Ad Hoc Networks最新文献_第5页

Supervised machine learning-based ETX optimization for energy-efficient routing in IoT-enabled WSNs 基于监督机器学习的ETX优化，用于支持物联网的wsn的节能路由

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-16 DOI: 10.1016/j.adhoc.2025.103972

Oussama Senouci, Nadjib Benaouda

{"title":"Supervised machine learning-based ETX optimization for energy-efficient routing in IoT-enabled WSNs","authors":"Oussama Senouci, Nadjib Benaouda","doi":"10.1016/j.adhoc.2025.103972","DOIUrl":"10.1016/j.adhoc.2025.103972","url":null,"abstract":"<div><div>This paper addresses the challenge of energy-efficient and reliable data routing in Wireless Sensor Networks (WSNs) within Internet of Things (IoT) environments by optimizing the Expected Transmission Count (ETX) metric for efficient routing. Traditional ETX-based routing struggles with dynamic network conditions, leading to suboptimal path selection and increased energy consumption. To overcome these limitations, we propose a Machine Learning-Based ETX Optimization Approach, which dynamically adjusts ETX values based on real-time network conditions and historical transmission patterns. The approach employs a supervised learning model, specifically a CatBoost classifier, to predict the most energy-efficient and reliable routes. The model achieves a high classification accuracy of 98.9%, enabling precise differentiation between optimal and non-optimal links, thereby reducing retransmissions and balancing energy consumption across the network. Our approach is evaluated using extensive simulations, analyzing key performance metrics such as energy consumption, network lifespan, Packet Delivery Ratio (PDR), and communication overhead. Experimental results demonstrate that the proposed method significantly enhances routing efficiency, minimizes energy expenditure, and improves overall network performance. Specifically, our method improves network lifetime by 14.3%, energy efficiency by 16.7%, PDR by 26.4% and communication overhead by 8.06% compared to existing protocols. These results highlight the robustness and predictive power of our approach, making it a highly effective solution for integrating WSNs into IoT ecosystems while ensuring sustainable and efficient operation.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103972"},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent positioning method for underwater vehicle under hydroacoustic noises 一种水声噪声下水下航行器智能定位方法

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-15 DOI: 10.1016/j.adhoc.2025.103963

Xiyun Ge, Junbo Zhao, Yue Cheng, Jianfeng Yang, Hao Geng

{"title":"An intelligent positioning method for underwater vehicle under hydroacoustic noises","authors":"Xiyun Ge, Junbo Zhao, Yue Cheng, Jianfeng Yang, Hao Geng","doi":"10.1016/j.adhoc.2025.103963","DOIUrl":"10.1016/j.adhoc.2025.103963","url":null,"abstract":"<div><div>Underwater vehicle positioning is the foundation of ocean exploration, target tracking, and multi aircraft collaboration. As the only carrier for long-distance information transmission, underwater acoustic positioning is easily affected by spatiotemporal frequency variations and multipath interference, resulting in large errors. Hence, this paper proposes an intelligent positioning method for underwater vehicle that can resist underwater acoustic noises. Firstly, based on the slant range, azimuth angle, pitch angle, and motion parameters, the measurement equations and state equations are established for underwater vehicle estimation. Secondly, utilizing the cubature Kalman filter to approximate the motion characteristics of positioning system, the Particle filtering is jointly used to reduce the deviation between importance density and posterior distribution. Thirdly, a sequence weighted correction strategy is introduced to dynamically update particle weights, and the hierarchical resampling is performed based on particle quality evaluation indicators. Finally, the multiple parameter simulations combined with platform experiments are conducted for accuracy comparisons and analyses on the lake. The experimental results show that the proposed positioning method has better positioning accuracy and stability compared to the relevant algorithms, and can be used for the positioning and tracking of actual underwater vehicles.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103963"},"PeriodicalIF":4.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Traffic Normalization Location Attention Network for cyber attack detection in Industrial Cyber-Physical Systems 面向工业信息物理系统网络攻击检测的流量归一化位置关注网络

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-14 DOI: 10.1016/j.adhoc.2025.103965

Minpeng Cheng, Yongyi Chen, Dan Zhang

引用次数: 0

Distributed serverless function scheduling in ad-hoc drone networks ad-hoc无人机网络中的分布式无服务器功能调度

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-14 DOI: 10.1016/j.adhoc.2025.103951

Giuseppe De Palma , Saverio Giallorenzo , Alexandre Heideker , Matteo Trentin , Angelo Trotta , Gianluigi Zavattaro

{"title":"Distributed serverless function scheduling in ad-hoc drone networks","authors":"Giuseppe De Palma , Saverio Giallorenzo , Alexandre Heideker , Matteo Trentin , Angelo Trotta , Gianluigi Zavattaro","doi":"10.1016/j.adhoc.2025.103951","DOIUrl":"10.1016/j.adhoc.2025.103951","url":null,"abstract":"<div><div>The increasing use of Unmanned Aerial Vehicles (UAVs) in critical applications, such as disaster response, compels efficient communication and computation frameworks for highly dynamic ad-hoc networks. We present an interpretation of Function-as-a-Service serverless computing within the distributed settings of drone swarms to address their peculiar challenges in functionality deployment, resource allocation, and mission adaptability. We propose a novel two-layer network overlay architecture, combining a gossip-based topology management layer with a function scheduling layer to support distributed function scheduling. Our system introduces a declarative language of Ad-Hoc Allocation Priority Policies (AHAPP), tailored for ad-hoc drone networks, enabling flexible function deployment based on resource constraints and operational needs. The resulting combination addresses the volatility of UAVs networks by supporting execution semantics for stable- and dynamic-topology scenarios, function offloading, and resilience to network disruptions. We present experiments confirming that the features provided by our proposal support the efficient execution of serverless functions in ad-hoc drone networks, effectively handling their dynamic and heterogeneous nature, while achieving strong performance in terms of reliability, scheduling time, and communication overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103951"},"PeriodicalIF":4.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lightweight Decentralized Federated Learning Framework for the Industrial Internet of Things 面向工业物联网的轻量级分散联邦学习框架

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-12 DOI: 10.1016/j.adhoc.2025.103962

Changsong Yang , Jianran Wang , Yueling Liu , Yong Ding , Zhen Liu , Shuo Wang

{"title":"A Lightweight Decentralized Federated Learning Framework for the Industrial Internet of Things","authors":"Changsong Yang , Jianran Wang , Yueling Liu , Yong Ding , Zhen Liu , Shuo Wang","doi":"10.1016/j.adhoc.2025.103962","DOIUrl":"10.1016/j.adhoc.2025.103962","url":null,"abstract":"<div><div>Federated learning (FL) has recently gained significant attention in edge computing, the Industrial Internet of Things (IIoT), and Internet of Things (IoT) due to its ability to enable distributed clients to train models collaboratively while keeping the original data local. However, existing works usually suffer from limited communication resources, dynamic network conditions, and heterogeneous client properties, which hinder effective FL in IIoT scenarios. To address the above challenges simultaneously, we propose a Lightweight Decentralized Federated Learning Framework for the Industrial Internet of Things (LDFLF). LDFLF uses the ternary quantization technique to compress the client model, reduce the communication overhead, and improve model transmission efficiency. Experiments show the proposed method’s superiority in communication efficiency, model accuracy, and convergence speed, making it particularly suitable for resource-constrained IIoT environments. Compared to traditional federated learning methods, LDFLF framework achieves an average communication cost reduction of 80% and an average model accuracy improvement of 5.3% on IID data and 10.2% on Non-IID data, while significantly accelerating the convergence speed.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103962"},"PeriodicalIF":4.4,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A self-supervised deep reinforcement learning for Zero-Shot Task scheduling in mobile edge computing environments 移动边缘计算环境下零间隔任务调度的自监督深度强化学习

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-12 DOI: 10.1016/j.adhoc.2025.103977

Parisa Khoshvaght , Amir Haider , Amir Masoud Rahmani , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Jan Lansky , Mehdi Hosseinzadeh

{"title":"A self-supervised deep reinforcement learning for Zero-Shot Task scheduling in mobile edge computing environments","authors":"Parisa Khoshvaght , Amir Haider , Amir Masoud Rahmani , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Jan Lansky , Mehdi Hosseinzadeh","doi":"10.1016/j.adhoc.2025.103977","DOIUrl":"10.1016/j.adhoc.2025.103977","url":null,"abstract":"<div><div>The rising need for swift response times makes it essential to use computing resources and network capacities efficiently at the edges of the networks. Mobile Edge Computing (MEC) handles this by processing user data near where it is generated rather than always relying on remote cloud centres. Yet, scheduling tasks under these conditions can be difficult because workloads shift, resources vary, and network performance is unstable. Traditional scheduling strategies often underperform in such rapidly changing settings, and even Deep Reinforcement Learning (DRL) solutions usually require extensive retraining whenever they encounter unfamiliar tasks. This paper proposes a self-supervised DRL framework for zero-shot task scheduling in MEC environments. The system integrates self-supervised learning to generate task embeddings, enabling the model to classify tasks into clusters based on resource requirements and execution complexity. A Soft Actor-Critic (SAC)-based scheduler then optimally assigns tasks to MEC nodes while dynamically adapting to network conditions. The training process combines contrastive learning for task representation and policy optimization to enhance scheduling decisions. Simulations demonstrate that the proposed approach reduces task completion time by up to 22 %, lowers energy consumption by 29 %, and improves latency by 18 % over baseline methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103977"},"PeriodicalIF":4.4,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TAGSCS: Trust aware data gathering technique based slicing and Compressive Sensing for IoT based WSN TAGSCS：基于物联网WSN的基于切片和压缩感知的信任感知数据收集技术

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-10 DOI: 10.1016/j.adhoc.2025.103954

Walid Osamy , Oruba Alfawaz , Ahmed M. Khedr , Ahmed Aziz

{"title":"TAGSCS: Trust aware data gathering technique based slicing and Compressive Sensing for IoT based WSN","authors":"Walid Osamy , Oruba Alfawaz , Ahmed M. Khedr , Ahmed Aziz","doi":"10.1016/j.adhoc.2025.103954","DOIUrl":"10.1016/j.adhoc.2025.103954","url":null,"abstract":"<div><div>The proliferation of the Internet of Things (IoT) network presents formidable challenges, primarily centered around data transmission reduction and safeguarding devices against potential adversaries. To address these challenges, a synergistic approach involving Compressive Sensing (CS) and Data Slicing (DS) can be employed. CS offers the dual benefit of lightweight encryption and compression, resulting in enhanced energy efficiency. On the other hand, DS is a well-known means of guaranteeing sensing data privacy. In this work, we propose a technique, called TAGSCS, that integrates CS and DS to enable efficient data gathering while ensuring high security, privacy, and minimal utilization of energy. By leveraging the benefits of DS for privacy preservation and the CS method for encryption and compression, TAGSCS achieves optimal results. Additionally, the data-gathering process is designed to ensure the energy efficiency and trustworthiness of all the involved nodes. The findings from the simulation show the effectiveness of the TAGSCS technique in delivering superior performance over the alternative approaches. When considering a 0.2 probability of eavesdropping on the communication link, TAGSCS exhibited remarkable enhancements in upholding privacy compared to TDSM, HEEPP, EEHA, and SMART. It achieved privacy boosts of 88.9%, 85.9%, 70.4%, and 68.6% respectively. Moreover, TAGSCS demonstrated a significant extension in the network lifetime, surpassing the performance of TDSM, HEEPP, EEHA, and SMART by 80%, 110%, 122%, and 170% respectively.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103954"},"PeriodicalIF":4.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolving Explainable Artificial Intelligence for electroencephalography-based mental health classification in digital twin systems 数字孪生系统中基于脑电图的精神健康分类的可解释人工智能进化

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-09 DOI: 10.1016/j.adhoc.2025.103964

Zihan Wang , Zhibo Zhang , Ahmed Y. Al Hammadi , Xueting Huang , Fusen Guo , Ernesto Damiani , Chan Yeob Yeun , Lin Li

{"title":"Evolving Explainable Artificial Intelligence for electroencephalography-based mental health classification in digital twin systems","authors":"Zihan Wang , Zhibo Zhang , Ahmed Y. Al Hammadi , Xueting Huang , Fusen Guo , Ernesto Damiani , Chan Yeob Yeun , Lin Li","doi":"10.1016/j.adhoc.2025.103964","DOIUrl":"10.1016/j.adhoc.2025.103964","url":null,"abstract":"<div><div>The classification of mental health conditions using electroencephalogram (EEG) signals has gained increasing attention due to its non-invasive nature and potential for early diagnosis. Explainable Artificial Intelligence (XAI) plays a crucial role in enhancing the interpretability of machine learning models; however, traditional XAI methods often suffer from high computational costs and redundant feature selection. In this study, we propose Envolving Explainable Artificial Intelligence (E-XAI), an evolutionary XAI framework that leverages Genetic Algorithms (GA) to efficiently search for the optimal EEG feature subset, reducing computational overhead while maintaining interpretability. Furthermore, this work integrates Digital Twin technology, enabling a dynamic and adaptive representation of EEG-based mental states. The proposed framework allows real-time monitoring, remote diagnosis, and personalized mental health interventions by continuously updating the digital twin model with real-time EEG data. This enhances model adaptability, robustness, and scalability for mental health classification. Experimental results on a benchmark EEG dataset demonstrate that E-XAI with Digital Twin technology significantly reduces the computational time of XAI techniques while improving the classification performance and interpretability of mental health classification systems. This advancement provides a promising pathway for real-time, scalable, and intelligent EEG-based mental health analysis.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103964"},"PeriodicalIF":4.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anableps: Priority-aware super-resolution Video Caching with low latency for QoE-centric multi-user MEC networks Anableps：优先级感知的超低延迟超分辨率视频缓存，适用于以qos为中心的多用户MEC网络

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-09 DOI: 10.1016/j.adhoc.2025.103961

Honghai Wu , Yuan Li , Jingcan Wang , Huahong Ma , Ling Xing , Kaikai Deng

{"title":"Anableps: Priority-aware super-resolution Video Caching with low latency for QoE-centric multi-user MEC networks","authors":"Honghai Wu , Yuan Li , Jingcan Wang , Huahong Ma , Ling Xing , Kaikai Deng","doi":"10.1016/j.adhoc.2025.103961","DOIUrl":"10.1016/j.adhoc.2025.103961","url":null,"abstract":"<div><div>As video traffic continues to dominate network data transmission, high playback latency has emerged as a critical bottleneck limiting the quality of streaming media services. While existing edge-assisted super-resolution methods mitigate latency in single-user scenes, they struggle to balance computational resource allocation and latency-sensitive task demands in multi-user scenes with constrained mobile edge computing (MEC) nodes. To this end, we propose <em>Anableps</em>, a multi-user MEC framework that integrates dynamic model selection and deep reinforcement learning-based scheduling to achieve efficient resource allocation and minimize latency. Specifically, we design a <em>dynamic multi-granularity super-resolution loading model</em> using optical flow field features to adaptively allocate light, medium, or heavy super-resolution models, optimizing both resource utilization and visual quality. Additionally, we design a <em>dynamic multi-task scheduling algorithm based on deep Q-Network</em> that dynamically adjusts system load and task urgency through adaptive penalty factors, ensuring efficient resource prioritization. Experimental results demonstrate that <em>Anableps</em> improves QoE by 12.7% compared to state-of-the-art methods, while enhancing video smoothness and reducing re-buffering time by 23.4% and 18.7%, respectively, which further highlights its effectiveness in optimizing streaming performance in resource-constrained environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103961"},"PeriodicalIF":4.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive learning FOA algorithm with energy consumption balancing for coverage optimization in WSNs 基于能量平衡的自适应学习FOA算法用于wsn的覆盖优化

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-08 DOI: 10.1016/j.adhoc.2025.103958

Yong Zhang , Zhen Zhang , Dengzhi Liu , Peng Zheng , Zhaoman Zhong

{"title":"Adaptive learning FOA algorithm with energy consumption balancing for coverage optimization in WSNs","authors":"Yong Zhang , Zhen Zhang , Dengzhi Liu , Peng Zheng , Zhaoman Zhong","doi":"10.1016/j.adhoc.2025.103958","DOIUrl":"10.1016/j.adhoc.2025.103958","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial manufacturing, disaster relief, healthcare, and energy management. However, the development of WSNs still faces many challenges related to coverage and energy balancing among the distributed nodes. To address the above issues, we propose an adaptive learning Fruit Fly Optimization Algorithm (FOA) to optimize the nodes’ coverage and energy balancing in 2D and more complex 3D environments. Adaptive learning FOA incorporates a fusion of adaptive virtual force modeling and adaptive small habitat techniques to enhance initial search capabilities and maintain search balance in later stages. Moreover, we employ dynamic oppositional learning (DOL) and adaptive dimensional learning (ADL) to avoid falling into local optima and to improve search accuracy. Additionally, we introduce a real-time node energy consumption model, which calculates energy consumption during movement, coverage, and iteration of nodes. The proposed model enables continuous monitoring of node energy, helping to prevent energy loss and node failure, thereby enhancing the overall performance and stability of WSNs. The simulation results demonstrate the effectiveness of our approach: in the 2D scenario, the adaptive learning FOA achieves a maximum coverage rate of 94.86% and an average coverage rate of 94.18%, while in the 3D scenario, it reaches a maximum coverage rate of 97.68% and an average coverage rate of 96.32%. These results highlight the significant improvements in coverage and energy balancing, confirming the potential of our method to optimize WSN performance in diverse environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103958"},"PeriodicalIF":4.4,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0