Integrated Sensing, Communication, and Computation (ISCC) has the potential to meet diverse requirements of Internet of Vehicles (IoV), such as high reliability and low power consumption. However, existing works have not fully considered the problems of unreliable communication links and inefficient data processing under resource constraints in non-ideal environments. To address these issues, this paper proposes a predictive Integrated Sensing, Communication and Computation Over-the-Air (ISCCO) approach based on Orthogonal Time Frequency Space (OTFS) modulation. It takes high Doppler shifts, network dynamics, and resource constraints into account. In particular, the Road Side Unit (RSU) performs target tracking while communicating with the downlink users through Space Division Multiplexing (SDM), and receives the transmission results of the uplink. For the downlink, a predictive beamforming approach based on Extended Kalman Filtering (EKF) is employed, while Over-the-Air computation (AirComp) is utilized for the uplink. The transmit power and receive beamformer at the RSU, along with the transmit power of the uplink users, are jointly optimized through two formulated optimization problems: sensing performance maximization and power consumption minimization. To solve these problems, we adopt an Alternating Optimization (AO)-based algorithm for finding the local optimal solution. Simulation results validate the effectiveness of the AO-based algorithm, and the analysis of the trade-offs between multi-dimensional performance of ISCC and power consumption is conducted.

Today, electric vehicles (EVs) have gained significant recognition in the global market as an innovative mode of transport. However, the development of EVs is based on the interaction with the energy Internet. The increasing number of EVs has presented significant challenges to the existing charging infrastructure. Traditional fixed-location charging stations are increasingly inadequate to meet fluctuating charging demand, leading to inefficiencies such as long waiting times and uneven distribution of charging load. To address the problem, this paper proposes a Mobile-Fixed Charging Stations Collaboration Network (MFCSCN), which integrates fixed charging stations with mobile charging stations to dynamically adapt to real-time charging demands. Firstly, LightGBM is utilized to predict the charging load at fixed charging stations considering historical data and real-time EV mobility patterns. Secondly, the reinforcement learning algorithm is employed to optimize the placement of mobile charging stations based on predicted demand. Third, within the MFCSCN framework, LPPO (LightGBM and Proximal Policy Optimization) is proposed, which combines predictive modeling and reinforcement learning to optimize the dynamic placement of mobile charging stations. Through extensive simulations, we demonstrate that the MFCSCN significantly improves the responsiveness and scalability of the EV charging infrastructure, offering a robust solution to the evolving needs of urban mobility.

In vehicular cooperative perception (CP), numerous resource allocation strategies have been proposed to enhance urban autonomous driving. However, existing studies often overlook the competition between self-perception and cooperative perception, where degrading a ground vehicle's (GV's) self-perception may introduce safety risks and reduce passenger comfort. Moreover, UAV–GV cooperation—which can improve sensing precision, reduce task execution delay, and enhance CP service availability—has received limited attention. It is worth noting that unmanned aerial vehicles (UAVs) are unavailable for cooperative perception during the recharging process. To address these issues, this paper investigates on-demand scheduling strategy in UAV–GV cooperative perception. At the millisecond timescale, resource competition is considered in real-time sensing, communication, and computation (SC2) resource allocation. At the minute timescale, the idle flying period between consecutive tasks is utilized for UAV recharging through attachment to GVs along the route. Specifically, we first develop a model that captures the mutual influence between UAVs and GVs on perception performance under resource constraints. Then, a mixed-timescale solution is proposed: at the small timescale, a multi-agent deep reinforcement learning (MA-DRL) algorithm with gradient-free projection and auxiliary supervision is designed to schedule SC2resources; at the large timescale, a Hungarian-based algorithm is employed to control UAV recharging. Simulation results show that the proposed approach outperforms benchmark schemes by reducing task execution delay and energy consumption, and enhancing CP service availability, while satisfying sensing precision, GV safety, and passenger comfort requirements.

Accurate Channel State Information is prerequisite for intelligent sensing and ubiquitous connectivity. However, the diversity of channel conditions—from sparse to dense and static to fast-varying—fundamentally challenges traditional single and fixed estimation algorithms. To address this issue, this paper proposes a Physics-Prioritized Mixture of Experts (PP-MoE) scheme, leveraging the MoE paradigm’s ability to allocate resources to specialized experts tailored for distinct physical environments. The proposed scheme features an innovative heterogeneous expert library, where the architecture of each expert is customized with embedded physical priors to match its specific propagation environment. To enable intelligent scheduling, we design a hybrid decision gating network that collaboratively leverages physical formula computation and data-driven deep learning to achieve accurate channel environment identification and expert routing. Furthermore, to overcome the expert collapse problem, we propose a three-stage training strategy—pretraining, freezing, and fine-tuning to ensure training stability specialization. Extensive simulations demonstrate that PP-MoE significantly outperforms traditional and deep learning baselines. Notably, in the low-SNR region (0–15 dB), it achieves an NMSE nearly an order of magnitude lower than LMMSE. Additionally, PP-MoE maintains high efficiency with only 0.0256 GFLOPs. This work provides an effective paradigm for designing adaptive and physically reliable wireless physical layers.

Recommending optimal medication combinations for patients is a crucial application of artificial intelligence in healthcare. Recent works typically use patients' electronic health record combined with their current health conditions. However, these efforts have the following issues: 1) they often reference historical visits unrelated to the current situation, and 2) there is a latent risk of side effects from historical prescriptions. Such issues raise concerns about the safety of medication recommendation. To address this, we propose GPSRec, a novel Graph augmented Patient Similarity network for medication Recommendation. By leveraging dual similarity measures to selectively integrate historical visits, GPSRec effectively filters out irrelevant information, improving the accuracy of recommendation. We further present a training strategy, which combines a pre-training method and a dual threshold loss adjustment, reduces the risk of adverse drug-drug interactions, enhancing the safety of recommendation. Extensive experiment results on two real datasets demonstrate that GPSRec significantly outperforms state-of-the-art methods. Notably, it achieves 30.11% and 24.92% improvements in safety, respectively, with higher accuracy.

Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset’s utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.

This paper investigates the secrecy performance optimization of an uplink satellite communication system exposed to dual-domain eavesdropping threats. Specifically, a cooperative relaying architecture is considered, where a ground user (GU) transmits confidential information to a target satellite (TS), assisted by an amplify-and-forward (AF) relay satellite (RS). Simultaneously, a ground-based malicious node acts as an AF relay to enhance the interception capability of a satellite eavesdropper (SE). To improve secure transmission, a secrecy rate maximization problem is formulated by jointly optimizing the transmit powers of the GU and RS, subject to quality-of-service constraints at the TS. The resulting non-convex problem is solved efficiently using a successive convex approximation algorithm. Simulation results demonstrate that the proposed cooperative relaying scheme significantly enhances secrecy performance compared to traditional non-cooperative baselines. These findings highlight the potential of cooperative multi-satellite relaying as an effective approach to securing next-generation satellite communication networks against sophisticated eavesdropping threats.

The occurrence of geological hazards is not only affected by natural factors, but is also closely related to human engineering activities. In order to systematically integrate information related to multi-source and heterogeneous geological hazards and to improve the ability of hazard awareness and risk assessment, this paper proposes a multi-level and multiscale geological hazards knowledge graph construction method. The method constructs a multilevel geohazard ontology model containing elements such as hazard types, hazard-causing factors, hazard events, and hazard-bearing bodies, and introduces a multiscale spatio-temporal coding mechanism, integrating geological and human activity data at different spatial and temporal scales, to achieve the structured expression and organization of the hazard knowledge.

Integrated sensing, communication, and computation (ISCC) in Internet of Vehicles (IoV) confronts cross-domain interference, limited computing capacity and stringent Quality of Service (QoS) requirements. To address these challenges, this paper proposes a Non-Orthogonal Multiple Access (NOMA)-enhanced cloud-edge collaborative framework for ISCC, which achieves multi-dimensional resource synergy through three key innovations. First, a two-tier task offloading architecture is designed, where computational tasks are dynamically allocated to mobile edge computing (MEC) servers and cloud server (CS), breaking the computing capacity bottleneck of standalone edge nodes. Second, leveraging successive interference cancellation (SIC), we develop a dual-domain interference suppression mechanism: intra-functional NOMA decoding eliminates uplink multi-user interference, while coordinated beamforming mitigates cross-functional impacts between communication and sensing signals. Third, we formulate the joint beamforming and resource allocation problem as a mixed-integer nonlinear programming (MINLP) problem, and propose an alternating direction method of multipliers with alternating optimization (ADMM-AO) algorithm. The algorithm decomposes the NP-hard problem into tractable subproblems via MMSE beamforming, convex-relaxed offloading, and closed-form resource allocation. Simulation results demonstrate that the proposed scheme improves the average computation rate by 29.45% compared to the traditional space division multiple access (SDMA) architecture.

As a privacy-preserving collaborative framework, federated learning (FL) enables connected and autonomous vehicles (CAVs) to achieve cooperative environmental perception and decision-making. However, in Internet of Vehicles (IoV) scenarios, multiple concurrent tasks with weak inter-task correlations and negative transfer make traditional FL inapplicable. To mitigate task correlation deficiencies and solve negative transfer, this paper proposes a hierarchical FL framework with personalized edge aggregation for multi-task scenarios. Specifically, we first decentralize the model aggregation process to CAV edges, and divide the convolutional neural network (CNN) model into a shared feature extraction layer and personalized decision layer based on task types, maintaining model accuracy in collaborative training. Next, to eliminate negative transfer during aggregation, we propose a representation similarity-based personalized model aggregation weight allocation algorithm. Extensive simulations demonstrate that the proposed algorithm improves model accuracy and convergence speed while reducing energy and time costs in multi-task IoV scenarios.

Overcoming the limitations of individual-vehicle line-of-sight (LOS) sensing holds significant importance for guaranteeing the safety of driving. With a wider perception field, vehicle-infrastructure (VI) collaborative perception can provide vehicles with more comprehensive perception assistance, which has received widespread attention in recent years. However, the perception data fusion between infrastructure and vehicles is still impeded by issues such as large data volume and complex processing procedures, constituting a threat to driving safety. To deal with these issues, this paper proposes a VI collaborative environment perception approach based on sparse bird's eye view (BEV) features. By leveraging the representation of sparse BEV, features can be fused within a unified perspective in a lightweight manner, thereby enhancing the efficiency of feature fusion and reducing redundancy. Additionally, we present a solution for processing the overlapping features between the EGO-vehicle and road side unit (RSU) by taking the union of the coordinate points. Finally, the applicable vehicle and RSU datasets are collected through Carla. The experimental results demonstrate that the proposed approach can effectively mitigate the limitations of individual-vehicle perception by compensating for occluded information and provide a more comprehensive perception field.

In vehicular edge computing networks, the realtime task processing for different vehicles by determining task offloading paths and allocating offloading resources is crucial. However, existing studies often overlook the impact of air-ground cooperation, the task burden on offloading relay nodes, and the limited resources on network performance. These oversights can lead to traffic congestion and performance degradation. To address these issues, this paper proposes a novel integrated sensing and communication (ISAC)-enabled unmanned aerial vehicle (UAV)-assisted task offloading strategy, where tasks are relayed and processed by ground vehicles and multiple UAVs based on the task load rate. Specifically, we first analyze the differences between offloading paths for ground vehicles and UAVs, the coupling relationship between communication, sensing, and computing resources, and the conflicts between UAV self-sensing and ground vehicle offloading assistance. We then formulate the offloading path determination and resource allocation problem as an optimization problem, to minimize overall offloading delay and UAV energy consumption. Subsequently, we propose a novel offloading algorithm based on deep reinforcement learning to schedule offloading paths and network resources intelligently. Finally, simulation results demonstrate the effectiveness of our proposal in reducing offloading delay and UAV energy consumption.

Currently, end-to-end autonomous driving systems that employ imitation learning effectively learn and optimize driving strategies by integrating the driving behaviors of human experts with modern deep learning techniques. However, challenges such as scene diversity, data security, and training time must still be addressed to develop a model that is applicable across various traffic scenarios while maintaining high accuracy. To tackle these issues, this paper proposes a personalized feder-ated imitation learning algorithm that aggregates locally trained imitation learning decision models from vehicles operating in different scenarios through a distributed training approach, thereby enhancing training efficiency and model accuracy. Specifically, considering the variability in communication link quality and potential disconnections caused by the mobility of vehicles in a connected vehicle network, we introduce a personalized user selection algorithm. Building on this foundation, we employ a federated imitation learning method to efficiently and rapidly train a driving decision model with comparable performance while safeguarding data privacy. Extensive simulation results confirm the superiority of the proposed algorithm in terms of training speed and model accuracy.

The perception of a single vehicle has limitations. In contrast, vehicle-infrastructure cooperative perception can share perception information among nodes, overcoming view limitations and occlusion for more comprehensive environmental awareness. However, vehicle-infrastructure cooperative perception encounters two main challenges: the trade-off between perception performance and communication bandwidth, and the negative impact of communication delay on perception. To address these challenges, this paper devises a cooperative data transmission algorithm with delay compensation that innovatively combines feature selection and delay compensation to reduce data volume and offset delays. The algorithm exploits spatial value differences for dynamic feature selection, reducing data transmission while maintaining key performance, and has a receiving-end designment to synchronize the asynchronous features. Simulation results show that the proposed a Sparse BEV Feature Transmission Algorithm with Delay Compensation (DC-SBEVTx) significantly reduces data transmission volume in bandwidth-limited scenarios while maintaining acceptable Average Precision (AP). It also maintains high AP under communication delays, outperforming baseline methods in terms of AP across various conditions, such as different average delay and position noise levels.

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL.

Federated learning (FL), as a typical distributed machine learning framework, has been effectively applied to traffic flow optimization, driving behavior analysis, and other areas. However, the stability and efficiency of FL systems heavily rely on the quality and cooperation of participants. If participants find no profit in the FL process, they may reduce their willingness to participate due to energy consumption and limited resources. To bridge these gaps, this article proposes a demand-balanced incentive mechanism for multiple FL tasks. First, considering the time-varying channel characteristics in the Internet of Vehicles (IoV) scenario, two optimization problems are constructed: 1) maximizing task-matching satisfaction and 2) minimizing communication energy consumption. Second, these problems are transformed into a distributed incentive mechanism based on a multileader-multifollower (MLMF) Stackelberg game, and a bi-level alternating direction method of multipliers (ADMM) algorithm is proposed to solve for the optimal resource allocation and reward schemes that balance the demands of all parties. Furthermore, this article designs a multiagent deep reinforcement learning-based method to solve the incentive problem, thereby avoiding the impact of information asymmetry. Simulation results verify that the proposed scheme not only balances the demands of all parties but also enhances user participation without being affected by the number of participants, making it suitable for IoV environments.

In the Internet of Vehicles (IoV), centralized transmission of vehicle data poses significant privacy risks. Applying federated learning (FL) to vehicular networks enables collaborative model training while preserving data privacy. However, due to heterogeneous computational capabilities across devices, some users may drop out due to insufficient computing resources, resulting in slow convergence and degraded model accuracy. Furthermore, the traditional sequential transmission of model parameters creates communication bottlenecks and increases training latency, particularly when dealing with a large number of participating vehicles. Over-the-air computation (AirComp) provides an innovative solution by exploiting the natural super-position property of wireless channels to enable simultaneous transmission and aggregation of signals from multiple users, significantly reducing communication rounds and system latency compared to conventional sequential approaches. In order to solve the above problems, this paper combines AirComp with FL to form an adaptive OA-FL algorithm that addresses computing resource heterogeneity and improves communication efficiency through parallel signal processing. Specifically, we first adaptively adjust the number of local iterations based on the assessment of users’ available remaining computational resources to improve user participation rates. Subsequently, we formulate and solve an optimization problem to minimize model parameter distortion caused by AirComp aggregation, thereby reducing communication overhead while accelerating model aggregation and enhancing model accuracy. Simulation results demonstrate that the proposed OA-FL method effectively reduces client unavailability, improves FL training efficiency, and achieves faster convergence compared to conventional FL approaches.

Federated learning (FL), as a distributed machine learning paradigm, facilitates collaborative training without sharing raw data and holds promise for effective application in the Internet of Vehicles (IoV) for tasks, such as traffic flow prediction and driving behavior analysis. However, the efficiency of FL systems relies on the integrity of the local dataset and the level of user contribution. Vulnerabilities to attacks by malicious users and suboptimal aggregation methods can compromise system performance. To address these issues, this article proposes a blockchain-based FL secure aggregation algorithm to bolster FL robustness. Specifically, in the absence of a centralized trust authority in the IoV, we establish a hierarchical blockchain-empowered IoV reputation management framework that leverages smart contracts to create a trustworthy environment for reputation sharing. Additionally, a lightweight consensus protocol tailored for blockchain efficiency is proposed, thus facilitating a flexible and effective implementation of FL in the IoV. Furthermore, we introduce a reputation-based model selection evaluation scheme and, based on this, a robust FL secure aggregation algorithm. This novel reputation assessment strategy mitigates the effects of interaction uncertainties and integrates a broader spectrum of IoV-specific reputation determinants, thereby enhancing the precision of model selection. The simulation results validate the proposed framework’s superiority in terms of robustness, adaptability, and security.

To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm.

Presents corrections to the paper, (Correction to “Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks”).

Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sensor networks (RSNs) into the CAV ecosystem presents a promising solution to improve sensing performance, but also introduces significant challenges, including heterogeneous sensing requirements, inconsistencies in multisource sensor data, and efficient resource utilization. To address these challenges, this article proposes a novel cooperative sensing framework that leverages multisource and multilevel sensing information from RSNs to optimize CAV sensing performance in resource-constrained scenarios. We develop an improved decision transformer (DT)-based approach that dynamically adapts to diverse driving conditions and efficiently fuses sensor data at various abstraction levels. To tackle the issue of long-delayed rewards, we introduce a reshaped reward function and a bi-level optimization framework that enables effective propagation of rewards along decision sequences. An advanced gradient approximation technique is employed to efficiently solve the optimization problem. Extensive simulations demonstrate the superior performance of our improved DT approach compared to state-of-the-art reinforcement learning (RL) methods in terms of sensing accuracy, coverage, and data efficiency under various traffic conditions.

In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands using non-orthogonal frequency division multiplexing. The decoy signals are used to deceive non-cooperative eavesdropper, while the true signals are hidden to counter interception threats. Definitions for the interception and deception probabilities are provided, and the mechanism of CTSF is discussed. To improve the secrecy performance of true signals while ensuring decoy signals achieve their deceptive purpose, we model the problem as maximizing the sum secrecy rate of true signals, with constraint on the decoy effect. Furthermore, we propose a bi-stage alternating dual-domain optimization approach for joint optimization of both power allocation and correlation coefficients among multiple sources, and a Newton’s method is proposed for fitting the T/F frequency multiplexing factor. In addition, simulation results verify the efficiency of anti-intercepting performance of our proposed CTSF scheme.

With the rapid evolution of vehicular network technology, the integration of Machine Learning (ML) with Connected and Autonomous Vehicles (CAVs) presents both remarkable opportunities and formidable challenges. This paper addresses the crucial need for efficient ML model training in the context of Federated Learning (FL) within vehicular networks. Recognizing the limitations imposed by the tradeoff between the high energy cost at the local level with the performance problem at the global level, we propose an innovative approach that harmonizes cost optimization with strategic model selection. Our strategy primarily focuses on optimizing energy consumption during model training and updating at the vehicle end, thereby resolving the prevalent issue of limited end-user participation in FL due to high energy demands. Additionally, we introduce an advanced model selection method, prioritizing local model uploads and adaptively allocating bandwidth to clients with more extensive training data. This method enhances the efficiency and reliability of model updates, ensuring robust global model performance. We validate our approach through extensive simulations, demonstrating not only improved learning performance but also a significant reduction in energy consumption among participating clients.

Vehicular cooperative perception (VCP) facilitates the exchange of sensing data among vehicles through vehicle-to-everything (V2X) communication, significantly increasing the sensing range and precision of individual autonomous vehicles (AVs). However, efficiently managing the sharing and processing of large volumes of sensing data presents challenges due to restricted communication and computation resources. This study introduces an integrated sensing, communication, and computation (ISCC)-based task offloading and resource allocation (ITORA) framework, which optimizes cooperative perception by determining what data to share, which vehicles to involve, and how to process the data effectively. We develop an information value function to evaluate the data quality for each vehicle. Subsequently, we design strategies for sensing task allocation, task offloading, and resource allocation to enable value-driven data selection at a subregion level, facilitating collaborative computing among edge servers and vehicles. Additionally, we formulate an optimization problem aimed at maximizing information value while minimizing delay and energy consumption, subject to constraints on a full region of interest (RoI) coverage, delay, wireless bandwidth, and computational resources. We decompose the mixed-integer nonlinear programming (MINLP) problem into two subproblems, devising a sensing task allocation algorithm and a proximal policy optimization (PPO)-based task offloading and resource allocation (PTORA) algorithm to address them. Comprehensive simulations validate the effectiveness of the proposed PTORA in optimizing information value, reducing task execution delay, and minimizing energy consumption.

In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to synchronize data to their DTs deployed in the cloud for decision-making. However, for a vehicle which is simultaneously covered by a group of heterogeneous network infrastructures, the DT of the vehicle (DT-V) can connect with the DTs of infrastructures (DT-Is) in different infrastructure groups across regions in the virtual networks so that each DT-V may deceive the DT-Is by interacting with multiple DT-I groups and selecting the optimal one to synchronize data. To this end, we propose an on-demand data synchronization scheme for DT-Vs and DT-Is. In the scheme, infrastructures and vehicles are grouped based on their geographical locations and the arrival time of each vehicle through which the DT-Vs and DT-Is can interact with each other to make decisions in groups. Then, the requirements of DT-Vs (i.e., minimize synchronization cost and maximize synchronization satisfaction) and DT-Is (i.e., maximize profits) are considered to design their utility functions and the decision-making process between the DT-Vs in each group and the DT-Is in each group is formulated as a Stackelberg game to obtain their optimal strategies. After that, considering the deceptive behavior of vehicles, a joint optimization algorithm that integrates the Stackelberg game and the selection of each DT-V is designed to obtain the real equilibrium solution for DT-Vs and DT-Is to maximize their utilities. Simulation results show that our scheme can obtain the highest utilities compared with the traditional schemes.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.

Connected and Autonomous Vehicles (CAVs) increasingly leverage sophisticated sensor systems integrated with emerging technologies like the sixth generation (6G) for enhancing driving safety and efficiency. Despite the potential of utilizing the advanced communication technology to enhance driving reliability, conficts between high-quality sensing needs of CAVs and insufficient sensing sharing wellness present significant challenges. To bridge the gaps, this paper proposes a novel cooperative sensing sharing framework that utilizes vehicle-to-vehicle (V2V) communication to extend the effective sensory range of CAVs, thereby reducing perception gaps and enhancing driving efficiency in complex driving environments. Aiming at incentivizing cooperative sensing sharing ecosystem within this decentralized framework, we first introduce a multi-tier blockchain architecture that ensures secure and transparent data sharing among CAVs. Concurrently, a custom-designed efficient consensus algorithm is proposed to minimize the overhead while guaranteeing transaction throughput. To tackle issues related to trust and motivate long-term cooperative behavior, we introduce a supervision-oriented model that utilizes evolutionary game to formulate incentive mechanisms discouraging malicious participation and promoting honest, active engagement. Finally, theoretical analysis and extensive simulations demonstrate that our system not only ensures healthy sensing sharing performance but also maintains system security.

Accurate future traffic flow prediction is essential for decision-making in travel recommendations and route planning, aiming to reduce congestion and enhance traffic safety. Traditional traffic flow prediction models often face limitations in quality and structure, leading to increased training costs and inefficiencies, due to data scarcity and centralized training modes that compromise data privacy. To address these issues, we propose a model called 2MGTCN, which combines Multi-modal Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN) for Cross-city Traffic Flow Prediction (TFP). Our 2MGTCN model utilizes federated transfer learning (FTL) to transfer the model from the source to the target domain, mitigating data scarcity. It also incorporates GCN and TCN to capture both spatial and temporal information, enhancing cross-city adaptability. Additionally, Grey Relation Analysis (GRA) and Dynamic Time Warping (DTW) methods are applied to capture road relationships, and a Federated Parameter Aggregation based on Spatial Similarity (FPASS) algorithm is proposed for ensuring effective parameter aggregation by considering spatial similarity. Simulation results show that our 2MGTCN algorithm outperforms traditional TFP models in both centralized and distributed training modes, ensuring higher accuracy and better privacy protection.

Currently, heterogeneous driving scenarios and complex traffic conditions challenge connected and autonomous vehicles (CAVs) to achieve accurate sensing of road conditions. Existing research on the sensing capabilities of vehicles only relys on adding more onboard sensors, which makes the driving safety unable to be guaranteed due to the installment and cost limit of various sensors. Therefore, this paper proposes an enhanced 3D sensor deployment method to break through the sensing capabilities of CAVs’ own equipment limitations. By efficiently utilizing road infrastructure, reasonable roadside sensor deployment will effectively assist CAVs to expand the sensing range and improve overall sensing accuracy. Firstly, in order to address the limitations of existing works that often rely on simplified sensor models and idealized road conditions, we propose a Bresenham-based sensor and environment model that can be used to construct realistic road environments. Secondly, a decision transformer (DT)-based method is adopted to solve the problem of optimal deployment of sensors in road environments. Our approach effectively addresses the limitations of traditional static deployment methods, which often fail to consider the complexities of real-world driving conditions and the diverse factors influencing optimal sensor deployment. Finally, in order to solve the problem of DT delayed rewards, we propose a two-layer optimization method to redistribute the reward function to solve the challenge of local optimization. A large number of simulations oriented to sensing effects not only verify the effectiveness of the sensor deployment method but also ensure the reliability of sensing assistance.

Wireless Traffic Prediction (WTP) plays a significant role in achieving intelligent resource management for communication systems. However, WTP still faces challenges such as inaccurate prediction resulting from the complex spatial-temporal characteristics due to user mobility, high communication overhead caused by the complexity of the prediction model, and user privacy issues stemming from Centralized Learning (CL). To address the aforementioned issues, this paper proposes a WTP framework under the Federated Learning (FL) strategy called Federated Spatial-Temporal Dual-attention based Network (FedSTDN). Aiming at improving communication efficiency and simultaneously representing various wireless traffic patterns, a data augmentation-based clustering algorithm is adopted, which groups cells into different regions using a small augmented dataset, facilitating subsequent processing. To improve prediction performance, a local prediction model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to capture the short- and long-term dependencies of traffic. Additionally, a novel Kolmogorov-Arnold Network (KAN) layer is introduced to replace the traditional Multi-Layer Perceptron (MLP) layer, further enhancing prediction performance. Simulations on two different real-world datasets verify the effectiveness and efficiency of FedSTDN. Compared to the well-performing baseline, the proposed FedSTDN achieves up to 32.83% and 24.30% improvements in Mean Square Error (MSE) and Mean Absolute Error (MAE) on the Milan dataset, respectively. For the Trentino dataset, FedSTDN achieves up to 17.25% and 5.86% improvements in MSE and MAE, respectively.

Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.

With the advent of the Industry 4.0 era, the widespread deployment of Automated Guided Vehicles (AGVs) in factories has enabled them to serve as sensor relays, assisting in collecting sensor data in areas with poor signal quality. Traditionally, the objective of sensor data collection has been primarily to reduce the delay in data acquisition. However, latency alone offers an incomplete reflection of the significance of sensor data to industrial tasks. Value of information (VoI) has emerged as a novel metric that more accurately reflects the impact of sensor data on the performance of upstream tasks. In this background, we introduce an innovative AGV-assisted sensor data collection strategy to minimize the loss of sensor data VoI. This strategy encompasses the selection of data fusion nodes, choice of transmission modes, and AGV path planning. We introduce a new metric called structural value entropy, which effectively reduces VoI loss during the data fusion process, and through the design of a metaheuristic algorithm based on ant colony optimization, achieves the selection of transmission modes and the planning of AGV paths with minimal VoI loss. Simulation experiments validate the effectiveness of the proposed strategy in maintaining VoI, demonstrating significant performance enhancements and acceptable convergence speed compared to baseline strategies, affirming the strategy's efficiency and feasibility in handling large-scale sensor data collection tasks.

The advent of deep learning (DL) technology has increasingly captivated the research community’s interest in harnessing DL to enhance data transmission efficiency. Notwithstanding, prevalent methodologies often overlook the computation delay of DL processing data during the inferencing procedure, and fail to adjust intelligent algorithm complexity based on user features. To bridge this gap, we introduce $\mathbf{C o m}^{2}$ (Communication-Computation) framework, to synergize the optimization of communication and computational delays. $\mathrm{Com}^{2}$ adeptly navigates the trade-offs between communication and computation delays, facilitated by autoencoders of varying depths, thus one user can reduce communication delay through more computation latency, and vice versa. Further enhancing this framework, we present an optimization algorithm that marries QMIX with a cascaded graph neural network (GNN), designed to select the optimal autoencoder depth and optimize transmission resources in a distributed manner. This algorithm pioneers a label-free training regime, employing reinforcement learning and unsupervised learning to adaptively improve without the need for high-quality labels. Simulation results show that $\mathrm{Com}^{2}$, alongside the proposed optimization algorithm, maximizes the utility of users’ computing and transmission resources, significantly curtailing the overall data transmission delay by intelligently managing delay trade-offs.

Due to the stringent latency requirements for computationally intensive rendering in virtual reality (VR) transmission and the limitations of computational resources on VR devices, extensive research has focused on task offloading with joint communication and computing resource scheduling to address these issues. Traditional model-based theoretical methods face challenges with long online processing times, while data-driven methods lack interpretability. This paper proposes a knowledge-driven rendering task offloading strategy for immersive wireless VR with mobile edge computing (MEC). The rendering approaches include local, MEC, and collaborative offloading between VR devices and MEC servers. First, we formulate an optimization problem to maximize user quality of experience (QoE), which is defined as the weighted sum of latency and video resolution. To solve the optimization problem, we propose a knowledge-driven belief propagation (KD-BP) algorithm where the structure of the BP algorithm is regarded as knowledge. Specifically, the operations with high computational complexity in the BP algorithm are replaced by a deep neural network, termed the knowledge-fused deep learning (DL) method. Finally, numerical results show that when the number of users reaches 10, the proposed KD-BP algorithm significantly reduces online processing latency and closely matches the convergence speed and performance compared to the BP algorithm.

The deepening process of urbanization poses formidable challenges to the current transportation carrying capacity. The utilization of near-ground space (NGS) and urban air mobility (UAM) greatly enhance spatial dimensions and traffic flexibility of the transportation system. However, the current limited sensing capability falls short in meeting the real-time collaborative environmental sensing and intelligent control requirements of aerial transportation. Integrated sensing and communication (ISAC) combines the sensing system of UAM with 6G communication technologies, enabling them to collaborate and achieve data sensing, transmission, processing, and decision control. The use of artificial intelligence-generated content (AIGC) facilitates real-time data fusion and decision-making, adapting to dynamic and unpredictable environments. In this paper, we first model and analyze the traffic flow in three-dimensional space, achieving knowledge embedding based on artificial potential energy field theory. Next, we design a multimodal data fusion neural network structure, which utilizes the Variational Autoencoder (VAE) to generatively achieve feature fusion and compression. Finally, we construct a UAM digital simulation platform using AirSim, which generates considerable aerial data. The simulation results demonstrate that our proposed approach achieves a feature recognition accuracy of 90.38%. The total latency is below 0.6ms, which exhibits high real-time performance.

FL enables collaborative training of autonomous driving models without sharing the original data. It enhances the model’s environmental adaptability and establishes an effective distributed paradigm for connected and autonomous vehicles (CAVs) to share driving experiences as well as make collaborative decisions. However, participants’ negative behavior, such as free riding due to selfishness, can significantly reduce federated learning (FL) training efficiency and model accuracy. Unlike previous studies that focused solely on a single FL task, this article proposes an incentive mechanism for long-term driving model training, which models the interactions between participants and the server during the long-term FL process as an infinitely repeated game. The incentive mechanism considers the relationship between participants’ historical behaviors and their future incomes, motivating participants to maintain positive behaviors throughout the long-term FL process and ensuring the efficient operation of the training process. Furthermore, in order to increase CAVs’ enthusiasm, we design reward rules that attract new participants and encourage sustained engagement. The simulation results demonstrate that the proposed incentive mechanism maximizes the profits of both CAVs and the server in long-term FL, which effectively reduces negative CAVs’ behaviors and improves the efficiency of FL training.

The integration of edge computing with digital twins (DTs) has been instrumental in driving substantial advancements in the Internet of Vehicles (IoV) domain in recent times, particularly within the 6G wireless networks where DTs enable real-time simulation, monitoring, analysis, and high-speed transmissions for connected vehicles. Despite these benefits, several challenges arise, including dynamic network topologies resulting from the high-speed vehicle mobility, frequent edge server switches causing instability and increased latency, and the limited computing resources struggling to cope with the demanding computational tasks. This article addresses these issues by proposing a framework where the vehicles serve as the auxiliary mobile edge computing (MEC) servers. It introduces an enhanced density-based spatial clustering of applications with the noise (DBSCAN) algorithm designed to improve the clustering of vehicles under high-speed movement scenarios. Moreover, a multi-to-multi matching algorithm is devised to effectively associate vehicles with the auxiliary MEC servers. To alleviate the problem of insufficient computing resources due to intense computational loads during DT updates, a deep reinforcement learning (DRL)-based approach is utilized to make the optimal computation offloading decisions. This work further refines the offloading strategy by adopting the improved double deep Q-network (DDQN) and the dueling deep Q-network algorithms. Simulation experiments validate that the proposed clustering improvement and the DRL-based offloading decision-making scheme outperform the existing baseline methods across multiple performance metrics, such as clustering effectiveness, processing latency reduction, algorithmic efficiency, and convergence rate.

With the proliferation of cellular vehicle-to-everything (C-V2X), connected and automated vehicles (CAVs) are gradually being commercialized. CAVs can interact with road infrastructure and human-driven vehicles (HDVs) to acquire relevant traffic information, thereby altering the characteristics of the traditional traffic flow. The emergence of CAVs is widely believed to bestow benefits to the traffic system in terms of safety, efficiency, and energy consumption. Nevertheless, as with most phenomena, there are two sides to the coin. Further exploration is necessary to determine whether the emergence of CAVs will trigger adverse effects and the underlying factors that may induce adverse effects. To be specific, this article first delves into how selfish driving behaviors (egoism CAV control strategy) can have an unfavorable impact on the performance of the traffic systems, thereby lowering the traffic efficiency. Subsequently, we develop an unselfish (altruism) CAV control strategy that aims to achieve the global optimization and improve the overall road operational capacity. Based on the simulation results obtained at different inflow and outflow rates on highway, it is evident that egoism driving behavior leads to a 11.55% decrease in average speed performance as compared to the noncontrol strategy, while altruism driving behavior results in a 20.14% improvement. Furthermore, we compare the proposed strategy with the current road infrastructure control, which only improves the average speed performance by 11.6%. This indicates that controlling CAVs has the potential to replace the deployment of the traditional road infrastructure, thereby optimizing the social and economic benefits. This article can provide insightful guidance for the future policy formulation in the transportation authorities, wherein the emergence of CAVs needs to be effectively regulated based on the altruism, thus fostering the establishment and development of a safe and efficient mixed traffic ecosystem.

With the proliferation of connected and automated vehicles (CAVs), densely distributed edge computing nodes have emerged on roadways. Consequently, leveraging CAVs as a mobile computing platform can integrate idle vehicle resources to provide computational services for ubiquitous Internet of Things (IoT) devices. Numerous studies have investigated task offloading strategy in the systems with full CAVs penetration. It is expected that the coexistence of CAVs and human-driven vehicles (HDVs) in mixed traffic systems will continue for a considerable period. However, due to the impact of HDVs on communication performance, the task offloading model designed for the systems with full CAVs penetration are no longer applicable in mixed traffic systems. We explore task offloading schemes using CAVs as a mobile computing platform in mixed traffic systems to address this issue. Specifically, we first model the communication model in mixed traffic systems, taking into account the influence of HDVs on link interference, the alteration of path loss due to the impact of HDVs on routing, and the additional sensing tasks arising from the inability of HDVs and CAVs to communicate. Subsequently, considering that delay and energy consumption are crucial factors affecting the performance of CAVs as a mobile computing platform, we formulate the task offloading scheme as an optimization problem. Additionally, we employ a distributed offloading based on deep learning (DODL) algorithm to obtain approximately optimal offloading decisions. Simulation results demonstrate the effectiveness of the proposed model in mixed traffic systems. By employing the DODL algorithm, the CAVs as a mobile computing platform can achieve enhanced performance in terms of convergence, thereby advancing the development of autonomous driving in mixed traffic systems.

Connected and Automated Vehicles (CAVs) are characterized by diverse communication attributes, embodying the trajectory of future automotive progress. Meanwhile, the transportation system will be in a mixed stage of CAVs and Human-Driven Vehicles (HDVs) for a long time. The study of communication capacity and strategies for mixed traffic systems is of great significance for the popularization of CAVs and the deployment of communication infrastructures. However, current research mainly focuses on the communication capacity analysis in the scenario with full penetration of CAVs, while the influence caused by HDVs on Vehicle-to-Vehicle (V2V) communications and the capacity analysis of connected vehicles in mixed traffic systems need further understanding. To address this issue, this paper considers the shadow fading caused by HDVs on wireless communication links and analyzes the communication capacity in mixed traffic systems. Specifically, we first synthesize the V2V and Vehicle-to-Infrastructure (V2I) communication modes to propose an analytical framework for vehicular network communication capacity in mixed traffic. Then, a predictive communication strategy is also provided that caches the required content at infrastructure in advance according to predicted vehicle trajectories to improve the capacity of vehicular networks in mixed traffic. Furthermore, the derived capacity analysis theorems reveal the communication capacity of mixed traffic is closely related to the CAV penetration rate, the vehicle arrival rate, and the infrastructure deployment interval. Simulation results prove the effectiveness of the proposed framework, and the proposed predictive communication strategy can increase the mixed traffic communication capacity compared to existing communication strategies. The theoretical results herein can guide the implementation of vehicular network applications and the design of communication strategies in mixed traffic systems.

Federated learning (FL) is a promising technology for autonomous driving, enabling connected and autonomous vehicles (CAVs) to collaborate in decision-making and environmental perception while preserving privacy. However, traditional FL algorithms face challenges related to imbalanced data distribution, fluctuating channel conditions, and potential security risks associated with malicious attacks on local models. This paper proposes a fair and secure FL algorithm that not only addresses the challenges arising from imbalanced data distribution and fluctuating channel conditions, but defends against malicious attacks. Specifically, we first propose a personalized local training round allocation algorithm to balance energy costs and accelerate model convergence. Next, in order to further guarantee security, we embed an attack module based on Gini impurity. Extensive simulations demonstrate that the proposed algorithm achieves energy fairness, reduces global iteration time, and exhibits resistance against malicious attacks.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

Air-ground Integrated Mobility (AIM), as a disruptive mode of travel, has the tremendous potential to alleviate ground traffic congestion issues substantially. However, the primary challenge in achieving this leapfrog development lies in ensuring driving safety. Receiving collision warnings in time within a limited distance can significantly reduce collision risks, which is crucial for ensuring driving safety in AIM. However, due to challenges in aerial network coverage, ensuring the communication quality of aerial Personal Aerial Vehicles (PAVs) remains difficult, thereby affecting the effective transmission of messages. Furthermore, the integration of ground Connected and Automated Vehicles (CAVs) with aerial PAVs in AIM results in significant differences in user resource requirements. Given the complexity of the AIM environment and the high mobility of PAVs, it is challenging to rapidly and accurately capture user communication quality. Therefore, addressing the differential resource requirements of users in this environment is particularly challenging. To this end, we propose a personalized resource allocation strategy assisted by a Channel Knowledge Map (CKM) in AIM. This strategy aims to meet the personalized resource requirements of users while maintaining the maximum Perception Response Time (PRT), thereby ensuring driving safety. Specifically, the CKM in AIM is constructed to obtain channel states through environment-aware communication. Next, a 3D collision warning system is designed to analyze rigorously the maximum PRT of vehicles under different motion states in avoiding collisions. On this basis, with the help of CKM, the channel knowledge of the user’s location is obtained to quantify the communication and computing resources required by each user to maintain the maximum PRT. Finally, we establish the PRT-driven resource optimization problem and employ Deep Reinforcement Learning (DRL) to seek the optimal resource allocation strategy. Simulation results indicate that the proposed method effectively enhances safety and resource utilization in AIM under resource constraints and uneven distribution.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.

Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

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.

As the number of connected and autonomous vehicles (CAVs) on road networks continues to increase, mixed transportation scenarios where CAVs and human-driven vehicles (HDVs) coexist are becoming more common. Establishing dedicated lanes (DLs) for CAVs is crucial for managing mixed traffic and improving road capacity. In this paper, we provide a theoretical analysis of the relationship between the market penetration rate (MPR) of CAVs and road capacity in both single-lane scenarios and multiple-lane scenarios with DLs. We derive a critical MPR for CAVs, at which they can be seamlessly accommodated within the DLs. Our numerical results show that CAVs should be prioritized to enter DLs first to optimize road capacity in mixed traffic. We also derive and validate the road capacity in multiple-lane scenarios and provide an optimal strategy for setting up DLs under varying MPRs to maximize road capacity. Overall, our study provides valuable insights into the significance of DLs for CAVs in mixed traffic and offers guidance on their implementation to improve road capacity.

Task offloading is a potential solution for computation-intensive vehicular applications due to limited on-board computing resources. However, traditional model-driven methods are hindered by long online processing time, while data-driven methods are deficient in interpretability and generalizability. To overcome this challenge, this paper formulates the resource allocation for task offloading in connected autonomous vehicles (CAVs) as a multi-objective optimization problem, and proposes a novel knowledge-driven algorithm that integrates both model-driven and data-driven methods. Specifically, the framework of a model-driven alternating minimization (AM) algorithm, which solves the formulated problem via alternatively optimizing power allocation subproblem and bandwidth and CPU frequency allocation subproblem, is regarded as knowledge. Inspired by such knowledge, our proposed knowledge-driven neural network consists of two long short term memory networks (LSTMs) to alternatively updating these two subproblems. Furthermore, to get away from the local optimum usually occurred in the AM algorithm, our proposed knowledge-driven neural network updates network parameters with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the LSTM without knowledge.

Urban air mobility (UAM) provides a new solution to relieve urban transportation pressure by expanding transportation resources of near-ground space. The vigorous development of emerging technologies such as artificial intelligence, intelligent transportation, and sixth-generation (6G) communication technologies have greatly promoted the progress of UAM. However, UAM also increases traffic safety hazards while introducing vertical dimension transportation resources. Traditional collision avoidance is not suitable for three-dimensional (3-D) air-ground integrated mobility scenario, which considers safety hazards in vertical dimensions as well as the resource supply and demand conflict due to the combined effect of directional antenna angle and limited communication distance. Therefore, a safety-oriented on-demand resource allocation strategy for air-ground integrated mobility is proposed. Specifically, we first model the 3-D safety distance model in the air-ground integrated mobility scenario and construct its quantitative relationship with communication and computing resources. Secondly, a 3-D safety distance optimization model is proposed with joint consideration of safety-oriented resource requirements and resource distribution, which can allocate resources in the scenario. Furthermore, a 3-D safety distance optimization algorithm based on deep reinforcement learning (DRL) is designed for solving the optimization model, which implements a safety-oriented resource allocation. Simulation results show that the proposed safety control strategy can effectively improve the safety of air-ground integrated mobility and alleviate the contradiction between the supply and demand of resources.

To investigate the diversified applications in vehicular networks, artificial intelligence, intelligent edge computing, and vehicular networks are combined. By offloading computation tasks to devices close to vehicles, Vehicular Edge Computing (VEC) has emerged as a new computing paradigm to tackle the problem. Most existing VEC methods simply slice the application into subtasks for offloading purposes without considering the dependencies between subtasks. In practice, the dependency information is critical to the efficiency of offloading strategies. If a subtask requires the computation result of another subtask, the latter has to be processed before the former is finished. In this paper, we propose a deep reinforcement learning based offloading strategy for multi-vehicle collaboration VEC, with task dependency taken into account. With the proposed strategy, we formulate the offloading problem as an Markov Decision Process (MDP) and use the Sequence-to-Sequence (S2S) neural network to represent the policy/value function of the MDP. Furthermore, we train the S2S neural network to obtain the appropriate offloading policy using the Proximal Policy Optimization (PPO) technique. Our simulation results indicate that, by considering task dependencies during offloading, the proposed strategy outperforms existing methods in effectively reducing task offloading latencies.

Edge intelligence and federated learning (FL), as key enablers of 6G, is a promising solution for networked Autonomous Driving (NAD). However, traditional federated learning is a server-client architecture, which makes the model training overly dependent on a fixed single aggregation server and makes the FL process insecure and unreliable due to the vulnerability of the aggregation server to a single point of failure. In this paper, we propose a rotating server FL scheme (RSFL) to solve the problem of single point of failure and limited resources and improve environmental adaptability. Specifically, we consider multiple factors to measure the vehicle performance and find the vehicle with the highest performance score in this round as the server for the next round while setting weights that are randomized in each round, which reduces even more the likelihood that a malicious user will recognize the regularity of the chosen server. Finally, the performance of RSFL is evaluated through a large number of experiments, and the results show that compared with baseline FL, FL with randomly selected servers, and peer-to-peer decentralized FL, RSFL can effectively reduce the cases of servers being detected and attacked by malicious adversaries, and improve the accuracy of the model.

The rapid development of Internet of Things (IoT) technology can significantly promote the development and deployment of smart roads, enabling efficient and reliable road information sensing and analysis. As an important part of smart roads, timely and accurate detection of road cracks can improve service life of roads and reduce road management and operating costs. In this paper, we propose a vibration-sensor-based crack detection scheme for smart roads. In this scheme, by deploying the vibration sensor on the roadside, the changes in the vibration signals caused by the vehicle passing through the range of the sensor can be collected in real time. Then, considering that the seismic waves caused by vehicle driving are mostly distributed in the low-frequency range, we perform low-pass filtering on the collected vibration signals to retain the low-frequency vibration signals. After that, in order to distinguish the crack state of the road, we extract the vibration signal features of the normal road and the cracked road in the time domain, frequency domain and time-frequency domain, respectively. Based on the extracted features, we use logistic regression (LR), support vector machine (SVM) and random forest classification (RFC) machine learning algorithms to realize road crack detection. Finally, we conduct experiments to evaluate the performance of the proposed road crack detection scheme. The experimental results verify the high accuracy of the proposed scheme, and the accuracy of LR, SVM and RFC are 93.3%, 93.3% and 96.7%, respectively.

Millimeter wave (mmWave) communication requires huge beam training overhead, which is highly undesirable in vehicle-to-infrastructure (V2I) communication, due to the requirement for low latency and high reliability. Sensory information can be utilized to reduce the beam training overhead. However, single-type sensors have limitations that result in inadequate performance when used independently for assistance, while employing multi-type sensors sensors for assistance presents challenges such as data redundancy and a large volume of data. To tackle the aforementioned issues, we propose a multisource low redundancy data-aided beam prediction (MLRDBP) scheme. Specifically, we first extract and fuse features from LiDAR, GPS, and camera data. Then, we employ the principal component analysis (PCA) algorithm to reduce the dimensionality and redundancy of the fused features. Finally, we design a classification model based on a multi-layer perceptron (MLP), training it with the fused low redundancy features to predict the optimal beam direction for communication parties. The simulation results indicate that the proposed scheme achieves the accuracy exceeding 76.9% for top-1 beam prediction, offering satisfactory performance with lower data redundancy compared to single-sensor-aided schemes and other existing schemes.

By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge.

With the dispersed and privacy-preserving features, federated learning (FL) enables connected and autonomous vehicles (CAVs) to achieve cooperative perception, decision-making, and planning by utilizing the learning capabilities and sharing model parameters. However, the discrepancies in local training cost and model upload durations between various CAVs make the energy and time costs caused by traditional FL algorithms unfair. In this paper, a fair and efficient FL algorithm is proposed with to address the challenges arising from imbalanced data distribution and fluctuating channel conditions. Specifically, to achieve uniformity in total time and energy cost among CAVs, a personalized approach is employed for the local training rounds of each CAV. This approach ensures fairness and training effectiveness while reducing the local training time in each round of global iteration. Furthermore, it enhances the convergence speed of the global model. Extensive simulations demonstrate that the proposed algorithm achieves fairness in energy cost while reducing the duration of each round of global iteration.

Digital Mobile Radio (DMR) is widely used in mission-critical communication due to its cost-effectiveness. However DMR only provide voice service for users in a small range. To address these limitations, a protocol conversion gateway named DoIP was designed and implemented to allow DMR devices to access a variety of communication services over long distances using the internet. In our proposed hierarchical model, DoIP works in an add-on mode. To meet the requirements of real-time, reliable, and multimedia applications, we designed the DMR frame structure, SPI packet structure, and Internet Protocol (IP) packet structure for inter-layer transmission. We also proposed the mapping rules between different protocols and achieved the conversion of DMR frames to IP packets. Finally, we implemented the DoIP on a commodity DMR repeater and evaluated its performance. The comprehensive evaluation revealed that DoIP successfully realized protocol conversion between DMR and TCP/IP, with a conversion delay of 9.10 ms, a packet loss rate of 0.3%, and an average jitter of 1.90 ms.

Integrated sensing, communication, and computation (ISCC) has become one of the research hotspots focused on the sixth generation (6G) communication systems. However, due to the demand for the coexistence of sensing, communication, and computing functions, there is uneven scheduling of resources among all parties, resulting in difficulties in accurately perceiving the environment, processing massive data efficiently in real-time, and experiencing large latency in task processing in the Internet of Vehicles (IoV). In this work, we propose a novel wireless scheduling architecture to enhance the coordination gains of sensing, communication, and computation from a perspective of joint optimization, enabling true on-demand services. Specifically, we first adopt the modified Cobb-Douglas utility function, the communication rate, perception of mutual information (MI), and calculation delay as coordinated gains. Next, we formulate a joint user association and subchannel assignment problem to capture the network externalities induced by resource competition among vehicle user devices (VUEs) with multi-functional requirements. To achieve a mutually satisfactory solution, we propose the dung beetle optimizer (DBO) algorithm to maximize the average utility of all VUEs in the network. Simulation results show that, compared with the baseline algorithm, the average utility gain of the proposed algorithm can reach up to 7.21%.

In digital twins enabled space-air-ground integrated networks (DT-SAGINs), the DT of a vehicle (DT-V) needs to constantly migrate between the infrastructures deployed on the path of the vehicle as the vehicle moves to provide stable and continuous driving services for the vehicle. However, each DT-V has differentiated migration requirements and the heterogeneous network infrastructures have various migration performances. Therefore, how to design a scheme that jointly considers the above factors to determine the optimal migration strategy for each DT-V becomes a challenge. In this paper, we propose a game-based migration scheme for the DT-Vs in DT-SAGINs. In this scheme, we first design a two-layer DT migration architecture, where each vehicle has two DTs and each network infrastructure only has one DT. The two DTs of the vehicle are respectively deployed in the cloud layer (Primary DT-V) and the edge layer (Second DT-V). In contrast, the DT of each network infrastructure is deployed in the cloud layer (DT-I). Based on the designed architecture, the interaction of the Primary DT-Vs and the DT-Is deployed in the cloud layer is formulated as a matching game, where an integrated algorithm that couples bilateral matching and dynamic programming is designed to obtain the optimal migration strategy for each Second DT-V deployed in the edge layer to maximize its average utility. The simulation results show that the proposed scheme can lead to a higher utility for each Second DT-V than the conventional schemes.

Federated learning (FL), as a distributed technology, has great application potential in autonomous driving. However, due to the lack of a quality verification mechanism for the client, it faces the problem of malicious users attacking the global model, which reduces the accuracy of FL model. In response to this problem, this paper proposes a hierarchical blockchain-enabled FL with reputation management scheme, which improves the efficiency and accuracy of FL while ensuring privacy and security. In this paper, we first propose a hierarchical blockchain framework and design a blockchain consensus algorithm based on parameter proof of quality (PoQ), which can provide FL workers with a secure and efficient data storage environment in a decentralized manner, while making up for the poor scalability of traditional single blockchains. On this basis, we design a reputation management method based on Bayesian theory and multiple subjective logic models to select high-quality local participating users. In particular, the weights of factors such as interactive activity and interactive position are taken into account to improve the accuracy of reputation calculations. Extensive simulations validate the performance of our scheme in improving FL accuracy and efficiency.

The fifth-generation network (5G) has made great progress. With the continuous development of communication technology, by analyzing the characteristics of 5G scenarios, the sixth-generation network (6G) technology combined with multiple scenarios provides effective solutions for the implementation of emerging services with stringent requirements. It is worth noting that the vigorous development of emerging services has been weakened due to the limited resources provided by a single scenario, cross-scenario technologies are urgently needed to enable emerging services in the 6G stage. However, most of the existing work only focuses on a single scenario, which leads to emerging services with complex requirements still difficult to achieve in practice. Therefore, we propose an efficient representation and scheduling framework to achieve the unification of cross-scenario resources, aiming to solve the problem of resource scheduling in cross-scenario. In the above framework, first of all, considering the strict resource requirements of emerging services, we establish a unified resource representation model based on the Time-Expanded Graph (TEG). Secondly, to maximize resource utilization, based on the representation model, a cross-scenario resource scheduling model is proposed. Then, considering the complexity of solving the scheduling model, a resource utilization maximization strategy is presented through the primal decomposition. Simulation results show that the unified framework can effectively improve resource allocation efficiency in complex 6G scenarios.

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

The dissemination of traffic incident information (TII) will greatly help to decrease fuel consumption and congestion under future Internet of Vehicles (IoV) environments. Compared with the current semitargeted dissemination strategies of TII that focus on communication performance, we propose a complete targeted-dissemination strategy by jointly considering the impact of the dissemination on the route planning of connected vehicles and the communication performance of information dissemination in the IoV environment. This strategy further alleviates the considerable challenges caused by the increasing number of vehicles and limited radio resources in dissemination, while reducing the additional fuel consumption caused by excessive and meaningless dissemination by selectively distributing traffic information to connected vehicles. Specifically, the proposed strategy consists of a radio resource allocation strategy guaranteeing communication quality and a traffic-influencing targeted dissemination strategy selecting the targets. Simulation results validate the effectiveness of the proposed strategy in ensuring communication performance, reducing total cost, and decreasing the carbon dioxide emission rate.

With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due to their flexibility, maneuverability, and economy, unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. UAVs can not only provide wireless access to IoT devices in the absence of a terrestrial network but can also perform rich IoT services and applications such as video surveillance, cargo transportation, pesticide spraying, and so forth. However, due to the high complexity, dynamics, and heterogeneity of the UAV-assisted IoT networks, growing attention has focused on using artificial intelligence (AI)-based methods to optimize, schedule, and orchestrate UAV-assisted IoT networks. In this article, we comprehensively analyze the impact of applying advanced AI architectures, models, and methods to different aspects of UAV-assisted IoT networks, including key IoT technologies, tasks, and applications. In addition, this article also explores challenges and discusses potential research directions of AI-enabled UAV-assisted IoT networks.

The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.

With the development of autonomous driving technology, it is foreseeable that connected and automated vehicles (CAVs) will be fully popularized in people’s lives. During this process, transportation systems are expected to evolve into the era of mixed autonomy, where CAVs and human-driven vehicles (HDVs) coexist in road networks and share available road resources. To materialize the much-anticipated potential of CAVs, a thorough understanding of CAVs’ effects on transportation systems is indispensable. On the one hand, attributing to advanced sensing, communication, and computation capabilities, CAVs provide opportunities to enhance mixed traffic safety, improve energy savings and suppress shockwave spread. On the other hand, due to advantages in large-scale information and cloud-computing resources, CAVs have the ability to occupy more road resources compared with HDVs, resulting in a reduction in the travel efficiency of HDVs, and even of the entire transportation systems. In this article, by clarifying the key differences between HDVs and CAVs, we comprehensively review the potential impacts of CAVs when they are appearing on road networks coexisting with HDVs. It can be regarded as the first-of-its-kind paper that systematically overviews the impacts of CAVs in the era of mixed autonomy on both positive and negative emotions. Specifically, the main focuses of this article are: 1) what are the key differences between CAVs and HDVs? 2) what are the positive impacts of CAVs’ appearance on mixed traffic systems? 3) will the introduction of CAVs cause some negative effects simultaneously? and 4) what kinds of strategies should be employed to relieve these negative effects? Hopefully, this article can not only call for an objective attitude toward the introduction of CAVs, but also provide foresighted advice to address possible challenges during the popularization of CAVs, so as to create a cooperative, safe, and efficient mixed traffic ecosystem.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

Currently, the complex traffic environment challenges the fast and accurate response of a connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to collaborate and share knowledge. To remedy that, this paper proposes a selective federated reinforcement learning (SFRL) strategy to achieve online knowledge aggregation strategy to improve the accuracy and environmental adaptability of the autonomous driving model. First, we propose a federated reinforcement learning framework that allows participants to use the knowledge of other CAVs to make corresponding actions, thereby realizing online knowledge transfer and aggregation. Second, we use reinforcement learning to train local driving models of CAVs to cope with collision avoidance tasks. Third, considering the efficiency of federated learning (FL) and the additional communication overhead it brings, we propose a CAVs selection strategy before uploading local models. When selecting CAVs, we consider the reputation of CAVs, the quality of local models, and time overhead, so as to select as many high-quality users as possible while considering resources and time constraints. With above strategic processes, our framework can aggregate and reuse the knowledge learned by CAVs traveling in different environments to assist in driving decisions. Extensive simulation results validate that our proposal can improve model accuracy and learning efficiency while reducing communication overhead.

Federated learning (FL), as a distributed machine learning technology, allows large-scale nodes to utilize local datasets for model training and sharing without revealing privacy, which has significant efficiency and advantages in artificial intelligence (AI)-based knowledge sharing of connected and autonomous vehicles (CAVs). However, for FL, there are challenges to ensure the security of knowledge, deal with the lazy behavior of participants, and enforce effective incentives. To bridge the gaps, in this paper, we first propose a hierarchical blockchain-supported FL architecture that utilizes the immutable and transparent properties of blockchain to enable secure storage and sharing of knowledge and transaction information with scalability. Then, considering the cost and laziness of the participants in the FL process, we propose an incentive mechanism combined with the supervision game to attract high-quality participants based on a comprehensive evaluation of model quality and participants’ reputation. Extensive simulation results validate that our proposal can improve learning accuracy and efficiency while ensuring security.

Traffic incident management in metropolitan areas is crucial for the recovery of road systems from accidents as well as the mobility and safety of the community. With the continuous improvement in computation and communication technologies, connected and automated vehicles (CAVs) exhibit the potential to relieve incident-induced traffic degradation. To understand the benefits of CAVs on traffic incidents, this paper models the impacts of CAVs with joint consideration of microscopic CAV driving behaviors and macroscopic traffic assignment in mixed traffic environment comprising both CAVs and human-driven vehicles (HDVs). Firstly, a generic traffic assignment model with mixed traffic is proposed to analyze the mixed traffic process from the macroscopic perspective. Then, we incorporate the traffic assignment model with bottleneck delays and incident effects from the microscopic perspective, to model the dynamic road system with incident effects in mixed traffic environment. Furthermore, cooperating with the mixed traffic assignment model, dynamic signal control policies are presented according to different incident severities, and the conditions for equilibrium existence, uniqueness and stability of the road system are derived. The analytical results indicate that road system stability with incident effects is closely related to the incident severity, signal control policy as well as penetration rate and spatial distribution of CAVs. Finally, simulation results are conducted to demonstrate the effectiveness of our proposed incident management policy in improving the recovery rate and system stability of road networks.

In 5G/B5G networks, the preemptive scheduling provides an efficient solution to the coexistence problem of eMBB/URLLC services. Current works usually assume that the downlink transmission duration of each URLLC service is within one mini-slot, which ignores the different requirements of URLLC users and may lead to the severe data rate loss of eMBB services and low resource utilization efficiency. To deal with above problem, we propose a novel URLLC preemptive strategy, where the arriving URLLC services could cross through multiple mini-slots rather than only one to puncture resources on demand. With the proposed strategy, considering the heterogeneous delay requirements of URLLC services and the preemptive influence on eMBB services, an efficient algorithm based on optimized sparrow search is also proposed. Through allocating time and frequency resources occupied by each URLLC service on de-mand, the number of URLLC services supported by the gNB is maximized while the satisfaction of eMBB services is ensured. The simulation results indicate that the proposed algorithm can achieve better performance compared with the benchmark schemes.

Vehicle platooning is an effective way to improve the efficiency and safety of transportation systems, in which a group of vehicles maintains a moving pattern by minimizing the tracking error of each vehicle. In this paper, a joint optimization of radio resource allocation for kinetic status information transmission and platoon control is considered under resource-constrained conditions to maintain the targeted inter-vehicle spacing. The formulated problem is approximately solved by the decomposition method, where the radio resource allocation and the platoon control are considered alternatively in two stages. In the first stage, a tracking error based scheduling strategy is presented for radio resource allocation. In the second stage, the control inputs of each vehicle are optimized based on the model predictive control (MPC). Simulation results show that the proposed scheme can achieve the objective of platoon control while having a low tracking error compared with other scheduling strategies.

The occurrence of traffic accidents in cities is often accompanied by property losses, environmental pollution, casualties, and congestion. Predicting the spatio-temporal range of accident-induced congestion can mitigate the negative effects by taking appropriate measures to respond to traffic accidents in a timely manner. Unlike most existing traffic accident spatial-temporal prediction strategies that depend on existing traffic models, this paper proposes a model-free method by using the macroscopic road network images, which relieves the restriction of precise modeling of traffic dynamics and the detailed traffic data. Specifically, we first design a digital twin road network to observe the traffic operation from a macro perspective. Then, after designing the structure of the Convolutional LSTM (Conv-LSTM) cell, we stack multiple Conv-LSTM layers to form an encoding-decoding structure to predict spatio-temporal congestion caused by accidents in urban road networks. Finally, the simulation results indicate that the proposed method improves the prediction accuracy compared with the model-based method and the LSTM network model. The proposed strategy provides a new approach to predict the spatio-temporal congestion caused by accidents from a macroscopic perspective.

With accurate network traffic prediction, communication systems can make self-management and embrace efficient automation. However, due to the lack of consideration of dynamic spatial interactions between regions in existing methods, the performance of network traffic prediction still needs to be improved. To solve the above problem, this paper proposes a spatiotemporal dynamic relative-flow network (STDRN) to capture the dynamic spatial dependency, thereby improving the accuracy of urban network prediction. In the proposed STDRN, we take both spatial and temporal dependence of network traffic into consideration. First, we propose relative-flow gating mechanism (RGM) combined with convolutional neural network (CNN) to learn the dynamic spatial dependency. Then, we adopt the periodic shifting attention mechanism to capture the long-term periodic temporal dependency while long short-term memory (LSTM) for the short-term temporal dependency. The STDRN is tested on real-world network traffic dataset. Experimental results demonstrate that compared with other benchmark methods, STDRN can reduce RMSE by up to 48.99% and MAPE by 13.61%, which proves the effectiveness of our proposal.

Autonomous driving has attracted huge research interest from both academia and industry. As one of the key components for the safe driving of autonomous vehicles, lane detection allows vehicles to correctly locate itself in the lane and follow the traffic rules. Unlike traditional detection methods that rely on the extraction of professional and hand-designed features, this paper proposes a robust lane detection method by mining semantic information via the deep learning model LaneNet, which can cope with more complex road scenes, and relieve the restriction of deep learning models that detect a fixed number of lanes. Specifically, we first utilize the LaneNet to segment lane pixels from the road scene. Then we distinguish different lane instances by using a clustering loss function based on the distance vector of lane pixels. Finally, we verify our method on two datasets, Tusimple and CULane. The results show that the detection accuracy of Tusimple is up to 94.3%, CULane’s normal level is 90.4% and other more complex levels can reach up to 70%. Furthermore, by comparing with existing approaches, simulation results confirm the robustness of the proposed method in lane detection. In addition, we combine lane detection with driving decision based on intelligent driving simulation platform PanoSim5, which illustrates the effectiveness of our proposed lane detection method for autonomous driving.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the “bottleneck” of knowledge acquisition during the construction of expert systems but also solves the “black box” phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety.

Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.

Traffic incidents are recognized as a key contributor to non-recurrent congestion, which causes many negative effects in economy, environment, health and lifestyle. In this article, we investigate an incident management policy considering both signal control and route choice, which presents a real-time systematic effort to provide a rapid recovery from an incident and mitigate incident-related congestion according to different incident effects. Firstly, we introduce a route choice method on a multiple-route urban road network with consideration of bottleneck delays. Then, we analyze the route travel costs under incident effects and give the equilibrium existence condition after the occurrence of an incident. Furthermore, combining with the route choice method, a novel traffic signal control policy is proposed and the condition for equilibrium existence is given with the consideration of dynamic signal control and route choice simultaneously. Sufficient conditions for the dynamic road system to be stable are also derived and validated by using Lyapunov stability theorem. The analytical results indicate that opposite signal control policies should be applied in road networks under different incident circumstances and the proposed control policy can achieve the improved recovery rate and system stability than existing control policies in terms of dynamic incident effects in road networks. Finally, numerical results have been conducted to demonstrate the effectiveness of our proposed incident control policy and confirm the conditions for road system stability when different incident circumstances had been identified.

Recent advancements in edge computing and edge caching provide a feasible solution to support a plethora of new applications such as on-demand videos, AR/VR, road surveillance. However, to apply edge caching in vehicular scenarios is still difficult due to the unkonwn request pattern of vehicular users and intermittent service links between vehicles and edge servers (e.g., Road Side Units, RSUs). In this paper, we aim to investigate the vehicular edge caching problem in practical vehicular scenarios by considering higher hit ratio, while avoiding interruption of caching services. Specifically, to obtain a higher hit ratio, we firstly propose an on-demand adaptive cache algorithm. The algorithm can adjust the eviction time of cached contents by tracking the dynamics of requests and content popularity. We then develop an analysis framework to model the interruption performance of caching services from RSUs. Through diffraction approximation theory, the service process can be modeled as a joint process of the movement and stopping of vehicles to deduce the interruption ratio. To apply the on-demand adaptive cache algorithm in practical scenarios, the final caching decisions should be corrected by incorporating the interruption performance. Therefore, a$\alpha $-fair utility-oriented vehicular edge caching scheme is developed, which can achieve the tradeoff of hit ratio and interruption ratio. Performance evaluation shows the advantages of our proposed vehicular caching scheme in hit ratio, accuracy of analysis model, utility, respectively.

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

The development of autonomous driving poses significant demands on computing resource, which is challenging to resource-constrained vehicles. To alleviate the issue, Vehicular edge computing (VEC) has been developed to offload real-time computation tasks from vehicles. However, with multiple vehicles contending for the communication and computation resources at the same time for different applications, how to efficiently schedule the edge resources toward maximal system welfare represents a fundamental issue in VEC. This article aims to provide a detailed analysis on the delay and cost of computation offloading for VEC and minimize the delay and cost from the perspective of multi-objective optimization. Specifically, we first establish an offloading framework with communication and computation for VEC, where computation tasks with different requirements for computation capability are considered. To pursue a comprehensive performance improvement during computation offloading, we then formulate a multi-objective optimization problem to minimize both the delay and cost by jointly considering the offloading decision, allocation of communication and computation resources. By applying the game theoretic analysis, we propose a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem. Extensive simulation results verify that our proposed PSOCO outperforms counterparts. Based on the results, we also present a comprehensive analysis and discussion on the relationship between delay and cost among the Pareto-optimal solutions.

Realizing driving safety is the fundamental goal pursued by artificial intelligence (AI)-enabled autonomous driving. However, due to the limited capacity of vehicles and the limited scenarios involved, the reliability and environmental adaptability of the current single-vehicle intelligence still need to be improved. In addition, driving knowledge only exists locally in each connected autonomous vehicle (CAV) and cannot be effectively reused or shared with other CAVs. To solve the above problems, this paper proposes to use federated learning (FL) to realize the collaboration of CAVs without revealing local data, thereby improving the accuracy of CAVs decision-making and driving safety. First, for the typical rear-end collision scenario, we propose a local decision-making model that comprehensively considers multiple influencing factors to fit the actual traffic environment. Then, we design a federated learning process for knowledge sharing. In particular, we propose a model similarity method to select high-quality local models for upload, thereby reducing communication overhead while improving the accuracy of the global model. Extensive simulations validate the performance of our proposal in reducing communication overhead and improving decision accuracy.

By driving vehicles in constant spacing, platooning is a promising way to enable a safer and faster mobility with higher lane capacity and energy efficiency. On one hand, recent advances in vehicular communication technologies improve the usefulness of platooning. On the other hand, the string instability in platooning is easily created by the unavoidable communication delays. In this paper, we focus on improving the performance of intraplatoon communications by considering the impact of non-line-of-sight (NLOS), which is typically isolated in most of present works. To do that, we first evaluate the impact of NLOS due to the vehicles as obstacles on signal attenuation among platoon members by developing an analytical mode based on the knife-edge model. To obtain the optimal communication performance, an power control problem is formulated by considering NLOS and multi-user interference. By resorting to the graph neural network (GNN), which can achieve an excellent performance in learning dynamic graph characteristics, an efficient power control policy is developed after modeling the inter-vehicle communication links in the platoon as a fully connected interference graph. Extensive simulations finally validate the performance of our method.

The travel delay caused by incidents severely reduces the efficiency of traffic. This symptom has been relieved with the development of advanced communication technologies. However, the emergency traffic information (ETI) is meaningless for vehicles which do not traverse the incident segment. Unlike most existing studies concentrating on the network performance during the dissemination of ETI to all vehicles, this paper proposes a joint traffic-communication optimization strategy (JTCS) to reduce the extra cost caused by unnecessary communication, which minimizes the total communication and traffic cost by transmitting the ETI to the worthy vehicles who need the ETI. Specifically, we capture the optimal targeted ETI transmission strategy with combination of radio resource allocation strategy (RRAS) and traffic-influencing transmission strategy (TTS), which can be converted into a bi-level optimization problem. The lower-level problem minimizes the total cost by Lagrangian method and obtains the optimal RRAS when the TTS is given. The upper-level problem develops the optimal TTS based on the optimal RRAS obtained in the lower-level problem. Simulation results using SUMO and MATLAB indicate that JTCS can achieve minimal total cost of ETI transmission and vehicle rerouting by comparing with existing approaches.

Recently, with a large number of on-board sensors, vehicles have been widely used for Vehicular Crowdsensing (VCS). Appropriate multi-task assignment strategy is crucial for V CS. However, sensing efficiency and benefit of the system of the existing works need to be improved due to the following challenges. On one hand, many sensing tasks have clustering characteristics in terms of their geographic distribution and sensing requirements, but current works are often ignored. On the other hand, existing multi-task assignment strategies often only design optimization problem for the benefit of one of the platform or participants, and fail to maximize the overall benefit of the system. To remedy that, this paper proposes a multitask assignment for tasks with clustering characteristics. First, we propose a task combination algorithm, which can greatly improve the task assignment efficiency and reduce the sensing cost. Next, we design a two-stage task assignment scheme, in which the benefits of the platform and participants are optimized respectively in two stages to maximize the benefit of the system. Finally, we carry out extensive simulations, and the simulation results verify the effectiveness of our proposal from clustering validity and sensing cost.

Neighbor information perception with high accuracy and low overhead is quite essential for vehicular networks, which is accomplished by the neighbor discovery scheme. Following the scheme, nodes exchange short discovery messages for advertising their existence and sensing neighboring vehicles. To combat high vehicle mobility and severe channel fading, the discovery message is always exchanged frequently in vehicular networks. This, however, introduces superabundant communication overhead. In this article, a novel neighbor discovery method with mobility awareness and channel randomness adaptability is proposed for investigating the aforementioned issue. First, a closed-form expression is derived, which captures the quantitive relation of the neighbor discovery performance to the vehicle mobility and channel randomness. Guided by our theoretical analysis, the optimal neighbor discovery scheme is developed to adjust the discovery frequency adaptively based on mobility and channel. Thus, an optimal tradeoff between the discovery accuracy and overhead is achieved in vehicular networks. Simulation results coincide with our analysis results, which further demonstrates that the proposed discovery scheme outperforms the periodic and existing adaptive methods in terms of discovery accuracy and overhead.

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.

With the fast development of Internet of Vehicles (IoV), various types of computation-intensive vehicular applications pose significant challenges to resource-constrained vehicles. The emerging Vehicular Edge Computing (VEC) and Edge Intelligence (EI) can alleviate this situation by offloading the computation tasks of vehicles to the roadside edge servers. However, with many vehicles contending for the communication and computation resources at the same time, how to quickly and efficiently make an optimal computation offloading decision for individual vehicles represents a fundamental research issue. In this paper, we propose a self-learning based distributed computation offloading scheme for IoV. Note that without any centralized controller, a fully distributed algorithm is necessary. The proposed scheme is devised based on a game-theoretic model. Specifically, through establishing an offloading framework with communication and computation for IoV, the computation offloading problem is first formulated as a distributed offloading decision-making game, in which each vehicle as a player makes its best response decision to minimize its joint cost (including latency and offloading cost). The existence of Nash Equilibrium can be proved. We then propose a self-learning based distributed computation offloading (DISCO) algorithm to reach the Nash Equilibrium, where a mutually satisfactory solution among vehicles is obtained and no vehicle is willing to change its decision. Using extensive simulations, we verify that DISCO can outperform the counterparts and achieve at least an order-of-magnitude improvement on time overhead and 88% performance gain on message overhead, only at up to 12% performance loss on joint cost over the centralized scheme.

Identifying the root cause in urban road networks and ranking the influential factors can benefit traffic management for improving traffic condition. Traditional congestion identification studies paid attention to identify traffic bottlenecks, namely the most vulnerable points in a road network, without consideration of root causes that leading to the congestion. In this paper, we propose a gradient boosting decision trees (GBDTs) based method to identify the root cause of road network congestion and rank the influential factors using different types of explanatory variables. Based on Sioux Falls network, different signal control strategies at intersections and number of lanes on road segments under different traffic flows are conducted as samples using Simulation of Urban Mobility (SUMO) to train and test the GBDT model. Simulation results indicate that the GBDT model can achieve superior performance in average travel speed prediction and identify the root causes of congestion by prioritizing the relative importance of influential factors, such as lane numbers and signal control strategies, compared with other algorithms.

As a sub-class of internet of things (IoTs), wireless sensor networks (WSNs) are becoming ubiquitous in recent years, which makes the efficient coverage of sensors challenging. Traditionally, WSNs are composed of omni-directional sensors, which, however, are still limited to unadjustable sensing angle and superfluous energy consumption. Fortunately, these limitations can be overcome by deploying directional sensors in WSNs, thus forming directional sensor networks, namely DSNs. Therefore, it is necessary to propose efficient coverage optimization methods for DSNs to solve the minimum exposure path (MEP) problem that refers to a path along which the intruder can go through WSNs with lowest detection probability. In this paper, a novel MEP-PSO algorithm-based coverage optimization mechanism is proposed to improve the coverage quality in DSNs. With our coverage optimization mechanism, the traditional MEP problem is analyzed by means of discrete geometric theories while the path searching performance is improved based on the particle swarm optimization (PSO) algorithm. Specifically, the deployment scenario is firstly discretized into multiple square grids with uniform sizes. The weighted undirected graph is thus constructed in which the path segment exposure of MEP can be analyzed by discrete geometric theory. Based on the analysis, the feasibility of PSO is evaluated and enhanced in terms of MEP searching. Using our algorithm, the coverage performance of DSNs can be improved significantly by dynamically adjusting the positions of directional sensors. Finally, we conduct extensive experiments to validate the effectiveness of our work.

Implementing real-time and onboard fault diagnosis on electric vehicles can effectively avoid potential dangers. However, the low calculating ability and limited storage capacity of electric vehicles hamper the development of real-time and onboard fault diagnosis. To address the issue, combining neural network and fuzzy logic, we propose a low complexity onboard vehicle fault diagnosis method to monitor the vehicle status and give early warning of accidents. In twelve months, we first utilize three electric vehicles and collect 6. 52GB real data related to vehicle components. Motivated by those data, we conducted an in-depth research on the major vehicle faults, and divided them into four types which are no fault, battery fault, sensor fault, and module fault. Furthermore, we propose a BP neural network based multiple training method to define the correlation between data types and fault types. Then, applying the correlation and data, a fuzzy logic based classification method is proposed to evaluate the vehicle status and give early warning. Finally, a comprehensive simulation is conducted, which indicates that the accuracy is 88%.

Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module (${i}$PDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multi-sensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, ${i}$PDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of ${i}$PDM. The results validate the high reliability of ${i}$PDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, ${i}$PDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that ${i}$PDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single ${i}$PDM is only US $ 30, which makes it suitable to large-scale deployment.

Smart roads can achieve a comprehensive, real-time and accurate perception of road environment, which is of great significance for intelligent transportation systems (ITS). However, due to massive data needed to be computed, cloud computing usually imposes pressure on backhaul and produces high delay. In this context, mobile edge computing (MEC) provides a promising solution. Meanwhile, current researches of the task offloading based on MEC lack global considerations and ignore IoT devices along the roadside, so optimization on three-side is very necessary and worth researching. To this end, we consider a scenario of smart roads including vehicular terminals (VTs), IoT devices and MEC servers. And we formulate an optimization problem aiming at minimizing a weighted sum of the costs of energy consumption and time delay for users side and cost for MEC servers. On this basis, we propose a three-side dynamic joint task offloading and resource allocation (TDJORA) scheme. Moreover, considering that the optimization problem is a multi-objective optimization problem, we utilize a combination of the particle swarm optimization (PSO) algorithm and Pareto optimality to obtain the optimal solution. Simulation results show that our proposed TDJORA can realize reasonable task offloading and optimal resource allocation for three sides.

Three-dimensional (3D) highly dynamic unmanned aerial vehicle networks (UAVNets) could serve as a pivotal intermediate architecture in air and space integration networks. For UAVNets, efficient routing protocols for data packet delivery are crucial to its wide use. However, communication in UAVs has been a challenging project because of frequent failures at forwarding nodes. In this paper, we propose a localized heuristic solution, called the reliability-aware adaptive greedy-multicast routing (RAGM-3D), to reduce the negative impact of failure on route forwarding. RAGM-3D dynamically adjusts the next-hop forwarding node set which, initially selected by a new multi-factor hybrid greedy strategy, are based on the reliability evaluation to route packets to the destination efficiently. Simulation results show that comprehensive performance of RAGM-3D in terms of packet delivery ratio, delay, and routing overhead outperforms other compared protocols.

Leveraging the Internet of Vehicles (IoV) to accurately and timely disseminate traffic information, especially for emergency information, is an important way to improve traffic safety and efficiency. However, privacy and security issues in the information transmission process will seriously affect the reliability of the information dissemination system. In this paper, we propose a decentralized targeted information dissemination framework in vehicular networks based on blockchain techniques. In this framework, the information collected by users is uniformly judged and identified by Roadside Units (RSUs), and finally packaged and uploaded to the blockchain that makes the system run more stable and reliable. Further more, we utilize reputation evaluation algorithm to comprehensively process the collected information, and we design the corresponding reputation management system to stimulate more users to participate in the system and provide as accurate information as possible. Extensive simulation results reveal that the proposed framework is effective and feasible in collecting, filtering, conducting, and disseminating in vehicular networks.

Inappropriate lane following and changing behaviors of connected and autonomous vehicles (CAVs) can result in accidents, such as rear-end collision and side collision. To remedy that, the use of deep reinforcement learning (DRL) for autonomous driving decisions is currently a widely used promising solution. In this case, the accuracy and effectiveness of such a machine learning (ML) model is quite essential for this artificial intelligence (AI)-enabled CAVs. This article proposes a blockchain-based collective learning (BCL) framework for autonomous lane-changing systems. Four key issues, namely, learning efficiency, data security, users' privacy, as well as communication burden, are addressed by applying collective learning, vehicular blockchain, and knowledge transfer. First, we model the lane-changing problem as a DRL process and learn the autonomous lane-changing strategy through the deep deterministic policy gradient (DDPG) algorithm. Second, a single CAV involves a limited number of driving scenarios, and the independent learning method has the problem of inefficiency. Therefore, we propose a collective learning framework to utilize the “collective intelligence” shared by CAVs. Third, a vehicular blockchain is then applied to ensure the security and privacy of the user and data. In addition, the introduction of the blockchain can incentivize more users to participate in collective learning. Finally, in order to accelerate the learning process and achieve higher level performance while further reducing the communication burden, we use the corresponding knowledge extracted from the ML model such as human learning, as privileged information for sharing instead of directly sharing local ML models. Extensive simulation results validate the effectiveness and efficiency of our proposal in terms of learning efficiency, driving safety, as well as system security and robustness.

Vehicular edge computing (VEC), which integrates mobile-edge computing (MEC) into vehicular networks, can provide more capability for executing resource-hungry applications and lower latency for connected vehicles. Distributing the result content to connected vehicles is vital for them to take proper actions based on computing results. However, the increasing number of connected vehicles and the limited communication resources make the content distribution a challenge. Besides, the diversity of connected vehicles and contents makes it more challenging for content distribution. To address this issue, in this article, we propose EdgeVCD, an intelligent algorithm-inspired content distribution scheme. Specifically, we first propose a dual-importance (DI) evaluation approach to reflect the relationship between the Priority of Vehicles (PoV) and the Priority of Contents (PoC). To make use of the limited communication resources, we then formulate an optimization problem to maximize the system utility for content distribution. To solve the complex optimization problem effectively, we first divide the road into small segments. Then, we propose a fuzzy-logic-based method to select the most proper content replica vehicle (CRV) for aiding content distribution and redefine the number of content request vehicles in each segment. Thereafter, the optimization problem is transformed into a nonlinear integer programming problem. Inspired by the artificial immune system, we propose an immune clone-based algorithm to solve it, which has a fast convergence to an optimal solution. Extensive simulations validate the effectiveness of our proposed EdgeVCD in terms of system utility, average utility, and convergence.

With the development of autonomous driving, the surging demand for data communications as well as computation offloading from connected and automated vehicles can be expected in the foreseeable future. With the limited capacity of both communication and computing, how to efficiently schedule the usage of resources in the network toward best utilization represents a fundamental research issue. In this article, we address the issue by jointly considering the communication and computation resources for data scheduling. Specifically, we investigate on the vehicular edge computing (VEC) in which edge computing-enabled roadside unit (RSU) is deployed along the road to provide data bandwidth and computation offloading to vehicles. In addition, vehicles can collaborate among each other with data relays and collaborative computing via vehicle-to-vehicle (V2V) communications. A unified framework with communication, computation, caching, and collaborative computing is then formulated, and a collaborative data scheduling scheme to minimize the system-wide data processing cost with ensured delay constraints of applications is developed. To derive the optimal strategy for data scheduling, we further model the data scheduling as a deep reinforcement learning problem which is solved by an enhanced deep $Q$ -network (DQN) algorithm with a separate target $Q$ -network. Using extensive simulations, we validate the effectiveness of the proposal.

Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction. However, due to the various behaviors of vehicles, not all the vehicles are trustworthy and willing to join in the service request relay process. Therefore, how to exploit the cooperation between ECDs and vehicles to relay the service request by considering the dynamic traffic status and the behaviors of vehicles becomes a challenge. As an effort to address this problem, we propose a trusted relay selection scheme for edge services to facilitate the proposed reservation service framework. Specifically, we first design the request relay mechanism based on the dynamic traffic status to guarantee the efficiency of the relay process. Then, the reputation management mechanism is presented to constrain the behaviors of vehicles, where a vehicle with high reputation value can enjoy the price discount for computing service. Based on the designed request relay and reputation management mechanisms, a reputation-based auction approach is then proposed to select relay vehicles (RVs) to reduce the cost of the relay service. Simulation results show that the proposed reservation service framework can manage vehicles efficiently and lead to the lowest cost for the relay services compared with the conventional schemes.

Secure message dissemination is an important issue in vehicular networks, especially considering the vulnerability of vehicle-to-vehicle message dissemination to malicious attacks. Traditional security mechanisms, largely based on message encryption and key management, can only guarantee secure message exchanges between a known source and destination pairs. In vehicular networks, however, every vehicle may learn its surrounding environment and contributes as a source, while in the meantime, acting as a destination or a relay of information from other vehicles, and message exchanges often occur between “stranger” vehicles. This makes secure message dissemination against malicious tampering much more intricate. For secure message dissemination in vehicular networks against insider attackers, who may tamper the content of the disseminated messages, ensuring the consistency and integrity of the transmitted messages becomes a major concern which the traditional message encryption and key management-based approaches fall short to provide. However, it is challenging for a vehicle to distinguish which message is true when the messages received from multiple nearby vehicles are conflicting. In this paper, by incorporating the underlying network topology information, we propose an optimal decision algorithm that is able to maximize the chance of making a correct decision on the message content, assuming the prior knowledge of the percentage of malicious vehicles in the network. Furthermore, a novel heuristic decision algorithm is proposed that can make decisions without the aforementioned knowledge of the percentage of malicious vehicles. The simulations are conducted to compare the security performance achieved by our proposed decision algorithms with that achieved by the existing ones that do not consider or only partially consider the topological information to verify the effectiveness of the algorithms. Our results show that by incorporating the network topology information, the security performance can be much improved. This paper sheds light on the optimum algorithm design for secure message dissemination.

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under the condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm for rear-end collision avoidance. Three key issues are addressed by applying the neural network approach, including noises in positioning, inaccurate risk assessment, and enhanced comfort level of passengers. First, to filter the noises in positioning, wireless vehicular communications are leveraged; accurate relative lane positioning can be achieved to justify when two vehicles are in the same lane. Second, an online neural network model is developed to assess the risk of collisions in real time while driving. The algorithm can converge fast to a globally optimal solution and adapt to different traffic environments. Third, to maximize the comfort of passengers during the braking process, a graded warning strategy is developed at the prerequisite of guaranteed safety. With the above schemes sewed in to one framework, our proposal can achieve rear-end warning with reduced missing alarm rate, accurate risk assessment and enhanced comfort to passengers. The extensive simulations validate the effectiveness and accuracy of our proposal in terms of relative lane positioning, risk assessment, and collision avoidance.

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.

Wireless Body Area Networks (WBANs) have been developed rapidly with the increasing popularity of wireless network and wearable technologies. The inherent characteristics of convenience and efficiency for health monitoring facilitate the depth and width of WBAN applications. However, the inter-WBAN interference problem affects the network performance in intensive WBAN scenarios, degrading reliability and increasing latency of health data. In this paper, we propose a Two-Tier Multi-channel Medium Access Control (2TM-MAC) protocol with interference mitigation for reliable health monitoring. Specially, the 2TM-MAC establishes an inter-WBAN interference matrix for every WBAN to show the mutual interference among coexisting WBANs. We design a multi-channel selection algorithm at the first tier to select different numbers of channels for each WBAN to avoid inter-WBAN interference and collisions. At the second tier, the hub of each WBAN schedules the available channels assigned from the first tier to sensor nodes according to their traffic requirements, mitigating the intra-WBAN interference as well. 2TM-MAC protocol enhances the reliability of emergency data and service experience in healthcare applications. Simulation results show the 2TM-MAC protocol significantly improves the network throughput and decreases the average packet delay compared with IEEE 802.15.6 for densely deployed coexisting WBANs scenarios.

Highly dynamic network has a growing demand with the popularization and application of high-speed mobile equipments, such as aircraft, unmanned boat and etc. There are many new features for highly dynamic networks over mobile networks, which include high link failure rate, high frequency enter-and-exit the network and frequent changes of the topology. Therefore, the design of access protocols is more stringent. Particularly, the packets would be invalid when waiting to be sent to the next hop node due to the link breaking with high- speed motion of the nodes in highly dynamic network. In order to better adapt to highly dynamic network scenarios, we propose a novel access protocol based on link validity prediction. Firstly, the protocol obtains the valid time of the packet in the buffer by considering the validity of the next hop link. Further, we designed a backoff mechanism based on packet valid time differentiation. We derive the mathematical expressions for the performance metrics. The performance evaluation exhibits our protocol by providing a lower latency and higher success transmission rate.

In order to meet the requirement of dual-band MIMO operation in 5G smartphones, a 12-port dual-band inverted F-shaped antenna array for 3.5 GHz (3400- 3600 MHz) and 5.8 GHz (5725-5785 MHz) bands is designed. The proposed dual-band antenna array elements are located symmetrically on the inner surface of side frames instead of on the circuit board. Thus, the structure without ground clearance along the long edges is suitable for wide-screen smartphones. To embed 12 antenna elements into the limited space of a smartphone without additional decoupling structures, we propose a structure that bend the branches of inverted-F antenna to a smaller size (8mm×6mm) reducing the occupied space of antennas. A prototype of the proposed 12-antenna array is manufactured and measured. Both simulation and experimentally measured results show that our designed antenna array achieves desirable antenna impedance matching (better than 6/10 dB return loss), acceptable isolation (better than 10 dB) and antenna efficiency (better than 40%). Envelope correlation coefficients and channel capacities are also calculated to validate that the proposed dual- band antenna arrays have good radiation and MIMO capacity performance.

With the surge service requirements from vehicular users, especially for automated driving, providing real-time high-rate wireless connections to fast-moving vehicles is ever demanding. This motivates the development of the emerging LTE-V network, a 5G cellular-based vehicular technology. However, note that the vehicular user group is typically of a very large scale, whereas the bandwidth spectrum available for vehicular communications is very limited. To efficiently allocate and utilize the slim spectrum resource to vehicle users are therefore important. This paper develops a service priority oriented spectrum allocation scheme in an LTE-V network, which explores the features of vehicular networks toward economic yet QoS guaranteed spectrum allocation. Specifically, the work exploits two features of the vehicular networks. First, vehicles in the proximity typically have similar information requirements, e.g., road conditions. As a result, the location-based multicast (i.e., geocast) could be applied to save the spectrum. Second, different types of vehicles, e.g., ambulances, buses, and private cars, are of different bandwidth and service requirements. Therefore, differential services and spectrum allocations should be applied. By jointly considering the above features, we develop a 2-D service importance oriented framework for LTE-V network spectrum allocations. The spectrum allocation issue is finally modeled as a mixed integer programming problem to maximize the system utility, and solved using an immune clonal based algorithm. The convergence of the proposed algorithm is proved, and using numerical results, we show that our proposal can outperform the typical heuristics-based spectrum resource allocation in terms of convergence and average delay.

In vehicular ad hoc networks (VANETs), three-dimensional scenarios are always ignored, even though they are attractive for their effectiveness in land use. Focusing on those scenarios, we propose and prove their severe impacts on the performance of a vehicular network. We first conduct a transmission experiment. The results prove that the existence of those scenarios induces the inter-layer communication, and then significantly reduces the transmission range. Furthermore, we demonstrate that the variation of the transmission range makes an enormous difference in the neighbor number, which severely affects the network performance. At last, our extensive simulations show that the aforementioned analysis are in fact quite accurate.

Real-time and accurate prediction about current and future traffic conditions is one of the effective ways to alleviate traffic problems. However, missing data problem is inevitable for various reasons when obtaining real-time traffic flow information. Incomplete traffic information may seriously affect the prediction accuracy. To address this problem, in this paper, we propose a method that predicts the traffic flow in real time under missing data. Considering the spatio-temporal characteristics of traffic flows and the spatial location of road segments, we first evaluate the importance of traffic flows using a spatio-temporal correlation function to analyze the correlations of traffic flows. Then, we present a PPCA-based minimum data imputation optimization (P-MDIO) algorithm to reduce computation time of data imputation. Finally, we utilize the complete traffic data and relevant road segments sequences to predict real-time traffic flows. The experimental data are obtained from the real-time traffic data collected by loop detectors in Taipei, Taiwan. Our experimental results show the performance and validity of the proposed approach, particularly in large-scale prediction.

© 2018 IEEE. Neighbor discovery, providing the neighbor information by broadcasting discovery messages, is a promising solution for cooperative perception of Intelligent Vehicles (IVs). However, the high vehicle mobility and severe channel randomness of IV environments call for a frequent discovery, which results in a superabundant overhead. In this paper, we propose a new framework for cooperative perception of IVs by novelly introducing an improved neighbor discovery method. We first establish an analytical framework to capture the quantitive relation between the hitting probability of neighbor discovery with the vehicle mobility and channel randomness using a closed-form expression. Based on the analysis, an adaptive neighbor discovery method is developed to adaptively make tradeoff between the discovery accuracy and overhead at varying driving status of IVs. Applying the improved neighbor discovery, the process of cooperative perception is discussed. Accordingly, simulations in three IV scenarios are conducted whose results are consistent with our analysis.

Wireless Body Area Network (WBAN) technology jumps into popularity owing to its real-time ability and high reliability in health monitoring. The accompanying interference problem must be highly concerned in coexisting densely deployed WBANs since the inter-WBAN interference results in high delay and low reliability data transmissions, especially with the movement of human body. In the paper, we analyze the dynamic interference with human mobility in multiple coexisting WBANs with the consideration of different distances between inter-WBANs and varying number of coexisting WBANs. Moreover, we investigate the influence of inter- WBAN interference on the performance of normalized throughput and average access delay of different traffic types. The results show that the interference generated by mobile neighbour WBANs extremely decreases the throughput of the target WBAN and increases the average packet delay 1.76 times of emergency data compared with the target WBAN without interference. The dynamic interference analysis provides insights on the practical WBAN management and interference mitigation protocol design, especially for the deeply deployed coexisting WBAN scenarios.

Traffic congestion has seriously caused various problems in society, economy and environment, especially in urban areas. A traffic bottleneck is always seen as the root cause of congestion which frequently deduces the congestion emergence, queues formation and congestion propagation. However, bottlenecks are caused by many complicated factors and vary with spatial and temporal environment which are difficult to be defined and identified in urban areas. In this paper, we first propose a novel definition of bottlenecks in urban area based on the congestion propagation costs and the congestion weights of road segments. Then according to the definition, we present an urban bottleneck identification method using causal congestion trees and causal congestion graphs to identify some bottlenecks. This paper implements some experiments based on the urban inductive loop detector data. According to our proposed method, we identify several bottleneck groups around the urban area. Furthermore, we also improve the road capacity of identified bottlenecks and compare the congestion level and congestion propagation range before and after the improvement to verify the identified bottlenecks.

MmWave systems with most prior work focused on its narrowband hybrid analog/digital precoding, however, will likely operate on wideband channels with frequency selectivity. Therefore, in this paper we investigate wideband angular beamforming schemes for mmWave massive MIMO-OFDM systems. First, for the RF analog beamforming, the optimal beamforming of an unconstrained antenna array (UAA) is given as the performance benchmark. Then, the optimal angular beamforming (OAB) and a simple dominant angular beamforming (DAB) of a shared antenna array (SAA) are compared in received SNR and implementation cost. Second, for the baseband digital precoding, the space-frequency vector perturbation (SFVP) precoding is proposed to collect both spatial and multi-path diversity. Finally, analytical and simulation results show that: a) DAB is a cost- effective RF beamforming scheme under LOS channel environment; b) the proposed hybrid DAB-SFVP beamforming scheme achieves the array gain equaling the number of transmit antennas Ntand diversity gain equaling the product of the number of RF chains K and the number of temporal resolvable clusters ℒ.

The increasing per capita vehicle ownership has led to extremely serious traffic congestion, energy crisis and environment pollution. Electric vehicle, as a representative of energy structure transition and traffic component changes can effectively solve the mentioned issues. In this paper, we propose a novel energy consumption model for electric vehicles, Green wave band based Energy Consumption Model (GECM), which is built upon traffic signal control theory and combines with the distribution of energy consumption in a macroscopic view. This model designs green wave scenarios based on the traffic signal data in real traffic surroundings, and involves specific parameters like offset and green split for the analysis of energy consumption. Simulation results show that the energy saving and efficiency improvement are available and feasible for electric vehicles under the proposed model.

Electric Vehicles (EVs) is a promising transportation to alleviate traffic congestion and pollution problems and its route planning can save the limited energy. However, designing a reliable route planning strategy to achieve optimal route remains a challenging problem, especially when various objectives of both energy consumption and time cost are taken into consideration. Therefore, based on the traffic signal control technologies in urban areas and an EV energy consumption model, we formulate EV route planning problem as a multi-objective optimization problem and propose a Green wave band-based Multi-objective Ant Colony Optimization (G-MACO) algorithm to solve it. Moreover, relying on the field measurement data, the graphical model of urban road network containing relevant weights is also analyzed. Finally, simulation results show that the proposed algorithm can achieve a trade-off between energy consumption and time cost to realize the multi-objective optimization route planning.

Wireless body area networks (WBANs) can be widely used in wireless medical, motion detection etc. However, the existence of interference results in the increase in energy consumption and delay. To mitigate interference, nodes are prevented from using the same or similar spectrum resources by adopting multi‐channel media access control (MAC) protocols. Here, the authors propose a multi‐channel MAC protocol towards energy efficiency and low interference (EIMAC). Firstly, the states of each channel are clarified by the channel mapping mechanism. A novel channel selection strategy, considering the unfairness between high or low priorities, then is carried out. After that, considering the characteristics of node including residual energy, user priority, and data volume, the authors propose a low energy consumption enabled transmission mechanism. Lastly, the authors utilise a novel collision avoidance mechanism to reduce the collision probability of packets. Numerical results show that EIMAC significantly enhance the performance of WBANs in terms of delay, throughput, and energy consumption.

Intelligent wireless body area networks (WBANs) have entered into an incredible explosive popularization stage. WBAN technologies facilitate real-time and reliable health monitoring in e-healthcare and creative applications in other fields. However, due to the limited space and medical resources, deeply deployed WBANs are suffering severe interference problems. The interference affects the reliability and timeliness of data transmissions, and the impacts of interference become more serious in mobile WBANs because of the uncertainty of human movement. In this paper, we analyze the dynamic interference taking human mobility into consideration. The dynamic interference is investigated in different situations for WBANs coexistence. To guarantee the performance of different traffic types, a health critical index is proposed to ensure the transmission privilege of emergency data for intra- and inter-WBANs. Furthermore, the performance of the target WBAN, i.e., normalized throughput and average access delay, under different interference intensity are evaluated using a developed three-dimensional Markov chain model. Extensive numerical results show that the interference generated by mobile neighbor WBANs results in 70% throughput decrease for general medical data and doubles the packet delay experienced by the target WBAN for emergency data compared with single WBAN. The evaluation results greatly benefit the network design and management as well as the interference mitigation protocols design.

In this paper, we propose a cooperative communication strategy that explores the combined use of vehicle-to- infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, mobility of vehicles and cooperation among vehicles and infrastructure to improve the achievable capacity of vehicular network. An analytical framework is developed to model the data dissemination process using this strategy, and a closed form expression of the achievable capacity is obtained, which reveals the relationship between the achievable capacity and its major performance- impacting parameters such as inter-infrastructure distance, radio ranges of infrastructure and vehicles, sensing range of vehicles, transmission rates of V2I and V2V communications, vehicular density and the proportion of vehicles with download requests. Numerical result shows that the proposed cooperative communication strategy significantly increases the capacity of vehicular networks, especially when the proportion of vehicles with download request is low. Our results provide guidance on the optimum deployment of vehicular network infrastructure and the design of cooperative communication strategy to maximize the capacity.

Effective file transfer between vehicles is fundamental to many emerging vehicular infotainment applications in the highway Vehicular Ad Hoc Networks (VANETs), such as content distribution and social networking. However, due to fast mobility, the connection between vehicles tends to be short-lived and lossy, which makes intact file transfer extremely challenging. To tackle this problem, we presents a novel Cluster-based File Transfer (CFT) scheme for highway VANETs in this paper. With CFT, when a vehicle requests a file, the transmission capacity between the resource vehicle and the destination vehicle is evaluated. If the requested file can be successfully transferred over the direct Vehicular-to-Vehicular (V2V) connection, the file transfer will be completed by the resource and the destination themselves. Otherwise, a cluster will be formed to help the file transfer. As a fully-distributed scheme that relies on the collaboration of cluster members, CFT does not require any assistance from roadside units or access points. Our experimental results indicate that CFT outperforms the existing file transfer schemes for highway VANETs.

The Intelligent Transportation System (ITS) targets to a coordinated traffic system by applying the advanced wireless communication technologies for road traffic scheduling. Towards an accurate road traffic control, the short-term traffic forecasting which predicts the road traffic at the particular site in a short period is often useful and important. In existing works, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular approach. The scheme however encounters two challenges: (1) the analysis on related data is insufficient whereas some important features of data may be neglected; and (2) with data presenting different features, it is unlikely to have one predictive model that can fit all situations. To tackle above issues, in this work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we first explore the autocorrelation and distribution features existed in traffic flow to amend structure of the time series model. Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA. We show the efficiency and accuracy of our proposal using both analysis and experimental studies. Using the real-world trace data, we show that the proposed predicting approach can achieve satisfactory performance in practice.

Data dissemination is the basis of implementing any kind of applications in Vehicular Ad Hoc Networks (VANETs). However, due to the high mobility of vehicles, severe channel fading, and limited transmission range, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications both tend to be transient and unreliable, which extremely restricts the efficiency of data dissemination. To tackle this problem, we propose a Cooperative Content Dissemination Scheme (CCDS), especially for large-size content in resource-intensive applications of highway vehicular networks. CCDS coordinates the cooperation of multiple infrastructures, multiple vehicles in two moving directions and vehicles' mobility to facilitate the target vehicle to download data. Based on CCDS, the process of data dissemination and achievable data download volume are derived and analysed theoretically. Finally, through extensive simulations, both CCDS's significant benefits in terms of data download volume and the accuracy of theoretical analysis are demonstrated. Moreover, the results are of great value in guiding the effective division of target content among multiple infrastructures and vehicles.

Urban Rail Transit (URT) system has been one of the major trip modes in cities worldwide. As the passengers arrive at variable rates in different time slots, e.g., rush and non-rush hours, the service frequency at a site directly relates to perceived service quality of passengers; the high service frequency, however, incurs increased operation cost to the running of URT. Therefore, a tradeoff between the interest of railway operator and the service quality of passengers needs to be addressed. In this paper, we develop a model on the method of train operation scheduling using a Stackelberg game model. The railway operator is modeled as the game leader and the passengers as the game follower; an optimal service frequency can be determined according to strike the tradeoff between passengers' service quality and the operation cost of URT. Numerical experiments based on the operation data from Nanjing transit subway at China are presented and the results demonstrate that the proposed model can significantly improve the traffic efficiency.

Wireless Body Area Networks (WBANs) have attracted significant attentions because of their important role in medical applications with the development of requirements in health monitoring and diagnosis. IEEE 802.15.6, as the international standard for WBAN, supports network in operating on, in or around human body. Owing to the special propagation characteristics as affected by human body, WBAN needs reliable access mechanism to guarantee the stability of nodes access and information transmission. To achieve the high slot utilization rate and low average packet delay, we propose a gated scheduled access mechanism based on IEEE 802.15.6 and study the impact of allocation slot length on network performance. To examine the performance of our proposal, we conduct hardware experiment through a novel prototype system based on IEEE 802.15.6 standard. The experiment results show that the obtained optimal allocation slot length under the gated scheduled access mechanism can well satisfy the Quality of Service (QoS) requirements in different data rates, which is consistent with the results of theoretical analysis. The comparison results also show that our prototype system can be a practical reference in the future study of wireless body area network.

In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

Due to the time-varying channel conditions and dynamic topology of vehicular networks attributable to the high mobility of vehicles, data dissemination in vehicular networks, especially for content of large-size, is challenging. In this paper, we propose a cooperative communication strategy for vehicular networks suitable for dissemination of large-size content and investigate its achievable throughput. The proposed strategy exploits the cooperation of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications and the mobility of vehicles to facilitate the transmission. Detailed analysis is provided to characterize the data dissemination process using this strategy and a closed-form result is obtained on its achievable throughput, which reveals the relationship between major performance-impacting parameters such as distance between infrastructure, radio ranges of infrastructure and vehicles, transmission rates of V2I and V2V communications and vehicular density. Simulation and numerical results show that the proposed strategy significantly increases the throughput of vehicular networks even when the traffic density is low. The result also gives insight into the optimum deployment of vehicular network infrastructure to maximize throughput.

Collision on road is a classic and important problem as it brings about great loss on humans' safety and the efficiency of the traffic system. Numerous algorithms have been proposed to address the issue, and most of the existing researches are focused on rear-end, overtaking and lane change scenarios. However, due to the massive computing process, limitation of prediction time and complex layouts at intersections, most conventional algorithms are not suitable for intersection collision avoidance. This paper makes a theoretical analysis on the variations of vehicle states taking advantage of the Dynamic Bayesian Networks (DBNs). Based on the analysis, the risk assessment process is made out to identify a dangerous situation. Different collision avoidance strategies are implemented to avoid accidents if a potential danger is detected and the driver is informed of warning. Simulations are carried out to evaluate the ability of the proposed algorithms on detecting and mitigating a vehicle collision. Results show that the proposed algorithms have great performance in handling the vehicle collisions at intersections.

Traffic congestion has seriously hindered the healthy and sustainable development of economy and society. Many research have been done to address the issue. Intelligent Transportation System (ITS) is key to improve the performance of urban traffic. As one of the essential components of an ITS, green ITS navigation for people and freight is critical, its research must base on the accurate grasp of real-time traffic conditions and the complex nature of the transport network. In order to provide more accurate traffic state information for ITS navigation, we study the traffic bottleneck which is the source of congestion based on complex network and user equilibrium model in this paper. We identify the bottleneck and evaluate its importance according to the consequences of the road failure from two aspects of the traveling cost and the network effectiveness. Furthermore, we verify the performance of the proposed identification method and evacuation strategy by simulating on VISSIM. The research outcome shows that the proposed algorithm is correct and effective.

Resource management is a crucial task in vehicular ad hoc networks (VANETs) due to the existence of various resources, such as text, audio and video. However, the highly dynamic network feature and the limited memory of the local server pose challenges to resource management, which not only lead to the failure of resource presentations to users, but the transmission of the invalid fragment data would also result in the significant waste of precious bandwidth and memory. To address the issue, our paper proposes a Fuzzy Logic based Resource Management scheme (FLRM) under fog computing platform in VANETs. In the scheme, we first gather and record the request time and download time for each resource by the designed Vehicle to Infrastructure (V2I) communication mode. Depending on the above information, we define a survival time for each stored resource by the proposed fuzzy logic based popularity evaluation algorithm. Motivated by the defined survival time, the local server can update the resource list in real time. In the end, we conduct simulations to verify the performance of FLRM. Results demonstrate that the proposed scheme performs well in terms of throughput, which increases the user experience with fresh resources.

Mobility models play a vital role in Vehicular Ad Hoc Networks (VANETs) simulations. Moreover, mobility models with higher extent of reality to fit into the characteristics of VANETs better make simulations more credible and accurate. However, most of traditional mobility models only devote to planar and ideal scenarios. It is rare for them to reflect more realistic environments, such as ubiquitous 3D scenarios where accidents occur frequently because of the complex vehicle motion and human factors. To address these issues, a Three-dimensional Accident Driver Model (T-ADM) is proposed for VANETs in this paper to reflect the real world more realistically. With characteristics based on the combination of plane and space in VANETs, T-ADM can produce 3D scenarios like viaducts in VANETs. Furthermore, T-ADM can mimic the non-standard driver behavior and generate accident scenarios. Finally, a comparison among T-ADM and other mobility models is generated by Matlab and VanetMobiSim. It demonstrates that T-ADM is able to not only reflect the corresponding realistic characteristics mentioned above but also give an objective description of vehicle density and velocity in VANETs.

The precise spatio-temporal position data of vehicles is useful for most studies, such as wireless link lifetime and node degree in vehicular ad hoc networks. However, due to the system errors and random errors, the existing Global Positioning System (GPS) only provides the positional accuracy about 10m or even worse. In this paper, to address the issue of positional accuracy, a Clustering and Approximating (C-A) algorithm is proposed. We first divide each road into several small parts which are described by linear functions. Then a linear regression algorithm is utilized to approximate traces under system errors, which is reliable for reducing GPS errors. Particularly, when two roads are very close, GPS points may be mapped on adjacent roads. A clustering algorithm is taken to separate GPS points and their positions are revised by the iterative utilization of the linear regression algorithm. In the end, the method mentioned above smoothes raw GPS data of buses in Taiwan to make it available for further researches. Compared with existing methods, the method described in this paper characterized with low cost and high reliability in different situations. Besides, its simple model will make the process of revising data more convenient.

Wireless Body Area Networks (WBAN) are becoming increasingly important for health care with the development of health consciousness and health detection requirements. A lot of researches have been devoted to the progress of WBAN, for example, the improvement of protocols designed for WBAN, the optimization of parameters and the system performance evaluation. However, the main premise of all the directions above is to provide an efficient and reliable simulation platform, since the existing platforms cannot respond to accord with the reality with high performance. To work this issue out, we construct the WBAN simulation platform on Network Simulation 3 (NS-3) which specifies in good expandability and resources saving and agrees with real networks in many aspects comparing with the other simulators. In this paper, we propose our implementation of WBAN module based on IEEE 802.15.6 standard. Our simulation platform consists of Medium Access Control (MAC) and Physical (PHY) layer of IEEE 802.15.6. Then in order to make the simulation more objective, we design a proper simulation scenario according to the practical application and analyze the simulation results. Finally, we compare the throughput saturation threshold on NS-2 and NS-3 with the analysis results respectively to indicate the superiorities of NS-3 and verify the validity and effectiveness of our WBAN module.

The shortest path problem has become an important issue in the increasingly complex road networks nowadays, especially for these applications which are strict with high timeliness. However, searching the shortest path is difficult as road traffic flows are time-varying. An important issue in searching the shortest path is how to obtain the time expired on each segment at the given time. To this purpose, we propose a hybrid speed model to calculate the travel time in this paper, which considers the difference between the speed in congested and uncongested road networks. And analysis of speed in both conditions are given, respectively. Subsequently, a metric is also proposed to distinguish between congested and uncongested networks. Our work also utilizes the huge traffic data to reflect and analyze the real scenario. Compared to the previous work, this paper considers a more complex urban traffic scenario and some verifications are made with our data. Finally, a numerical study is carried out in the urban road network in Kaohsiung, Taiwan. The results show the hybrid speed model can give travel time prediction in an accurate way and can provide useful information for road designers.

Even the greedy routing is widely used in wireless networks, its theoretical study is still limited in vehicle environments. In this paper, we theoretically analyze the performance of the greedy routing under three typical vehicle scenarios, i.e., the single-lane road, the multilane road, and the multilevel road. We first propose the analytical model by analyzing characteristics of traffic environments, which contain the width and multilevel features of roads. Specifically, we prove that the road-width is ignorable under certain conditions, whereas the data measured in an outdoor experiment reveal that the multilevel feature is non-ignorable because its existence dramatically degrades the transmission range. Based on the model, we analyze the routing length of the greedy routing for all scenarios in the following three aspects. 1) We derive the distribution function for the first one-hop progress. 2) We prove that routing increments are history-dependent and give one sufficient condition that ensures these increments are approximately i.i.d. 3) We calculate the routing length described by the$h$-hop coverage and hop count using the renewal theory. Finally, simulations are conducted to verify the accuracy of our analysis.

Auction is often applied in cognitive radio networks due to its efficiency and fairness properties. An important issue in designing an auction mechanism is how to utilize the limited spectrum resource in an efficient manner. In order to achieve this goal, we propose a predictive double spectrum auction model in this paper. Our auction model first obtains the bidding range from statistical analysis, and then separates the interval into independent states and employees a Markovian prediction based algorithm to generate guidelines for the bidding range of primary and secondary users, respectively. Comparing with existing approaches, our proposed auction model is more efficient in spectrum utilization and satisfies the economic properties. Extensive simulation results show that our work achieves an utilization ratio up to 91%.

As a technical method over VANETs, video delivery has the potential power to enhance the application experience associated with traffic safety, management and infotainment. The experience of user is seriously affected by the quality of the video display. Therefore, Quality of Experience (QoE) enhancement for video delivery in VANETs is an important issue. However, the high mobility and dynamic nature of VANETs cause the dynamic topology which poses a significant challenge for video delivery. In this paper, we propose a user-oriented cluster-based solution called CDOV (Cluster and Dynamic Overlay based video delivery over VANETs), which combine the novel clustering algorithm and the dynamic overlay structure into a novel structure. Simulation results show that CDOV scheme for video delivery over VANETs significantly reduces the startup delay and increases the delivery rate compared with the gossiping-based scheme.

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

In this paper, we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m=nb(0v) (0 <; v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes' communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes' topologies for any given obstacles' distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithm's performance.

The internal collision managing mechanism in Enhanced Distributed Channel Access (EDCA) makes it more difficult to successfully access the channel for the data with low priority. To address this issue, a novel Mechanism to solve the Internal Collision problem (MIC) is proposed to improve the performance of EDCA. The main concept of MIC is to decrease the backoff time of the Access categories (ACs) with low priority. In this way, MIC can improve the performance of the low priorities without compromising that of the high priorities. A Markov chain model is used to analyze the performance of MIC under saturated conditions. The correctness of the analysis results are verified by simulating MIC with NS-2. Both the analysis results and simulation results confirm that MIC outperforms EDCA in terms of the average access delay.

In realistic Vehicular Ad hoc NETworks (VANETs), due to the existence of three-dimensional (3D) scenarios, such as the viaduct, tunnel and ramp, the distribution of vehicles is non-planar. However, existing routing protocols in VANETs are mainly analyzed and designed based on ideal plane scenarios. We call them plane-based routing protocols. In this paper, we focus on routing issues in 3D scenarios of VANETs. Through analysis, we demonstrate that applied in 3D scenarios, the plane-based routing protocols suffer a series of severe problems, i.e., hop count increases and delivery ratio decreases. To address the issues, we propose a Three-dimensional scenario oriented Routing (TDR) protocol for VANETs. Utilizing three-dimensional information, TDR establishes a route hop by hop and transmits packets as far as possible to the optimal immediate neighbor node which is located on the same plane with the current forwarding node. In the end, a comparison between TDR and the existing protocol GPSR is conducted on the network simulator NS2. The results show that TDR has higher delivery ratio but lower end-to-end delay and average hops.

Vehicular ad hoc network (VANET) is suffering from intermittent connectivity problems due to vehicles mobility, which challenge routing protocols. To address the issue, we propose a novel strategy called Reactive Pseudo-suboptimal-path Selection routing protocol (RPS). It is different from existing solutions which rely on vehicles physical movement to carry packets in intermittent connectivity scenarios. RPS gives the recently passed intersection a chance to select a new path from suboptimal-path unilaterally determined by local knowledge. Thus it improves the probability of transmission through wireless channels. A comparison between RPS and current protocols is presented and results show that the proposed RPS has higher packet delivery ratio and lower end-to-end delay.

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Until now, however, various extended VP techniques proposed to apply in multiuser precoding are almost restricted to one antenna configuration of each user. The restriction does not meet the development of next generation wireless systems. So, the well-known block diagonal (BD) algorithm and VP is naturally combined and proposed, named BD-VP for short, to solve this problem. However, the BD-VP completely suppressing multi user interference (MUI) at the expense of noise enhancement results in performance degradation. To overcome the shortcoming of BD-VP, we propose generalized channel inversion VP (GCI-VP) algorithms. Analysis and simulation results show that the proposed ZF GCI-VP is equivalent to the BD-VP, while the algorithm MMSE GCI-VP I and MMSE GCI VP II greatly outperform the BD-VP.

Wireless body area network (WBAN) solution is an emerging technology to resolve the small area connection issues around human body, especially for the medical applications. Based on the integrated superframe structure of IEEE 802.15.4, this paper proposes a modified medium access control (MAC) protocol for WBAN with focus on the simplicity, dependability and power efficiency. Considering the support to multiple physical layer (PHY) technologies including ultra-wide band (UWB), the slotted ALOHA is employed in the contention access period (CAP) to request the slot allocation. Mini-slot method is designed to enhance the efficiency of the contention. Moreover, sufficient slot allocation in the contention-free period (CFP) makes the proposed protocol adaptive to the different traffic including the medical and non-medical applications. Simulation results show that the protocol effectively decreases the probability of collision in a CAP and extends the CFP slots to support more traffic with quality of service (QoS) guarantee.

In order to save power to prolong battery life of subscriber stations (SSs) in IEEE 802.16e networks, the standard defines a sleep mode for SS. When there is no traffic for an SS to transmit or to receive, the SS switches to sleep mode periodically. The sleep interval is doubled each time until a maximum sleep interval threshold Tmaxis reached. Obviously, the performance of this power saving mechanism depends on the idle period distribution, which is user-specific. In network traffic modeling, it is commonly accepted that frame interarrival times have heavy-tailed distributions. Since heavy-tailed distributions make analysis and design challenging, in this paper we propose to use mixtures of exponentials to approximate heavy-tailed idle times. With a mixture of exponentials approximating the idle times, performance can be explicitly derived and optimized. An online EM algorithm is proposed to fit the mixture of exponential distributions to the idle times. Numerical examples show the effectiveness of the proposed procedures.

Smart antenna is a promising technology to improve the capacity of wireless networks. Implementing smart antenna in WLAN urgently demands new multiple access protocols. In this paper we propose a multiple access protocol for WLAN to effectively support smart antenna. The proposed protocol makes use of the hybrid superframe structure of polling and contention, which is illumed from IEEE 802.11, and distinguishes the stations residing within the AP's broadcast range from those out of the broadcast range. For the stations residing within the AP's broadcast range, the AP uses polling to transmit in broadcasting mode; for those residing out of the AP's broadcast range but in its directional range, the AP uses contention-based method to transmit in beamforming mode. We have evaluated and optimized the protocol performance against its parameters by simulation. To show the efficiency of the proposed protocol, we also compare it with other protocols in average packet delay and throughput. The simulation results show that the proposed protocol performs well; especially, under heavy traffic load, it achieves the smallest average delay and the highest throughput.

The use of smart antenna to extend the coverage range and capacity of wireless local area networks (WLAN) dictates the employment of novel multiple access protocols, with which the access point (AP) can provide access to remote stations. In this paper, we propose an adaptive multiple access protocol for WLAN with smart antenna. To efficiently support the differentiation of the stations residing within the broadcasting coverage range of the AP from those residing out of the range, the proposed protocol uses the hybrid superframe structure of contention-free period and contention period. Moreover, our protocol can adaptively adjust the antenna mode between beamforming and broadcasting according to the beam width and the number of active stations in the service area. Using simulation we compared the adaptive protocol with other non-adaptive ones. The results show that constructing our adaptive protocol by switching among non-adaptive protocols turns out to be the best strategy which allow us to take advantage of the best protocol providing low delay and high throughput.

A novel MAC protocol called adaptive beamforming time division multiple access (ABF-TDMA) for mobile ad hoc networks with smart antennas is proposed and analyzed ABF-TDMA is based on TDMA and smart antenna and takes the advantage of collision-free reservation. After successful reservation and training, data packet can be transmitted from source node to destination node with adaptive beamform. We analyze the throughput and delay in ABF-TDMA for the case of a fully connected network with variable-length packets and different number of nodes. The simulation results show that ABF-TDMA can achieve high throughput and keep low delay in the traffic-load ranges of interest, especially when the average packet length and the number of nodes are large.

Abstract The IEEE 802.11 wireless local area network (WLAN) media access control (MAC) specification is a hybrid protocol of random access and polling when both distributed coordination function (DCF) and point coordination function (PCF) are used. Data traffic is transmitted with the DCF, while voice transmission is carried out with the PCF. Based on the performance analysis of the MAC protocol for integrated data and voice transmission by simulation, this paper puts forward a self‐adaptive transmission scheme to support multi‐service over the IEEE 802.11 WLAN. The simulation results show that, on the premise of satisfying the maximum allowable delay of packet voice, the self‐adaptive transmission scheme can improve the data traffic performance and increase the WLAN capacity through dynamic and appropriate adjustment of the protocol parameters. Especially, voice traffic is sensitive to delay jitter, and the self‐adaptive scheme can effectively decrease it. Finally, it is worth noting that the adaptive scheme is easy to be realized, whereas no change in the MAC protocol is needed. Copyright © 2006 John Wiley & Sons, Ltd.

The use of smart antennas in extending coverage range and capacity of wireless LANs dictates the employment of novel media access control (MAC) schemes, with which the access point (AP) provides access to users by learning their spatial signatures. This paper puts forward an exact approach to analyze the performance of the schemes in terms of required time so that the AP becomes aware of user locations and grants access to the system. The numerical results show that the scheme which distinguishes the users residing in or out of broadcasting coverage range of the AP can provide rapider media access. In addition, the simulation results verify the theoretical approach.

This paper studies the performance of the IEEE 802.11 standard MAC protocol for integrated data and voice transmission with the DCF (distributed coordination function) and the PCF (point coordination function). By simulation, we evaluate the network performance for various protocol parameters, especially, the delay jitter for voice traffic. The main factor to influence delay jitter is given. Numerical results show that it is important to choose appropriate parameters and compromise the number of voice stations and the data traffic throughput to get the enhanced performance of IEEE 802.11. The performance of protocol in theory is derived and is verified by the simulation results.

The IEEE 802.11 standard MAC is a hybrid protocol of random access and polling when both DCF (distributed coordination function) and PCF (point coordination function) are used. On the base of the performance analysis of the MAC protocol for integrated data and voice transmission by simulation, this paper puts forward a selfadaptive transmission scheme to support multiservice over the IEEE 802.11 WLAN. The simulation results show that, on the premise of satisfying the maximum allowable delay of packet voice, the self-adaptive transmission scheme can improve the data traffic performance and increase the WLAN capacity through dynamic and appropriate adjustment of the protocol parameters. Especially, the scheme is easy to be realized for no change in the MAC protocol is needed.

The IEEE 802.11 standard MAC is a hybrid protocol of random access and polling when both DCF (distributed coordination function) and PCF (point coordination function) are used. This paper evaluates the performance of the MAC protocol for integrated data and voice transmission with the two access mechanisms. By simulation, we evaluate the network performance for various values of the protocol parameters. Especially, voice traffic is sensitive to delay jitter and here we point out the main factors to influence it. Numerical results show that it is important to choose appropriate parameters and we should compromise the number of the voice stations and the data traffic throughput to get the enhanced performance of IEEE 802.11. Finally, the performance of protocol in theory is derived and is verified by the simulation results.