Computer Networks最新文献

{"title":"Delay-energy-aware joint multi-cell association, service caching, and task offloading in hybrid-task heterogeneous edge computing networks","authors":"Bassant Tolba ,&nbsp;Maha Elsabrouty ,&nbsp;Mohammed Abo-Zahhad ,&nbsp;Akira Uchiyama ,&nbsp;Ahmed H. Abd El-Malek","doi":"10.1016/j.comnet.2025.111231","DOIUrl":"10.1016/j.comnet.2025.111231","url":null,"abstract":"<div><div>In highly dense networks with huge computational requirements, mobile edge computing has been proposed to alleviate network traffic congestion and reduce system latency by offloading the intensive computational tasks to the network edges for execution. As a result, achieving low energy consumption and reduced system latency has become increasingly important under this paradigm. In this paper, we propose a delay-energy-aware algorithm for minimizing the overall system latency, energy consumption and balancing the load among base stations, particularly in the case of hybrid-task scenarios. A novel crafted weighted-sum objective function for the total system latency and energy consumption is designed to formulate a non-convex joint optimization problem. The Gibbs sampling algorithm is used to solve the formulated optimization problem through updating the caching and offloading decision variables. The proposed framework investigates the optimal multi-cell association, power allocation, service data caching, and computational task offloading for multi-tier communication and edge computing networks. The effect of limited quota on multi-tier heterogeneous networks is investigated under Rayleigh fading channels. Simulation results demonstrate the superiority of the proposed algorithms over the state-of-the-art works in terms of reducing the system latency and energy consumption.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111231"},"PeriodicalIF":4.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715570","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":"Multi-channel real-time access with starvation avoidance for heterogeneous data in smart factories","authors":"Huaguang Shi ,&nbsp;Jian Huang ,&nbsp;Hengji Li ,&nbsp;Tianyong Ao ,&nbsp;Wei Li ,&nbsp;Yi Zhou","doi":"10.1016/j.comnet.2025.111236","DOIUrl":"10.1016/j.comnet.2025.111236","url":null,"abstract":"<div><div>In Industrial Wireless Control Networks (IWCNs), Industrial Devices (IDs) generate massive amounts of Data Packets (DPs) with different Quality of Service (QoS) requirements. However, most of the existing works set different priorities for differentiated transmission of heterogeneous data, and the high-priority DPs will access the channel immediately after they are generated. This may result in the access starvation of low-priority DPs in time-frequency resource-constrained IWCNs. In this paper, we study the collaborative transmission algorithm of heterogeneous data to avoid access starvation for lower-priority DPs while guaranteeing QoS for higher-priority DPs. Specifically, we first design an edge-assisted learning architecture with multi-access edge computing to assist the training of the algorithm. Then, to mitigate access conflicts among IDs, a gated recurrent unit enhanced Multi-Agent Deep Reinforcement Learning (MADRL) framework was adopted. Based on the framework, we propose a Multi-criteria Decision based dynamic Multi-channel Access (MDMA) algorithm, where high-priority DPs can consider waiting for access according to their own criteria to avoid preempting the channel access opportunity of low-priority DPs approaching the deadline. Extensive simulations show that the proposed MDMA algorithm outperforms the existing algorithms in terms of the average channel utilization rate and the average completion rate of heterogeneous data.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111236"},"PeriodicalIF":4.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748011","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":"GEN-DRIFT: Generative AI-driven drift handling for beyond 5G networks","authors":"Venkateswarlu Gudepu ,&nbsp;Bhargav Chirumamilla ,&nbsp;Venkatarami Reddy Chintapalli ,&nbsp;Piero Castoldi ,&nbsp;Luca Valcarenghi ,&nbsp;Bheemarjuna Reddy Tamma ,&nbsp;Koteswararao Kondepu","doi":"10.1016/j.comnet.2025.111237","DOIUrl":"10.1016/j.comnet.2025.111237","url":null,"abstract":"<div><div>Beyond fifth-generation (B5G) networks enable high data rates, low latency, and massive machine communications, driving digital transformation across sectors. The integration of Artificial Intelligence and Machine Learning (AI/ML) technologies plays a vital role in enhancing the performance and efficiency of B5G networks. However, the dynamic and ever-evolving service demands associated with B5G use cases lead to the occurrence of drift, which can significantly degrade the performance of AI/ML models. Drift occurrence often results in violations of Service Level Agreements (SLAs) and over- or under-provisioning of resources, ultimately impacting user experience and network reliability.</div><div>Drift detection and adaptation are essential for addressing the dynamic service demands of B5G networks. Existing threshold approach and various other frameworks, have significant limitations, — SLA violations from delayed drift detection and inefficient resource management due to frequent retraining. This paper proposes a drift handling framework that determines drift promptly after its occurrence using Generative Artificial Intelligence (Gen-AI). The proposed Gen-AI framework is evaluated for a Quality of Service Prediction use case on the Open Radio Access Network (O-RAN) Software Community (OSC) platform and compared to the existing threshold and other frameworks. Also, a real-time dataset from the Colosseum testbed is considered to evaluate the Network Slicing (NS) use case with the proposed Gen-AI framework for drift handling.</div><div>The results demonstrate that the proposed Gen-AI framework leverages both Generative Adversarial Network (GAN) and Variational AutoEncoder (VAE), significantly enhances drift detection and adaptation time in B5G networks. Specifically, in the QoS prediction use case, GAN achieves 98% drift detection accuracy, while the VAE achieves 95% , compared to 85% for the classifier framework, 25% for the threshold-based approach. In addition, a similar kind of results is observed in case of the network slicing use case. These results highlight the effectiveness of the proposed Gen-AI framework in proactively handling drift with reduced detection and adaptation time, making it a promising solution for B5G networks.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111237"},"PeriodicalIF":4.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706246","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":"Joint DRL and GCN-based Cloud-Edge-End collaborative cache optimization for metaverse scenarios","authors":"Zheng Wan ,&nbsp;Shenglu Zhao ,&nbsp;Cuifang Wang ,&nbsp;Yifeng Tan ,&nbsp;Xiaogang Dong","doi":"10.1016/j.comnet.2025.111239","DOIUrl":"10.1016/j.comnet.2025.111239","url":null,"abstract":"<div><div>The rapid emergence of the Metaverse demands deeply immersive and highly responsive virtual experiences, necessitating ultra-high transmission speeds and extremely low latency. Centralized data processing methods are facing increasing constraints in managing large-scale user data due to limitations in computing power, storage capacity, and network bandwidth. To address these challenges, this paper presents a Cloud-Edge-End transmission framework for Metaverse scenarios aimed at optimizing resource allocation, reducing latency, and enhancing rendering efficiency. We propose a distributed trajectory prediction (DTP) algorithm and develop a distributed trajectory prediction cluster system that utilizes the FastDTW algorithm to predict trajectory segments and calculate subscene popularity. Additionally, a real-time collaborative cache optimization scheme (GCNAC), based on GCN and DRL, is introduced to dynamically adjust caching strategies according to subscene popularity, thereby improving cache hit rates and reducing cache replacement frequencies. Simulations demonstrate that the GCNAC scheme markedly outperforms existing methods in target subscene cache hit rates and transmission latency across varying capacities, achieving a 43.49% reduction in edge replacement frequency. This study provides a practical solution for terminal pre-rendering of Metaverse scenes, offering both theoretical support and practical guidance for the development of scene rendering technologies and the generative Metaverse.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111239"},"PeriodicalIF":4.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706247","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":"AF-MCDC: Active Feedback-Based Malicious Client Dynamic Detection","authors":"Hongyu Du ,&nbsp;Shouhui Zhang ,&nbsp;Xi Xv ,&nbsp;Yimu Ji ,&nbsp;Sisi Shao ,&nbsp;Fei Wu ,&nbsp;Shangdong Liu","doi":"10.1016/j.comnet.2025.111234","DOIUrl":"10.1016/j.comnet.2025.111234","url":null,"abstract":"<div><div>Federated Learning (FL) has long been known for separating the training and model construction processes, ensuring the privacy of participating clients. However, this separation also introduces a new attack surface. Due to the decentralization feature, Federal Learning is prone to Byzantine attacks. Attackers can deliberately corrupt or malfunction one or more participants in the federated network, disrupting the overall model training process. Researchers have proposed many defense mechanisms to mitigate Byzantine attacks. Their main ideas include eliminating malicious updates that deviate from the overall distribution through similarity detection and avoiding malicious parameters using statistical characteristics. Yet, these defense mechanisms are usually passive, detection only happens on the central server, neglecting the important role of clients. Thus we propose AF-MCDC: Active Feedback-Based Malicious Client Dynamic Detection, a byzantine-robust federated learning method taking advantage of valid clients. What sets AF-MCDC apart from existing robust federated learning methods is its three-pronged defense approach. First, a detection mechanism is deployed on each client to verify the integrity of the distributed global model. If the model fails the integrity check, it will not be used to initialize the local model. On the server side, a decision is made based on the detection results uploaded by the clients, followed by performance scoring using cosine similarity among federated clients. Finally, a dynamic weighting mechanism based on client score rankings is applied to weigh the local models uploaded by all clients, effectively filtering out malicious clients Evaluation of two datasets, MNIST and CIFAR-10, demonstrates that AF-MCDC is robust against a significant portion of malicious clients. Furthermore, even when over half of the clients are malicious, AF-MCDC can still train a global model with performance comparable to the global model learned by FedAvg under non-adversarial conditions.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111234"},"PeriodicalIF":4.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706249","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":"Towards layer-wise quantization for heterogeneous federated clients","authors":"Yang Xu,&nbsp;Junhao Cheng,&nbsp;Hongli Xu,&nbsp;Changyu Guo,&nbsp;Yunming Liao,&nbsp;Zhiwei Yao","doi":"10.1016/j.comnet.2025.111223","DOIUrl":"10.1016/j.comnet.2025.111223","url":null,"abstract":"<div><div>Federated Learning (FL) has arisen to train deep learning models on massive private data, which are produced and possessed by geographically dispersed clients at the network edge. However, in edge computing scenarios, FL usually suffers from the constrained and heterogeneous communication resource. To achieve communication-efficient FL, we concentrate on the technique of model quantization. The existing researches in FL mainly perform model quantization at the grain of the entire model. However, according to our empirical analysis, when quantizing each layer of a model with the same quantization level, the amount of saved memory differs significantly across layers. Besides, the model exhibits different decreases in test accuracy when each layer is separately quantized to the same degree. To this end, we propose a more efficient and flexible Layer-wise Quantization scheme for FL, termed FedLQ. We further theoretically analyze the relationship between the convergence bound and the quantization level. Furthermore, considering that the quantization of each layer will yield different effects on the communication cost and model accuracy, we develop a joint metric (<em>i.e.</em>, layer significance) to evaluate the comprehensive influence of layer-wise quantization on model training, and design a significance-aware algorithm to determine adaptive layer-wise quantization levels for different clients. Extensive experiments in simulation environment illustrate that FedLQ is able to effectively reduce communication consumption while still achieving promising accuracy even with low-bit quantization. Compared to the baselines, FedLQ can achieve up to 5.77<span><math><mo>×</mo></math></span> speedup when reaching the target accuracy, or obtain at most 27% improvement in test accuracy under low-bits quantization scenarios.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111223"},"PeriodicalIF":4.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738224","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 new approach on self-adaptive trust management for social Internet of Things","authors":"Elham Moeinaddini ,&nbsp;Eslam Nazemi ,&nbsp;Amin Shahraki","doi":"10.1016/j.comnet.2025.111187","DOIUrl":"10.1016/j.comnet.2025.111187","url":null,"abstract":"<div><div>The emergence of the Social Internet of Things (SIoT) represents a significant evolution of the traditional Internet of Things (IoT) paradigm. While earlier IoT systems prioritized the integration of intelligent devices, the SIoT paradigm shifts the focus toward enabling social interaction and collaboration among connected entities. Within the SIoT, various nodes can autonomously establish social connections to access the desired services. Given the dynamic and decentralized nature of this open environment, effective trust management becomes crucial. In recent years, machine learning (ML) techniques have made significant progress in enhancing trust computing within the SIoT ecosystem. However, challenges such as scalability, dynamic behavior, and device resource limitations continue to pose difficulties. This study introduces SATM-SIoT, a decentralized self-adaptive trust management model for SIoT that integrates the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop. By adjusting thresholds based on perceived hostile behavior, this model effectively identifies malicious devices, enhancing adaptivity in trust management. To address resource limitations and ensure scalability, our model assigns trust evaluation tasks to Fog nodes, leveraging their distributed computational power. We utilize ML techniques, specifically multi-layer perceptron (MLP), to analyze device behavior and assess trustworthiness. Federated learning (FL) enhances local ML models, enabling collaborative learning and incremental updates based on new data to adapt to the dynamics of SIoT. The primary goal of SATM-SIoT is to improve overall trustworthiness and accurately detect malicious devices while tackling challenges related to scalability, dynamic behavior, and resource constraints. We conducted experiments on a simulated SIoT network to validate our model. The results show that SATM-SIoT effectively identifies almost all malicious devices in a SIoT network. Utilizing the FL technique and MAPE-K loop led to an average increase in Success Rate of 7% and 9%, respectively.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111187"},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706250","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":"Latency minimization in IRS-UAV assisted WPT-MEC systems: An ID-AOPDDQN-based trajectory and phase shift optimization approach","authors":"Wenjie Zhou,&nbsp;Linbo Zhai,&nbsp;Zekun Lu,&nbsp;Kai Xue,&nbsp;Tian Zhang","doi":"10.1016/j.comnet.2025.111215","DOIUrl":"10.1016/j.comnet.2025.111215","url":null,"abstract":"<div><div>Intelligent Reflectors (IRS) assisted Wireless Power Transmission and Mobile Edge Computing (WPT-MEC) are considered as solutions for implementing sustainable Internet of Things (IoT) networks that can effectively improve network performance and reduce data transmission latency. There are still challenges such as flexible deployment of IRSs and multivariate joint optimization remain. In this paper, we study the task offloading problem of unmanned aerial vehicles (UAVs) carrying IRSs (IRS-UAV) assisted WPT-MEC, which exploits the flexibility of the UAV to dynamically improve the energy harvesting and task offloading channel transmission between ground equipment (GD) and access point (AP). In this system, we consider the association relationship between the hovering points (HPs) of the IRS-UAV and the GDs, the phase shift of the IRS-UAV, the flight trajectory, the beamforming vector, and the offloading decision and transmit power of the GDs to minimize latency performance under the constraint of energy consumption. To solve this multivariable non-convex problem, we propose an ID-AOPDDQN (ISODATA clustering, successive convex approximation and parametric Dueling deep <span><math><mi>Q</mi></math></span>-network) algorithm. At first, we cluster the HPs and the association relationship between GDs and HPs through an efficient load balancing algorithm (ISODATA), so as to cover all GDs to the maximum extent. Secondly, on the basis of clustering, we divide the target problem into two sub-problems. For the first sub-problem, Successive Convex Approximation (SCA) is used to transform it into a convex problem, and the phase shift and beamforming vectors of radio energy transmission are optimized alternately. For the second subproblem, we design the PDDQN (a combination of DDPG and Dueling DQN) algorithm to process the mixed space based on the first problem of the solution, where DDPG processes continuous motion (such as phase shift) and Dueling DQN processes discrete action (such as offloading decisions). Simulation results show that the ID-AOPDDQN algorithm significantly improves the performance of the system in latency.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111215"},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715571","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":"Accelerating traffic engineering optimization for segment routing: A recommendation perspective","authors":"Linghao Wang,&nbsp;Miao Wang,&nbsp;Chungang Lin,&nbsp;Yujun Zhang","doi":"10.1016/j.comnet.2025.111224","DOIUrl":"10.1016/j.comnet.2025.111224","url":null,"abstract":"<div><div>Traffic engineering (TE) is important for improving network performance. Recently, segment routing (SR) has gained increasing attention in the TE field. Many segment routing traffic engineering (SR-TE) methods compute optimal routing policies by solving linear programming (LP) problems, which suffer from high computation time. Therefore, various methods have been proposed for accelerating TE optimization. However, prior methods solve individual TE optimization problems from scratch, overlooking valuable information from existing historical solutions. We argue that these data can imply the distribution of optimal solutions for solving future TE problems. In this paper, we provide a new perspective on accelerating SR-TE optimization. First, we generated and analyzed historical solutions of a widely used LP model, and revealed two key findings from the data: Flows are predominantly routed through a small subset of intermediate nodes; similar decisions can be made for some flows. Then, inspired by the findings, we propose RS4SR, the first framework to our knowledge leveraging historical solutions for SR-TE acceleration. It can significantly reduce the size of LP model by performing candidate recommendation and flow clustering. Experiments on real-world topologies and various traffic matrices demonstrate that a simple implementation of RS4SR is sufficient to obtain near-optimal solutions within the time limit of two seconds on large-scale networks, utilizing a small number of historical solutions.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111224"},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738225","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":"Socially beneficial metaverse: Framework, technologies, applications, and challenges","authors":"Xiaolong Xu ,&nbsp;Xuanhong Zhou ,&nbsp;Muhammad Bilal ,&nbsp;Sherali Zeadally ,&nbsp;Jon Crowcroft ,&nbsp;Lianyong Qi ,&nbsp;Shengjun Xue","doi":"10.1016/j.comnet.2025.111198","DOIUrl":"10.1016/j.comnet.2025.111198","url":null,"abstract":"<div><div>In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"263 ","pages":"Article 111198"},"PeriodicalIF":4.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706251","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}