A Self-Adaptive Back-off Optimization Scheme Based on Beacons Probability Prediction for Vehicle Ad-Hoc Networks

In order to improve the broadcast reception rates of beacon messages in vehicle ad-hoc networks,a conclusion that the relationship between collision probability and minimum contention window size and the relationship between expiration probability and minimum window size was reached by building a Markov model. According to this conclusion, a back-off algorithm based on adjusting the size of minimum contention window called CEB is proposed, and this algorithm is on the basis of the differential size between the number of expiration beacons and preset threshold. Simulations were done to compare the performance of CEB with that of RBEB and BEB, and the results show that the performance of the new proposed algorithm is better than that of RBEB and BEB.

Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad-Hoc Networks

This paper comes up with a SDN Based Vehicle Ad-Hoc On-Demand Routing Protocol(SVAO),which separates the data forwarding layer and network control layer,as in software defined networking(SDN),to enhance data transmission efficiency within vehicle ad-hoc networks(VANETs).The roadside service unit plays the role of local controller and is in charge of selecting vehicles to forward packets within a road segment.All the vehicles state in the road.Correspondingly,a two-level design is used.The global level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded.On the other hand,the local level is in charge of selecting forwarding vehicles in each road segment determined by the global level.We implement two routing algorithms of SVAO,and compare their performance in our simulation.We compare SVAO with popular ad-hoc network routing protocols,including Optimized Link State Routing(OLSR),Dynamic Source Routing(DSR),Destination Sequence Distance Vector(DSDV),and distance-based routing protocol(DB)via simulations.We consider the impact of vehicle density,speed on data transmission rate and average packet delay.The simulation results show that SVAO performs better than the others in large-scale networks or with high vehicle speeds.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Vehicle Positioning Method Based on RFID in Vehicular Ad-Hoc Networks

With the rapid development of vehicular ad hoc network( VANET) technology,VANET applications such as safe driving and emergency rescue demand high position accuracy,but traditional GPS is difficult to meet new accuracy requirements. To overcome this limitation,a new vehicle positioning method based on radio frequency identification( RFID) is proposed. First RFID base stations are divided into three categories using fuzzy technology,and then Chan algorithm is used to calculate three vehicles' positions,which are weighed to acquire vehicles' accurate position. This method can effectively overcome the problem that vehicle positioning accuracy is not high resulting from the factors such as ambient noise and base distribution when Chan algorithm is used. Experimental results show that the performance of the proposed method is superior to Chan algorithm and 2-step algorithm based on averaging method,which can satisfy the requirements of vehicle positioning in VANETs.

Innovation and Firm Co-ownership Network in China’s Electric Vehicle Industry

Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Coordinated Voltage Control of Distribution Network ConsideringMultiple Types of Electric Vehicles

The couple between the power network and the transportation network(TN)is deepening gradually with the increasing penetration rate of electric vehicles(EV),which also poses a great challenge to the traditional voltage control scheme.In this paper,we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV.In the first stage,the action of on-load tap changer and capacitor banks,etc.,are determined by optimal power flow calculation,and the node electricity price is also determined based on dynamic time-of-use tariff mechanism.In the second stage,multiple operating scenarios of multiple types of EVs such as cabs,private cars and buses are considered,and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory,Floyd-Warshall algorithm and traffic flow information.In the third stage,the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode.The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation,and the amount of regulation is calculated based on the fair voltage control mode of EVs.Finally,we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN.The simulation results show that the proposed scheme can mitigate voltage violations well.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks

Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.

Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks(RNN) is proposed. Firstly, considering the inaccu?rate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty(SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering?reduction method is proposed based on sigmoid function. In chattering?reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapu?nov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can e ectively achieve high?precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and e ectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool?experi?ments of AUV.

Quantitative Survivability Analysis Using Probability Model Checking: A Study of Cluster-Based Vehicle Networks with Dual Cluster Heads

As an important part of future 5G wireless networks,a vehicular network demands safety,reliability and connectivity.In this context,networking survivability is usually considered an important metric to evaluate network performance.In this paper,we propose a survivability model for vehicle communication networking based on dual cluster heads,wherein a backup cluster head(CH)will be activated if the primary CH fails,thereby effectively enhancing the network lifetime.Additionally,we introduce a software rejuvenation strategy for the prime CH to further improve the survivability of the entire network.Using the Probabilistic Symbolic Model Checker(PRISM),we verify and discuss the proposed survivability model via numerical simulations.The results show that network survivability can be effectively improved by introducing an additional CH and further enhanced by adopting the software rejuvenation technique.

Outage Performance of Non-Orthogonal Multiple Access Based Unmanned Aerial Vehicles Satellite Networks

With rapid development of unmanned aerial vehicles(UAVs), more and more UAVs access satellite networks for data transmission. To improve the spectral efficiency, non-orthogonal multiple access(NOMA) is adopted to integrate UAVs into the satellite network, where multiple satellites cooperatively serve the UAVs and mobile terminal using the Ku-band and above. Taking into account the rain fading and the fading correlation, the outage performance is first analytically obtained for fixed power allocation and then efficiently calculated by the proposed power allocation algorithm to guarantee the user fairness. Simulation results verify the outage performance analysis and show the performance improvement of the proposed power allocation scheme.

An Automatic System of Vehicle Number-Plate Recognition Based on Neural Networks

This paper presents an automatic system of vehicle number-plate recognition based on neural networks. In this system, location of number-plate and recognition of characters in number-plate can be automatically completed. Pixel colors of Number-plate area are classified using neural network, then color features are extracted by analyzing scanning lines of the cross-section of number-plate. It takes full use of number-plate color features to locate number-plate. Characters in number-plate can be effectively recognized using the neural networks. Experimental results show that the correct rate of number-plate location is close to 100%, and the time of number-plate location is less than 1 second. Moreover, recognition rate of characters is improved due to the known number-plate type. It is also observed that this system is not sensitive to variations of weather, illumination and vehicle speed. In addition, and also the size of number-plate need not to be known in prior. This system is of crucial significance to apply and spread the automatic system of vehicle number-plate recognition.

An Anonymous Authentication Scheme with Controllable Linkability for Vehicle Sensor Networks

Vehicle sensor networks(VSN)play an increasingly important part in smart city,due to the interconnectivity of the infrastructure.However similar to other wireless communications,vehicle sensor networks are susceptible to a broad range of attacks.In addition to ensuring security for both data-at-rest and data-in-transit,it is essential to preserve the privacy of data and users in vehicle sensor networks.Many existing authentication schemes for vehicle sensor networks are generally not designed to also preserve the privacy between the user and service provider(e.g.,mining user data to provide personalized services without infringing on user privacy).Controllable linkability can be used to facilitate an involved entity with the right linking key to determine whether two messages were generated by the same sender,while preserving the anonymity of the signer.Such a functionality is very useful to provide personalized services.Thus,in this paper,a threshold authentication scheme with anonymity and controllable linkability for vehicle sensor networks is constructed,and its security is analyzed under the random oracle model.

An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks

The vehicle edge network(VEN)has become a new research hotspot in the Internet of Things(IOT).However,many new delays are generated during the vehicle offloading the task to the edge server,which will greatly reduce the quality of service(QOS)provided by the vehicle edge network.To solve this problem,this paper proposes an evolutionary algorithm-based(EA)task offloading and resource allocation scheme.First,the delay of offloading task to the edge server is generally defined,then the mathematical model of problem is given.Finally,the objective function is optimized by evolutionary algorithm,and the optimal solution is obtained by iteration and averaging.To verify the performance of this method,contrast experiments are conducted.The experimental results show that our purposed method reduces delay and improves QOS,which is superior to other schemes.

Secrecy Outage Probability for Two-Way Integrated Satellite Unmanned Aerial Vehicle Relay Networks with Hardware Impairments

In this paper,we investigate the secrecy outage performance for the two-way integrated satellite unmanned aerial vehicle relay networks with hardware impairments.Particularly,the closed-form expression for the secrecy outage probability is obtained.Moreover,to get more information on the secrecy outage probability in a high signalto-noise regime,the asymptotic analysis along with the secrecy diversity order and secrecy coding gain for the secrecy outage probability are also further obtained,which presents a fast method to evaluate the impact of system parameters and hardware impairments on the considered network.Finally,Monte Carlo simulation results are provided to show the efficiency of the theoretical analysis.

Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

Vehicle Ad hoc Networks(VANETs)have high mobility and a rando-mized connection structure,resulting in extremely dynamic behavior.Several challenges,such as frequent connection failures,sustainability,multi-hop data transfer,and data loss,affect the effectiveness of Transmission Control Protocols(TCP)on such wireless ad hoc networks.To avoid the problem,in this paper,mobility-aware zone-based routing in VANET is proposed.To achieve this con-cept,in this paper hybrid optimization algorithm is presented.The hybrid algo-rithm is a combination of Ant colony optimization(ACO)and artificial bee colony optimization(ABC).The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another.The optimal routing process is used to avoid traffic and link failure.Thefitness function is designed based on Link stability and Residual energy.The validation of the proposed algorithm takes solution encoding,fitness calculation,and updat-ing functions.To perform simulation experiments,NS2 simulator software is used.The performance of the proposed approach is analyzed based on different metrics namely,delivery ratio,delay time,throughput,and overhead.The effec-tiveness of the proposed method compared with different algorithms.Compared to other existing VANET algorithms,the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay.

ADAPTIVE PREDICTIVE CONTROL OF NEAR-SPACE VEHICLE USING FUNCTIONAL LINK NETWORK

A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.

Fairness Assurance through TXOP Tuning in IEEE 802.11p Vehicle-to-Infrastructure Networks for Drive-Thru Internet Applications

This paper addresses an unfairness problem that exists among vehicles of distinct velocities in IEEE 802.11p based vehicle-to-infrastructure (V2I) networks used for drive-thru Internet applications. The standard IEEE 802.11p does not take into account, the residence time of vehicles within the coverage of each road side unit (RSU), for granting channel access. Due to this, a vehicle moving with higher velocity has less chance to communicate with the RSU, as compared to vehicles with lower velocity, due to its shorter residence time in the coverage area of RSU. Accordingly, the data transfer performance of a higher velocity vehicle gets degraded significantly, as compared to that of the vehicle with lower velocity, resulting in unfairness among them. In this paper, our aim is to resolve this unfairness problem by assigning the transmission opportunity (TXOP) limits to vehicles according to their mean velocities. Using an analytical model, we prove that tuning TXOP limit proportional to mean velocity can ensure fairness among vehicles belonging to distinct classes of mean velocities, in the sense of equal chance of communicating with RSU. Analytical results are validated using extensive simulations.