Journal of Network and Computer Applications最新文献

{"title":"Task offloading strategy of vehicle edge computing based on reinforcement learning","authors":"Lingling Wang,&nbsp;Wenjie Zhou,&nbsp;Linbo Zhai","doi":"10.1016/j.jnca.2025.104195","DOIUrl":"10.1016/j.jnca.2025.104195","url":null,"abstract":"<div><div>The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function to evaluate the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104195"},"PeriodicalIF":7.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847508","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":"An overview and solution for democratizing AI workflows at the network edge","authors":"Andrej Čop,&nbsp;Blaž Bertalanič,&nbsp;Carolina Fortuna","doi":"10.1016/j.jnca.2025.104180","DOIUrl":"10.1016/j.jnca.2025.104180","url":null,"abstract":"<div><div>With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applications and systems at the network edge. While AI models and services are well-managed in cloud systems, achieving similar maturity for serving network needs remains an open challenge. Existing open solutions are emerging and are yet to consider democratization requirements. In this work, we identify key requirements for democratization and propose NAOMI, a solution for democratizing AI/ML workflows at the network edge designed based on those requirements. Guided by the functionality and overlap analysis of the O-RAN AI/ML workflow architecture and MLOps systems, coupled with the survey of open-source AI/ML tools, we develop a modular, scalable, and distributed hardware architecture-independent solution. NAOMI leverages state-of-the-art open-source tools and can be deployed on distributed clusters of heterogeneous devices. The results show that NAOMI performs up to 40% better in deployment time and up to 73% faster in AI/ML workflow execution for larger datasets compared to AI/ML Framework, a representative open network access solution, while performing inference and utilizing resources on par with its counterpart.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104180"},"PeriodicalIF":7.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834932","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":"Innovative detection of IoT cyber threats using a GBiTCN-Temformer and MKOA framework","authors":"Vipin Rai ,&nbsp;Pradeep Kumar Mishra ,&nbsp;Shivani Joshi ,&nbsp;Rajiv Kumar ,&nbsp;Avinash Dwivedi ,&nbsp;Amrita","doi":"10.1016/j.jnca.2025.104192","DOIUrl":"10.1016/j.jnca.2025.104192","url":null,"abstract":"<div><div>In the digital era, the Internet of Things (IoT) platform is progressively utilized in several applications due to its versatility, adaptability, and accessibility. Despite that, the lack of security protocols, improper device updates, and unauthorized access exposes the IoT environment to diverse cyber threats, which affect network security and user confidentiality. To enhance cybersecurity, various deep learning (DL) approaches have been developed, yet these techniques have struggled to detect various attacks affecting the IoT platform, often yielding imprecise results and high false positive rates. This research proposes a novel framework that leverages the Modified Kepler optimization algorithm (MKOA) based feature selection model and the Gated Bidirectional Temporal Convolutional Network (GBiTCN) based Temporal transformer (Temformer) detection model to improve the cybersecurity and integrity of the IoT devices. The MKOA technique is introduced to select optimal features from the normalized data, thereby analyzing the characteristics of attacks and reducing data dimensionality. In addition, this study introduces a hybrid detection model, which combines the Gated BiTCN and Temformer approaches. The Gated BiTCN captures the bidirectional semantic features of network traffic, while the Temformer extracts correlations among different sequences for precise cyber-attack detection. Further, the effectiveness of this proposed technique is measured by utilizing IoT attack datasets with distinct parameters and it attains a higher accuracy of 98.76 % during attack detection tasks. These results demonstrate that the proposed technique provides exceptional performance in cyber threat detection. Experimental validation shows that the model significantly contributes to cyber-attack detection and enhances the cybersecurity of IoT networks effectively.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104192"},"PeriodicalIF":7.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859544","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":"Corrigendum to ‘Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey’ [J. Netw. Comput., Volume 238, June 2025, 104152]","authors":"Rana Zeeshan Ahmad ,&nbsp;Muhammad Rizwan ,&nbsp;Muhammad Jehanzaib Yousuf ,&nbsp;Mohammad Bilal Khan ,&nbsp;Ahmad Almadhor ,&nbsp;Thippa Reddy Gadekallu ,&nbsp;Sidra Abbas","doi":"10.1016/j.jnca.2025.104193","DOIUrl":"10.1016/j.jnca.2025.104193","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104193"},"PeriodicalIF":7.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899937","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":"A dynamic spectrum access scheme for Internet of Things with improved federated learning","authors":"Feng Li ,&nbsp;Junyi Yang ,&nbsp;Kwok-Yan Lam ,&nbsp;Bowen Shen ,&nbsp;Hao Luo","doi":"10.1016/j.jnca.2025.104189","DOIUrl":"10.1016/j.jnca.2025.104189","url":null,"abstract":"<div><div>The traditional spectrum management paradigm is no longer sufficient to meet the increasingly urgent demand for efficient utilization of spectrum resources by Internet of Things (IoT) devices. Dynamic spectrum access, as an emerging solution, allows devices to intelligently select appropriate spectrum resources based on real-time demands and environmental changes. In this paper, we propose a dynamic spectrum access scheme based on a federated deep reinforcement learning framework, incorporating federated learning, graph neural networks (GNN), and deep Q networks (DQN). In the method, the GNN undertakes the Q-value prediction task, giving full play to its ability to capture inter-device relationships and environmental features. Meanwhile, the DQN learns by interacting with the environment and continuously adapts its strategy to maximize long-term cumulative rewards. To enhance the stability and learning efficiency of the model, we also apply techniques such as empirical playback buffering and updating the target network at fixed intervals. In particular, the use of the FedAge algorithm in federated learning helps to coordinate knowledge sharing and model updates across multiple devices, further enhancing the performance and operational efficiency of the entire system. After several simulation training, the results show that the system model of this paper’s scheme is close to or even better than the traditional federated deep reinforcement learning model in terms of convergence effect and stability while maintaining the privacy-preserving advantages of federated learning. Particularly noteworthy is that in terms of operational efficiency, this paper’s scheme significantly outperforms traditional federated deep learning models.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104189"},"PeriodicalIF":7.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829213","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":"A survey of secure semantic communications","authors":"Rui Meng ,&nbsp;Song Gao ,&nbsp;Dayu Fan ,&nbsp;Haixiao Gao ,&nbsp;Yining Wang ,&nbsp;Xiaodong Xu ,&nbsp;Bizhu Wang ,&nbsp;Suyu Lv ,&nbsp;Zhidi Zhang ,&nbsp;Mengying Sun ,&nbsp;Shujun Han ,&nbsp;Chen Dong ,&nbsp;Xiaofeng Tao ,&nbsp;Ping Zhang","doi":"10.1016/j.jnca.2025.104181","DOIUrl":"10.1016/j.jnca.2025.104181","url":null,"abstract":"<div><div>Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104181"},"PeriodicalIF":7.7,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806912","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":"On the network coding-based D2D collaborative recovery scheme for scalable video broadcasting","authors":"Lei Wang ,&nbsp;Jie Sun ,&nbsp;Liang Chen ,&nbsp;Jun Yin","doi":"10.1016/j.jnca.2025.104179","DOIUrl":"10.1016/j.jnca.2025.104179","url":null,"abstract":"<div><div>This paper examines the collaborative recovery issue for the scalable video broadcasting (SVB) system, where two proximate user nodes are able to maintain a local out-of-band device-to-device (D2D) pair to cooperatively recover their lost broadcasted packets. Traditional error protection methods, such as Forward Error Correction (FEC) and error concealment, often require the source node to dynamically adjust its broadcast content based on feedback from user nodes. However, in many practical SVB scenarios, such as mobile TV broadcasting systems and satellite-based video broadcast virtual file systems, user nodes are merely recipients of broadcast messages and cannot feasibly report their reception status back to the source node (i.e., feedback-free). To address these challenges, we propose the Network Coding based Collaborative recovery scheme for SVB, named NC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>–SVB. NC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>–SVB deviates from previous studies by employing a feedback-free transmission model, wherein the source node neither receives updates on reception status nor channel conditions from the user nodes, nor does it dynamically modify its broadcast content. By utilizing the designed coding window sliding mechanism and the collaborative video layer scheduling algorithm, each user node can independently maintain a sliding coding window, generate optimal network coded packets, and collaborate recovery for the partner in a timely manner. The theoretical bounds of reliability and decoding delay for NC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>–SVB have been analyzed. Experimental results demonstrate that NC<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>–SVB, compared to existing schemes, enhances the collaboration throughput, achieves higher decoding rates, offers lower decoding delays, as well as improved video playback quality.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104179"},"PeriodicalIF":7.7,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791890","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":"Enhancing DDoS defense in SDN using hierarchical machine learning models","authors":"Sukhveer Kaur,&nbsp;Krishan Kumar,&nbsp;Naveen Aggarwal","doi":"10.1016/j.jnca.2025.104168","DOIUrl":"10.1016/j.jnca.2025.104168","url":null,"abstract":"<div><div>Software Defined Networking (SDN) enhances network management by decoupling the control plane from the data plane, centralizing control in a software-based controller. While this architecture simplifies network administration, it also introduces vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the central controller and disrupt network operations. Current DDoS defense mechanisms, often based on conventional network datasets, fail to address SDN-specific challenges and typically focus on high-rate attacks, overlooking other critical types. To address these issues, we propose a hierarchical DDoS defense system (HDDS) tailored for SDN environments, capable of adapting to various network conditions through retraining. To support this, we introduce SDN-DAD, a dataset tailored for SDN that includes diverse attack traffic, such as legitimate traffic, flash traffic, and a variety of DDoS attacks, including low-rate, slow, and flood attacks targeting both the application and transport layers. Furthermore, we identify optimal features for attack detection that minimize computational load on the SDN controller. Our HDDS model achieves 95% detection accuracy for a wide range of DDoS attacks, with 100% accuracy for high-rate attacks, ensuring robust defense while preventing bottlenecks during high-traffic events. This approach enhances the security and resilience of SDN environments against a broad spectrum of DDoS threats, ensuring robust defense across varying network conditions.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104168"},"PeriodicalIF":7.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776694","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":"ParallelC-Store: A committee structure-based reliable parallel storage mechanism for permissioned blockchain sharding","authors":"Lin Qiu ,&nbsp;Kaimin Zhang ,&nbsp;Xingwei Wang ,&nbsp;Bo Yi ,&nbsp;Fei Gao ,&nbsp;Yanpeng Qu ,&nbsp;Min Huang","doi":"10.1016/j.jnca.2025.104171","DOIUrl":"10.1016/j.jnca.2025.104171","url":null,"abstract":"<div><div>The storage performance of blockchain suffers from serious limitations due to its employed full-replication strategy, especially in large-scale network services such as Jointcloud computing and big data processing. To address this challenge, some storage partitioning mechanisms integrating Erasure Coding with Byzantine Fault Tolerant (BFT) consensus protocol are developed, like BFT-Store and PartitionChain. Whilst promising, there still exist three major issues impacting system effectiveness, scalability and stability. Firstly, the high computational complexity of coding consumes substantial computing time. Secondly, the signature schemes for verifying the integrity and correctness of encoded data lead to massive transmitted data over the network. Thirdly, each process necessitates the participation of all nodes, causing extended time overhead and interruption of system operation. To optimize the above three aspects, this paper presents a parallel storage partitioning mechanism called ParallelC-Store, where the nodes are divided into <span><math><mi>g</mi></math></span> Storage Committees (SCs) based on the existing BFT sharding protocol. Firstly, the <span><math><mi>g</mi></math></span> SCs engage in parallel implementation of data encoding and decoding of <span><math><mi>g</mi></math></span> distinct original blocks in a synchronous manner. Hence, the computational complexity/throughput per block of encoding and decoding can be decreased/increased by about <span><math><mi>g</mi></math></span>/<span><math><msup><mrow><mi>g</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and <span><math><msup><mrow><mi>g</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>/<span><math><msup><mrow><mi>g</mi></mrow><mrow><mn>3</mn></mrow></msup></math></span> times respectively. Secondly, Merkle Tree and Bloom Filter are employed to generate the verification proof of encoded data, which avoids heavy communication burdens. Thirdly, all processes for different scenarios can be implemented exclusively within a specific SC when a node joins/quits the system or a single crashed node needs repair. The experimental results demonstrate that the proposed mechanism generally outperforms the comparison mechanisms in terms of storage consumption, coding efficiency and system scalability.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104171"},"PeriodicalIF":7.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746968","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":"Multi-agent reinforcement learning based dynamic self-coordinated topology optimization for wireless mesh networks","authors":"Qingwei Tang ,&nbsp;Wei Sun ,&nbsp;Zhi Liu ,&nbsp;Qiyue Li ,&nbsp;Xiaohui Yuan","doi":"10.1016/j.jnca.2025.104177","DOIUrl":"10.1016/j.jnca.2025.104177","url":null,"abstract":"<div><div>Wireless mesh network (WMN) technology enhances wireless communication coverage and increases end-to-end (E2E) delay. The delay in WMNs is influenced by various mesh topologies, which are shaped by transmission power factors. Due to the dynamic nature of WMNs, conventional offline topology optimization methods are ineffective. This paper presents a reinforcement learning (RL)-based dynamic collaborative optimization method to minimize E2E delay and power consumption in WMNs. First, we develop a numerical model that simulates real-world communication environments. Next, we design a baseline and a reward function tailored to this environment. We propose a novel dynamic self-coordinated topology optimization algorithm to address the challenges associated with high-dimensional state–action spaces and the need for coordinated actions among multiple terminal devices. By leveraging coordination signals from terminal agents and multiple optimization strategies, the algorithm enables automatic agent coordination. Experimental results and real-world simulations demonstrate that the proposed algorithm effectively reduces both E2E delay and device power consumption.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104177"},"PeriodicalIF":7.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725854","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}