Digital Communications and Networks最新文献_第7页

SecureBadger: a homomorphic encryption-based framework for secure medical inference SecureBadger：用于安全医学推断的基于同态加密的框架

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-05-01 Epub Date: 2025-08-18 DOI: 10.1016/j.dcan.2025.08.006

Zhaoyang He , Wenti Yang , Longfei Wu , Zhitao Guan

{"title":"SecureBadger: a homomorphic encryption-based framework for secure medical inference","authors":"Zhaoyang He , Wenti Yang , Longfei Wu , Zhitao Guan","doi":"10.1016/j.dcan.2025.08.006","DOIUrl":"10.1016/j.dcan.2025.08.006","url":null,"abstract":"<div><div>With the rapid development of Artificial Intelligence of Things (AIoT) technology, its adoption in the field of smart healthcare is becoming increasingly pervasive. Leading cloud service providers like IBM Watson Health now offer neural network inference services tailored for smart healthcare applications - users simply need to send data to the server to get the diagnosis results. However, a growing concern arises regarding the potential compromise of user privacy. Currently, researchers propose the use of secure multi-party computation and homomorphic encryption techniques to address this issue. Nevertheless, further exploration and improvement are needed to mitigate the side effects, such as increased latency and challenges in meeting real-time monitoring requirements. In this paper, we propose a secure homomorphic encryption-based inference framework named SecureBadger for two typical medical inference scenarios: disease diagnosis based on image analysis and health monitoring with smart wearable devices. We design two inference modes—large-scale batch inference and small-scale low-latency inference. Additionally, different ciphertext packaging schemes are designed to enhance inference efficiency for different inference modes, different input data types and different network layers. Experimental evaluations are conducted on several datasets, and the results indicate that SecureBadger can significantly reduce the inference time overhead in both inference modes.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 5","pages":"Pages 743-754"},"PeriodicalIF":7.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148170045","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

FSCM constellation mapping fusion for HF fading channels 高频衰落信道的FSCM星座映射融合

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-05-01 Epub Date: 2025-12-08 DOI: 10.1016/j.dcan.2025.12.001

Jie Wang , Guojun Li , Zheming Zhang , Yun Lin

{"title":"FSCM constellation mapping fusion for HF fading channels","authors":"Jie Wang , Guojun Li , Zheming Zhang , Yun Lin","doi":"10.1016/j.dcan.2025.12.001","DOIUrl":"10.1016/j.dcan.2025.12.001","url":null,"abstract":"<div><div>To resolve the problems of unreliable transmission and low communication capacity caused by the time-frequency domain dual-selective fading of High Frequency (HF) channels, a constellation mapping fusion technology for HF Frequency Shift Chirp Modulation (FSCM) is proposed. On one hand, the FSCM technology is employed to compensate for the fast fading introduced by frequency domain expansion, and the spreading factor is used to adjust the transmission rate. On the other hand, based on the FSCM matching, by constructing a two-dimensional constellation mapping, the soft information and symbol weights of multipaths are extracted, thereby realizing single-channel RAKE fusion and multi-channel diversity fusion with symbol-by-symbol weighting. This effectively improves transmission reliability in HF dual-selective channels where the main and secondary paths switch. In typical HF scenarios defined by ITU-R F.1487, simulation results demonstrate that, compared with the traditional hard demodulation algorithms, the proposed soft information fusion algorithm can still ensure reliable transmission under low signal-to-noise ratio conditions, even with high multipath separation degrees. Moreover, through 24-h field tests, the practical applicability of the proposed algorithm is further verified.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 5","pages":"Pages 802-812"},"PeriodicalIF":7.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148232182","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

Statistical energy consumption analysis and optimization for relaying transmission with wireless power transfer 无线功率传输中继传输的统计能耗分析与优化

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2025-07-05 DOI: 10.1016/j.dcan.2025.06.012

Fang Xu , Yuanchen Wang , Xinyu Zhang , Yiyuan Xie , Ramy Samy

引用次数: 0

Data grading for intelligent connected vehicles: an inference strength-driven framework 智能网联汽车数据分级：一个推理强度驱动的框架

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2026-02-26 DOI: 10.1016/j.dcan.2026.02.004

Shichun Yang , Bowen Zheng , Yi Shi , Haoran Guang , Tianyang Gong , Weifeng Gong , Xinjie Feng , Mingjie Chen , Yaoguang Cao , Hao Wu , Tian Liu , Jia Zhao

{"title":"Data grading for intelligent connected vehicles: an inference strength-driven framework","authors":"Shichun Yang , Bowen Zheng , Yi Shi , Haoran Guang , Tianyang Gong , Weifeng Gong , Xinjie Feng , Mingjie Chen , Yaoguang Cao , Hao Wu , Tian Liu , Jia Zhao","doi":"10.1016/j.dcan.2026.02.004","DOIUrl":"10.1016/j.dcan.2026.02.004","url":null,"abstract":"<div><div>Intelligent Connected Vehicles (ICVs) generate massive heterogeneous multi-modal data during operation, and due to the limited computing resources on board, graded data encryption protection is of great significance for balancing data security and efficient utilization. However, the current data grading processes struggle to address the evolving inference attacks and dynamic operational environments, and traditional grading approaches relying on static expert judgment or information-theoretic metrics. To bridge this gap, this paper proposes a novel inference strength-driven data grading framework, where inference strength quantifies the susceptibility of one dataset to infer another through adversarial reasoning. The framework employs a systematic methodology combining graph theory, optimization, and Large Language Models to construct an inference library and calculate inference strength. The framework also provides a PageRank-based algorithm to generate interpretable data grading lists for both static policy and vehicle-end application, prioritizing core data protection while respecting computational constraints. Validated on the Audi A2D2 dataset and real vehicle controller, our approach demonstrates improved protection utility compared to default grading baselines. The results highlight its potential to enhance data security in ICVs through prioritized protection of core data under computational constraints.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 698-715"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147996559","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

Computing power networks for unmanned aerial vehicles: a hierarchical resources trading market 无人机计算能力网络：分层资源交易市场

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2025-01-08 DOI: 10.1016/j.dcan.2024.12.002

Xiaofei Wang , Hui Deng , Chao Qiu , Zheyuan Chen , Tao Luo , Zhao Ming

引用次数: 0

NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment NPO-BAND：回程感知无人机网络部署的数量和布局优化

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2024-11-22 DOI: 10.1016/j.dcan.2024.11.016

Sining Yang , Bo Zhang , Jinshu Su

{"title":"NPO-BAND: Number and placement optimization for backhaul-aware UAV network deployment","authors":"Sining Yang , Bo Zhang , Jinshu Su","doi":"10.1016/j.dcan.2024.11.016","DOIUrl":"10.1016/j.dcan.2024.11.016","url":null,"abstract":"<div><div>In emergency communication scenarios, exploiting Unmanned Aerial Vehicles (UAVs) as relays to provide wireless communication services for ground users has emerged as a promising application. A key challenge in this resource-constrained application is deploying the minimum number of UAVs to form an aerial backhaul network to ensure coverage, which composes the Number and Placement Optimization for the Backhaul-Aware Network Deployment (NPO-BAND) problem. In this paper, we first formulate the NPO-BAND problem based on the geometric disk coverage model. Then, we propose a low-complexity heuristic method to solve this NP-hard problem. The proposed method contains a Very Important Point-Choosing (VIPC) strategy and a Backhaul-Aware Local Coverage (BALC) algorithm. Specifically, the VIPC strategy weighs up the backhaul connectivity constraint and the ground user coverage to choose the VIP, while the BALC algorithm solves the extended 1-center problem to determine the deployment location of each UAV. Simulation results show that the proposed method can effectively reduce the number of deployed UAVs, saving up to 25%-50% of that compared to existing methods across varying numbers and area sizes in clustered distribution patterns of ground users.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 630-639"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147852539","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

Distributed context-aware Transformer-enabled dynamic energy consumption prediction for smart building networks 面向智能建筑网络的分布式环境感知变压器动态能耗预测

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2025-03-10 DOI: 10.1016/j.dcan.2025.03.006

Rui Dai, Ge Bai

{"title":"Distributed context-aware Transformer-enabled dynamic energy consumption prediction for smart building networks","authors":"Rui Dai, Ge Bai","doi":"10.1016/j.dcan.2025.03.006","DOIUrl":"10.1016/j.dcan.2025.03.006","url":null,"abstract":"<div><div>The integration of communication networks and artificial intelligence enables the effective collection of data over smart building networks, facilitating more accurate predictions of Building Energy Consumption (BEC). However, existing schemes for BEC prediction suffer from limited dynamic adaptability, risks of privacy leakage, and the inability to accurately capture actual energy consumption patterns. To improve prediction accuracy while ensuring privacy and dynamic adaptability, we propose a novel BEC prediction design that incorporates dynamic threshold participation and privacy-preserving mechanisms. Specifically, we design a three-tier network architecture integrated with threshold participation tokens to support dynamic access and dropout of building entities during the BEC model construction process. Furthermore, we develop a Context-Aware Transformer (CAT) network integrated into Federated Learning (FL) to enhance feature sensitivity and facilitate the sharing of knowledge derived from Internet of Things (IoT) data and BEC features. Finally, we evaluate the performance of our design using real-world data, and the results demonstrate that our design achieves superior performance in distributed BEC prediction.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 640-648"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147852564","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

CGGM: a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks CGGM：一种物联网网络节点异常检测的自适应稀疏条件图生成模型

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2025-05-28 DOI: 10.1016/j.dcan.2025.05.013

Munan Li , Xianshi Su , Runze Ma , Tongbang Jiang , Zijian Li , Tony Q.S. Quek

{"title":"CGGM: a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks","authors":"Munan Li , Xianshi Su , Runze Ma , Tongbang Jiang , Zijian Li , Tony Q.S. Quek","doi":"10.1016/j.dcan.2025.05.013","DOIUrl":"10.1016/j.dcan.2025.05.013","url":null,"abstract":"<div><div>Dynamic graphs are increasingly utilized for detecting anomalous behaviors in nodes within the Internet of Things (IoT). Graph generative models play a pivotal role in addressing the challenge of imbalanced node categories in dynamic graphs. However, these models encounter several limitations, including the monotonicity of adjacency relationships, the complexity in constructing multi-dimensional features for nodes, and the absence of an end-to-end method for generating multiple categories of nodes. In this study, we introduce a novel graph generation model, designated as Conditional Graph Generation Model (CGGM), aimed specifically at generating samples from minority classes. The architecture comprises two principal modules: a conditional graph generation module and a graph-based anomaly detection module. The generative module adjusts to matrix sparsity by downsampling a noise adjacency matrix and integrates a multi-dimensional feature encoder based on multi-head self-attention to capture latent feature dependencies. Furthermore, a latent space constraint coupled with distribution distance is utilized to approximate the latent distribution of real data. The graph-based anomaly detection module employs the generated balanced dataset to predict node behaviors. Extensive experiments demonstrate that CGGM surpasses contemporary state-of-the-art methods in accuracy and divergence. The results further reveal that CGGM can produce diverse data categories, thereby enhancing the performance in multi-category classification tasks.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 686-697"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147996560","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 comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 人工智能在支持无人机的无线网络中的应用的综合调查

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2024-11-20 DOI: 10.1016/j.dcan.2024.11.005

Li Zhou , Hao Yin , Haitao Zhao , Jibo Wei , Dewen Hu , Victor C.M. Leung

{"title":"A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks","authors":"Li Zhou , Hao Yin , Haitao Zhao , Jibo Wei , Dewen Hu , Victor C.M. Leung","doi":"10.1016/j.dcan.2024.11.005","DOIUrl":"10.1016/j.dcan.2024.11.005","url":null,"abstract":"<div><div>This comprehensive survey paper examines the applications of Artificial Intelligence (AI) in Unmanned Aerial Vehicle (UAV)-enabled wireless networks. With the increasing demand for efficient and adaptive communication systems, the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication. The paper first outlines the background and motivation behind AI integration, highlighting the potential for enhanced network performance, autonomy, and adaptability. It then delves into the key AI applications across different network layers, including data sensing and collection, placement and trajectory optimization, radio resource management, routing and topology control, edge computing and caching, as well as security and privacy enhancement. For each application, the paper discusses relevant AI techniques, main findings, optimization objects, and the potential benefits and challenges. The survey also identifies open issues, such as the practical implementation gap, standardization issues, and real-world application barriers, and proposes future directions to address these challenges and further advance the field. In conclusion, the integration of AI with UAV-enabled Wireless Networks (UWNs) holds tremendous potential for transforming wireless communication, enabling new applications and services with unprecedented capabilities.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 561-583"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147743639","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

Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems 基于用户关联的URLLC和eMBB 5G多rat系统的能源最小化联合资源分配和任务卸载

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2026-04-01 Epub Date: 2025-07-04 DOI: 10.1016/j.dcan.2025.07.002

Dongjun Jung , Jong-Moon Chung , Hea-Sook Park

{"title":"Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems","authors":"Dongjun Jung , Jong-Moon Chung , Hea-Sook Park","doi":"10.1016/j.dcan.2025.07.002","DOIUrl":"10.1016/j.dcan.2025.07.002","url":null,"abstract":"<div><div>In this paper, a multi-agent deep reinforcement learning-based joint resource allocation and task offloading control technique is proposed to minimize the energy consumption of user equipment (UE) supporting 5G ultra reliability low latency communications (URLLC) and enhanced mobile broadband (eMBB) services while meeting strict quality of service (QoS) requirements in 5G multi-radio access technology (RAT) networks. An optimization problem involving transmission power, channel resource, user association, offloading rate, and central processing unit (CPU) frequency is formulated using a queueing system-based mathematical design to support services with different characteristics while minimizing the energy consumption. It is proven in this paper that this problem is nondeterministic polynomial (NP) hard, in which multi-agent deep reinforcement learning (DRL) is used to solve the problem. To increase the learning efficiency and stability of deep reinforcement learning, prioritized experience replay (PER) and delayed target network and policy updates are applied. Simulation results show that the proposed scheme provides an improved energy consumption performance compared to the benchmarked schemes.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"12 4","pages":"Pages 662-676"},"PeriodicalIF":7.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147953100","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