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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Nijaguna, G.S. Babu, J. Ananda Ramya, R. Saravanan, V. Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Department of ISE Malnad College of Engineering Hassan India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai Thandalam India

ISBN: (纸本)9789819778751

Cognitive radio sensor networks (CRSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve network lifetime. In many CRSN applications, node mobility poses challenges due to changing network topology. In this research, the Adaptive Spider Monkey Optimization (ASMO) algorithm is proposed for obtaining the energy efficient Cluster Head (CH) selection and routing in CRSN. The consumption of energy in CRSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based clustering and routing approach is developed for designing the optimal transmission path by CH to destination. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network lifetime (NL), and end-to-end delay (EED) are employed to evaluate suggested methodology. Comparative analysis shows that the ASMO methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： clustering algorithms

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Efficiently Transfer User Profile Across Networks

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IEEE TRANSACTIONS ON BIG DATA 2025年第1期11卷 271-285页

作者： Diao, Mengting Zhang, Zhongbao Su, Sen Gao, Shuai Cao, Huafeng Ye, Junda Beijing Univ Posts ands Telecommun BUPT State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.

关键词： Social networking (online) Transfer learning Knowledge engineering Computational complexity clustering algorithms Big Data Adversarial machine learning User profiling transfer learning social network

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Dynamic Weighted Energy Minimization for Aerial Edge Computing Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 683-697页

作者： Li, Yihang Gao, Xiaozheng Shi, Minwei Kang, Jiawen Niyato, Dusit Yang, Kai Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

In this article, we develop a dynamic weighting strategy which considers the residual energy of different devices in aerial edge computing networks, and formulate a weighted energy consumption optimization problem aimed at extending device operating duration. To solve the formulated problem, we develop a clustering algorithm using K-means++ to establish optimal user-to-unmanned-aerial-vehicle access relationships, and the optimization problem is decomposed into trajectory, transmit power, and bandwidth subproblems. Each subproblem is sequentially solved by using the successive convex approximation algorithm, and the entire optimization problem is resolved by using the block coordinate descent algorithm. Simulation results demonstrate the effectiveness of our proposed weighting strategy in managing the energy levels of users, which prolongs the operational duration of the devices.

关键词： Optimization Autonomous aerial vehicles Energy consumption Heuristic algorithms clustering algorithms Bandwidth Approximation algorithms Costs Vehicle dynamics Time division multiple access Aerial edge computing network (AECN) dynamic weighting strategy mobile edge computing (MEC) trajectory optimization weighted energy minimization

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Unconstrained Fuzzy C-Means Based on Entropy Regularization: An Equivalent Model

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 979-990页

作者： Nie, Feiping Zhang, Runxin Duan, Yu Wang, Rong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Sch Comp SciMinist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Shaanxi Peoples R China

Fuzzy c-means based on entropy regularization (FCER) is a commonly used machine learning algorithm that uses maximum entropy as the regularization term to realize fuzzy clustering. However, this model has many constraints and is challenging to optimize directly. During the solution process, the membership matrix and cluster centers are alternately optimized, easily converging to poor local solutions, limiting the clustering performance. In this paper, we start with the optimization model and propose an unconstrained fuzzy clustering model (UFCER) equivalent to FCER, which reduces the size of optimization variables from (n+d)xc to dxc. More importantly, there is no need to calculate the membership matrix during the optimization process iteratively. The time complexity is only linear, and the convergence speed is fast. We conduct extensive experiments on real datasets. The comparison of objective function value and clustering performance fully demonstrates that under the same initialization, our proposed algorithm can converge to smaller local minimums and get better clustering performance.

关键词： clustering algorithms Optimization Entropy Linear programming Time complexity Optimization models Machine learning algorithms Convergence Complexity theory Vectors Fuzzy c-means gradient descent local minimum maximum entropy

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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems

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IEEE ACCESS 2025年 13卷 21265-21278页

作者： Babichev, Sergii Liakh, Igor Skvor, Jiri Kherson State Univ Dept Phys UA-73008 Kherson Ukraine Jan Evangelista Purkyne Univ Usti nad Labem Dept Informat Usti Nad Labem 40096 Czech Republic Uzhgorod Natl Univ Dept Informat Sci Phys & Math Disciplines UA-88016 Uzhgorod Ukraine

The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer's disease using gene expression data derived from both DNA microarray experiments and mRNA sequencing. It outlines a conceptual framework and provides a block diagram of the stepwise procedure for analyzing gene expression data, aiming to enhance the accuracy and objectivity of disease diagnosis. The research methodology involves initial gene ontology analysis, followed by the application of the Self Organizing Tree Algorithm (SOTA) for clustering gene expression profiles, an ensemble algorithm for data biclustering, and CNN for sample classification. Bayesian optimization method was employed to determine the optimal hyperparameters for all models. The analysis of simulation results demonstrates the high efficacy of the proposed approach. Specifically, for Alzheimer's data, the number of genes analyzed was reduced from 44,662 to 4,004. Subsequent cluster-bicluster analysis divided this data into two subsets containing 1,158 and 2,846 genes, respectively. Classification accuracy for samples within these subsets reached 89.8% and 91.8%. In cancer data analysis, the gene count was reduced from 60,660 to 10,422, with 3,955 and 6,467 genes in the first and second clusters, respectively. The classification accuracy for these subsets was 97.4% and 97.6%, respectively. To our mind, the implementation of this model promises to significantly improve the efficacy of early diagnosis systems for complex diseases.

关键词： Gene expression Ontologies Accuracy Optimization Bayes methods Medical diagnosis clustering algorithms Alzheimer's disease Predictive models Precision medicine Gene expression data gene ontology analysis clustering biclustering convolutional neural network Bayes optimization classification Alzheimer's disease cancer disease

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An Intelligent Framework for Cluster-Based Side-Channel Analysis on Public-Key Cryptosystems

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1962-1973页

作者： Wei, Congming He, Shulin Wang, An Sun, Shaofei Ding, Yaoling Zhang, Jingqi Zhu, Liehuang Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China

Classical cluster-based side-channel analysis (SCA) uses clustering algorithms to analyze power traces and often, principal component analysis to reduce the dimension of data, resulting in that clustering may not deal well with high-dimensional traces, such as cryptographic algorithm implementations with countermeasures. In this article, we propose an intelligent framework for cluster-based SCA, which includes three steps of clustering, classification and correction, for processing large high-dimensional data. By combining unsupervised clustering and supervised deep learning techniques, the framework succeeds in mining the data for additional in-depth information. In addition, unlike traditional cluster-based SCA, our approach focuses on deep learning and deliberately avoids over-reliance on cluster labels during classification. And metrics for correction are adopted to achieve a high level of reliability in key recovery. Experiments on the RSA smart card based on Montgomery ladder implementation and FPGA-based ECC with random delay demonstrate that our framework can significantly improve the success rate with strong robustness.

关键词： clustering algorithms Deep learning Noise Convolutional neural networks Classification algorithms Cryptography Computational modeling Cluster deep learning public-key cryptosystems side-channel analysis (SCA)

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Handover-Free Multi-Connectivity Mobility Management for Downlink FD-RAN: A Hierarchical DRL-Based Approach

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 1281-1296页

作者： Zhang, Tianqi Xue, Jianzhe Xu, Yunting Jiao, Luofang Chen, Jiacheng Zhou, Haibo Zhao, Lian Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Peng Cheng Lab Dept Strateg & Adv Interdisciplinary Res Shenzhen 518066 Peoples R China Toronto Metropolitan Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

Seamless connectivity and on-demand service provision are considered as the fundamental capabilities in next-generation mobile networks (6G). However, current configuration of single base station (BS) connection and increasingly denser BS deployment pose great challenges for mobile user equipment (UE), due to the frequent handover and limited communication serving capacity. To this end, we investigate the handover-free multi-connectivity mobility management problem in downlink over a novel 6G architecture, namely fully-decoupled radio access network (FD-RAN). Particularly, we formulate the problem as a two-layer task involving UE-BS association and link power control, whose objective is to minimize the long-term absolute difference between UE's serving rate and rate demand. We propose a hierarchical deep reinforcement learning (HDRL)-based scheme to decompose the original problem into two subproblems for efficient resolution. Specifically, a double deep Q-network (DDQN) algorithm is employed to update the multi-connectivity BS cooperation set for each UE at the first layer of HDRL. Then at the second layer, we design a transformer-assisted soft actor-critic (TSAC) algorithm to jointly determine transmission power for all links associated with each UE. Extensive simulations validate the effectiveness of proposed scheme over benchmarks, which is capable of providing seamless connectivity and fine-grained on-demand service for mobile UEs.

关键词： Handover 5G mobile communication Downlink Heuristic algorithms clustering algorithms Resource management 6G mobile communication FD-RAN multi-connectivity mobility management power control hierarchical deep reinforcement learning

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LRD-Raft: Log Replication Decouple for Efficient and Secure Consensus in Consortium-Blockchain-Based IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 8807-8820页

作者： Yang, Heru Feng, Yuming Zhang, Weizhe Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Peng Cheng Lab Dept New Networks Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Peoples R China Peng Cheng Lab Dept New Networks Shenzhen 518055 Peoples R China

Currently, consortium blockchain has been used in Internet of Things (IoT) systems to ensure secure data sharing across organizations. Consortium blockchain typically uses the Raft algorithm because of its high performance. However, in geo-distributed IoT environments, the single-point overhead problem of leader nodes affects the security and efficiency of consensus due to the high latency and frequency of client requests. To address this challenge, we propose a novel solution: log replication decoupling raft (LRD-Raft), which enables follower nodes to participate in log replication as well. This scheme reduces the overhead of the leader node by delegating part of the log replication task to the follower node. We also propose an adaptive coding protocol that dynamically adjusts the erasure code parameters according to the number of cluster healthy nodes to save the cluster's network traffic. We evaluated LRD-Raft in different network latency environments and different cluster sizes, and the experimental results show that LRD-Raft has a more extensive performance system compared to Raft in high network latency and large data block transmission environments, and also has a certain level of resistance to DoS attacks, which improves the performance and security of the consensus mechanism.

关键词： Internet of Things Codes Security Telecommunication traffic clustering algorithms Voting Performance evaluation Consensus protocol Scalability Denial-of-service attack Consensus mechanisms consortium blockchain erasure code Internet of Things (IoT) log replication decouple

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System Design and Prototyping of the CMS Level-1 Calorimeter Trigger at the High-Luminosity LHC

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2025年第3期72卷 385-391页

作者： Kumar, P. Gomber, B. Univ Hyderabad Sch Phys CASEST Hyderabad 500046 India

The high-luminosity Large Hadron Collider (HL-LHC) proposes an ambitious physics program covering high-precision standard model (SM) measurements and advancing the searches for new physics. The efficient data gathering and precise events reconstruction in the intense environment of 200 proton-proton interactions per bunch-crossing (40 MHz) are essential for attaining the success of the HL-LHC program. To fulfill these requirements, the Compact Muon Solenoid (CMS) experiment is building entirely new data acquisition (DAQ) and trigger systems. The Phase-2 CMS Level-1 trigger system will drive the massive detector input data bandwidth of 75 Tb/s and is aimed to complete the single-event processing within 12.5 mu s. For this purpose, the CMS plans to replace the Phase- 1 mu Telecommunications Computing Architecture (TCA)-based processor cards and crates with an Advanced TCA (ATCA) specification. Each ATCA board is powered with a Xilinx UltraScale+ family field-programmable gate array (FPGA) that supports over 100 high-speed optical links at 25 Gb/s, capable of handling the high bandwidth and processing requirements of the HL-LHC. Along with the upgrade in hardware, the Level-1 trigger system will use highly flexible, modular, and adequately sophisticated algorithms currently feasible solely in offline reconstruction, such as a particle-flow (PF) algorithm. The flexible and modular architecture will assist in addressing the HL-LHC physics requirements. This article discusses the system design, prototyping, and algorithms being developed for the Phase-2 Level-1 Calorimeter trigger system.

关键词： Field programmable gate arrays Crystals Computer architecture Large Hadron Collider Detectors Mesons Poles and towers Physics Correlators clustering algorithms Calorimeter field-programmable gate arrays (FPGAs) high-luminosity Large Hadron Collider (HL-LHC) physics trigger

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Secure and Efficient Cross-Modal Retrieval Over Encrypted Multimodal Data

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IEEE TRANSACTIONS ON COMPUTERS 2025年第4期74卷 1405-1417页

作者： Yang, Li Zhang, Wei Miao, Yinbin Liang, Yanrong Li, Xinghua Choo, Kim-Kwang Raymond Deng, Robert H. Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xidian Univ Engn Res Ctr Big data Secur State Key Lab Integrated Serv Networks Sch Cyber EngnMinist Educ Xian 710071 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

With the popularity of social media, mobile devices and the Internet, a large amount of multimodal data (e.g, text, image, audio, video, etc.) is increasingly being outsourced to cloud to save local computing and storage costs. To search through encrypted multimodal data in the cloud, privacy-preserving cross-modal retrieval (PPCMR) techniques have attracted extensive attention. However, most of the existing PPCMR schemes lack the ability to resist quantum attacks and have low search efficiency on large-scale datasets. To solve above problems, we first propose a basic PPCMR scheme FECMR using the enhanced Single-key Function-hiding Inner Product Functional Encryption for Binary strings (SFB-IPFE) and cross-modal hashing technology, which achieves the measurement of similarity over encrypted multimodal data while resisting quantum attacks. Then, we design an efficient index KM-tree utilizing the K-modes clustering algorithm. On this basis, we propose an improved scheme FECMR+, which achieves sub-linear search complexity. Finally, formal security analysis proves that our schemes are secure against quantum attacks, and extensive experiments prove that our schemes are efficient and feasible for practical application.

关键词： Cryptography clustering algorithms Cross modal retrieval Security Computers Correlation Complexity theory Feature extraction Accuracy Hash functions Privacy-preserving cross-modal retrieval cross-modal hashing K-modes clustering algorithm KM-tree indexing post-quantum security

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