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Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

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Journal of communications and information Networks 2024年第2期9卷 137-150页

作者： Yuting Wei Sheng Wu Zhe Ji Zhigang Yu Chunxiao Jiang Linling Kuang School of information and communication engineering Beijing University of Posts and TelecommunicationsBeijing 100876China China Academy of Electronics and information technology Beijing 100041China Beijing National Research Center for information Science and technology Tsinghua Space CenterTsinghua UniversityBeijing 100084China

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.

关键词： UAV-based edge computing multi-UAV collaboration joint task offloading and wireless channel allocation simulated annealing(SA)algorithm multi-agent deep deterministic policy gradient(MADDPG)

Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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Computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and information Science Fu Jen Catholic UniversityNew Taipei City24205Taiwan Department of Computer Science and information engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354Taiwan Department of Electrical and Electronics engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical engineering and information technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trainedCNN(PCNN)MobileNetV2 architecture combined with a SupportVectorMachine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy forMobileNetV2-SVM,InceptionV3,andMobileNetV2 is 90.11%,86.88%,and 89

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything communications

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China communications 2024年第1期21卷 242-259页

作者： Hao Jiang Hongming Zhang Ting Liu College of Artificial Intelligence Nanjing University of Information Science and Technology210044China National Mobile communications Research Laboratory Southeast UniversityNanjing 210096China School of information and communication engineering Beijing University of Posts and TelecommunicationsChina

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)*** the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless *** statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and *** results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and *** proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

关键词： complex CIRs LoS and NLoS propagation components MIMO V2X communication environments SoS simulation channel model statistical properties

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Two-layer Lagrange-based relay network topology and trajectory design for solar system explorations

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Science China(information Sciences) 2024年第6期67卷 342-358页

作者： Jun CAO Jian JIAO Hao LIU Rongxing LU Qinyu ZHANG Guangdong Provincial Key Laboratory of Aerospace communication and Networking technology Harbin Institute of Technology (Shenzhen) faculty of Computer Science (FCS) University of New Brunswick

Exploration targeting outer planets and even the edge of the solar system is an emerging direction for the deep-space exploration in the next decades. To address this challenge, a novel two-layer Lagrangebased relay network topology is proposed in this study. Specifically, we utilize the Sun-Mars and Sun-Saturn Lagrange points(LPs) L4 and L5 to build a two-layer backbone relay network, which ensures continuous and high-efficiency communication capability for the exploration of the solar system. Furthermore, we utilize the planetary gravity assist and design the transfer trajectory of backbone relays with the help of planetary celestial body motion equations and Kepler's laws. Moreover, we conduct link budget analysis for multihop relay transmission under Gamma-Gamma distribution, shadowed Rician fading, and additive white Gaussian noise channels in several typical exploration scenarios and validate that the LP relays can effectively support future deep-space exploration missions.

关键词： deep-space communication Lagrangian points backbone relay network transfer trajectory relay selection

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and communication engineering University of Science and Technology BeijingBeijing100083China Beijing engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China faculty of Informatics engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

Hybrid Optimization Algorithm for Handwritten Document Enhancement

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Computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and engineering Shandong University of Science and TechnologyQingdao266590China School of information and Control engineering Qingdao University of TechnologyQingdao266520China faculty of Electrical engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

Security of quantum key distribution with virtual mutually unbiased bases

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 13-21页

作者： Hong-Wei Li Chen-Peng Hao Zhi-Jiang Chen Li Gong Yi-Fei Lu Yang Wang Jia-Ji Li Chun-Mei Zhang Rong Wang Zhen-Qiang Yin Qing-Yu Cai Henan Key Laboratory of Quantum information and Cryptography SSF IEUZhengzhou 450000China Institute of Quantum information and technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Physics University of Hong KongHong Kong 999077China Key Laboratory of Quantum information University of Science and Technology of ChinaHefei 230026China School of information and communication engineering Hainan UniversityHaikou 570228China

In a perfect quantum key distribution(QKD)protocol,quantum states should be prepared and measured with mutually unbiased bases(MUBs).However,in a practical QKD system,quantum states are generally prepared and measured with imperfect MUBs using imperfect devices,possibly reducing the secret key rate and transmission *** analyze the security of a QKD system with imperfect MUBs,we propose virtual MUBs to characterize the quantum channel against collective attack,and analyze the corresponding secret key rate under imperfect state preparation and measurement *** generally,we apply the advantage distillation method for analyzing the security of QKD with imperfect MUBs,where the error tolerance and transmission distance can be sharply *** analysis method can be applied to benchmark and standardize a practical QKD system,elucidating the security analysis of different QKD protocols with imperfect devices.

关键词： quantum key distribution mutually unbiased bases advantage distillation

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Rethinking attribute localization for zero-shot learning

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Science China(information Sciences) 2024年第7期67卷 184-196页

作者： Shuhuang CHEN Shiming CHEN Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI School of Electronic information and communication Huazhong University of Science and Technology School of Computer Science and engineering Nanjing University of Science and Technology School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

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RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

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Journal of Systems engineering and Electronics 2024年第3期35卷 558-574,F0002页

作者： FAN Rong SI Chengke HAN Yi WAN Qun School of information and communication engineering University of Electronic Science and Technology of ChinaChengdu 611731China Institute of Electronic and Electrical engineering Civil Aviation Flight University of ChinaGuanghan 618307China

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of *** this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,***,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train *** introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is *** improves physical interpretability of ***,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of ***,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is ***,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction *** results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

关键词： specific emitter identification(SEI) deep learning(DL) radio frequency fingerprint(RFF) multidimensional feature extraction(MFE) variational mode decomposition(VMD)

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