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Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management System Utilizing Crypto-Asset and Multisignature

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5765-5778页

作者： Kharjana, Mebanjop Sahana, Subhas Chandra Saha, Goutam North-Eastern Hill University Department of Information Technology Shillong793022 India National Institute of Technology Department of Computer Science and Engineering Durgapur713209 India

Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures. © 2002-2012 IEEE.

关键词： Blockchains Autonomous aerial vehicles Security Authentication Drones Public key Mobile computing Smart contracts Collaboration Global Positioning System

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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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science China(information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

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A verifiable multi-secret image sharing scheme based on DNA encryption

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Multimedia Tools and Applications 2025年第4期84卷 1967-1983页

作者： Chattopadhyay, Arup Kumar Saha, Sanchita Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret input images into n share images. This encoding ensures that if a subset of participants collude, no information about the secret images can be revealed. The secret images can only be recovered if all participants cooperate. Most of these schemes are built using basic Boolean operations, primarily XOR operations. MSIS schemes that rely on Boolean logic offer various benefits compared to other methods, such as the ability to recover data without any loss, no increase in pixel size, and efficient computation. This article presents a verifiable MSIS scheme that relies on a secure two-variable one-way function and a secure hash function. The scheme allows for independent verification of both the dealer and the participants to detect any cheating attempts. The proposed scheme utilizes the Deoxyribonucleic acid (DNA) encryption technique to leverage the inherent benefits of DNA computing, such as enhanced security, fast computation, minimal storage needs, and low power usage. The share images produced by the proposed scheme exhibit an average entropy of 7.99, indicating that the generated 8-bit share images are completely random. Additionally, the correlation coefficient is approximately 0.005, suggesting that neighboring pixels in the images are not related to each other. The proposed scheme can detect any instance of cheating, whether it is done by the dealer or by a participant. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： DNA

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UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

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Digital Communications and Networks 2024年第4期10卷 837-852页

作者： Jing Bai Jinsong Gui Guosheng Huang Shaobo Zhang Anfeng Liu School of Computer Science and Engineering Central South UniversityChangSha410083China School of Information Science and Engineering Hunan First Normal UniversityChangsha410205China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangTan411100China

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the *** this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for *** main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data ***,we conducted extensive experiments to evaluate the UAV-ITD *** results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

关键词： Unmanned aerial systems Trust computing Truth discovery Deep matrix factorization Low-cost communications

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Designing an Ultrasonic-Assisted Machining System by Using a Network Mapping Fusion–Convolutional Neuro-Fuzzy Network Model

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IEEE Transactions on Industrial Informatics 2024年第7期20卷 1-12页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Lin, Xue-Qian the Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung 411030 Taiwan the Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung 404336 Taiwan

In the field of machining, product quality must meet customer specifications. In general, surface roughness is an essential indicator of machining quality. Low surface roughness correlates with increased fatigue strength and corrosion resistance. However, the main factor that affects surface roughness is the selection of the machining parameters. When different parameters are combined, the resulting machining quality varies. Therefore, to achieve the desired machining quality, appropriate machining parameters must be selected. In this study, an ultrasonic-assisted machining system (UAMS) was designed to help users determine the machining parameters and machine SiC materials. To establish a prediction model for surface roughness, a novel network mapping fusion (NMF) convolutional neuro-fuzzy network (CNFN) model was used in the designed UAMS. The differential evolution algorithm was then used to search for optimized machining parameters. To explain the prediction model, which can help analyze the factors that have the greatest influence on surface roughness, a Shapley additive explanations method is proposed. The proposed NMF–CNFN model was more accurate than were the other deep learning models and exhibited a MAPE of 1.98%. When optimized machining parameters were selected, the desired surface roughness was obtained, thereby confirming the effectiveness and accuracy of the proposed UAMS. Moreover, the proposed model was implemented in a field-programmable gate array (FPGA) to reduce its power consumption and increase its computational performance. Experimental results indicated that the computational speed of the FPGA was 99.64%and 99.16%higher than those of the CPU and GPU, respectively. IEEE

关键词： Convolutional neuro-fuzzy network (CNFN) differential evolution (DE) algorithm neuro-fuzzy network Shapley additive explanations (SHAP) ultrasonic-assisted machining system (UAMS)

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Modern Standard Arabic speech disorders corpus for digital speech processing applications

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International Journal of Speech technology 2024年第1期27卷 157-170页

作者： Alqudah, Assal A. M. Alshraideh, Mohammad A. M. Abushariah, Mohammad A. M. Sharieh, Ahmad A. S. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan

Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purposes. Although there are available speech corpora, speech data for speakers suffering speech disorders are hardly available for many languages including Arabic language. Consequently, developing digital speech processing applications that target the entire society becomes hard due to the unavailability of speech corpora that contain sufficient speakers’ variations including healthy and disordered speech. This research presents our work towards developing a Modern Standard Arabic (MSA) speech corpus for speakers suffering distortion and substitution articulation disorders. The speech corpus was recorded by 40 (20 male and 20 female) Jordanian speakers who suffer either distortion or/and substitution articulation disorders. This speech corpus can be used for various applications including ASR, speech and hearing, and others. Part of this speech corpus is used for developing and evaluating an ASR for MSA using the Carnegie Mellon University (CMU) Pocketsphinx tools based on Mel-Frequency Cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) techniques. Furthermore, Linear Discriminant Analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) optimization techniques were applied. Using three different testing data sets, this work obtained 98.38% and 1.76% average word recognition correctness rate (WRCR) and average Word Error Rate (WER), respectively, for speaker-dependent and text-independent. For speaker-independent and text-dependent, this work obtained 99.37% and 0.68% average WRCR and average WER, respectively, whereas for speaker-independent and text-independent this work obtained 96.53% and 4.00% average WRCR and average WER, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Hidden Markov models

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Towards smart technologies with integration of the internet of things, cloud computing, and fog computing

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International Journal of Networking and Virtual Organisations 2023年第1期29卷 73-124页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida201309 India

In the recent era of technology, the internet of things (IoT) plays a tremendous role in enhancing the quality of human life through smart devices and sensing the real-world environment. IoT aims to interconnect anything from anywhere via the internet. The existing literature lacks in providing deeper research insight toward a broader perspective of integrating IoT, cloud computing (CC), and fog computing (FC). Hence, this paper aims to provide a comprehensive review of integrating these main emerging technologies and four major objectives are addressed. The first objective identifies the primary characteristics of IoT to solve real-life problems. The second objective addresses the issues that necessitate the integration of IoT with other emerging technologies. The third objective involves how the integration of IoT with CC elevates the scope of providing a solution to real-life problems. The final objective addresses the limitations of the integration of IoT with CC and how FC overcomes these limitations. © 2023 Inderscience Enterprises Ltd.

关键词： Internet of things

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2025年第17期84卷 18327-18350页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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