Traditional federated learning (FL) approaches face significant challenges when applied to dynamic and heterogeneous Internet of Vehicles (IoV) environments, which are characterized by frequent node mobility, unstable communication links, and highly non-independent and identically distributed (Non-IID) data. In particular, decentralized network topologies exacerbate the difficulty of maintaining model consistency, thereby impairing overall learning performance. To address these challenges, we propose a new hierarchical decentralized federated learning (HDFL) framework. This framework combines the advantages of centralization and decentralization, builds a three-layer collaborative structure, and improves communication flexibility through an asynchronous model exchange mechanism between the edge and the client. Simultaneously, HDFL introduces a local fine-tuning strategy based on knowledge distillation to enhance the generalization ability and stability of the model. Experimental results using an urban traffic simulation platform show that HDFL consistently outperforms representative decentralized FL methods in terms of the achieved accuracy and convergence speed under heterogeneous IoV environments.

This paper introduces a real-time semantic communication system for remote driving, addressing the challenges of video transmission over constrained and fluctuating wireless communication links. Instead of transmitting raw video streams, the proposed system extracts and transmits compact semantic representations, significantly reducing bandwidth requirements while preserving task-relevant visual information. An end-to end semantic encoding-decoding pipeline enables low latency operator-view reconstruction with low latency, improving robustness without relying on high-throughput links. Implemented and evaluated on the real-time prototype, the proposed system demonstrates the practicality of semantic communication for enhancing responsiveness and reliability in remote driving scenarios.

The convergence of Federated Learning (FL), a nascent decentralized machine learning paradigm, with the Internet of Things (IoT) presents unprecedented opportunities for promoting sustainable agricultural development. However, this synergy faces severe challenges from untargetd poisoning attacks which undermine the performance of global models. Due to factors such as seasonality and location, agricultural data exhibit a high degree of non-IID characteristics which further exacerbates this risk. In this context, the model updates submitted by benign clients tend to become dispersed, allowing malicious updates to blend in and go undetected. This dispersion renders traditional defense frameworks based on global anomaly detection ineffective, preventing FL from realizing its full potential in the agricultural domain. In this article, we propose FedCoguard, aimed at ensuring the integrity of FL in smart agriculture. FedCoguard shifts the perspective of defense from the server to the client. By leveraging the intrinsic differences in training objectives between benign and malicious clients, it excludes malicious updates spontaneously, thereby protecting the global model. Experiments on five benchmark datasets demonstrate that even in scenarios of highly non-IID data and a substantial presence of malicious clients, FedCoguard can achieve high accuracy and robust performance. By addressing security issues in the collaborative training process, this research alleviates the challenge of data silos in agricultural data sharing, unlocking the potential of collaborative intelligence and promoting the development of sustainable agricultural systems.

Vehicular Edge Computing (VEC) is pivotal for latency-sensitive vehicular applications but confronts three critical challenges: traditional one-time identity authentication cannot adapt to high mobility, leaving privacy and data security vulnerabilities, offloading system security levels lack quantifiability, and security-performance optimization objectives are inherently conflicting. To address these limitations, we propose a lightweight continuous identity authentication-based secure offloading scheme for VEC. First, a three-entity collaborative architecture is designed, which leverages chameleon hash function (CHF) to reduce vehicle-side signature overhead, and Bloom filter (BF) to enable real-time verification during vehicle-roadside unit (RSU) handovers. Second, a dual-dimensional security framework that quantifies authentication and data signature levels is established, enabling on-demand security adjustment for diverse tasks. Third, to balance task latency minimization and security maximization, Unlike prior works that optimize security and offloading separately, this framework unifies both identity authentication and data transmission security into a holistic latency-oriented offloading design, filling critical research gaps in high-mobility VEC scenarios. To tackle this intricate problem, we decompose it into four sub-problems. These sub-problems are solved using Lagrangian duality, the Newton-Raphson method, and the branch-and-bound algorithm to obtain stable and highquality feasible solutions efficiently. Extensive simulations against four baseline schemes demonstrate that the proposed approach achieves fast convergence and priority performance.

With the rapid development of Generative Artificial Intelligence (GAI), large-scale AI-Generated Content (AIGC) has been widely produced, raising critical challenges in trustworthy copyright management. Blockchain-based copyright registration or trading have become a research hotspot, but existing solutions focus on isolated stages and fail to support the full lifecycle of AIGC content, while copyright management performance, infringement detection capability, and copyright query efficiency remain challenging. To address these challenges, we designed a dual-layer blockchain framework for full-lifecycle AIGC copy-right management, which supports coordinated copyright registration, verification, trading, and traceability. The proposed framework adopts a dual-layer architecture with a main chain and multiple sub-chains, and integrates sharding with a Directed Acyclic Graph (DAG) parallel ledger to improve system scalability. Specifically, a Perceptual Hash (pHash)-based similarity detection method is introduced for copyright registration to identify plagiarism and unauthorized duplication; a hybrid indexed sharded query mechanism is designed for efficient and verifiable copyright verification; and cryptographic techniques together with zero-knowledge proofs are incorporated to enable secure and non-repudiable copyright trading. Experimental results show that the designed framework delivers about 1.1× higher throughput and achieves roughly a 29× reduction in transaction latency compared with single-chain blockchains, while the proposed query mechanism reduces query latency by up to 56× across different shard scales. These results validate the capability of the proposed framework to support secure, efficient, and scalable AIGC copyright management.

Network automation is critical for Intelligent Internet of Things (IIoT) systems, such as industrial networks and smart cities, where managing heterogeneous devices at scale poses significant challenges. Intent-Based Networking (IBN) powered by Large Language Models (LLMs) enables natural language intent translation into executable policies, yet struggles with accuracy in few-shot In-Context Learning (ICL) due to inefficient example retrieval for multi-intent scenarios. To address this issue, we propose SubmodICL, a submodular optimization-based method that retrieves contextual examples to maximize query-guided mutual information, thereby ensuring both coverage and representativeness. Integrated with LLMs, SubmodICL significantly improves intent translation performance. We validate our approach through a use case translating flow-table operation intents into API calls, and evaluate LLMs across different parameter scales. Experiments demonstrate that SubmodICL outperforms baselines by 9.121%–12.377% in accuracy for resource-constrained settings (small LLMs, limited examples, or multi-intent tasks). The proposed method provides a generalizable framework for enhancing intent translation in network automation, with potential applicability to IIoT systems.

The regional edge cloud data centers support numerous latency-sensitive applications, including autonomous driving, Augmented Reality/Virtual Reality (AR/VR), smart grids. However, dynamic workloads often trigger spurious Virtual Machine (VM) migrations that degrade real-time service guarantees. To address this challenge, we propose a lightweight, proactive VM scheduling framework based on a hierarchical structure (HLFVM). By combining logical region partitioning with low-complexity migration algorithms, it enables rapid localized migration decisions. First, by leveraging the Enhanced Harris Hawk Optimization (EHHO) to optimize the parameters of the Long Short Term Memory (LSTM) model, we propose a Load Forecast method based on the EHHO-LSTM (LFEL) model. This algorithm accurately predicts multiple resource loads on PMs and reduces the lag in migration decision-making. Then, we propose the zone-aware LFEL-based VM Migration (LFVM) algorithm, which includes PM status classification and migration selection mechanism. The migration selection mechanism chooses the VM destinations according to the cost function to expedite the migration decision. Numerous experiments have shown that the execution time of the LFVM algorithm is reduced by at least 70.4% compared to traditional algorithms, while VM migration time is improved by 5.7%. Concurrently, it achieves superior control over energy consumption and enhances resource utilization.

Epidemic forecasting is vital for public health, yet privacy concerns impede inter-institutional data sharing and limit model performance. Federated learning has emerged as a promising approach, but previous research has been limited to specific datasets and temporal prediction. In this paper, we present a privacy-preserving framework, federated framework for epidemic on networks (FFEN), for node-level epidemic prediction on networks that leverages federated learning (FL) to model the spatio-temporal propagation of epidemic severity across data-isolated subnetworks. A Spatio-Temporal Graph Attention Network (STGAT) is proposed to enhance federated epidemic prediction by effectively capturing spatio-temporal dependencies. Extensive simulations on various epidemic processes within a real-world airline network comprehensively evaluate FL’s efficacy under diverse scenarios. To further assess robustness, we introduce the efficacy energy metric, systematically analyzing key factors affecting FL performance. Numerical results validate the effectiveness of FFEN in complex epidemic prediction and demonstrate that STGAT outperforms traditional temporal approaches in capturing dynamic epidemic propagation.

As an emerging communication technology, fluid antenna (FA) offers remarkable diversity and multiplexing gains due to its port mobility, which significantly reduces transmission delays in communication processes. This capability makes FA a promising solution for enhancing mobile edge computing (MEC) by optimizing communication delay. This paper establishes an FA-aided MEC offloading architecture and proposes a game theory-assisted multi-agent deep reinforcement learning (DRL) scheme to minimize the system delay of MEC. We aim to address the joint optimization problem of FA port selection, beamforming, user transmit power design, and MEC server computation resource allocation. However, the dynamic nature of FA ports and the variability of the associated large number of parameters introduce significant challenges, such as non-convexity and high dimension, in the optimization problem. In this paper, we employ game theory to reduce the dimension of the optimization variables by modeling the power control problem among multiple users as a non-cooperative game. Therefore, we propose a multi-agent deep deterministic policy gradient (MADDPG) algorithm, featuring two types of agents that collaboratively solve the problem. Simulation results validate the effectiveness of the proposed scheme, achieving 19.1-65.8% lower delays than benchmarks in MEC efficiency across all scenarios.

Traditional reinforcement learning methodologies, primarily based on multi-layer perceptron (MLP) architectures, require extensive, fully connected layers for complex nonlinear representations. Such an approach increases computational demands and enhances the likelihood of model overfitting. In this paper, we present an innovative reinforcement learning approach, leveraging the Kolmogorov-Arnold Networks (KAN) framework, to enhance decision-making processes and resource management strategies in edge networks. We develop a KAN-based hybrid proximal policy optimization algorithm (KANH-PPO) to address this issue. This algorithm effectively addresses the challenge of hybrid action spaces within edge networks, which include discrete action spaces characterized by computational offloading decisions and continuous action spaces characterized by power allocation. Furthermore, the KANH-PPO algorithm innovatively integrates the KAN architecture, significantly reducing the number of training parameters and enhancing the algorithm's fitting capability and overall performance. Simulation experiments indicate that our proposed KANH-PPO algorithm outperforms the benchmark algorithm in terms of convergence speed and edge network system power, and it requires significantly fewer training parameters than benchmark algorithms. This helps to reduce the power consumption of communication and promote the development of green communication.

In decentralized learning systems over wireless device-to-device (D2D) networks, training latency is a key metric that needs to be minimized by link selection and resource allocation, thereby accelerating model training. However, it may cause large computational complexity in general. To tackle the challenge, this paper proposes a graph neural network (GNN)-based algorithm to minimize the training latency. Under modeling the D2D network as a graph, the link selection and resource allocation can be efficiently obtained based on the local computing power and link quality. By the constraint on the network connectivity, the training latency can be significantly reduced while guaranteeing accuracy with a low complexity. The simulation results demonstrate that the GNN-based approach outperforms traditional approaches, offering superior scalability and robustness in heterogeneous large-scale D2D networks.

Federated learning (FL) is a promising approach that effectively and securely harnesses the vast amounts of data generated by the rapid proliferation of internet-connected devices. In FL, the transmission of model parameters over wireless channels plays a pivotal role in determining system performance. To optimize the wireless environment and boost communication efficiency, we present a novel FL beamforming design scheme that integrates multi-sector beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with over-the-air computation (AirComp). The scheme leverages the waveform superposition property of wireless signals, using AirComp to rapidly aggregate the global model in FL. Additionally, the scheme utilizes BD- RIS to flexibly manip-ulate communication beams, improving user channel conditions and further reducing model aggregation errors. Specifically, we evaluate the impact of this design on FL systems and derive an upper limit on the gap between training loss and optimal loss. To minimize this gap, we formulate a joint optimization problem of BD- RIS passive beamforming and base station receive beamforming, and we propose an optimization algorithm based on successive convex approximation (SCA) and block coordinate descent (BCD) to solve it. Simulation results confirm that our de-sign significantly enhances user channel conditions and improves FL performance, with the benefits becoming more pronounced as the number of BD- RIS reflecting elements increases.

As the development of B5G and 6G continues to progress, higher network bandwidth and increasingly complex vehicle connectivity are driving greater concurrency in highly dynamic and delay-sensitive transportation tasks within the Internet of Vehicles (IoV). Existing resource allocation methods such as Deep Reinforcement Learning (DRL), Graph Neural Network (GNN), Lyapunov and simple Transformer series often result in insufficient individual consideration or unprioritized attention on key resource characteristics, causing high task execution time cost and energy consumption. To overcome above problems, this paper proposes a Dynamic Self-Feedback (DSF) resource allocation approach. First, DSF models task latency and requirements along with diverse computing power to support allocation and dynamically adjusts the dimensions of self-attention heads according to resource consumption prediction in a self-feedback manner. Then, DSF adjusts dimensions of attention embedding to light and heavy tasks as feedback and leads next round of allocation optimization. Therefore, DSF enables individually tailored and energy-efficient allocation of computing resources for high concurrent IoV tasks. Simulations show the proposed mechanism achieves up to 85% tasks execution efficiency and 38% fewer timeout tasks, more than 50% of low energy consumption tasks after allocation, with almost 100% units having a workload lower than 40%.

In active distribution networks (ADNs), the rapid changes in photovoltaic (PV) generation can easily lead to short-term voltage stability issues. However, achieving real-time voltage control under limited communication resources is a major challenge. This paper addresses this issue by introducing a novel co-scheduling scheme for volt/var control and wireless resources allocation. We model the nonlinear dynamics between PV generation and communication delay into a co-scheduling optimization problem, targeting the minimization of system voltage deviations. To efficiently solve this problem, we propose a multi-agent reinforcement learning (MARL) algorithm, termed Meta-learning Equivalent model-based Hierarchical Reinforcement Learning (MEHRL). This algorithm employs a hierarchical reinforcement learning (HRL) framework to segment the complex action space and incorporates a meta-learning equivalent (ME) model to enhance adaptability during distributed training and decentralized execution (DTDE). Simulation results validate the efficacy of the proposed co-scheduling scheme in ADNs and underscore the advanced capabilities of the MEHRL algorithm in addressing the optimization challenge.

In the fields of intelligent security and human-computer interaction, the rapid development of noncontact identity recognition technology based on Wi-Fi signals has shown promising application potential. To address the significant decrease in recognition accuracy in close-range scenarios, an close-range noncontact identity recognition method named WiDoor is proposed. During the data collection phase, the Fresnel propagation model is utilized by WiDoor to optimize the deployment layout of the receiving antennas. Gait information is reconstructed from the multiple antennas to enable the acquisition of more rich gait features. In the identity recognition stage, WiDoor employs a lightweight model that combines self-attention mechanisms with multiscale convolutional neural networks. This combination effectively enhances the model’s capability to capture key features while significantly reducing computational complexity and maintaining a high recognition accuracy. Experimental results show that WiDoor achieves a recognition accuracy of up to 99.3% on an expanded dataset that includes ten participants, with a distance of 1 m between the receiving and transmitting ends, and the parameter quantity of the built-in model is only 2% of the compared model with the same accuracy, offering a significant advantages over similar methods. Additionally, the model can achieve a high-precision recognition across different distances between the transmitter and the receiver using a limited number of samples, showing strong robustness of the model.

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. Additionally, the code for our method is available athttps://github.com/gaoyafeiyoo/ADBR.

Autonomous driving technology has achieved remarkable advancements, offering substantial potential to revolutionize traffic safety and smart mobility. However, when faced with rare scenarios (weather, accident scenes, and lighting), autonomous driving systems can still only play a limited role due to insufficient learning in these rare situations. To address this challenge, we propose a novel approach that leverages low-rank adaptation (LoRA) and Mixture of Experts (MoEs) technologies to enhance the performance of pretrained autonomous driving models in handling rare situations. Specifically, we first use LoRA to fine tune the pretrained model of autonomous driving to focus on capturing knowledge related to rare scenarios and enhance the model’s ability to handle rare situations. Furthermore, we introduce MoEs and propose local, global, and hybrid adaptive solutions to overcome LoRA’s fixed intrinsic rank limitation. These approaches enable adaptive adjustment of LoRA’s rank, and improve the model’s performance from both local and global perspectives. Finally, we design detailed algorithms for different adaptation schemes. Extensive experiments demonstrate that our proposed solutions not only effectively improve the performance of the autonomous driving perception model in rare scenarios but also maintain lower inference latency compared to baseline methods.

In this article, we consider deploying multiple unmanned aerial vehicles (UAVs) to enhance the computation service of mobile edge computing (MEC) through collaborative computation among UAVs. In particular, the tasks of different types and service requirements in MEC network are offloaded from one UAV to another. To pursue the goal of low-carbon edge computing, we study the problem of minimizing system energy consumption by jointly optimizing computation resource allocation, task scheduling, service placement, and UAV trajectories. Considering the inherent unpredictability associated with task generation and the dynamic nature of wireless fading channels, addressing this problem presents a significant challenge. To overcome this issue, we reformulate the complicated nonconvex problem as a Markov decision process and propose a soft actor-critic-based trajectory optimization and resource allocation algorithm to implement a flexible learning strategy. Numerical results illustrate that within a multi-UAV-enabled MEC network, the proposed algorithm effectively reduces the system energy consumption in heterogeneous tasks and services scenarios compared to other baseline solutions.

This letter proposes a computing resource sharing strategy based on blocked job offloading in the low-Earth orbit (LEO) satellite network. The proposed strategy allows a satellite to share all or part of its computing resources with other satellites, and each satellite offloads or receives the blocked jobs from the adjacent satellites on the same meridian and latitude lines. Furthermore, we analyze the job execution probability, which evaluates the likelihood of the job being executed in the satellite network, for resource sharing strategies with different blocked job offloading hops. The numerical results validate the performance advantages of the computing resource sharing strategies and indicate a way to select the proper strategy.

Developing trustworthy reinforcement learning (RL) agents for safety-critical control tasks, such as end-to-end autonomous driving, has been a longstanding challenge due to low sample efficiency. Prior works have attempted to address this challenge by performing self-supervised auxiliary tasks like self-reconstruction or predicting long-term future states. However, there still remain unexplored sequential features and causality relationships inherent in sequential state, action, and reward signals in the frequency domain. To fully exploit the temporal and frequential features, we propose a contrastive RL framework called BRain-Inspired causalitY-Aware coNTrastive learning (BRYANT) to achieve efficient representation learning and human-like autonomous driving. Different from existing temporal predictive methods, we transform the sequential latent representations, reward, and action signals into the frequency domain, followed by the symmetric temporal prediction pattern for real and imaginary parts of the frequential signals. To capture the temporal causality for the latent representations, we introduce a brain-inspired network structure called Closed-form Continuous-time (CfC) network to parameterize the derivative of the latent representations and establish the neural dynamic model. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of BRYANT in efficient representation learning, enabling agents to concentrate on potential risks and decrease the collision rate compared to several state-of-the-art RL methods. Furthermore, through the visualization of the latent representation prediction process, we reveal the causal relationships between the critic Q values and the latent representation vectors in the frequency domain, and demonstrate the effectiveness of the frequency domain prediction.

With the rapid proliferation of the Internet of Things (IoT), network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this article, we propose multirepresentation spatial-temporal graph convolutional networks (MRSTGCNs), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a multirepresentation graph convolutional network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules—Historical Embedding and Temporal Embedding—to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons.

The combination of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and an unmanned aerial vehicle (UAV) can further improve channel quality and extend coverage. However, the high-quality air-to-ground link is more vulnerable to eavesdropping by adversaries. In this paper, we investigate STAR-RIS-assisted covert communication in UAV non-orthogonal multiple access (NOMA) networks with a warden Willie, where Alice intends to transmit the covert signal to a near user Bob under the cover of a far user Carol via STAR-RIS. We aim to maximize the covert transmission rate by jointly optimizing the active and passive beamforming as well as the UAV location. The error detection probability and optimal detection threshold for Willie are first derived to obtain an analytic solution for the minimum detection error probability. Then, an alternating optimization algorithm is proposed to maximize the covert transmission rate under the condition of guaranteeing the communication of Carol and satisfying the covertness constraint of Bob. Specifically, the nonconvex problem is decomposed into three sub-problems by block coordinate descent, which are then solved using semidefinite relaxation and successive convex approximation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed covert communication scheme for STAR-RIS assisted UAV air-ground networks.

In 6G-enabled vehicle-to-edge networks, through deploying computing power resources closer to mobile vehicles for providing low latency and highly reliable services, Mobile Edge Computing (MEC) as an emerging paradigm has promoting mobile vehicles with limited capacities to come with diversified artificial intelligence (AI) applications. Nevertheless, the computing power of MEC server is still finite, seeking the optimal resource pricing and allocation strategies for MEC servers, and determining the optimal computation offloading for intelligent vehicle applications, still remain challenging issues that are necessary to be reasonably solved to improve the service experience. To address this issue, a scenario that multiple intelligent vehicle applications cooperatively initiate computation offloading requests in 6G-enabled multi-server multi-access vehicle-to-edge computing systems is considered in this paper, and with the defined reasonable utilities for MEC servers and intelligent vehicle applications, a solution of bidding-enabled dynamic resource pricing for computation offloading is proposed. Concretely, through considering the relationship of resource supply and demand, and the bidding among MEC servers, a dynamic resource pricing scheme is designed for MEC servers, meanwhile, with the complete consideration of dynamic resource pricing and time-varying wireless channel interference in multi-cell networks, a Q-learning based offloading decision algorithm is proposed for intelligent vehicle applications. Simulation experiments finally are conducted to demonstrate the efficiency in achieving the win-win situation with guaranteed utilities for both MEC servers and intelligent vehicle applications.

AI-empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this article first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on the ASVspoof2019 dataset show that GEMINI reduces the Equal Error Rate and tandem Detection Cost Function by up to 96.75% and 96.35% in the physical access scenario, and by up to 86.62% and 87.71% in the logical access scenario compared to peer methods.

Vital sign detection, based on Channel State Information (CSI) from commercial off-the-shelf (COTS) WiFi devices, has become a popular research area. Previous works in this field mainly focus on respiration, while heartbeat sensing has not been well studied yet, because its signal is very weak and overwhelmed by hardware noises and the respiration signal. Different from existing research that exploits directional antenna, the proposed WiCG ( Wi Fi C ardio G ram) system uses common antennas, and not only accurately senses heartbeat rate but also provides the heartbeat signal for further analysis in complex real-life home scenes. Specifically, we first propose an effective denoising solution for Wi-Fi CSI by exploiting its spatial structure, which exhibits strong correlation among the In-phase/Quadrature components. Leveraging this characteristic with Principal Component Analysis (PCA) achieves effective reduction of ambient noise in both the amplitude and phase of the CSI. Then, we introduce a heartbeat enhancement scheme that utilizes the periodicity of the heartbeat signal. By applying Singular Spectrum Analysis (SSA), the complex effects of residual noise and respiratory interference are effectively mitigated. Extensive experiments have proven that WiCG can effectively sense the heartbeat rate. In a real deployment environment, the average detection error can be reduced to 0.28 bpm, close to current commercial heartbeat sensors.

In the rapidly evolving landscape of federated learning (FL), hierarchical architectures are pivotal for improving computational efficiency and safeguarding data privacy. A key challenge in this research area is the optimal selection of edge servers, crucial for executing distributed learning tasks across multiple clients and servers efficiently. Traditional selection methods falter due to their inability to dynamically handle the uncertainties in network conditions and server capabilities. To addressing this weakness, we propose a fuzzy logic-based approach that optimizes edge server selection in a novel smart way, thus enhancing resource allocation by efficiently handling the unpredictable nature of network environments and servers performance. This method is integrated with a previously developed scheme for selecting an optimal subset of clients, thereby establishing a comprehensive framework that significantly boosts the performance and reliability of FL networks. The performance of our approach is validated through real-world experiments and the results demonstrate its superiority over existing methods in terms of accuracy and processing time.

Millimeter wave (mmWave) Device-to-Device (D2D) communication networks suffer high path loss and dynamic physical obstructions. Meanwhile, eavesdroppers can intercept confidential information by residing in the main or side lobe of the transmission beam. Fortunately, multiple distributed Reconfigurable Intelligent Surfaces (RISs) offer a valuable approach to support mobile D2D devices, mitigating blocking effects and enhancing data security. In this paper, we propose a deep reinforcement learning (DRL) based communication scheme for the multi-RIS aided dynamic mmWave D2D networks, which aims to maximize the total secrecy data volume of D2D users over each service period by jointly optimizing the RIS resource allocation and multi-RIS phase shift design. To implement the intelligent collaboration of the RISs, we design the DRL approach with a nested structure. Specifically, we adopt a proximal policy optimization (PPO) network with discrete actions to realize the RIS-User association. Subsequently, we integrate the multi-agent PPO (MAPPO) framework to derive the phase shift design, containing intricate dynamic competition and cooperation among RIS agents. In addition, we divide the RIS into multiple subarrays, each sharing the same reflection coefficient. This approach controls more RIS phases while ensuring training stability. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the secrecy performance of dynamic mmWave D2D networks.

We consider the problem of reducing the learning latency of layerwise federated learning through joint device selection and bandwidth allocation. Specifically, we examine practical scenarios with heterogeneous devices with varying system parameters (e.g., CPU frequency, transmit power, etc.) and energy budgets. We formulate a long-term optimization problem, which is difficult to solve even with perfect channel state information. To address the issue, we employ Lyapunov theory to transform the problem into a series of online optimization problems, each of which can be efficiently solved using an alternating optimization-based method. Simulation results show that our scheduling scheme surpasses baseline schemes not only in terms of reducing the learning latency but also in reducing the energy deficit.

Satellite Terrestrial Networks (STN) are known to enhance the quality of service and to provide a better user experience. However, STNs are primarily utilized as wide-range relays and are characterized by a lack of effective intersatellite collaboration. The communication efficiency and quality of near real-time remote sensing tasks in 6G space-air-ground integrated networks are enhanced by the proposed Orbital Edge Computing-Collaborative Offloading Scheme (OEC-COS), which utilizes Service Function Chains (SFC) and the processing and collaboration capabilities of satellite nodes to allocate near real-time remote sensing tasks to optimal satellite nodes for execution. The remote sensing task offloading problem has been formulated as a delay minimization problem, and the advantages of the OEC-COS scheme in terms of computational resource utilization ratio and latency are validated through simulation experiments by comparing it with average orbit allocation and co-orbit allocation schemes. The proposed OEC-COS scheme achieves the lowest average task computing delay among these methods.

The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic.

Deep mutual learning (DML) is one of the emerging technologies for mobile intelligent applications that has attracted much attention in recent years. To effectively deploy DML at a large scale, in this paper, we propose a novel hierarchical asynchronous deep mutual learning (HADML) system that enables devices to collaborate in model training without the exchange of local datasets. To further improve the learning efficiency, the average energy cost for model exchanging is minimized by jointly optimizing the link scheduling and communication resource allocation. To efficiently solve this problem, the graph neural network and deep unfolding network are employed to obtain the link scheduling and resource allocation, respectively. Finally, the simulation results demonstrate that our proposed algorithm can achieve a balance between knowledge sharing and communication energy consumption.

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network.

Low Earth Orbit (LEO) satellites are gradually developing towards to a large scale, in order to achieve a desired vision of ubiquitous connectivity and broadband access at anytime and anywhere. However, the increasing scale and highly dynamic nature of LEO constellation pose challenges to network management on its flexibility and scalability. Clustering is introduced as an effective approach to manage LEO satellite networks in a flexible manner. Unfortunately, satellite clusters encounter instability and high communication load due to frequent topology changes and traffic growth. In this paper, we design LEO satellites clustering models that jointly optimize cluster reliability and network communication load in the GEO/LEO network architecture. The coalition game framework is introduced to obtain a stable cluster structure by adopting an automated and centralized approach. A coalition formation algorithm based on the optimization of reliability and communication load is developed for the clustering problem. Finally, numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed grouping and clustering scheme.

The combination of millimeter-wave (mmWave) and massive MIMO techniques can fulfill high data rate requirements for vehicular networks. However, due to the elevated path loss and severe blocking effects in mmWave propagation, the downlink data service of vehicles will seriously deteriorate. Fortunately, reconfigurable intelligent surface (RIS) can serve as a single-user relay to mitigate individual performance degradation without additional power consumption. In this paper, we propose a deep reinforcement learning (DRL)-based joint user schedule and RIS-User pairing scheme for the dynamic mmWave vehicular network to alleviate the blocking effects and maximize the total transmission data volume while ensuring the quality of service (QoS) for all target vehicles. In this scheme, each target vehicle has a distinct QoS constraint called minimum service data volume, which is a long-term and posterior optimization problem. Thus, QoS constraints are introduced in the reward design of the DRL algorithm, and the problem of high-dimensional action spaces is addressed by utilizing two nested Dueling Double-DQN (D-D3QN) networks. Simulation results demonstrate the superiority of our scheme in mmWave vehicular networks.

Unmanned Aerial Vehicle (UAV) technologies have garnered significant attention, particularly in the context of UAV-assisted wireless networks, which are seen as a pivotal component in the development of Sixth-Generation (6G) mobile communication systems. In this research, we delve into the realm of UAV-assisted wireless communication networks, where a single UAV efficiently caters to numerous random mobile users on the ground. Our focus lies in optimizing user movement tracking, beamforming, and UAV trajectory to maximize the data transmission rates for users within a specified time frame, all while adhering to stringent power constraints and the UAV's limited flight range. We harness the power of deep reinforcement learning (DRL) to monitor mobile users and predict the ever-changing channel state information. As beamforming and UAV trajectory adjustments operate on different timescales, we introduce a dual-layer deep unfolding network to fine-tune the transmit beamformer and UAV trajectory simultaneously. The outcomes of our simulations demonstrate the effectiveness and commendable performance of the proposed scheme.

The digital twin technology facilitates the application of Metaverse in autonomous driving. Particularly, this paper focuses on investigating an unmanned aerial vehicle (UAV)-aided vehicular Metaverse. In specific, the moving vehicles in physical world collect the real-time traffic data, which is synchronized through the UAV to the virtual world to help the autonomous vehicle (AV) simulation. For such a physical-virtual synchro-nization process, we define the age of incorrect information (AOII) to measure the traffic data freshness. Accounting for the randomness in the physical world, we jointly optimize the UAV trajectory, the vehicle scheduling and the semantic extraction of collected data under the Markov decision process (MDP) framework. Our objective is to minimize the expected long-term system AOII. Without the statistical knowledge of physical-world randomness, we propose to leverage a proximal policy optimization based deep reinforcement learning algorithm to solve the optimal control policy to the MDP formulation. We conduct numerical experiments to verify the accuracy of the theoretical analysis, and the results demonstrate the performance gains from our proposed algorithm.

The Internet of Vehicles (IoV) enhances smart city functionalities by interconnecting diverse components, yet it introduces significant challenges in terms of user privacy, communication efficiency, and energy consumption. Traditional federated learning frameworks, while adept at addressing these concerns, fall short in personalization due to heterogeneous data distributions among clients. To overcome this, we introduce Hier-FedMeta, a novel framework that combines hierarchical federated learning with meta-learning to provide tailored and efficient solutions. Our comparative analyses with four estab-lished methods show Hier-FedMeta's superior generalization capabilities and adaptability, achieving enhanced performance with minimal computational overhead after just one update step. Furthermore, our in-depth analysis of aggregation parameters offers valuable insights for the optimization of hierarchical federated meta-learning architectures, representing a significant step forward in personalized learning for IoV in smart cities.

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks.

The massive data transmission in vehicular networks has given rise to the demand for high-capacity communication and information security. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology to escalate the communication capacity of multiple vehicle users (VUs), and design artificial noise (AN)-based secure transmission schemes for this new NOMA-mmWave communication architecture. The AN beamforming matrix is derived from the mmWave discrete angular channel model to fully exploit the characteristics of mmWave propagation and facilitate the analysis. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the analytical expressions for the performance metrics. Numerical results demonstrate that the proposed scheme can effectively improve the secrecy performance of the NOMA-mm Wave vehicular communications.

In the 6G era, space–air–ground integrated networks (SAGINs) can provide ubiquitous coverage for Internet of Things (IoT) devices. Multiaccess edge computing (MEC) and blockchain are two enabling technologies, which can further enhance the services capabilities of SAGINs, where MEC demonstrates a notable capability in efficiently minimizing both the task execution delays and system energy consumption, and blockchain can provide trust guarantee for task offloading and wireless data transmission among the entities operated by different operators in SAGIN. In this article, we present an MEC and blockchain enabled SAGIN architecture, which consists of two subsystems. In the MEC subsystem, a satellite and multiple unmanned aerial vehicles (UAVs) act as the edge nodes to provide IoT devices with computing power. Moreover, the satellite serves as the block generator and the client, and the UAVs serve as the consensus nodes of the blockchain subsystem. We intend to minimize the energy consumption within the network, which is achieved through the IoT devices’ task segmentation, the UAVs, and satellite’s bandwidth allocation among their served IoT devices. And moreover, the computing power of UAVs and the satellite also allocated in task processing and blockchain consensus. Considering the high dynamics of the network, it is impossible to obtain real-time and accurate channel information, so we remodel this problem as a Markov decision process, and propose a low-complexity adaptive optimization algorithm based on the deep deterministic policy gradient (DDPG). Our simulation results indicate that the proposed algorithm exhibits commendable performance in minimizing the network energy consumption and DDPG agent’s accumulated reward maximization.

As a vital enabler for ubiquitous connectivity in space-air-ground integrated networks, the satellite-terrestrial communication system has been envisioned to be a crucial complement to terrestrial networks because of its superior capability of providing wide coverage. However, there are many practical limitations that degrade system performance, including on-board power constraints, severe path loss, high delay, and Doppler frequency shift. Reconfigurable intelligent surface (RIS), an attractive candidate technology for future networks, has been expected to be a promising solution to tackle these challenges. In this paper, we propose an RIS-assisted multi-satellite cooperative downlink transmission scheme, where an RIS is deployed near the terrestrial receiver to enhance the communication via joint design of beamforming vectors at satellites and phase shift optimization at RIS. Specifically, we first optimize the channel gain of the proposed transmission scheme under the continuous-time propagation model. Then, we formulate the problem as a maximization optimization of the received signal power during the duration of multi-satellite communication service, and we apply an alternating optimization (AO) algorithm to solve the non-convex problem. To reduce computational complexity, we further propose a low-complexity optimization algorithm under line-of-sight (LoS) transmission conditions and a scalable distributed optimization algorithm without the need to share the multi-satellite individual channel state information. Simulation results validate the superiority of our proposed transmission scheme and demonstrate that the low-complexity algorithm and the distributed algorithm can achieve system performance comparable to that of the AO algorithm under LoS channel conditions.

Unmanned aerial vehicle (UAV) assisted data collection has been extensively employed in various application scenarios, e.g., nonterrestrial networks for disaster management, agricultural crop protection, environmental monitoring. However, data collection and transmission model in different applications are not universal, and the timeliness of large-scale data collection and transmission also has been remained as a challenge. To address this issue, artificial intelligence (AI)-empowered intelligent search algorithms for path planning in UAV-assisted data collection networks are investigated in this article. With the constraints, including energy consumption, transmission distances, and full coverage of sensors, a data collection model using UAV in hovering mode is first established for minimizing the flight distances of UAVs, and an adaptive full coverage algorithm (AFCA) is proposed to optimize the Quality of Service through using the model. Subsequently, for optimizing the path planning of UAVs, an intelligent path planning algorithm (IPPA) is proposed through considering the loop and noncrossing characteristics presented by the optimal paths. In six testing cases with different sensor sizes, the experimental results have been shown to demonstrate that the proposed solution outperforms the traditional algorithms.

In the Internet of Things (IoT) ecosystem, optimizing processing capabilities of devices through Wireless Powered Mobile Edge Computing (WP-MEC) is crucial. This research addresses the challenge of efficiently scheduling task offloading from devices to an edge server, which is vital for enhancing system performance. Prior studies often overlook the necessity for rapid adaptation to changing wireless conditions, resulting in suboptimal offloading strategies. Our work introduces the Intelligent Online Computation Offloading (IOCO) algorithm, leveraging Deep Neural Networks (DNNs) to make informed, real-time offloading decisions based on previous experiences. This approach not only optimizes the allocation of wireless and computing resources but also incorporates novel quantization and sampling methods to improve robustness and adaptability. Simulation results demonstrate that IOCO can achieve near-optimal efficiency swiftly and adapt effectively to significant resource changes, highlighting its practicality in dynamic WP-MEC environments.

Metaverse is a visually rich virtual space in which users can interact with each other. By introducing metaverse into vehicular networks, vehicular metaverse can provide users real-time immersive experiences based on augmented technologies. Vehicular edge computing is a desirable approach to support computation-intensive vehicular metaverse services by network resource collaboration. User collaboration needs to reach a consensus on perception information, operation control and so on to realize user autonomy. However, the existing consensus algorithms often require computational proof or frequent communication, making them unsuitable for dynamically changing vehicular edge metaverse with low latency and energy restrictions. In this paper, we have proposed a reputation model maintained in the vehicular edge metaverse to score the vehicles, so the vehicles with a high reputation can be selected to participate in practical Byzantine fault tolerant (PBFT) consensus, which improves the probability of success and credibility of consensus without increasing the number of participating vehicles. Meanwhile, an optimization problem is formulated for each vehicle to allocate its computation and communication resources to reach a PBFT consensus. Also, the optimized communication time interval of each phase in the PBFT consensus can be used as a reference for setting the agreed upper time, which reduces the waiting time of vehicles and the probability of re-consensus. Simulation results have demonstrated that the proposed scheme effectively achieves PBFT information consensus with lower latency and energy consumption, and thus is more scalable and efficient.

In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.

Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we proposeHare, a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster.Hareadopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Given full knowledge of job arrival time and sizes, we propose a fast heuristic algorithm to minimize the average job completion time and derive its theoretical bound is derived. Without prior knowledge of jobs, we propose an online algorithm based on the Heterogeneity-aware Least-Attained Service (HLAS) policy. We evaluateHareusing a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art, achieving a performance improvement of about 2.94×.

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Background subtraction forms the basis of many safety applications in airport traffic management, such as the visual conflict warning system. However, deep learning methods often mistakenly identify stationary aircraft as foreground, mainly because they prioritize learning appearance over motion features. This means that stationary aircraft with a similar appearance to moving ones are often incorrectly classified as foreground. To address this issue, a Motion-enhanced Background Subtraction Network (MBSNet) is proposed in this paper. MBSNet is designed to focus more on motion information within an encoder-decoder framework. Firstly, a Motion Augmentation Encoder Module (MAEM) is introduced, which generates a clean background frame without foreground from previous frames. This module compares the background frame with the current frame containing moving objects, indirectly enhancing the motion component in the encoded features. Because targets on the airport ground are relatively sparse, MAEM ensures a clean background image. Secondly, a Motion Accumulation Decoder Module (MADM) is designed, which accumulates motion-augmented features from the current frame and past frames based on feature dissimilarity measurement. Since aircraft exhibit consistent motion patterns, such as continuous straight travel with occasional turns, MADM further enhances the motion component in the accumulated feature vector. Finally, MBSNet is evaluated on the AGVS dataset, and our experiments demonstrate the effectiveness of the proposed method for airport background subtraction.

Since the requirements of cross-domain/layer communications, the Space-ground Integrated Information Network (SIIN) becomes a strategic research area. To improve the service sustainability and reduce the latency of data transmission, literature works focus on evaluating the status of channels between ground stations and satellites, but underestimate the power of dynamic data allocation for handover management. This paper explores the relationship of data allocation and seamless handover in SIIN to provide high-reliability and service sustainability. We propose a Channel Perceiving-based Handover Management (CPHM) strategy to optimize the utilization of channels and dynamically adjust the data allocation strategy. Specifically, CPHM perceives the motion status of satellites to accurately evaluate their service time and reconstruct connectivities, e.g., altitude, velocity, motion direction, and location. Furthermore, CPHM evaluates the service capability of satellites to generate the strategy of data allocation and dynamically adjust this strategy. Then, to improve utilization of channels, CPHM manages transmission queues according the strategy of data allocation and length of queues. Extensive simulation results show that CPHM outperforms other baseline algorithms in terms of delivery ratio, average delivery latency, and interruption ratio.

Federated learning has been combined with graph learning for modeling spatial-temporal data while maintaining data confidentiality and safety. However, there are still several issues: 1) In practical usage, some clients may be unable to participate in the model inference due to poor network signal, malicious attacks, etc. 2) In the communication process, the uploaded information is easily disturbed by noise. The performance of the graph model will be seriously affected by its low robustness. Additionally, the assumption of identical distribution between the training and testing domain does not hold in practical scenarios, resulting in overfitting and poor generalization ability of the trained models. 3) The relations that exist among clients may change dynamically over time and manually constructing the graph structure of clients may not accurately represent the relations among clients. In this paper, we address all the above limitations by proposing a robust hierarchical split-federated graph model named DCSFG. Specifically, DCSFG combines split-federated learning and spatial-temporal graph model to better capture the spatial-temporal dependencies. We propose a Dropclient method and introduce the uncertainty estimation to enhance the robustness and generlization ability of the model. We also design a dual-sub-decoders structure for clients so that they can perform predictions locally and independently when they are unable to participate in the inference process. A novel hierarchical graph message passing structure is proposed to enable each client to perceive the global and local information. The extensive experimental results demonstrate the effectiveness of DCSFG.

This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it.

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and meanwhile jointly exploits the pilot sequences in the channel estimation stage and user information in the transmission stage to assist target detection. We investigate the joint design of the pilot matrix, training duration, and transmit beamforming to maximize the probability of target detection, subject to the minimum achievable rate required by the user. However, designing the optimal pilot matrix is rather challenging since there is no closed-form expression of the detection probability with respect to the pilot matrix. To tackle this difficulty, we resort to designing the pilot matrix based on the information-theoretic criterion to maximize the mutual information (MI) between the received observations and BS-target channel coefficients for target detection. We first derive the optimal pilot matrix for both channel estimation and target detection, and then propose a unified pilot matrix structure to balance minimizing the channel estimation error (MSE) and maximizing MI. Based on the proposed structure, a low-complexity successive refinement algorithm is proposed. In addition, we rigorously analyze the impact of pilot length and pilot matrix on two fundamental tradeoffs, namely MSE-MI and Rate-MI. Simulation results demonstrate that the proposed pilot matrix structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the significant region improvement of our proposed design as compared to other benchmark schemes. Furthermore, it is unveiled that as the communication channel is more spatially correlated, the Rate-MI region can be further enlarged.

Deep mutual learning (DML) is one of the most high-profile technologies emerging in the field of machine learning during the past few years. DML has the potential of exchanging knowledge on the premise of ensuring data privacy, while retaining the characteristics of local models. In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), which allows neighbor mobile devices to learn from each other via bidirectional device-to-device links over unlicensed spectrum (D2D-U). On this basis, we formulate a non-convex optimization problem for the one-to-one pairing scenario with the goal of minimizing the average communication energy cost for sharing knowledge. We further propose a two-layer iterative algorithm that includes the outer layer based on the enumeration method and the inner layer based on the sum-of-ratios optimization, aiming to find the optimal pairing scheme between devices and obtain the global optimal communication resource allocation scheme, respectively. The numerical results validate the effectiveness of the proposed algorithm in improving the DML performance.

We study efficient federated learning (FL) using random pruning in resource-constrained edge intelligence networks. We propose an edge device selection strategy to identify appropriate edge devices for participating in FL at the beginning of each training iteration. We then formulate an optimization problem that jointly optimizes the pruning ratio, CPU frequency, uplink power, and bandwidth allocation for the selected edge devices. Since the optimization problem is non-convex and challenging to solve directly, we decompose it into three subproblems and propose efficient algorithms or closed-form solutions for each subproblem. Based on the solutions to the subproblems, an alternating optimization algorithm is constructed to solve the original problem. Simulation results demonstrate that our scheme outperforms baseline schemes in terms of both learning accuracy and energy consumption.

Open radio access network (O-RAN) slicing allows the flexible control of network components and resources to satisfy the ever increasing demand of mobile applications. To optimize service provisioning, efficient management of limited radio resources is challenging due to the orchestration among network slices in the long-timescale and the slice configurations according to the mobile user (MU) statistics in the short-timescale. In this paper, we first propose a novel meta Markov decision process framework to mathematically formulate the problem of two-timescale radio resource management (RRM) in O-RAN slicing. The original RRM problem is then decoupled into a long-timescale master problem and a short-timescale subproblem, which are solved by a hierarchical reinforcement learning (RL) mechanism. Our proposed hierarchical RL mechanism includes a deep RL algorithm, solving the optimal long-timescale RRM policy, and a linear-decomposition based meta-RL algorithm, solving the optimal short-timescale RRM policy. Numerical experiments verify the theoretical analysis and show that our proposed hierarchical RL mechanism outperforms the most representative state-of-the-art baselines.

Cooperative perception is a promising approach to improve safety in autonomous driving by utilizing sensing data from cooperative devices. However, the real-time transmission and processing of large amounts of sensing data with limited network resources and different vehicle requirements pose significant challenges. To address this issue, we propose a joint sensing tasks and communication-computation resource allocation approach. The proposed approach considers a multi-objective optimization problem of total value of information and delay-energy consumption under resource-constrained and differentiated information quality conditions. To solve the formulated problem, we decompose it into two stages. In the first stage, the sensing tasks allocation algorithm is proposed to select optimal sensing data for vehicle requirements based on differences in information quality. In the second stage, the communication-computation resource allocation algorithm is proposed to balance the delay and energy consumption of sensing tasks execution. Simulation results demonstrate that the proposed scheme is more effective than the benchmark schemes in addressing the challenges posed by cooperative perception with limited resources.

Achieving seamless connectivity is one of the key visions for sixth-generation (6G) wireless communication systems. Satellite communication (SatCom) system, especially the multi-satellite cooperative communication, has become an indispensable part of space-air-ground integrated communication network due to its superior capability in providing wide area coverage. However, the performance of satellite-terrestrial communication is severely limited by high path loss. Reconfigurable Intelligent Surface (RIS) has been considered as a potential enabling technology in 6G to improve the service quality of various communication systems. In this paper, a RIS-aided multi-beam cooperative transmission scheme is proposed to boost the system performance of satellite-terrestrial communication. By optimizing the RIS reflection phase dynamically, the system capacity is considerably improved during the continuous service time of dual satellites. Based on the simulation results, we also point out the impact of the discrete phase optimization and RIS's inclination angle on the system performance considering the deployment of RIS in three-dimensional (3D) space.

The low earth orbit (LEO) satellite constellation is regarded as an effective complement to the terrestrial communication system due to its seamless coverage and ultra-low latency. Unfortunately, the highly dynamic traffic volume, as well as the inherent nature of dynamic topology changes caused by frequent link handover and uncertain hardware failures, pose severe challenges in the design of reliable routing. However, most existing reliable routing approaches with distributed schemes only focus on information exchange between adjacent nodes, which makes them fail to perceive real-time global network changes and make optimal decisions. In this paper, we propose a software defined networking (SDN) based intelligent satellite routing (SISR) method to increase the adaptivity and reliability during the packet transmission process. With the facilitation of SDN, we manage the network in a hierarchical and centralized paradigm, and further implement a more refined form of reinforcement learning (RL) to enhance the fault-tolerant ability of satellite network routing. Experimental results show that our solution can reduce latency and packet loss ratio by more than 42% and 29% compared to baselines.

Federated learning (FL) in unmanned aerial ve-hicles (UAVs) networks demands considerable communication resources to transfer model data between the central server and UAVs (FL clients). However, different UAVs may have different communication capabilities due to the UAV's maneuverability and heterogeneity, where limited communication resource could be bottle neck for FL performance. In this paper, we first introduce a knowledge distillation based approach that places two different models with different sizes, namely the teacher model and student model, for FL client to make a trade-off between the FL performance and communication cost. Then, we propose a novel model switching method to switch between the teacher model and student model to adapt to the dynamic feature of UAV networks. Specifically, considering available communication bitrate and learning accuracy, we design a threshold-based model switching algorithm (TBMSA) and determine the threshold based on the k-means method (DTBKM) to accurately and quickly determine the switching point. In addition, for the knowledge transfer between models, we design a knowledge inheritance based on a transfer learning (KIBTL) algorithm, which transfers knowledge from one model to another. Experiments show that the proposed model switching algorithm achieves significant performance improvements as compared to existing baselines.

Terahertz communication with abundant spectrum resources is envisioned as the key technology of 6G. Despite its narrow beam, terahertz transmission is still vulnerable to eaves-dropping attacks in indoor scenarios. In this paper, we propose a blockage feature-based artificial noise scheme to safeguard the indoor network in the presence of multiple access points (APs), users, and eavesdroppers. Those APs with blocked links to the typical user are selected to emit artificial noise to deteriorate the reception of eavesdroppers. Thus, communication security is ensured without escalating the instability of legitimate connections caused by the small coverage nature of terahertz beams. By comprehensively considering the propagation characteristics of terahertz, such as the three dimensions narrow beam and the human blocking effect, we derive the theoretical expressions of the connection outage probability, the secrecy outage probability, and the average number of perfect links per unit area. Numerical results demonstrate that the proposed scheme outperforms the traditional schemes in terms of connection stability and secrecy performance.

In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), where edge devices are allowed to learn from each other via bidirectional device-to-device communications over unlicensed spectrum. We further formulate a non-convex optimization problem to minimize energy consumption and accelerate knowledge sharing with constrained power, bandwidth and transmission latency. Under this context, we propose a two-layer iterative algorithm, which contains an enumeration-based outer layer for the pairing scheme and a sum-of-ratios-based inner layer for obtaining a globally optimal allocation of communication resources. Simulation results verify that our obtained algorithm converges fast and finds efficiently the balance between knowledge sharing and communication energy consumption.

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

The number of vehicles in developed countries has grown more rapidly than available road capacity, resulting in increased congestion, air pollution, and more accidents. A recent UN report predicts that the increasing size of cities and levels of population mobility will mean 2.9 billion vehicles on the road in cities alone by 2050. To mitigate the consequences of this increase without dramatically increasing the number of built roads, novel methods to better utilise existing road capacity are required. To that end, this paper introduces COALITION, a cognitive radio-enabled probabilistic offloading of congested lanes, as an innovative solution to efficiently handle traffic congestion in urban areas. This solution builds upon and improves the performance of our previous work, named CRITIC, and makes use of Electric Connected and Autonomous Vehicles (ECAVs) features to maximize the usage of road capacity through opportunistic exploitation of under-utilized reserved lanes while fostering the use of electric vehicles to support carbon neutral transportation objectives. Simulation results have proven the effectiveness of COALITION and its potential impact in real-world scenarios.

Despite the high directionality of antennas in terahertz communication, there remains a risk of confidential message interception when eavesdroppers are within the beam coverage area. This paper proposes a blockage-based cooperative jamming scheme to enhance the security of terahertz communication. Due to significant signal attenuation caused by blockages in the terahertz frequency band, we select idle users with blockages between them and the typical user in the indoor three-dimensional (3D) space to act as cooperative jammers. Thus, the jamming signal can deteriorate the reception of eavesdroppers while effectively minimizing interference to the typical user. Taking into account the influence of terahertz channel characteristics, blockage, and 3D antenna model, we derive analytical expression for the secrecy outage probability (SOP). Besides, we analyze the effects of access point (AP) density, blockage density, and user idle factor on network performance. Our results demonstrate that the blockage-based cooperative jamming scheme effectively improves the secrecy performance of the terahertz network.

Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.

Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information, such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multikeyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multikeyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks.

Mobile-edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which, in turn, deteriorates the overall quality of computation experience. This article, hence, investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a noncooperative stochastic game. We propose an approximation-based multiagent Markov decision process without the global system state observations, under which a multiagent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a nonstationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multiagent meta-PPO algorithm, which rapidly adapts the control policy learning to the nonstationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

With the massive deployment of the Internet of Things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing FedL schemes either take expensive computation costs (e.g., public-key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving FedL scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret-sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

Wireless virtual reality (VR) is expected to be a technology that revolutionizes human interaction and perceived media, where the quality of experience (QoE) is an important indicator to measure user service perception. However, existing schemes only consider general and time-invariant QoE optimization, which may suffer performance degradation. Moreover, it is also necessary to ensure the fairness of the individual user’s performance in wireless VR. To address these challenges, we employ digital twin technology to investigate a max-min QoE-optimal problem for wireless VR systems in this paper. Specifically, we maximize the QoE of the worst-case head-mounted displays (HDMs) client, where the QoE model is the linear weighting combination of video quality, service delay, and energy efficiency. The formulated optimization problem is characterized by multidimensional control, which jointly optimizes model selection, transmit power, computation time, and GPU-cycle frequency. Due to the mixed combinatorial features of the optimization problem, we give a low-complexity algorithm design by decoupling the optimization variables. Notably, we first obtain the allocation of the transmit power by employing the generalized fractional programming theory and the Lagrangian dual decomposition, followed by attaining the optimal allocation of GPU-cycle frequency in VR mode is derived by the proposed adaptive modified harmony search algorithm, and finally achieve the computation time by the barrier method. Meanwhile, we devise a greedy-style heuristic algorithm for mode selection. In the simulation, three baseline schemes are established as comparisons to assess the effectiveness of the proposed scheme. Meanwhile, the simulation results manifest that the proposed algorithms have good convergence performance and better increase the QoE of the DT-enabled wireless VR system compared to benchmark solutions.

Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received considerable attention by AI community and various DGNN models have been proposed, building a distributed system for efficient DGNN training is still challenging. It has been well recognized that how to partition the dynamic graph and assign workloads to multiple GPUs plays a critical role in training acceleration. Existing works partition a dynamic graph into snapshots or temporal sequences, which only work well when the graph has uniform spatio-temporal structures. However, dynamic graphs in practice are not uniformly structured, with some snapshots being very dense while others are sparse. To address this issue, we propose DGC, a distributed DGNN training system that achieves a 1.25× - 7.52× speedup over the state-of-the-art in our testbed. DGC's success stems from a new graph partitioning method that partitions dynamic graphs into chunks, which are essentially subgraphs with modest training workloads and few inter connections. This partitioning algorithm is based on graph coarsening, which can run very fast on large graphs. In addition, DGC has a highly efficient run-time, powered by the proposed chunk fusion and adaptive stale aggregation techniques. Extensive experimental results on 3 typical DGNN models and 4 popular dynamic graph datasets are presented to show the effectiveness of DGC.

-1To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique by collaboratively training a shared learning model on edge devices. The number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to performone-shotmodel aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a novel performance-oriented long-term design scheme that integrated design multiple communication rounds to minimize the optimality gap of the loss function is proposed. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline schemes to tackle the resulting design problem. Simulation results demonstrate that such a long-term design strategy can achieve higher test accuracy than the conventional isolated design approach in FL. Both the theoretical analysis and numerical results exhibit a “later-is-better” principle, which demonstrates the later rounds in the FL procedure are more sensitive to aggregation error, and hence more resources are required over time.

Vehicle platoon is a group of vehicles moving in the same direction at a certain speed while maintaining a stable inter-vehicle distance, and so the vehicles are closely connected with each other in a virtual way. While a vehicle platoon can improve traffic efficiency, there are some challenges in maintaining communication among platoon members, especially when some vehicles join an existing platoon that includes long-body, heavy-duty or special vehicles. In order to solve this problem, we propose a spectrum sensing scheduling (SSS) scheme for communication resource management in platooning. First, we propose a three-level platoon architecture, which includes the cloud, road side unit (RSU), and vehicles to increase the communication coverage of a platoon and multiple platoons. Second, the spectrum sensing model, platoon control error model, and platoon communication delay model are established for the platoon delay under the SSS scheme. Third, we propose a greedy algorithm for resource allocation, which is based on the SSS scheme and vehicle-to-vehicle (V2V) communications, to minimize platoon delay. Finally, we simulate platoon delay and platoon safety status of the SSS scheme when some vehicles join an existing platoon. Simulation results show that the proposed SSS scheme reduces the platoon communication delay by around 50% and achieves a smaller platoon error as compared with existing baseline schemes for resource scheduling.

In this paper, a novel decentralized mutual learning (DML) network is designed, where each mobile device can share knowledge with its neighbour devices via bidirectional device-to-device (D2D) communication. We subdivide and discuss mutual learning scenarios, and investigate the user association and resource allocation problems for the one-to-many scenario. With constraints on power, bandwidth and communication latency, we formulate a non-convex optimization problem to minimize the average communication energy consumption for sharing new knowledge. On the basis, a two-layer iterative algorithm is proposed, which consists of an outer layer algorithm based on particle swarm optimisation (PSO) for searching a suitable user association strategy and an inner layer algorithm based on sum-of-ratios optimization for achieving a globally optimal allocation of communication resource. Numerical results are presented to verify the fast convergence and the effectiveness of the proposed algorithm in terms of a trade-off between energy consumption and knowledge sharing efficiency.

Massive data drives the development of machine learning (ML) for a long time. However, at present, data is starting to hinder ML's development. The first reason is that the privacy of data is increasingly valued by the public. Therefore, Federated Learning (FL) has emerged, which realizes model training through distributed computing and centralized aggregation. Second, due to the popularity of FL, edge devices need to store all data, which may quickly occupy the entire storage space of edge devices, resulting in fatal errors. To address these challenges, we proposed a storage-aware joint user scheduling and spectrum allocation algorithm, named FedSUS, to reduce the storage stress of each device and guarantee traditional FL metrics, i.e., learning accuracy and training latency. First, a probabilistic framework is adopted for user scheduling. Second, we introduce a data influence evaluation method to FL and analyze its convergence. Based on this, two problems are formulated to tradeoff the storage resource, the influence of data, and the learning latency and to minimize the transmission latency, respectively. Then, the closed-form results to the above problems are both developed. Finally, FedSUS is validated by using a popular convolutional neural network (CNN) and datasets (CIFAR-10). And numerical results demonstrate that our algorithm can effectively reduce the local data size while keeping (even improving) the learning accuracy as compared with baseline.

Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we propose Hare, a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster. Hare has three novel designs. First, Hare optimizes GPU execution environment to reduce task switching overhead by exploiting unique features of DML scheduling. Second, Hare adopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Finally, we propose a fast heuristic algorithm to minimize the total weighted job completion time by jointly considering job features and hardware heterogeneity. Its theoretical bound is derived. We evaluate Hare using a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art by about 2x.

Traffic flow prediction is of particular interest since its massive applications in intelligent transportation systems (ITS). The problem is challenging due to the complex spatio-temporal correlations and nonlinearities of traffic flows. However, existing methods based on the graph neural networks cannot efficiently extract the dynamic and long-range spatial correlations, thus producing unsatisfactory prediction results. In this paper, we propose an AdaBoost Spatio-temporal Network (Ada-STNet). Similar to AdaBoost, Ada-STNet stacks several base neural networks as "layers" which capture spatial and temporal correlations simultaneously. Each layer learns an adaptive adjacency matrix from weights and embedding of nodes. The adjacency matrix is layer-wise adjusted to extract information from distant neighbors and adapt to dynamic correlations. Experiments are conducted on three real-world benchmark datasets, demonstrating that the Ada-STNet outperforms the state-of-the-art methods.

Human pulmonary function declines with age. Elders, especially those with lung or cardiovascular diseases, yearn for daily lung function tests for timely diagnosis and treatment. However, current clinical spirometers are cumbersome and ex-pensive while home-use portable ones’ accuracy is questionable. Moreover, both kinds require contact measurements and could cause cross infection, especially hazardous for contagious diseases like COVID-19. To this end, we propose SpiroFi, a contactless system that leverages WiFi Channel State Information (CSI) for convenient yet accurate Pulmonary Function Testing (PFT) out of clinic. The key enabler underlying SpiroFi is a set of algorithms that can extract chest wall movement from WiFi signal variations and interpret such information into lung function indices. We have realized SpiroFi on low-cost commodity WiFi devices and tested it in a home-like site where it achieves 2.55% monitoring error over healthy youths. Then, with the Ethics Committee (EC) approval, we conducted a 2-month clinic study in a city hospital over elders with basic diseases. SprioFi still yields 6.05% monitoring error despite elders’ degenerated pulmonary function and body control. Also, the correlation between lung function and age as well as chronic diseases has been revealed, highlighting the importance of daily PFT for the elderly.

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.

Despite the wide utilization of unmanned aerial vehicles (UAVs), UAV communications are susceptible to eavesdropping due to air-ground line-of-sight channels. Intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, and thus is an attractive solution for integrating with UAV to facilitate the security in wireless networks. In this paper, we investigate the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper. With the aim at maximizing the average secrecy rate, the trajectory of UAV, the transmit beamforming, and the phase shift of IRS are jointly optimized. To address this sophisticated problem, we decompose it into three sub-problems and resort to an iterative algorithm to solve them alternately. First, we derive the closed-form solution to the active beamforming. Then, with the optimal transmit beamforming, the passive beamforming optimization problem of fractional programming is transformed into corresponding parametric sub-problems. Moreover, the successive convex approximation is applied to deal with the non-convex UAV trajectory optimization problem by reformulating a convex problem which serves as a lower bound for the original one. Simulation results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.

Change detection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for change detection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). Change detection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded fromhttps://www.agvs-caac.com. Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of change detection for airport ground surveillance.

Video object segmentation (VOS) is the fundamental problem of vision-based intelligent transportation, and many VOS algorithms relying on inference from reference masks have been proposed. Due to the inherent defects of the inference strategy and the complex changes of targets, VOS methods that perform well on public datasets are usually ineffective in airport scenarios. We propose a spatiotemporal alignment network (STA-Net) that makes use of Automatic Dependent Surveillance-Broadcast (ADS-B) data as prior information to guide the long-term segmentation of aircraft. ADS-B is an airport-specific signal, which indicates the location of aircraft in real time. Based on ADS-B, we continuously generate new reference masks instead of using previous masks for inference, which greatly reduces the accumulation of inference errors. To achieve this, previous masks of each aircraft are aligned on the temporal domain based on the position information in ADS-B. All temporally-aligned masks are compared, and the one most similar to the current instant is reserved. This mask is both temporally and spatially aligned; hence it is a better reference mask for inference. Aligned masks are updated every time new ADS-B data arrive, so that they can support long-term inference. With the selected mask as a reference, aircraft of interest are segmented within a unified encoder-decoder framework over the long term. Experiments on a benchmark dataset and in a real airport scenario verify the effectiveness of the presented method.

Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. However, existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), which cannot efficiently handle graph data that are popular in many applications. Graph Convolutional Network (GCN) has been proposed as one of the most promising techniques for graph learning, but its federated setting has been seldom explored. In this article, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph. FedGraph provides strong graph learning capability across clients by addressing two unique challenges. First, traditional GCN training needs feature data sharing among clients, leading to risk of privacy leakage. FedGraph solves this issue using a novel cross-client convolution operation. The second challenge is high GCN training overhead incurred by large graph size. We propose an intelligent graph sampling algorithm based on deep reinforcement learning, which can automatically converge to the optimal sampling policies that balance training speed and accuracy. We implement FedGraph based on PyTorch and deploy it on a testbed for performance evaluation. The experimental results of four popular datasets demonstrate that FedGraph significantly outperforms existing work by enabling faster convergence to higher accuracy.

This paper addresses a joint radio and computational resources allocation problem for mobile edge computing (MEC) networks. To alleviate the shortage of licensed spectrum resources, computing tasks can be offloaded to the MEC server through not only the licensed channels but also the unlicensed channels, where the adaptive duty-cycle-muting (DCM) mechanism is employed at the user terminals (UTs) to guarantee the fair coexistence with the WiFi networks. Moreover, Stackelberg game formulation is used to build up a decentralized radio and computational resources allocation framework, where the MEC server is modeled as a leader to set the price of the licensed spectrum, while UTs as followers compete for the radio and computational resources as a non-cooperative game. The objective of each UT is to minimize the long-term energy consumption with the guarantee of task buffer stability. Accordingly, we develop a distributed algorithm to achieve the equilibrium solution for the formulated Stackelberg game. Numerical results are presented to demonstrate that the proposed scheme is effective with respect to the reduction on energy consumption of UTs with limited signaling overheads.

Mobile edge computing (MEC) is a promising paradigm for alleviating the computation burden of resource-constrained mobile devices. Nevertheless, the majority of existing efforts concentrate on offloading computations from mobile de-vices to an edge computing server through the cellular networks only. With the development of wireless connectivity technologies, WiFi networks over unlicensed spectrum provide a “green” (i.e., cost-efficient and economical) alternative for computation offloading. In this paper, we investigate the problem of computation offloading in a heterogeneous WiFi and cellular MEC system, where both the WiFi and the cellular networks are possible for offloading the arriving computation tasks at a mobile user (MU). The objective of an MU is to minimize the long-term cost, which can be described as a single-agent Markov decision process (MDP) by accounting for the inherent system dynamics in the MU mobility, sporadic computation task arrivals and wireless connectivity variations. To solve the optimal strategy for the formulated MDP with a high-dimensional state space but without the statistical knowledge of system dynamics, we resort to a model-free deep reinforcement learning algorithm. Numerical experiments verify that the proposed algorithm is able to significantly reduce the average computation offloading cost compared with other baselines.

In this paper, a distributed power and spectrum allocation scheme is proposed to maximize the system energy efficiency (EE) for unlicensed device-to-device (D2D-U) networks. A non-convex optimization problem is formulated while considering the co-channel interference on licensed bands as the global constraint. To deal with the non-convex situation, the object function is converted into an equivalent convex one. Then, a distributed algorithm is developed to solve the optimization problem in which each D2D-U pair can find the optimal allocation strategy independently. The signaling overheads are analyzed and numerical results are presented to show that the proposed scheme is capable of achieving the optimal EE while confining the cochannel interference and guaranteeing the fair coexistence.

Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

With the rapid increase of industrial systems, industrial spectrum is stepping into the era of big data, and at the same time spectrum resources are facing serious shortage. Cognitive industrial system (CIS) based on cognitive radio can improve spectrum utilization by accessing the idle spectrum licensed to primary user. However, the CIS must find enough idle channels by performing spectrum sensing. In this article, a reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion is proposed to sense industrial big spectrum data, which can find required idle channels faster while guaranteeing spectrum sensing performance. Double thresholds are set to guarantee both high detection probability and spectrum access probability, and weighed energy detection is proposed to maximize detection probability when the energy statistic falls into the confusion area between the double thresholds. Bayesian fusion is proposed to get a final decision on the channel availability by combining the local sensing decisions of all the time slots. A prediction and selection algorithm for idle channels is proposed to predict the idle probability of each channel and find required idle channels from the sorted channel set. From simulation results, the proposed spectrum sensing scheme outperforms cooperative spectrum sensing and energy detection, which can predict idle channels accurately and get needed idle channels with fewer sensing operations.

The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

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.

By enabling direct communications between nearby user equipment (UE), device‐to‐device (D2D) communication has become one of the key technologies in 5th generation (5G) mobile networks. D2D communication brings new communication opportunities for mobile devices, especially in a highly dense network. In this paper, D2D communication in the unlicensed spectrum, namely, D2D‐Unlicensed (D2D‐U), is discussed. The use of unlicensed frequency bands can ease the shortage of spectrum resources and improve network performance. However, the D2D‐U in 5G has significant effects on the network performance of existing unlicensed networks sharing the same frequency bands, such as Wi‐Fi and Bluetooth. Therefore, it is necessary to design a fair coexistence scheme for D2D‐U. To understand the coexistence problem, in this paper, we first formulate the network performance of D2D‐U and Wi‐Fi under two different coexistence schemes, namely, listen before talk (LBT) and duty cycle mechanism (DCM). Then, we use computer simulations to investigate a mode selection scheme that switches between these two schemes and point out the best possible solution for the coexistence between D2D‐U and Wi‐Fi.

Although the millimeter wave (mmWave) massive multiple‐input and multiple‐output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping‐based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low‐complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple‐signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed‐form solution for updating the step‐size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

This paper investigates the problem of age of information (AoI)-aware resource awareness in an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, which is deployed by an infrastructure provider (InP). A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long-term computation performance. We formulate the non-cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q-networks to approximate the Q-factor and the post-decision Q-factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.

After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (Movable and Deployable Resource Unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In this kind of heterogeneous disaster response networks, it is of great importance to minimize the packet delay from user terminals by performing optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, which enables the smart relay selection and transmitting power control. We evaluate the performance by extensive simulations, and validate the superiority of the proposed mechanism by comparing with baseline schemes.

Nowadays, fifth-generation (5G) technology is promoted to support massive data transmissions and low-latency communications, which enables diverse vehicular applications. There have been some studies discussing the use of vehicle clustering to save scarce spectrum resources, and prevent network congestions in hybrid cellular/IEEE 802.11p vehicular environments. However, different types of applications could have different levels of quality-of-service (QoS) requirements, and thus this issue should be considered in the clustering of vehicles. We propose a novel vehicle clustering scheme that utilizes the global view and programmable advantage of software defined networking (SDN) technology to conduct an efficient clustering in vehicular environments. The proposed scheme first classifies the existing applications to three different types, the delay-sensitive type, traffic-intensive type, and computation-intensive type, according to the QoS requirements. Then the scheme forms three different clusters based on this classification to satisfy the QoS requirement for each type. We use computer simulations to show the advantage of the proposed scheme over the conventional approach.

The Delay Tolerant Network (DTN) is a network architecture that plays an important role in intermittently connected networks. DTNs can support transmissions between clients even there are no end-to-end connections by using “store-carry-forward” mechanism, where some nodes called ferry nodes could be employed into DTN to enhance the network performance. In this paper, we use Unmanned Aerial Vehicles (UAVs) to act as ferry nodes, and a probabilistic routing protocol based on the encounter connection time between nodes is proposed. The proposed protocol not only considers the efficiency of message transmission but also the reliability between connected nodes. The proposed protocol and some existing protocols are compared and analyzed using ONE simulator. The simulation results show that the proposed protocol increases the delivery probability and reduces the average latency.

Device-to-Device (D2D) communication, which enables direct connection between the nearby user equipments (UEs), is one of the key technologies in 5G network. In this paper, D2D communication on the unlicensed spectrum, namely, D2D-U is discussed. D2D system performance can be efficiently enhanced by utilizing the unlicensed band. However, it has a huge impact on the performance of other unlicensed networks. Therefore, the fairness of the coexistence scheme is a key problem for D2D-U. To solve the coexistence problem, two access schemes called Listen Before Talk (LBT) and Duty Cycle Mechanism (DCM) have been discussed extensively. D2D users choose the transmission mode according to transmission environments. In this paper, we discuss the performance of these two modes. Furthermore, the problem of how to access the unlicensed band with combination of these two modes is also discussed.

Change detection in airport ground is important for airport security. Due to the particularity of ground environment, e.g. haze and camouflage, airport ground change detection is generally incomplete. If an incomplete detection is used as reference for the detection in subsequent frames, it may result in noticeable detection defects across the frames. In this paper, extended motion diffusion (EMD) is proposed to address the problems. The core idea of the EMD is to design a novel model insensitive to incomplete detection. Firstly the one-to-many correspondence in traditional motion diffusion is extended in the prediction step of EMD to build up correspondence from incomplete detection to intact objects. Prior information, e.g. aircraft motion prior and ground structure prior, is employed in the development of the correspondence. Then based on the correspondence a number of new samples are synthesized and filtered in the identification step of the EMD to compensate possible detection defects. Finally, the reserved samples are collected to train a foreground model, which is used in conjunction with another background model for classification. The proposed method is verified based on the Airport Ground Video Surveillance (AGVS) benchmark. Experimental results show effectiveness of the proposed algorithm in dealing with haze and camouflage.

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.

Network slicing is a proposing technology to support diverse services from mobile users (MUs) over a common physical network infrastructure. In this paper, we consider radio access network (RAN)-only slicing, where the physical RAN is tailored to accommodate both computation and communication functionalities. Multiple service providers (SPs, i.e., multiple tenants) compete with each other to bid for a limited number of channels across the scheduling slots, aiming to provide their subscribed MUs the opportunities to access the RAN slices. An eavesdropper overhears data transmissions from the MUs. We model the interactions among the non-cooperative SPs as a stochastic game, in which the objective of a SP is to optimize its own expected long-term payoff performance. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game using the channel auction outcomes. Then we linearly decompose the per-SP Markov decision process to simplify the decision- makings and derive a deep reinforcement learning based scheme to approach the optimal abstract control policies. TensorFlow-based experiments verify that the proposed scheme outperforms the three baselines and yields the best performance in average utility per MU per scheduling slot.

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.

Constructing Radio Environment Map (REM) accurately and cost-efficiently is of great importance to realize dynamic spectrum access. Two kinds of approaches are widely investigated recently, i.e., radio propagation model based approaches and sensor monitoring based approaches. However, these existing approaches are suffering from either inaccurate spectrum availability or high deployment cost. To this end, outsourcing the spectrum sensing task to mobile users that are outfitted with spectrum sensors could greatly reduce the operator's expenditure, and meanwhile, achieve a satisfactory accuracy. The key of crowdsourced REM construction is to attract user participation. In this paper, we propose a novel online incentive mechanism for constructing a fine-grained REM with crowdsourcing in a realistic scenario, where the mobile users arrive and leave in an online manner. The proposed mechanism is proven to satisfy the truthfulness, individual rationality, computational efficiency and consumer sovereignty. Evaluation results demonstrate that the proposed mechanism outperforms the baseline schemes substantially.

The introduction of Electric Vehicles (EVs) leads to new concern on the E-Mobility. Making charging reservation, by considering the EV's arrival time and its expected charging time at Charging Stations (CSs) has been studied to predict the dynamic status of CSs. In this paper, we propose a Mobile Edge computer Geared v2x for E-mobility Ecosystem (MEGEE), as a decentralized alternative to the conventional centralized cloud based architecture. MEGEE enables the Vehicular Delay/Disruption Tolerant Networking (VDTN)-driven anycasting for information delivery, and Mobile Edge Computing (MEC) functioned CSs for information mining and aggregation. MEGEE efficiently and timely processes essential charging reservations and charging control information, through the Internet of EVs and MEC servers. Our studies show that MEGEE can achieve the close charging performance as performed by the centralized system, while offers a significant saving in communications cost.

The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an "all- mmWave" cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the "all- mmWave" cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time- varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of- art baselines, in terms of average queue length and average packet dropping rate.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

This paper addresses the problem of wireless resource scheduling in a vehicle-to-vehicle (V2V) communication network. The technical challenges lie in the fast changing network dynamics, namely, the channel quality and the data traffic variations. For a road segment covered by a road side unit (RSU), especially in a dense urban area, the vehicle density tends to be stable. The incoming service requests from the vehicle user equipment (VUE)-pairs compete with each other for the limited frequency resource in order to deliver data packets. Such competitions are regulated by the RSU via a sealed second-price auction at the beginning of scheduling slots. Each incumbent service request aims at maximizing the expected long-term payoff from bidding the frequency resource for packet transmissions. Markov perfect equilibrium (MPE) can be utilized to characterize the optimal competitive behaviors of the service requests. When the number of incumbent VUE-pairs becomes large, solving the MPE becomes infeasible. We adopt an oblivious equilibrium to approximate the MPE, which is theoretically proven to be error-bounded. The decision making process at each service request is hence transformed into a single-agent Markov decision process, for which we propose an on-line auction based learning scheme. Through simulation experiments, we show the potential performance gains from our proposed scheme, in terms of per-service request average utility.

We propose a vehicle-to-roadside communication protocol based on a distributed clustering algorithm where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by taking into account multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory-based reward discount is employed to guide each vehicle to select the route which can maximize the whole network performance. We conduct extensive computer simulations to show the performance advantage of the protocol over other approaches.

Decentralized communication technologies (i.e., ad hoc networks) provide more opportunities for emerging wireless Internet of Things (IoT) due to the flexibility and expandability of distributed architecture. However, the performance degradation of wireless communications with the increase of the number of hops becomes the main obstacle in the development of decentralized wireless IoT systems. The main challenges come from the difficulty in designing a resource and energy efficient multihop communication protocol. Transmission control protocol (TCP), the most frequently used transport layer protocol for achieving reliable end-to-end communications, cannot achieve a satisfactory result in multihop wireless scenarios as it uses end-to-end acknowledgment which could not work well in a lossy scenario. In this paper, we propose a multihop data delivery virtualization approach which uses multiple one-hop reliable transmissions to perform multihop data transmissions. Since the proposed protocol utilizes hop-by-hop acknowledgment instead of end-to-end feedback, the congestion window size at each TCP sender node is not affected by the number of hops between the source node and the destination node. The proposed protocol can provide a significantly higher throughput and shorter transmission time as compared to the end-to-end approach. We conduct real-world experiments as well as computer simulations to show the performance gain from our proposed protocol.

The Tor network is an open network that helps its users to defend against adversaries who performed traffic analysis and threatens the personal privacy and confidential security. Tor protects its users by preventing the sites they visit from revealing their physical location. As the number of Tor user increases, the performance of Tor degrades badly due to Tor's scheduler is not fairly distributing the traffic in Tor. In this paper, we highlight the problems in Tor performance due to scheduling design of Tor, which degrades the web and bulk traffic performances that multiplexes on a single TCP connection. To solve these problems, this paper emphasis on applying a simple method of active circuit switching on Tor application level, in order to distribute the bulk and web traffics on different TCP connections when congestion is detected in the Tor network. We evaluate our modified switching algorithm with the other default schedulers in Tor, such as vanilla Tor and priority EWMA scheduler in a shadow wide deployment network emulator. The experimental results show that our switching technique considerably offers stable improvement of traffic performance for all the web and bulk traffics.

The Tor network has its user grown by thousands every year. The increasing number of users around the world makes Tor become one of the widely used privacy networks today. However, as the number of Tor user increases, the performance of Tor degrades badly due to traffic is not fairly distributed. As a result, the Tor network does not provide a better quality of communication for all its users. To improve these problems, we proposed a simple method of dynamic circuit switching on the Tor application level to distribute the bulk and light traffic on Tor. We calculated the fairness indexes of transferred bulk and light traffic to observe any improvement and, compared our method with the default entry onion router algorithm. Our experimental results show the proposed method improves the distribution of traffic and achieves fairness of throughputs for all Tor users.

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.

We propose a context-aware routing protocol for vehicular ad hoc networks (VANETs). Two types of context information is considered in this paper specifically communication type (broadcast or unicast) and packet size. The proposed protocol constructs route based on virtual clustering which only exchanges beacon messages in one-hop neighborhood area. The packets are forwarded by the cluster heads, and the last 2-hop route is optimized by using a reinforcement learning algorithm which can attain good performance with low overhead. The advantage of the proposed protocol is shown by using computer simulations.

It is particularly challenging to design an efficient routing protocol for vehicular ad hoc networks due to the vehicle movement, limited wireless resources, and lossy feature of the wireless channel. We propose a protocol which uses common backbone vehicles for different traffic flows, as well as employs interflow network coding to encode packets at the backbone vehicles. A reliably connected backbone is selected by taking into account vehicle movement dynamics and link quality between vehicles. By using interflow network coding at the backbone vehicles, the protocol is able to reduce the number of generated packets by 25% in most cases compared with the conventional routing approach. As a result, the proposed protocol can provide a high packet delivery ratio, low overhead, and low delay. We show the effectiveness of the protocol using theoretical analysis and computer simulations.

We propose a QoS supporting ad hoc network scheme which employs a hybrid approach utilizing both TDMA and 802.11 DCF and present performance evaluation results of the scheme. In the scheme, TDMA period provides contention free transmissions for QoS flows, and DCF period is used to provide contention-based access for best effort or low priority flows. We evaluate the proposed scheme for various numbers of TCP flows and different CBR data rates with QualNet simulator. Simulation results show that the protocol is able to provide an efficient solution for QoS control in ad hoc networks by combing TDMA and 802.11 DCF.

This paper addresses energy-aware traffic offloading in stochastic heterogeneous cellular networks (HCNs). The objective is to minimize energy consumption of the HCN while maintaining Quality-of-Service experienced by the mobile users. For each cell, the energy consumption depends on its associated system load, which is coupled with system loads in other cells due to the sharing over a common spectrum band. Such a traffic offloading problem is modeled by a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller learns to solve the optimal traffic offloading strategy with no prior knowledge of the DTMDP statistics. To deal with the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named as QC-learning. Moreover, a decentralized QC-learning algorithm is developed such that the macro-cell base stations (BSs) can independently manage the operations of small-cell BSs by making use of the network information obtained from the network controller. Simulations validate the proposed studies.

Existing multi-hop routing protocols for vehicular ad hoc networks (VANETs) do not consider the packet payload size and some important MAC layer issues for the route selection. In this paper, we first solve the performance anomaly problem by providing the same transmission time for different nodes which have different channel qualities using an adaptive frame aggregation mechanism. Next, we propose a packet size-aware routing protocol where a communication route is determined by taking into account the payload size of data packets. We consider multiple metrics for the route selection specifically vehicle mobility, frame aggregation efficiency, and link quality. The proposed protocol is evaluated using real-world experiments as well as computer simulations.

We propose a routing protocol for drive-thru Internet access in delay tolerant vehicular networks. The contribution of this paper is two-folds. First, we propose a new approach which utilizes the concept of delay tolerant network (DTN) to supplement the conventional communication approach. Second, we propose an algorithm to schedule DTN transmissions in order to maximize the system throughput. The proposed protocol is able to attain higher TCP throughput than the conventional approach by providing more efficient wireless resource utilization. We use theoretical analysis and computer simulations to evaluate the proposed protocol.

Designing an efficient multi-hop broadcast protocol is very important for the realization of collision avoidance systems and other many interesting applications in vehicular ad hoc networks (VANETs). Existing protocols are optimized for a specific scenario, and are not capable of working in various scenarios. Therefore, designing an intelligent protocol which can tune itself in relation to the change of network environment is particularly important. In this paper, we propose a broadcast protocol which is able to make forwarding decision based on a self-learning mechanism. The protocol employs a fuzzy logic-based relay node selection approach to take into account multiple metrics for the forwarding algorithm. The parameters used for the fuzzy logic are tuned online using a reinforcement learning approach. Transfer learning is used to transfer knowledge to new arriving vehicles (agents) in order to shorten the convergence time. The combination of reinforcement learning, transfer learning and fuzzy logic can provide an intelligent solution for broadcasting in VANETs. We conduct computer simulations to evaluate the proposed protocol.

In vehicular ad hoc networks (VANETs), due to the limited radio propagation range of wireless devices, many safety applications require a multihop broadcast protocol to disseminate traffic warning information. However, providing an efficient multi-hop forwarding of broadcast messages has been a challenging problem due to vehicle movement, limited wireless resources, and unstable signal strength. In this paper we propose a broadcast protocol that can provide a low message overhead and a high packet dissemination ratio. The proposed scheme uses a fuzzy logic algorithm to choose the next hop relay nodes and uses network coding to improve the packet dissemination ratio without increasing the message overhead. By using the fuzzy logic algorithm, the protocol can choose the best relay node by taking intervehicle distance, vehicle velocity, and link quality into account. Network coding is used to improve the packet reception ratio by utilizing the broadcast nature of wireless channels. We show the effectiveness of the proposed scheme by using both theoretical analysis and computer simulations.

Multihop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and many other interesting applications. However, designing an efficient data dissemination protocol for VANETs has been a challenging issue due to vehicle movements, limited wireless resources, and the lossy characteristics of wireless communication. In this paper, we propose a protocol that can provide a lightweight and reliable solution for data dissemination in VANETs. The protocol employs dynamically generated backbone vehicles to disseminate broadcast packets to reduce the MAC-layer contention time at each node while maintaining a high packet dissemination ratio by taking into account vehicle movement dynamics and the link quality between vehicles for the backbone selection. The protocol also uses network coding to reduce the protocol overhead and to improve the packet reception probability as compared with conventional approaches. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

In vehicular ad hoc networks (VANETs), multi-hop broadcast communications are required for many applications including driver assistance systems. However, providing a low end-to-end latency has been very challenging. In this paper, we propose a joint MAC network layer multi-hop broadcast protocol. For the network layer, the proposed protocol reduces the number of sender nodes by using common forwarder nodes for different traffic flows. At the MAC layer, the proposed protocol uses a Q-Learning algorithm to adjust the contention window size. By interacting with the environment, the protocol can find the best contention window size to transmit data packets and therefore can provide a high packet dissemination ratio and low end-to-end delay for various scenarios. The simulation results demonstrate the advantage of the proposed protocol over other existing alternatives.

We propose a QoS supporting ad hoc network protocol which employs a hybrid approach utilizing both TDMA and 802.11 DCF. TDMA period provides contention free transmissions for QoS flows, and DCF period is used to provide contention-based access for best effort or low priority flows. TDMA time slots are assigned on admission basis in a distributed manner by utilizing network routing information and considering current assignment situation. In DCF period, frames are transmitted not to disturb the TDMA period. By combing TDMA and 802.11 DCF, the proposed ad hoc network is able to provide an efficient solution for QoS control in ad hoc networks. We use QualNet simulator to evaluate the proposed scheme.

Packet delivery ratio and end-to-end delay are the two most important metrics for vehicular ad hoc network applications. In this paper, we propose a MAC layer protocol which can provide a high packet delivery ratio, low end-to-end delay, and high fairness for various scenarios. The proposed protocol uses a Q-Learning algorithm to adjust the contention window size in order to provide an efficient channel access scheme for various network situations. The simulation results demonstrate the advantage of the proposed protocol over other alternatives.

In vehicular ad hoc networks (VANETs), multi-hop broadcast protocols are required for many applications including driver assistance systems. However, providing a low end-to-end latency has been very challenging. In this paper, we propose a protocol which can provide a fast channel access for broadcast traffic flows in VANETs. The protocol introduces new control messages to reserve time slots for the forwarder nodes and eliminate the hidden terminal problem. By providing a contention-free multi-hop forwarding scheme based on reliably generated forwarding backbone, the protocol can provide low end-to-end delay and high packet dissemination ratio. The theoretical analysis and simulation results demonstrate the advantage of the proposed protocol over other existing alternatives.

Multi-hop data dissemination in vehicular ad hoc networks (VANETs) is very important for the realization of collision avoidance systems and other many interesting applications. However, designing an efficient data dissemination protocol in VANETs has been a challenging issue due to vehicle movements, limited wireless resources and lossy characteristics of wireless communication. In this paper, we propose a protocol which employs intra-flow and inter-flow network coding to reduce the protocol overhead as compared to traditional protocols. The protocol also can improve the packet reception probability at the receiver nodes by using the network coding. Therefore, the protocol can provide a lightweight and reliable solution for data dissemination in VANETs. We use theoretical analysis and computer simulations to show the advantage of the proposed protocol over other existing alternatives.

Many routing protocols in vehicular ad hoc networks (VANETs) utilize location information to find a route to the destination. However, tracking the location information of other nodes is very challenging in highly dynamic VANETs. We propose a location service which can provide location information with low overhead and low delay. The location service periodically disseminates the location of each vehicle to 3-hop distance for every second with very low overhead. By eliminating location errors by taking account of the velocity of a vehicle, the proposed protocol can provide accurate position information. The location service also provides a lightweight location query mechanism for longer distance destination nodes. We show the effectiveness of the protocol by using theoretical analysis and computer simulations.

In vehicular ad hoc networks (VANETs), designing an efficient routing protocol is particularly challenging due to the vehicle movement and lossy wireless channel. A packet can be lost at a forwarder node even when a proper node is selected as the forwarder. In this paper, we propose a loss-tolerant scheme for unicast routing protocols in VANETs. The proposed scheme uses multiple forwarder nodes to improve the packet reception ratio at the forwarders. The scheme uses network coding to reduce the number of required transmissions, resulting in a significant improvement of end-to-end packet delivery ratio without increasing the message overhead. We show the effectiveness of the proposed scheme by using both theoretical analysis and computer simulations.

Point-to-point data delivery in Vehicular Ad hoc NETworks (VANETs) has been a challenging issue due to the vehicle movement, limited wireless resources and lossy feature of wireless channel. In VANETs, due to the frequent topology changes, traditional ad hoc routing protocols incur a high overhead for the route maintenance. In this paper, we propose a new approach to route data messages in VANETs. The proposed protocol uses dynamically generated backbone vehicles to forward data messages. The protocol generates the backbone by using only hello messages. The protocol selects the backbone vehicles by considering vehicle movement dynamics and link quality between vehicles. As a result, the selected backbone vehicles generate a reliably connected network. By using packet forwarding with the backbone nodes, the proposed protocol can reduce the message overhead and MAC layer contention time at each node while maintaining a high packet delivery ratio. We show the effectiveness of the proposed protocol with both theoretical analysis and computer simulations.

Efficient data dissemination in Vehicular Ad hoc NETworks (VANETs) has been a challenging issue due to vehicle movements, limited wireless resources and lossy characteristics of wireless communication. In this paper, we use dynamically generated backbone vehicles to disseminate information. The proposed protocol selects the backbone vehicles by considering vehicle movement dynamics and link quality between vehicles. The proposed approach can significantly reduce the MAC layer contention time at each node while maintaining a high packet dissemination ratio. We show the effectiveness of the proposed protocol by using theoretical analysis and computer simulations.

In vehicular ad hoc networks (VANETs), a multi-hop broadcast protocol is required to disseminate traffic warning information. Reducing broadcast message overhead while maintaining a high dissemination ratio is a very challenging task. In this paper, we study how to improve the performance of sender-oriented broadcast protocols using network coding. We propose a scheme which uses network coding to improve the packet dissemination ratio without increasing the message overhead. In the proposed scheme, the source node specifies two relay nodes. With cooperation between two relay nodes, the proposed scheme significantly increases the packet reception ratio by utilizing the broadcast nature of wireless channel. We show the effectiveness of the proposed scheme by using both theoretical analysis and computer simulations.

Wireless sensor networks (WSNs) can be used for a variety of applications. For many applications, a high reliability and low delay are required. Due to lossy feature of wireless channel, providing a reliable communication is very challenging. The most frequently used approach for providing the reliability is to use the acknowledgement based retransmission mechanism which increases the end-to-end delay especially when the number of hops from a sensor to the sink node is large. In this paper, we propose a redundancy-based approach to provide a high reliability while maintaining a low end-to-end delay. The proposed mechanism uses a redundant transmission when a link is unreliable or the end-to-end delay requirement is strict. We implement the proposed mechanism on a wireless sensor network which consists of IRIS motes, and evaluate the performance of the proposed mechanism.

Vehicular ad hoc networks (VANETs) can be used for the purpose of driving assistance, environment monitoring and entertainment. However, due to the vehicle movement, limited wireless resources and lossy feature of wireless channel, providing a reliable multi-hop communication in VANETs is particularly challenging. In this paper, we propose a VANET routing protocol which learns the optimal route by employing a fuzzy constraint Q-Learning algorithm. The protocol uses a fuzzy logic to evaluate a wireless link is whether good or not by considering multiple metrics of signal strength, available bandwidth and relative vehicle movement. Based on the evaluation of each wireless link, the proposed protocol learns the best route using the route request messages and hello messages. Upon reception of a route request message, each node maintains an evaluation value for each possible next hop node. In this way, the protocol can choose the best route, which is difficult to acquire in a typical reactive routing protocol. We show the effectiveness of the proposed protocol by using computer simulations.