Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Flow prediction of mountain cities arterial road network for real-time regulation","authors":"Xiaoyu Cai ,&nbsp;Zimu Li ,&nbsp;Jiajia Dai ,&nbsp;Liang Lv ,&nbsp;Bo Peng","doi":"10.1016/j.jksuci.2024.102190","DOIUrl":"10.1016/j.jksuci.2024.102190","url":null,"abstract":"<div><div>This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102190"},"PeriodicalIF":5.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The evolution of the flip-it game in cybersecurity: Insights from the past to the future","authors":"Mousa Tayseer Jafar ,&nbsp;Lu-Xing Yang ,&nbsp;Gang Li ,&nbsp;Xiaofan Yang","doi":"10.1016/j.jksuci.2024.102195","DOIUrl":"10.1016/j.jksuci.2024.102195","url":null,"abstract":"<div><div>Cybercrime statistics highlight the severe and growing impact of digital threats on individuals and organizations, with financial losses escalating rapidly. As cybersecurity becomes a central challenge, several modern cyber defense strategies prove insufficient for effectively countering the threats posed by sophisticated attackers. Despite advancements in cybersecurity, many existing frameworks often lack the capacity to address the evolving tactics of adept adversaries. With cyber threats growing in sophistication and diversity, there is a growing acknowledgment of the shortcomings within current defense strategies, underscoring the need for more robust and innovative solutions. To develop resilient cyber defense strategies, it remains essential to simulate the dynamic interaction between sophisticated attackers and system defenders. Such simulations enable organizations to anticipate and effectively counter emerging threats. The Flip-It game is recognized as an intelligent simulation game for capturing the dynamic interplay between sophisticated attackers and system defenders. It provides the capability to emulate intricate cyber scenarios, allowing organizations to assess their defensive capabilities against evolving threats, analyze vulnerabilities, and improve their response strategies by simulating real-world cyber scenarios. This paper provides a comprehensive analysis of the Flip-It game in the context of cybersecurity, tracing its development from inception to future prospects. It highlights significant contributions and identifies potential future research avenues for scholars in the field. This study aims to deliver a thorough understanding of the Flip-It game’s progression, serving as a valuable resource for researchers and practitioners involved in cybersecurity strategy and defense mechanisms.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102195"},"PeriodicalIF":5.2,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework to improve software effort estimation accuracy using novel ensemble rule","authors":"Syed Sarmad Ali ,&nbsp;Jian Ren ,&nbsp;Ji Wu","doi":"10.1016/j.jksuci.2024.102189","DOIUrl":"10.1016/j.jksuci.2024.102189","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the &lt;em&gt;Novel Incomplete Value Imputation Model (NIVIM)&lt;/em&gt;, a hybrid model using &lt;em&gt;Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta)&lt;/em&gt;, and the &lt;em&gt;Heterogeneous Ensemble Model (HEM)&lt;/em&gt;. The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from &lt;em&gt;11.05%&lt;/em&gt; to &lt;em&gt;17.72%&lt;/em&gt; and MAE improvements from &lt;em&gt;9.62%&lt;/em&gt; to &lt;em&gt;21.96%&lt;/em&gt;. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a &lt;em&gt;25.61%&lt;/em&gt; reduction in MSE. Additionally, the proposed CFS-Meta achieves a &lt;em&gt;10%&lt;/em&gt; (MAE) improvement against the hybrid PSO-SA model, an &lt;em&gt;11.38%&lt;/em&gt; (MAE) improvement compared to the Hybrid ABC-SA model, and &lt;em&gt;12.42%&lt;/em&gt; and &lt;em&gt;12.703%&lt;/em&gt; (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of &lt;em&gt;21.8%&lt;/em&gt; (Pred()) and the homogeneous ensemble model by &lt;em&gt;15%&lt;/em&gt; (Pred()). This","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102189"},"PeriodicalIF":5.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous emotional contagion of the cyber–physical society","authors":"Heqi Gao ,&nbsp;Jiayi Zhang ,&nbsp;Guijuan Zhang ,&nbsp;Chengming Zhang ,&nbsp;Zena Tian ,&nbsp;Dianjie Lu","doi":"10.1016/j.jksuci.2024.102193","DOIUrl":"10.1016/j.jksuci.2024.102193","url":null,"abstract":"<div><p>When emergencies occur, panic spreads quickly across cyberspace and physical space. Despite widespread attention to emotional contagion in cyber–physical societies (CPS), existing studies often overlook individual relationship heterogeneity, which results in imprecise models. To address this issue, we propose a heterogeneous emotional contagion method for CPS. First, we introduce the Strong–Weak Emotional Contagion Model (SW-ECM) to simulate the heterogeneous emotional contagion process in CPS. Second, we formulate the mean-field equations for the SW-ECM to accurately capture the dynamic evolution of heterogeneous emotional contagion in the CPS. Finally, we construct a small-world network based on strong–weak relationships to validate the effectiveness of our method. The experimental results show that our method can effectively simulate the heterogeneous emotional contagion and capture changes in relationships between individuals, providing valuable guidance for crowd evacuations prone to emotional contagion.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102193"},"PeriodicalIF":5.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002829/pdfft?md5=f933d896a76a94be422b19df9a07b8ff&pid=1-s2.0-S1319157824002829-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced prediction model of short-term sea surface wind speed: A multiscale feature extraction and selection approach coupled with deep learning technique","authors":"Jin Tao ,&nbsp;Jianing Wei ,&nbsp;Hongjuan Zhou ,&nbsp;Fanyi Meng ,&nbsp;Yingchun Li ,&nbsp;Chenxu Wang ,&nbsp;Zhiquan Zhou","doi":"10.1016/j.jksuci.2024.102192","DOIUrl":"10.1016/j.jksuci.2024.102192","url":null,"abstract":"<div><div>Accurate prediction of short-term sea surface wind speed is essential for maritime safety and coastal management. Most conventional studies encounter challenges simply in analyzing raw wind speed sequences and extracting multiscale features directly from the original received data, which result in lower efficiency. In this paper, an enhanced hybrid model based on a novel data assemble method for original received data, a multiscale feature extraction and selection approach, and a predictive network, is proposed for accurate and efficient short-term sea surface wind speed forecasting. Firstly, the received original data including wind speed are assembled into correlation matrices in order to uncover inherent associations over varied time spans. Secondly a novel Multiscale Wind-speed Feature-Enhanced Convolutional Network (MW-FECN) is designed for efficient and selective multiscale feature extraction, which can capture comprehensive characteristics. Thirdly, a Random Forest Feature Selection (RF-FS) is employed to pinpoint crucial characteristics for enhanced prediction of wind speed with higher efficiency than the related works. Finally, the proposed hybrid model utilized a Bidirectional Long Short-Term Memory (BiLSTM) network to achieve the accurate prediction of wind speed. Experimental data are collected in Weihai sea area, and a case study consist of five benchmarks and three ablation models is conducted to assess the proposed hybrid model. Compared with the conventional methods, experiment results illustrate the effectiveness of the proposed hybrid model and demonstrate effective balancing prediction accuracy and computational time. The proposed hybrid model achieves up to a 28.45% MAE and 27.27% RMSE improvement over existing hybrid models.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102192"},"PeriodicalIF":5.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks","authors":"Liying Zhao ,&nbsp;Chao Liu ,&nbsp;Entie Qi ,&nbsp;Sinan Shi","doi":"10.1016/j.jksuci.2024.102187","DOIUrl":"10.1016/j.jksuci.2024.102187","url":null,"abstract":"<div><div>Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102187"},"PeriodicalIF":5.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel edge intelligence-based solution for safer footpath navigation of visually impaired using computer vision","authors":"Rashik Iram Chowdhury,&nbsp;Jareen Anjom,&nbsp;Md. Ishan Arefin Hossain","doi":"10.1016/j.jksuci.2024.102191","DOIUrl":"10.1016/j.jksuci.2024.102191","url":null,"abstract":"<div><p>Navigating through a tactile paved footpath surrounded by various sizes of static and dynamic obstacles is one of the biggest impediments visually impaired people face, especially in Dhaka, Bangladesh. This problem is important to address, considering the number of accidents in such densely populated footpaths. We propose a novel deep-edge solution using Computer Vision to make people aware of the obstacles in the vicinity and reduce the necessity of a walking cane. This study introduces a diverse novel tactile footpath dataset of Dhaka covering different city areas. Additionally, existing state-of-the-art deep neural networks for object detection have been fine-tuned and investigated using this dataset. A heuristic-based breadth-first navigation algorithm (HBFN) is developed to provide navigation directions that are safe and obstacle-free, which is then deployed in a smartphone application that automatically captures images of the footpath ahead to provide real-time navigation guidance delivered by speech. The findings from this study demonstrate the effectiveness of the object detection model, YOLOv8s, which outperformed other benchmark models on this dataset, achieving a high mAP of 0.974 and an F1 score of 0.934. The model’s performance is analyzed after quantization, reducing its size by 49.53% while retaining 98.97% of the original mAP.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102191"},"PeriodicalIF":5.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002805/pdfft?md5=67af390c0280c8b6ae2c05684fbae69f&pid=1-s2.0-S1319157824002805-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph contrast learning for recommendation based on relational graph convolutional neural network","authors":"Xiaoyang Liu ,&nbsp;Hanwen Feng ,&nbsp;Xiaoqin Zhang ,&nbsp;Xia Zhou ,&nbsp;Asgarali Bouyer","doi":"10.1016/j.jksuci.2024.102168","DOIUrl":"10.1016/j.jksuci.2024.102168","url":null,"abstract":"<div><div>Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive Graph Attention with Graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102168"},"PeriodicalIF":5.2,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400257X/pdfft?md5=d69bd7bfcc27dc9c754378e21af4a8b9&pid=1-s2.0-S131915782400257X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving embedding-based link prediction performance using clustering","authors":"Fitri Susanti ,&nbsp;Nur Ulfa Maulidevi ,&nbsp;Kridanto Surendro","doi":"10.1016/j.jksuci.2024.102181","DOIUrl":"10.1016/j.jksuci.2024.102181","url":null,"abstract":"<div><p>Incomplete knowledge graphs are common problem that can impair task accuracy. As knowledge graphs grow extensively, the probability of incompleteness increases. Link prediction addresses this problem, but accurate and efficient link prediction methods are needed to handle incomplete and extensive knowledge graphs. This study proposed modifications to the embedding-based link prediction using clustering to improve performance. The proposed method involves four main processes: embedding, clustering, determining clusters, and scoring. Embedding converts entities and relations into vectors while clustering groups these vectors. Selected clusters are determined based on the shortest distance between the centroid and the incomplete knowledge graph. Scoring measures relation rankings, and link prediction result is selected based on highest scores. The link prediction performance is evaluated using Hits@1, Mean Rank, Mean Reciprocal Rank and prediction time on three knowledge graph datasets: WN11, WN18RR, and FB13. The link prediction methods used are TransE and ComplEx, with BIRCH as the clustering technique and Mahalanobis for short-distance measurement. The proposed method significantly improves link prediction performance, achieving accuracy up to 98% and reducing prediction time by 99%. This study provides effective and efficient solution for improving link prediction, demonstrating high accuracy and efficiency in handling incomplete and extensive knowledge graphs.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102181"},"PeriodicalIF":5.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002702/pdfft?md5=e31143cd70a22f8ffa2da3a54e983856&pid=1-s2.0-S1319157824002702-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration","authors":"Jiaying Wu ,&nbsp;Lingyun Yuan ,&nbsp;Tianyu Xie ,&nbsp;Hui Dai","doi":"10.1016/j.jksuci.2024.102184","DOIUrl":"10.1016/j.jksuci.2024.102184","url":null,"abstract":"<div><p>Sharding is a critical technology for enhancing blockchain scalability. However, existing sharding blockchain protocols suffer from a high cross-shard ratio, high transaction latency, limited throughput enhancement, and high account migration. To address these problems, this paper proposes a sharding blockchain protocol for enhanced scalability and performance optimization through account transaction reconfiguration. Firstly, we construct a blockchain transaction account graph network structure to analyze transaction account correlations. Secondly, a modularity-based account transaction reconfiguration algorithm and a detailed account reconfiguration process is designed to minimize cross-shard transactions. Finally, we introduce a transaction processing mechanism for account transaction reconfiguration in parallel with block consensus uploading, which reduces the reconfiguration time overhead and system latency. Experimental results demonstrate substantial performance improvements compared to existing shard protocols: up to a 34.7% reduction in cross-shard transaction ratio, at least an 83.2% decrease in transaction latency, at least a 52.7% increase in throughput and a 7.8% decrease in account migration number. The proposed protocol significantly enhances the overall performance and scalability of blockchain, providing robust support for blockchain applications in various fields such as financial services, supply chain management, and industrial Internet of Things. It also enables better support for high-concurrency scenarios and large-scale network environments.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102184"},"PeriodicalIF":5.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002738/pdfft?md5=107fe417689144e59c75fddd0f5b671f&pid=1-s2.0-S1319157824002738-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}