such as vehicle-to-infrastructure and vehicle-to-vehicle, as well as data QoS requirements pose critical issues on efficient transmission scheduling. Based on this motivation, in this paper, we adopt a deep Q -learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. Furthermore, we investigate the characteristics of communication modes and spectrum resources chosen by vehicles in different network states, and propose an efficient learning algorithm for obtaining the optimal scheduling strategies. Numerical results are presented to illustrate the performance of the proposed scheduling schemes.

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

In recent years, Advanced Persistent Threats (APTs) have become a major issue for critical infrastructures that are increasingly integrating modern IT technologies. This requires the development of advanced cyber-security services that can holistically detect and trace these attacks, beyond traditional solutions. In this sense, Opinion Dynamics has been proven as an effective solution, as they can locate the most affected areas within the industrial network. With this information, it is possible to put in place accurate response techniques to limit the impact of attacks on the infrastructure. In this paper, we analyze the applicability of Opinion Dynamics to trace an APT throughout its entire life cycle, by correlating different anomalies over time and accounting for the persistence of threats and the criticality of resources. Moreover, we run various experiments with this novel technique over a testbed that models a real control system, thereby assessing its effectiveness in an actual industrial scenario.

Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.

The papers in this special section focus on blockchain for the Industrial Internet of Things (IoT). Industrial IoT is reshaping various industrial sectors, such as manufacturing, logistics, transportation, healthcare, energy, and utilities. IIoT consists of various smart objects distributed throughout the whole industrial system to collect massive ambient data, which can be used to identify performance bottlenecks, troubleshoot faults, and detect malicious behaviors consequently enforcing effective control to the physical world. However, there are several challenges posed on IIoT before the formal adoption of IIoT across various industrial sectors. Among them, security and privacy preservation on IIoT data are the most crucial concerns. On the other hand, the blockchain technology is transforming industries by enabling anonymous and trustful transactions in decentralized and trustless environment. As a result, blockchains help to reduce system risks, mitigate financial fraud, and cut down operational cost. The convergence of IIoT and blockchains can potentially overcome the deficiencies of IIoT consequently resulting in the realization of IIoT in various industrial sectors. Both industry practitioners and academic researchers aim at realizing general, scalable and deployable blockchain-based IIoT platforms in various application domains while there are a number of challenges such as distributed consensus algorithms and data analytics with privacy-preservation in IIoT systems.

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

With the development of social media technologies, sharing photos in online social networks has now become a popular way for users to maintain social connections with others. However, the rich information contained in a photo makes it easier for a malicious viewer to infer sensitive information about those who appear in the photo. How to deal with the privacy disclosure problem incurred by photo sharing has attracted much attention in recent years. When sharing a photo that involves multiple users, the publisher of the photo should take into all related users' privacy into account. In this paper, we propose a trust-based privacy preserving mechanism for sharing such coowned photos. The basic idea is to anonymize the original photo so that users who may suffer a high privacy loss from the sharing of the photo cannot be identified from the anonymized photo. The privacy loss to a user depends on how much he or she trusts the receiver of the photo. And the user's trust in the publisher is affected by privacy loss. The anonymiation result of a photo is controlled by a threshold specified by the publisher. We propose a greedy method for the publisher to tune the threshold, in the purpose of balancing between the privacy preserved by anonymization and the information shared with others. Simulation results demonstrate that the trust-based photo sharing mechanism is helpful to reduce the privacy loss, and the proposed threshold tuning method can bring a good payoff to the user.

With the increasing computation and storage capabilities of mobile devices, the concept of fog computing was proposed to tackle the high communication delay inherent in cloud computing, and also improve the security to some extent. This paper concerns with the privacy issue inherent in the sustainable fog computing platform. However, there is no universal solution to the privacy problem in fog computing due to the device heterogeneity. In this paper, we proposed a differential privacy-based query model for sustainable fog computing supported data center. We designed a method that can quantify the quality of privacy preserving through rigorous mathematical proof. The proposed method uses the query model to capture the structure information of the sustainable fog computing supported data center, and the datasets for the query result are mapped to real vectors. Then, we implemented the differential privacy preserving by injecting Laplacian noise. The experiment results demonstrated that the proposed method can effectively resist various popular privacy attacks, and achieve relatively high data utility under the premise of better privacy preserving.

The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions.

Concerns have been raised regarding the economical viability for each operator to have a full regional 5G coverage. A possible solution is to have traffic offloaded to competitors. In this work we present a new scheme for optimal offloading in a stochastic environment. This is more in line with the conditions 5G base stations will face with changing link and traffic conditions. The problem is formulated as a Stackelberg game, and the players' utility functions are derived though queuing models. Numerical results illustrate that our scheme provides a global optimal resource allocation up to a threshold. The threshold is a function of the traffic load and the number of offloading candidates. Beyond the threshold players still have incentives to participate, but the market equilibrium is not globally optimal.

Unmanned Aerial Vehicles (UAVs) communications have received increasing attention in both military and civilian applications due to low cost and ease of deployment. Security is an unavoidable yet challenging issue during the data transmission process of communication networks. In this paper, we concentrate on the resource allocation in secure relay network assisted by a UAV in the presence of multiple eavesdroppers. Our target is to maximize the secrecy rate by jointly designing the transmit beamformer and artificial noise subject to the transmit power constraint of UAV. The resulting optimization problem is highly intractable and the key observation is that the original optimization problem can be equivalently transformed into a two- level problem. In particular, the inner-level problem can be solved by exploiting Semi-Definite Relaxation (SDR) and Charnes-Cooper transformation techniques, and the outer-level problem is handled by performing one-dimensional algorithm. Also, the tightness of the rank-relaxation is analyzed. Finally, simulation results are provided to validate the performance of our proposed scheme.

Data leakage is a growing insider threat to data owners. Several studies have been done on data leakage prevention (DLP). In the era of big data, massive data has been generated constantly by various institutions. In many applications, multiple institutions may be interested in sharing data with each other to extract more value, without leaking private data. It is a new challenge for DLP in the big data scenario to train a global detection model over distributed big data sets without breaking the data privacy of each party. Moreover, as the forms of data become much complicated, the model should also be capable of tolerating data transformation. In this paper, we propose a Collaborative graph-based mechanism for Distributed big data Leakage Detection (CoDLD). CoDLD addresses the collaborative DLP problem in three aspects. First, it transfers the text detection problem into graph space. The local weighted graphs of data owners are iteratively constructed in turn. Second, it protects the privacy of each data owner by using graph masking on local weighted graphs. Third, it applies partition-based method on the graph to perform accurate matching detection efficiently. Experimental results show that our method can perform collaborative data leak detection over distributed data with high accuracy and efficiency.

Packet loss rate (PLR) is a crucial and popular link quality metric for wireless sensor networks (WSNs). In this paper, we investigate how to estimate PLR of an IEEE 802.15.4 link from the information that is easily obtained from radio chip. Specifically, we aim to establish a generalized model that connects PLR to link quality indicator (LQI), a physical layer link quality measure, and packet length under diverse environmental conditions. To this aim, an extensive experimental study considering various environmental factors and packet lengths is conducted, from which rich observations are made on the spatio-temporal characteristics of the dependency of PLR on LQI and packet length. Based on the observations, we propose a packet loss rate model as a function of LQI and packet length, that is applicable in all experimented scenarios. Besides, a comparison with a literature LQI-only based PLR model shows that our proposed model has higher accuracy for various packet lengths. Finally, we provide the implications of the empirical study and the guidelines for real-world WSN applications to construct and adapt the proposed PLR model in different environments.

Modeling and prediction of Packet Loss Rate (PLR) of wireless links using hardware information is essential for the design of higher-layer protocols in Wireless Sensor Networks. While many previous studies revealed the spatio-temporal variation of various link quality metrics, how environment impacts on the mapping between PLR and hardware indicators still remains unclear. Without a comprehensive understanding of such environmental impact, the acquired empirical PLR models are severely limited to specific scenarios. In this paper, we present the results of indoor and outdoor experimental campaigns focusing on the impact of various environmental factors (e.g., obstacles, human activities, climate conditions) on the dependency between the link PLR, signal to noise ratio (SNR) and packet length. Rich observations are made on the spatio-temporal characteristics of the PLR-SNR relationship and our analysis shows that link PLR can be modeled, in all experimented scenarios, as an exponential function of SNR and packet length with two model parameters that may vary over space and time. Besides, implications of the observations are summarized, providing guidelines to construct and adapt PLR models in different environments.

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.

Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. The overall network performance of existing IoT is restricted by limited network lifetime. Hence, energy harvesting technology with energy replenishment from mobile charger is proposed to prolong the network lifetime. Energy harvesting IoT is emerged. Nodes can not only request energy replenishment from the mobile charger, but also transfer surplus energy to the mobile charger for improving energy utilization. This gives rise to bidirectional energy flows in the network. A new paradigm that energy flows coexist with data flows is further resulted in. But there exist great challenges on controlling these flows. Toward centralized flow control, we exploit software defined networking to simplify and optimize network management, thus introduce software defined energy harvesting IoT (SEANET). In our proposed architecture, the data plane, energy plane, and control plane are decoupled to support enhanced communications and flexible energy scheduling. We consider reliable communications for SEANET, and propose to relay data packets among the nodes with high reputation values and sufficient energy. In particular, reputation values of nodes are computed by the multiweighted subjective logic for higher accuracy. Besides, a Nash bargaining game is formulated to solve the benefit allocation problem for energy trading in SEANET. Numerical results indicate that SEANET improves data traffic by reducing packet loss, optimizes energy utilization, and saves energy.

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1-ϵ)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ϵ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(ϵ), O(1/ϵ)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

The nine papers included in this special section focus on cyber security methods to protect social network services. The emerging paradigm of social network provides an enormous number of novel approaches to implementing advanced networking communications and data analysis schemes efficiently using existing datasets, networks, and infrastructure. Social networks have had a great impact on people’s daily life and global businesses, as has been addressed by recent research. However, the security issue is also a critical concern when adopting social network technologies in practice. Considering the uniqueness of social networks, the mechanism is now facing a variety of security challenges from multiple dimensions, such as mobile apps, wireless communication, cloud systems, big data, and security operations. Compared with traditional security issues, the applications of social networks are operated in a dynamic circumstance involving different internal and external inputs and factors, which requires new security mechanisms in distinct operational environments. The complexity of the technical implementations may result in unexpected consequences when adopting social network technologies. It is therefore important for current researchers and practitioners to address the security issues and seek out efficient ways to handle different hazards. For the purpose of preventing social network-based solutions from the threats of social networks, a variety of cyber security approaches or mechanism have been proposed.

Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users' privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP-an online aggregate monitoring framework over infinite streams with w-event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying w-event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two w-event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.

In industrial Internet of things (IIoT), peer-to-peer (P2P) energy trading ubiquitously takes place in various scenarios, e.g., microgrids, energy harvesting networks, and vehicle-to-grid networks. However, there are common security and privacy challenges caused by untrusted and nontransparent energy markets in these scenarios. To address the security challenges, we exploit the consortium blockchain technology to propose a secure energy trading system named energy blockchain. This energy blockchain can be widely used in general scenarios of P2P energy trading getting rid of a trusted intermediary. Besides, to reduce the transaction limitation resulted from transaction confirmation delays on the energy blockchain, we propose a credit-based payment scheme to support fast and frequent energy trading. An optimal pricing strategy using Stackelberg game for credit-based loans is also proposed. Security analysis and numerical results based on a real dataset illustrate that the proposed energy blockchain and credit-based payment scheme are secure and efficient in IIoT.

This paper studies the secure transmission rate issue between sensors and the remote controller to defend the jamming and eavesdropping attacks in green cyber-physical transportation systems. In this system, the traffic sensor transmits the transportation state information to the remote controller via wireless networks. Due to the broadcast characteristics of the wireless communication, the systems are vulnerable to the eavesdropping and jamming attacks. In this paper, we study how to maximize the secure transmission rate between sensors and the controller in the presence of a malicious eavesdropper and a jammer. Specifically, the malicious jammer is smart and can choose the optimal power strategy to maximize the side effect with the knowledge of sensor's transmission power. For the purpose of achieving the maximum utility, the optimal strategy is determined via adjusting the sensor's transmission power according to the control feedback conditions. We consider the single-antenna model and the multiantenna model to formulate this problem as an optimization problem based on a Stackelberg game. We then prove the existence of the Stackelberg equilibrium via the interaction between the sensor and the jammer. Moreover, we present two algorithms to obtain the optimal transmission strategy, i.e., a stochastic algorithm with feedback and renewed intelligent simulated annealing. Finally, extensive simulations and trace experimental results are presented to verify our theoretical analysis.

The nineteen articles in this special section focus on energy informatics, a new and emerging nterdisciplinary research field. The main goal is to tackle the future global warming, energy crisis, and climate change challenges by exploiting advanced information and communication (ICT) theories and tools to address energy-related problems. The scope of energy informatics includes the next-generation communications, networking, computing, sensing, and control technologies (e.g., big data, machine learning, 5G, cloud computing, and fog computing); and their applications in the energy sectors (e.g., smart cities, smart grid, electric vehicles, and PV systems).

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Industrial Internet of unmanned aerial vehicles (IIoUAVs) that enable autonomous inspection and measurement of anything anytime anywhere have become an essential component of the future industrial Internet of things (IIoT) ecosystem. In this paper, we investigate how to apply IIoUAVs for power line inspection in smart grid from an energy-efficiency perspective. First, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization, such as trajectory scheduling, velocity control, and frequency regulation, and the small-timescale optimization, such as relay selection and power allocation. Then, the original NP-hard problem is transformed into a two-stage suboptimal problem by exploring the timescale difference and the energy magnitude difference between the large-timescale and the small-timescale optimizations, and is solved by combining dynamic programming (DP), auction theory, and matching theory. Finally, the proposed algorithm is verified based on real-world map and realistic power grid topology.

As a promising branch of Internet of Things, Internet of Vehicles (IoV) is envisioned to serve as an essential data sensing and processing platform for intelligent transportation systems. In this paper, we aim to address location privacy issues in IoV. In traditional pseudonym systems, the pseudonym management is carried out by a centralized way resulting in big latency and high cost. Therefore, we present a new paradigm named Fog computing supported IoV (F-IoV) to exploit resources at the network edge for effective pseudonym management. By utilizing abundant edge resources, a privacy-preserved pseudonym (P3) scheme is proposed in F-IoV. The pseudonym management in this scheme is shifted to specialized fogs at the network edge named pseudonym fogs, which are composed of roadside infrastructures and deployed in close proximity of vehicles. P3scheme has following advantages: 1) context-aware pseudonym changing; 2) timely pseudonym distribution; and 3) reduced pseudonym management overhead. Moreover, a hierarchical architecture for P3scheme is introduced in F-IoV. Enabled by the architecture, a context-aware pseudonym changing game and secure pseudonym management communication protocols are proposed. The security analysis shows that P3scheme provides secure communication and privacy preservation for vehicles. Numerical results indicate that P3scheme effectively enhances location privacy and reduces communication overhead for the vehicles.

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.

Mobile edge computing is a promising paradigm that brings computing resources to mobile users at the network edge, allowing computing-intensive and delay-sensitive applications to be quickly processed by edge servers to satisfy the requirements of mobile users. In this chapter, we first introduce a hierarchical architecture of mobile edge computing that consists of a cloud plane, an edge plane, and a user plane. We then introduce three typical computation offloading decisions. Finally, we review state-of-the-art works on computation offloading and present the use case of joint computation offloading.

New wave of urbanization, ever more stringent emission standards, and high pressure on improving efficiency of private and public transport have made the development of more sustainable transportation systems one of the fundamental societal challenges of the next decade. Connected vehicles have been envisioned to provide enabling key technologies to enhance transportation efficiency, reduce incidents, improve safety, and mitigate the impacts of traffic congestion. The seamless integration and convergence of vehicular communication networks, information and transportation systems, and mobile devices and networks will face a number of technical, economic, and regulatory challenges. It is of paramount importance to (i) design vehicular communication systems that enable road users and other actors to exchange information in real time with high reliability, (ii) enable pervasive sensing to monitor the status of vehicles and the surroundings, (iii) develop data analytics tools for processing large amounts of data generated by the connected vehicles, and (iv) develop middleware platforms for data management and sharing.

By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

This paper study a new paradigm of heterogeneous charging stations (CSs) for the excellent charging services of Electric Vehicles (EVs) in a dense urban environment. When a large number of EVs are driving on the roads, their travel demands and charging activities have significant impact on the charging management of multiple CSs including large-CSs (LCSs) and small-CSs (SCSs). The problem of charging scheduling can be formulated as a two-side Hospital/Residents (HR) matching game. In this matching game, we not only consider the economic interest of multiple CSs and charging experience of EVs but also utilize the natural advantages of SCSs to balance the traffic on the roads around LCSs. Furthermore, a two-stage scheme that is composed of distributed HR admission scheme and centralized HR reassignment scheme is proposed to reduce the computational complexity of the matching process. Simulation results indicate that the proposed algorithms can effectively offer the scheduling management among multiple CSs while providing good satisfaction to EV drivers.

The increasing number of smart vehicles and their resource hungry applications pose new challenges in terms of computation and processing for providing reliable and efficient vehicular services. Mobile Edge Computing (MEC) is a new paradigm with potential to improve vehicular services through computation offloading in close proximity to mobile vehicles. However, in the road with dense traffic flow, the computation limitation of these MEC servers may endanger the quality of offloading service. To address the problem, we propose a hierarchical cloud-based Vehicular Edge Computing (VEC) offloading framework, where a backup computing server in the neighborhood is introduced to make up for the deficit computing resources of MEC servers. Based on this framework, we adopt a Stackelberg game theoretic approach to design an optimal multilevel offloading scheme, which maximizes the utilities of both the vehicles and the computing servers. Furthermore, to obtain the optimal offloading strategies, we present an iterative distributed algorithm and prove its convergence. Numerical results indicate that our proposed scheme greatly enhances the utility of the offloading service providers.

Mobile applications and Internet of Things applications increasingly require one mobile device to exchange data with another collocated device. Despite the introduction of multiple standard device-to-device communication protocols (i.e., WiFi direct, Bluetooth, Bluetooth Low Energy, NFC), transferring data across devices remains hard for mobile programmers for three reasons. 1) different devices support different D2D communication interfaces, and therefore the available D2D communication channels are dynamically decided by the peers; 2) different D2D interfaces have different features (connection establish time, data rate, energy consumption) and different performances under different contexts, complicating the decision which channel to use; 3) implementing and debugging data transmission functionalities requires knowing the low-level details of communication protocols, which is difficult and error prone. To solve the above mentioned problem, this paper presents a runtime framework for context-sensitive device-to-device communication. The presented framework consists of two major components: 1) a set of encapsulated D2D data transmission interfaces to reduce the programming efforts; 2) a context-sensitive communication channel selection algorithm to select the optimal communication channel as defined by the dynamic context. We design and implement the runtime framework, and evaluate its performance in terms of the required programming effort, energy consumption and data transmission latency under different contexts.

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

Geo-distributed mobile cloud computing (GMCC) integrates location information into mobile cloud computing, that has high potential for a large variety of applications. In a vehicular environment, a GMCC provides a large number of resources to vehicles that are geographically close to them. However, there are few studies that focus on security and privacy issues in a GMCC scenario. Vehicles need sufficient pseudonyms to periodically change for privacy preservation. In this paper, we focus on pseudonym management in GMCC system for vehicular environment. We design a three-layer on-demand pseudonym system to manage the pseudonyms. Moreover, we propose a secure pseudonym distribution scheme for secure communication among vehicles. As the number of demanded pseudonyms varies with traffic loads in different clouds, we use a newsvendor model to address the optimal on-demand pseudonym distribution problem. Numerical results indicate our proposed schemes not only improve utility of the clouds, but also maximize utilization of the pseudonyms.

In the future wireless heterogeneous network, it is significantly critical to rationally and efficiently utilize network communication resource for satisfying the diversification and differentiation of user and service requirements. In this paper, we propose a user-centric wireless access and service matching scheme in heterogeneous networks with multiple radio access technologies (Multi-RATs). We model the interaction between user requirements and the quality of cloud services as the Markov decision process with Multi-RATs. The optimal matching scheme is derived by an iteration algorithm. To reduce time complexity, we further present a heuristic matching algorithm for determining whether the current stationary scheme is optimal in an off-line. Moreover, the difference between the current stationary scheme and the optimal matching scheme is derived by an upper bound. Performance evaluation demonstrates the achievable quality of experience by employing the two algorithms.

Vehicular ad hoc networks (VANETs) have been attracting interest for their potential roles in intelligent transport systems (ITS). In order to enable distributed ITS, there is a need to maintain some information in the vehicular networks without the support of any infrastructure such as road side units. In this paper, we propose a protocol which can store the data in VANETs by transferring data to a new carrier (vehicle) before the current data carrier is moving out of a specified region. For the next data carrier node selection, the protocol employs fuzzy logic to evaluate instant reward by taking into account multiple metrics specifically throughput, vehicle velocity, and bandwidth efficiency. In addition, a reinforcement learning-based algorithm is used to consider the future reward of a decision. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

Demand response management (DRM) is envisaged to play a key role in the smart grid operation, which requires reliable communications between power providers and consumers. To reduce communication cost, the cognitive radio technique is proposed to transmit the meter data from consumers to the control center. Unlike most existing studies on cognitive enabled smart grid, where only individual spectrum sensing is considered, in this paper, both cooperative spectrum sensing and harvested renewable energy are incorporated. The tradeoff between spectrum sensing cost and the DRM management gain makes the optimization of cooperation scheme a challenging research problem. To solve the problem, we analytically study the degradation of communication reliability of the renewable energy supplied consumers who attend the cooperative sensing as well as the DRM gain obtained by the communication improvement. Further, the optimization problem of selecting the number of cooperative consumers is formulated. We prove that there exists a unique optimal cooperative number under some constraints and propose an efficient searching algorithm. Numerical results are presented to validate our theoretical analysis.

Due to the ever-increasing use of electric vehicles (EVs) and renewable energy sources, the charging station with renewable energy supply is made as one promising energy solution. Moreover, grouping vehicles into platoons could greatly improve road capacity and reduce energy consumption. These tendencies urgently demand a theoretical performance analysis framework of the EV platoons charging at renewable energy supplied stations. In this paper, we present such a framework based on a queuing network model. In this model, the fluctuation of the renewable energy, the uncertainty of the EV platoons arrival, the variation of the charging price, and the serving capacity limitation of the charging station are taken into account. The steady-state distribution of the queuing system have been presented based on the equilibrium equations. Then, various performance metrics of the charging system together with the charging gain of EVs have been proposed. The queuing network model, validated by the simulation results, can be used in the planning of practical charging stations and the charging scheduling of EV platoons.

Nowadays gigantic crowd-sourced data collected from mobile phone users have become widely available, which enables the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of these data to the public will pose a considerable threat to mobile users' privacy. To solve this problem, the notion of differential privacy has been proposed to provide privacy with theoretical guarantee, and recently it has been applied in streaming data publishing. However, most of the existing literature focus on either event-level privacy on infinite streams or user-level privacy on finite streams. In this paper, we investigate the problem of real-time spatiotemporal crowd-sourced data publishing with privacy preservation. Specifically, we consider continuous publication of population statistics for monitoring purposes and design RescueDP-an online aggregate monitoring scheme over infinite streams with privacy guarantee. RescueDP's key components include adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, which are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. We show that RescueDP can achieve w-event privacy over data generated and published periodically by crowd users. We evaluate our scheme with real-world as well as synthetic datasets and compare it with two w-event privacy-assured representative benchmarks. Experimental results show that our solution outperforms the existing methods and improves the utility with strong privacy guarantee.

In battery-powered Cognitive Machine-to-Machine\nCommunications (CM2M), the energy consumption, opportunis-\ntic data access capacity and interference to the licensed system\nneed to be optimized simultaneously. We consider this as joint\ncooperative spectrum sensing and power allocation, and model\nthis as a constraint multiobjective optimization problem of three\nobjectives. Our model helps to find a Pareto optimal variable\nset of sensing duration, detection threshold and transmission\npower for each individual sensor in cooperative spectrum sensing.\nThe evaluation of our model shows that energy consumption,\nopportunistic data capacity and interference are optimized\nsimultaneously while keeping the total cooperative spectrum\nsensing error lower than a predefined threshold. Pareto optimal\nresults show that better energy efficiency [bits/joule] makes lower\nharmful interference to the primary system.

Cloud-enabled vehicular network is an emerging paradigm which utilizes cloud computing to enhance the performance of vehicular network. But some issues still need to be addressed and we focus on the pseudonym resources management, which is crucial for vehicles to guarantee location privacy. A new three-plane hierarchical architecture with software defined network technology is proposed to manage the pseudonym resources. We use two-sided matching theory to solve the pseudonym resources allocation problem among pseudonym pools in different roadside unit clouds. Numerical results show that our proposed approach optimizes the pseudonym resources utilization and also improves the privacy entropy of vehicles.

Vehicular social network (VSN) is envisioned to serve as an essential data sensing, exchanging and processing platform for the future Intelligent Transportation Systems. In this paper, we aim to address the location privacy issue in VSNs. In traditional pseudonym-based solutions, the privacy-preserving strength is mainly dependent on the number of vehicles meeting at the same occasion. We notice that an individual vehicle actually has many chances to meet several other vehicles. In most meeting occasions, there are only few vehicles appearing concurrently. Motivated by these observations, we propose a new privacy-preserving scheme, called MixGroup, which is capable of efficiently exploiting the sparse meeting opportunities for pseudonym changing. By integrating the group signature mechanism, MixGroup constructs extended pseudonym-changing regions, in which vehicles are allowed to successively exchange their pseudonyms. As a consequence, for the tracking adversary, the uncertainty of pseudonym mixture is accumulatively enlarged, and therefore location privacy preservation is considerably improved. We carry out simulations to verify the performance of MixGroup. Results indicate that MixGroup significantly outperforms the existing schemes. In addition, MixGroup is able to achieve favorable performance even in low traffic conditions.

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and power grid, which provides flexible demand response management (DRM) for the reliability of smart grid. EV mobility is a unique and inherent feature of the V2G system. However, the inter-relationship between EV mobility and DRM is not obvious. In this paper, we focus on the exploration of EV mobility to impact DRM in V2G systems in smart grid. We first present a dynamic complex network model of V2G mobile energy networks, considering the fact that EVs travel across multiple districts, and hence EVs can be acting as energy transporters among different districts. We formulate the districts' DRM dynamics, which is coupled with each other through EV fleets. In addition, a complex network synchronization method is proposed to analyze the dynamic behavior in V2G mobile energy networks. Numerical results show that EVs mobility of symmetrical EV fleet is able to achieve synchronous stability of network and balance the power demand among different districts. This observation is also validated by simulation with real world data.

Multimedia crowdsourcing possesses a huge potential to actualize many new applications that are expected to yield tremendous benefits in diverse fields including environment monitoring, emergency rescues during natural catastrophes, online education, sports, and entertainment. Nonetheless, multimedia crowdsourcing unfolds new challenges such as big data acquisition and processing, more stringent quality of service requirements, and heterogeneity of crowdsensors. Consequently, incentive mechanisms specifically tailored to multimedia crowdsourcing applications need to be developed to fully utilize the potential of multimedia crowdsourcing. In this paper, we design an optimal incentive mechanism for the smartphone contributors to participate in a cloud-enabled multimedia crowdsourcing scheme. We establish a condition that determines whether the smartphones are eligible to participate, and provide a close form expression for the optimal duration of service from the contributors, for a given reward from the crowdsourcer. Consequently, we derive the conditions for existence of an optimal reward for the contributors from the crowdsourcer, and prove its uniqueness. We numerically illustrate the performance of our model considering logarithmic and linear cost functions for the cloud resources. The similarity of the results for different cost models corroborates the validity of our model and the results, whereas the difference in the magnitudes suggests that the strategy of the crowdsourcer as well as the strategies of the smartphone participants considerably depend on the cloud cost model.

Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper, we study the QoS differential scheduling problem in the CR-based smart grid communications networks. The scheduler is responsible for managing the spectrum resources and arranging the data transmissions of smart grid users (SGUs). To guarantee the differential QoS, the SGUs are assigned to have different priorities according to their roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler adjusts the channels allocation to minimize the transmission delay of SGUs. The entire transmission scheduling problem is formulated as a semi-Markov decision process and solved by the methodology of adaptive dynamic programming. A heuristic dynamic programming (HDP) architecture is established for the scheduling problem. By the online network training, the HDP can learn from the activities of primary users and SGUs, and adjust the scheduling decision to achieve the purpose of transmission delay minimization. Simulation results illustrate that the proposed priority policy ensures the low transmission delay of high priority SGUs. In addition, the emergency data transmission delay is also reduced to a significantly low level, guaranteeing the differential QoS in smart grid.

Vehicular ad hoc networks (VANETs) which are deployed along roads make traffic systems safer and efficient. Existing theoretical results on capacity scaling laws provide insights and guidance for the design and deployment of VANETs. In this paper, we propose a novel fundamental framework RVWNM (Real Vehicular Wireless Network Model), which enables a more realistic capacity analysis in VANETs. We first introduce a Euclidean planar graph which can be constructed from any real map of urban area, and represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph which considers the transmission interference relations among the nodes in the network. Finally, we analyze theoretically the interference relationships in the interference relationship graph. As far as we know, we are the first to use a practical geometry structure to calculate the asymptotic capacity of VANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of urban area VANETs with the consideration of social- proximity based mobility of vehicles.

Mobile cloud computing is a promising technology for pervasive healthcare, which guarantees real-time health monitoring and electronic medical records sharing in different environments. In this paper, we present a hierarchical mobile cloud computing framework with three layers for pervasive healthcare. The scalable and hierarchical mobile cloud framework can be used to disperse the global storage and management load. We also study the social characteristics among patients and divide the patients into different social groups for privacy protection. A secure electronic medical records sharing scheme and a real-time health information transmission scheme are proposed. The security analysis shows that our schemes not only provide secure communication but also protect privacy of the patients.

In Mobile Social Networks (MSNs), data caching techniques are widely applied to enhance the performance of data delivery by using storage devices called throwboxes. A throwbox is usually placed at a particular place and acts as a stationary relay. When putting throwboxes into a network, the deployment and storage allocation of throwboxes are two fundamental problems. Although throwbox deployment has been studied, optimal storage allocation is often ignored in these approaches. In this paper, we investigate the two problems jointly. Contact strength between a user and a particular place is evaluated with the aid of the contact history of users. Moreover, a joint optimization model is established to calculate the optimal throwbox deployment and storage allocation. Simulation results show that the proposed scheme performs well in decreasing data loss incurred by storage saturation and improving the efficiency of data delivery.

Because of the space and time dynamics of moving vehicles, network connectivity is an important performance metric to affect packet delivery in Vehicular Ad Hoc Networks (VANETs). Grouping vehicles into platoons in VANETs can improve road safety, change the network connectivity, and even reduce channel access collisions. Unfortunately, network connectivity is often ignored in the design of exiting MAC protocols for VANETs. In this paper, we analyze the connectivity probability and present a connectivity-aware Medium Access Control (MAC) protocol for platoon-based VANETs. A multi-priority Markov model is presented to derive the relationship between the connectivity probability and the system saturated throughput. Based on variable traffic status and network connectivity, a multi-channel reservation scheme is adopted to dynamically adjust the length of the Control CHannel (CCH) interval and the Service CHannel (SCH) interval for the improvement of the system performance, in terms of network throughput and the priority packet transmission opportunities for platoons. As a result, some important observations to the design and analysis of such communication systems are provided.

False data injection attacks have serious threat to the smart grid, e.g., may incur power outage or blackout. Normally, an intruder should have priori knowledge of the linear structure matrix and then control all smart meters to perform attacks. State-of-the-art studies have proven in theory that false data injection attacks can be unobservable when an intruder coordinately controls a small number of smart meters. However, there are no practical or implementable unobservable false data injection attacks with low-sparsity yet in the literature. In this paper, we propose a two-stage attacking scheme to demonstrate the practical feasibility of unobservable false data injection attacks in the smart grid. In the first stage, we explore the parallel factor analysis to derive the linear structure matrix of the smart grid using the intercepted data. In the second stage, we construct the sparse attack vector via a linear-based relaxation approach, which is used as the false data. Results indicate that we can realize highly successful attacking performance with a low detection probability.

With the aid of advanced Information and Communication Technologies (ICT), Vehicle-to-Grid (V2G) networks will play an important role in supporting and enhancing the distributed electricity supply in the next generation power grid-smart grid. In order to ensure stability of the power grid and satisfy the Quality of Energy (QoE) requirements of Electric Vehicles (EVs), this paper proposes a two-level group bidding mechanism for the electric energy trade between the grid and EVs. Trading information between the grid and EVs is exchanged through communication networks. EVs acted as mobile energy storage are organized to form an electricity feedback group by aggregators. The grid aims at minimizing the cost of given electricity demand while EVs expect to maximize their profits. A quantity based feedback electricity unit pricing scheme is proposed to incentivize the participation of EVs in V2G networks. Moreover, Vickrey-Clarke-Groves (VCG) auction-based algorithms are designed to implement our proposed mechanisms. Simulation results indicate that our mechanism is able to reduce the cost of the grid while offer EVs significant incentives to participate in the V2G power market.

This paper studies the physical layer security in cognitive relay network (CRN) with multiple antennas in the presence of multiple eavesdroppers. Under spectrum sharing scenario and orthogonal space-time block code (OSTBC) transmission, we derive both the exact and asymptotic expressions of secrecy outage probability over Rayleigh fading channels and give the reliability-security tradeoff analysis. The results which have been verified by Monte Carlo simulation show that the loss of the secrecy outage performance caused by an increase of eavesdroppers number can be totally overcome by multiple antenna diversity. It's illustrated that increasing the number of antennas can also improve both reliability and security of the system. Besides, the asymptotic analysis indicates the secrecy diversity order is only determined by number of antennas and is independent with number of eavesdroppers. The secrecy array gain is relevant with both number of antennas and eavesdroppers. In other words, the existence of eavesdroppers will degrade the secrecy outage performance but will not affect the diversity order of the system.

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and grid, which provides powerful demand response, balancing the electricity demand and supply in smart grid. Mobility is the key feature of EVs, which is also a significant challenge for V2G systems. In order to model the EV mobility in V2G systems, we propose a complex networking modeling for V2G mobile energy network. Each district has a V2G system. EVs travel among different districts. The EV fleets transport energy and impact the V2G systems of districts. The theory of complex network synchronization is employed to analyze the dynamics of the mobile energy network. Numerical results show the energy transportation of EV fleets may achieve synchronous stability of demand level of different districts, balancing the demand response in the mobile energy network.

Many applications of wireless sensor networks (WSNs) need to balance multiple yet often conflicting performance requirements such as high energy efficiency, high throughput, low delay and low loss. Finding appropriate WSN parameter configuration to achieve the best trade-off requires in depth understanding of the joint effect of key parameters residing at different layers on the performance. In this paper, we present an extensive experimental study on the data delivery performance of aWSN link, where 4 major performance metrics, namely energy, throughput, delay and loss, were measured over 6 months under around 50 thousand parameter configurations of 7 key stack parameters. Different from existing work, rich observations are made out of the extensive measurement data, with the focus on the joint effect of these parameters on the performance. Specifically, for each of the four performance metrics, a set of guidelines is derived for parameter optimization. In addition, we propose empirical models for each performance metric to quantify the joint effects, which enable finding optimal settings for parameters such as payload size or retransmissions, in consideration of link quality and other parameter settings, to achieve better performance trade-offs. To demonstrate the potential of this work, the obtained joint parameter optimization results are applied to an example. The outcome is compared with those achieved by following representative single-parameter tuning guidelines from the literature. The comparison reveals that by considering the joint effect of multi-layer parameters together, a WSN application can obtain a much improved performance trade-off.

In future, a massive number of devices are expected to communicate for pervasive monitoring and measurement, industrial automation, and home/building energy management. Nevertheless, such Machine-to-Machine (M2M) communications are prone to failure due to depletion of machines energy if the communication system is not designed properly. A key step in building energy-efficient protocols for large-scale M2M communications is to assess, model or characterize a network energy consumption profile. To meet this need, we develop a theoretical and numerical framework to evaluate the cumulative distribution function (CDF) of the total energy consumption by fully exploiting the properties of stochastic geometry. Unlike the other existing approaches, we model the transmission energy as a function of transmission power, packet size, and link affordable capacity that is a logarithmic function of experienced Signal to Interference plus Noise Ratio (SINR). Since it is very difficult, if not impossible, to derive a closed-form expression for the CDF, we derive numerically computable first- and second-order moments of energy consumption. Applying these moments we then propose Log-normal and Log-logistic distributions to approximate the CDF. Our simulation results show that Log-logistic almost precisely approximates the exact CDF.

In the context of Mobile Social Networks (MSNs), a type of wireless storage device called throwbox has emerged as a promising way to improve the efficiency of data delivery. Recent studies focus on the deployment of throwboxes to maximize data delivery opportunities. However, as a storage device , the storage usage of throwboxes has seldom been addressed by existing work. In this paper, the storage allocation of throwboxes is studied as two specific problems: (1) if throwboxes are fixed at particular places, how to allocate storage to the throwboxes; and (2) if throwboxes are deployable, how to conduct storage allocation in combination with throwbox deployment. Two optimization models are proposed to calculate the optimal storage allocation with a knowledge of the contact history of users. Real trace based simulations demonstrate that the proposed scheme is able to not only decrease data loss on throwboxes but also improve the efficiency of data delivery.

Providing direct communications among a rapidly growing number of wireless devices within the coverage area of a cellular system is an attractive way of exploiting the proximity among them to enhance coverage and spectral and energy efficiency. However, such device-to-device (D2D) communications create a new type of interference in cellular systems, calling for rigorous system analysis and design to both protect mobile users (MUs) and guarantee the connectivity of devices. Motivated by the potential advantages of cognitive radio (CR) technology in detecting and exploiting underutilized spectrum, we investigate CR-assisted D2D communications in a cellular network as a viable solution for D2D communications, in which devices access the network with mixed overlay-underlay spectrum sharing. Our comprehensive analysis reveals several engineering insights useful to system design. We first derive bounds of pivotal performance metrics. For a given collision probability constraint, as the prime spectrum-sharing criterion, we also derive the maximum allowable density of devices. This captures the density of MUs and that of active macro base stations. Limited in spatial density, devices may not have connectivity among them. Nevertheless, it is shown that for the derived maximum allowable density, one should judiciously push a portion of devices into receiving mode in order to preserve the connectivity and to keep the isolation probability low. Furthermore, upper bounds on the cellular coverage probability are obtained incorporating load-based power allocation for both path-loss and fading-based cell association mechanisms, which are fairly accurate and consistent with our in-depth simulation results. Finally, implementation issues are discussed.

The capacity of cognitive radio (CR) systems can be enhanced significantly by deploying relay nodes to exploit the spatial diversity. However, the inevitable imperfect sensing in CR has vital effects on the policy of relay selection, channel access, and power allocation that play pivotal roles in the system capacity. The increase in transmission power can improve the system capacity, but results in high energy consumption, which incurs the increase of carbon emission and network operational cost. Most of the existing schemes for CR systems have not jointly considered the imperfect sensing scenario and the tradeoff between the system capacity and energy consumption. To fill in this gap, this paper proposes an energy-aware centralized relay selection scheme that takes into account the relay selection, channel access, and power allocation jointly in CR with imperfect sensing. Specifically, the CR system is formulated as a partially observable Markov decision process (POMDP) to achieve the goal of balancing the system capacity and energy consumption as well as maximizing the system reward. The optimal policy for relay selection, channel access, and power allocation is then derived by virtue of a dynamic programming approach. A dimension reduction strategy is further applied to reduce its high computation complexity. Extensive simulation experiments and results are presented and analysed to demonstrate the significant performance improvement compared to the existing schemes. The performance results show that the received reward increases more than 50% and the network lifetime increases more than 35%, but the system capacity is reduced less than 6% only.

In this paper, we present performance analysis for underlay cognitive decode-and-forward relay networks with the Nth best relay selection scheme over Nakagami-m fading channels. Both the maximum tolerated interference power constraint and the maximum transmit power limit are considered. Specifically, exact and asymptotic closed-form expressions are derived for the outage probability of the secondary system with the Nth best relay selection scheme. The selection probability of the Nth best relay under limited feedback is discussed. In addition, we also obtain the closed-form expression for the ergodic capacity of the secondary system with a single relay. These expressions facilitate in effectively evaluating the network performance in key operation parameters and in optimizing system parameters. The theoretical derivations are extensively validated through Monte Carlo simulations. Both theoretical and simulation results show that the fading severity of the secondary transmission links has more impact on the outage performance and the capacity than that of the interference links does. Through asymptotic analysis, we show that the diversity order for the Nth best relay selection scheme is min(m1, m3) × (M - N) + m3, where M denotes the number of cognitive relays, and m1and m3represent the fading severity parameters of the first-hop transmission link and the second-hop transmission link, respectively.

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

Many applications, such as product promotion advertisement and traffic congestion notification, benefit from opportunistic content exchange in Delay Tolerant Networks (DTNs). An important requirement of such applications is timely delivery. However, the intermittent connectivity of DTNs may significantly delay content exchange, and cannot guarantee timely delivery. The state-of-the-arts capture mobility patterns or social properties of mobile devices. Such solutions do not capture patterns of delivered content in order to optimize content delivery. Without such optimization, the content demanded by a large number of subscribers could follow the same forwarding path as the content by only one subscriber, leading to traffic congestion and packet drop. To address the challenge, in this paper, we develop a solution framework, namely Ameba, for timely delivery. In detail, we first leverage content properties to derive an optimal routing hop count of each content to maximize the number of needed nodes. Next, we develop node utilities to capture interests, capacity and locations of mobile devices. Finally, the distributed forwarding scheme leverages the optimal routing hop count and node utilities to deliver content towards the needed nodes in a timely manner. Illustrative results verify that Ameba achieves comparable delivery ratio as Epidemic but with much lower overhead.

Abstract In cognitive vehicular networks (CVNs), spectrum sensing and access are introduced as the promising technologies to fully exploit the underutilized licensed spectrum. Because the sensing ability of a single secondary vehicular user (SVU) is affected by high mobility, dynamic topology, and unreliable wireless environment, collaborative sensing is developed to increase the sensing accuracy and efficiency. Generally, the synchronization is required in the collaborative sensing in CVN. However, it is difficult to keep all SVUs synchronized with others for sensing under the high dynamic network topology, and the sensing overhead of the synchronous cooperative action may be significant. In this paper, we first propose an asynchronous cooperative sensing scheme in which each SVU provides an energy information (EI) that is tagged with location and time information. The sensing decision will be made on account of the EI. Considering the temporal and spatial diversities of each SVU, we assign different weights to each EI and formulate the probabilities of detection and false alarm as the optimization problems to find the optimal weight of each EI. Then, based on the asynchronous sensing, the specifications of the opportunistic spectrum access mechanism are elaborated in both centralized and decentralized CVNs for the sake of practical implementation. We analyze the system performance in terms of achievable throughput and transmission delay. Numerical results show that the proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Providing adequate and reliable spectrum resources for unlicensed users in spectrum database-based cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities and radio environment. In this paper, we propose an adaptive spectrum access method based on spectrum database for cognitive radio (CR) networks. While making decision to access the licensed spectrum, the secondary users (SUs) not only use the spectrum information informed by the spectrum database but also use the local sensing to confirm the specific condition of the spectrum. The adaptive sensing and access process is modeled as an optimal decision process by maximizing the achievable throughput of CR networks. The dynamic programming algorithm is developed to find the optimal sensing and access policy for each SU. Simulation results show that the proposed sensing and access policies can provide reliability guarantees for finding spectrum opportunities in terms of dynamic radio environment.

In this paper, we study the routing problem in multi-hop cognitive radio networks (CRNs). We observe that Primary Users (PUs) in CRNs exhibit unevenly geographic distribution due to their social behaviors. In the area with densely-distributed PUs, the low spectrum availability will severely decrease the Secondary Users (SUs) communication reliability. We are motivated to exploit Primary User (PU) social features to set up a multi-hop routing path providing reliable end-to-end communications. There are two main challenges: 1) how to quantitatively represent and characterize PU social features in CRNs; 2) given the knowledge of the PU social features, how to design an efficient route to improve the packet-forwarding reliability. We first propose the concept of PU community in CRNs. Maximum likelihood estimation method is then employed to predict the number of potential PUs. The estimated results effectively reflect the spectrum utilization, and hence, provide important information for PU community formulation. After that, considering the existence of PU community, we propose a hybrid routing scheme to alleviate the impact of PU community on packet-forwarding reliability. Simulation results demonstrate that the proposed scheme significantly improves the reliability of end-to-end communications.

This paper studies the discriminatory channel estimation (DCE) performance between a legitimate receiver (LR) and an unauthorized receiver (UR) in the multiple-input multiple-output (MIMO) wireless systems. DCE is a recently developed concept that intentionally degrades the channel estimation at the UR so as to minimize the probability of confidential information being eavesdropped by the UR. Usually, the existing DCE scheme is based on the linear minimum mean square error (LMMSE) method with two-way training. In this paper, we propose a new two-way training for DCE based on semiblind approach, e.g., the whitening-rotation (WR)-based channel estimator. To characterize the DCE performance, we derive the closed-form of the normalized mean squared error (NMSE) to the channel estimation at both the LR and the UR. Simulation results show that the proposed two-way training achieves higher performance compared to the two-way training designs in the literature.

Plug-in hybrid electric vehicles (PHEVs) are receiving growing attention to achieve a sustainable transport system and society. Due to the limited vehicle battery capacity, PHEVs perform charging and re-charging from time to time. It is visioned that the charging load with high PHEVs penetration will pose a considerable impact on the residential distribution network. Therefore, implementation of coordinated PHEVs charging becomes necessary for smart grid. For maintaining the household load, the limited energy supply may not fulfill all PHEVs charging load at any time. Thus, the fairness of energy scheduling among PHEVs should be considered. In this paper, charging fairness (CF) and discouraging-charging fairness (DCF) are proposed to guarantee the charging opportunity of each PHEV and fast recovery of PHEV driving ability. We formulate the problem of the fair energy scheduling in residential distribution network as a Semi Markov Decision Process (SMDP). The technique Neuro-Dynamic Programming (NDP) is exploited to solve the corresponding problem in SMDP. In the scheduling process, Entropy Weight Method (EWM) is proposed to consider three key metrics: CF, DCF and cable power loss. Simulation results illustrate that the proposed scheduling scheme is able to avoid a number of peak load caused by PHEVs charging and at the same time reduce power loss without affecting traveling plan.

In this paper, we consider a three-node wireless network comprising of a source and two users. Both users need to decode the transmitted data correctly. User 1 has better position to the source than user 2 most of time slots. User 1 has buffer to store the transmitted information by the source. Thus, in the case of wrong decoding at user 2, user 1 can resend data to user 2 some time slots later. In this paper, we propose a novel incremental relaying based adaptive link selection policy that exploits incremental relaying and buffer to maximize the throughput of the network. That is, based on the channel quality of the available links, each time slot is allocated either to the source or user 1 to transmit data. Both delay constrained and delay tolerant transmission schemes are studied. We model the variation of the buffer at user 1 as a Markov Chain and calculate the outage probability of the proposed policy. Our simulation results show that the proposed scheme achieves higher throughput and lower outage probability compared to the recently proposed link selection policies with or without buffer.

Vehicular Ad Hoc Networks (VANETs) can provide safety and non-safety related applications and services to improve the passenger safety and comfort. Due to the space and time dynamics of moving vehicles, network connectivity is an important performance metric to indicate the quality of the network and the user's satisfaction. Grouping vehicles into platoons in the highway can improve road safety, reduce fuel consumption, and decrease traffic congestion. In this paper, we study the connectivity characteristic of platoon-based VANETs. The connectivity probabilities are analyzed for the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios. The relationships between the connectivity probability and the key parameters are investigated, including the traffic density, the coverage of the ordinary vehicle, the coverage of the platoon, the coverage of the Road Side Unit (RSU), the distance between two adjacent RSUs and the ratio of the platoon in the VANET. The results can help the transport system designer to control the traffic on the highway to satisfy the connectivity requirement. Analysis results show that the connectivity probability can be significantly improved when there are platoons in a network.

It is expected that the Internet of Things (IoT) provides the foundational infrastructure for smart cities, and making ICT an enabling technology to meet major challenges associated with climate change, energy efficiency, mobility and future services. On the other hand a smart city with these requirements is usually evolving through incremental automation and integration of new components, that are digital or physical components or smart devices. To handle the growing scale and complexity of a system, an adaptive modelling method is needed for dynamic analysis and verification and/or validation, and integration. In this paper, we consider the case study of a Demand Response (DR) Programme that is to be realized by the deployment of a network of smart meters. Through this case study, we propose a component-based modelling approach and demonstrate how it deals with the growing complex architecture.

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR, which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are twofold: Compared with the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; and the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.

Vehicle-to-grid (V2G), involving both charging and discharging of battery vehicles (BVs), enhances the smart grid substantially to alleviate peaks in power consumption. In a V2G scenario, the communications between BVs and power grid may confront severe cyber security vulnerabilities. Traditionally, authentication mechanisms are solely designed for the BVs when they charge electricity as energy customers. In this paper, we first show that, when a BV interacts with the power grid, it may act in one of three roles: 1) energy demand (i.e., a customer); 2) energy storage; and 3) energy supply (i.e., a generator). In each role, we further demonstrate that the BV has dissimilar security and privacy concerns. Hence, the traditional approach that only considers BVs as energy customers is not universally applicable for the interactions in the smart grid. To address this new security challenge, we propose a role-dependent privacy preservation scheme (ROPS) to achieve secure interactions between a BV and power grid. In the ROPS, a set of interlinked subprotocols is proposed to incorporate different privacy considerations when a BV acts as a customer, storage, or a generator. We also outline both centralized and distributed discharging operations when a BV feeds energy back into the grid. Finally, security analysis is performed to indicate that the proposed ROPS owns required security and privacy properties and can be a highly potential security solution for V2G networks in the smart grid. The identified security challenge as well as the proposed ROPS scheme indicates that role-awareness is crucial for secure V2G networks.

As a special sensor network, a wireless body area network (WBAN) provides an economical solution to real-time monitoring and reporting of patients' physiological data. After a WBAN is deployed, it is sometimes necessary to disseminate data into the network through wireless links to adjust configuration parameters of body sensors or distribute management commands and queries to sensors. A number of such protocols have been proposed recently, but they all focus on how to ensure reliability and overlook security vulnerabilities. Taking into account the unique features and application requirements of a WBAN, this paper presents the design, implementation, and evaluation of a secure, lightweight, confidential, and denial-of-service-resistant data discovery and dissemination protocol for WBANs to ensure the data items disseminated are not altered or tampered. Based on multiple one-way key hash chains, our protocol provides instantaneous authentication and can tolerate node compromise. Besides the theoretical analysis that demonstrates the security and performance of the proposed protocol, this paper also reports the experimental evaluation of our protocol in a network of resource-limited sensor nodes, which shows its efficiency in practice. In particular, extensive security analysis shows that our protocol is provably secure.

The Vehicular Ad hoc Networks (VANETs) have been recently introduced to provide high-speed Internet access to vehicles by deploying 802.11 enhanced Roadside Units (RSUs) along roads. However, few content downloading oriented RSU deployment strategies have been proposed. In this paper, we propose a new RSU deployment strategy for content downloading in VANETs. The encounters between vehicles and RSUs are modeled as a time continuous homogeneous Markov chain. The optimal inter-meeting time between vehicles and RSUs is analyzed based on the encounter model. Then, the road network is modeled as a weighted undirected graph, and a RSU deployment algorithm is designed based on the depth-first traversal algorithm for edges of a graph. Simulation results show that the proposed RSU deployment algorithm can satisfy the file downloading service requirements with the lowest RSU deployment cost.

We propose a novel anonymous access control protocol which is formulated as a series of coalitional games, where the players are the owners of shared private resources (big volumes of data) in online social networks (OSNs). Basically, co-owners cooperate to generate by themselves an attribute-based boolean formula to control access to their shared resource. By means of this boolean formula, co-owners are able to secretly express their privacy preferences over a common shared resource and requesters can anonymously access the secured private resource. In this paper, we formally analyze our protocol's fairness from a cooperative game theory point of view, and how OSN users, which are mostly cooperative, evaluate their expected gains and costs to adopt such cooperative privacy management scheme in many different settings.

Along with radio frequency identification (RFID) becoming ubiquitous, security issues have attracted extensive attentions. Most studies focus on the single-reader and single-tag case to provide security protection, which leads to certain limitations for diverse applications. This paper proposes a grouping-proofs-based authentication protocol (GUPA) to address the security issue for multiple readers and tags simultaneous identification in distributed RFID systems. In GUPA, distributed authentication mode with independent subgrouping proofs is adopted to enhance hierarchical protection; an asymmetric denial scheme is applied to grant fault-tolerance capabilities against an illegal reader or tag; and a sequence-based odd-even alternation group subscript is presented to define a function for secret updating. Meanwhile, GUPA is analyzed to be robust enough to resist major attacks such as replay, forgery, tracking, and denial of proof. Furthermore, performance analysis shows that compared with the known grouping-proof or yoking-proof-based protocols, GUPA has lower communication overhead and computation load. It indicates that GUPA realizing both secure and simultaneous identification is efficient for resource-constrained distributed RFID systems.

In this work, we study the quality-delay tradeoff for video streaming over mobile ad hoc network by utilizing a class of scheduling schemes. We show that node spatial mobility indeed impacts on the performance of wireless video transmission under the assumption that all the nodes can identically and uniformly visit the entire network. To describe a practical mobile scenario, we consider a random walk mobility model in which each node can randomly and independently choose its mobility direction at each time-slot. The contributions of this work are twofold: 1) It investigates the optimal node velocity for the mobile video network which helps to identify the impact of mobility on the video performance; 2) It derives the achievable quality-delay tradeoff range for any node mobility velocity, and thus it is helpful to design appropriate quality and delay requirements. These results provide insights on network design and fundamental guidelines on establishing an efficient mobile wireless video transmission system.

Previous cooperative sensing schemes require the cooperative Secondary Users (SUs) to behave in a synchronous way. This requires each SU to start cooperations at the same time by stopping their own transmissions. In multi-hop cognitive radio networks, it is very difficult to keep all SUs synchronized with others for sensing. In this paper, we propose an asynchronous cooperative sensing scheme for multi-hop cognitive radio networks in which each SU only provides its energy information in stead of ceasing its own transmission to perform the cooperative sensing. Each energy information is assigned an appropriate weight by considering the temporal and spatial diversities of each SU. We formulate the probabilities of detection and false alarm as optimization problems to find the optimal weight for every energy information. The achievable throughput has been derived. Numerical results show that the proposed scheme is able to achieve substantially higher throughput compared with the existing schemes.

Cognitive Radio (CR) is a promising technology to efficiently utilize the limited spectrum resources for the rapidly increasing demands on wireless applications and services. Security is very challenging in a CR network due to its unique characteristics, e.g. systems co-existence and interference guarantee. In this paper, we will study the performance of CR networks under Primary User Emulation Attacks (PUEA) that is a typical security issue. A multi-dimensional Markov model is built and the system performance is evaluated under generic system models, including the common control channel (CCC) adoption and a joint PUEA detection and call admission control scheme. We introduce a new performance metric CCC recovery time and redefine outage probability to identify and evaluate the important impact of CCC on the CR network. In addition, we calculate blocking rate, dropping rate, and outage probability of the CR networks. Numerical results show that the system performance may deteriorate severely when attacked. The joint PUEA detection mechanism and guard channel strategy are able to significantly alleviate the performance degradation.

Localization is a vital foundation in Wireless Sensor Networks (WSNs). However, most previous localization methods assume an idealistic radio propagation model that is far from reality. This will lead to inaccurate localization, since unknown nodes cannot receive enough location messages under the radio irregularity model. In our previous work “LMAT”, we solved the path planning problem of the mobile anchor node without taking into account radio irregularity. This paper further studies how localization performance is affected by radio irregularity. In order to improve localization accuracy, the anchor node's radio range is adjusted to guarantee that all unknown nodes can receive sufficient localization information. Furthermore, it points out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability in presence of 2-hop localization. Finally, simulation results show that, compared with 1-hop localization algorithm, 2-hop localization along with a good trajectory reduces average localization error.

Vehicular Ad Hoc Networks (VANETs) and their diverse applications experience growing interest in both academic and industry. Different types of traffic packets delivered through vehicle-to-vehicle and vehicle-to-infrastructure communications are intended to improve passenger safety and comfort. In this paper, we propose a multiple priority supported Medium Access Control (MAC) protocol for VANETs based on the time slotted p-persistent channel access mechanism. The protocol differentiates the services packets into multi-priority on the Control Channel (CCH). Theoretical analysis based on Markov model is presented to optimize the transmission probabilities of the packets with different priorities, as well as the adjustable intervals of the CCH and the Services Channels (SCHs). Both analytical results and simulation experiments show that the proposed MAC protocol is able to ensure the prioritized transmission of the safety packets and also achieve optimal system performance with respect to saturated throughput.

In cognitive radio networks, spectrum sensing and access scheme affects the system performance. In this paper, a new wideband mixed access scheme is proposed, in which the Secondary Users (SUs) sense the channels via wideband spectrum sensing, and access them with a mixed access strategy. In order to maximize the ergodic throughput of SUs, we find optimal sensing time and transmission power of each channel, while protecting the Primary Users (PUs) from interference. It is shown that the optimization problem can be formulated as a convex problem. Moreover, we present a QoS-aware low complexity scheme, in which the SUs select several specific channels to sense. An effective sensing channels selection criterion is proposed. Numerical results show that the proposed schemes can effectively improve the system performance.

In the smart grid, an efficient and reliable communication infrastructure is crucial for improving system efficiency and stability. In this paper, we leverage cognitive radio technology to construct the smart grid communication infrastructure. The proposed network architecture consists of three sub-areas, cognitive Home Area Networks (HAN), cognitive Neighborhood Area Networks (NAN) and cognitive Wide Area Networks (WAN). We focus on the spectrum resource management in cognitive NANs for efficient smart grid services. A new spectrum access paradigm called hybrid spectrum access is proposed, in which both licensed and unlicensed spectrum bands are intelligently scheduled for the transmission of smart grid services. The admission control problem under hybrid spectrum access is deliberately investigated. The optimal number of leased and reserved channels are derived through two-dimension Markov chain analysis. Numeric results indicate that the hybrid spectrum access strategy significantly improves the network capacity in supporting the smart grid services, compared to the traditional fixed spectrum access strategy.

SUMMARY The open radio frequency identification (RFID) air interface may suffer from severe threats that make security problem become a critical issue for RFID systems and applications. This paper proposes a dual cryptography authentication protocol (DCAP) for RFID systems. DCAP partitions randomly the tag identifier into two partial identifiers that are used in the forward link and in the backward link, respectively. The protocol applies hash function and shared‐key encryption algorithm to safeguard both forward and backward links and provides a three‐round authentication mode on each tag and reader in a session. Then, authentication is carried out by the primary, secondary, and final verifications. For a formal analysis, a graphical method Colored Petri Nets is applied to model and analyze the correctness of DCAP. We prove that the protocol owns tag anonymity and forward security and has the capability to resist major attacks such as replay, reader forgery, and tag forgery. Finally, the performance in terms of storage, communication overhead, and computation load is evaluated to demonstrate that the protocol has modest complexity and high efficiency. Copyright © 2011 John Wiley & Sons, Ltd.

In recent years, governments, standardization bodies, automobile manufacturers, and academia are working together to develop vehicular ad hoc network (VANET)-based communication technologies. VANETs apply multiple channels, i.e., control channel (CCH) and service channels (SCHs), to provide open public road safety services and the improve comfort and efficiency of driving. Based on the latest standard draft IEEE 802.11p and IEEE 1609.4, this paper proposes a variable CCH interval (VCI) multichannel medium access control (MAC) scheme, which can dynamically adjust the length ratio between CCH and SCHs. The scheme also introduces a multichannel coordination mechanism to provide contention-free access of SCHs. Markov modeling is conducted to optimize the intervals based on the traffic condition. Theoretical analysis and simulation results show that the proposed scheme is able to help IEEE 1609.4 MAC significantly enhance the saturated throughput of SCHs and reduce the transmission delay of service packets while maintaining the prioritized transmission of critical safety information on CCH.

Cooperative spectrum sensing improves the reliability of detection. However, if the secondary users are selfish, they may not collaborate for sensing. In order to address this problem, Medium Access Control (MAC) protocols can be designed to enforce cooperation among secondary users for spectrum sensing. In this paper, we investigate this problem using game theoretical framework. We introduce the concept of correlated equilibrium for the cooperative spectrum sensing game among non-cooperative secondary users and formulate the optimization problem for the case where secondary users have heterogeneous traffic dynamics. We show that the correlated equilibrium improves the system utility, as compared to the mixed strategy Nash equilibrium. While maximizing system payoff is important, fairness is also equally important in systems with dissimilar users. In order to address fairness issue, we propose a new fair social welfare correlated equilibrium, which maximizes the system utility and ensures that the less well-off users do not starve. We employ a no-regret learning algorithm for distributed implementation of the correlated equilibrium. Finally, we propose a neighbourhood based learning algorithm and show that it achieves better performance than the no-regret algorithm.

The emerging wireless vehicular communication technology is intended to improve safety and comfort of transportation systems. Different types of traffic information could be delivered through vehicle-to-vehicle and vehicle-to-infrastructure communications. This paper proposes a Quality-of-Service (QoS) supported multi-channel MAC scheme for Vehicular Ad Hoc Networks (VANETs), which can adaptively tune the contention window for different services at each node, and dynamically adjust the intervals of the Control Channel (CCH) and the Service Channels (SCHs) working in multi-rate. Theoretical model is proposed to obtain the contention window and optimize the intervals based on traffic conditions. Analysis and simulation results show that the proposed MAC is able to help IEEE 1690.4 MAC support QoS services, while ensuring the high saturation throughput and the prioritized transmission of critical safety information.

In this paper, we study the reliable packet forwarding in Wireless Sensor Networks (WSNs) with the multiple-input multiple-output (MIMO) and orthogonal space time block codes (OSTBC) techniques. The objective is to propose a cross-layer optimized forwarding scheme to maximize the Successful Transmission Rate (STR) while satisfying the given end-to-end power consumption constraint. The channel coding, power allocation, and route planning are jointly considered to significantly improve the transmission quality in terms of STR. The joint optimization design is formulated as a global deterministic optimization and also a local stochastic optimization issues. It is found that the stochastic optimization approach can effectively model, analyze, and solve the routing problem. In order to substantially reduce the implementation complication of the global optimization, we propose a low-complexity distributed scheme. The determination of relaying nodes and power budgets are decoupled, i.e. performing route planning and power allocation separately. We have shown that the result in the distributed scheme is able to provide sufficiently accurate predication of the global optimization. In addition, the proposed scheme can clearly reduce the Symbol Error Rate (SER) and achieve higher STR compared with two existing energy-efficient routing protocols, in which no joint design is considered.

Abstract The distinguishing properties of VehicularAd hocwireless Networks (VANETs) strongly challenge the design of Medium Access Control (MAC) protocols, which are responsible for the medium access coordination among active vehicles, as well as the accommodation of both driving safety applications and non‐safety applications. In this paper, we focus on a comprehensive survey of VANET MAC schemes by integrating various related issues and challenges. Our analysis not only deepens the understanding of MAC techniques in VANETs but also presents the key ideas and potential directions for future research in this area. In order to significantly improve the communication performance of VANETs, more research efforts on MAC techniques must be made for optimizing multichannel coordination and allocation approaches, enhancing the Quality of Service (QoS) capability, and combating the hidden terminal problem, broadcast storm problem and even ACK (acknowledgment) explosion problem. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Wireless mesh networks (WMNs) is an emerging communication paradigm to enable resilient, cost‐efficient and reliable services for the future‐generation wireless networks. In this paper, we study the broadcasting (one‐to‐all communication) in WMNs with known topology, i.e. where for each primitive the schedule of transmissions is pre‐computed based on full knowledge about the size and the topology of the network. We show that broadcasting can complete in D + O(logn) time units in the WMN with sizenand diameterD. Moreover, we also propose an optimal O(D)‐time deterministic energy efficient broadcasting scheduling, under which each node in the WMN is only allowed to transmit at most once. Furthermore, we explore the fault‐tolerant broadcasting in the WMN. We show an O(n)‐time deterministic broadcasting schedule with large number of link failures. This is an optimal schedule in the sense that there exists a network topology in which the broadcasting cannot complete in less than Ω(n) units of time. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Wireless sensor network (WSN) is an important instrument to realize wireless monitoring and control in various application fields. Energy conservation are crucial for WSNs to prolong the network lifetime. In this paper, we study sleeping management schemes which can efficiently control the network topology and significantly reduce the energy consumption by selectively turning off the radios of abundant sensor nodes. Based on the observation that a Matérn Hard‐core Process (MHP) could thin out evenly distributed nodes from a dense graph, we propose Backbone Energy Efficient Sleeping (BEES) management scheme, whose central idea is to generate and maintain the backbone by simulating MHPs. There are three attractive features of BEES: (i) the backbone size could be conveniently scaled according to the practical requirements; (ii) the backbone is energy efficient in the point of view of packet forwarding (or routing); (iii) the construction of backbone is robust to possible ranging errors, which ensures the feasibility and reliability in practice. Theoretical analysis and simulation experiments are carried out to demonstrate the correctness and effectiveness of BEES. Numerical results indicate that, compared with two existing sleeping management schemes, BEES provides about 30% wider range of scalability, consumes about 13% less routing energy, and achieves 11% ∼ 20% longer network lifetime under different traffic loads. Copyright © 2009 John Wiley & Sons, Ltd.

Nowadays, online gaming is one of the emerging industry on the Internet. Massively Multiplayer Online Games (MMORPG) is one of the most important type of online games. Research on MMORPGs always pay attention to the network situations, such as flow imbalance, system optimization and traffic identification. The results help the game designers and network protocol engineers to improve user game experience. In this paper, we perform traffic analysis and modeling in three distinct game scenarios over two different wireless network connections in World of Warcraft(WoW), which is one of the most popular MMORPGs among the world. In addition, we contribute a random traffic generator base on ns-2 which could be a open development platform for the MMORPGs.

Sensor nodes in Cognitive Sensor Networks (CSNs) can work on different frequency bands (or channels) according to dynamically available wireless resources. This paper proposes a spectrum-aware routing scheme for CSNs, which jointly considers traffic balance, route configuration and power control for reliable end-to-end communications. Bayesian learning method is used to estimate the number of neighboring Primary Users (PUs) and Secondary Users (SUs). The estimated results effectively reflect the spectrum utilization and provide important information for route configuration. Multiple Attribute Decision Making (MADM) method is employed to combine the routing objectives of reliability, energy efficiency and path delay into a single target function. Randomized route selection strategy is adopted for traffic balance. The simulation results demonstrate that the proposed spectrum-aware routing scheme significantly improves the communication reliability, and simultaneously has satisfying performances in energy efficiency and end-to-end delay.

Link adaptation is a critical component of IEEE 802.11 systems, which adapts transmission rates to dynamic wireless channel conditions. In this paper we investigate a general cross-layer link adaptation algorithm which jointly considers the physical layer link quality and random channel access at the MAC layer. An analytic model is proposed for the link adaptation algorithm. The underlying wireless channel is modeled with a multiple state discrete time Markov chain. Compared with the pure link quality based link adaptation algorithm, the proposed cross-layer algorithm can achieve considerable performance gains of up to 20%.

In this paper, we review some results on the multiplexing gain of of the sum rate of the Gaussian multi-antenna compound broadcast channel in the high SNR regime. The transmitter transmits to each user one private message. The channel realization for each user is arbitrarily chosen from a finite set known to the transmitter. To achieve the multiplexing gain region of the channel, we discuss the methods of interference alignment and subspace coding. We interpret the interference alignment scheme for compound MIMO broadcast channel as a special case of subspace coding. We also point out that the subspace coding method could be applied in wireless multihop network to improve the efficiency of intra-cluster broadcasting.

This paper aims at mitigating the so-called Funneling Effect for S-MAC, particularly by improving the throughput and fairness of S-MAC. Wireless sensor networks (WSNs) exhibit some phenomenon named Funneling Effect resulting from the accumulation of disproportionate large number of packets in the regions close to the sink. The collision and congestion due to the Funneling Effect strongly weaken the vitality and robustness of WSNs. As for S-MAC which achieves great energy efficiency, the mitigation of funneling effect seems more significant and urgent. In this paper, targeted to alleviate the funneling effect for S-MAC, we propose a SMAC-based proportional fairness backoff scheme (SPFB). Based on the schedule and contention scheme in S-MAC, SPFB employs Kelly's shadow price theory to achieve the proportional fairness as well as optimizes the back off mechanism to improve the throughput. The contention window range is dynamically adjusted according to the load of individual node. With extensive simulations, we can show that SPFB can achieve much higher throughput than traditional S-MAC, especially when the network is heavy loaded. SPFB can also gain good energy efficiency.

Radio frequency identification (RFID) is a kind of electronic identification technology that is becoming widely deployed. Compared to traditional RFID system, tags in the proposed smart RFID system would store not only the fixed ID information but also some information which is “active” and encoded in the form of mobile codes indicating the up-to-date situation and associated services' directives. In the proposed system, the service that the RFID tag bearer needs can be explained in a context-aware decision making system to provide a situation-aware system response and offer a good quality of service (QoS).

In Cognitive Radio (CR) networks, spectrum sensing is a crucial technique to discover spectrum opportunities for the Secondary Users (SUs). The Quality-of-Service (QoS) of spectrum sensing is characterized by both sensing accuracy and sensing efficiency. Here, sensing accuracy is represented by the false alarm probability and the detection probability while sensing efficiency is represented by the metrics sensing overhead and throughput. The literature has mainly focused on improving sensing accuracy while sensing efficiency has been largely ignored. In this paper, we propose a group-based cooperative Medium Access Control (MAC) protocol, which concentrates on improving sensing efficiency without degrading spectrum sensing accuracy. The MAC protocol is specified and implemented in three phases: reservation, sensing and transmission. The protocol incorporates a group-based cooperative spectrum sensing scheme. In particular, the cooperative SUs are grouped into several teams. During a sensing period, each team senses a different channel. As a consequence, multiple distinct channels can be simultaneously detected within one sensing period. Then, we formulate throughput maximization problems in both time-invariant and time-varying channel scenarios to determine the key design parameters. In addition, an SU-selecting algorithm is presented to selectively choose the cooperative SUs based on the channel dynamics and usage patterns in order to substantially reduce sensing overhead. Numerical results indicate that the proposed strategy is able to significantly decrease sensing overhead and increase throughput with guaranteed sensing accuracy.

This paper focuses on studying the impacts of a duty-cycle based CKN sleep scheduling algorithm for our previous designed TPGF geographical multipath routing algorithm in wireless sensor networks (WSNs). It reveals the fact that waking up more sensor nodes cannot always help to improve the exploration results of TPGF in a duty-cycle based WSN. Furthermore, this study provides the meaningful direction for improving the application-requirement based QoS of stream data transmission in duty-cycle based wireless multimedia sensor networks.

Real time traffic such as voice and video have strict requirements on the acceptable end-to-end packet delay. When there are different types of traffic with different requirements on tolerable latency, priority based packet scheduling schemes are normally used in order to reduce the queuing delay for real time services. However, in cognitive radio networks, the time that the system spends on spectrum sensing adds further delay to the packet transmission. In this paper, we propose a new scheme to significantly reduce the overall packet delay, including the delay due to sensing for real time services in cognitive radio networks. We derive the expression for average packet delay for the proposed scheme and the simulation results match well with the analytical results. The numerical results show that the priority based scheduling scheme combined with our scheme substantially reduces the packet delay for real time applications.

Femtocell is attracting an increasing interest in the telecommunication sectors. In this paper, we study the efficient resource allocation in femtocell networks with completed closed access method. The interaction between a macrocell and its underlying femtocells is identified, characterized, and qualified. The study answers the fundamental question in the femtocell networks: how to allocate spectrum to the femtocells and their overlaying macrocells subject to the Quality-of-Service (QoS) requirement of femtocell networks. Results indicate that an appropriate spectrum splitting is able to substantially increase the service availability in the macrocells, which is essentially due to the accommodated indoor traffics in the femtocells.

Handoff is an indispensable operation in wireless networks to guarantee continuous, effective, and resilient services during a mobile station (MS) mobility. Handoff counting, handoff rate, and handoff probability are important metrics to characterize the handoff performance. Handoff counting defines the number of handoff operations during an active call connection. Handoff rate specifies the expected number of handoff operations during an active call, or equivalently, the average handoff counting. Handoff probability refers to the probability that an MS will perform a handoff before call completion. In the literature, the fading channel is not incorporated in deriving these metrics. In addition, the teletraffic parameters are usually simplified into exponentially distributed variables for the sake of analytical tractability. In this paper, we derive the formulas for these metrics over Rayleigh fading. In particular, the results can demonstrate the explicit relationship between the handoff metrics and the physical layer characteristics, e.g., carrier frequency, maximum Doppler frequency, and fade margin. Furthermore, the formulas are developed with the generalized teletraffic parameters. Numerical examples are presented to demonstrate the impact of physical layer on the handoff metrics. The techniques and the results are significant for both deploying practical wireless networks and evaluating the system resilience over an unreliable radio channel.

By Lingyang Song, Yan Zhang, Nirwan Ansari, Jianwei Huang and Bechir Hamdaoui, Guest Editors Welcome to this special issue of Wiley Journal of Wireless Communications and Mobile Computing (WCMC). The title of this special issue literally adopts the theme of the 2010 International Wireless Communications and Mobile Computing Conference (IWCMC 2010), as it attempts to represent the ‘best’ of IWCMC 2010 by soliciting representative quality research works presented at the conference for inclusion in this issue via a rigorous selection and review process. This special issue covers a quite broad range of topics of wireless networks, wireless communications, and mobile computing, from the physical layer through application and system design. The goal of this special issue is to create a great opportunity for high impact research from both the mobile communications industry and academia to present and discuss new trends, developments, emerging technologies, and new industrial standards. To guarantee the quality, in this special issue, we have selectively collected 11 expanded papers from the proceedings of IWCMC 2010, and clustered them in three groups: three papers dealing with physical layer aspects, six papers investigating MAC and network layer issues, and two papers focusing on applications as well as prototypes. Detailed overview of the selected works is given below. The first group includes three papers, which provide physical layer results for wireless communications and mobile computing. The first paper, by Takeda et al., studies joint transmit and iterative receive frequency-domain equalization for DS-CDMA. In the proposed scheme, simple one-tap frequency-domain equalization at the transmitter and iterative one-tap FDE at the receiver are jointly performed using the common knowledge of channel state information, and at the same time taking channel estimation constraints into account. The paper by Fan et al. investigates the relay position selection problem for the diamond network over Nakagami-m fading channels. This paper discusses the impact on the performance of diamond network caused by the relays' position for a general Nakagami-m fading channel, which extends the previous work for a special Rayleigh fading case, and gives clear restrictions of their positions based on the requirement of the throughput improvement and network stability. The third paper, by Stuber et al., studies outage probability for cooperative diversity with selective combining in cellular networks. The analysis mainly focuses on outage probability for amplify-and-forward and decode-and-forward cooperative diversity systems with selective combining, for the case of a log-normal Nakagami faded desired signal and log-normal Rayleigh faded co-channel interferers. The second group of papers mainly investigates MAC and network layer issues. The first paper, by Wong et al., deals with switching cost minimization in the IEEE 802.16e mobile WiMAX sleep mode operation in order to improve the battery lifetime of the mobile station. The paper proposes a novel approach to resolve this issue by making a heuristic decision during the listening interval to minimize the switching frequency for better energy efficiency. The second paper by Lin et al. studies Multicast Broadcast Service (MBS) zone configuration for wireless multicast and broadcast service. Two schemes, the overlapping scheme and the enhanced overlapping scheme, are provided for more flexible MBS zone configuration to achieve better performance for MBS in terms of QoS and radio resource utilization. The third paper, by Kumar et al., investigates the issue of trust advisory and its establishment in mobile networks, with application to ad hoc networks, including DTNs. The authors utilized encounters in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. The fourth paper by Znati et al. proposes robust multicast routing algorithms for mobile wireless networks by considering more practical challenges, e.g., the mobility of nodes, the tenuous status of communication links, limited resources, and indefinite knowledge of the network topology. This paper addresses these difficulties by providing a framework and architecture with proactive and reactive components to support multicasting to guarantee reliability and efficiency of end-to-end packet delivery. The fifth paper, by Pu et al., redefines the fairness concept regarding the application utility for time-constraint flows and then presents novel utility-based fair bandwidth sharing approaches in vehicular networks. Accordingly, two practical bandwidth-sharing schemes are provided for transferring data by fast-moving wireless nodes such as vehicles in order to guarantee QoS. The sixth paper, by Ali et al., provides a MAC protocol for cognitive wireless sensor body area networks to increases the critical traffic throughput. The proposed cognitive radio based MAC protocol prioritizes the critical packets access to the transmission medium by transmitting them with higher power while transmitting lower priority packets using lower transmission power. The third group consists of two papers focusing on applications and prototypes. The first paper by Fantacci et al. introduces a novel communication infrastructure for emergency management to interconnect several heterogeneous systems and provide multimedia access to groups of people involved in emergency operations as foreseen by the In.Sy.Eme. (Integrated System for Emergency) project. The main scope of the In.Sy.Eme system is to facilitate functional integration of new technologies with actual or off-the-shelf technologies to provide fast responses to any emergency situations and efficient use of all available resources. The second paper, by Manfrin et al., demonstrates the CalRAdio-Based advanced Spectrum Scanner, an open platform developed to monitor the ISM 2.4-2.499 GHz band, and reveals opportunities for a better utilization of the available spectrum resources. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it capitalizes on the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. In conclusion, this issue of WCMC offers a state-of-the-art view of recent advances in wireless network, wireless communications, and mobile computing. It also offers both academic and industry appeal—the former as a basis toward future research directions and the latter toward viable commercial applications. In the long term, innovative wireless communications and mobile computing techniques will be characterized by their criticalness in consumer, business, and government applications to enhance the development of the whole world in realizing a better future. Finally, we would like to thank all the authors who have submitted their papers for consideration for publishing their work in this issue. We would like to extend our gratitude to the anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also would like to thank the devoted staff of Wiley for their high level of professionalism, and particularly express our gratitude to the Editor-in-Chief of WCMC, Professor Mohsen Guizani, for his advice, patience, and encouragement from the beginning until the final stage. We hope you will enjoy reading the great selection of papers in this issue.

Abstract Multiple‐input multiple‐output (MIMO) enabled wireless sensor networks (WSNs) are becoming increasingly important since significant performance enhancement can be realized. In this paper, we propose a packet forward strategy for MIMO sensor networks by jointly considering channel coding, rate adaptation, and power allocation. Each sensor node has multiple antennas and uses orthogonal space time block codes (OSTBC) to exploit both spatial and temporal diversities. The objective is to determine the optimal routing path that achieves the minimum symbol error rate (SER) subject to the source‐to‐destination (S‐D) energy consumption constraint. This SER‐based quality‐of‐service (QoS) aware packet forwarding problem is formulated into the framework of dynamic programming (DP). We then propose a low‐complexity and near‐optimal approach to considerably reduce the computation complexity, which includes state space partition and state aggregation techniques. Simulations indicate that the proposed protocol significantly outperforms traditional algorithms. Further still, the performance gain increases with tighter S‐D energy constraint. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In cognitive radio (CR) networks, medium access control (MAC) protocols play an important role to exploit the spectrum opportunities, manage the interference to primary users (PUs), and coordinate the spectrum access amongst secondary users (SUs). In this paper, we first introduce the challenges in the design and implementation of CR MAC protocols. Then, we make a comprehensive survey of the state‐of‐the‐art CR MAC protocols and categorize them on the basis of spectrum sharing modes, i.e., overlay and underlay modes. We also introduce some other classification metrics such as architecture (centralized or distributed), sharing behaviors (cooperative or non‐cooperative), and access modes (contention based or contention free). Through the study, we find out that most CR MAC protocols are designed for the overlay mode. The CR MAC protocols in underlay mode could yield higher spectrum utilization efficiency with the cost of more complicated power and admission control schemes. The centralized CR MAC protocols are more suitable to do spectrum sensing using quiet period of whole network, while the distributed CR MAC protocols are more flexible to deploy. We identify several future directions, such as more practical CR MAC protocols for underlay mode, MAC protocols considering security, MAC protocols considering heterogeneous coexistence, etc. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the joint optimization problem on channel coding, power allocation, and route planning in wireless sensor networks (WSN) using dynamic programming (DP). Each sensor node has multiple antennas and applies orthogonal space time block codes (OSTBC) in order to improve the transmission reliability. A decode-and-forward protocol is adopted to relay the signals. The objective function is to determine the packet forwarding route that has the maximum successful transmission rate (STR) subject to the source-to-destination (S-D) energy consumption constraint. Specifically, we cast this energy and quality-of-service (QoS) aware packet forwarding problem into the framework of DP, such that adaptive power allocation can be jointly realized at each sensor node. State space partition techniques and state aggregation approximation architecture are introduced to derive the value function. Simulation results show that the proposed protocols significantly outperform classical routing algorithms, especially when the energy constraint becomes stringent.

The IEEE 802.16e is a compatible and reasonable technology to solve the last mile access problem. Since the multiple services in the system are connection-oriented, it is crucial to provide effective admission control and resource allocation mechanisms to guarantee the quality of service (QoS) of the system. In this paper, we investigate the joint bandwidth reservation and admission control in the IEEE 802.16e networks. Foremost, we consider two system models: model without buffer and model with buffer. Then two optimization problems are formulated for the two models separately. To solve the two min-max optimization problems, we propose a bandwidth reservation algorithm. In addition, the bandwidth reservation thus obtained can be used with admission control jointly. Our aim is to enhance fairness while satisfying QoS requirement. Numerical results are presented to demonstrate the performance of the proposed scheme in terms of maximal call blocking probability and fairness.

Wireless mesh networks (WMNs) is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. We study the minimum-latency communication primitive of gossiping (all-to-all communication) in multi-hop ad-hoc WMNs. Each mesh node in the WMN is initially given a message and the objective is to design a minimum-latency schedule such that each mesh node distributes its message to all other mesh nodes. Minimum-latency gossiping problem is known to be NP-hard even for the scenario in which the topology of the WMN is known to all mesh nodes in advance. We show an approximation scheme that can complete gossiping task in O(n log3/2n) time units with high probability at least 1 - 1/n in any ad-hoc WMN of size n. Our algorithm allows the labels (identifiers) of the mesh nodes to be polynomially large in n. To the best of our knowledge, this is the first time that randomized algorithm has been considered in ad-hoc WMNs with large labels. Moreover, our gossiping scheme also significantly improved all current gossiping algorithms in terms of approximation ratio. Our work has approximation ratio at most O(log3/2n) which is a great improvement of the current best known state-of-the-art algorithm with approximation ratio O(log2n) but for linearly large node labels by Czumaj and Rytter [FOCS'03].

Spectrum handoff is an indispensable component in cognitive radio networks to provide resilient service for the secondary users. In this paper, we explore the spectrum handoff procedure and then propose four metrics to characterize both short-term and long-term spectrum handoff performance: link maintenance probability, the number of spectrum handoff, switching delay, and non-completion probability. In particular, the probability mass function (pmf) and the average number of spectrum handoff are developed. The tele-traffic parameters are relaxed to follow a general distribution function, which will enable a wide applicability and theoretical significance of the derived formulae. Both opportunistic and negotiated spectrum access strategies are investigated. Results show that these two mechanisms will generate significantly different performance. Numerical examples are presented to demonstrate the performance trade-off and the interaction between the primary users and the secondary users. The impact of key parameters on spectrum handoff is also discussed. The techniques as well as the results are important for evaluating the primary and second users co-existence, and hence helpful for design and optimization of cognitive radio networks.

In Cognitive Radio (CR) networks, spectrum handoff is an important functionality to guarantee the reliability and continuity of the communications of Secondary Users (SUs). In this paper, we integrate soft-QoS based spectrum handoff mechanism into the framework of Call-Admission-Control (CAC) for CR networks. By doing so, the proposed CAC strategy has in-built capability to deal with resilient services of wireless applications, and hence obviously improves the QoS and spectrum utilization of CR networks. We adopt Markov chain model to describe the procedures of the proposed CAC and spectrum handoff strategy. After that, the problem is formulated as a nonlinear optimization where the dropping probability is minimized under the constraint of blocking probability. The method of branch-and-bound is employed to make the optimization problem tractable. The proposed CAC strategy is evaluated through simulation experiments. The numerical results indicate that the proposed CAC strategy outperforms two traditional CAC strategies. It achieves a much better tradeoff in dropping and blocking probabilities, and meanwhile significantly enhances the spectrum utilization of CR networks.

This paper addresses the problem of optimal cooperative spectrum sensing in a cognitive-enabled sensor network where cognitive sensors can cooperate in the sensing of the spectrum. Such sensor networks are assumed to be power resource constrained. With a given threshold for the accuracy of the spectrum detection, we find the optimal number of cognitive sensors participating in the cooperative spectrum sensing and the optimal sensing interval that minimize the total energy consumption of the cooperative sensing. First, the mathematical lower bound and upper bound for the number of cooperative cognitive sensors are found. Then the optimization problem to minimize the total energy consumed by a group of sensors is presented. Finally, an efficient approximate solution to the optimization problem is proposed. Numerical calculations validate the accuracy and the performance of the proposed scheme. The impact of the noise uncertainty, the choice of the energy detection threshold, and the spectrum bandwidth on the detection accuracy and the minimum total energy consumption is also studied.

Wireless Mesh Network (WMN) is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. In this paper, we study the problem of multipoint-to-multipoint (M2M) multicasting in a WMN which aims to use the minimum number of time slots to exchange messages among a group of k mesh nodes in a multi-hop WMN with n mesh nodes. We study the M2M multicasting problem in a distributed environment where each participant only knows that there are k participants and it does not know who are other k -1 participants among n mesh nodes. It is known that the computation of an optimal M2M multicasting schedule is NP-hard. We present a fully distributed deterministic algorithm for such an M2M multicasting problem and analyze its time complexity. We show that if the maximum hop distance between any two out of the k participants is d, then the studied M2M multicasting problem can be solved in time O(d log2n+k log3n/log k) with a polynomial-time computation, which is an almost optimal scheme due to the lower bound Omega(d+ k log n/log k) given in [5]. Our algorithm also improves the currently best known result with running time O(d log2n + k log4n) in [13]. In this paper, we also propose a distributed deterministic algorithm which accomplishes the M2M multicasting in time O(d+k) with a polynomial-time computation in unit disk graphs. This is an asymptotically optimal algorithm in the sense that there exists a WMN topology, e.g., a line, a ring, a star or a complete graph, in which the M2M multicasting cannot be completed in less than Omega(d+k) units of time.

The diversity gains achievable in the generalised distributed antenna system with cooperative users (GDAS-CU) are considered. A GDAS-CU is comprised of M largely separated access points (APs) at one side of the link, and N geographically closed user terminals (UTs) at the other side. The UTs are collaborating together to enhance the system performance, where an idealised message sharing among the UTs is assumed. First, geometry-based network models are proposed to describe the topology of a GDAS-CU. The mean cross-correlation coefficients of signals received from non-collocated APs and UTs are calculated based on the network topology and the correlation models derived from the empirical data. The analysis is also extendable to more general scenarios where the APs are placed in a clustered form due to the constraints of street layout or building structure. Subsequently, a generalised signal attenuation model derived from several stochastic ray-tracing-based pathloss models is applied to describe the power-decaying pattern in urban built-up areas, where the GDAS-CU may be deployed. Armed with the cross-correlation and pathloss model preliminaries, an intrinsic measure of cooperative diversity obtainable from a GDAS-CU is then derived, which is the number of independent fading channels that can be averaged over to detect symbols. The proposed analytical framework would provide critical insight into the degree of possible performance improvement when combining multiple copies of the received signal in such systems.

This paper presents a novel k-hop compound metric based clustering (KCMBC) scheme, which uses the host connectivity and host mobility jointly to select cluster-heads. KCMBC is a fast convergent and load balancing clustering approach that is able to offer significant improvement on scalability for large-scale ad hoc networks. On the other hand, since host mobility has been taken into account in terms of the average link expiration time, the clusters constructed by KCMBC are more stable than many other schemes. Simulation results show that the clusters created by using the KCMBC approach retain modest but more uniform cluster size, and cluster-head life-time can be increased by KCMBC up to 50%. Moreover, the control overheads for cluster formation using the KCMBC scheme are kept relatively low if compared to other clustering schemes.

Ultra-wideband (UWB) is a new technology that enables wireless connectivity with consistent high data rates across multiple devices, such as high-definition television (HDTV) receivers, PCs, printers and digital cameras, within the digital home, and the office. In this paper, we focus on UWB transmissions where multiple accesses to the channel are coordinated by the IEEE 802.15.3 medium access control mechanism proposed in the IEEE 802.15.3a task group. Advanced encryption standard (AES), the most popular encryption cipher used nowadays, is used to ensure the security of the transmission. We study the overhead introduced by applying the AES cipher to the transmitted frames. Specifically, we analyze the tradeoff between throughput, payload size, and channel error when AES is used to encrypt the frames.

We propose a hierarchical pipeline paging (HPP) for multi-tier hierarchical cellular networks, in which different tiers overlay with one another to provide overlapped coverage of cellular service, and each mobile terminal can be paged in any tier of a network. Paging requests (PRs) are queued in different waiting queues, and multiple PRs in each waiting queue are served in a pipeline manner. We study HPP, hierarchical sequential paging (HSP), and hierarchical blanket paging (HBP) schemes analytically in terms of discovery rate, total delay, paging delay, and cost. It is shown that HPP scheme outperforms both HBP and HSP schemes in terms of discovery rate while maintaining the same cost as HSP scheme. The HPP scheme outperforms HSP scheme in terms of total delay and has a lower total delay than HBP scheme when traffic load is high.

Abstract In cognitive radio cellular networks (CogCell), the secondary users (SUs) are allowed to access the channels licensed to the primary users (PUs) including Primary Transmitters (PTs) and Primary Receivers (PRs), only if the interference to the PRs is less than the predefined threshold, and the quality of service (QoS) requirements of PTs are guaranteed. In addition, different SUs may require different levels of QoS, and pay differently depending on the provided QoS. The network operator achieves different secondary revenues by admitting SUs in different QoS levels. The problem we address in this paper is to maximize the total secondary revenue relative to the interference constraints on PRs, and QoS requirements for both PTs and SUs. We formulate this optimization problem, and propose a power control scheme for both PTs and SUs. Then, we introduce three solutions including an exact solution using dynamic programming, a greedy heuristic algorithm, and a minimal signal‐interference‐plus‐noise‐ratio (SINR) removal algorithm. Based on these algorithms, we propose three QoS aware admission and power control (QAPC) schemes, one optimal solution called QAPC‐dynamic, and two approximate solutions called QAPC‐greedy and QAPC‐minimal SINR removal algorithm (MSRA), respectively. Numerical results show that QAPC‐dynamic always achieves the highest secondary revenue while QAPC‐MSRA gives the lowest secondary revenue. Since the time complexity of QAPC‐dynamic is much higher than the other two schemes, QAPC‐greedy is recommended considering the trade‐off between the computation complexity and performance gain. Copyright © 2009 John Wiley & Sons, Ltd.

In a cognitive radio wireless network, each node can sense and opportunistically access the under-utilized spectrums in the primary system. Since the unoccupied spectrum is location-dependent and time-dependent, the available spectrums in each node are different. With this spectrum heterogeneity and different Quality-of-Service (QoS) requirement, different nodes may have different preferences in using a particular channel to communicate with its neighboring nodes. In this paper, we formulate this channel selection problem using a cooperative game theoretical approach such that the QoS requirement of each node is guaranteed and the network capacity is maximized. To further improve the performance, a learning negotiation mechanism is introduced. The key motivation is to derive new mixed strategies of the nodes with the reference to the historical profiles of all selected strategies in the past. Simulation results are presented to show the fast convergence of the game, the high efficiency of the learning mechanism, and the effect of mobility.

Channel capacity and node energy represent resources and constraints in designing efficient routing schemes for Wireless Sensor Networks(WSNs). To delivery more data, a higher rate is desirable, which however consumes more energy and may demand more bandwidth. Hence, data transmission in WSNs should take into account both limited capacity and constrained energy. In this paper, we propose an utility-based nonlinear convex optimization formulation to maximize utility subject to the capacity and energy constraints. To achieve this, we introduce link interference set to represent all flows contention over a link, and it offers the capacity constraint over the specific link. For each node, we express the energy constraint with network lifetime requirement. A distributed solution with dual decomposition approach is proposed to address the optimization formulation. In addition, an Optimal Rate Routing (ORR) is developed by incorporating the optimization result to select the optimal rate route. Comparing with the previous schemes, ORR is able to achieve the highest utility, optimal rate selection during routing, and well-balanced performance.

Access authentication is an indispensable component in wireless mobile networks for providing network resilience and ensuring secure communications. Normally, more complicated secure algorithms will consume more resources, including bandwidth, power, computation time and storage space. This will result in higher authentication overhead, which, however, is not fully studied in the literature. In this paper, we will analyze and exam the authentication overhead in wireless networks. The number of user authentication request (UAR) is formulated as the performance metric to demonstrate the authentication traffic load. The probability mass function and the average number of UAR are developed. In deriving the formula, Registration Area residence time is relaxed to follow a general probability density function. In addition, an efficient recursive algorithm is developed to significantly reduce the computation complexity. Numerical examples are presented to investigate the interaction between authentication overhead and the key tele-traffic parameters. The analysis has been validated by the simulation.

Call admission control is an effective mechanism to guarantee robust services in wireless networks. In this paper, we present several call admission control algorithms and queueing models for the subcarrier allocation in the OFDM-based wireless multimedia networks. Call connection requests are differentiated into narrow-band call and wide-band call. For either class of calls, the traffic process is characterized as batch arrival since each call may request multiple subcarriers to satisfy its quality-of- service (QoS) requirement. The batch size is a random variable following a probability mass function (pmf) with realistically maximum value. In addition, the service time for various classes are different. The formulae for the significant performance metrics call blocking probability and bandwidth utilization are developed. Numerical investigations are presented to demonstrate the interaction between key parameters and performance metrics. The performance tradeoff among different call admission control algorithms are discussed. Moreover, the analytical model has been validated by the simulation. The methodology as well as result provides an efficient tool for planning the future generation OFDM-based broadband wireless access system.

Cognitive radio wireless networks is an emerging communication paradigm to effectively address spectrum scarcity challenge. Spectrum sharing enables the secondary unlicensed system to dynamically access the licensed frequency bands in the primary system without any modification to the devices, terminals, services and networks in the primary system. In this paper, we propose and analyze new dynamic spectrum access schemes in the absence or presence of buffering mechanism for the cognitive secondary subscriber (SU). A Markov approach is developed to analyze the proposed spectrum sharing policies with generalized bandwidth size in both primary system and secondary system. Performance metrics for SU are developed with respect to blocking probability, interrupted probability, forced termination probability, non-completion probability and waiting time. Numerical examples are presented to explore the impact of key systems parameters like the traffic load on the performance metrics. Comparison results indicate that the buffer is able to significantly reduce the SU blocking probability and non-completion probability with very minor increased forced termination probability. The analytic model has been verified by extensive simulation.

In this paper, we develop kappa-connectivity in wireless multi-hop networks in the presence of both the log-normal shadowing and Nakagami-m fading. The formula are generally applicable in a variety of realistic physical situations. Based on the results, we are able to find the critical node density to satisfy node kappa-connectivity almost with probability 1. In addition, kappa-connectivity shows significant difference with distinct channel conditions, which necessitates a generic result. The result is useful for the design of a wireless multi-hop network in practice.

With the rapid progress in information technology and an enormous amount of media appearing over Internet, e.g., text, audio, speech, music, image, and video, guaranteeing information security is becoming increasingly important. Several pivotal challenges include copyright protection, integrity verification, authentication, and access control etc. As a consequence, the subject of security protection in multimedia communication has attracted intensive research activities in academia, industry, and also government. Nowadays, multimedia data are used more and more widely in human's daily life. The typical applications include audio broadcasting, Digital TV, Mobile TV, etc., which are constructed on multimedia communication techniques. Associated with these applications, there are some security issues, e.g., multimedia content security, payment security and user privacy. To solve these issues, some means are required. During the past decades, various techniques have been reported for secure multimedia communication, including key management, multimedia encryption, authentication, digital watermarking, digital fingerprinting, access control, and digital rights management. Among them, multimedia encryption, authentication, digital watermarking, and digital fingerprinting aim to protect multimedia content's confidentiality, integrity, ownership, and traitor traceability. For example, digital fingerprinting embeds a unique customer code into multimedia content in order to produce a unique copy for the certain customer. Thus, the illegal redistribution of the media copy can be traced by detecting and comparing the embedded customer code. Some other techniques, e.g., key management, access control, and digital rights management, are able to protect payment security and user privacy. Taking access control for example, it permits only the authorized customer to access the multimedia content, while some other customers without payment often have limited access right. Additionally, in different networks such as Internet, 3G wireless, DVB-H, and p2p, different secure protocols and algorithms are required to provide the system security. For example, in DVB-H, the broad cast mode is used for data transmission, while in Internet-based services, the unicast or multicast mode is often used. Different from them, in p2p, the multi-hop transmission without the server is often used. When different networks are converged, the secure system with interactivity and interoperability is expected. All these topics are in active development. Furthermore, devices like digital cameras, mobile/video phones, graphics processing units, DVD player, etc. are expected to be equipped with such security mechanisms. In these situations, software solutions may not be adequate to provide high real-time performance. On the other hand, hardware assisted solutions are much better for easy integration with multimedia hardware, which has low power consumption, higher reliability/availability, and low cost. The aim of this special issue is to present a collection of high-quality research papers that report the latest research advances in secure transmission or distribution of multimedia content than on multimedia content protection. In this special issue, we selected 7 papers, which can demonstrate advanced works in this field. A detailed overview of the selected works is given below. The first paper, A Secure Virtual Point of Service for Purchasing Digital Media Content over 3G Wireless Networks, presents the secure virtual point of service (SVPOS) for secure multimedia content transactions and payments. The proposed solutions based on 3rd generation partnership project (3GPP) generic authentication architecture (GAA) and standard charging and billing protocols (e.g., Parlay-X) guarantees some desirable privacy and security properties, including privacy of subscribers, protection of 3G operator and merchant, and interaction security. The scheme is suitable for cell phone based merchant purchase in 3G networks. The second paper, Realising Time-limitation for Cryptographic Keys in Secure Multimedia Distribution, reports a promising transmission infrastructure and key construction scheme for future multimedia delivery. In particular, the scheme is transmitting the encoded layers on separate streams which will be secured using SRTP. This allows fast adaptation by just dropping layers if necessary. The session keys used for SRTP are time-limited and hierarchically dependent such that only one key is necessary for the client to decrypt all desired layers. Although the prerequisites for choosing the initial values of the time-limited keys are fairly high, this structure implies less administration effort. The third paper, Integrating Fingerprint with Cryptosystem Internet-Based Live Pay-TV System, proposes a new architecture to protect contents from unauthorized viewing and illegal redistribution for an internet-based live pay-TV system. Code embedding is considered in order to enhance the resistance against collusion of fingerprinted video. The paper has demonstrated that the proposed scheme can provide higher security and better trade-off between image and video encryption and fingerprinting imperceptibility than existing works. The paper also indicates the ongoing implementation of the pay broadcasting system based on the proposed architecture. The fourth paper, User Plane Security Alternatives in the 3G Evolved Multimedia Broadcast Multicast Service (e-MBMS), proposes a secure multicast overlay (SMO) approach and describe how to implement based on a proof-of-concept test-bed over public domain Linux routers. The paper demonstrates that SMO is able to achieve a low risk of denial of service attacks, significant advantages in terms of impact on the architecture and on device requirements, and high security association management and key management. The fifth paper, Step-wise Inter-frame Correlation-based Steganalysis System for Video Streams, reports a collusion scheme among video frames to find out whether secret data is hidden in frames or not. The effect of local motion interfering detection precision is studied. The local motion and message embedded in video frame is treated as a bimodal noise. To reliably detect the existence of the embedded message, block-wise correlation-based steganalysis scheme is proposed to reduce the local motion interfering effect. The video steganalysis process is divided into two stages. In the first stage, suspicious video frames will be recognized by decision module employing features extracted with a light-weight collusion scheme. In the second stage, suspicious frames will be analyzed critically by the present powerful image steganalysis schemes. In addition, the determine principle is also studied to reduce the false positive rate in first stage. Experimental results have been presented to show the performance of the proposed strategy. The sixth paper, AuthoCast - a Mobility-compliant Protocol Framework for Multicast Sender Authentication, introduces a protocol framework for authenticating multicast sources and securing their mobility handovers. Using a self-consistent, one-way authentication based on cryptographically generated addresses, a common design is derived to jointly comply with the mobile any source and source specific multicast protocols that are currently proposed. This light-weight scheme smoothly extends the unicast enhanced route optimization for mobile IPv6 and adds only little overhead to multicast packets and protocol operations. The seventh paper, Early Security Key Exchange for Encryption in Mobile IPv6 Handoff, proposes an early security key exchange for encryption in Mobile IPv6 handoff in order to reduce the security latency. In the approach, two issues are addressed in dealing with the latency within the encryption technology during the handover. First, the study extends the early binding update method to deal with the long security exchange negotiation time for the Mobile IPv6 handoff. Secondly, the study adopts the security access gateway (SAG) to solve the limited computing and memory in the mobile node. In conclusion, this issue of Security and Communications Networks offers a ground-breaking view into the recent advances in secure multimedia communications. This issue offers both academic and industry appeal- the former as a basis toward future research directions, and the latter toward viable commercial applications. Finally, we would like to express our gratitude to the Editor-in-Chief, Dr. Hsiao-Hwa Chen for his advice, patience, and encouragements since the beginning until the final stage. Special thanks go to Michelle in Wiley during the production. We thank all anonymous reviewers who spent much of their precious time reviewing all the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also thank all authors who have submitted their papers for consideration for this issue. We hope you will enjoy reading the great selection of papers in this issue.

In this paper, we perform a tele-traffic queueing analysis for OFDM subcarrier allocation in wireless multiservice networks. For this purpose two call admission control algorithms, the batch blocking scheme and the partial blocking scheme, are proposed. Call connection requests are classified into two different types, narrow-band and wide-band. For either class of calls, the traffic process is characterized as batch arrival, as each call may request multiple subcarriers to satisfy its quality-of-service (QoS) requirements. The batch size is a random variable which obeys a probability mass function (pmf) with a realistic maximum value. In addition, the service times for various call classes are different. Consequently, an OFDM-based broadband wireless multiservice network can be formulated as a multiclass multiserver batch arrival queueing system. The methodology and results are further generalized into a multiple-class scenario with service priority provision. Formulae are developed for evaluation of the following performance metrics: the probability that a call will be blocked, the average number of subcarriers used, and bandwidth utilization. Numerical results are presented to demonstrate the interactions between key parameters and performance metrics. The analytical model was validated by the simulation results, showing the fact that it can be used as an efficient tool for design of future-generation broadband wireless access networks.

Abstract In the literature, there are two common assumptions for the tele‐traffic parameter in analyzing the wireless network performance, that is, the tele‐parameter follows a specific probability density function (pdf) and additionally the pdf exists closed‐form Laplace Transform (LT). However, taking into account the cell irregular shape, the specific pdf may be unavailable while only the measured statistical moments are available. Moreover, the pdf function may not exist a closed‐form LT, for example, lognormal distribution function. In this paper, based on the Central Limit Theorem and hyper‐Erlang universal approximation property, we propose an approximation method applicable in the situations when only the statistical moments are available or LT of pdf does not exist. We then employ the technique in diverse applications, including the performance analysis of wireless network and the cost evaluation of mobility management. Extensive numerical examples demonstrate the good approximation capability to the exact formula and the simulation results. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Access security is an indispensable component in the future generation wireless networks to enable the trusted communications. During the User Equipment (UE) access procedure, the authentication overhead analysis is able to provide insights in understanding the security management performance. To unify the model for analyzing the authentication traffics under different location management schemes, we introduce the concept location update inter‐arrival time. Furthermore, based on the UE's typical behavior, we propose a system model and define the metric number of User Authentication Request (UAR) in SGSN residence time to evaluate the authentication cost. Numerical results are illustrated to show the interaction between the parameters and the performance metrics. The sensitivity of call arrival process and the effects of location management schemes are also discussed. Copyright © 2006 John Wiley & Sons, Ltd.

As a fundamental component in wireless networks, location management consists of two operations: location update and paging. These two supplementary operations enable the mobile user ubiquitous mobility. However, in case of failed location update, a significant consequence is the obsolete location identity in the network databases and thereafter the incapability in establishing the valid route for the potential call connection, which will seriously degrade the network quality-of-service (QoS). This issue is not theoretically studied in the literature. In this paper, we perform a quantitative analysis of the location management effect on QoS in the wireless networks. The metrics call blocking probability and the average number of blocked calls are introduced to reflect the QoS. For the sake of general applicability, the performance metrics are formulated with the relaxed tele-traffic parameters. Namely, the call inter-arrival time, cell residence time, location area residence time and location update inter-arrival time follow a general probability density function. The formulae are additionally specified in the static and several dynamic location management mechanisms. Numerical examples are presented to show the interaction between the performance metrics and location management schemes. The discussions on the sensitivity of tele-parameters are also given. Index Terms -Location management, QoS, location update, wireless networks, call blocking probability, dynamic location management

We consider energy efficient packet forwarding with quality-of-service (QoS) guarantee for wireless sensor networks (WSNs). In most existing wireless network protocols, route planning and transmission rate setting are considered independently, however, we observe that energy consumption can be significantly reduced by combining these two aspects together when making forwarding decision. This is because this combination potentially increases one more degree of freedom (i.e., the temporal dimension) for the original routing problem. We are motivated to exploit this additional degree of freedom and devise forwarding protocol that conserves more energy than traditional algorithms. In particular, we cast the packet forwarding problem into the framework of dynamic programming. State space partition technique and state aggregation approximating architecture are introduced to produce an approximating optimal value function. Simulation experiments are carried out to evaluate the performance of the proposed forwarding protocol. The results indicate that our protocol outperforms classical routing algorithms, especially when the QoS constraint becomes stringent.

Handoff probability is one of the significant metrics to characterize the handoff operation in wireless mobile networks. Handoff probability refers to the probability that an Mobile Station (MS) will handoff to a neighboring cell without call completion upon the instant arriving at the border of the serving cell. In the literature for developing the metrics, the physical fading channel is not taken into account. In addition, the tele-traffic parameters are usually simplified as exponential distributed variables for the sake of analytical tractability. In this paper, by proposing a new cross-layer approach, we formulate the closed-form result for the handoff probability under the Rayleigh fading scenario. In particular, the results could demonstrate the explicit relationship between the handoff probability and the physical link characteristics, e.g. carrier frequency, maximum Doppler frequency and fade margin. In addition, the formulae is developed with the generalized tele- traffic parameters for the aim of general applicability. The numerical investigations demonstrate that the fading channel has a substantial impact on handoff probability.

In this paper, geometry-based channel models are proposed to describe the topology of generalized distributed antenna systems with cooperative diversity (GDAS-CD). The system architecture comprises M largely separated access points (APs) at one side of the link, and N geographically closed user terminals (UTs) at the other. The UTs are assumed to be operating in cooperative mode to enhance the system diversity. The average cross-correlation of signals received from noncollocated APs and UTs is derived based on the network topology and the correlation models derived from the empirical data. The analysis is also extended to more general scenarios when the APs are placed in a clustered form. The presented results would provide useful insight into the degree of possible performance improvement from the GDAS-CD.

A geometry-based channel model is proposed to describe the topology of a generalized distributed antenna system with cooperative diversity (GDAS-CD). The system architecture comprises a number of largely separated access points (APs) each with multiple antennas within an AP at one side of the link, and several geographically closed user terminals (UTs) each having multiple antennas within a UT at the other side. The UTs are assumed to be cooperative devices. The average cross-correlation of signals received from non-collocated APs and UTs is derived based on the system topology and the empirical models proposed by Sorensen. Subsequently, we investigate the diversity gains obtainable from the GDAS-CD based on the proposed model, which would provide insight into the degree of possible performance improvement when combining multiple copies of the received signal.

Call admission control (CAC) is a significant component in wireless networks to guarantee quality-of-service (QoS) requirements and also to enhance the network resilience. In the literature, CAC is normally evaluated by considering the limited bandwidth in the radio interface while the physical fading wireless channel is not considered. This work proposes a cross-layer analytical technique to study a CAC mechanism in wireless networks over Rayleigh fading channel. The tele-traffic parameters are relaxed to follow general distributions in stead of exponential variables for the sake of general applicability in various scenarios. The explicit relationship is derived between the performance metrics and the Rayleigh fading channel characteristics. Numerical investigation demonstrates that the fading channel has a substantial impact on the network performance.

Energy management strategy is an important component in the emerging standard IEEE 802.16e supporting mobility. The efficient energy saving mechanism is the foundation to guarantee long lifetime of mobile subscriber station (MSS). In this paper, we characterize the standardized sleep mode in IEEE 802.16e and perform an extensive analysis of the specified energy management scheme in terms of energy consumption and packet delay. The analytical model is developed based on the generalized traffics processes to match the recent state-of-art research on tele-traffic modeling. Simulation results are presented to validate the analytical model; and the illustrative examples are shown to study the interaction between the key parameters and the aforementioned metrics.

Due to the inherent flexibility, scalability and reliability advantages of mesh networking architecture, the IEEE 802.16 working group is actively standardizing the mesh mode. Compared with the point-to-multipoint (PMP) mode, the newly introduced mesh mode has not specified any solution for providing quality-of-service (QoS) mechanisms in the literature. In this paper, a simple and effective scheme achieving QoS differentiation in the WiMAX mesh networking mode was proposed and analyzed. In addition, by taking account into the dynamics of networks topologies and also the variations of different protocols, a characteristics matrix for a mesh network from stochastic point of view was introduced. Based on the matrix, a new formula was developed for the performance metrics which enables the theoretical evaluation of a random topology. Illustrative numerical examples are presented to demonstrate the effectiveness of the proposed strategy, and also the impacts of the key parameters in differentiating the various services.

To provide differentiated service is one of important issues in the design of wireless ad hoc networks. Traditionally, the provisioning of the differentiated service to different types of traffic streams is mainly supported at the medium access control layer with strict priority scheduling or weighted priority scheduling. In this paper, we propose a novel solution, named as Power-Controlled Rate-Adaptive medium access control (PCRA-MAC) scheme, to provide differentiated service by cross-layer consideration and design. The PCRA-MAC scheme exploits the features of multi-rate and power adjustable wireless transceiver system to achieve different transmission rates to support differentiated service. To the best of our knowledge, the PCRA-MAC scheme is the first and the only MAC scheme that generates different transmission rates for different types of traffic by tuning transmission power. Simulation experiments have proved that the PCRA-MAC scheme can effectively supports differentiated service in the wireless ad hoc networks.

Traditionally, the wireless networks tele-traffic modeling is performed without taking into account the physical link inherent unreliability. In order to reflect the unique characteristics of the channel erroneous property, we introduce the concept resource occupancy time (ROT) and derive its property in terms of probability density function and statistical moments under the general scenarios, i.e. general call holding time and the generalized wireless channel model. Numerical investigation is carried out to discuss the impacts of the call holding time and the wireless channel characteristics upon ROT.

There are two major contributions in this paper. The first is to exam the Poisson assumption for the authentication traffic process triggered by the location update (LU) requests. For this, we develop an analytical model and efficient, recursive algorithm to derive the LU inter-arrival time in static as well as dynamic location management scheme. The analysis is validated by simulation and the numerical investigation indicates that the Poisson process is invalid in approximating the authentication requests. The second goal is to investigate the issue: whether different mobility management schemes have a significant effect on the authentication traffic evaluation. To answer this, we propose a system model to incorporate different LU policies. Via simulation, we evaluate the authentication traffics under static and dynamic location management schemes. The comparison demonstrates that, due to the diverse network architecture as well as the different event triggering LU message, there are significant discrepancy in generating authentication traffic load. The result reveals the fact that the performance of security management and the mobility management in wireless mobile networks interact with each other.

In PCS networks, location management operation expends the limited wireless resources to keep track the location information of a mobile terminal. Various dynamic location update (LU) schemes have been proposed to improve the efficiency of location management. However, most of them only work for certain mobility patterns. In this paper, we propose a new scheme that the LU area is adaptively designed according to the mobility pattern and traffic parameters. The 2D Markov walk is used as the mobility model which describes a broad class of mobility patterns. A recursive algorithm is developed to compute the location management cost of a general LU area shape. An iterative greedy heuristic algorithm is then used to find the LU area shape with minimum location management cost. The effects of the mobility patterns and traffic parameters on the designed LU area shape are investigated. Experimental results show that the LU area designed by the heuristic algorithm can adaptively change according to the given mobility pattern and traffic parameters. Compared with some existing dynamic LU schemes, the proposed adaptive LU is more flexible and efficient for location management.

An improvement for the mutual authentication in the third-generation mobile networks is proposed to address the potentially long delay in waiting for authentication vector (AV) when SGSN and HLR/AuC are located far away from each other, or when the two entities are operated by different operators, or when the mobile terminal (MT) call/mobility activity is frequent. In the 3GPP Technical Specification TS33.102, the probability that an action triggering authentication and key agreement (AKA) has to wait for an AV is fixed as high as 20%. Our proposed scheme is able to achieve this probability smaller than 2% with negligible increased signaling overhead and low storage cost.

New closed-form formulas for the call complete probability and the probability density function (pdf) of the completed call holding time (CCHT) are developed under the concurrent impacts of the resource insufficiency as well as the wireless link unreliability in the wireless mobile networks performance evaluation. The results are obtained with the general scenario, i.e. general call holding time, general cell residence time and the generalized wireless channel model. The analysis result is validated by the simulation model under typical call holding time and cell residence time distributions, Gilbert-Elliott or Fritchman wireless channel model. The comparison indicates that the wireless networks performance will be greatly overestimated without taking into account the unreliable wireless link effect.

In the hierarchical cellular system, the take-back procedure usually takes place when the overflowed slow or fast call crosses the microcell boundary. In this paper, three new take-back schemes, micro-macro, macro-micro and macro-macro, based on cell boundary crossing are proposed. In terms of call blocking probability and call dropping probability, the comparison shows that micro-macro outperforms the other schemes.

New results for the handoff probability in wireless mobile networks are developed taking into account the effects of the limited bandwidth and unreliable wireless channel, either of which may result in the forced termination of an ongoing call connection. The formulae are derived based upon the general scenario, i.e. general call holding time, general cell residence time and the generalized wireless channel model. Numerical investigation is carried out to discuss the impact of the wireless channel characteristics upon the handoff probability.

In this paper, we propose a model to study the call performance in mobile network taking into account the inherent time varying wireless channel. The performance metrics call complete probability and call complete ratio are introduced and further derived with the general call holding time. The impacts of the wireless channel error characteristics and the system parameters on the call performance are illustrated and discussed. The sensitivity problem with respect to the call holding time distribution is investigated. The result indicates that both call complete probability and call complete ratio exhibit a significantly different variation under different call holding time. The analytical model is verified by simulation result.

In this paper, we study a fixed wireless access (FWA) system supporting both fixed and mobile users. A multi-dimensional Markov chain is developed which captures the mobility and call arrival pattern of the users. Three different policies including complete sharing, guard channel and reserved trunk are evaluated. Analytical results are provided to demonstrate the effects of various parameters on the system performance.