Internet of Things最新文献

{"title":"Acheron: a market-based multi-relay architecture for adaptive and secure cross-chain communication","authors":"Tuan-Dung Tran ,&nbsp;Quang Vu ,&nbsp;Bao Huynh ,&nbsp;Van-Hau Pham","doi":"10.1016/j.iot.2025.101836","DOIUrl":"10.1016/j.iot.2025.101836","url":null,"abstract":"<div><div>The growing fragmentation of the blockchain ecosystem has intensified the demand for secure and scalable interoperability protocols. Existing cross-chain solutions face an ‘interoperability trilemma’, struggling to simultaneously achieve decentralization, security, and scalability amidst a fragmented blockchain ecosystem. This paper introduces Acheron, a market-based multi-relay architecture designed to navigate this trilemma by decoupling security from a single monolithic entity and parallelizing message transport across independent, sovereign Relay Chains. Each Relay Chain operates as a self-contained Proof-of-Stake (PoS) network with slashing-based cryptoeconomic security, eliminating monolithic bottlenecks. Routing decisions are governed by a Multi-Attribute Utility Theory (MAUT) model that dynamically optimizes for security, latency, and cost, while the Acheron DAO and Watchtower Network ensure verifiable governance and continuous relay telemetry. Experimental validation using the Hardhat framework and the Base Sepolia testnet demonstrates that throughput scales linearly from 0.13 to 1.27 transactions per second as relays increase from one to ten, while latency variance decreases by over 90 % and average transaction costs remain stable. Compared with established baselines, Acheron achieves a 7 % reduction in mean latency and over 2.2 ×  higher throughput under identical workloads. These results demonstrate that Acheron’s market-based paradigm presents a viable and quantitatively superior path toward achieving secure, scalable, and decentralized interoperability for both financial and IoT-driven ecosystems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"35 ","pages":"Article 101836"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736531","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}

{"title":"Decentralized proximity-aware clustering for collective self-federated learning","authors":"Davide Domini ,&nbsp;Nicolas Farabegoli ,&nbsp;Gianluca Aguzzi ,&nbsp;Mirko Viroli ,&nbsp;Lukas Esterle","doi":"10.1016/j.iot.2025.101841","DOIUrl":"10.1016/j.iot.2025.101841","url":null,"abstract":"<div><div>In recent years, Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training in IoT systems, enabling clients to learn a global model for tasks like classification, prediction, or anomaly detection in IoT environments without sharing raw data. However, traditional centralized FL architectures face bottlenecks, single points of failure, and struggle with non-IID data. These limitations hinder effective Collective Intelligence in large-scale IoT systems where numerous devices operate across diverse and dynamic environments. Existing clustered FL approaches often retain centralization or overlook how the spatial distribution inherent in IoT deployments directly influences data heterogeneity, challenging both the integration of spatially correlated devices and the establishment of intelligence distributed across the entire system. Creating such intelligence demands both decentralized architectures for scalability and effective integration of devices with similar data distributions. For these reasons, this article introduces <em>Proximity-Aware Self-Federated Learning (PSFL)</em>, a novel decentralized approach embodying collective intelligence principles. PSFL leverages field-based coordination to enable IoT devices to form <em>self-federations</em>, dynamically clustered groups that train specialized models based on both spatial proximity and local model characteristics. These self-federations reflect underlying data distributions, creating a distributed ecosystem of specialized models across the network. This approach overcomes global model limitations in non-IID settings through specialized federations based on local data distributions, enhancing performance while maintaining decentralization. We evaluate our approach using the Extended MNIST and CIFAR-100 datasets against state-of-the-art baselines, demonstrating its effectiveness in forming coherent, localized models under non-IID conditions.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"35 ","pages":"Article 101841"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736534","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}

{"title":"Human-centered and context-aware smart ML-based IoT framework for online fatigue detection: A real-world study of football training","authors":"Abdelkarim Mamen ,&nbsp;Elisabetta De Giovanni ,&nbsp;Teodoro Montanaro ,&nbsp;Ilaria Sergi ,&nbsp;Luigi Patrono","doi":"10.1016/j.iot.2025.101847","DOIUrl":"10.1016/j.iot.2025.101847","url":null,"abstract":"<div><div>Fatigue is one of the factors that most influences competitive athletes’ performance, leading to injuries and overtraining. To effectively monitor and predict fatigue levels during real-world training, it is necessary to integrate Internet of Things (IoT) technology with machine learning (ML). In this context, the paper presents three main contributions : a) a smart IoT framework that integrates edge and cloud-based modules to collect physiological parameters, monitor fatigue during real-world sessions, and assist coaches in optimizing exercise strategies ; b) a dataset collected through the proposed framework in a real pilot study with eight futsal players over five training sessions, each lasting between 35 and 50 m depending on performed exercises, using ECG and PPG-based sensors ; c) an online ML-based fatigue detection module and on-cloud analysis of various ML models, traditional and deep learning, including CNN+GRU, XGBoost, and Transformer architectures, and context-aware feature sets. We evaluated the accuracy of our fatigue detection method using standard metrics, achieving an F1-score of up to 95 % with pilot study data. Our framework incorporates a context-aware design, where contextual information (exercise type) and sensing modality (ECG- or PPG-based) are explicitly integrated with physiological features (HRV and HR) in the fatigue prediction model to adapt it to different settings, improving robustness and interpretability. Finally, we evaluated the framework’s efficacy and the value of user and expert input, highlighting the benefits of integrating IoT and ML within a human-centered, context-aware approach to balance sensor accuracy, comfort, and efficiency in competitive sports training.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"35 ","pages":"Article 101847"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736533","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}

{"title":"A TinyML device for risk identification for people with hearing loss","authors":"Cristian Bautista-Villalpando ,&nbsp;Victor Lomas-Barrie","doi":"10.1016/j.iot.2025.101840","DOIUrl":"10.1016/j.iot.2025.101840","url":null,"abstract":"<div><div>This paper presents an innovative approach to enhancing the quality of life for people with hearing impairment by implementing a portable and discreet TinyML device. The Support System for Identifying Emergency Sounds (SSIES) is designed to recognize four characteristic emergency sounds: car horn, scream, ambulance sirens, and crying babies. Through unique vibration patterns for each sound, the device provides a haptic response that allows the user to be aware of their surroundings and react if necessary. In addition, the device provides information on the direction of arrival (DOA) of the sound. In the state of the art, various supervised machine learning techniques have been explored to achieve this behavior. In this work, we focus primarily on artificial neural network algorithms (ANN) and their optimization for execution on devices with limited computational resources, a trend known as Machine Learning at the Edge.</div><div>The methodology used in this project is based on a combination of the HW/SW co-design and development lifecycle model for embedded systems and the lifecycle of ML-based solutions.</div><div>The results obtained indicate that the proposed TinyML device is feasible and has the potential to significantly improve environmental awareness for people with hearing impairment.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"35 ","pages":"Article 101840"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736532","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}

{"title":"LoRaGeo-PSW: a prompt-aligned large language model for few-shot fingerprint geolocation in urban LoRaWAN networks","authors":"Wenbin Shi ,&nbsp;Zhongxu Zhan ,&nbsp;Jingsheng Lei ,&nbsp;Xingli Gan","doi":"10.1016/j.iot.2025.101821","DOIUrl":"10.1016/j.iot.2025.101821","url":null,"abstract":"<div><div>Accurate and efficient geolocation remains a critical need for low-power IoT networks, particularly in large urban environments where GNSS-based positioning is often infeasible. Fingerprint-based localization using LoRaWAN signals offers a scalable alternative, but conventional methods depend on rigid matching algorithms and static radio maps, leading to poor performance in complex cityscapes. This work proposes LoRaGeo-PSW, a novel geolocation framework based on large language models (LLMs) that aligns structured wireless signal features with prompt-driven reasoning. Built upon a GPT-2 foundation, the model encodes LoRaWAN fingerprints—including RSSI and SNR from multiple gateways—as token sequences, and interprets them in context using a structured workflow-aware architecture. By integrating signal processing workflows via lightweight adapter modules and employing low-rank adaptation (LoRA), LoRaGeo-PSW offers both interpretability and parameter efficiency. Crucially, the model enables few-shot localization by conditioning on a handful of example fingerprints, thereby adapting to new environments without retraining. Evaluated on a public LoRaWAN dataset from urban Antwerp with over 130,000 transmissions, the model achieves a median localization error of approximately 150 m, substantially surpassing classical fingerprinting and deep learning baselines. This work introduces a new paradigm for wireless localization, demonstrating that LLMs can effectively bridge structured signal reasoning and geospatial inference through prompt-driven alignment.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"35 ","pages":"Article 101821"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521123","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}

{"title":"Smart-contract-based blockchain-enabled decentralized scheme for improving smart-grid security","authors":"Bhabendu Kumar Mohanta ,&nbsp;Ali Ismail Awad ,&nbsp;Tarek Elsaka ,&nbsp;Hamza Kheddar ,&nbsp;Ezedin Baraka","doi":"10.1016/j.iot.2025.101811","DOIUrl":"10.1016/j.iot.2025.101811","url":null,"abstract":"<div><div>Intelligent devices with embedded technology have proliferated dramatically over the past decade. The Internet of Things (IoT) has emerged as a transformational force, advancing traditional systems to previously unattainable levels of intelligence. Smart cities, transportation, healthcare, supply-chain management, agriculture, water management, and smart grid (SG) systems are among the industries where the IoT has found applications. These developments are demonstrated by the integration of IoT systems into SG networks, offering significant improvements in sustainability, dependability, and efficiency. Such systems use various IoT devices to continuously monitor the environment and transmit data for processing and analysis. Nonetheless, the growth of the IoT has introduced security vulnerabilities, including concerns about user identification, data integrity, and trust, especially in SG applications. This study aims to resolve several security challenges in IoT-enabled SG applications to support sustainability. The proposed scheme effectively tackles critical security requirements such as data integrity, user anonymity, distributed storage, trust management, and decentralized architecture. The security concerns addressed by blockchain technology include preserving data integrity, fostering trust, providing secure communication, and enabling effective monitoring. Smart contracts automate system processes and are effective in maintaining user trust. The experimental findings support the viability of the proposed system, demonstrating a computational cost of 3.150 ms and a communication overhead of 992 bits, both representing improvements over various existing solutions. Additionally, the deployment cost for the smart contract is found to be 5.64 USD with a writing cost of 2.89 USD, both of which are lower than the costs associated with comparable approaches.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101811"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415712","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}

{"title":"Resource-efficient fog computing vision system for occupancy monitoring: A real-world deployment in university libraries","authors":"Alejandro S. Martínez-Sala,&nbsp;Lucio Hernando-Cánovas,&nbsp;Juan C. Sánchez-Aarnoutse,&nbsp;Juan J. Alcaraz","doi":"10.1016/j.iot.2025.101748","DOIUrl":"10.1016/j.iot.2025.101748","url":null,"abstract":"<div><div>This paper presents a fog computing system for real-time occupancy monitoring across three university libraries, using ceiling-mounted, top-view cameras positioned above each entrance. Video streams from low-cost cameras are securely transmitted to a fog server deployed within the university’s intranet. Top-view person tracking ensures privacy compliance by inherently eliminating facial recognition, but introduces challenges such as non-standard human appearance, occlusions, and lighting variations. For person detection, we employ a YOLOv5 model initially trained on top-view human annotations, further refined through transfer learning using a curated dataset from the three libraries. The system features a two-stage processing pipeline. First, a lightweight background subtraction algorithm filters frames with potential motion, which are queued via RabbitMQ for sequential processing. Second, a People Flow Counting module applies the optimized YOLOv5 model to detect and count individuals in each frame, followed by a custom tracking algorithm and virtual line-crossing logic to ensure accurate flow tracking. Each library is handled independently through a batch processing approach, updating occupancy estimates with bounded delay using a single CPU-only fog server. This architecture maintains low latency while avoiding server overload and minimizing energy use. The system has been in continuous production for over twelve months, demonstrating reliable performance across all three libraries on commodity hardware. Quantitative evaluation confirms 94 % accuracy in people flow detection, validating the system’s robustness, scalability, and practical utility for long-term, privacy-preserving deployment in smart campus environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101748"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989076","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}

{"title":"CARES: A Hybrid caregivers recommendation system using deep learning and knowledge graphs","authors":"Qiaoyun Zhang ,&nbsp;Sze-Han Wang ,&nbsp;Chung-Chih Lin ,&nbsp;Chih-Yung Chang ,&nbsp;Diptendu Sinha Roy","doi":"10.1016/j.iot.2025.101769","DOIUrl":"10.1016/j.iot.2025.101769","url":null,"abstract":"<div><div>Recommendation systems have prospered by leveraging user-item interactions and their features for personalized recommendations. Recent advancements in deep learning further enhance these recommendation systems with powerful backbones for learning from user-item data. However, solely depending on these interactions often leads to the cold-start problem, where items lacking historical data cannot be effectively recommended. Additionally, the issue of high similarity between user and item features frequently goes unresolved. This paper introduces a Hybrid Caregiver Recommendation mechanism, called CARES, designed to recommend suitable caregivers for postpartum women using deep learning and knowledge graphs. Initially, the proposed CARES utilizes Extreme Gradient Boosting (XGBoost) to identify important features, addressing the issue of feature similarity. Then it employs <em>K</em>-Means clustering to group postpartum women and caregivers based on similar features. Subsequently, it utilizes a Deep &amp; Cross Network (DCN) to automatically learn feature interactions and constructs knowledge graphs to tackle the cold start problem. The proposed CARES also integrates exploration and exploitation strategies to balance the accuracy and diversity of recommendations. The proposed CARES compares with existing mechanisms on real datasets, and the simulation results demonstrate its effectiveness in terms of precision, recall, and F1-Score.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101769"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158240","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}

{"title":"Enhancing big data analysis in IoT applications and optimizing the performance of machine learning models using hybrid dimensionality optimization approach","authors":"Ihab Nassra,&nbsp;Juan V. Capella","doi":"10.1016/j.iot.2025.101764","DOIUrl":"10.1016/j.iot.2025.101764","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The proliferation of Internet of Things (IoT) applications generates high-dimensional datasets characterized by substantial velocity, variety, and complexity, imposing severe computational constraints on machine learning systems. Such data's high dimensionality complicates identifying meaningful correlations among features. Thus, high-dimensional datasets pose substantial challenges for machine learning, as the abundance of variables tends to obscure meaningful correlations and hinder practical data analysis, particularly regarding computational resource consumption (e.g., memory usage), processing time, and machine learning models' training efficiency and performance. Dimensionality reduction techniques address these challenges by decreasing the number of input variables and preserving the intrinsic structure of the data while alleviating computational burdens. Nevertheless, most contemporary methods are optimized for either linear or nonlinear data patterns, but rarely both. Hybrid strategies integrating linear and nonlinear reduction techniques have increasingly addressed these constraints. Specifically, the combination of Principal Component Analysis (PCA) as a preprocessing stage with Restricted Boltzmann Machines (RBMs) offers a complementary solution, wherein PCA condenses the feature space into a lower-dimensional representation, thereby improving training efficiency and enabling RBMs to capture complex nonlinear dependencies with enhanced convergence and generalization. While this combination can theoretically exploit the data's linear and nonlinear characteristics, conventional PCA-RBM frameworks often struggle to retain essential local manifold structures, limiting their effectiveness in capturing the full complexity of real-world datasets. This study addresses these challenges by proposing a novel hybrid dimensionality reduction framework that integrates PCA's global linear projection capabilities with RBMs' nonlinear feature learning strengths through an adaptive graph regularization mechanism that preserves critical local manifold properties, which address the limitations of conventional PCA-RBM combinations. The adaptive regularization mechanism ensures that proximate data points in input space retain similarity in the reduced feature space, effectively bridging global and local structure preservation. Compared to conventional methods, experimental validation demonstrates superior performance across multiple evaluation metrics, including data reduction efficiency, classification accuracy, precision, recall, and F-score. The framework addresses critical limitations in high-dimensional data processing while maintaining model performance, establishing a methodologically significant contribution to dimensionality reduction techniques applicable across scientific disciplines handling complex IoT-generated datasets. Our findings indicate that dimensionality reduction constitutes a viable and efficacious approach to simplifying ","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101764"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158239","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}

{"title":"Robust priority aware multi-criterion offloading in digital twin UAVs networks","authors":"Muhammad Yahya ,&nbsp;Muhammad Naeem ,&nbsp;Zeeshan Kaleem ,&nbsp;Waleed Ejaz","doi":"10.1016/j.iot.2025.101763","DOIUrl":"10.1016/j.iot.2025.101763","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) play a critical role in replenishing the energy of power-constrained Internet of Things (IoT) devices, particularly in public safety operations, thereby maintaining continuous system functionality. Integrating Mobile Edge Computing (MEC) into UAV platforms enables offloading computational tasks to aerial nodes, optimizing resource utilization. Efficient orchestration of communication, computation, caching, and energy resources is imperative to maximize the benefits of UAV-assisted MEC networks. Additionally, ensuring high situational awareness is essential for supporting priority-based latency-sensitive applications. Digital twin technology can be instrumental in minimizing latency by generating a real-time digital representation of the physical infrastructure, enabling enhanced system monitoring and optimization. Accordingly, we have formulated an optimization problem to maximize the number of IoT devices UAVs can support while adhering to predefined constraints. The formulated problem is a mixed integer non-linear programming model. Additionally, the dynamic management of tasks with varying priorities and computational demands introduces a significant resource allocation and scheduling challenge. Our proposed approach entails an efficient task offloading and priority-based scheduling strategy that prioritizes tasks, allocating computational resources to those with higher priority. The approach encompasses a multi-stage offloading strategy combining an interior-point method with a learning algorithm to address the inherent complexity and provide a viable solution. Simulation results validate the effectiveness of the proposed approach, outperforming conventional methods. Specifically, the Penalty Function Method Heuristic combined with the Interior Point Method achieves superior user connectivity compared to the Simple Relaxation Heuristic strategy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101763"},"PeriodicalIF":7.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158236","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}