Digital Communications and Networks最新文献_第10页

Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles 数字孪生为动态车联网提供轻便高效的区块链

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.08.004

Haoye Chai , Supeng Leng , Jianhua He , Ke Zhang

{"title":"Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles","authors":"Haoye Chai , Supeng Leng , Jianhua He , Ke Zhang","doi":"10.1016/j.dcan.2023.08.004","DOIUrl":"10.1016/j.dcan.2023.08.004","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) has great potential for Intelligent Transportation Systems (ITS), enabling interactive vehicle applications, such as advanced driving and infotainment. It is crucial to ensure the reliability during the vehicle-to-vehicle interaction process. Although the emerging blockchain has superiority in handling security-related issues, existing blockchain-based schemes show weakness in highly dynamic IoV. Both the transaction broadcast and consensus process require multiple rounds of communication throughout the whole network, while the high relative speed between vehicles and dynamic topology resulting in the intermittent connections will degrade the efficiency of blockchain. In this paper, we propose a Digital Twin (DT)-enabled blockchain framework for dynamic IoV, which aims to reduce both the communication cost and the operational latency of blockchain. To address the dynamic context, we propose a DT construction strategy that jointly considers the DT migration and blockchain computing consumption. Moreover, a communication-efficient Local Perceptual Multi-Agent Deep Deterministic Policy Gradient (LPMA-DDPG) algorithm is designed to execute the DT construction strategy among edge servers in a decentralized manner. The simulation results show that the proposed framework can greatly reduce the communication cost, while achieving good security performance. The dynamic DT construction strategy shows superiority in operation latency compared with benchmark strategies. The decentralized LPMA-DDPG algorithm is helpful for implementing the optimal DT construction strategy in practical ITS.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1698-1707"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46508635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overview of RIS-enabled secure transmission in 6G wireless networks 6G无线网络中支持ris的安全传输概述

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.02.005

JungSook Bae , Waqas Khalid , Anseok Lee , Heesoo Lee , Song Noh , Heejung Yu

引用次数: 0

A game incentive mechanism for energy efficient federated learning in computing power networks 计算能力网络中高效节能联合学习的博弈激励机制

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.10.006

Xiao Lin , Ruolin Wu , Haibo Mei , Kun Yang

{"title":"A game incentive mechanism for energy efficient federated learning in computing power networks","authors":"Xiao Lin , Ruolin Wu , Haibo Mei , Kun Yang","doi":"10.1016/j.dcan.2023.10.006","DOIUrl":"10.1016/j.dcan.2023.10.006","url":null,"abstract":"<div><div>Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1741-1747"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136093691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-class Bitcoin mixing service identification based on graph classification 基于图分类的多类比特币混合服务识别

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.08.010

Xiaoyan Hu , Meiqun Gui , Guang Cheng , Ruidong Li , Hua Wu

{"title":"Multi-class Bitcoin mixing service identification based on graph classification","authors":"Xiaoyan Hu , Meiqun Gui , Guang Cheng , Ruidong Li , Hua Wu","doi":"10.1016/j.dcan.2024.08.010","DOIUrl":"10.1016/j.dcan.2024.08.010","url":null,"abstract":"<div><div>Due to its anonymity and decentralization, Bitcoin has long been a haven for various illegal activities. Cyber-criminals generally legalize illicit funds by Bitcoin mixing services. Therefore, it is critical to investigate the mixing services in cryptocurrency anti-money laundering. Existing studies treat different mixing services as a class of suspicious Bitcoin entities. Furthermore, they are limited by relying on expert experience or needing to deal with large-scale networks. So far, multi-class mixing service identification has not been explored yet. It is challenging since mixing services share a similar procedure, presenting no sharp distinctions. However, mixing service identification facilitates the healthy development of Bitcoin, supports financial forensics for cryptocurrency regulation and legislation, and provides technical means for fine-grained blockchain supervision. This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification (BMSI-GC) model. First, BMSI-GC constructs 2-hop ego networks (2-egonets) of mixing services based on their historical transactions. Second, it applies graph2vec, a graph classification model mainly used to calculate the similarity between graphs, to automatically extract address features from the constructed 2-egonets. Finally, it trains a multilayer perceptron classifier to perform classification based on the extracted features. BMSI-GC is flexible without handling the full-size network and handcrafting address features. Moreover, the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification. Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification, achieving an average identification F1-score of 95.08%.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1881-1893"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143355759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs 实现低成本NOMA的F-RAN的联合用户关联和资源分配

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.08.001

Yuan Ai , Chenxi Liu , Mugen Peng

{"title":"Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs","authors":"Yuan Ai , Chenxi Liu , Mugen Peng","doi":"10.1016/j.dcan.2023.08.001","DOIUrl":"10.1016/j.dcan.2023.08.001","url":null,"abstract":"<div><div>Integrating Non-Orthogonal Multiple Access (NOMA) into Fog Radio Access Networks (F-RANs) has shown to be effective in boosting the spectral efficiency, energy efficiency, connectivity, and reducing the latency, thus attracting significant research attention. However, the performance improvement of the NOMA-enabled F-RANs is at the cost of computational overheads, which are commonly neglected in their design and deployment. To address this issue, in this paper, we propose a hybrid dynamic downlink framework for NOMA-enabled F-RANs. In this framework, we first develop a novel network utility function, which takes both the network throughput and computational overheads into consideration, thus enabling us to comprehensively evaluate the performance of different access schemes for F-RANs. Based on the developed network utility function, we further formulate a network utility maximization problem, subject to practical constraints on the decoding order, power allocation, and quality-of-service. To solve this NP-hard problem, we decompose it into two subproblems, namely, a user equipment association and subchannel assignment subproblem and a power allocation subproblem. Three-dimensional matching and sequential convex programming-based algorithms are designed to solve these two subproblems, respectively. Through numerical results, we show how our proposed algorithms can achieve a good balance between the network throughput and computational overheads by judiciously adjusting the maximum transmit power of fog access points. We also show that the proposed NOMA-enabled F-RAN framework can increase, by up to 89%, the network utility, compared to OMA-based F-RANs.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1686-1697"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48715483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles 基于dag的群学习：一种安全的车联网异步学习框架

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.10.004

Xiaoge Huang , Hongbo Yin , Qianbin Chen , Yu Zeng , Jianfeng Yao

{"title":"DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles","authors":"Xiaoge Huang , Hongbo Yin , Qianbin Chen , Yu Zeng , Jianfeng Yao","doi":"10.1016/j.dcan.2023.10.004","DOIUrl":"10.1016/j.dcan.2023.10.004","url":null,"abstract":"<div><div>To provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. However, there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles (IoV), such as unreliable communications and malicious attacks. In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. Moreover, to enhance the anti-attack property of the DSL algorithm, a malicious attack detection method is adopted, which could recognize malicious vehicles by the site confirmation rate. Furthermore, an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors. Finally, simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy, convergence rates and security compared with existing algorithms.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1611-1621"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey on security aspects of distributed software-defined networking controllers in an enterprise SD-WLAN 企业SD-WLAN中分布式软件定义网络控制器的安全性研究

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.09.004

Neena Susan Shaji, Raja Muthalagu

引用次数: 0

Robust beamforming design for energy harvesting efficiency maximization in RIS-aided SWIPT system 在 RIS 辅助 SWIPT 系统中实现能量收集效率最大化的稳健波束成形设计

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.01.004

Xingquan Li , Hongxia Zheng , Chunlong He , Xiaowen Tian , Xin Lin

引用次数: 0

Rate distortion optimization for adaptive gradient quantization in federated learning 联合学习中自适应梯度量化的速率失真优化

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2024.01.005

Guojun Chen , Kaixuan Xie , Wenqiang Luo , Yinfei Xu , Lun Xin , Tiecheng Song , Jing Hu

{"title":"Rate distortion optimization for adaptive gradient quantization in federated learning","authors":"Guojun Chen , Kaixuan Xie , Wenqiang Luo , Yinfei Xu , Lun Xin , Tiecheng Song , Jing Hu","doi":"10.1016/j.dcan.2024.01.005","DOIUrl":"10.1016/j.dcan.2024.01.005","url":null,"abstract":"<div><div>Federated Learning (FL) is an emerging machine learning framework designed to preserve privacy. However, the continuous updating of model parameters over uplink channels with limited throughput leads to a huge communication overload, which is a major challenge for FL. To address this issue, we propose an adaptive gradient quantization approach that enhances communication efficiency. Aiming to minimize the total communication costs, we consider both the correlation of gradients between local clients and the correlation of gradients between communication rounds, namely, in the time and space dimensions. The compression strategy is based on rate distortion theory, which allows us to find an optimal quantization strategy for the gradients. To further reduce the computational complexity, we introduce the Kalman filter into the proposed approach. Finally, numerical results demonstrate the effectiveness and robustness of the proposed rate-distortion optimization adaptive gradient quantization approach in significantly reducing the communication costs when compared to other quantization methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1813-1825"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fusion deep learning framework based on breast cancer grade prediction 基于乳腺癌等级预测的融合深度学习框架

IF 7.5 2区计算机科学

Digital Communications and Networks Pub Date : 2024-12-01 DOI: 10.1016/j.dcan.2023.12.003

Weijian Tao , Zufan Zhang , Xi Liu , Maobin Yang

{"title":"A fusion deep learning framework based on breast cancer grade prediction","authors":"Weijian Tao , Zufan Zhang , Xi Liu , Maobin Yang","doi":"10.1016/j.dcan.2023.12.003","DOIUrl":"10.1016/j.dcan.2023.12.003","url":null,"abstract":"<div><div>In breast cancer grading, the subtle differences between HE-stained pathological images and the insufficient number of data samples lead to grading inefficiency. With its rapid development, deep learning technology has been widely used for automatic breast cancer grading based on pathological images. In this paper, we propose an integrated breast cancer grading framework based on a fusion deep learning model, which uses three different convolutional neural networks as submodels to extract feature information at different levels from pathological images. Then, the output features of each submodel are learned by the fusion network based on stacking to generate the final decision results. To validate the effectiveness and reliability of our proposed model, we perform dichotomous and multiclassification experiments on the Invasive Ductal Carcinoma (IDC) pathological image dataset and a generated dataset and compare its performance with those of the state-of-the-art models. The classification accuracy of the proposed fusion network is 93.8%, the recall is 93.5%, and the F1 score is 93.8%, which outperforms the state-of-the-art methods.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"10 6","pages":"Pages 1782-1789"},"PeriodicalIF":7.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0