Vehicular Communications最新文献

{"title":"Detection of zero-day attacks via sample augmentation for the Internet of Vehicles","authors":"Bingfeng Xu, Jincheng Zhao, Bo Wang, Gaofeng He","doi":"10.1016/j.vehcom.2025.100887","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100887","url":null,"abstract":"Detecting zero-day attacks is a critical challenge in the Internet of Vehicles (IoV). Due to the limited availability of labeled attack data, anomaly-based methods are predominantly employed. However, the variability in the driving environment and behavioral patterns of vehicles introduces significant fluctuations in normal behavior, which in turn leads to high false positive rates when using these methods. In this work, we propose a novel detection method for zero-day attacks in IoV through sample augmentation. We first analyze the similarities between known and zero-day attacks in IoV. Based on the analysis, a Few-shot Learning Conditional Generative Adversarial Network (FLCGAN) model with multiple generators and discriminators is developed. Within this framework, an attack sample augmentation algorithm is designed to enhance input data by expanding the known attack dataset, thereby reducing false positives. To address the data imbalance caused by the limited number of input attack samples, an ensemble focal loss function is incorporated into the generator to ensure diversity and dispersion of the generated samples. Additionally, a collaborative focal loss function is introduced into the discriminator to improve the classification of difficult-to-classify data. A theoretical analysis is also conducted on the coverage of samples generated by the model. Extensive experiments conducted on the IoV simulation tool Framework For Misbehavior Detection (F2MD) demonstrate that the proposed method surpasses existing approaches in both detection effect and detection delay for zero-day attacks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"23 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel cylindrical filtering-based greedy perimeter stateless routing scheme in flying ad hoc networks","authors":"Amir Masoud Rahmani, Amir Haider, Khursheed Aurangzeb, May Altulyan, Entesar Gemeay, Mohammad Sadegh Yousefpoor, Efat Yousefpoor, Parisa Khoshvaght, Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100879","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100879","url":null,"abstract":"Flying ad hoc networks (FANETs) are a new example of ad hoc networks, which arrange unmanned aerial vehicles (UAVs) in an ad hoc form. The features of these networks, such as the movement of UAVs in a 3D space, high speed of UAVs, dynamic topology, limited resources, and low density, have created vital challenges for communication reliability, especially when designing routing methods in FANETs. In this paper, a novel cylindrical filtering-based greedy perimeter stateless routing scheme (CF-GPSR) is suggested in FANETs. In CF-GPSR, cylindrical filtering reduces the size of the initial candidate set to accelerate the selection of the next-hop node. In this phase, the formulation of the cylindrical filtering construction process is expressed in the cylindrical coordinate system because the filtered area is a cylinder enclosed within the communication range of flying nodes. The cylindrical filtering construction process includes three steps, namely transferring coordinate axes, rotating coordinate axes, and cylinder construction. When selecting the next-hop node, CF-GPSR first uses this cylindrical filtering to limit the candidate set of each flying node. Then, CF-GPSR decides on the best next-hop UAV based on a merit function, which includes four criteria, namely velocity factor, ideal distance, residual energy, and movement angle, and selects a candidate node with the highest merit value as the next-hop UAV. Finally, the simulation process is performed using the NS 3.23 simulator, and four simulation scenarios are defined based on the number of UAVs, the communication area of nodes, network connections, and the size of packets to evaluate CF-GPSR. In the simulation process, CF-GPSR is compared with the three GPSR-based routing schemes, namely UF-GPSR, GPSR-PPU, and GPSR in terms of delay, data delivery ratio, data loss ratio, and throughput. In the first scenario, namely the change in the number of flying nodes, CF-GPSR improves delay, PDR, PLR, and throughput by 17.34%, 4.83%, 16%, and 7.05%, respectively. Also, in the second scenario, namely the change in communication range, the proposed method optimizes delay, PDR, PLR, and throughput by 4.91%, 5.71%, 6.12%, and 8.45%, respectively. In the third scenario, namely the change in the number of connections, CF-GPSR improves EED, PDR, PLR, and throughput by 18.41%, 9.09%, 9.52%, and 7.03%, respectively. In the fourth simulation scenario, namely the change in the packet size, CF-GPSR improves delay, PDR, PLR, and throughput by 14.81%, 19.39%, 7.19%, and 0.39%, respectively.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"48 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RORA: Reinforcement learning based optimal distributed resource allocation strategies in vehicular cognitive radio networks for 6G","authors":"Mani Shekhar Gupta, Akanksha Srivastava, Krishan Kumar","doi":"10.1016/j.vehcom.2025.100882","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100882","url":null,"abstract":"The next generation (5G/B5G) vehicular cognitive radio networks (VCRNs) flag the track to intelligence-based autonomous driving in the initiation of future wireless networking and make daily vehicular operation more convenient, greener, efficient, and safer. However, with the continuous evolution of vehicles, the vehicular network becomes large-scale, dynamic, and heterogeneous, making it tough to fulfill the strict necessities, such as high security, resource allocation, massive connectivity, and ultralow latency. The combination of cognitive radio (CR) networks (different network coexistence) and machine learning (ML) has arisen as an influential artificial intelligence (AI) approach to make both the communication system and vehicle more adaptable and efficient. Naturally, applying ML to VCRNs has become an active research area and is being extensively considered in industry and academia. In this work, a reinforcement learning (RL) based optimal resource allocation (RORA) technique is proposed to solve the myopic decision-making problem by an autonomous vehicle (RL agent) takes its action to select the power level and optimal sub-band and maximize long-term rewards with a maximum payoff in VCRNs. The aim of this work is to design and implement an intelligent, resource allocation framework that ensures efficient and adaptive spectrum utilization while minimizing communication latency, energy consumption, and transmission cost in VCRNs. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network inter-working with Wi-Fi network with constant inter-space between Wi-Fi access points (APs) installed along the pathway is analysed. This framework is further analysed with variable inter-space between Wi-Fi APs. The key research problem addressed in this work is the challenge of optimizing spectrum and power allocation in highly dynamic vehicular environments characterized by rapid mobility, fluctuating network conditions, and interference from multiple vehicular CR nodes. The results show that the proposed RORA technique is more operative and outperforms other resource allocation schemes in terms of prediction accuracy and throughput.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"105 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing connected vehicle security through real-time sensor anomaly detection and recovery","authors":"Akshit Singh, Heena Rathore","doi":"10.1016/j.vehcom.2025.100876","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100876","url":null,"abstract":"Connected Vehicles (CVs) are a crucial element in the evolution of smart transportation systems, utilizing communication and sensing technologies to interact with each other and with infrastructure. As these vehicles become more interconnected, the risk of their components being affected by anomalies or intentional malicious attacks grows. It is essential, therefore, to identify and filter out any anomalous data to ensure reliable decision-making. Existing solutions for anomaly detection in CVs include methods such as kalman filter, cumulative summation, convolutional neural networks and other machine learning models. However, a prevalent issue is the limited universality of anomaly datasets along with the variability introduced by simulated data. Additionally, there are few methods for recovering the network from anomalies using sensor information. In this paper, we address these limitations by utilizing the Tampa CV (TCV) dataset and incorporating anomalies such as bias, noise, and spikes. Furthermore, we present a novel method for real-time anomaly detection in CVs using Bayesian Online Change Point Detection (BOCPD). We propose a unique recovery mechanism that employs Bayesian forecasting to interpret identified anomalies, marking the first of its kind in this field. This approach significantly enhances the security of CV systems by seamlessly merging instant detection with swift recovery, ensuring continuous protection against data integrity threats. Results demonstrate that the proposed model achieves an average accuracy improvement of 53.83 % over other machine learning models. This paper makes advancement through real-time anomaly detection and recovery mechanisms, thus significantly improving the resilience of smart transportation systems against data integrity threats.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"10 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-enhanced multi-region data aggregation for Internet of Vehicles","authors":"Haibin Dai, Yuanyuan Zhang, Li Lin, Jinbo Xiong, Youliang Tian","doi":"10.1016/j.vehcom.2025.100886","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100886","url":null,"abstract":"Data aggregation is evolving into an extremely crucial role for facilitating decision-making in Internet of Vehicles (IoV). Multi-region data is a typical attribute of IoV, containing sensitive information and driving trajectories. However, the existing privacy-preserving schemes face problems such as regional statistics, messages integrity, and collusion attacks. In order to tackle this challenge, we propose a privacy-enhanced multi-region data aggregation (PRDA) scheme for IoV. Specifically, PRDA protects both sensing data and location as masked values by multi-secret sharing. We design regional vectors to generate mask keys using symmetric bivariate polynomial without interaction. In addition, vehicles spontaneously generate verifiable and aggregatable signature to ensure messages integrity in insecure communication networks. Batch verification of bilinear pairing can improve efficiency while resisting tampering attacks by malicious adversaries. Experiments demonstrate that as the number of regions increases, comparing with existing works, PRDA has lower communication overhead, and decreases computational cost by over 32.6%.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"14 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considering both energy effectiveness and flight safety in UAV trajectory planning for intelligent logistics","authors":"Zhiyang Liu, Liuhuan Li, Xiao Zhang, Wan Tang, Zhen Yang, Ximin Yang","doi":"10.1016/j.vehcom.2025.100885","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100885","url":null,"abstract":"In low-altitude economic logistics scenarios, trajectory planning for unmanned aerial vehicles (UAVs) can be treated as a typical traveling salesman problem (TSP). High-rise buildings in urban areas not only severely impact the flight safety of UAVs, but also increase their energy consumption when avoiding obstacles, thereby affecting their delivery ranges. To address these issues, this paper proposes a two-stage trajectory planning solution called ACO-DQN-TP for logistics UAVs. In the first stage, the ant colony optimization (ACO) algorithm is applied to solve the sequence for multi-target point deliveries, to obtain the optimal flight paths. The ant tabu table is reopened to allow for retracing of the movement paths in order to avoid forward search dilemmas. In the second stage, a deep Q-network (DQN) is combined with the traditional artificial potential field method to enhance the interaction between UAVs and their environment. The rewards are accumulated using two potential functions generated based on the target points and obstacles, to minimize the changes in the yaw angles and smooth the flight trajectory of the UAV. Simulation experiments were conducted on UAV trajectory planning for delivery missions with four to ten target points. The simulation results show that the average path length obtained by ACO-DQN-TP is 65% and 79% shorter than that of Greedy+DQNPF and BACO, respectively, and the sum of turning angles along the path is 56% of Greedy DQNPF and 72% of BACO on average. It indicates that the proposed ACO-DQN-TP scheme not only optimizes delivery routes compared to traditional ACOs but also effectively controls the magnitude of the changes in heading angle during flight. This ensures flight safety for the UAV through obstacle avoidance while reducing flight energy consumption. In particular, the heading angle optimization mechanism proposed in this paper has universal guiding significance for low-altitude flights in the areas of traffic and transportation using UAVs.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"158 3 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-assisted Internet of vehicles: A framework empowered by reinforcement learning and Blockchain","authors":"Ahmed Alagha, Maha Kadadha, Rabeb Mizouni, Shakti Singh, Jamal Bentahar, Hadi Otrok","doi":"10.1016/j.vehcom.2025.100874","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100874","url":null,"abstract":"This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination processes are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for efficient and autonomous UAV coordination, and finally, a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the coordination between the selected UAVs is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). MDRL offers decentralized control and intelligent decision-making for the UAVs to maintain coverage and connectivity over the assigned vehicles. The evaluation results demonstrate that the proposed selection mechanism improves the stability of the selected relays, while MDRL maximizes the coverage and connectivity achieved by the UAVs. Both methods show superior performance compared to several benchmarks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"38 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative service caching for delay minimization in vehicular edge computing networks","authors":"Man Zhou, Jie Tian, Dongyang Li, Tiantian Li, Ji Bian","doi":"10.1016/j.vehcom.2025.100877","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100877","url":null,"abstract":"Vehicular edge computing (VEC) is beneficial to reduce task offloading delay and service acquisition delay by pushing cloud functions to the edge of the networks. Edge servers have the computation and storage capacity to execute vehicular tasks and cache the services required for tasks execution. Due to the limited caching resources of a single edge server, the vehicles will obtain services from cloud servers when they can't obtain from their own associated edge server, which results to the increase of service acquisition delay. To this end, we establish a multiple edge servers collaboration caching framework to minimize the heterogeneity tasks execution delay of the all vehicles, including tasks offloading delay, services acquisition delay and tasks processing delay. Specifically, the edge servers collaboratively make slot level caching decisions, i.e., what to be cached in each slot according to vehicular tasks requirements. Based on this framework, we formulate a long-term optimization problem to minimize the heterogeneity tasks execution delay of the all vehicles under the long-term energy constraints. To solve it, we firstly construct a virtual energy deficit queue, and then we transform the target problem into a delay drift-plus-energy consumption minimization problem by utilizing Lyapunov optimization theory. The equal transformation problem is a 0-1 multi-knapsack problem, which is a NP-hardness problem. To solve it, we improved the greedy algorithm that retains the selection process of the greedy algorithm and the comparison and selection of the genetic algorithm. Extensive simulations illustrate that the proposed scheme achieves near optimal delay performance while strictly satisfying long-term energy constraints, and outperforms other baseline schemes in terms of time-averaged delay and time-averaged energy consumption.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"122 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicles-digital twins matching scheme in vehicular edge computing networks: A hierarchical DRL approach","authors":"Huanxin Lin, Chao Yang, Shaoan Wu, Xin Chen, Yinan Liu, Yi Liu","doi":"10.1016/j.vehcom.2025.100883","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100883","url":null,"abstract":"Digital twin (DT) provides a powerful framework to enable various intelligence applications in vehicular edge computing networks. DT servers are used to model and optimize the resource allocation of the whole dynamic system, to provide low latency services for the vehicles. However, the dynamic topology and varying network status make it a challenge to construct the DT model timely, especially in the urban traffic scenario, both the base station (BS) and roadside unit (RSU) cover the ground vehicles overlapped. In this paper, we propose an optimal vehicles-DT servers matching scheme in urban road networks with respect to the dynamic topology and time-varying network status, the DT model selection, building, synchronization and migration latencies are analyzed and optimized mainly. To deal with the complex non-convex problem, we propose a hierarchical reinforcement learning-based solution scheme. The formulated joint optimization problem is decomposed into two subproblems: DT model building and migration. We solve these two subproblems orderly, an improved hierarchical deep reinforcement learning (HDRL)-based algorithm is proposed to find the final optimal solutions. Numerical results demonstrate the convergence of the proposed algorithms, and the effectiveness of the proposed schemes in reducing the system latency.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"8 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum-resilient blockchain-enabled secure communication framework for connected autonomous vehicles using post-quantum cryptography","authors":"Anjum Mohd Aslam, Aditya Bhardwaj, Rajat Chaudhary","doi":"10.1016/j.vehcom.2025.100880","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100880","url":null,"abstract":"Connected and Autonomous Vehicles (CAVs) are pivotal to the evolution of Intelligent Transportation Systems (ITS), offering enhanced connectivity and automation. However, the emergence of quantum computing poses significant security challenges to existing cryptographic protocols. This study addresses these challenges by proposing a hybrid security approach that combines Kyber Post-Quantum Cryptography (PQC) with an Adaptive Grouping Score-based Practical Byzantine Fault Tolerance (AGS-PBFT) blockchain mechanism. The key contribution of this study is the integration of lattice-based Kyber PQC, which is resistant to quantum attacks, with a dynamically adaptive AGS-PBFT blockchain. This integration aims to secure vehicular communications by ensuring data integrity, authenticity, and confidentiality while enhancing the scalability and efficiency of consensus processes in dynamic CAVs environments.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"59 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}