Internet of Things最新文献

Key–value data collection with local differential privacy for urban air quality monitoring in crowdsensing 基于局部差分隐私的众感城市空气质量监测关键值数据采集

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-19 DOI: 10.1016/j.iot.2025.101670

Yanming Fu , Haodong Lu , Jiayuan Chen , Binyang Luo

{"title":"Key–value data collection with local differential privacy for urban air quality monitoring in crowdsensing","authors":"Yanming Fu , Haodong Lu , Jiayuan Chen , Binyang Luo","doi":"10.1016/j.iot.2025.101670","DOIUrl":"10.1016/j.iot.2025.101670","url":null,"abstract":"<div><div>The growth of IoT and mobile devices has led to Mobile Crowdsensing (MCS), a cost-effective data collection method crucial for smart cities. While MCS outperforms wireless sensor networks, it may expose workers’ sensitive data, such as location and identity, in air quality monitoring. Traditional privacy-preserving techniques, such as location obfuscation and data perturbation, have inherent limitations in ensuring strong privacy protection. Moreover, the frequent uploading of numerical data during task execution requires a larger privacy budget, thereby increasing the risk of privacy leakage. To solve these problems, this paper proposes a key–value data collection scheme based on local differential privacy for air quality monitoring in smart cities. The proposed scheme aims to protect user privacy while ensuring data utility. It consists of two main phases: data collection and data prediction. During the data collection phase, workers locally perturb both the task location (key) and the sensed data (value), utilizing the correlation between keys and values to enhance data utility. The system subsequently aggregates the perturbed data and applies bias correction to ensure unbiased estimation. In the prediction phase, an exponential smoothing technique is introduced to mitigate the impact of privacy-preserving mechanisms on prediction accuracy. This method effectively reduces random fluctuations in the data, thereby enhancing the overall prediction performance. Experiments on real-world datasets show that the proposed scheme outperforms other privacy-preserving algorithms in efficiency while maintaining nearly the same prediction accuracy as non-privacy-preserving methods, effectively balancing privacy and data utility.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101670"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321702","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 multi-layer echo state network for efficient DDoS detection in resource-constrained environments 一种用于资源受限环境下高效DDoS检测的多层回波状态网络

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-17 DOI: 10.1016/j.iot.2025.101665

M. Franckie Singha, Ripon Patgiri, Laiphrakpam Dolendro Singh

{"title":"A multi-layer echo state network for efficient DDoS detection in resource-constrained environments","authors":"M. Franckie Singha, Ripon Patgiri, Laiphrakpam Dolendro Singh","doi":"10.1016/j.iot.2025.101665","DOIUrl":"10.1016/j.iot.2025.101665","url":null,"abstract":"<div><div>This study proposes a multi-layer Echo State Network (ESN) model for effectively detecting DDoS attacks on resource-constrained and low-memory devices. Generally, these low-memory devices, common in smart homes, healthcare, and industrial applications, do not have enough computational resources to run traditional deep learning methods of DDoS attack detection. This makes the devices much more vulnerable to attacks. While previous works have focused mainly on improving detection accuracy, they have failed to consider vital trade-offs between resource utilization and detection performance. The proposed ESN model achieves 99.33% and 99.99% accuracy in CICDDoS2019 and CICIoT2023 datasets respectively. With only 640 trainable parameters, it ensures high performance with minimum consumption of computational resources. The proposed model has 1.27% and 0.06% CPU utilization in CICDDoS2019 and CICIoT2023. The CPU utilization is much lesser compared to LSTM, RNN, CNN, and state-of-the-art models, respectively. This makes our model a lightweight architecture suitable for devices with limited memory and processing power. The paper presents an efficient, lightweight model for the security of low-resource environments and robust DDoS detection without loss of accuracy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101665"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313990","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 comprehensive and realistic performance evaluation of post-quantum security for consumer IoT devices 对消费物联网设备的后量子安全进行全面而现实的性能评估

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-16 DOI: 10.1016/j.iot.2025.101650

Yacoub Hanna , Jessica Bozhko , Samet Tonyali , Ricardo Harrilal-Parchment , Mumin Cebe , Kemal Akkaya

{"title":"A comprehensive and realistic performance evaluation of post-quantum security for consumer IoT devices","authors":"Yacoub Hanna , Jessica Bozhko , Samet Tonyali , Ricardo Harrilal-Parchment , Mumin Cebe , Kemal Akkaya","doi":"10.1016/j.iot.2025.101650","DOIUrl":"10.1016/j.iot.2025.101650","url":null,"abstract":"<div><div>The computational capacity envisaged for quantum computers poses a significant threat to today’s traditional cryptographic algorithms. Although they are not yet large enough to compromise current cryptographic protocols, one can practice retrospective decryption since data packets traveling through the Internet can be easily sniffed. This threat extends to wireless communication security within consumer IoT devices that use lightweight cryptography due to limited computational power. Thus, countermeasures against potential quantum attacks should be preemptively adopted. NIST is leading efforts to standardize several algorithms as quantum-resistant Key Exchange Mechanisms (KEMs) and Digital Signatures. In this paper, we investigate the viability of these Post-Quantum algorithms in the Transport Layer Security (TLS) of power-constrained IoT devices. Specifically, it focuses on two widely used IoT network protocol stacks, i.e., Bluetooth Low Energy (BLE) and Wi-Fi. To this end, we build a realistic IoT testbed running IP over BLE. Our evaluation considers the impact of several realistic factors for the first time, such as using a chain of certificates on the server side and incorporating certificate validation methods such as Online Certificate Status Protocol (OCSP) and Certificate Revocation Lists (CRL). We also evaluate the impact of mutual authentication between the server and the client. Utilizing the outcomes of this evaluation, we then propose a novel approach for IoT devices to dynamically choose the most efficient KEM algorithm for TLS based on the device’s physical network interface. The performance results provide valuable insights with respect to the TLS latency and energy consumption of consumer IoT devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101650"},"PeriodicalIF":6.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306578","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

LT-YOLOv10n: A lightweight IoT-integrated deep learning model for real-time tomato leaf disease detection and management LT-YOLOv10n：用于番茄叶片病害实时检测和管理的轻量级物联网集成深度学习模型

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-14 DOI: 10.1016/j.iot.2025.101663

Abdelaaziz Bellout , Mohamed Zarboubi , Mohamed Elhoseny , Azzedine Dliou , Rachid Latif , Amine Saddik

{"title":"LT-YOLOv10n: A lightweight IoT-integrated deep learning model for real-time tomato leaf disease detection and management","authors":"Abdelaaziz Bellout , Mohamed Zarboubi , Mohamed Elhoseny , Azzedine Dliou , Rachid Latif , Amine Saddik","doi":"10.1016/j.iot.2025.101663","DOIUrl":"10.1016/j.iot.2025.101663","url":null,"abstract":"<div><div>The challenges posed by a growing global population and the impacts of climate change have intensified concerns about food security, particularly in agriculture. This study introduces LT-YOLOv10n, a lightweight and efficient deep learning model tailored for detecting and localizing tomato leaf diseases. The model incorporates advanced architectural features, such as Convolutional Block Attention Modules (CBAM) and C3f layers, to improve feature extraction and emphasize disease-relevant areas. By adopting a reduced depth and width scaling approach, LT-YOLOv10n achieves high detection accuracy while ensuring computational efficiency, making it ideal for use on devices with limited resources.</div><div>The LT-YOLOv10n model was thoroughly tested against leading models, showcasing competitive performance in key areas like accuracy, inference speed, and parameter efficiency. It achieved an mAP50 of 98.7% on the test dataset and an inference speed of 87.28 FPS on the Jetson Orin Nano, demonstrating its effectiveness for real-time applications. Furthermore, the model was deployed in a mobile application, enabling real-time disease detection and offering actionable pesticide recommendations. The application integrates with ThingsBoard, an open-source IoT platform, to centralize prediction data and support remote monitoring through an intuitive dashboard. Features such as disease mapping, image-based predictions, and customized pesticide suggestions provide a comprehensive farm management solution.</div><div>This integrated system equips farmers with accessible, cost-effective tools for timely disease management, enhancing crop health and productivity while supporting sustainable agricultural practices. LT-YOLOv10n represents a significant step forward in agricultural AI, offering a scalable and efficient solution for precision farming.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101663"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313885","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

Smart systems: A review of theory, applications, and recent advances 智能系统：理论、应用和最新进展综述

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-14 DOI: 10.1016/j.iot.2025.101667

Naseem Alsadi , Waleed Hilal , Alex McCafferty-Leroux , S.A. Gadsden , John Yawney

{"title":"Smart systems: A review of theory, applications, and recent advances","authors":"Naseem Alsadi , Waleed Hilal , Alex McCafferty-Leroux , S.A. Gadsden , John Yawney","doi":"10.1016/j.iot.2025.101667","DOIUrl":"10.1016/j.iot.2025.101667","url":null,"abstract":"<div><div>Rapid technological advancements have permeated numerous professional fields, transforming mundane tasks and complex operations alike, with examples evident in smart cities, healthcare, and various industries. As a result, a significant surge in the literature concerning smart systems is observed, as is the rise of pragmatic implementations of such systems. In this comprehensive survey paper, we decompose the cumulative smart system architecture into five fundamental components, namely: control, perception, knowledge, communication, and security. Inspired by the underlying notions of cognitive dynamics theory, each component is discussed in detail and categorized, thoroughly detailing necessary concepts and functionality. To add, we discuss the state of the art with respect to each of these components and the most impactful applications of smart systems. From this, gaps in smart systems literature can be identified, where future work is proposed to rectify shortcomings in published methods. This work therefore has foremost utility to those investigating smart systems from an academic standpoint, with the goal of examining the smart system taxonomy and the most modern methods. In addition to further defining the smart system framework, our analysis concluded that the most increasingly researched, and most important components in advancing smart systems applications are knowledge and security. Primarily, this is motivated by aspirations towards safe, adaptive, and robust data-driven autonomy in large scale systems. We conclude that blockchain, IoT, and machine learning protocols and technologies are continuously developing topics that will be essential in smart system advancement.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101667"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321703","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 rendezvous of computing and communication services at the optical layer in optical computing–communication integrated network 光计算-通信综合网络中计算与通信业务在光层的会合

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-13 DOI: 10.1016/j.iot.2025.101668

Dao Thanh Hai , Isaac Woungang

{"title":"A rendezvous of computing and communication services at the optical layer in optical computing–communication integrated network","authors":"Dao Thanh Hai , Isaac Woungang","doi":"10.1016/j.iot.2025.101668","DOIUrl":"10.1016/j.iot.2025.101668","url":null,"abstract":"<div><div>With the significant advancements in optical computing platforms recently capable of performing various primitive operations, a seamless integration of optical computing into very fabric of optical communication links is envisioned, paving the way for the advent of <em>optical computing–communication integrated network</em>, which provides computing services at the ligthpath scale, alongside the traditional high-capacity communication ones. This necessitates a paradigm shift in optical node architecture, moving away from the conventional optical-bypass design that avoids lightpath interference crossing the same node, toward leveraging such interference for computation. Such new computing capability at the optical layer appears to be a good match with the growing needs of geo-distributed machine learning, where the training of large-scale models and datasets spans geographically diverse nodes, and intermediate results require further aggregation/computation to produce the desired outcomes for the destination node. To address this potential use case, an illustrative example is presented, which highlights the merit of providing in-network optical computing services in comparison with the traditional optical-bypass mode in the context of distributed learning scenarios taking place at two source nodes, and partial results are then optically aggregated to the destination. We then formulate the new <em>routing, wavelength and computing assignment problem</em> arisen in serving computing requests, which could be considered as an extension of the traditional routing and wavelength assignment, that is used to accommodate the transmission requests. Simulation results performed on the realistic COST239 topology demonstrate the promising spectral efficiency gains achieved through the <em>optical computing–communication integrated network</em> compared to the optical-bypass model.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101668"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297096","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

UoCAD2: An unsupervised online contextual anomaly detection approach using optimized hyperparameters of RNNs for multivariate time series UoCAD2：一种无监督在线上下文异常检测方法，使用优化的rnn超参数用于多变量时间序列

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-13 DOI: 10.1016/j.iot.2025.101664

Aafan Ahmad Toor, Jia-Chun Lin, Ernst Gunnar Gran

{"title":"UoCAD2: An unsupervised online contextual anomaly detection approach using optimized hyperparameters of RNNs for multivariate time series","authors":"Aafan Ahmad Toor, Jia-Chun Lin, Ernst Gunnar Gran","doi":"10.1016/j.iot.2025.101664","DOIUrl":"10.1016/j.iot.2025.101664","url":null,"abstract":"<div><div>Internet of Things (IoT) based smart devices are gradually becoming part of daily lives through their increasing usage in industry, healthcare, agriculture, environmental monitoring, energy, transportation, and smart cities, buildings, and homes. IoT devices generate fast-paced time-bound data known as time series. Time series often contain anomalies, i.e., unusual patterns or deviations from the norm, that can disrupt services and must be detected quickly. Many researchers have tried to detect unlabeled anomalies by employing unsupervised online anomaly detection approaches based on Recurrent Neural Networks (RNN). RNNs are specially designed to process sequential data. However, selecting the right type of RNN and appropriate hyperparameters for a specific data domain is challenging. Another challenge in the online processing of time series is to pick out an appropriate sliding window size, that is small enough to process the incoming data in a limited time and large enough to capture the underlying deviations in the data. This study extends the Unsupervised Online Contextual Anomaly Detection (UoCAD) approach to overcome these challenges by proposing UoCAD2. UoCAD2 conducts hyperparameter optimization on six RNN variants in an offline phase and uses fine-tuned hyperparameters to detect anomalies during the online phase. The experiments evaluate the proposed framework on three IoT datasets containing contextual anomalies. Precision, Recall, F1 score, and detection time are the evaluation metrics used in this study. This study recommends selecting the best combination of RNN-based models, optimal hyperparameters, and window sizes for contextual anomaly detection in multivariate time series data.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101664"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313910","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

Enhancing face recognition attendance system utilizing real-time face tracking 利用实时人脸跟踪增强人脸识别考勤系统

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-09 DOI: 10.1016/j.iot.2025.101660

Emmanuel Bugingo , Obed Imbahafi , Athnatius Caius Umeonyirioha , Tohari Ahmad , Ntivuguruzwa Jean De La Croix , Anne Marie Uwumuremyi

引用次数: 0

IoT-based system for individual dairy cow feeding behavior monitoring using cow face recognition and edge computing 基于物联网的奶牛个体摄食行为监测系统，采用奶牛面部识别和边缘计算

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-09 DOI: 10.1016/j.iot.2025.101674

Yueh-Shao Chen , Dan Jeric Arcega Rustia , Shao-Zheng Huang , Jih-Tay Hsu , Ta-Te Lin

{"title":"IoT-based system for individual dairy cow feeding behavior monitoring using cow face recognition and edge computing","authors":"Yueh-Shao Chen , Dan Jeric Arcega Rustia , Shao-Zheng Huang , Jih-Tay Hsu , Ta-Te Lin","doi":"10.1016/j.iot.2025.101674","DOIUrl":"10.1016/j.iot.2025.101674","url":null,"abstract":"<div><div>This study presents an IoT-enabled cow face recognition system leveraging edge computing to enable real-time, automated monitoring of individual cow feeding behavior. The system integrates a lightweight YOLOv4-tiny model for cow face detection with MobileNetV2 for feature extraction, optimized for embedded devices with limited computational power. A key innovation is the incorporation of few-shot learning (FSL), allowing the system to adapt efficiently to newly introduced cows with minimal training data. The algorithm achieved robust performance, with an F1-score of 0.98 for detection and a recognition accuracy of 0.97 using FSL. Feeding times estimated by the system were validated against manually observed data, demonstrating high precision with a mean absolute error (MAE) of 1.7 min per cow. Long-term experiments conducted under varying seasonal conditions showcased the system's effectiveness in monitoring feeding behavior year-round. Results revealed significant seasonal differences, with cows feeding longer in winter (197.0 min/day) than in summer (115.5 min/day), likely due to the effects of heat stress during warmer months. This IoT-driven system offers scalable, non-invasive monitoring solutions for dairy farm environments, enabling real-time insights to support herd management, early health issue detection, and individualized feeding strategies. By integrating advanced IoT technologies with agricultural practices, this system provides a pathway to enhancing productivity and animal welfare in precision dairy farming.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101674"},"PeriodicalIF":6.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262799","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 deep transfer learning-based blockchain-assisted cooperative network architecture for internet of unmanned any vehicle things 一种基于深度迁移学习的区块链辅助无人车物联网协同网络架构

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-06-07 DOI: 10.1016/j.iot.2025.101659

Anik Islam , Md Masuduzzaman , Soo Young Shin

{"title":"A deep transfer learning-based blockchain-assisted cooperative network architecture for internet of unmanned any vehicle things","authors":"Anik Islam , Md Masuduzzaman , Soo Young Shin","doi":"10.1016/j.iot.2025.101659","DOIUrl":"10.1016/j.iot.2025.101659","url":null,"abstract":"<div><div>An Internet of Unmanned Any Vehicle Things (IUxVTs) enables cooperative operations among different unmanned vehicles, providing enhanced mission efficiency such as extended coverage, especially with the support of Internet of Things (IoT) sensors, edge computing, and dew computing. However, IUxVT communication encounters significant security and connectivity challenges. Conventional deep learning models are difficult to deploy effectively in dynamic mission environments due to their inability to adapt rapidly without extensive retraining, especially in remote areas with limited connectivity. To address these issues, this paper proposes a novel deep transfer learning (DTL)-based cooperative network architecture integrated with blockchain technology. Specifically, a lightweight blockchain and nonce-based authentication scheme were adopted to enhance protection against security threats, including spoofing, tampering, and unauthorized access. The DTL approach facilitates real-time model adaptation for IUxVTs without the need for extensive retraining, leveraging a dew computing-based delay-tolerant network for secure data storage and transmission. Experimental validation through a practical disaster rehabilitation and recovery scenario demonstrates that the proposed scheme significantly outperforms conventional methods, achieving over 97% model accuracy within fewer training epochs and reducing the training time by more than 30%. Additionally, the scheme effectively counters cybersecurity threats, showcasing robust resilience against unauthorized access and ensuring secure, low-latency data processing in dynamic and resource-constrained environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101659"},"PeriodicalIF":6.0,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253540","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