Vehicular Communications最新文献

{"title":"Optimizing road side units connectivity in intelligent transportation systems with optical network solutions","authors":"Camilo Anzola-Rojas,&nbsp;Ignacio de Miguel,&nbsp;Noemí Merayo,&nbsp;Juan Carlos Aguado,&nbsp;Rubén M. Lorenzo,&nbsp;Ramón J. Durán Barroso","doi":"10.1016/j.vehcom.2025.100975","DOIUrl":"10.1016/j.vehcom.2025.100975","url":null,"abstract":"<div><div>A fundamental component of Intelligent Transportation Systems (ITS) is connectivity. For connected vehicles to be aware of events occurring nearby, or even far from them, roadside infrastructure is essential. Roadside Units (RSUs) are electronic equipment placed along highways to provide connectivity and share data with vehicles, other RSUs, and networks. Connected vehicles require wireless communication with RSUs; however, depending on the complexity of tasks and the number of users, spectrum resources may be insufficient to handle all required communication between vehicles, RSUs and external networks. Since RSUs are stationary, optical fiber is an ideal technology for interconnecting them and linking them to the Internet and the cloud, providing reliable, high-performance connectivity, with low signal attenuation and high bandwidth. This paper proposes a model for deploying fiber networks to connect RSUs, with a focus on minimizing capital expenditures, including costs for civil works, cables, and devices, which are critical considerations given the large distances involved. Specifically, we consider and compare two established optical network technologies: point-to-point (PtP) and passive optical networks (PON). To support this comparison, we present and test two novel Integer Linear Programming (ILP) formulations: one for PtP and one for PON. Additionally, we introduce a genetic algorithm that improves upon a previously proposed heuristic, achieving near-optimal results comparable to the ILP formulation, while efficiently solving large-scale scenarios. The results show that the optimal choice between PtP and PON depends on the deployment area and density of RSUs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100975"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221463","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":"RSU-assisted V2X message fusion via PC5 and Uu with actor–critic modeling for autonomous driving under intersection scenario","authors":"Sawera Aslam,&nbsp;Daud Khan,&nbsp;Sudeb Mondal,&nbsp;KyungHi Chang","doi":"10.1016/j.vehcom.2025.100972","DOIUrl":"10.1016/j.vehcom.2025.100972","url":null,"abstract":"<div><div>Autonomous driving systems rely heavily on effective data fusion from Vehicle-to-Everything (V2X) networks, where accurate decisions depend on integrating diverse messages from multiple communication interfaces. However, current single-interface communication methods, either PC5 or Uu, limit the achievable autonomy level due to insufficient reliability and situational awareness. To address these limitations, this paper proposes an efficient RSU-centered Message-level fusion framework tailored for intersection-based autonomous driving. The proposed approach strategically assigns CAM, CPM, and SPATEM to the PC5 interface, while DENM and MAPEM are assigned to the Uu interface. A confidence-weighted fusion algorithm is implemented at the RSU aligns timestamps, filters inconsistent inputs, and resolves conflicts to generate unified situational awareness messages every 100<!--> <!-->ms. The onboard decision-making model employs a CNN–GRU enhanced Actor–Critic network to optimize decisions for intelligent lane changing, collision avoidance, and traffic flow management. Simulation outcomes confirm that the proposed dual-interface fusion significantly enhances performance compared to single-interface systems, improving the packet delivery ratio to 0.75 at 300<!--> <!-->m and achieving decision accuracy improvements of approximately 14–25% across key use cases. Consequently, our framework meets the criteria for autonomy sub-level L4-C, providing a robust foundation for advanced intersection-based autonomous driving systems.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100972"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159196","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":"Budget feasible strategy-proof mechanism design for elastic mobile service in vehicle computing","authors":"Xi Liu ,&nbsp;Jun Liu","doi":"10.1016/j.vehcom.2025.100982","DOIUrl":"10.1016/j.vehcom.2025.100982","url":null,"abstract":"<div><div>In the context of the Internet of Things, edge computing can no longer fully meet the needs of the diversified task requirements; hence, vehicle computing has been developed. Concerning the new feature of intelligent vehicles (IVs) mobility, a novel elastic mobile service model is proposed for vehicle computing, where vehicle computing dynamically changes computing power according to the demands. This paper considers the elastic mobile service in two communication scenarios. The first is that IVs from different areas quickly move to the area with insufficient computing power, and the second is that IVs move to the specified area by the user. Owing to the heterogeneous costs of IVs, the pricing is based on the auction. Our goal is to maximize the total value, which is the sum of the values of all tasks running on the IVs. The problem is formulated as an integer linear program and proven <span><math><mrow><mi>N</mi><mi>P</mi></mrow></math></span>-hard. Then, a budget-feasible strategy-proof mechanism is proposed to allocate IVs one by one based on the marginal contribution. We first show that the proposed mechanism achieves strategy-proofness, individual rationality, consumer sovereignty, and budget balance, which drives the system into equilibrium. The approximation ratio of the proposed mechanism is analyzed. The experimental results show that under budget constraints, the proposed mechanism not only approaches the optimal performance in terms of total value but also effectively allocates tasks to provide the elastic mobile service.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100982"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397951","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":"HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks","authors":"Chen Sun,&nbsp;Liang Hou,&nbsp;Suqi Yu,&nbsp;Jian Shu","doi":"10.1016/j.vehcom.2025.100947","DOIUrl":"10.1016/j.vehcom.2025.100947","url":null,"abstract":"<div><div>Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100947"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491511","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":"Hybrid RF/VLC intelligent vehicular communications: A secrecy analysis","authors":"E. Soleimani-Nasab ,&nbsp;S. Coleri","doi":"10.1016/j.vehcom.2025.100964","DOIUrl":"10.1016/j.vehcom.2025.100964","url":null,"abstract":"<div><div>Next generation intelligent transportation systems (ITS) are expected to use visible light communications (VLC) as a complementary technology to the existing radio frequency (RF)-based technologies in vehicle-to-everything (V2X) communication to provide secure and reliable transmission by exploiting the directivity and impermeability of light. Moreover, reconfigurable intelligent surfaces (RIS) are a promising solution to enhance the coverage and reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications by modifying the phase, amplitude and polarization of incoming electromagnetic waves. Most previous works assumed double Rayleigh and Rayleigh fading channels for the RF links, with RIS-assisted setup lacking direct links between vehicles, and non-random distributions for vehicle movement in the VLC links. In this paper, we analyze the physical layer security performance of RIS-assisted hybrid RF/VLC links for both V2V and V2I scenarios. We also assume a direct line-of-sight (LoS) link between legitimate vehicles. In the existence of co-channel interference (CCI), an eavesdropper attempts to receive the information. We employ an accurate method to derive an exact expression for the cumulative distribution function (CDF) of RIS-assisted links combined with a direct link. More specifically, we derive closed-form expressions of secrecy outage probability (SOP), average secrecy capacity (ASC), probability of strictly positive secrecy capacity (PSPSC), effective secrecy throughput (EST), and intercept probability (IP). We assume double Nakagami-<em>m</em> fading for the V2V links, Nakagami-<em>m</em> channel for the V2I links, and log-Normal fading and uniform distribution for both longitude separation of Tx and Tx-Rx distance, leading to random path-loss. The correctness of the derivations is verified by using extensive Monte Carlo simulations for both V2V and V2I scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100964"},"PeriodicalIF":6.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865752","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":"Energy-harvesting relay-assisted STAR-RIS-enhanced vehicular NOMA networks","authors":"Jianghui Liu ,&nbsp;Saibing Wang ,&nbsp;Baofeng Ji ,&nbsp;Ruijuan Zheng ,&nbsp;Hao Li ,&nbsp;Guoqiang Zheng","doi":"10.1016/j.vehcom.2025.100960","DOIUrl":"10.1016/j.vehcom.2025.100960","url":null,"abstract":"<div><div>The integration of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with non-orthogonal multiple access (NOMA) technology represents an effective approach for enabling massive device connectivity and achieving 360° network coverage. Under the NOMA scheme, the communication performance of weak users can be significantly enhanced, thereby improving user fairness; however, this often comes at the cost of performance degradation for strong users. Therefore, an energy harvesting relay-assisted STAR-RIS enhanced vehicular NOMA network is considered in this paper. Specifically, the paper provides a detailed analysis of the signal-to-noise ratio (SNR) at the near and far vehicles within the STAR-RIS-assisted vehicular NOMA network. The channel gain from the base station to the near vehicle via STAR-RIS and the composite channel gain between the base station and the far vehicle are approximated using Gamma distributions, with the accuracy of these approximations validated through Monte Carlo simulations. Based on the end-to-end SNR and the statistical characteristics of the channels, closed-form approximate expressions for the outage probabilities of near and far vehicles are rigorously derived, where the direct communication link exists between the far vehicle and the base station, while the energy-harvesting relay provides an auxiliary communication for the near vehicle. All analytical results are validated through simulations. The numerical and simulation results show that, without increasing the number of STAR-RIS elements, the outage performance of the near and far vehicles can be effectively controlled by adjusting the power allocation at the base station and the reflection/transmission coefficients of the STAR-RIS elements. This approach promotes fairness among vehicle users. Moreover, comparison with the orthogonal multiple access (OMA) scheme demonstrates that NOMA achieves better fairness between vehicle users and significantly reduces the outage probability for the far vehicle. Additionally, the energy-harvesting relay helps alleviate the negative impact of NOMA on the near vehicle, thereby further enhancing its communication stability.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100960"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703541","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":"Optimizing end-to-end latency in C-V2X networks: A novel FD-RAN and MEC integration approach","authors":"Saber F. Mohammed ,&nbsp;Pan Zhiwen ,&nbsp;Haithm M. Al-Gunid ,&nbsp;Zeyad A.H. Qasem","doi":"10.1016/j.vehcom.2025.100955","DOIUrl":"10.1016/j.vehcom.2025.100955","url":null,"abstract":"<div><div>The increasing demand for low-latency services in cellular vehicle-to-everything (C-V2X) communications is crucial for the efficient operation of connected vehicles and autonomous driving systems. As C-V2X networks become integral to modern transportation infrastructure, minimizing end-to-end (E2E) latency remains a significant challenge in ensuring system reliability and effectiveness. To this end, we propose a novel approach that integrates a fully decoupled radio access network (FD-RAN) with multi-access edge computing (MEC) in C-V2X networks, aiming to optimize latency-sensitive applications. We apply a tractable analytical model for the E2E latency that accounts for latency contributions across the radio, backhaul, network, and processing layers. By leveraging FD-RAN's decoupled access and MEC's distributed processing, our approach effectively mitigates latency bottlenecks inherent in conventional RAN architectures. Additionally, we propose a stochastic optimization-based resource allocation method using Lyapunov techniques and Markov decision processes to dynamically manage base station selection and bandwidth allocation, thereby enhancing the system performance. Simulation results demonstrate that FD-RAN with MEC significantly reduces E2E latency compared with conventional RAN architectures, even under high traffic densities, while maintaining high data rates. These findings validate the proposed approach and offer key insights for developing low-latency infrastructures for next-generation V2X applications.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100955"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570531","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":"MIEC: A magnetism-inspired framework for MS deployment and joint task offloading and resource allocation optimization in LMREC","authors":"Mingyu Zhang ,&nbsp;Zhibo Sun ,&nbsp;Fengjie Li ,&nbsp;Hong Zhang","doi":"10.1016/j.vehcom.2025.100948","DOIUrl":"10.1016/j.vehcom.2025.100948","url":null,"abstract":"<div><div>With the rapid growth of Internet of Things (IoT) devices, Mobile Edge Computing (MEC) faces challenges in meeting increasing computational demands, especially in resource-constrained environments. To address this issue, we propose the LEO Satellite-MS-RSU Edge Computing (LMREC) framework, which integrates Mobile Servers (MSs), Low Earth Orbit (LEO) satellite networks, and Roadside Units (RSUs) into an innovative edge computing architecture. We first introduce “attraction” and “repulsion” metrics to model the willingness of vehicular satellite servers to serve specific users. Subsequently, we design a Magnetic Equilibrium Algorithm (MEA), which dynamically adjusts the MS deployment and service allocation by balancing user-driven attraction and server repulsion. To address the latency sensitivity of task scheduling and user satisfaction in LMREC, we formulate a mixed-integer nonlinear programming (MINLP) optimization problem for task offloading and resource allocation. Since this optimization problem is intractable to solve in polynomial time, we propose a Magnetic Domain Migration Algorithm (MDMA) to obtain a near-optimal solution. In MDMA, tasks are modeled as magnetic domains migrating in a magnetic field, and the optimization problem is decomposed into subproblems, which are solved using Exact Potential Game Theory, convex optimization, and a hybrid genetic algorithm. Finally, simulation results validate the effectiveness of the LMREC framework, demonstrating its superiority over existing methods and its potential to enhance collaboration among end devices, RSUs, and LEO satellite networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100948"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491512","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 comprehensive survey of deep reinforcement learning in UAV-assisted IoT data collection","authors":"Oluwatosin Ahmed Amodu ,&nbsp;Huda Althumali ,&nbsp;Zurina Mohd Hanapi ,&nbsp;Chedia Jarray ,&nbsp;Raja Azlina Raja Mahmood ,&nbsp;Mohammed Sani Adam ,&nbsp;Umar Ali Bukar ,&nbsp;Nor Fadzilah Abdullah ,&nbsp;Nguyen Cong Luong","doi":"10.1016/j.vehcom.2025.100949","DOIUrl":"10.1016/j.vehcom.2025.100949","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) play a critical role in data collection for a wide range of Internet of Things (IoT) applications across remote, urban, and marine environments. In large-scale deployments, UAVs often face complex decision-making challenges, for which Deep Reinforcement Learning (DRL) has emerged as a promising solution. This paper presents a comprehensive review of research on UAV-assisted IoT utilizing DRL, covering key research questions relating to DRL algorithm variants, deployment objectives, architectural features, integrated technologies, UAV roles, optimization constraints, energy management strategies, and performance metrics. Findings indicate that value-based and actor-critic algorithms are the most commonly employed, targeting objectives such as path planning, transmit power control, scheduling, velocity and altitude control, and charging optimization. Other architectural considerations include clustering, security, obstacle avoidance, buffered sensors, and multi-UAV coordination. Beyond data collection, UAVs are also used for tasks such as device selection, data aggregation, and sensor charging, with energy management primarily achieved through charging and energy harvesting techniques. Performance is typically assessed using metrics like energy efficiency, throughput, latency, packet loss, and Age of Information (AoI). The paper concludes by outlining several promising research directions and open challenges critical to the successful deployment of UAVs as aerial communication platforms, especially in IoT data collection. By organizing existing work across key themes and outlining promising future directions, this review offers a valuable reference for researchers and technology professionals alike.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100949"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515946","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":"Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach","authors":"Zhenzheng Shi,&nbsp;Liang Wang,&nbsp;Yaguang Lin,&nbsp;Anna Cai,&nbsp;Jiamin Fan,&nbsp;Cong Liu","doi":"10.1016/j.vehcom.2025.100952","DOIUrl":"10.1016/j.vehcom.2025.100952","url":null,"abstract":"<div><div>Mobile edge computing (MEC) technology can provide stable and efficient computing services for ground vehicles and users. However, maintaining stable MEC services becomes challenging in scenarios where ground MEC servers are damaged or unavailable, such as in post-disaster or remote areas. To tackle this issue, this paper proposes a novel space-air-ground integrated network (SAGIN) based emergency vehicular edge computing (VEC) framework, leveraging the rapid deployment characteristic of unmanned aerial vehicle (UAV) to provide VEC services for ground vehicles. A distance-based UAV clustering (DUC) algorithm is designed for efficient multi-UAV collaboration, executed by low earth orbit (LEO) satellite with wide coverage. Within each cluster, a task splitting algorithm based on a novel expected computing delay (ECD) metric is performed by the cluster-head UAV (CHU). Focusing on the issue of limited line-of-sight (LoS) range of UAV and computing sustainability during vehicle moving, we propose a dynamic offloading strategy. Simulation results show that the proposed framework enhances UAV utilization by 60% and significantly reduces task process delays across varying scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100952"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563851","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}