International Journal of Communication Systems最新文献

{"title":"A Lightweight CoDeV–HyPerion-T Deep Learning Framework for Threat Detection in Underwater Wireless Sensor Networks (UWSNs)","authors":"N. Dhanalakshmi,&nbsp;N. Babu,&nbsp;M. Anto Bennet,&nbsp;S. Pournima","doi":"10.1002/dac.70595","DOIUrl":"https://doi.org/10.1002/dac.70595","url":null,"abstract":"<div>\u0000 \u0000 <p>The use of underwater wireless sensor networks (UWSNs) has become a major security concern due to their increased use in environmental monitoring, military surveillance, and offshore applications. The networks are highly susceptible to various cyber threats due to their dynamic topology, limited energy resources, and unreliable communication networks. The existing intrusion detection techniques tend to exhibit low detection rates, high false alarms, and high complexity, making them inapplicable to real-time and resource-constrained UWSN systems. Moreover, many conventional and deep learning-based models cannot effectively model feature interactions and emerging attack patterns in heterogeneous IoTs and underwater environments. To address these weaknesses, this paper presents a new hybrid intrusion detection model, which integrates CoDeV based feature extraction algorithm and the HyPerion-T model. To capture both the spatial and temporal characteristics of network traffic and leverage a transformer-based structure to enhance intrusion detection by exploiting adaptive attention, the article presented here was based on sophisticated feature description and dependency modeling. These combinations will ensure greater accuracy, strength, and efficiency in the calculations. The results of the proposed methodology are tested on the benchmark datasets, i.e., WSN-DS, IoT-23, and N-BaIoT. Among all models, CoDeV+HyPerion-T has reached the highest average accuracy (98.96%) with an accuracy of more than 98% and recall of more than 98% and also achieved the lowest average inference time (9.5 ms), demonstrating great real-time performance. The inference energy is also measured to be 3.42 mJ for per inference, achieving up to 50.1% energy reduction compared with basis approach models.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784824","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":"An Advanced Framework Leveraging Contrastive Multilevel Graph Neural Networks With Snow Avalanches Algorithm to Enhance IoT Network Security for Intrusion Detection","authors":"P. A. Mathina,&nbsp;K. Valarmathi","doi":"10.1002/dac.70580","DOIUrl":"https://doi.org/10.1002/dac.70580","url":null,"abstract":"<div>\u0000 \u0000 <p>Internet of Things (IoT) applications are becoming increasingly popular owing to their extensive application in a variety of real-world services. Many devices in the IoT ecosystem are connected to each other over the internet, making IoT networks vulnerable to different sorts of cyberattacks; network security and user privacy are important concerns in its deployment. In this paper, an advanced framework leveraging contrastive multilevel graph neural networks with snow avalanches algorithm to enhance IoT network security for intrusion detection (CMGNN-SAA-IoT-ID) is proposed. Initially, the raw network traffic is collected from CICIoT2023 and CIC-MalMem-2022 datasets. The collected data are given to the preprocessing, where the Bayesian boundary trend filtering (BBTF) is applied for data cleaning, missing values, and data normalization. Then the preprocessed data are fed to feature selection using banyan tree growth optimization (BTGO) to select best features. Then the selected features are fed to intrusion detection using contrastive multilevel graph neural networks (CMGNN), which is used to detect the attacks as recon, DoS, DDoS, brute force, web-based, spoofing, and Mirai from CICIoT2023. Additionally, the CIC-MalMem-2022 dataset is used to detect attacks as benign and malware. Finally, snow avalanches algorithm (SAA) is proposed to optimize the CMGNN for precisely classifying the intrusion detection. The proposed CMGNN-SAA-IoT-ID is implemented, and performance metrics such as accuracy, recall, precision, computational time, and detection rate are analyzed. Finally, the performance of the CMGNN-SAA-IoT-ID provides 26.68%, 25.75%, and 26.16% high accuracy and 29.08%, 30.70%, and 16.26% high precision compared to the existing models.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784826","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":"SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP","authors":"Yu Qiao,&nbsp;Hao Ji,&nbsp;Guowei Li","doi":"10.1002/dac.70590","DOIUrl":"https://doi.org/10.1002/dac.70590","url":null,"abstract":"<div>\u0000 \u0000 <p>The widespread adoption of the Internet of Vehicles (IoV) and location-based services (LBS) has generated massive trajectory data, which drives intelligent transportation innovations but also causes severe privacy leakage risks. Existing trajectory privacy protection methods face problems of low-quality synthetic data, insufficient semantic protection, and lack of personalization. To address these issues, this paper proposes a Semantic-Aware Personalized Trajectory Privacy Protection algorithm (SAP-TrajWGP) based on Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). First, a WGAN-GP model integrating LSTM and self-attention mechanisms is constructed to generate high-authenticity synthetic trajectories with spatiotemporal consistency. Second, a Bidirectional Gated Recurrent Unit (Bi-GRU) model dynamically identifies sensitive trajectory segments, and a hierarchical differential privacy (DP) perturbation mechanism is implemented based on user-specific privacy preferences. Experimental results on real trajectory datasets show that the proposed algorithm achieves a mean RMSE of 37.72 m, spatial and temporal JSD of 0.18 and 0.21, respectively, a TUL attack success rate of 18.4%, and an MI value of 1.86, outperforming mainstream baseline methods in both data utility and privacy protection. This research provides an effective technical pathway for secure and controllable trajectory data sharing.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783956","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":"Modeling a Software-Defined Network for Analyzing Route and Energy With Sensor Nodes","authors":"M. Meenakshi Dhanalakshmi,&nbsp;M. Karthiga","doi":"10.1002/dac.70556","DOIUrl":"https://doi.org/10.1002/dac.70556","url":null,"abstract":"<div>\u0000 \u0000 <p>The practical architecture known as software-defined networking (SDN) enables the Internet of Things (IoT) to function in various applications. Also, SDN has been adopted for effective routing in wireless networks. The controller intends to work using an algorithm to offer secure routing. However, some existing algorithms must provide optimized and secured routing paths. This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM-RLA). The aim is to ensure that the chain and reward functions align with the Quality of Service (QoS). The reward regarding the following successive routing path is analyzed where SDN-enabled IoT enhances the routing based on the prior routing ideas. Moreover, the entire network is managed via the network remotely. The performance of the anticipated is compared with various prevailing approaches. Multiple metrics like packet delivery rate (PDR), network lifetime, routing overhead, energy efficiency, and delay are compared to attain suitable WSN performance via efficient routing.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784393","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":"Enhanced WSN-IoT Security in Cyber-Physical Systems Employing a Directed Acyclic Graph Triplet Attention Network and Draco Lizard Optimizer","authors":"R. Bhaskaran,&nbsp;M. Jehosheba Margaret,&nbsp;Helina Rajini Suresh,&nbsp;T. L. Kayathri","doi":"10.1002/dac.70589","DOIUrl":"https://doi.org/10.1002/dac.70589","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless Sensor Network-Internet of Things security in Cyber-Physical Systems (CPSs) focuses on protecting interconnected sensor networks and devices from cyber threats while ensuring reliable operation of physical processes. However, these systems face challenges such as limited device resources, high vulnerability to attacks, and difficulties in maintaining data integrity and privacy. This research introduces a novel blockchain-based wireless sensor network and Internet of Things (WSN-IoT) security framework that makes use of a Directed Acyclic Graph Triplet Attention Network coupled with the Draco Lizard Optimizer (DAGTAN-DLO) as an optimization technique. It first collects network traffic data from the UNSW-NB15 and NSL-KDD datasets, followed by pre-processing using Adaptive Multiple Imputation of Missing Values with Class Center (AMIMVC), which reduces noise and normalizes features. Then the Empowering Decision Transformer (EDT) is used to extract compact and discriminative features, and the Wonderful Fairy Wren Optimization Algorithm (SFOA) is used to select the most relevant features. Ultimately, these features are classified by Directed Acyclic Graph Triplet Attention Network (DAGTAN) into normal or anomalous traffic, with classification accuracy improved through weight optimization using the Draco Lizard Optimizer (DLO). The encrypted data is then securely stored using Blockchain-Based Ultra-Low Storage overhead Practical Byzantine Fault Tolerance (ULS-PBFT), thus allowing for privacy-preserving storage with the integration of smart contracts and cloud services. The proposed network traffic monitoring framework not only effectively and accurately detects cyber threats but also manages data securely and efficiently in the WSN-IoT-enabled CPS environments. The proposed technology achieves up to 99.89% accuracy, 99.43% F1-score, and exceptionally high specificity and precision on UNSW-NB15 and NSL-KDD datasets, offering a scalable, precise, and blockchain-secured solution for cyber threat detection in WSN-IoT-enabled CPS environments.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784859","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":"Quantum Resilient Explainable Autonomous IoT Security Framework Using Dual-Layer Blockchain for Edge Intelligence","authors":"M. Namratha,&nbsp;Kunwar Singh","doi":"10.1002/dac.70588","DOIUrl":"https://doi.org/10.1002/dac.70588","url":null,"abstract":"<div>\u0000 \u0000 <p>The fast explosion of Internet of Things (IoT) technology across smart homes, autonomous vehicles, healthcare, and industrial infrastructure has led to real-time data transmission, which is highly susceptible to sophisticated cyber-attacks. The scalability of security protocols, trust management, and robustness against quantum-based attacks is limited for existing security protocols. To address these challenges, this research proposes a Quantum Resilient Explainable framework with Dual Layer Blockchain (QRE-DLB) for secure edge intelligence. The proposed framework utilizes CRYSTALS-Kyber512 and Dilithium2 for quantum-resistant authentication and communication, whereas dual-layer blockchain technology is used for decentralized authorization and trust verification, which is achieved through static cryptographic identities and dynamic behavioral analysis. For intelligent threat detection, the trio-based rethinking transformer-enhanced temporal convolutional network is used for adaptive anomaly detection, whereas the multi-view heterogeneous deterministic-probabilistic graph attention network is used for attack detection. The proposed framework achieves 99.4% detection accuracy, 99.3% precision, 99.2% recall, 24% reduction in detection latency, 18% reduction in energy consumption, and 13.7% improvement in attack mitigation rate. These findings demonstrate the practical applicability of QRE-DLB for secure edge-enabled IoT deployments and provide an effective engineering solution for building scalable, explainable, and quantum-resilient cyber-physical systems.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784865","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":"Learning-Based Reliable Channel Ranking Without CSI for Efficient Spectrum Sensing in Cognitive Radio Networks","authors":"Pallavi Pant,&nbsp;Neelam Srivastava","doi":"10.1002/dac.70593","DOIUrl":"https://doi.org/10.1002/dac.70593","url":null,"abstract":"<div>\u0000 \u0000 <p>The need for effective spectrum usage has increased with the rapid growth of wireless communication systems, which has accelerated the development of cognitive radio (CR) networks. In CR networks, reliable spectrum sensing (SS) and channel selection are major concerns particularly under fading and noise uncertainty where conventional detectors experience considerable performance degradation. This paper proposes a machine learning (ML) assisted reliable pre-sensing channel ranking framework that is independent of instantaneous channel state information (CSI). Instead of relying upon explicit channel estimation, it uses statistically observable information obtained from the received signal to perform intelligent channel ranking prior to SS. A reliability metric proportional to effective signal-to-noise ratio (SNR) is determined by a supervised random forest regression model to enable the selection of the most reliable channel for sensing and access. The proposed method is tested under multiple wireless fading environments, including AWGN, Rayleigh, Rician and Weibull channels. Performance is evaluated in terms of detection probability, ranking reliability, channel selection accuracy, switching rate, and achievable throughput. Simulation outcome indicates that the learning-based ranking scheme performs close to the oracle approach while providing a relative improvement of up to 8% for the achievable throughput and channel selection accuracy compared to estimation schemes under moderate SNR values in fading channels.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784864","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":"Server-Controlled Intelligent Reflecting Surface Networks for Optimized Beam Routing","authors":"Nitin Panuganti,&nbsp;Pinku Ranjan,&nbsp;Anupam Shukla","doi":"10.1002/dac.70598","DOIUrl":"https://doi.org/10.1002/dac.70598","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent reflecting surfaces (IRS) have emerged as a promising solution to enhance signal propagation and mitigate interference in next-generation wireless networks. The purpose of this study is to develop and evaluate an optimized beam routing framework for IRS-assisted wireless communication in complex urban environments. To achieve this, this study proposes an IRS-assisted beam routing framework that leverages metaheuristic optimization to determine optimal signal paths in complex urban environments. The key contribution lies in the design of a server-controlled IRS network that dynamically adjusts phase shifts based on environmental factors and user locations, enabling adaptive and efficient signal routing. A server-controlled IRS network dynamically adjusts phase shifts to ensure efficient transmission, overcoming obstacles and improving network coverage. Extensive simulation results show that the proposed method significantly enhances signal reliability, reduces path loss, and outperforms existing IRS-based approaches in terms of path optimization and communication efficiency. Specifically, the proposed framework achieves a 40% improvement in path efficiency compared to prior state-of-the-art methods, as confirmed through comparative analysis. Comparative analysis with prior research confirms a 40% improvement in path efficiency, validating the system's effectiveness. These findings highlight the potential of IRS-based beamforming as a scalable and robust solution for sixth-generation (6G) wireless networks and beyond. Future work will focus on real-world validation and the integration of machine learning techniques for adaptive IRS configurations.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784337","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":"Blockchain-Enabled Risk-Aware RPL With Real-Time Rank Evaluation and Trust-Weighted Consensus for Secure IoT Routing","authors":"Abhay Deep Seth,&nbsp;Ankur Ratmele,&nbsp;Vikas Kumar Jain","doi":"10.1002/dac.70592","DOIUrl":"https://doi.org/10.1002/dac.70592","url":null,"abstract":"<div>\u0000 \u0000 <p>The need for efficient routing arises in Internet of Things (IoT)–based networks that comprise low-power and lossy nodes. Routing Protocol for Low-Power and Lossy Networks (RPL) has multiple limitations, including rank manipulation attacks, inefficient energy utilization, poor link quality, and vulnerability to malicious nodes. Moreover, conventional routing metrics typically evaluate only a limited set of parameters and are unable to capture the multiple risks and dynamic behaviors encountered in practical IoT environments. To overcome these limitations, this research proposes a Blockchain-enabled Risk-Aware RPL (Bc-RARPL) framework that enhances routing security, adaptability, and performance. The proposed framework establishes a unified routing architecture by integrating real-time rank verification, Multi-Attribute Utility Theory (MAUT)–based risk assessment, trust-aware consensus, and blockchain-based route validation into a continuous routing decision process. Each node evaluates candidate parent nodes using a composite risk utility score derived from multiple factors, including residual energy, link quality, latency, and node failure probability, thereby enabling secure and intelligent parent selection. Routing path optimization is performed using a risk-aware decision-making strategy supported by the Starfish Optimization Algorithm, which jointly minimizes energy consumption, improves route stability, and mitigates dynamic network risks. In addition, a lightweight Zigbee blockchain securely stores routing histories, node rankings, and risk values, whereas a Trust-Weighted Byzantine Fault Tolerance (TW-BFT) consensus protocol restricts validation to trusted nodes, thereby reducing the impact of malicious attacks. Experimental results demonstrate that the proposed framework achieves a packet delivery ratio of up to 96%, reduces end-to-end delay and energy consumption, and improves routing reliability while providing enhanced resilience against rank manipulation and insider attacks.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753790","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":"Optimizing Real-Time Vehicular Communications With a Cross-Layer Model for Energy-Efficient Clustering and Blockchain-Based Congestion Control","authors":"K. Satheshkumar,&nbsp;S. Ramalingam,&nbsp;A. Suresh Babu,&nbsp;S. Murugesan","doi":"10.1002/dac.70578","DOIUrl":"https://doi.org/10.1002/dac.70578","url":null,"abstract":"<div>\u0000 \u0000 <p>Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate real-time communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and ever-changing network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestion-free communication simultaneously. To address these challenges, this work introduced an integrated cross-layer framework featuring three innovative algorithms: mobility-aware black hole clustering (M-BHC), energy-aware piranha optimization algorithm (EPOA), and cross-layer multi-attribute blockchain routing with congestion control (CL-MABRC). The M-BHC algorithm enhances the stability of clusters and counters black-hole attacks by forming clusters dynamically based on real-time vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bio-inspired resource allocation strategy modeled on piranha predation behavior. CL-MABRC addresses network congestion and security using blockchain-based verification and cross-layer routing decisions informed by multi-attribute metrics. Extensive simulations were conducted with a setting of 100 veh/km². The proposed framework showed significant performance improvements over benchmark protocols, such as optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR), enhanced location-aided ant colony routing (ELAACR), trust-based multi-objective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, end-to-end delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the M-BHC, EPOA, and CL-MABRC algorithms create a robust, energy-efficient, and secure communication framework for VANETs, enhancing scalability, reliability, and real-time performance in transportation systems.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"39 14","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753558","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}