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A Blockchain System for QoS Monitoring in Decentralized Edge Computing

IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第1期17卷 263-276页

作者： Wang, Puwei Li, Haoran Fu, Hang Sun, Zhouxing Chen, Jinchuan Du, Xiaoyong Renmin Univ China Sch Informat Minist Educ Beijing 100872 Peoples R China Renmin Univ China Key Lab Data Engn & Knowledge Engn Minist Educ Beijing 100872 Peoples R China

In edge computing, applications are usually delivered as services, each of which runs independently and cooperates to construct complicated applications. QoS (Quality of Service) monitoring is an important way to detect and locate faulty services. In a decentralized environment, QoS monitoring will face trust problem because it is difficult to guarantee the trustworthiness of monitoring results. This article builds a blockchain system for QoS monitoring. However, there are two challenges. First, although the blockchain consensus ensures the consistency of on-chain data among nodes, there is no guarantee that the monitoring data collected in the decentralized environment are authentic, because malicious nodes may report falsified data. Second, in order to handle service faults in time, the real-time query is usually required for obtaining monitoring data. But, blockchains suffer from inefficient querying, because the sequential data storage of blockchain is designed for write intensive applications at the expense of some read performance. To address these challenges, this article proposes a clustering-based algorithm for validating the authenticity of monitoring data collected in the decentralized environment, and proposes a probabilistic threshold query over blockchain, which supports efficient querying and guarantees the probability that the query results are correct is not less than a given threshold. This article implements the proposed blockchain system based on the blockchain platform Hyperledger Fabric and the edge computing platform KubeEdge. The experiment results demonstrate the proposed blockchain system provides high-throughput and low-latency monitoring ability, and can efficiently obtain monitoring results close to real QoS data.

关键词： Monitoring Blockchains Quality of service Edge computing Probabilistic logic Measurement clustering algorithms QoS monitoring edge computing blockchain probabilistic threshold query

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Dual-Task GPR Method: Improved Generative Adversarial Clutter Suppression Network and Adaptive Target Localization Algorithm in GPR Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Hou, Feifei Fang, Mengqi Luo, Tess Xianghuan Fan, Xinyu Guo, Ying Cent South Univ Sch Automat Changsha 410083 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China Hefei Natl Lab Hefei 230088 Peoples R China

Ground-penetrating radar (GPR), as a geophysical survey method, offers high resolution and fast detection capabilities obtaining rich information about the underground structure. However, under real conditions, GPR is susceptible to interference and clutter. Therefore, there is an urgent need to develop methods for suppressing clutter, highlighting and positioning targets in GPR images. This article proposed an integrated method consisting of two modules: GPR image decluttering and underground target localization, which are used for clutter suppression and hyperbola vertex estimation from GPR image. In the first module, an unsupervised generative adversarial network (GAN) model is developed to map cluttered GPR images to clutter-free GPR images, where the CycleGAN is adapted as the main network architecture. To further improve the performance of the model, the first module proposes a novel loss function that introduces a perceptual loss into the previous CycleGAN loss. It utilizes a multiscale structural similarity index (MS-SSIM) loss to obtain richer and more realistic context information. In the second module, based on the obtained decluttered image, a novel adaptive target characterization algorithm, called gradient points clustering-based localization (GPCL), is proposed for the recognition and localization of GPR target signatures. This algorithm is designed to leverage changes in target position and local characteristics, thereby achieving efficient target vertex evaluation. Experimental results conducted on field datasets demonstrate that the proposed methods can significantly improve the quality and clarity of various cluttered images and exhibit strong applicability for the localization of underground targets.

关键词： Clutter Location awareness Feature extraction clustering algorithms Transforms Generative adversarial networks Data mining clustering algorithm clutter elimination generative adversarial network (GAN) ground-penetrating radar (GPR) perceptual loss subsurface target localization

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DACA: Distributed adaptive grid decision graph based clustering algorithm

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第5期52卷 1199-1215页

作者： He, Jing Zhou, Jun Wang, Haoyu Cai, Li Yunnan Univ Natl Pilot Sch Software Kunming Yunnan Peoples R China

clustering algorithms play a very important role in machine learning. With the development of big-data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large-scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to adaptively mesh the data to form an obvious sparse grid and dense grid. Then, the decision graph is used to determine the cluster center mesh object. Finally, the KD-tree is used to accelerate the determination of the cluster center of sparse points to complete clustering. The algorithm is implemented using the popular Apache Spark computing framework, compared with other distributed clustering algorithms, DACA can adaptively divide the grid according to the data distribution to obtain better clustering effect. At the same time, KD tree algorithm is used to speed up the decision-making of clustering center. Numerous experiments show that the DACA algorithm has excellent performance and accuracy on six standard datasets and real GPS trajectory datasets.

关键词： adaptive grid division clustering algorithms decision graphs distributed KD-tree

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Automatic Dual-Polarized Ground Penetrating Radar for Enhanced 3-D Tree Roots System Architecture Reconstruction

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Luo, Wenhao Lee, Yee Hui Hao, Tong Yusof, Mohamed Lokman Mohd Yucel, Abdulkadir C. Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Ctr Spatial Informat Sci & Sustainable Dev Applica Shanghai 200092 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Pk Board Singapore 259569 Singapore

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.

关键词： Three-dimensional displays Image reconstruction Radar antennas Vegetation Signal processing algorithms Accuracy Surveys Permittivity Ground penetrating radar clustering algorithms 3-D reconstruction dual-polarized ground-penetrating radar (GPR) optimized slice relation clustering (OSRC) radar signal processing framework root system architecture (RSA)

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SVM-Assisted Adaptive Kernel Power Density clustering Algorithm for Millimeter Wave Channels

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 4014-4026页

作者： Du, Fei Zhao, Xiongwen Zhang, Yu Wen, Yang Fu, Zihao Geng, Suiyan Qin, Peng Zhou, Zhenyu Xu, Chen Liu, Yongsheng Fan, Wei North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Sch Elect & Elect Engn Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Power Internet Things Technol Baoding 071003 Hebei Peoples R China China Res Inst Radiowave Propagat Qingdao 266107 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Cluster-based channel modeling has gradually become a trend in the development of a channel model, since it is a good compromise between accuracy and complexity. However, most of the existing clustering algorithms require prior knowledge of clusters, initialization, and threshold choices. An accurate and automatic cluster identification algorithm is therefore highly desirable for channel modeling. In this article, adaptive kernel-power-density (AKPD) and support vector machine-assisted AKPD (SVM-AKPD) algorithms are proposed. First, a new distance-based metric is proposed to calculate an adaptive-K for each multipath component (MPC), in which the AKPD can be used in scenarios where we have a complex distribution of MPCs, especially for the cluster with small MPCs. Furthermore, the SVM is applied in clustering by the full partition of MPCs' feature space to overcome the limitation of the AKPD, where the MPCs lying at a large distance from the cluster centroids will be clustered into surrounding clusters when the clusters are closely spaced in the AKPD. Finally, the performance of the proposed AKPD and SVM-AKPD is validated with measured and simulated channels data at millimeter waveband, respectively. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm. The proposed algorithms enable applications in multiple-input-multiple-output (MIMO) channels with no prior knowledge about the clusters, such as number and initial locations. It also does not need to adjust cluster parameters manually and can be implemented for cluster-based channel modeling with a fairly low complexity.

关键词： clustering algorithms Delays Support vector machines MIMO communication Channel models Adaptation models Wireless communication Channel measurement and modeling cluster kernel density multipath component (MPC) multiple-input-multiple-output (MIMO) support vector machine (SVM) wireless channel

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Deep Fuzzy Envelope Sample Generation Mechanism for Imbalanced Ensemble Classification

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1248-1262页

作者： Li, Fan Li, Yongming Shen, Yinghua Pedrycz, Witold Zhang, Xiaoheng Wang, Pin Li, Pufei Zhou, Chuanyan Cheng, Huan Chongqing Univ Sch Microelectorn & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton T6G 2R3 AB Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn TR-34010 Sariyer Turkiye

Ensemble methods are widely used to tackle class imbalance problem. However, for existing imbalanced ensemble (IE) methods, the samples in each subset are resampled from the same dataset, and are directly input to the classifier for training, so the quality (diversity and separability) of the subsets is unsatisfactory usually. To solve the problem, a deep fuzzy envelope sample generation mechanism is proposed. First, the fuzzy C-means clustering based deep sample envelope prenetwork (DSEN) is designed to mine correlation information among samples, thereby increasing the quality of the subsets. Second, the local manifold structure metric and global structure distribution metric are designed to construct local-global structure consistency mechanism (LGSCM) to enhance distribution consistency of interlayer samples of DSEN. Third, the DSEN and LGSCM are combined to form the final deep sample envelope network-DSENLG to refresh the existing subsets. Finally, base classifiers are applied on the new subsets generated by the DSENLG and then fused, thereby realizing a new IE algorithm. The experimental results show that the proposed algorithm is significantly better than existing representative IE algorithms and it achieves the highest improvement of 10.64%, 19.5%, 18.67% and 22.33% on four criteria over the state-of-the-art methods. The originality of the article is threefold: proposing the concept of "deep fuzzy samples" or "envelope samples", which comprehensively considers the correlation information among original samples;proposing the LGSCM to resolve the distribution inconsistency of interlayer samples;and forming an fuzzy envelope sample based IE algorithm.

关键词： Ensemble learning Correlation Classification algorithms Training Manifolds Measurement clustering algorithms Class imbalance problem domain adaption ensemble approach envelope learning fuzzy C-means (FCMs) clustering

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Semantic Maps for Knowledge Graphs: A Semantic-Based Summarization Approach

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IEEE ACCESS 2024年 12卷 6729-6744页

作者： Camarillo-Ramirez, Pablo Cervantes-Alvarez, Francisco Gutierrez-Preciado, Luis Fernando Western Inst Technol & Higher Educ Guadalajara 45601 Jalisco Mexico

Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic structured information and enabling the development of intelligent systems. This paper focuses on the generation of semantic maps as summarization method for KGs. We propose a strategy that utilizes centroid-based clustering algorithms, namely Affinity Propagation and Partitioning Around Medoids (PAM), to capture the semantic distance between nodes in the KG and generate meaningful clusters. Our experiments demonstrate divergent results between the two clustering algorithms, with Affinity Propagation showing qualitative coherence and meaningfulness, while PAM performs well in terms of internal validation metrics. We leverage the computed centroids to infer a main term of the semantic map, which contributes to the visually appealing and informative representation of the KG. The combination of semantic distance capture, clustering algorithms, and centroid-based inference facilitates a comprehensive understanding of the KG. Our findings highlight the importance of considering both qualitative and quantitative evaluation measures in assessing clustering results. The effectiveness of semantic maps is showcased in visualizing KGs and advancing the field of knowledge graph visualization. The integration of centroid-based clustering algorithms, qualitative evaluation, and inference methods offers improved clarity and interpretability for complex KG analysis.

关键词： Semantics Knowledge graphs Visualization clustering algorithms Data visualization Ontologies Measurement knowledge graph visualization semantic distance semantic mapping visual data exploration clustering

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Feature Selection of Gene Expression Data Using a Modified Artificial Fish Swarm Algorithm With Population Variation

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IEEE ACCESS 2024年 12卷 72688-72706页

作者： Li, Zong-Zheng Wang, Fang-Ling Qin, Feng Yusoff, Yusliza Binti Zain, Azlan Mohd Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Bahru Malaysia

Microarray data is of great significance for cancer identification at the gene level. In the microarray dataset, only a small number of characteristic genomes have significant classification and identification rates for cancer. How to extract a small number of characteristic genes from a large number of microarray data is a classic NP-hard problem. This paper proposes a practical hybrid approach to implement the feature selection of gene expression from the microarray by combining the F-score algorithm and an improved artificial fish swarm algorithm with population variation (FSA-PV). Firstly, the F-score algorithm eliminates a large number of useless and redundant features in the dataset. Then, FSA-PV is discussed to obtain the ability to jump out of the local optimum while retaining the excellent feature of the subset as much as possible, and the adaptive step and visual are used to adjust the search space and to move the range of the algorithm in different environments to improve the local optimization and global optimization abilities. In addition, a naive Bayesian classifier is used to test the classification accuracy of subsets. Eight classical datasets are used to verify the performance of the proposed mechanism in the experiment part. The results reveal that the classification accuracy using the FSA-PV is significant superior to other algorithms in Breast dataset, and the classification accuracy is more than 90% in 8 cases. It further indicates the robustness and feasibility of the FSA-PV in the gene selection process.

关键词： Classification algorithms Feature extraction Visualization Support vector machines clustering algorithms Wrapping Gene expression Fish schools Feature selection gene expression microarray data modified artificial fish swarm algorithm population variation

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A Multilevel Autoadaptive Denoising Algorithm Based on Forested Terrain Slope for ICESat-2 Photon-Counting Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 16831-16846页

作者： Tang, Jie Xing, Yanqiu Wang, Jiaqi Yang, Hong Wang, Dejun Li, Yuanxin Zhang, Aiting Northeast Forestry Univ Forest Transportat Engn Harbin 150040 Peoples R China Northeast Forestry Univ Sch Mech & Elect Engn Harbin 150040 Peoples R China Northeast Forestry Univ Harbin 150040 Peoples R China Northeast Forestry Univ Forest Engn Harbin 150040 Peoples R China

In complex mountainous terrain, the terrain slope causes the scattering of pulsed lasers, generating a lot of noise in photon cloud data (PCD) collected from forestland, which seriously affects the accurate retrieval of forest structure parameters. To address this problem, a multilevel autoadaptive denoising (MLAD) algorithm was proposed in this article. First, random noise photons were removed through the ordering points to identify the clustering structure (OPTICS) algorithm in the coarse denoising process. Second, in the fine denoising step, the circular search domain in the OPTICS algorithm was replaced with an elliptical search domain. The photons after coarse denoising were automatically divided along-track direction into several continuous segments of 100 m each. The median slope method was used to automatically calculate the slope of the forested terrain in each interval segment, so that the range of the ellipse search domain was automatically adjusted to achieve accurate denoising of PCD. Finally, the denoising results of the MLAD algorithm in three different forested terrain areas were compared with those of the difference, regression, and Gaussian adaptive nearest neighbor (DRAGANN) algorithms, and the performance of the MLAD algorithm was evaluated for both different terrain slopes and different vegetation coverages. The results indicated that compared with the DRAGANN algorithm, the MLAD algorithm has higher denoising capability in different regions. The denoising results of the MLAD algorithm exhibit slight changes with the variation in slope, and the F-values are around 0.96, demonstrating good robustness. The F-value of the MLAD algorithm mostly exceeds 0.95 in different vegetation coverages. Overall, the MLAD algorithm exhibits stronger noise identification capabilities for complex forest environments. These results can provide a reference for subsequent accurate extraction of forest structural parameters.

关键词： Photonics Forestry clustering algorithms Noise reduction Vegetation mapping Optics Feature extraction Denoising accuracy forested terrain slope ice cloud and land elevation satellite-2 (ICESat-2) ordering points to identify the clustering structure (OPTICS) photon cloud data (PCD)

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Analysis of Network Intrusion Detection Based on Semi-Supervised and SS-DGM

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IEEE ACCESS 2024年 12卷 170148-170160页

作者： Yu, Xiao Liu, Chang Wang, Jie Liu, Chang Tian, Li Zhou, Liang State Grid Hubei Elect Power Res Inst Wuhan 430074 Peoples R China

The rapid advancement of technology has made network security a hot topic of concern for researchers worldwide. Therefore, to improve the accuracy and real-time response capability of network intrusion detection systems, and to effectively detect and analyze network attack forms in complex network environments, starting from the drawbacks of existing network intrusion detection methods and the vulnerable attack modes of networks, this study first introduces variational auto-encoder to improve the semi-supervised intrusion detection algorithm. Subsequently, the labeled dataset is expanded using K-means clustering algorithm and collaborative training algorithm. Finally, a novel network intrusion detection model based on multi-level semi-supervised intrusion detection algorithm is proposed. The experimental outcomes denoted that the model achieved the highest classification accuracy of 93.08%, 92.12%, 91.22%, and 96.38% for four common types of network attacks: denial of service attacks, unauthorized access from remote machines, unauthorized access attacks by ordinary users to local superuser privileges, and cross-site scripting attacks, respectively. The results of the network attack confusion matrix indicated that the proposed model could detect and classify all types of network attacks, with excellent detection applicability and classification efficiency, and its scores were all above 60 points. In addition, the running time of the proposed model was only 25.64 seconds. From this, the proposed method effectively improves the dynamic adaptability and accuracy of network intrusion detection, providing an efficient and accurate solution for network security defense.

关键词： Accuracy Data models Deep learning Network security Feature extraction Decoding Classification algorithms Anomaly detection clustering algorithms Training Semisupervised learning Intrusion detection Encoding Semi-supervised SS-DGM variational auto-encoder clustering intrusion detection

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