Digital Communications and Networks最新文献_第9页

Personalized and sustainable federated learning in integrated Internet of Things 集成物联网中的个性化可持续联邦学习

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.dcan.2026.01.001

Xu Zheng , Yifu Zheng , Tingqi Wang , Chong Mu , Ke Yan , Ling Tian

{"title":"Personalized and sustainable federated learning in integrated Internet of Things","authors":"Xu Zheng , Yifu Zheng , Tingqi Wang , Chong Mu , Ke Yan , Ling Tian","doi":"10.1016/j.dcan.2026.01.001","DOIUrl":"10.1016/j.dcan.2026.01.001","url":null,"abstract":"<div><div>Integrated Internet of Things (IoT) brings novel opportunities for pervasive smart services, as these systems allow for seamless information and resource sharing among IoT devices. Meanwhile, Federated Learning (FL) has emerged as a new framework for distributed deployment of machine learning models and a promising approach for implementing intelligent IoT systems. However, integrated IoT systems are usually composed of diverse IoT devices from different systems, and thus their ownership, roles, data distribution, and capabilities are heterogeneous. Current FL algorithms mainly focus on handling non-Independent and Identically Distributed (Non-IID) issues, but often result in reduced and unsustainable performance in integrated IoT systems. Therefore, we investigate in this paper the problem of personalized and sustainable FL in integrated IoT systems. First, we argue that different parties in integrated IoT are heterogeneous and limited in available resources for FL, and these parties are also selfish and expect rational outcomes during cooperation, which is essential for guaranteeing the sustainability of integrated IoT. Then, this paper provides a novel framework for device selection in FL. It first sets one instance of the model for each device, and iteratively selects devices to participate in model training based on the joint consideration of local model accuracy, similarity of parameters, and remaining resources per device. The proposed method guarantees the rational allocation of resources to maintain balanced model performance across all devices. In this way, the sustainability of the whole IoT system is improved such that no devices will suffer extreme resource exhaustion or poor performance. Finally, extensive evaluation is conducted to validate the advanced performance of the proposed method in integrated IoT systems.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 441-450"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147603329","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}

引用次数: 0

Federated anomaly detection in smart grid systems 智能电网系统中的联邦异常检测

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.dcan.2026.02.001

Tre’ R. Jeter , Raed Alharbi , Jung Taek Seo , My T. Thai

{"title":"Federated anomaly detection in smart grid systems","authors":"Tre’ R. Jeter , Raed Alharbi , Jung Taek Seo , My T. Thai","doi":"10.1016/j.dcan.2026.02.001","DOIUrl":"10.1016/j.dcan.2026.02.001","url":null,"abstract":"<div><div>Smart Grid infrastructures have enhanced energy distribution efficiency, reliability, and sustainability, but their proper operation requires robust anomaly detection to mitigate risks from equipment failures, cyberattacks, and natural disasters. Federated Learning (FL) offers a privacy-preserving solution by allowing power plants and grid sectors to collaboratively train models without sharing raw data, addressing privacy concerns, regulatory compliance, and single points of failure that often emerge in centralized approaches. FL also improves real-time anomaly detection and scalability by adapting dynamically to different grid topologies while incurring minimal communication overhead. Within our FL framework, Transformer models excel in anomaly detection due to their self-attention mechanisms that capture intricate temporal dependencies in sensor data. Unlike traditional models, Transformers effectively learn long-range patterns, enhancing detection accuracy and responsiveness. This work conducts a comparative study of two state-of-the-art Tranformer models in an FL environment, evaluating their anomaly detection performance across four diverse smart grid datasets. To assess robustness, we introduce a GAN-based Anomaly Injection Attack (GAIA) that generates and injects realistic syntheitc anomalies. Our results indicate that both federated Transformer models achieve high detection performance across seven metrics, even under adversarial conditions, offering valuable insights into their capabilities in decentralized smart grid applications.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 451-461"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703262","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}

引用次数: 0

GCRA-FWVAE: Anomaly detection for IIoT univariate time series using time-frequency domain analysis gra - fwvae：基于时频域分析的工业物联网单变量时间序列异常检测

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-08-18 DOI: 10.1016/j.dcan.2025.08.007

Xiaoling Tao , Haowei Liu , Wenbo Zhao , Weikun Li , Yaqi Nie , Jingqi Fu

{"title":"GCRA-FWVAE: Anomaly detection for IIoT univariate time series using time-frequency domain analysis","authors":"Xiaoling Tao , Haowei Liu , Wenbo Zhao , Weikun Li , Yaqi Nie , Jingqi Fu","doi":"10.1016/j.dcan.2025.08.007","DOIUrl":"10.1016/j.dcan.2025.08.007","url":null,"abstract":"<div><div>With the rapid development of Artificial Intelligence of Things (AIoT) technologies, the security of Industrial Internet of Things (IIoT) data faces increasing challenges, particularly in time series anomaly detection. IIoT data are typically scarce in abnormal samples and noisy, making unsupervised learning a common solution. The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods. While Variational Autoencoders (VAEs) excel in noise resilience, they face two critical challenges in IIoT data: difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features. To address these challenges, we propose the Greater Cane Rat Algorithm-enhanced Fourier-Wavelet Conditional Variational Autoencoder (GCRA-FWVAE). Our method introduces a time-frequency dual-branch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization. These complementary representations jointly regulate the Conditional Variational Autoencoder (CVAE) reconstruction process, effectively preserving critical anomaly signatures while suppressing noise interference. The architecture is further optimized through bioinspired Greater Cane Rat Algorithm (GCRA) to improve adaptive learning capabilities. Extensive validation on the Yahoo benchmark indicates state-of-the-art performance, achieving an F1-score of 93.6% (an improvement of 4.5% over baseline VAEs) and a precision of 95.1%. These improvements significantly increase anomaly detection accuracy and robustness, particularly in the AIoT environment, where it effectively handles more complex and dynamic industrial data.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 405-416"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552410","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}

引用次数: 0

RADAR: A rapid discovery algorithm for routes in SDN-based IoV 雷达：一种基于sdn的车联网路由快速发现算法

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2024-08-08 DOI: 10.1016/j.dcan.2024.08.001

Zahid Khan , Nauman Khan , Anis Koubaa , Adel Ammar , Wadii Boulila

{"title":"RADAR: A rapid discovery algorithm for routes in SDN-based IoV","authors":"Zahid Khan , Nauman Khan , Anis Koubaa , Adel Ammar , Wadii Boulila","doi":"10.1016/j.dcan.2024.08.001","DOIUrl":"10.1016/j.dcan.2024.08.001","url":null,"abstract":"<div><div>Link failures are a critical issue that disrupts consistent communications in the fast-evolving Internet-of-Vehicles (<em>IoV</em>) environment. In response to the aforementioned limitation, we introduce the rapid discovery algorithm for routes (<em>RADAR</em>), a novel scheme designed for rapid route discovery in the event of abrupt link failures or topological changes. Built on a heuristic <span><math><mi>T</mi><mi>h</mi><mi>e</mi><mi>t</mi><msup><mrow><mi>a</mi></mrow><mrow><mo>⁎</mo></mrow></msup></math></span> algorithm, <em>RADAR</em> operates within a three-layered Software-Defined Networking (<em>SDN</em>) architecture to ensure seamless communications. The first layer collects vital vehicle information, including location, speed, direction, and vehicle <em>ID</em>. The second layer uses an <em>SDN</em> edge controller to carry out local and intra-zone path discovery with the <span><math><mi>T</mi><mi>h</mi><mi>e</mi><mi>t</mi><msup><mrow><mi>a</mi></mrow><mrow><mo>⁎</mo></mrow></msup></math></span> heuristic algorithm. The third layer introduces a global controller that oversees inter-zone route discovery, thereby expanding the network's reach and capabilities. To validate <em>RADAR</em>'s effectiveness, we compare its performance to those of <em>Dijkstra</em>-based and <span><math><msup><mrow><mi>A</mi></mrow><mrow><mo>⁎</mo></mrow></msup></math></span> path planning algorithms across key metrics—Route Discovery Messages (<em>RDM</em>), Path Length (<em>PL</em>), and Route Discovery Time (<em>RDT</em>). Our analysis shows significant improvements with <em>RADAR</em> over <em>Dijkstra</em>, achieving up to an 80% increase in <em>RDM</em>, 20% in <em>PL</em>, and 70% in <em>RDT</em>. It also surpasses <span><math><msup><mrow><mi>A</mi></mrow><mrow><mo>⁎</mo></mrow></msup></math></span> by up to 70% in <em>RDM</em> and 60% in <em>RDT</em>. These results indicate that <em>RADAR</em> enhances routing reliability in <em>IoV</em> systems, thereby improving overall network performance.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 495-504"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703265","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}

引用次数: 0

DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers 针对智能窃听器的MIMO-OFDM物联网系统的drl支持安全上行传输

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-08-18 DOI: 10.1016/j.dcan.2025.08.004

Yingzhen Wu , Yan Huo , Xin Fan , Qinghe Gao , Jian Mao , Tao Jing

{"title":"DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers","authors":"Yingzhen Wu , Yan Huo , Xin Fan , Qinghe Gao , Jian Mao , Tao Jing","doi":"10.1016/j.dcan.2025.08.004","DOIUrl":"10.1016/j.dcan.2025.08.004","url":null,"abstract":"<div><div>Physical layer security has emerged as a promising approach to counter eavesdropping threats in the Internet of Things (IoT). With the advancement of technology, eavesdroppers (Eves) are increasingly sophisticated, exacerbating the difficulties in ensuring the security of uplink transmissions. In situations where transmitters are unable to deter Eves from approaching at close range, these intelligent Eves can attain substantial channel gains, thereby posing significant security threats. To address these challenges, we first propose a time-domain artificial noise (AN) scheme to eliminate interference at the legitimate receiver in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Then, we model the adversarial interaction between legitimate transceivers and intelligent Eves as a game-theoretic problem. To solve the dynamic game problem, we introduce a deep deterministic policy gradient (DDPG)-based framework, where an optimal beamforming fractional programming algorithm (OBFP) and a lower-complexity zero-forcing beamforming difference of convex algorithm (ZFDC) are designed to ascertain the most effective strategy for legitimate transceivers. Simulation results validate the effectiveness of our proposed algorithms and highlight the critical role of time-domain AN in combating intelligent eavesdropping.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 428-440"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552411","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}

引用次数: 0

A latency sensitive and agile IIoT architecture with optimized edge node selection and task scheduling 具有优化的边缘节点选择和任务调度的延迟敏感和敏捷IIoT架构

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-05-08 DOI: 10.1016/j.dcan.2025.04.012

Mona Kumari, Maheswari Prasad Singh, Amit Kumar Singh

{"title":"A latency sensitive and agile IIoT architecture with optimized edge node selection and task scheduling","authors":"Mona Kumari, Maheswari Prasad Singh, Amit Kumar Singh","doi":"10.1016/j.dcan.2025.04.012","DOIUrl":"10.1016/j.dcan.2025.04.012","url":null,"abstract":"<div><div>The Industrial Internet of Things (IIoT) has revolutionized conventional manufacturing industries by improving control, monitoring, and management, resulting in increased agility and long-term sustainability. The demand for processing and storage resources increases as the number of IoT devices and the data they generate increases. Cloud computing often serves these needs, but in certain scenarios, real-time data processing near the source is necessary to guarantee low latency, minimize bandwidth usage, and enhance data security. This work proposes a novel edge computing-based IIoT architecture for delay-sensitive monitoring and control in manufacturing industries. In contrast to previous work, this work is designed to enhance system performance by ensuring the availability of edge nodes and distributing task execution loads evenly across multiple edge computing nodes. The proposed architecture is further optimized for efficient allocation of resources and scheduling of tasks by the addition of a task scheduling algorithm by using Round Trip Time (RTT) and Resource Logger (RL). In an effort to optimize the response time of both the complete system and individual Device Nodes (DN), the work also considers the deployment of heterogeneous Edge Nodes (EN). The evaluation results show that the proposed architecture reduces the communication delay and overhead by 88.28% compared to the traditional cloud-based approach and 12.32% compared to the previously proposed methods ATS-FOA and RBEC/PENB, respectively, for executing IIoT applications. The proposed Resource Logger, Round Trip Time-Based Edge Node Selection (RLRTT-ESA), and task scheduling algorithms further improved the total application response time by 47.25% in comparison to the previously proposed methods ATS-FOA and RBEC/PENB, respectively.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 482-494"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703264","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}

引用次数: 0

GCN and DRL based on dependent task offloading mechanism in edge computing 边缘计算中基于相关任务卸载机制的GCN和DRL

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-10-17 DOI: 10.1016/j.dcan.2025.10.001

Ruiqi Tong , Shaoyong Guo , Xuesong Qiu , Feng Qi , Dongxiao Yu

引用次数: 0

Video frame interpolation based on invertible neural network for Internet of things 基于可逆神经网络的物联网视频帧插值

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-01-30 DOI: 10.1016/j.dcan.2025.01.005

Lingfan Wu , Keyuan Ye , Huan Gao , Huchen Jiang , Hong Zhang

{"title":"Video frame interpolation based on invertible neural network for Internet of things","authors":"Lingfan Wu , Keyuan Ye , Huan Gao , Huchen Jiang , Hong Zhang","doi":"10.1016/j.dcan.2025.01.005","DOIUrl":"10.1016/j.dcan.2025.01.005","url":null,"abstract":"<div><div>Video frame interpolation focuses on directly synthesizing intermediate frames by utilizing inter-frame changes, relying heavily on large, high-frame-rate video datasets for supervised training, which imposes significant demands on bandwidth and computational resources in the Internet of Everything (IoE) environments. Since video frame rate down and up conversion are inverse processes, an Invertible Neural Network (INN) provides an efficient solution by ensuring lossless and symmetrical information transfer in forward and backward processes. This paper introduces a self-supervised video frame rate conversion method based on an INN to reconstruct missing intermediate frames. By leveraging an invertible coupling structure, the model encodes the spatio-temporal features of sparse input frames into a Gaussian distribution, which effectively simulates the frame rate downsampling process. Through the network's invertibility and lossless processing capabilities, the intermediate frames are then reconstructed through reverse inference. This approach captures missing information from a Gaussian prior, ensuring stability and realism in the generated frames. Extensive experiments on public video datasets show that the proposed method surpasses existing state-of-the-art algorithms in accuracy and efficiency, offering superior visual quality, faster processing speeds, and reduced model parameters, especially for high-frame-rate recovery.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 3","pages":"Pages 540-549"},"PeriodicalIF":7.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147703266","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}

引用次数: 0

DDMI: a model information evaluation method based on deep dream DDMI：一种基于深度梦的模型信息评价方法

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-03-01 Epub Date: 2025-03-17 DOI: 10.1016/j.dcan.2025.03.008

Yana Yang , Haoyu Li , Shuai Xiao , Guipeng Lan , Yong Zhu , Ziqiang Huo

引用次数: 0

A security authentication scheme for mobile industrial IoT supply chains based on blockchain and group key management 基于区块链和组密钥管理的移动工业物联网供应链安全认证方案

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-02-01 Epub Date: 2025-08-18 DOI: 10.1016/j.dcan.2025.08.003

ChaoYue Wang , Xian Zhao , Qingyuan Liu , Ting Chen , Tao Liu

{"title":"A security authentication scheme for mobile industrial IoT supply chains based on blockchain and group key management","authors":"ChaoYue Wang , Xian Zhao , Qingyuan Liu , Ting Chen , Tao Liu","doi":"10.1016/j.dcan.2025.08.003","DOIUrl":"10.1016/j.dcan.2025.08.003","url":null,"abstract":"<div><div>Driven by globalization and digitization, the Mobile Industrial Supply Chain Internet of Things (IoT) has gradually developed, utilizing mobile devices and IoT technologies to enable real-time monitoring and efficient responses across various stages. However, with the growing demand for high-frequency data exchange, the Mobile Industrial Supply Chain IoT faces significant challenges in data security, authentication, and privacy protection. This paper proposes a security authentication scheme based on blockchain and group key management, leveraging the decentralized and tamper-resistant features of blockchain, the privacy-preserving authentication method of Zero-Knowledge Proofs (ZKP), and a hierarchical key management mechanism based on binary key trees. This approach aims to enhance the security and scalability of Mobile Industrial Supply Chain IoT. The experimental section simulates scenarios such as dynamic node addition and key updates, evaluating the performance in terms of encryption, decryption, and key management efficiency, thus demonstrating its superiority in multi-party collaborative environments.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 2","pages":"Pages 283-293"},"PeriodicalIF":7.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423859","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}

引用次数: 0