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TRBFT: An Efficient Blockchain Consensus for Edge Computing-Enabled IoT Systems

IEEE Internet of Things Journal 2025年第11期12卷 15853-15868页

作者： Zheng, Jiali Zhang, Yuxi Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi Nanning530004 China

With the continuous expansion of the Internet of Things (IoT), and despite reductions in communication latency enabled by edge computing, the decentralized nature of node structures poses significant security challenges. Blockchain is considered a critical technology for enhancing IoT security. However, traditional consensus exhibits limitations in efficiency and security when facing the exponential growth of IoT nodes necessitating further optimization and innovation. This paper proposes the threshold signature and reputation-enhanced Byzantine fault tolerance (TRBFT) algorithm. First, we integrate IoT with edge computing architectures, conduct a comprehensive evaluation of node reputation based on behavioral quality and contribution to consensus, and periodically update and categorize nodes into different sets with varying permissions. Secondly, the algorithm ensures that the selection of primary nodes strikes a balance between reliability and randomness, while employing a segmented screening scheme that progressively restricts the participating nodes in consensus, aiming to enhance accuracy. Subsequently, the communication structure is improved by incorporating threshold signature with reputation weight, which reduces communication complexity. Ultimately, simulation results demonstrate that TRBFT reduces the average consensus delay by 71.1% and increases the average throughput by 3.95 times compared to the advanced consensus HotStuff. In scenarios with faulty nodes, TRBFT increases the chain growth rate by 65.12% and decreases the block interval by 54.26% relative to HotStuff. The findings show the superior scalability and fault tolerance of TRBFT, making it better suited for large-scale IoT scenarios integrated with edge computing. © 2025 IEEE.

关键词： Blockchain

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Toward zero trust in 5G Industrial Internet collaboration systems

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Digital communications and networks 2025年第2期11卷 547-555页

作者： Han Zhang Ziyan Zhang Liquan Chen School of Cyber Science and Engineering Southeast University ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Purple Mountain Laboratories for Network and Communication Security

With the introduction of 5G, users and devices can access the industrial network from anywhere in the world. Therefore, traditional perimeter-based security technologies for industrial networks can no longer work well. To solve this problem, a new security model called Zero Trust(ZT) is desired, which believes in “never trust and always verify”. Every time the asset in the industrial network is accessed, the subject is authenticated and its trustworthiness is assessed. In this way, the asset in industrial network can be well protected, whether the subject is in the internal network or the external network. However, in order to construct the zero trust model in the 5G Industrial Internet collaboration system, there are still many problems to be solved. In this paper, we first introduce the security issues in the 5G Industrial Internet collaboration system, and illustrate the zero trust architecture. Then, we analyze the gap between existing security techniques and the zero trust architecture. Finally, we discuss several potential security techniques that can be used to implement the zero trust model. The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA) in the 5G Industrial Internet collaboration system.

关键词： Zero trust 5G Industrial Internet collaboration system Trust assessment Access control Authentication

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A Throughput and Priority Optimization Strategy for High Density Healthcare IoT

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Intelligent and Converged networks 2025年第1期6卷 41-52页

作者： Su, Zhenlang Ren, Junyu Huang, Yeheng Liao, Yang Qin, Tuanfa School of Computer Electronic and Information Guangxi University Nanning530004 China School of Physics and Electronic Information Guangxi Minzu University Nanning530006 China School of Computer Electronic and Information Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China

In the field of wireless body area networks (WBANs), for solving the complex interference problem of inter-WBANs, a density-based adaptive optimization strategy (DAOS) is proposed in this paper. Firstly, the complex interference problem among WBANs is converted into a distance-based graph coloring model, then time division multiple access and a two-level split clustering methods are adopted to allocate initial time slots for nodes. Secondly, the particle swarm optimization algorithm is used to optimize the time slot of each node for maximizing the throughput. We simulate the scenario on MATLAB simulator. Experimental results show that compared with the traditional scheme in high-density healthcare Internet of Things (IoT) scenarios, DAOS has obvious advantages compared with three comparison strategies of faster convergence rate of 48.94%, 60.76%, and 96.82%, and higher throughput of 5.60%, 8.08%, and 8.05% in traffic priorities 7 to 4. © 2020 Tsinghua University Press.

关键词： Particle swarm optimization (PSO)

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ALFC-Point: Adaptive Laplacian Feature Convolution network for 3D Point Cloud Understanding 41st

ALFC-Point: Adaptive Laplacian Feature Convolution Network f...

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41st Computer Graphics International Conference, CGI 2024

作者： Liao, Junjie Yin, Mengxiao Li, Ming Zhu, Congyang Yang, Zhiqiang Zhan, Feng School of Computer and Electronics Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9783031820236

To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Laplace transforms

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MCFTNet: Multimodal Cross-layer Fusion Transformer network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Predictive Generation for Digital Twin Channel:Long-Batch Time-Frequency Construction

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China communications 2025年第4期22卷 161-173页

作者： Che Chang Hu Jie Kang Honghui Rui Hua Lyu Xingzai Wang Bo School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE CorporationShenzhen 518055China

Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision *** key component is the creation of DT channels,which form the basis for upcoming ***,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication ***,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT *** generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the *** loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability ***,the accumulating error from the slicing window has been *** simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.

关键词： channel generation channel prediction deep learning digital twin(DT) generative adversarial network(GAN)

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and Computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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A two Stage Mixed Integer Programming Model for Distributionally Robust State Based Non-Intrusive Load Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Chen Chai, Zimo Yang, Linfeng University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China Guangxi University School of Computer Electronics and Information the Guangxi Key Laboratory of Multimedia Communication and Network Technology Nanning China

This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces fewer integer variables and richer absolute error function of load decomposition, which makes the power state selection of each device more accurate and the power consumption more accurate fitting. First, to tackle the issues related to noisy load data, an innovative load feature extraction model based on Kullback-Leibler distributionally robust optimization principles is introduced. Then the key features (power boundary/fluctuation features) of each device identified through this robust model are integrated into the constraints of the two-stage NILM model. The two-stage complementary framework includes: the determination of device state interval in the first stage;and the accurate fitting of device power consumption within the device state interval in the second stage. Comparative validation against existing optimization-based models on the AMPds, REFIT, and actual laboratory data sets demonstrate that our proposed model significantly enhances power decomposition accuracy and computational efficiency. In addition, the two-stage complementary framework and load feature extraction model can be applied to other optimization-based models of NILM to improve the computational efficiency of each model and the accuracy of load decomposition. © 2025 IEEE.

关键词： Mixed-integer linear programming

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Classification of Zhuang Dialect combined with Bert and SimAM

Classification of Zhuang Dialect combined with Bert and SimA...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Min Huang Xuejun Zhang Wenkang Chen School of Computer Electronics and Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Electrical Engineering Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Language recognition refers to the automatic recognition of the language type of the given speech segments. Zhuang is an important language in Southeast Asia, with more than 16 million native speakers. Due to the scarcity of data resources, there is relatively little research about Zhuang in the field of natural language processing, especially in the area of speech-level exploration. Because of the existence of a variety of dialects in Zhuang, this paper is the first to create a speech dataset for the study of the Zhuang language. In the experiment, it was observed that the output of different encoder layers of the BERT has an impact on classification, so this paper innovatively proposes to integrate the SimAM attention mechanism with the BERT model. The model uses a phone recognizer to extract phone sequences from speech segments. Then the Bert model combined with the integrated SimAM attention mechanism performs deep feature representation learning on the semantic information and word order of the sequence. Finally, the classifier realizes the efficient classification of the Zhuang language. Compared with Baseline Systems, the accuracy of the dialect recognition model proposed in this paper is 91.37%, which fully proves the effectiveness of the model.

关键词： Representation learning Attention mechanisms Semantics Speech recognition Bidirectional control Signal processing Feature extraction Encoding Natural language processing Speech processing

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A base station microgrid traffic prediction method based on IOOA-CNN-BiLSTM 3

A base station microgrid traffic prediction method based on ...

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3rd International Conference on Smart Energy and Electrical Engineering, SEEE 2024

作者： Yan, Ming Guo, Wenhao Xian, Shixuan Qin, Tuanfa School of Electrical Engineering Guangxi University Nanning530004 China School of Computer Electronics and Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

The rapid advancement of 5G technology has raised significant concerns regarding the energy consumption of base stations for mobile network operators. Integrating traditional base station power supply systems with microgrids to maximize the utilization of renewable energy has demonstrated considerable potential in addressing the energy consumption challenges faced by base stations. However, the inherent randomness of communication traffic loads adversely affects the reliable operation of base station microgrids. To tackle this issue, we propose a traffic prediction model for base station microgrids based on deep learning methods. Initially, a reference scenario for the base station microgrid is established, followed by the employment of an Improved Osprey Optimization Algorithm (IOOA) to enhance convergence speed and mitigate the risk of local optima. Ultimately, the key parameters of the CNN-BiLSTM model are optimized using the IOOA. Experimental results from real datasets corroborate the superiority of the proposed model concerning MAPE and R2 indicators, as well as perform effectively in energy savings. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Energy utilization

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