Journal of Network and Computer Applications最新文献

{"title":"An enhanced RAN for future converged audiovisual services: The bcNode","authors":"Rufino Cabrera ,&nbsp;Jon Montalban ,&nbsp;Orlando Landrove ,&nbsp;Erick Jimenez ,&nbsp;Eneko Iradier ,&nbsp;Pablo Angueira ,&nbsp;Sung-Ik Park ,&nbsp;Sunhyoung Kwon ,&nbsp;Namho Hur","doi":"10.1016/j.jnca.2025.104274","DOIUrl":"10.1016/j.jnca.2025.104274","url":null,"abstract":"<div><div>The current architecture of terrestrial broadcast network limits the evolution of terrestrial audiovisual services (broadcasting). A drastic change is required before the unavoidable future convergence with 5G/6G networks for more advanced services, including interactivity, 360°video, etc. Recent studies have explored the feasibility of incorporating a higher entity called a Broadcast Core Network (BCN) based on the equivalent model of mobile communication networks. This new paradigm requires upgrading the existing access network to manage both the modules of the new core network and those corresponding to the radio access technology. The present work proposes an intelligent software-based node (bcNode) that manages the Radio Access Network (RAN) and the BCN instructions after analyzing different aspects, such as the state of the art of CN-based RAN architectures and the challenges and limitations of the current broadcasting network. This manuscript details the main blocks of the Broadcast Node in relationship with transmitting facilities. The proposal also explains the necessary extensions to the ATSC 3.0 ALP protocol to support new services. Eventually, the paper presents numerical results to evaluate the performance of the proposal based on standard KPI parameters and compares it with the legacy infrastructure.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104274"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"5G slicing under the hood: An in-depth analysis of 5G RAN features and configurations","authors":"André Perdigão,&nbsp;José Quevedo,&nbsp;Rui L. Aguiar","doi":"10.1016/j.jnca.2025.104298","DOIUrl":"10.1016/j.jnca.2025.104298","url":null,"abstract":"<div><div>There has been extensive discussion on the benefits and improvements that 5G networks can bring to industry operations, particularly with network slicing. However, to fully realize network slices, it is essential to thoroughly understand the mechanisms available within a 5G network that can be used to adapt network performance. This paper surveys and describes existing 5G network configurations and assesses the performance impact of several configurations using a real-world commercial standalone (SA) 5G network, bringing the challenges between purely theoretical mathematical models into realizations with existing equipment. The paper discusses how these features impact communication performance according to industrial requirements.</div><div>The survey describes and demonstrates the performance impact of various 5G configurations, enabling readers to understand the capabilities of current 5G networks and learn how to leverage 5G technology to enhance industrial operations. This knowledge is also crucial to fully realize network slices tailored to industrial requirements.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104298"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks","authors":"Jinglin Li ,&nbsp;Haoran Wang ,&nbsp;Sen Zhang ,&nbsp;Peng-Yong Kong ,&nbsp;Wendong Xiao","doi":"10.1016/j.jnca.2025.104301","DOIUrl":"10.1016/j.jnca.2025.104301","url":null,"abstract":"<div><div>Mobile charging provides a new way for energy replenishment in Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging sensor nodes sequentially according to the mobile charging sequence scheduling result. Event detection is an essential application of WRSN, but when the events occur stochastically, Mobile Charging Sequence Scheduling for Optimal Stochastic Event Detection (MCSS-OSED) is difficult and challenging, and the non-deterministic detection property of the sensor makes MCSS-OSED complicated further. This paper proposes a novel Multistage Exploration Q-learning Algorithm (MEQA) for MCSS-OSED based on reinforcement learning. In MEQA, MC is taken as the agent to explore the space of the mobile charging sequences via the interactions with the environment for the optimal Quality of Event Detection (QED) evaluated by both considering the sensing probability of the sensor and the probability that events may occur in the monitoring region. Particularly, a new multistage exploration policy is designed for MC to improve the exploration efficiency by selecting the current suboptimal actions with a certain probability, and a novel reward function is presented to evaluate the MC charging action according to the real-time detection contribution of the sensor. Simulation results show that MEQA is efficient for MCSS-OSED and superior to the existing classical algorithms.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104301"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"NoRDEx: A decentralized optimistic non-repudiation protocol for data exchanges","authors":"Fernando Román-García,&nbsp;Juan Hernández-Serrano,&nbsp;Oscar Esparza","doi":"10.1016/j.jnca.2025.104291","DOIUrl":"10.1016/j.jnca.2025.104291","url":null,"abstract":"<div><div>This article introduces the Non-Repudiable Data Exchange (NoRDEx) protocol, designed to ensure non-repudiation in data exchanges. Unlike traditional non-repudiation and fair exchange protocols, NoRDEx can be considered decentralized as it eliminates the need for a centralized Trusted Third Party (TTP) by using a Distributed Ledger Technology (DLT) to store cryptographic proofs without revealing the exchanged message. NoRDEx is an optimistic non-repudiation protocol, as it only uses the DLT in case of a dispute. The protocol has been implemented and tested in real-world environments, with performance assessments covering cost, overhead, and execution time. A formal security analysis using the Syverson Van Oorschot (SVO) logical model demonstrates NoRDEx’s ability to resolve disputes securely.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104291"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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-based Deep Learning Models for Intrusion Detection in Industrial Control Systems: Frameworks and Open Issues","authors":"Devi Priya V.S. ,&nbsp;Sibi Chakkaravarthy Sethuraman ,&nbsp;Muhammad Khurram Khan","doi":"10.1016/j.jnca.2025.104286","DOIUrl":"10.1016/j.jnca.2025.104286","url":null,"abstract":"<div><div>Critical infrastructure and industrial systems are both becoming more and more networked and equipped with computing and communications tools. To manage processes and automate them where possible, Industrial Control Systems (ICS) manage a variety of components, including monitoring tools and software platforms. More complicated data is now being run on the networks, including data(past), money(present), and brains (future). In order to predictably detect specific services and patterns (deep learning) and automatically check authenticity and transfer value (blockchain), deep learning and blockchain are integrated into the ICS network. Hence, we conducted a thorough examination of the models published in the literature in order to comprehend how to integrate machine learning and blockchain efficiently and successfully for intrusion detection services. We also provide useful guidance for future research in this area by noting significant issues that must be addressed before substantial deployments of IDS models in ICS.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104286"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Joint container orchestrating and request routing for serverless edge computing-based simulation applications","authors":"Yong Peng ,&nbsp;Miao Zhang ,&nbsp;Zhi Zhou ,&nbsp;Hao Huang","doi":"10.1016/j.jnca.2025.104284","DOIUrl":"10.1016/j.jnca.2025.104284","url":null,"abstract":"<div><div>Serverless edge computing dynamically invokes functions based on events, enabling on-demand code execution at the network edge and minimizing infrastructure management overhead. This computing paradigm is naturally suitable for event-driven distributed simulation applications, which involves frequent event interactions and stringent latency constraints. When running on top of geographically dispersed edge clouds, container orchestration and request routing have a significant impact on the performance of serverless edge computing-based simulations. In this paper, we propose an online orchestration framework for cross-edge serverless computing-based-simulations, which aims to minimize the resource cost and carbon emission under performance (i.e., latency) constraint, via jointly optimizing the container retention and requesting routing on-the-fly. This long-term cost minimization problem is difficult since it is NP-hard and involves future uncertain information. To simultaneously address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method in a joint optimization framework. This framework first temporally decomposes the long-term time-coupling problem into a series of one-shot fractional problem via Lyapunov optimization, and then applies randomized dependent scheme to round the fractional solution to a near-optimal integral solution. The resulting online algorithm achieves an outstanding performance, as verified by extensive trace-driven simulations.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104284"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Balancing function performance and cluster load in serverless computing: A reinforcement learning solution","authors":"Menglin Zhou ,&nbsp;Bingbing Zheng ,&nbsp;Li Pan ,&nbsp;Shijun Liu","doi":"10.1016/j.jnca.2025.104299","DOIUrl":"10.1016/j.jnca.2025.104299","url":null,"abstract":"<div><div>Serverless computing, as an emerging cloud computing service model, enables developers to focus on business logic without concerning underlying resource management by decomposing applications into fine-grained functions that execute on demand. However, in heterogeneous server cluster environments, the bursty and transient nature of function requests presents significant resource scheduling challenges. To ensure the performance of function execution, newly created function instances are often scheduled to nodes with abundant resources. This leads to resource allocation imbalances under high loads, which could potentially trigger node failures. In this paper we model function scheduling as an optimization problem that balances performance and load. We then propose a scheduling method based on the PPO algorithm, which guides decisions by analyzing node load and performance metrics in real time. For validation, we conducted experiments on the OpenFaaS platform using both real and simulated traces. The experimental results demonstrate that our method not only effectively reduces the risks associated with load imbalance but also achieves improvements in function performance.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104299"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Real-time high-resolution hardware–software co-design neural architecture search for unmanned mobile platforms","authors":"ZiWen Dou,&nbsp;Jun Tian,&nbsp;HaiQuan Sang,&nbsp;MingMing Zhang","doi":"10.1016/j.jnca.2025.104282","DOIUrl":"10.1016/j.jnca.2025.104282","url":null,"abstract":"<div><div>Traditional manually designed high-resolution networks on mobile computing platforms often struggle to balance accuracy and inference speed. To address the issue of large computational costs in high-resolution neural networks, which makes them difficult to deploy on mobile computing platforms, we simplified the traditional multi-scale feature extraction process by reducing the three-branch fusion to a two-branch fusion, establishing a lightweight network-level search space. We applied gradient descent to iteratively optimize the two-layer parameters within the search space and used the pareto optimal algorithm to balance inference speed and accuracy. After convergence, we obtained a multi-scale feature extraction neural network structure that satisfies the balance inference speed and accuracy. When combined with different feature decoders, this structure enables real-time semantic segmentation and monocular depth estimation tasks on mobile platforms. An self-constructed unmanned mobile platform, built on a mobile computing platform, was used to collect image data from real-world environments to create a custom dataset. This dataset was used to validate the perception capabilities of the designed semantic segmentation and monocular depth estimation model on the mobile platform in real-world scenarios. The experiments demonstrate that our semantic segmentation model, designed for the NVIDIA NX mobile computing platform, achieves an accuracy of 71.7% for 1024 ×2048 high-resolution images, with an inference speed of 25.25 FPS. This represents a 39.2% improvement in inference speed over existing SOTA methods. Meanwhile, our monocular depth estimation model on the NVIDIA NX achieves an absolute relative error (Abs Rel) of 0.091, with an inference speed of 14.46 FPS. This method improves inference speed by 87.7% compared to existing methods, while preserving high accuracy. The code is available: <span><span>https://github.com/douziwenhit/RealtimeSeg</span><svg><path></path></svg></span> and <span><span>https://github.com/douziwenhit/RealtimeMDE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104282"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Secure and efficient data collaboration in cloud computing: Flexible delegation via hierarchical attribute-based signature","authors":"Wenrui Jiang,&nbsp;Yongjian Liao,&nbsp;Qishan Gao,&nbsp;Han Xu,&nbsp;Hongwei Wang","doi":"10.1016/j.jnca.2025.104328","DOIUrl":"10.1016/j.jnca.2025.104328","url":null,"abstract":"<div><div>Data collaboration allows multiple parties to jointly share and modify data stored in the cloud server. As unauthorized users may create or modify the shared data as they want by tampering with requests sent by authorized users to replace them with what the unauthorized users want to send, secure data collaboration in cloud computing requires data integrity protection of requests and precise privilege verification of users. However, while maintaining data integrity, it is difficult for current signature schemes to achieve the following demands: fine-grained access control, high scalability, a flexible and controllable hierarchical delegation mechanism, and efficient signing and verification. Therefore, we designed a scalable and flexible hierarchical attribute-based signature (HABS) model and proposed a signing policy HABS construction using the linear secret sharing scheme to construct an access structure. Furthermore, we proved the unforgeability of our HABS scheme in the standard model. We also analyzed and tested the performance of our HABS scheme and related scheme, and we found that our scheme has less signing computation consumption in large-scale systems with complex policies. Finally, we provided a specified application scenario of HABS used in data collaboration based on cloud computing.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104328"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"C-PFL: A committee-based personalized federated learning framework","authors":"Lifan Pan ,&nbsp;Hao Guo ,&nbsp;Wanxin Li","doi":"10.1016/j.jnca.2025.104327","DOIUrl":"10.1016/j.jnca.2025.104327","url":null,"abstract":"<div><div>Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to train a shared model while preserving data privacy collaboratively. However, malicious clients pose a significant threat to FL systems. This interference not only deteriorates model performance but also exacerbates the unfairness of the global model caused by data heterogeneity, leading to inconsistent performance across clients. We propose C-PFL, a committee-based personalized FL framework that improves both robustness and personalization. In contrast to prior approaches such as FedProto (which relies on the exchange of class prototypes), Ditto (which employs regularization between global and local models), and FedBABU (which freezes the classifier head during federated training), C-PFL introduces two principal innovations. C-PFL adopts a split-model design, updating only a shared backbone during global training while fine-tuning a personalized head locally. A dynamic committee of high-contribution clients validates submitted updates without public data, filtering low-quality or adversarial contributions before aggregation. Experiments on MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and AGNews show that C-PFL outperforms six state-of-the-art personalized FL baselines by up to 2.89% in non-adversarial settings, and by as much as 6.96% under 40% malicious clients. These results demonstrate C-PFL’s ability to sustain high accuracy and stability across diverse non-IID scenarios, even with significant adversarial participation.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104327"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}