Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Real-Time Cyber Security Risk Assessment in Smart Home IoT Networks Using a Hybrid Fuzzy–Neural Soft Computing Framework","authors":"Ramachandiran Kanimozhi","doi":"10.1002/ett.70467","DOIUrl":"https://doi.org/10.1002/ett.70467","url":null,"abstract":"<div>\u0000 \u0000 <p>The high pace of the development of smart home IoT devices, such as cameras, thermostats, and door sensors, has made life more convenient and automated but more susceptible to cyber-attacks. Current cybersecurity options either work with uncertainty with fuzzy logic or offer predictive abilities with neural networks, but in few cases do both in concert to give real-time, interpretable risk evaluation in heterogeneous IoT settings. In this paper, a hybrid fuzzy–neural soft computing framework of cybersecurity risk assessment and decision support in smart home IoT networks is proposed. The model integrates a fuzzy logic module to process uncertain and noisy device data with the neural network module to learn the complex nonlinear patterns of attacks. The unified system creates risk scores for every device and classifies them as low, medium, or high risk in order to make timely mitigation decisions. Simulation of a smart home is carried out, and various attack scenarios on cameras, thermostats, and door sensors are experimented with. Experimental results show that the proposed hybrid model outperforms all baseline models such as Fuzzy only model, Neural Only model, and the Random Forest classifier with 94.1% accuracy on the classification problem and also having low-latency real-time alerts which are suitable for smart home IoT environments. This framework is scalable, interpretable, and flexible to changing threats of IoT, and it makes it a potentially effective solution to cybersecurity in smart homes.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"SkyVehiGuard-Net: A Mamba-MoE Framework for IoV Security Over Space–Air–Ground-Integrated Networks","authors":"Xiaobo Liu,&nbsp;Cheranrach Mahandren,&nbsp;Mohammed Okmi,&nbsp;Yen-Lin Chen,&nbsp;Mohammad Alhefdi,&nbsp;Hadeel Alsolai,&nbsp;Por Lip Yee","doi":"10.1002/ett.70463","DOIUrl":"https://doi.org/10.1002/ett.70463","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Vehicles (IoV) operating within Space–Air–Ground-Integrated Network (SAGIN) environments faces escalating cybersecurity threats, including network intrusion attacks on Controller Area Network (CAN) bus communications and malicious URL-based phishing campaigns targeting in-vehicle infotainment (IVI) systems. Existing intrusion detection systems (IDS) and malicious URL classifiers for IoV predominantly assume stable terrestrial network conditions, rendering them fundamentally unsuitable for heterogeneous SAGIN environments characterized by long satellite propagation delays, intermittent UAV relay channels, and bandwidth-constrained links. To address these compounding challenges, this article proposes SkyVehiGuard-Net, a novel dual-task hybrid architecture that synergistically integrates a Selective State Space Model (Mamba) backbone with a Deformable Multi-Scale Axial Attention Transformer (DMSA-T) module, orchestrated through a Sparse Gated Mixture-of-Experts (SGMoE) routing mechanism specifically engineered for latency-aware vehicular security inference over SAGIN. The Mamba backbone captures long-range temporal dependencies in CAN bus traffic sequences and URL character embeddings with linear computational complexity, while the DMSA-T module introduces learnable deformable sampling offsets for adaptive multi-scale feature aggregation across heterogeneous vehicular traffic patterns. A Channel-Aware Adaptive Knowledge Distillation (CA-AKD) framework compresses the full-scale model into lightweight student variants deployable across SAGIN tiers—satellite edge, UAV relay, and roadside units (RSUs). A Federated Split Learning with Differential Privacy (FSL-DP) protocol safeguards sensitive vehicular communication data traversing multi-domain SAGIN segments. Explainability is ensured through dual-pathway Grad-CAM and SHAP interpretation modules. Comprehensive experiments on CICIDS2017, CSE-CIC-IDS2018, Car-Hacking, CAN-intrusion, and a curated malicious URL dataset under emulated SAGIN channel conditions demonstrate that SkyVehiGuard-Net achieves state-of-the-art intrusion detection accuracy of 99.47% on CICIDS2017, 99.31% on Car-Hacking, and malicious URL detection accuracy of 98.92%, while reducing inference latency by 64.8% and model size by 73.2% compared to Vision Transformer baselines. The FSL-DP protocol maintains detection performance degradation below 0.9% under strict privacy budget <span></span><math></math>.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Optimized Multi-Channel Graph Neural Network Framework for Intrusion Detection in Internet-of-Things Environments","authors":"Robin Cyriac,&nbsp;Sundaravadivazhagan Balasubaramanian,&nbsp;R. Karthikeyan,&nbsp;V. Balamurugan,&nbsp;Saleem Raja Abdul Samad","doi":"10.1002/ett.70458","DOIUrl":"https://doi.org/10.1002/ett.70458","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) and its applications occupy a prominent place in contemporary research. The IoT, with its inherently heterogeneous behavior offers solutions to numerous problems and has become inseparable from human life. In this paper, an Optimized Multi-Channel Graph Neural Network Framework for Intrusion Detection in Internet-of-Things Environments (DGSEMGNN-IDF-IoT) is proposed. Here, the data collected from the Telemetry of Network-Internet of Things (ToN-IoT) dataset are used. To execute this, the input data are given into the pre-processing stage using the Dual Central Difference Kalman Filter (DCDKF) to eliminate redundant values and replace missing ones. The pre-processed data are supplied to the feature selection stage using the Ebola Optimization Search Algorithm (EOSA) to select the optimum features. The selected features are fed into the Dynamic Global Structure Enhanced Multi-Channel Graph Neural Network (DGSEMGNN) to effectively categorize the data as normal, backdoor, scanning, password, Cross-Site Scripting (XSS), ransomware, injection, Denial of Service (DOS), Distributed Denial of Service (DDOS) and Man-in-the-Middle (MITM) attack. Then, the Wolf-Bird Optimizer (WBO) is used to enhance the weight parameters of DGSEMGNN. The DGSEMGNN-IDF-IoT is implemented, and the performance metrics, such as accuracy, f1-score, specificity, sensitivity, Receiver Operating Characteristic (ROC), error rate, and computation time, are analyzed. The DGSEMGNN-IDF-IoT approach attains 6.80%, 10.41%, and 6.60% higher accuracy, 6.95%, 8.02%, and 10.9% higher f1-score when compared with existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Compact Quad-Port MIMO-Based 5G mm-Wave Antenna for V2X Connectivity","authors":"Poonam Tiwari,&nbsp;Jayant Kumar Rai,&nbsp;Rachit Jain,&nbsp;Pinku Ranjan,&nbsp;Rakesh Chowdhury,&nbsp;Vishant Gahlaut","doi":"10.1002/ett.70464","DOIUrl":"https://doi.org/10.1002/ett.70464","url":null,"abstract":"<div>\u0000 \u0000 <p>This article presents a compact quad port MIMO antenna working in a 5G millimeter wave for vehicle-to-everything (V2X) connectivity. The proposed design addresses challenges in achieving high isolation and radiation efficiency in compact multi-port configurations. The antenna achieves a wide input impedance bandwidth of 50% (21.5–32.3 GHz) with an excellent gain of 8.9 dBi and a radiation efficiency of 93%. An isolation of over 25 dB is achieved in the entire operating band through the strategically modified ground structure. The envelope correlation coefficient (ECC) value is less than 0.0001, which indicates minimal signal interference and strong diversity performance. This innovative design offers reliable and effective communication, making it suitable for V2X applications in 5G networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Hybrid Encryption With Digitally Signed Optimal Key for Secured EHR Data Storage in Cloud Environment","authors":"V. Ambica,&nbsp;A. Viji Amutha Mary","doi":"10.1002/ett.70461","DOIUrl":"https://doi.org/10.1002/ett.70461","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Problem/Purpose</h3>\u0000 \u0000 <p>The electronic healthcare data (EHR) makes privacy preservation as paramount as it is composed of highly sensitive medical information. The conventional approaches have issues in managing the keys and the access control process. In modern society, the deployment of EHR storage has attained enormous effects. In the cloud-based EHR storage, security concerns may occur. The main purpose of the research is to ensure the security of the EHR data in the cloud environment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To provide security to the EHR data, a hybrid encryption with a digitally signed optimal key is proposed in this research work. The objective of the research is to protect the EHR data from unauthorized users, and it helps to maintain secure EHR transactions between the authorized parties.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methodology</h3>\u0000 \u0000 <p>In this developed scheme, the hybrid adaptive encryption scheme (HAES) is utilized for performing encryption over the EHR data, where encryption mechanisms such as elliptic curve cryptography (ECC) and attribute-based encryption (ABE) are used for developing the HAES. The optimal keys are generated for encrypting the EHR data using the modified bear smell search algorithm (MBSSA). The integrity of the EHR health data is improved by digitally signing an optimal key. For the secure transmission of data, the encrypted data is stored in the cloud.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Electronics Findings</h3>\u0000 \u0000 <p>The developed MBSSA–HAES method used 15 s for encryption and 8.5 s for the decryption process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The implemented data security mechanism highly protects the sensitive EHR data in the cloud environment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148221846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Hybrid AI Model for Anomaly Detection and Sustainable Electric Power Utilization in VANET-Connected Human Activity Monitoring Networks","authors":"Zhihe Zhu","doi":"10.1002/ett.70445","DOIUrl":"https://doi.org/10.1002/ett.70445","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of vehicular ad hoc networks (VANETs) with human activity monitoring networks (HAMNs) is advancing intelligent mobility and connected healthcare. However, achieving accurate anomaly detection while maintaining sustainable electric power utilization remains challenging due to dynamic vehicular mobility and fluctuating energy demands. Existing approaches such as VANET security awareness framework (VSAF), secure passenger health assessment using vehicular communications (SPHA-VC), and bio-inspired VANET routing (BIVR) mainly focus on security awareness, passenger health monitoring, or routing optimization, but they lack adaptive mechanisms for joint anomaly detection and energy-efficient network management in highly dynamic environments. To address these limitations, this paper proposes ECO-SHIELD, a hybrid energy-conscious self-healing intelligent learning and detection framework that integrates graph neural networks (GNNs) with a modified particle swarm optimization (M-PSO) algorithm. The GNN module models spatial–temporal interactions among vehicular and human activity nodes, while M-PSO dynamically optimizes network parameters and energy thresholds to improve detection reliability and power efficiency. The comparative analysis reports detection accuracy values of 86.3%, 88.1%, and 89.4% for VSAF, SPHA-VC, and BIVR, respectively, in comparison with 98.4% achieved by ECO-SHIELD. Similarly, response latency values of 168, 149, and 134 ms and energy consumption values of 345, 318, and 296 mAh have been included for the baseline methods, which clearly highlight the improvements obtained by the proposed framework. In addition, the comparative results now present packet delivery ratios of 91.2%, 92.8%, and 94.1% for the baseline methods against 97% for ECO-SHIELD.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Machine Learning-Based Real-Time State of Health Estimation for Electric Vehicle Batteries in VANET Using GRU Networks and On-Board Diagnostic Data","authors":"Xiaoyu Zhang","doi":"10.1002/ett.70462","DOIUrl":"https://doi.org/10.1002/ett.70462","url":null,"abstract":"<div>\u0000 \u0000 <p>The recent growth of electric vehicles (EVs) and vehicular ad-hoc networks indicates the need for accurate and lightweight health state estimation in EV batteries. Lithium-ion batteries are the major contributors in this field. Real-time health state estimation is important to confirm the performance and lifetime of EVs, particularly in a VANET-enabled environment where battery status must be continuously shared across connected vehicles. They share information across connected vehicles. Traditional physical-based estimations often struggle with high computational demands and are not suitable for complex practical scenarios. To address this problem in the upcoming state of health (SOH) estimation, we propose an adaptive, lightweight attention-augmented gated recurrent unit (AL-GRU) model. The GRU model captures the temporal dependencies in battery operational data by using gated recurrent units that selectively update information at each time step, and enable the model to effectively study degradation patterns. Then, an attention mechanism is applied to focus only on the important time steps. The suggested model restricts its branches to avoid unnecessary computational complexity. The lightweight design of the proposed model makes it suitable for VANET-connected vehicles. The model is implemented in PyTorch and demonstrates strong predictive performance in terms of mean absolute error (MAE) of 0.039, root mean square error (RMSE) of 0.092, and <i>R</i>² of 0.859. Residual analysis further confirms the model's suitability for a real-time VANET environment for practical battery management applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Intelligent Real-Time Traffic Prediction in VANETs Using Federated Ensemble Neural Networks","authors":"A. Anushya,&nbsp;Ameni Filali,&nbsp;Tahani Abdalla Attia Gasmalla,&nbsp;Meiad Jrad Aljrad,&nbsp;Bedour Alrashidi,&nbsp;Abrar Alamr,&nbsp;Rasha Almarshdi,&nbsp;Asmaa Alrayzah","doi":"10.1002/ett.70459","DOIUrl":"https://doi.org/10.1002/ett.70459","url":null,"abstract":"<div>\u0000 \u0000 <p>Traffic congestion creates a significant challenge in urban environments that highly impacts infrastructure efficiency. Exact traffic prediction is important to avoid such challenges in difficult traffic scenarios. In this study, we propose a Federated Ensemble Neural Network (FENN) framework that integrates a lightweight Convolutional Neural Network (CNN), a Residual Dense (ResDense) network, and a Multi-Layer Perceptron (MLP) within a distributed federated learning architecture with an adaptive soft-voting mechanism. The suggested model predicts traffic congestion by extracting temporal features with a CNN and ResDense to model congestion patterns, capturing complex nonlinear traffic patterns with an MLP, and combining the predictions through soft voting to improve prediction efficiency. The model leverages knowledge from FedAvg-based training to ensure consistent performance across multiple RSU clients. The model is simulated using a traffic prediction dataset with complex traffic patterns. Experimental results proved that the model achieves 94.27% accuracy and an <i>R</i>² score of 0.82 in capturing dense traffic dynamics. As a result, the model provides a promising solution for traffic prediction and congestion reduction in urban platforms.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Performance Evaluation and Optimization of Network Parameters for UAV Enabled Dual Hop Wireless Communication System","authors":"Deepika Latka,&nbsp;Mona Aggarwal,&nbsp;Swaran Ahuja,&nbsp;Pooja Sabherwal,&nbsp;Neeraj Kumar Shukla","doi":"10.1002/ett.70460","DOIUrl":"https://doi.org/10.1002/ett.70460","url":null,"abstract":"<div>\u0000 \u0000 <p>This investigation delves into the operational performance of a dual-hop wireless communication system facilitated by an unmanned aerial vehicle (UAV) operating as a decode-and-forward (DF) relay. In scenarios where direct ground-based communication between a base station and mobile users is hindered by environmental or structural barriers, the UAV provides an aerial link. The first hop, from the base station to the UAV, is modeled using the generalized <span></span><math></math>-<span></span><math></math>-<span></span><math></math> fading distribution to capture a wide range of channel conditions. The second hop, from the UAV to the selected user, employs opportunistic user scheduling and experiences Rayleigh fading. The study presents exact closed-form results for key performance parameters like outage probability and average symbol error rate. To better understand system behavior at high signal-to-noise ratio (SNR) regimes, we also perform an asymptotic outage analysis, from which we extract the system's diversity order and coding gain. Additionally, we examine the optimal UAV placement strategy that minimizes the asymptotic outage probability, thereby improving system reliability and efficiency.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Security and Private Collaboration IDS for VANET: Utilizing Distributed Machine Learning to Detect Network Attacks","authors":"Bo Qi,&nbsp;Guohua Zhang","doi":"10.1002/ett.70442","DOIUrl":"https://doi.org/10.1002/ett.70442","url":null,"abstract":"<div>\u0000 \u0000 <p>In intelligent transportation systems, Vehicular Ad Hoc Networks (VANETs) provide real-time vehicle-roadside infrastructure communication to improve traffic safety and efficiency. However, VANETs are vulnerable to sophisticated network assaults like DoS, Sybil attacks, and message tampering due to their mobility, lack of centralization, and changeable topology. Traditional IDSs fail to detect such threats without sacrificing vehicle privacy. A novel IDS system, the Distributed Adaptive Network Anomaly Guard (DynaGuard), leverages distributed machine learning via privacy-preserving federated learning to address these concerns. DynaGuard lets cars train local models using network traffic patterns, detect abnormalities in real time, and adjust threat levels using ensemble-based learning. To conclude, DynaGuard protects vehicle networks from emerging cyber threats and enables intelligent transportation systems with secure, dependable communications. In a simulated VANET dataset, DynaGuard outperforms typical IDS techniques with a 97.1% detection rate, 19% fewer false positives, and low communication cost.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}