In numerous applications of multi-agent systems (MASs), e.g., social networks and biological networks, the relationship between agents may shift from competition to cooperation or vice versa. With that in mind, this paper investigates flocking behavior of MASs with evolving cooperation-competition relationships. The relationship between neighboring agents is characterized by a state-dependent nonlinear function: competition is triggered when the state discrepancy between agents exceeds a predefined threshold, whereas cooperation is maintained otherwise. A complete analysis is conducted on flocking behavior using the infinite products of substochastic matrices. As algebraic conditions regarding agent states and cooperative ranges are established to ensure the emergence of flocking behavior, and a lower bound for the convergence rate of flocking behavior is established. Finally, the theoretical results are validated through numerical simulations and an indoor multi-UAV system platform.

Accurate channel state information (CSI) prediction is essential for 6G massive multiple-input multiple-output (m-MIMO) IoT systems. Deep learning models, such as Transformers, exhibit quadratic computational complexity, resulting in significant efficiency bottlenecks when processing high-dimensional, long sequences channel data in high-mobility scenarios. The Mamba architecture, distinguished by its unique selective state-space model (SSM), presents a promising solution which combines linear computational complexity with robust capabilities for modeling long-range dependencies. Building on this foundation, we propose ChannelMamba, an end-to-end model specifically designed for channel prediction. First, the model employs a dual-domain input module that captures comprehensive channel features by concurrently processing frequency-domain CSI and delay-domain channel impulse response (CIR) data. Sub-sequently, we develop a cross-path parameter-sharing strategy for the Mamba modules to efficiently capture temporal channel dynamics while enhancing model generalization. Furthermore, to address the multi-dimensional dependencies and global context inherent in channel data, we design a bidirectional Mamba module for cross-feature modeling, enhanced with a lightweight attention mechanism. Finally, extensive experimental evaluations across various standard scenarios demonstrate the significant advantages of ChannelMamba over baseline methods in terms of prediction accuracy, robustness, generalization and computational efficiency, achieving new state-of-the-art performance in channel prediction tasks.

Highly flexible Automated Guided Vehicles (AGVs) are interconnected via Industrial Wireless Control Networks (IWCNs) in Multi-access Edge Computing (MEC)-assisted smart factories. The MEC alleviates the lack of computational resources in AGV systems through task offloading. However, IWCNs with limited communication resources struggle to support the highly concurrent offloading of AGVs. In the distributed AGV systems with multi-MEC servers, AGV mobility leads to uneven distribution across MEC server areas, potentially resulting in severe competition for communication resources. Therefore, in this paper, we design a Transferable joint Task Offloading and Multi-Channel Access (T2OMCA) algorithm based on multi-agent deep reinforcement learning. Specifically, AGV observations are modelled as graphs, in which edge relationships are learned through Transformer. This enables AGVs to utilize domain information to collaborate and alleviate concurrent offloading. Moreover, the T2OMCA algorithm converts network input into fixed embeddings to accommodate varying numbers of AGVs. Finally, to encourage exploration in the high-dimensional action space, the T2OMCA algorithm introduces a noisy network and a prioritized experience replay mechanism. Extensive simulations show that the T2OMCA algorithm outperforms existing algorithms in terms of average completion rate, processing delay, and access conflict rate under time-varying AGV topologies.

Localization security is crucial to the widespread applications of wireless sensor networks (WSNs) in various fields. This article mainly studies the issue of distributed localization in WSNs subject to deception attacks, in which the attacker randomly compromises communication channels and injects false data, resulting in the codification of data received by sensor nodes. A distributed iterative localization algorithm based on detection-holding strategy is proposed with the help of barycentric coordinate representations. This algorithm detects modified data in communication links through residual detection and communication encryption. It is proved theoretically that the proposed localization algorithm can achieve accurate convergence to the sensors' locations under general random false-data-injection attacks. Finally, the algorithm performance is demonstrated through simulation examples.

Hybrid energy storage system (HESS) in microgrid applications is controlled to balance the power between generation and load sides. However, power loss of converting and model parameter mismatch would affect the control performance. To this end, a deadbeat control algorithm for HESS combined with deep reinforcement learning is proposed in this article. In the proposed method, the variation of optimal HESS current reference caused by nonlinear power loss and model mismatch is regarded as a centralized disturbance that can be compensated by a deep deterministic policy gradient agent, and a deadbeat control generates an optimal duty cycle based on a precise reference current to eliminate system steady-state error and improve dynamic response speed. The effectiveness of the proposed algorithm is verified through simulation and hardware experiments. Results demonstrate that the steady-state error can be maintained within 1%. Compared to conventional deadbeat control methods, the proposed method reduces the bus voltage spike and settling time by 34.24%–44.44% and 16.66%–40.00%, respectively.

With electric vehicles (EVs) and charging facilities as a bridge, the coupling of transportation network (TN) and distribution network (DN) is getting closer, and EV charging navigation considering TN-DN convergence is a current research hotspot. In order to reduce the total cost of EV charging navigation and the impact of charging load on the grid, this paper proposes a novel bi-layer coordinated charging navigation model (Bi-CCNM). The upper layer model is to minimize the total cost of EV charging navigation. To precisely estimate travel costs, a dynamic spatio-temporal energy consumption estimation model is established, which considers the impact of dynamic traffic flow on EV travel resistance. The lower layer model aims to reduce energy exchange between the charging station (CS) and the main grid, and maximize the utilization of local renewable energy sources. To cope with the intermittent nature of renewable energy generation, this paper utilizes Vehicle-to-Grid (V2G) technology to effectively mitigate the impact of EV charging loads on the grid. To efficiently tackle the Bi-CCNM, the Joint Optimization algorithm combining Generalized Benders Decomposition and Logarithmic Barrier Function Method (JO-GBLB) is developed. Ultimately, an optimal solution can be obtained through the interaction of information between the two layers. Real-world case validates the effectiveness of the proposed the Bi-CCNM, energy consumption estimation model, and JO-GBLB algorithm. The results indicate which it provides a low-cost charging navigation solution while significantly reducing the power exchange between the CS and the main grid, effectively preventing safety issues caused by load fluctuations. Besides, the accuracy of energy consumption estimation of EVs increases by 5.1% - 6.7%.

Accurate trajectory prediction for all agents within complex environments is a crucial step toward realizing autonomous driving navigation. However, this task poses significant challenges due to the uncertainty surrounding the agent's intentions and the intricate road topology. Existing trajectory prediction methods struggle to strike a balance between accuracy and efficiency. To address this challenge, we propose the graph-based trajectory prediction network (DGATP). The model utilizes a two-layer graph representation to capture both the geometric and topological features of the driving environment information and encodes the static and dynamic driving environments hierarchically. An inter-layer network employing an attention mechanism is employed for feature aggregation, leading to improved local-global feature fusion. Furthermore, we introduce a joint prediction framework for all agents in the scenario, which utilizes dynamic weight learning. This adaptive head enhances the model's capacity without increasing its size, thereby maintaining the efficiency of the inference process and leading to accurate and efficient trajectory predictions.

6G Internet of Things (IoT) is envisioned to provide large-scale network connections and high data transmission rates to satisfy the diverse needs of IoT nodes. The wireless powered communication network (WPCN) is the essential part of the future 6G IoT, which can provide nodes with reliable and efficient data and energy transmission. In complex environments, wireless power transmissions are inefficient due to transmission distance and obstacles. To address these concerns, we propose a novel quality of experience (QoE)-driven framework for aerial intelligent reflective surface (IRS)-assisted WPCN, which exploits the maneuverability of unmanned aerial vehicle (UAV) to improve the network performance. In the framework, we construct a nonlinear satisfaction function to quantify the QoE and design an adaptive reflective units configuration scheme based on the QoE to reduce resource consumption (e.g., energy) while satisfying the QoE requirements. The optimization problem of maximizing average throughput is formulated by jointly optimizing the aerial IRS flight trajectory, node association variable, time slot allocation ratio, and IRS phase. The existence of coupling between optimization variables and the nonconvexity lead to the difficulty of solving the optimization problem directly. To effectively solve the above optimization problem, the block coordinate descent (BCD) algorithm is utilized to decompose the optimization problem into four subproblems to be solved separately. Simulation results demonstrate that the proposed scheme can significantly enhance the throughput compared with other schemes.

Federated learning (FL) is a distributed learning framework that allows clients to build models without disclosing local data. However, in resource-constrained scenarios, it is costly to participate in FL for all clients. Hence, selection strategy should be designed to select the most appropriate client groups. Current selection strategies are mainly cost and accuracy oriented, ignoring the autonomy of clients, which leads to the inability of clients to make autonomous decisions when participating in model training and updating. To realize autonomous selection of clients, we design a novel model accuracy-aware bidirectional client selection (MABCS) algorithm. The MABCS algorithm implements selection from both server and client dimensions. Specifically, the server evaluates the contributions of clients and design an accuracy-aware dynamic incentive mechanism. The client measures participation autonomy based on the reward and cost to decide whether or not to participate in FL. Thus, the client selection problem is modeled as a joint nonconvex optimization problem that maximizes the system revenue by optimizing the selection strategy and resource allocation strategy. The block coordinate descent algorithm is utilized to decouple the selection strategy and resource allocation strategy, and a linear approximation is employed to transform the selection strategy problem into a convex problem. An alternating optimization algorithm is used for the subproblems after the decomposition to obtain a near-optimal solution. Simulation results indicate that the MABCS algorithm exhibits superior convergence performance compared with other benchmark schemes.

Localization is a key technology to ensure the effective operation of wireless sensor networks in different environments. Due to the prevalence of cyber-attacks in real-world application scenarios, ensuring the accuracy of localization under denial-of-service (DoS) attacks is a growing concern. Existing research focuses on distributed localization ofstatic sensor networksunder DoS attacks. This article aims to extend the study of distributed localization inmobile sensor networkssubject to DoS attacks. Under DoS attacks, communication between sensor nodes can become intermittent, resulting in the time-varying characteristic for communication networks among all sensor nodes, which poses a challenge for successful localization. To overcome this challenge, this article proposes a distributed iterative localization algorithm using relative barycentric coordinates and distance measurements. Based on a hybrid approach composed of graph composition and sub-stochastic matrix, a comprehensive analysis of the convergence, rate and complexity of the localization algorithm is presented. At last, the theoretical results are verified by experimental examples.

A reliable driving trajectory prediction of surrounding vehicles is an essential reference for decision-making and safe driving of an autonomous vehicle. Although predicting short-term trajectories can be well achieved, it is still very challenging for long-term prediction of trajectories since the prediction space grows exponentially. In this paper, we propose a novel architecture for trajectory prediction from factored intention estimation (I2T), which decouples the trajectory prediction space into a high-level space for intention estimation and a low-level space for motion prediction. The long-term dependencies between intention cues and future motions during driving are naturally extended to the internal sharing mechanism of I2T, leading to improved performance. Furthermore, we design a Prioriformer model to serve as the backbone network for I2T so that it can accurately capture the long-term dependency couplings related to the task of intention estimation or motion prediction. Prioriformer model adopts a personalized normalization method, which facilitates learning latent representations of long-term features and avoids getting stuck on local optimum. A designed multi-scale fusion encoder extracts features from various receptive fields and then learns richer information from the representation subspaces. An efficient non-autoregressive decoder reduces the pressure in long-term prediction of trajectories while avoiding cumulative errors. Experiments on three real-world motion datasets show that I2T can significantly outperform the state-of-the-art.

Flocking aims to drive a group of agents connecting with each other to interact in a complex way, thereby emerging the behavior of group aggregation. Owing to the prevalence and complexity of cooperation and competition relationships among agents, it is practical and challenging to explore the realization of flocking behavior on multiagent coopetition networks. With that in mind, this article focuses on the study of flocking behavior for Cucker-Smale multiagent model over coopetition networks with communication delays. In this model, the cooperation/competition degree between agents is portrayed as a weight function with respect to communication distance, e.g., the closer (farther) the communication distance, the stronger (weaker) the cooperation/competition degree. With the help of analysis tools consisting of edge composites and substochastic matrices, the algebraic relationships between cooperation and competition degrees are established to ensure the emergence of flocking behavior. Moreover, mathematical expressions are given to show the effect of different communication delays on the agents’ final aggregation upper bound and convergence rate. In the end, the theoretical results are validated through computer simulations.

In the task of traffic flow forecasting, various external factors need to be considered to interfere with the flow, such as weather conditions, traffic accidents, emergency events, and Points of Interest (POIs). While capturing the spatio-temporal dependencies, it is essential to effectively capture the external factors. However, existing studies cannot effectively cascade the information contained in these external factors to traffic features, and lack the co-capture of spatio-temporal features. To address these challenges, we present a Knowledge Representation learning-actuated Spatio-Temporal Graph Neural Network (KR-STGNN) for traffic flow prediction. The Gated Feature Fusion Module (GFFM) is utilized to combine the knowledge embedding with the traffic features, and the traffic features are updated adaptively and dynamically according to the importance of external factors. To conduct the co-capture of spatio-temporal dependencies, we subsequently propose a spatio-temporal feature synchronous capture module combining dilation causal convolution with GRU. Experimental results on a real-world traffic dataset demonstrate that KR-STGNN has superior forecasting performances with different prediction steps, especially for short-term prediction.

Hybrid energy storage systems (HESS) are used to satisfy the power demand in microgrids. The supercapacitor (SC) is responsible for the high-frequency charge and discharge behaviors. For model predictive control (MPC) methods, the inaccurate modeling of the supercapacitor and control delay may cause the fluctuation of bus voltage. This paper proposes a fractional-order model predictive control (FOMPC), which provides more adjustable parameters, so it can optimize the control effect through parameter tuning. The method is validated in the simulation results.

Accurately forecasting the future motions of road participants is essential for proactive hazard avoidance and safety planning of autonomous vehicles. Existing methods for motion prediction based on probabilistic generative models are limited to low-accuracy likelihood calculations and relatively finite mode distributions. Recent studies show that score-based models can naturally overcome these limitations. In this work, we present a novel paradigm of conditional score-based models for vehicle motion prediction, called Motion-CSM. First, we model scene contextual representations of interaction regions at the feature level via graph convolutional networks. We then interpolate these representations as conditions into the solution process of the continuous-time reverse stochastic differential equation (SDE) to guide trajectory generation, which progressively converts the known prior distributions into multimodal trajectories including the ground truth modes. The designed stacked Transformer structure with dual control conditions is adopted to learn the score function approximation of the Gaussian perturbation kernel. Finally, we develop multiple consistency constraints to align the inference results of Motion-CSM in reverse SDE solving to improve the self-consistency and stability of multimodal trajectory generation. Experimental results on the real-world motion dataset demonstrate that the multimodal forecasting accuracy of Motion-CSM outperforms state-of-the-art methods.

Batteries of electronic quantities detection and state of health have always been the core of the battery management system of electric vehicles, which is capable of estimating SOC accurately and quickly and ensuring the safe operation of electric vehicles. Aiming at the problem of large estimation deviation of SOC and SOH in the whole life cycle of lithium battery, this paper proposes a multi-innovation dual Unscented Kalman Filter based on fractional-order model. Firstly, the fractional-order model of lithium battery is established and the parameters of the model are identified by a genetic algorithm. Secondly, the fractional-order multi-innovation Unscented Kalman Filter is proposed to estimate SOC, and the ohmic resistance and SOH are estimated by Unscented Kalman Filter to improve the SOC estimation accuracy in the whole life cycle. Finally, the proposed algorithm is verified by Urban Dynamometer Driving Schedule(UDDS) dynamic condition data.

Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforementioned constraints. In this article, we propose a Quality of Experience (QoE)-driven and energy-efficient adaptive deployment strategy for multi-UAV networks based on hybrid deep reinforcement learning (DRL) to solve the problem of incomplete information game, where the UAVs can adjust their moving directions and distance to serve users who move randomly in the target area. Through the hybrid DRL with centralized training and distributed testing, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and temporal difference error-based resampling optimization mechanism. The simulation results show that the hybrid DRL algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of QoE, energy consumption, and average throughput, by which average throughput can be increased by 20%–60%.

How to alleviate the traffic congestion and improve the traffic capacity of road networks through smart prediction has become a top priority for the realization of Intelligent Transportation Systems (ITS). It is necessary to capture the complex spatio-temporal correlation through the traffic data of road networks to achieve an accurate traffic prediction. In this paper, we propose a prediction method of spatio-temporal optimal graph neural network (STOG). It can obtain the spatio-temporal features of road networks through diffusion graph convolution (DGC) and recurrent neural network (RNN). We further leverage a new spatial attention mechanism to gain the aggregated features of the sampled nodes through pooling operations. It not only avoids excessive parameters, but also makes the model pays more attention to the sampled nodes, thereby reducing the prediction error. Through comparison with various baseline methods on METR-LA dataset, the results show that the proposed model can achieve higher prediction accuracy.

Collaborative unmanned aerial vehicle (UAV) networking has the characteristics of flexibility, efficiency, ubiquity, etc., which can enhance wireless network coverage and improve the quality of service to ground users. However, collaborative UAV networking has main challenges such as location deployment and energy optimization. For collaborative networking scenarios, it is necessary to focus on solving key issues such as algorithm convergence and time complexity. In response to the above problems, we propose to leverage hybrid deep reinforcement learning (DRL) with spatial attention mechanism to reduce time complexity according to actual application requirements of location deployment. Firstly, the convolutional neural network is used to improve the ability of discriminating state features. Then, an improved spatial attention mechanism is introduced to apply different weights to state features of different spatial locations, and focuses on the state features that are favorable for UAV deployment in spatial locations. Finally, the offline training state attention model is added to the state input of hybrid deep reinforcement learning for adaptive deployment training. The simulation results show that the training time of the algorithm can be greatly reduced while both mean opinion score (MOS) and energy consumption performance are reduced by about 10%.

Transportation system and power grid are tightly coupled by electric vehicles (EVs) and charging facilities. Finding the optimal charging path is crucial to mitigate EV user's mileage anxiety and guarantee the safety and stability of the power grid. To this end, this paper builds a charging path optimization model considering the constraints of transportation system and power grid, wireless charging power system, and dynamic energy consumption of EV. In addition, a charging path optimization algorithm based on Dijkstra and road section weighting is proposed, which can generate different optimal charging paths according to EV users' diverse preferences. To verify the feasibility and performance of the proposed algorithm, power grid is modeled as the standard IEEE-27 bus model and transportation system is the real road network of the high-tech zone of Kaifeng, China. Two scenarios with/without wireless charging power system are simulated in MATLAB, and the results are compared. The results show that with the support of wireless charging EVs with low initial energy can travel a longer distance, which can significantly mitigate EV users' mileage anxiety. The algorithm can find the optimal charging path for different EV users with different preferences. Simulation results with 100 EVs show that as the number of EVs increases, the algorithm still works well.

Vehicle trajectory prediction technology is of great significance in autonomous driving and intelligent transportation systems. Ego-vehicles can judge the future motion state considering nearby vehicles by predicting their trajectories, which facilitates safe and effective decisions to avoid collisions. It is a challenging task to accurately predict the future trajectories of surrounding vehicles. To solve this problem, we propose a Self-Attention Social Generative Adversarial Networks (SA-SGAN) model to predict trajectories of surrounding vehicles. We use the Self-Attention mechanism to capture the correlation between the features in the vehicle trajectory sequence to effectively solve the problem of missing important information due to a long input sequence, and use training characteristic of Generative Adversarial Networks (GAN) to effectively learn the distribution of real trajectory data and improve prediction accuracy. We evaluate the proposed model through NGSIM dataset, use the trained model to investigate the vehicle trajectory in the next 5s in a three-segment scenario of the US-101 highway, and use the Average Displacement Error (ADE) and Final Displacement Error (FDE) as the evaluation indicators. Compared with baseline methods, the proposed model reduces the evaluation indicators to 4.97 and 8.92 respectively.

Unmanned aerial vehicles (UAVs) can be used as air base stations to provide fast wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforemetioned constraints. In this paper, we propose an adaptive deployment strategy for UAV-aided networks based on hybrid deep reinforcement learning, where a UAV can adjust its movement direction and distance to serve users who move randomly in the target area. Through hybrid deep reinforcement learning, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and TD-error-based resampling optimization mechanism. Simulation results show that the hybrid deep reinforcement learning algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of coverage, energy consumption and average throughput, by which average throughput can be increased by 20% to 60%.

The deployment of drone small cells has emerged as a promising solution to agile provisioning of Internet backbone access for Internet of Things devices, and many other types of users/devices. In this paper, we consider the problem of deploying a set of drone cells operating on multiple channels in a target area to provide access to the backbone/core network, which is formulated as a combinatorial network utility maximization problem. Since an offline and centralized solution to such a problem is not feasible, a low-complexity and distributed online algorithm is highly desired. Therefore, we propose a measurement-aided dynamic planning (MAD-P) algorithm, where the dispatched drones perform position and channel configurations autonomously on the fly based on the real-time measurement of network throughput to solve the problem in a distributed fashion during flight with minimal centralized control. We prove that the proposed MAD-P algorithm is asymptotically optimal, and investigate how long it takes for the convergence to stationarity under the MAD-P algorithm by giving a mixing time analysis. We also derive an upper bound of the performance gap in presence of measurement errors. Simulation results are provided to validate our analytic results and demonstrate the effectiveness of our algorithm.

Next-generation intelligent transportation systems (ITS) are envisioned to greatly improve the transportation safety and efficiency by incorporating wireless communication and informatics technologies in the transportation system [1][2][3].As the cornerstone for ITS, vehicular communication networks enable vehicles to exchange information with other vehicles and the external environments and play a significant role in supporting a variety of services such as road safety, traffic management, and entertainment Vehicular communication networks face many technical challenges such as network scalability, highly dynamic topology, vulnerable wireless links, energy consumption of roadside units, poor network coverage, and bursty traffic.To address these challenges, various emerging technologies have been introduced in vehicular communication networks, such as software defined space-air-ground integrated vehicular network [4], fog computing in vehicular networks [5], droneassisted vehicular networks [6], and machine learning for data delivery [7].This special issue collection aims to present the vision, research, and dedicated efforts on the emerging technologies for vehicular communication networks.In this special issue, there are 15 submissions in total.After peerreview, 6 papers are selected for publication.The first article, "Software-Defined Collaborative Offloading for Heterogeneous Vehicular Networks" by W. Quan et al., proposes a software-defined collaborative offloading (SDCO) solution for heterogeneous vehicular networks, to efficiently manage the offloading nodes and paths.The offloading controller is equipped with two specific functions:

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy‐rule‐based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.

Clustering has been extensively used in Vehicular Ad- hoc NETworks (VANETs) for routing optimization and radio resource management, and continues to be considered to facilitate data dissemination in heterogeneous vehicular networks with the ever- increasing data traffic demands. Most of the existing clustering mechanisms in VANETs operate in a distributed mode. However, there is redundant control overhead and transmission decisions, such as cluster maintenance, parameter tuning and forwarding scheduling, which are costly in distributed modes. In this paper, a centralized clustering based hybrid vehicular networking architecture (CC-HVNA) is proposed, in which the collaborative control between IEEE 802.11p and LTE is realized to achieve clustering and to coordinate message delivery. In CC-HVNA, a volatile node state SN is set to reflect ever-changing network topology and to update clusters. Location-based Vehicle to Infrastructure (V2I) communications are utilized to gather regional information so as to perform centralized clusters partition and maintain cluster info table in infrastructures. We leverage a control center to integrate cluster info from the Evolved Node (eNodeB) and Road Side Units (RSUs). Owing to the possession of global cluster info, cluster changes can be detected and targeted data dissemination can be supported according to content-oriented service. The performance evaluation demonstrates that the proposed CC-HVNA clustering scheme can achieve a significant improvement of safety data dissemination.

In this paper, we studied the transmission performance of underwater wireless networks, where underwater network users (UNUs) can transmit their data through wireless optical and acoustic communication in a certain range to improve the overall underwater networks. By jointly considering UNUs' volume of data transferred and overall network transmission performance, we introduced an auction game based optical and acoustic communication mechanism (AGOC). With AGOC mechanism, the base transceiver station (BTS) sells wireless optical communication chances through auctions. The users will decide whether to bid according to their own situation, and then the winner could use wireless optical to transmit finally. The simulation results verified the effectiveness of our proposed algorithm. It also be concluded that AGOC mechanism could improve the overall underwater wireless network performance through reducing the number of UNUs contending for the wireless optical channel.

To improve the safety, traffic efficiency, and fairness among vehicles at intersections, it is urgent to study intelligent collaborative strategies and make intersections smarter. In this paper, a 3-Level Buffer (TLB) based Virtual Traffic Light (VTL) scheme, named TLB-VTL, is proposed for intelligent collaborative intersections. The intersection is divided into three adaptive areas according to the traffic flow of each lane, and the sequence of each timing cycle is calculated in realtime according to the flow in TLB around an intersection. To ensure fairness, the difference in probability of each lane to pass an intersection is restricted to a lower level. The VTL is realized based on communications of vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), and vehicle-to- infrastructure (V2I), which could improve the safety and fairness without involving traffic lights. Moreover, a Cooperative Collision Avoidance Predictive control (CCAP) algorithm is proposed, which can assist vehicles to go across the next intersection without stopping through predicting the time conflict and generating an efficient traffic schedule for the entire road network. The simulation results indicate that the proposed TLB-VTL algorithm improves the fairness by 331%, decreases the average delay by 88%, and improves the ability to solve congestion by 12% compared with the traditional traffic light algorithm. Besides, the CCAP algorithm increases the traffic fluency by 45% at the intersection.