Internet of Things最新文献_第5页

Artificial intelligence of things and distributed technologies as enablers for intelligent mobility services in smart cities-A survey 人工智能和分布式技术作为智慧城市智能交通服务的推动因素--一项调查

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-18 DOI: 10.1016/j.iot.2024.101399

Bokolo Anthony Jnr

{"title":"Artificial intelligence of things and distributed technologies as enablers for intelligent mobility services in smart cities-A survey","authors":"Bokolo Anthony Jnr","doi":"10.1016/j.iot.2024.101399","DOIUrl":"10.1016/j.iot.2024.101399","url":null,"abstract":"<div><div>The society is witnessing an accelerated large-scale adoption of technology with transformative effects on daily transport operations, with cities now depending on data driven mobility services. Disruptive technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and decentralized technologies for example Distributed Ledger Technologies (DLT) are being deployed in smart cities. However, AI is faced with data security and privacy issues due to its centralized mode of deployment. Conversely, DLT which employs a decentralized architecture can be converged with AI to provide a secure data sharing across various IoT thereby overcoming the existing setbacks faced in deploying AI in smart cities. Evidently, the convergence of AI and IoT as AIoT and DLT have great potential to create novel business models for improved data driven services such as intelligent mobility in smart cities. Although research on the convergence of AI, IoT and DLT exists, our understanding of its integration in achieving intelligent mobility services in smart cities remains fragmented as current research in this area remains scarce. This study bridges the gap between theory and practice by providing researchers and practitioners with insights on the potential benefits of converging AIoT and DLT. Grounded on the Technology Organization Environment (TOE) framework this study presents the technological, organizational, and environmental factors that impacts the convergence of AIoT and DLT in smart cities. Additionally, findings from this study present use cases on the applicability of AIoT and DLT to support intelligent mobility services in smart cities.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101399"},"PeriodicalIF":6.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking LPF-IVN：具有智能车联网功能机制的轻量级隐私增强方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-17 DOI: 10.1016/j.iot.2024.101400

Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu

{"title":"LPF-IVN: A lightweight privacy-enhancing scheme with functional mechanism of intelligent vehicle networking","authors":"Haijuan Wang , Weijin Jiang , Yirong Jiang , Yixiao Li , Yusheng Xu","doi":"10.1016/j.iot.2024.101400","DOIUrl":"10.1016/j.iot.2024.101400","url":null,"abstract":"<div><div>Due to decentralization and effective prevention of privacy leakage, Differential Private Federated Learning(DP-FL) has emerged as an efficient technique in the Internet of Vehicles (IoV). However, the essence of key industrial is big data. When applying the DP-FL model to the IoV, these large-scale nonlightweight data such as Non-IID and high-dimensional will decrease the security and accuracy of the model. Therefore, for the security and accuracy of the IoV, we proposed a lightweight DP-FL framework called DPF-IVN, considering the impact of heterogeneous and privacy leak in the context of IoV. It adopts the idea of “lowering dimension first and then optimization” to process non-light quantified data in the IoV. Specifically, we novelly design a Federated Randomized Principal Component Analysis (FRPCA) algorithm, allowing users to map local data to low-dimensional data. Then, we propose the Functional Mechanism(FM) to disturb the gradient parameters to solve the problem of low training accuracy caused by gradient cutting. Besides, to reduce model differences, we used the Bregman dispersal as a regularized item update loss function to improve the accuracy of the model. Extensive experiments demonstrate the superior performance of DPF-IVN in the heterogeneous environment compared to other methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101400"},"PeriodicalIF":6.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531777","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

Securing the Internet of Things with Ascon-Sign 利用 Ascon-Sign 保障物联网安全

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-16 DOI: 10.1016/j.iot.2024.101394

Alexander Magyari, Yuhua Chen

引用次数: 0

IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen IoT-HGDS：基于物联网集成机器学习的智能厨房有害气体检测系统

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-15 DOI: 10.1016/j.iot.2024.101396

Kanak Kumar , Anshul Verma , Pradeepika Verma

{"title":"IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen","authors":"Kanak Kumar , Anshul Verma , Pradeepika Verma","doi":"10.1016/j.iot.2024.101396","DOIUrl":"10.1016/j.iot.2024.101396","url":null,"abstract":"<div><div>This paper proposes an Internet of Things (IoT) and Machine Learning (ML) integrated Hazardous Gas Detection System (IoT-HGDS) for smart kitchens. The design incorporates six tin-oxide-based metal–oxide–semiconductor (MOS) gas sensor arrays and one DHT22 (temperature & humidity sensor). This IoT-HGDS can detect different hazardous gases, Volatile Organic Compounds (VOCs), and odors responses released from the kitchen’s materials and transmit them to a Remote Data Processing Centre (RDPC) through Amazon-Web Services (AWS) in real time. In this experiment, we collected <span><math><mrow><mn>150</mn><mo>×</mo><mn>9</mn><mo>=</mo><mn>1350</mn></mrow></math></span> samples from 9 kitchen materials like ghee, milk, liquid petroleum gas (LPG), bread, mustard oil, compressed natural gas (CNG), pigeon peas, refined oil, and kerosene. The Standardized Independent Component Analysis (SICA) pre-processing technique has been used to clean data, standardize the features, and remove outliers. ML approaches like Logistic Regression (LR), Adaptive Boosting (AdaBoost) and Regularized Discriminant Analysis (RDA) have been applied for accurate identification of gases/VOCs class and provide immediate alerts to improve kitchen safety. The SICA-RDA classifier outperformed (highest accuracy at 97.78 %) as compared to LR and AdaBoost in terms of performance and balanced precision, recall, and F1-Score. LR has the lowest performance in all metrics. LPG has the lowest Mean Squared Error (MSE) of <span><math><mrow><mn>6</mn><mo>.</mo><mn>62</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>7</mn></mrow></math></span>, while CNG has the highest MSE of <span><math><mrow><mn>3</mn><mo>.</mo><mn>60</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>4</mn></mrow></math></span>. This system can intelligently preserve gases, ensure safety precautions, and prevent accidents in the kitchens.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101396"},"PeriodicalIF":6.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531781","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

Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks Fed-Evolver：在支持 SDN 的网络中使用对抗性自动编码器的联合入侵检测系统自动演进方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-14 DOI: 10.1016/j.iot.2024.101397

Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham

{"title":"Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks","authors":"Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham","doi":"10.1016/j.iot.2024.101397","DOIUrl":"10.1016/j.iot.2024.101397","url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) have garnered escalating significance in response to the evolving landscape of cyberattacks, driven by the adaptability and versatility of Software Defined Networking (SDN)-based networks in enhancing security orchestration. Although Machine Learning (ML) models have been developed for IDS, they require large amounts of labeled data to achieve high performance. However, acquiring labels for attacks is a time-consuming process and can cause problems in deploying the existing ML models in new systems or lower performance due to a shortage of labeled data on pre-trained datasets. Additionally, such ML-based IDS models lack the self-learning function to automatically adapt to new cyberattacks during network operations. To overcome these challenges, our work proposes Fed-Evolver, an automated evolving approach for federated IDS that combines Generative Adversarial Networks (GANs) with Auto Encoder (AE) and a semi-supervised adversarial Autoencoder (SSAAE) for spotting intrusion actions. Our Fed-Evolver leverages supervised and unsupervised learning strategies to build efficient IDS models in the context of labeled data scarcity with the help of Federated Learning (FL). It allows data owners to collaborate for training intrusion detection models to provide the self-evolving capability in SDN-enabled networks. Our proposed framework is evaluated on 6 cyberattack datasets, including CICIDS2018, CIC-ToN-IoT, NF-UNSW-NB15, InSDN, InSecLab-IDS2021, DNP3 Intrusion Detection, and it outperforms other ML methods even when trained with only 1% proportion of labeled data, achieving consistently high performance across all metrics on the datasets.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101397"},"PeriodicalIF":6.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531780","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

Automated image-based fire detection and alarm system using edge computing and cloud-based platform 使用边缘计算和云平台的基于图像的火灾自动探测和报警系统

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-10 DOI: 10.1016/j.iot.2024.101402

Xueliang Yang, Yenchun Li, Qian Chen

{"title":"Automated image-based fire detection and alarm system using edge computing and cloud-based platform","authors":"Xueliang Yang, Yenchun Li, Qian Chen","doi":"10.1016/j.iot.2024.101402","DOIUrl":"10.1016/j.iot.2024.101402","url":null,"abstract":"<div><div>To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101402"},"PeriodicalIF":6.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications and emerging trends of blockchain technology in marketing to develop Industry 5.0 Businesses: A comprehensive survey and network analysis 区块链技术在市场营销中的应用和新兴趋势，以发展工业 5.0 企业：全面调查与网络分析

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-10 DOI: 10.1016/j.iot.2024.101401

Ali Nikseresht , Sajjad Shokouhyar , Erfan Babaee Tirkolaee , Nima Pishva

{"title":"Applications and emerging trends of blockchain technology in marketing to develop Industry 5.0 Businesses: A comprehensive survey and network analysis","authors":"Ali Nikseresht , Sajjad Shokouhyar , Erfan Babaee Tirkolaee , Nima Pishva","doi":"10.1016/j.iot.2024.101401","DOIUrl":"10.1016/j.iot.2024.101401","url":null,"abstract":"<div><div>With the availability of enormous amounts of data come the difficulties of big data, privacy, and ransomware assaults, which result in Marketing fraud and spam. Blockchain offers an extensive array of possible applications in the Marketing field. Nevertheless, both Marketing research and practice exhibit a degree of hesitance toward using Blockchain technology and have not yet come around to completely understand and adopt the technology. Here, the aim is to examine the Blockchain concepts and their applications in Marketing through bibliometrics, network, and thematic analyses, which can provide several novel insights and perspectives into current research trends in this field by evaluating the most significant and cited research publications, keywords, institutions, authors' collaboration network, and finally countries that promote Industry 5.0 (I5.0) businesses. This study performs a detailed bibliometric and thematic-based Systematic Literature Review (SLR) on 124 of over 15000 research papers. Major outcomes include the identification of emerging themes such as the role of Blockchain in advertising, and dynamic pricing, as well as the need for further exploration of underdeveloped areas (e.g., consumer behavior and brand equity). The results contribute to theoretical and practical management elements and provide the groundwork for future study in this area. The overarching target of this research is to give a complete overview of applications and emerging trends of Blockchain technology in Marketing, thereby serving as a resource for future research topics for Marketing scholars and experts aiming to implement solutions based on Blockchain technology and algorithms to develop an I5.0 business.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101401"},"PeriodicalIF":6.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445491","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

Gaussian process-based online sensor selection for source localization 基于高斯过程的源定位在线传感器选择

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-09 DOI: 10.1016/j.iot.2024.101388

Obadah Habash, Rabeb Mizouni, Shakti Singh, Hadi Otrok

{"title":"Gaussian process-based online sensor selection for source localization","authors":"Obadah Habash, Rabeb Mizouni, Shakti Singh, Hadi Otrok","doi":"10.1016/j.iot.2024.101388","DOIUrl":"10.1016/j.iot.2024.101388","url":null,"abstract":"<div><div>This paper addresses the sensor selection problem for source localization within cyber–physical systems (CPSs). While recent machine learning and reinforcement learning approaches aim to optimize sensor selection and placement within the Area of Interest (AoI), their need for intensive data collection and training precludes online operation. Furthermore, these methods often require prior knowledge of the unknown source’s characteristics and lack adaptability to the dynamic nature of CPSs, leading to inefficiencies in unseen environments. This paper addresses these shortcomings using Gaussian process Optimization coupled with an active sensor selection mechanism to locate the unknown source within the AoI. The proposed approach first builds a probabilistic model of the environment, which is discretized into a grid, without prior training using a Gaussian Process surrogate model. Next, the model iteratively and systematically learns the underlying spatial phenomenon using Gaussian Process optimization. Concurrently, the approach selects a subset of sensors by optimizing a fitness function that advocates selecting informative and energy-efficient sensors. Next, the probabilistic model, having accurately learned the environment, directs the algorithm to the unknown source by identifying the cell with the highest likelihood of containing it. Finally, a peak refinement step is performed, which computes the exact location of the source within the designated cell. The proposed method’s efficacy is validated through experiments in radioactive source localization, validation studies, and adaptability assessments across various environments. In terms of quality of localization (QoL), it outperforms recent localization benchmarks, such as a reinforcement learning-based approach and DANS, by around 18% and 100%, respectively.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101388"},"PeriodicalIF":6.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419211","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 quantum-safe authentication scheme for IoT devices using homomorphic encryption and weak physical unclonable functions with no helper data 使用同态加密和无辅助数据的弱物理不可克隆函数的物联网设备量子安全认证方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-08 DOI: 10.1016/j.iot.2024.101389

Roberto Román, Rosario Arjona, Iluminada Baturone

{"title":"A quantum-safe authentication scheme for IoT devices using homomorphic encryption and weak physical unclonable functions with no helper data","authors":"Roberto Román, Rosario Arjona, Iluminada Baturone","doi":"10.1016/j.iot.2024.101389","DOIUrl":"10.1016/j.iot.2024.101389","url":null,"abstract":"<div><div>Physical Unclonable Functions (PUFs) are widely used to authenticate electronic devices because they take advantage of random variations in the manufacturing process that are unique to each device and cannot be cloned. Therefore, each device can be uniquely identified and counterfeit devices can be detected. Weak PUFs, which support a relatively small number of challenge-response pairs (CRPs), are simple and easy to construct. Device authentication with weak PUFs typically uses helper data to obfuscate and recover a cryptographic key that is then required by a cryptographic authentication scheme. However, these schemes are vulnerable to helper-data attacks and many of them do not protect conveniently the PUF responses, which are sensitive data, as well as are not resistant to attacks performed by quantum computers. This paper proposes an authentication scheme that avoids the aforementioned weaknesses by not using helper data, protecting the PUF response with a quantum-safe homomorphic encryption, and by using a two-server setup. Specifically, the CRYSTALS-Kyber public key cryptographic algorithm is used for its quantum resistance and suitability for resource-constrained Internet-of-Things (IoT) devices. The practicality of the proposal was tested on an ESP32 microcontroller using its internal SRAM as a SRAM PUF. For PUF responses of 512 bits, the encryption execution time ranges from 16.41 ms to 41.08 ms, depending on the desired level of security. In terms of memory, the device only needs to store between 800 and 1,568 bytes. This makes the solution post-quantum secure, lightweight and affordable for IoT devices with limited computing, memory, and power resources.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101389"},"PeriodicalIF":6.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442001","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

Hybrid vehicular access protocol and message prioritization for real-time safety messaging 实时安全信息的混合车辆接入协议和信息优先级排序

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-05 DOI: 10.1016/j.iot.2024.101390

Mayssa Dardour, Mohamed Mosbah, Toufik Ahmed

{"title":"Hybrid vehicular access protocol and message prioritization for real-time safety messaging","authors":"Mayssa Dardour, Mohamed Mosbah, Toufik Ahmed","doi":"10.1016/j.iot.2024.101390","DOIUrl":"10.1016/j.iot.2024.101390","url":null,"abstract":"<div><div>Real-time safety services rely on the exchange of messages to enhance the operations of connected and automated vehicles (CAVs). These safety messages convey vital information about traffic conditions, enabling drivers to take necessary measures to prevent accidents. The timely and reliable delivery of these messages is essential, necessitating efficient channel access. Vehicular Deterministic Access (VDA) is employed as a channel access scheme with distinct priorities and stringent timing guidelines, particularly for urgent safety warnings. In this paper, we propose a hybrid approach that combines VDA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols, along with a message prioritization algorithm, to ensure efficient and reliable communication of safety messages in vehicular networks. Our approach leverages the strengths of both VDA and CSMA/CA to avoid message collisions; VDA is more efficient under high traffic loads, while CSMA/CA is better suited for low traffic loads. Additionally, the incorporation of the message prioritization algorithm ensures strict deadline guarantees for high-priority messages, such as Decentralized Environmental Notification Messages (DENMs). We evaluate our proposed solution using the Artery simulation framework. Our results show over a 93% delivery rate for DENM exchanges while maintaining low collision probability across various traffic loads. This research provides practical guidance for the development of efficient and reliable communication systems for CAVs. It also offers a detailed analysis of the trade-offs among different access protocols and message prioritization strategies in vehicular networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101390"},"PeriodicalIF":6.0,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419212","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