Vehicular Communications最新文献

{"title":"Adaptive backhaul optimization with hybrid FSO/RF links using multi-agent intelligence in 6G UAV networks","authors":"Umar Draz ,&nbsp;Tariq Ali ,&nbsp;Sana Yasin ,&nbsp;Mohammad Hijji ,&nbsp;Muhammad Ayaz ,&nbsp;Saleh Albelwi","doi":"10.1016/j.vehcom.2026.101001","DOIUrl":"10.1016/j.vehcom.2026.101001","url":null,"abstract":"<div><div>Free-Space Optical (FSO) communication is a promising candidate for high-capacity backhaul in beyond 5G (B5G) and 6G networks due to its exceptional bandwidth efficiency, minimal interference, and elevated data rates. However, its vulnerability to adverse weather conditions–such as fog, rain, and turbulence–poses significant limitations. To overcome these challenges, hybrid FSO/RF architectures have been introduced; however, current implementations typically rely on rigid resource allocation schemes, static channel selection, and suboptimal UAV positioning, which limit their adaptability in dynamic environments. In this study, we introduce the Smart Backhaul Framework for UAV Communication (SBF-UC), an intelligent, simulation-validated architecture designed to enhance hybrid FSO/RF backhauling. The framework employs Multi-Agent Reinforcement Learning (MARL) in conjunction with Matching Game Theory (MGT) to enable UAVs to act as autonomous agents that optimize flight altitude, communication link selection, and bandwidth distribution based on visibility-aware environmental metrics. The hybrid switching mechanism ensures continuous connectivity by dynamically selecting between FSO and RF links in response to atmospheric degradation. Extensive simulations under parameterized meteorological scenarios validate the effectiveness of SBF-UC, achieving up to 88% throughput under 30 dB/km attenuation, 25% energy efficiency gains, and latency below 200 ms for a network of 350 users. It contributes a unified multi-agent framework that combines MARL-driven UAV autonomy with matching-based hybrid FSO/RF backhaul optimization, enabling resilient link switching and efficient resource allocation under dynamic atmospheric conditions.The proposed framework offers a robust, scalable, and adaptive solution for resilient aerial backhauling in next-generation mobile communication systems.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101001"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962603","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":"Secure and efficient V2ES authentication protocol and faster-RCNN based object detection scheme for connected autonomous vehicles","authors":"Shucheng Ying ,&nbsp;Xiumei Li ,&nbsp;Qi Xie","doi":"10.1016/j.vehcom.2025.100997","DOIUrl":"10.1016/j.vehcom.2025.100997","url":null,"abstract":"<div><div>Connected Autonomous Vehicles (CAVs) can significantly enhance perception accuracy, optimize driving routes, and improve traffic efficiency and safety through collaborative road environment sensing. However, sharing image data among vehicles raises serious privacy concerns. Although various privacy-preserving computation techniques, such as homomorphic encryption, garbled circuits, and additive secret sharing, have been proposed to address this issue, most existing methods lack secure communication protocols between vehicles and edge servers (Vehicle-to-Edge Server, V2ES) as well as between edge servers. As a result, they remain vulnerable to collusion attacks, device capture attacks, and side-channel attacks, as well as incur high computational overhead. To address these challenges, an efficient and privacy-preserving computation scheme designed specifically for the faster region-convolutional neural network (R-CNN) object detection of CAVs is proposed, which has several advantages: (1) The first safe and practical object detection system model for CAVs is established; (2) The first secure and lightweight road side unit(RSU)-assisted V2ES authentication protocol and secure communication mechanism between edge servers are proposed to effectively resist collusion attacks and side channel attacks in the object detection scheme for CAVs; and (3) The multiplication and division protocols of Bi et al.’s scheme are optimized, significantly improving both computational and communication efficiency. The proposed RSU-assisted V2ES authentication protocol is provably secure under the Canetti-Krawczyk (CK) model and the extended security model. The experimental results further confirm that the proposed scheme significantly improves computational performance while ensuring data privacy, with multiplication efficiency improved by about 3.13 times and its communication overhead reduced by 50%, and division efficiency improved by 2.26 times and its communication overhead reduced by 60%.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 100997"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823369","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 gated transformer MADDPG algorithm for latency and energy aware task offloading in digital twinning Aerial edge computing","authors":"Md. Abdullah Al Sami ,&nbsp;Ibrahim Tanvir ,&nbsp;Palash Roy ,&nbsp;Md. Abdur Razzaque ,&nbsp;Md Rafiul Hassan ,&nbsp;Mohammad Mehedi Hassan","doi":"10.1016/j.vehcom.2026.101005","DOIUrl":"10.1016/j.vehcom.2026.101005","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) have seen breakthroughs in forming Aerial Edge Computing (AEC), which executes computationally intensive tasks generated by Internet of Things (IoT) devices, thanks to their ease of deployment, especially in scenarios where traditional terrestrial base stations are damaged and unable to process tasks due to natural disasters. However, an AEC faces significant challenges due to the limited battery capacity of UAVs and the need for efficient collaboration among them to execute tasks. Existing studies often overlook fine-grained task prioritization and balanced load distribution across UAVs, leading to inefficiencies in energy usage and service delay. In this paper, we have developed an optimization framework for efficiently offloading computationally intensive IoT tasks in a three-stage Digital Twin-enabled multi-UAV-based AEC network environment, which jointly minimizes service latency and energy consumption while ensuring the expected load distribution among the UAVs. The formulated framework is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is inherently NP-hard. To address this, we design GLEMATO, a scalable GTrXL-assisted MADDPG framework that learns high-quality offloading policies through memory-aware task prioritization and cooperative multi-agent decision-making in dynamic AEC scenarios. In GLEMATO, while the GTrXL model ensures adaptive task prioritization by considering factors such as task generation time, energy budget, and application deadlines, while the MADDPG enables decentralized policy learning through sharing cooperative state-actions among UAVs. The experimental results, carried out on the OpenAI Gym simulator platform, demonstrate that the developed GLEMATO framework reduces average energy consumption and service latency by 21.8% and 23.3%, respectively, and increases the average task completion ratio by up to 20.1% for computationally intensive tasks compared to the state-of-the-art approaches.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101005"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048182","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":"Deep learning-based analytical approach for coverage energy prediction in UAV-based energy scavenging networks","authors":"Thanh Binh Doan ,&nbsp;Tien Hoa Nguyen","doi":"10.1016/j.vehcom.2026.101009","DOIUrl":"10.1016/j.vehcom.2026.101009","url":null,"abstract":"<div><div>This study decisively investigates the performance of swarm-based networks comprised of unmanned aerial vehicles (UAVs), addressing the significant challenges posed by relaying communication protocols and co-channel interference (CCI). In which, UAVs are not limited to drawing energy from designated energy stations; they effectively scavenge energy from multiple interfering sources. Following that, we develop a robust framework that combines opportunistic UAV selection with amplify-and-forward (AF) relaying protocols, specifically targeting the impacts of CCI from various interferers. Under Nakagami-<em>m</em> fading channels, we conduct a thorough examination of how CCI affects the energy outage probability (EoP) in UAV swarm networks and ensure seamless connectivity between the source and destination through strategic UAV selection. Next, we derive precise analytical approximation expressions for EoP that comprehensively account for full interference effects, exploring three critical scenarios: CCI absent at UAVs, CCI absent at the destination, and CCI absent at both ends. To further enhance predictive capabilities, we implement a cutting-edge deep learning (DL) solution for real-time energy coverage probability (ECP) prediction to assist in the selection of the UAV, which has minimal EoP to maintain cooperative communication. Our numerical results, validated through extensive Monte-Carlo simulations, confirm the robustness of our mathematical framework and the effectiveness of the DL solution, while providing critical insights into the key design parameters that drive network performance.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101009"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135149","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 federated reinforcement learning-based energy efficiency optimization for IRS-assisted underlaying UAV communication","authors":"Haneef Khan ,&nbsp;Neeraj Joshi ,&nbsp;Abdoh Jabbari ,&nbsp;Hussein Zangoti ,&nbsp;Hussien T. Alrakah ,&nbsp;Ishan Budhiraja","doi":"10.1016/j.vehcom.2026.101003","DOIUrl":"10.1016/j.vehcom.2026.101003","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV)-assisted vehicular networks have garnered researchers’ attention as a promising solution to the limitations of vehicle-to-everything (V2X) communication, especially in the dynamic and dense urban scenario due to the non-line-of-sight (NLoS) setup, interference and unreliable links. Intelligent reflecting surfaces (IRS) further enhance communication quality by intelligently manipulating wireless signals when integrated with a UAV-assisted vehicular network. Although the IRS also supports simultaneous transmission and reflection (STAR), here, the passive reflection mode is considered alone. However, within such networks, due to dynamic topology, multi-dimensional state space, energy constraints and decentralized data, efficient resource management, power control and task offloading are compromised. Due to the limited adaptability, poor scalability and convergence of classical optimization techniques and deep reinforcement learning (DRL), we have presented a novel framework based on quantum federated reinforcement learning (QFRL) in this article. The suggested system makes effective use of the quantum properties of superposition and entanglement to facilitate decision-making. Task offloading, power allocation, and UAV trajectory are all taken into account when modelling the optimization problem as a Markov decision process (MDP). In order to guarantee privacy and decentralized intelligence while drastically cutting down on convergence time and computational overhead, a Quantum Neural Network (QNN) is used in federated learning (FL). The suggested QFRL framework performs better than the conventional Deep Deterministic Policy Gradient (DDPG) and Federated Reinforcement Learning (FRL) approaches, according to simulation results. In particular, the QFRL scheme outperforms FRL by 10.62% and DDPG by 49.32% in terms of energy efficiency. Additionally, QFRL exhibits better scalability and convergence speed as the number of vehicle terminals and IRS elements increases. A quantum-enhanced learning technique is established in this work as a potent remedy for the next generation of energy-efficient UAV communication networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101003"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014991","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":"Challenges and opportunities of synthetic data generation for machine learning-based intrusion detection systems in in-vehicle networks","authors":"Junhui Li ,&nbsp;Nikolaos Ersotelos ,&nbsp;Michail-Antisthenis Tsompanas ,&nbsp;Gregory Epiphaniou","doi":"10.1016/j.vehcom.2025.100998","DOIUrl":"10.1016/j.vehcom.2025.100998","url":null,"abstract":"<div><div>Machine learning-based intrusion detection systems (ML-IDS) for in-vehicle networks require diverse, high-quality datasets that are scarce because of privacy and data collection challenges. Collecting data in the real world often faces challenges, such as a lack of detailed attack scenarios and significant resource requirements. This survey examines synthetic data generation (SDG) as a solution and systematically reviews SDG methods, ML-IDS models, and their intersection in automotive security, which has not been addressed in prior surveys. We introduce a quantitative evaluation framework and apply it to synthetic and real datasets, such as SynCAN (Synthetic Controller Area Network), CAN-MIRGU (CAN Multi-Information Record Generating Unit) and Real ORNL (Oak Ridge National Laboratory) Automotive Dynamometer (ROAD) dataset. The results reveal critical limitations, since current synthetic approaches show reduced identifier coverage and unrealistic temporal patterns. Additionally, spatial network topology analysis reveals that synthetic datasets lack the hierarchical hub-and-spoke communication structures and functional subsystem coupling characteristic of real vehicular networks. Through a comprehensive analysis of more than 50 papers published in the time period from 2018 to 2025, we identified five research gaps,including temporal fidelity preservation, real-time constraints, cross-vehicle generalisation, attack diversity limitations, and quality validation requirements. Although SDG promises to address data scarcity and enable complex attack scenario simulations, current methods inadequately model authentic vehicular communications. We provide guidelines for developing temporally aware generation models and validation frameworks for practical deployment.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 100998"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894649","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 compensation scheme for non-ideal V2V communications in autonomous vehicle platoons","authors":"Xiaocheng Wang ,&nbsp;Qiaoni Han ,&nbsp;Jia Guo ,&nbsp;Guowen Cheng","doi":"10.1016/j.vehcom.2026.101004","DOIUrl":"10.1016/j.vehcom.2026.101004","url":null,"abstract":"<div><div>In recent years, the significant increase in the number of vehicles has led to driving safety, road congestion, and environmental pollution problems, while the connected and autonomous vehicles that form a platoon and are equipped with cooperative adaptive cruise control (CACC) can greatly improve traffic safety and road capacity. However, due to the limitation of communication resources and the high mobility of vehicles, the vehicle-to-vehicle (V2V) communications always suffer from time-varying delays and random packet dropouts, which seriously compromise platoon stability. Hence, in this work, considering the non-ideal V2V communications, firstly, the Smith predictor is introduced into the CACC-based platoon system to compensate for the equivalent communication delay caused by time-varying delays and random packet dropouts. Secondly, the analysis of plant stability provides guidance for the selection of control gains, and the analysis of string stability presents the theoretical minimum inter-vehicle distances under different communication delays. Further, the optimal control algorithm is applied to get the optimal values of control gains, so as to improve control accuracy and reduce energy consumption. Lastly, through comparisons with the CACC-based and model predictive control (MPC)-based counterparts, the simulation results validate the effectiveness of the proposed scheme in reducing inter-vehicle distance and enhancing tracking performance. They further reveal that the proposed scheme achieves a reduction in energy consumption by 34.24% and 22.58% relative to the CACC-based and MPC-based systems, respectively. Moreover, experiments conducted on the Xtark vehicle platform confirm the superior comprehensive performance of the proposed scheme.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101004"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014990","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":"Lightweight anonymous aggregate authentication in VANET based on offline/online certificateless signcryption using one-time key","authors":"Abdullahi Yahya Imam ,&nbsp;Fagen Li ,&nbsp;Hamisu Ibrahim Usman ,&nbsp;Muhammad Hanif Tunio","doi":"10.1016/j.vehcom.2025.100994","DOIUrl":"10.1016/j.vehcom.2025.100994","url":null,"abstract":"<div><div>Recent developments in the internet of things (IoT) and vehicular ad hoc network (VANET) significantly improve traffic management and safety. At the same time, several security challenges come alongside these improvements. Numerous research works have proposed different solutions to these security challenges using various cryptographic techniques. To maximize efficiency, recent works have developed several certificateless aggregate signcryption (CLASC) schemes without using the expensive bilinear pairing operations. However, recent studies have revealed various security flaws in many schemes, making them vulnerable to key replacement attacks that can lead to impersonation. Considering these security issues and the significance of high performance, we develop a novel pairing-free CLASC scheme for anonymous authentication in VANETs. To further improve the performance especially for real time communication, we devised a method of shifting the time consuming computations to offline operations. The confidentiality and unforgeability security of this scheme have been proved formally in random oracle model (ROM). Further analysis has demonstrated that the proposed scheme achieves other security requirements essential for anonymous authentication in VANET. Analysis of performances has shown that our scheme has shortest average transmission delay, specifically due to its very low computational overhead.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 100994"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796044","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 secure GNN-MADDPG framework with combinatorial action optimization for task offloading in vehicular networks","authors":"Yabin Zhu ,&nbsp;Xu Zhao ,&nbsp;Xin Zhang","doi":"10.1016/j.vehcom.2026.101000","DOIUrl":"10.1016/j.vehcom.2026.101000","url":null,"abstract":"<div><div>Vehicle-to-Everything (V2X) technology is rapidly developing. However, vehicular devices operate with limited computational power and energy. These constraints pose significant challenges for secure and energy-efficient task offloading. To address these challenges, this paper proposes a novel framework that integrates a Graph Neural Network (GNN) with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for secure task offloading and resource allocation. The framework employs a GNN (GraphSAGE) to capture the dynamic network topology and global interference, overcoming the limitations of partial observability. This spatial feature representation supports coordinated decision-making by multiple agents within the MADDPG architecture. To handle the high-dimensional and coupled action space, a combinatorial action selection strategy is proposed and QMIX value function decomposition is adopted. This “optimize-then-combine” mechanism enables efficient joint optimization of continuous resources and discrete decisions. Furthermore, a hybrid RSA-AES encryption scheme combined with frequency hopping is implemented to ensure end-to-end data security and anti-jamming capabilities. Extensive comparative experiments demonstrated that the proposed framework significantly outperformed baseline methods, including DQN and standard MADDPG, in terms of task completion rate, average latency, and energy consumption, especially in high-load scenarios. Ablation studies further validated the critical contributions of the GNN, combinatorial action design, and security mechanisms. This work provides an efficient, secure, and scalable solution for resource optimization in complex V2X environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101000"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902773","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 GWO-based approach for task scheduling in heterogeneous vehicular fog computing environments","authors":"Maryam Taghizadeh,&nbsp;Mahmood Ahmadi","doi":"10.1016/j.vehcom.2026.101006","DOIUrl":"10.1016/j.vehcom.2026.101006","url":null,"abstract":"<div><div>Vehicular Fog Computing (VFC) empowers automotive networks with fog computing, ensuring minimal delays for user services and vehicle operations. This study introduces a novel meta-heuristic algorithm for optimizing task scheduling in Vehicular Fog Computing (VFC). Leveraging Grey Wolf Optimization (GWO), the method differentiates between static and dynamic fog nodes, representing stationary servers and moving vehicles, respectively. Afterwards, a new stage is applied to refine the results of the GWO. The purpose of this step is to identify the fog node with the highest workload and distribute a percentage of its workload to several other fog nodes. This will reduce waiting time and improve makespan. Lastly, resource-intensive tasks are prioritized and allocated to these nodes. The paper includes a thorough evaluation of the GWO-based approach, analyzing the impact of various algorithm parameters. Performance is assessed using both real-world applications and synthetic data.Moreover, our implementation is considered ARM processor as computing resources in dynamic fog node. Experimental results demonstrate that the proposed algorithm achieves lower monetary costs than existing solutions and it shows the improvement at wait and makespan.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"58 ","pages":"Article 101006"},"PeriodicalIF":6.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072505","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}