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Clustering and power allocation for secured access to simultaneous transmissions and sensing Internet of Things networks

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WIRELESS networkS 2025年第1期31卷 345-359页

作者： Zhao, Juan Dai, Haibo Xu, Xiaolong Yan, Hao Zhang, Zheng Jinling Inst Technol Sch Network Secur Nanjing 211169 Peoples R China Nanjing Univ Posts & Telecommun Sch IoT Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210003 Peoples R China

The security of wireless access is studied for the integrated wireless communications and sensing in 6G Internet of Everything networks. The scaling law of the secured communications and that of the secured sensing are theoretically derived in closed form with respect to the number of the transmit nodes in the same transmit clustering. The energy efficiency of the secured communications is also theoretically analyzed. It is discovered that the gain from additional transmit nodes benefits both secured communication and secured sensing, yet the factors of these two gain scale in different coefficients. The methodology for the theoretical analysis is the statistical derivation approach. Moreover, the transmit power and the number of the transmit nodes for the cluster are jointly optimized to the global optimum in the analytical expression by either maximizing the secured ergodic throughput or maximizing the secured ergodic energy efficiency under the same constraint of the sensing required quality. Two algorithms are finally developed to jointly solve these two problems with the guaranteed convergence. Numerical simulations verify the performances of our derived results.

关键词： Internet of everything Secured energy efficiency Communications and sensing Secured access

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An improved lightweight ConvNeXt for rice classification

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ALEXANDRIA ENGINEERING JOURNAL 2025年 112卷 84-97页

作者： Lv, Pengtao Xu, Heliang Zhang, Qinghui Shi, Lei Li, Heng Chen, Youyang Zhang, Yana Cao, Dengke Liu, Zhongyang Liu, Yixin Han, Jingwen Zhang, Zhan Qi, Yiran Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Key Lab Grain Storage Informat Intelligent P Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Grain Big Data Anal & Applicat Engn Res Ctr Zhengzhou 450001 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100876 Peoples R China Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Minist Educ Kunming 650092 Peoples R China Minzu Univ China Key Lab Ethn Language Intelligent Anal & Secur Gov Beijing 100081 Peoples R China

Under the pressure of climate change, the international food market is facing great uncertainty. Rice is widely grown as a major worldwide food crop, and different rice seeds often influence the future merit of a country's rice growth. As a major food crop widely grown around the world, the seed type of rice plays a key role in ensuring food security and optimizing agricultural productivity. Therefore, identifying the types of rice grains is an important task in rice breeding and cultivation. To this end, this study proposes a new model based on the ConvNeXt framework for detecting rice types, aiming to improve the identification efficiency. Our improved model achieved an average accuracy of 94.69 %. Compared to the baseline model ConvNeXt, the proposed network is more lightweight and more accurate. We conducted comprehensive experiments on the rice dataset from the GrainSpace public dataset to ensure the thoroughness and rigor of the study. Compared to existing models, our proposed model achieved the highest accuracy while maintaining lower FLOPs and parameters.

关键词： Rice classification Lightweight model data augmentation Image classification

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Multiscale Adaptively Spatial Feature Fusion network for Spacecraft Component Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 3501-3513页

作者： Zhang, Wuxia Shao, Xiaoxiao Mei, Chao Pan, Xiaoying Lu, Xiaoqiang Xian Univ Posts & Telecommun Sch Comp Sci & Technol Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Ctr Opt Imagery Anal & Learning Xian 710119 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China

Spacecraft component recognition is crucial for tasks such as on-orbit maintenance and space docking, aiming to identify and categorize different parts of a spacecraft. Semantic segmentation, known for its excellence in instance-level recognition, precise boundary delineation, and enhancement of automation capabilities, is well-suited for this task. However, applying existing semantic segmentation methods to spacecraft component recognition still encounters issues with false detections, missed detections, and unclear boundaries of spacecraft components. In order to address these issues, we propose a multiscale adaptively spatial feature fusion network (MASFFN) for spacecraft component recognition. The MASFFN comprises a spatial attention-aware encoder (SAE) and a multiscale adaptively spatial feature fusion-based decoder (Multi-ASFFD). First, the spatial attention-aware feature fusion module within the SAE integrates spatial attention-aware features, mid-level semantic features, and input features to enhance the extraction of component characteristics, thus improving the accuracy in capturing size, shape, and texture information. Second, the multi-scale adaptively spatial feature fusion module within the Multi-ASFFD cascades four adaptively spatial feature fusion blocks to fuse low-level, middle-level, and high-level features at various scales to enrich the semantic information for different spacecraft components. Finally, a compound loss function comprising the cross-entropy and boundary losses is presented to guide the MASFFN better focus on the unclear component edge. The proposed method has been validated on the UESD and URSO datasets, and the experimental results demonstrate the superiority of MASFFN over existing spacecraft component recognition methods.

关键词： Space vehicles Feature extraction Semantics Accuracy Shape Semantic segmentation Target recognition Image edge detection Decoding YOLO Deep learning feature fusion multiscale semantic segmentation spacecraft component recognition

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Secure Indoor Localization Against Adversarial Attacks Using DCGAN

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 130-134页

作者： Yan, Qingli Xiong, Wang Wang, Hui-Ming Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China

The vulnerability of deep learning-based indoor Wi-Fi fingerprint localization methods to adversarial attacks significantly reduces localization performance. To overcome this challenge, we propose a defense strategy employing a deep convolutional generative adversarial network (DCGAN) to enhance the security of channel state information (CSI)-based localization methods while maintaining accuracy. Our approach eliminates adversarial perturbations before the adversarial samples are fed into the deep learning model for localization. The localization performance of the proposed DCGAN is evaluated through experiments conducted with commodity Wi-Fi devices in representative indoor environments. Experimental results demonstrate that the DCGAN model effectively mitigates adversarial interference while maintaining excellent localization accuracy under two white-box attacks and one black-box attack.

关键词： Location awareness Tensors Generators Training Perturbation methods Accuracy Fingerprint recognition Wireless fidelity Security Receiving antennas Indoor localization adversarial attack CSI DCGAN

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Error analysis of virtual element method for the Poisson-Boltzmann equation

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JOURNAL OF NUMERICAL MATHEMATICS 2025年

作者： Huang, Linghan Shu, Shi Yang, Ying Guilin Univ Elect Technol Guangxi Coll Sch Mathemat & Computat Sci Guilin 541004 Guangxi Peoples R China Guangxi Appl Math Ctr GUET Univ Key Lab Data Anal & Computat Guilin 541004 Guangxi Peoples R China Xiangtan Univ Sch Math & Computat Sci Hunan Key Lab Computat & Simulat Sci & Engn Key Lab Intelligent Comp & Informat ProcMinist Ed Xiangtan 411105 Hunan Peoples R China

The Poisson-Boltzmann equation, which incorporates the source of the Dirac distribution, has been widely applied in predicting the electrostatic potential of biomolecular systems in solution. In this paper we discuss and analyse the virtual element method for the Poisson-Boltzmann equation on general polyhedral meshes. Nearly optimal error estimates, approaching the best possible accuracy, are achieved for the virtual element approximation in both the L 2-norm and H 1-norm, even when the solution of the entire domain has low regularity. The efficiency of the virtual element method and the validity of the proposed theoretical prediction are confirmed through numerical experiments conducted on various polyhedral meshes.

关键词： Poisson-Boltzmann equation virtual element method error estimate polyhedral meshes

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An Impulsive Noise-Resistant Target Localization Approach With Unknown Model Parameter Learning

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 7421-7433页

作者： Yan, Qingli Luo, Zhe Wang, Hui-Ming Wang, Bin Gao, Cong Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Xian Univ Sci & Technol Sch Commun Engn Xian 710699 Peoples R China Xian Key Lab Heterogeneous Network Convergence Com Xian 710054 Peoples R China

Received signal strength (RSS)-based localization techniques have gained much attention in location-based services (LBSs). However, the coexistence of unknown path loss exponent (PLE), uncertain sensor positions, and impulsive noise poses serious challenges to localization accuracy. To address the problem, we first model the impulsive noise as a Mixture of Gaussian (MoG) distribution with unknown parameters. Thus, the noise model and the channel model can be refined using the observed data under the variational Bayesian inference (VBI) framework, which is defined as the model refinement learning. We then propose a corresponding online target localization procedure with the refined noise distribution, PLE and sensor positions. The Bayesian Cramer-Rao bound (BCRB) is finally derived in terms of all unknown parameters. Simulation results together with real experiment demonstrate that the proposed VBI algorithm can effectively learn the true noise distribution, and the developed localization method exhibits robust localization performance in various scenarios.

关键词： Location awareness Measurement errors Accuracy Parameter estimation Simulation Noise Taylor series data models Bayes methods Random variables Impulsive noise Mixture of Gaussians (MoGs) received signal strength (RSS) target localization variation Bayesian

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TransGAN-Based Secure Indoor Localization Against Adversarial Attacks

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5918-5930页

作者： Yan, Qingli Xiong, Wang Wang, Hui-Ming Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China

Received signal strength (RSS)-based WiFi fingerprint localization has attracted much attention for global positioning system denied-areas. Deep neural network (DNN) has introduced innovative techniques for indoor localization. However, deep learning models are susceptible to adversarial attacks, so the performance of indoor positioning methods is seriously threatened by adversarial attacks. To improve the localization performance, we first investigate the impact of adversarial attacks on indoor localization systems. A secure adversarial location guard framework, adv-LG, is then developed. It consists of a transformer-based generative adversarial network (TransGAN) and a cleaner module. TransGAN is developed to learn the mapping from adversarial samples to clean ones, while the cleaner module aims to remove adversarial perturbations from the adversarial samples using the learned mapping. The cleaned data is finally fed into a deep learning model to achieve online localization. We compare the localization performance of the proposed adv-LG method with adversarial training (AT), Gaussian smoothing (GS), and autoencoder (AE)-based approaches on two publicly available datasets, i.e., UJIIndoorLoc and UTSIndoorLoc. The results show that adv-LG exhibits significant advantages in classification and localization performance under several typical adversarial attack scenarios.

关键词： Adversarial attack deep neural network (DNN) defense generative adversarial network (GAN) defense generative adversarial network (GAN) indoor localization indoor localization received signal strength (RSS) received signal strength (RSS) transformer transformer transformer

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Meta-process-driven 3D skeleton feature learning for enhanced human action recognition

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VISUAL COMPUTER 2025年 1-21页

作者： Chen, Hao Wang, Chenxi Duan, Jiawei Yang, Qing Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Phys Educ Univ Sch Sport & Hlth Sci Xian 710068 Peoples R China

In contrast to two-dimensional skeletal data, which exhibits inherent susceptibility to environmental interference such as complex backgrounds and illumination variations, three-dimensional skeletal data demonstrates superior robustness against these confounding factors. This enhanced spatial representation capability enables more accurate feature extraction, thereby offering a more reliable framework for human action recognition tasks. However, feeding long videos into recognition models can significantly increase computational costs and potentially degrade accuracy due to redundant information. To address this, we propose a novel method based on meta-process-driven feature learning from 3D skeleton data. Our approach divides videos into meta-processes through clustering based on motion features of core joint points. Each meta-process is characterized using special Euclidean groups, and their parameters are derived using a Transformer network. By compressing data representation to eliminate redundancy and training a classifier with fused internal and external meta-process features, our method achieves efficient and accurate action recognition. Experimental results on NTU-RGB+D60, NTU-RGB+D120 and a mixed dataset demonstrate the effectiveness and advantages of our approach compared to 18 state-of-the-art algorithms.

关键词： 3D skeleton video Human action recognition Motion meta-process Special Euclidean groups Transformer

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Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2025年第4期26卷 4418-4431页

作者： Yuan, Xiaoming Luo, Zhenyu Zhang, Ning Guo, Ge Wang, Lin Li, Changle Niyato, Dusit Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Highway Minist Transport State Key Lab Intelligent Transportat Syst Res Inst Beijing 100088 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 308232 Singapore

Accurate future traffic flow prediction is essential for decision-making in travel recommendations and route planning, aiming to reduce congestion and enhance traffic safety. Traditional traffic flow prediction models often face limitations in quality and structure, leading to increased training costs and inefficiencies, due to data scarcity and centralized training modes that compromise data privacy. To address these issues, we propose a model called 2MGTCN, which combines Multi-modal Graph Convolutional networks (GCN) and Temporal Convolutional networks (TCN) for Cross-city Traffic Flow Prediction (TFP). Our 2MGTCN model utilizes federated transfer learning (FTL) to transfer the model from the source to the target domain, mitigating data scarcity. It also incorporates GCN and TCN to capture both spatial and temporal information, enhancing cross-city adaptability. Additionally, Grey Relation analysis (GRA) and Dynamic Time Warping (DTW) methods are applied to capture road relationships, and a Federated Parameter Aggregation based on Spatial Similarity (FPASS) algorithm is proposed for ensuring effective parameter aggregation by considering spatial similarity. Simulation results show that our 2MGTCN algorithm outperforms traditional TFP models in both centralized and distributed training modes, ensuring higher accuracy and better privacy protection.

关键词： data models Urban areas Predictive models Transfer learning Training Adaptation models Accuracy Roads data privacy Federated learning traffic flow prediction transfer learning graph convolutional network temporal convolutional network

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ASL: An Accurate and Stable Localization algorithm for multi-hop irregular networks

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JOURNAL OF network AND COMPUTER APPLICATIONS 2025年 239卷

作者： Xia, Xingsheng Yan, Jiajia Wu, Chenhuang Meng, Chao Yan, Xiaoyong Jiangsu Vocat Inst Commerce Sch Internet Things & Intelligent Engn Nanjing 211168 Peoples R China Nanjing Normal Univ Special Educ Sch Math & Informat Sci Nanjing 210038 Peoples R China Putian Univ Fujian Key Lab Financial Informat Proc Putian 351100 Fujian Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Jiangsu Peoples R China Hubei Univ Hubei Key Lab Big Data Intelligent Anal & Applicat Wuhan 430062 Hubei Peoples R China

Accurate geographical information about nodes is essential in wireless multi-hop networks. Most existing localization algorithms focus on locating nodes in regular network environments, posing challenges for irregular multi-hop networks. To mitigate the impact of irregularities on localization, we propose an Accurate and Stable Localization algorithm (ASL). ASL first infers a hop threshold based on the distribution characteristics of anchors, eliminating erroneous distances and avoiding them in the localization process. Next, under the constraint of the hop threshold, each normal node constructs its sub-region, including it, based on the estimated distance to the anchors. These sub-regions can avoid the occurrence of unreliable localization results and assist in decreasing communication overhead. Finally, the SMA (Slime Mould Algorithm) with the Halton sequence is introduced to search for the optimally estimated locations of normal nodes, which tends to accelerate convergence and improve localization accuracy. Extensive simulations demonstrate that our proposed ASL outperforms state-of-the-art algorithms regarding accuracy and stability when facing network irregularities. Specifically, our proposed ASL achieves a median improvement in localization accuracy ranging from 16.96% to 83.66%.

关键词： Irregular multi-hop networks Median absolute deviation Slime mould algorithm Halton sequence

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