Digital Communications and Networks最新文献

{"title":"Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain","authors":"Zhuo Chen ,&nbsp;Jiahuan Yi ,&nbsp;Yang Zhou ,&nbsp;Wei Luo","doi":"10.1016/j.dcan.2024.09.001","DOIUrl":"10.1016/j.dcan.2024.09.001","url":null,"abstract":"<div><div>Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 912-924"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329954","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":"CrowdRouting: Trustworthy and customized cross-domain routing based on crowdsourcing","authors":"Jialei Zhang ,&nbsp;Zheng Yan ,&nbsp;Huidong Dong ,&nbsp;Peng Zhang","doi":"10.1016/j.dcan.2024.11.008","DOIUrl":"10.1016/j.dcan.2024.11.008","url":null,"abstract":"<div><div>Cross-domain routing in Integrated Heterogeneous Networks (Inte-HetNet) should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements. However, current solutions face numerous challenges in continuously ensuring trustworthy routing, fulfilling diverse requirements, achieving reasonable resource allocation, and safeguarding against malicious behaviors of network operators. We propose CrowdRouting, a novel cross-domain routing scheme based on crowdsourcing, dedicated to establishing sustained trust in cross-domain routing, comprehensively considering and fulfilling various customized routing requirements, while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators. Concretely, CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains, thereby establishing sustainable and trustworthy cross-domain routing based on sustained trust in these routers. In addition, CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing, achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase. Moreover, CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing, thereby promoting optimal resource allocation and efficient utilization. Furthermore, CrowdRouting introduces a supervisory agency (e.g., undercover agent) to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators. Through comprehensive experimental evaluations and comparisons with existing works, we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services, stimulating active participation in cross-domain routing, inhibiting malicious operator behavior, and maintaining reasonable resource allocation, all of which outperform baseline schemes.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 734-756"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330054","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":"CMBA-FL: Communication-mitigated and blockchain-assisted federated learning for traffic flow predictions","authors":"Kaiyin Zhu ,&nbsp;Mingming Lu ,&nbsp;Haifeng Li ,&nbsp;Neal N. Xiong ,&nbsp;Wenyong He","doi":"10.1016/j.dcan.2025.04.011","DOIUrl":"10.1016/j.dcan.2025.04.011","url":null,"abstract":"<div><div>As an effective strategy to address urban traffic congestion, traffic flow prediction has gained attention from Federated-Learning (FL) researchers due FL's ability to preserving data privacy. However, existing methods face challenges: some are too simplistic to capture complex traffic patterns effectively, and others are overly complex, leading to excessive communication overhead between cloud and edge devices. Moreover, the problem of single point failure limits their robustness and reliability in real-world applications. To tackle these challenges, this paper proposes a new method, CMBA-FL, a Communication-Mitigated and Blockchain-Assisted Federated Learning model. First, CMBA-FL improves the client model's ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device. Second, to reduce the communication overhead during federated learning, we introduce a verification method based on parameter update consistency, avoiding unnecessary parameter updates. Third, to mitigate the risk of a single point of failure, we integrate consensus mechanisms from blockchain technology. To validate the effectiveness of CMBA-FL, we assess its performance on two widely used traffic datasets. Our experimental results show that CMBA-FL reduces prediction error by 11.46%, significantly lowers communication overhead, and improves security.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 724-733"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330131","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":"Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system","authors":"Adeel Akram ,&nbsp;Muhammad Bilal Khan ,&nbsp;Najah Abed Abu Ali ,&nbsp;Qixing Zhang ,&nbsp;Awais Ahmad ,&nbsp;Muhammad Shahid Iqbal ,&nbsp;Syed Atif Moqurrab","doi":"10.1016/j.dcan.2024.07.009","DOIUrl":"10.1016/j.dcan.2024.07.009","url":null,"abstract":"<div><div>The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 634-641"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330128","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":"K-B2S+: A one-dimensional CNN model for AF detection with short single-lead ECG waves from wearable devices","authors":"Bo Fang ,&nbsp;Zhaocheng Yu ,&nbsp;Li-bo Zhang ,&nbsp;Yue Teng ,&nbsp;Junxin Chen","doi":"10.1016/j.dcan.2024.05.004","DOIUrl":"10.1016/j.dcan.2024.05.004","url":null,"abstract":"<div><div>Wearable signal analysis is an important technology for monitoring physiological signals without interfering with an individual's daily behavior. As detecting cardiovascular diseases can dramatically reduce mortality, arrhythmia recognition using ECG signals has attracted much attention. In this paper, we propose a single-channel convolutional neural network to detect Atrial Fibrillation (AF) based on ECG signals collected by wearable devices. It contains 3 primary modules. All recordings are firstly uniformly sized, normalized, and Butterworth low-pass filtered for noise removal. Then the preprocessed ECG signals are fed into convolutional layers for feature extraction. In the classification module, the preprocessed signals are fed into convolutional layers containing large kernels for feature extraction, and the fully connected layer provides probabilities. During the training process, the output of the previous pooling layer is concatenated with the vectors of the convolutional layer as a new feature map to reduce feature loss. Numerous comparison and ablation experiments are performed on the 2017 PhysioNet/CinC Challenge dataset, demonstrating the superiority of the proposed method.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 613-621"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141142724","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":"Perceptual point cloud quality assessment for immersive metaverse experience","authors":"Baoping Cheng ,&nbsp;Lei Luo ,&nbsp;Ziyang He ,&nbsp;Ce Zhu ,&nbsp;Xiaoming Tao","doi":"10.1016/j.dcan.2024.07.001","DOIUrl":"10.1016/j.dcan.2024.07.001","url":null,"abstract":"<div><div>Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task. Firstly, because point cloud is formed by unstructured 3D points that makes the topology more complex. Secondly, the quality impairment generally involves both geometric attributes and color properties, where the measurement of the geometric distortion becomes more complex. We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System (HVS) and the intrinsic characteristics of the point cloud. The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling, and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors (KNN) clustering. For geometric distortion, the Point Feature Histogram (PFH) is extracted as the feature descriptor, and the Earth Mover's Distance (EMD) between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement. For color distortion, the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement. Finally, the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement. The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time. Meanwhile, the experimental results also demonstrate the robustness of the proposed method across various distortion types. The source codes are available at <span><span>https://github.com/llsurreal919/PointCloudQualityAssessment</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 806-817"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705011","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":"Resource allocation algorithm for downlink secure transmission in wireless EH cooperative networks with idle relay-assisted jamming","authors":"Xintong Zhou ,&nbsp;Kun Xiao ,&nbsp;Feng Ke","doi":"10.1016/j.dcan.2024.07.006","DOIUrl":"10.1016/j.dcan.2024.07.006","url":null,"abstract":"<div><div>In wireless Energy Harvesting (EH) cooperative networks, we investigate the problem of secure energy-saving resource allocation for downlink physical layer security transmission. Initially, we establish a model for a multi-relay cooperative network incorporating wireless energy harvesting, spectrum sharing, and system power constraints, focusing on physical layer security transmission in the presence of eavesdropping nodes. In this model, the source node transmits signals while injecting Artificial Noise (AN) to mitigate eavesdropping risks, and an idle relay can act as a jamming node to assist in this process. Based on this model, we formulate an optimization problem for maximizing system secure harvesting energy efficiency, this problem integrates constraints on total power, bandwidth, and AN allocation. We proceed by conducting a mathematical analysis of the optimization problem, deriving optimal solutions for secure energy-saving resource allocation, this includes strategies for power allocation at the source and relay nodes, bandwidth allocation among relays, and power splitting for the energy harvesting node. Thus, we propose a secure resource allocation algorithm designed to maximize secure harvesting energy efficiency. Finally, we validate the correctness of the theoretical derivation through Monte Carlo simulations, discussing the impact of parameters such as legitimate channel gain, power splitting factor, and the number of relays on secure harvesting energy efficiency of the system. The simulation results show that the proposed secure energy-saving resource allocation algorithm effectively enhances the security performance of the system.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 829-836"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330039","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":"AoI-aware transmission control in real-time mmwave energy harvesting systems: a risk-sensitive reinforcement learning approach","authors":"Marzieh Sheikhi ,&nbsp;Vesal Hakami","doi":"10.1016/j.dcan.2024.08.015","DOIUrl":"10.1016/j.dcan.2024.08.015","url":null,"abstract":"<div><div>The evolution of enabling technologies in wireless communications has paved the way for supporting novel applications with more demanding QoS requirements, but at the cost of increasing the complexity of optimizing the digital communication chain. In particular, Millimeter Wave (mmWave) communications provide an abundance of bandwidth, and energy harvesting supplies the network with a continual source of energy to facilitate self-sustainability; however, harnessing these technologies is challenging due to the stochastic dynamics of the mmWave channel as well as the random sporadic nature of the harvested energy. In this paper, we aim at the dynamic optimization of update transmissions in mmWave energy harvesting systems in terms of Age of Information (AoI). AoI has recently been introduced to quantify information freshness and is a more stringent QoS metric compared to conventional delay and throughput. However, most prior art has only addressed average-based AoI metrics, which can be insufficient to capture the occurrence of rare but high-impact freshness violation events in time-critical scenarios. We formulate a control problem that aims to minimize the long-term entropic risk measure of AoI samples by configuring the “sense &amp; transmit” of updates. Due to the high complexity of the exponential cost function, we reformulate the problem with an approximated mean-variance risk measure as the new objective. Under unknown system statistics, we propose a two-timescale model-free risk-sensitive reinforcement learning algorithm to compute a control policy that adapts to the trio of channel, energy, and AoI states. We evaluate the efficiency of the proposed scheme through extensive simulations.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 850-865"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330042","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 consensus-based solution for cryptocurrencies arbitrage bots in intelligent blockchain","authors":"Lingyue Zhang,&nbsp;Zongyang Zhang,&nbsp;Tianyu Li,&nbsp;Shancheng Zhang","doi":"10.1016/j.dcan.2024.09.004","DOIUrl":"10.1016/j.dcan.2024.09.004","url":null,"abstract":"<div><div>Intelligent blockchain is an emerging field that integrates Artificial Intelligence (AI) techniques with blockchain networks, with a particular emphasis on improving the performance of blockchain, especially in cryptocurrencies exchanges. Meanwhile, arbitrage bots are widely deployed and increasing in intelligent blockchain. These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning, generating substantial profits at the expense of ordinary users. In this paper, we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol, which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies. Specifically, we present two signal asynchronous common subset protocols, the more optimal one with only constant time complexity. We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment. The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 700-710"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330130","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":"Explainable AI for epileptic seizure detection in Internet of Medical Things","authors":"Faiq Ahmad Khan ,&nbsp;Zainab Umar ,&nbsp;Alireza Jolfaei ,&nbsp;Muhammad Tariq","doi":"10.1016/j.dcan.2024.08.013","DOIUrl":"10.1016/j.dcan.2024.08.013","url":null,"abstract":"<div><div>In the field of precision healthcare, where accurate decision-making is paramount, this study underscores the indispensability of eXplainable Artificial Intelligence (XAI) in the context of epilepsy management within the Internet of Medical Things (IoMT). The methodology entails meticulous preprocessing, involving the application of a band-pass filter and epoch segmentation to optimize the quality of Electroencephalograph (EEG) data. The subsequent extraction of statistical features facilitates the differentiation between seizure and non-seizure patterns. The classification phase integrates Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest classifiers. Notably, SVM attains an accuracy of 97.26%, excelling in the precision, recall, specificity, and F1 score for identifying seizures and non-seizure instances. Conversely, KNN achieves an accuracy of 72.69%, accompanied by certain trade-offs. The Random Forest classifier stands out with a remarkable accuracy of 99.89%, coupled with an exceptional precision (99.73%), recall (100%), specificity (99.80%), and F1 score (99.86%), surpassing both SVM and KNN performances. XAI techniques, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP), enhance the system's transparency. This combination of machine learning and XAI not only improves the reliability and accuracy of the seizure detection system but also enhances trust and interpretability. Healthcare professionals can leverage the identified important features and their dependencies to gain deeper insights into the decision-making process, aiding in informed diagnosis and treatment decisions for patients with epilepsy.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 3","pages":"Pages 587-593"},"PeriodicalIF":7.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330126","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}