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Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

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Digital Communications and Networks 2024年第1期10卷 53-62页

作者： Zhipeng Cheng Minghui Liwang Ning Chen Lianfen Huang Nadra Guizani Xiaojiang Du department of information and Communication engineering Xiamen UniversityXiamen361005China School of Electrical and Computer engineering University of Texas at ArlingtonArlingtonTX76019USA department of Electrical and Computer engineering Stevens Institute of TechnologyHobokenNJ07030USA

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in ***,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective *** this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground *** goal was to mitigate co-channel interference while maximizing long-term system *** problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this *** simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

关键词： UAV-user association Multi-connectivity Resource allocation Power control Multi-agent deep reinforcement learning

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

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Science China(Technological Sciences) 2024年第7期67卷 2074-2086页

作者： WANG Rui LI Ming-Jia WANG YiBo SUN QiuYe ZHANG PinJia College of information Science and engineering Northeastern UniversityShenyang 110819China School of Mechanical engineering Beijing Institute of TechnologyBeijing 100081China department of Electrical engineering Tsinghua UniversityBeijing 100084China

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

关键词： regional integrated electric-thermal energy systems real-time power balance virtual asynchronous machine adaptive inertia small-signal stability

WebFLex:A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

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Computers, Materials & Continua 2024年第3期78卷 4177-4204页

作者： Mai Alzamel Hamza Ali Rizvi Najwa Altwaijry Isra Al-Turaiki department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia department of Computer Science and engineering Punjab Engineering CollegeChandigarhIndia

Scalability and information personal privacy are vital for training and deploying large-scale deep learning *** learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web ***,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client ***,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for *** this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training *** a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning *** is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous ***,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central *** has actually been measured in various setups using the MNIST *** results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data *** addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network ***,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

Controlling a peristaltic robot inspired by inchworms

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Biomimetic Intelligence & Robotics 2024年第1期4卷 88-96页

作者： Yanhong Peng Hiroyuki Nabae Yuki Funabora Koichi Suzumori department of information and Communication engineering Graduate School of EngineeringNagoya UniversityNagoya 464-8601Japan department of Mechanical engineering Tokyo Institute of TechnologyTokyo 152-8550Japan

This study presents an innovative approach in soft robotics,focusing on an inchworm-inspired robot designed for enhanced transport *** explore the impact of various parameters on the robot’s performance,including the number of activated sections,object size and material,supplied air pressure,and command execution *** a series of controlled experiments,we demonstrate that the robot can achieve a maximum transportation speed of 8.54 mm/s and handle loads exceeding 100 g,significantly outperforming existing models in both speed and load *** findings provide valuable insights into the optimization of soft robotic design for improved efficiency and adaptability in transport *** research not only contributes to the advancement of soft robotics but also opens new avenues for practical applications in areas requiring precise and efficient object *** study underscores the potential of biomimetic designs in robotics and sets a new benchmark for future developments in the field.

关键词： McKibben artificial muscles Bionics Soft robotics Soft actuator Bio-inspired robotics

Luminescence properties and optical sensing behaviors of Sr_(2)GdSbO_(6):Eu^(3+) phosphors

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Journal of Rare Earths 2024年第8期42卷 1458-1469,I0002页

作者： Youfa Tai Ruirui Cui Jun Zhang Chunkai Wang Tingyu Zhao Beiqiao Zhang Chaoyong Deng Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and information engineering Guizhou University of CommerceGuiyang550014China School of Electronics and information engineering Guiyang UniversityGuiyang550005China

Novel orange-red Sr_(2)GdSbO_(6):xEu^(3+)(x=0,0.05,0.1,0.2,0.3,0.4,0.5 and 0.6) phospho rs were successfully prepared by the traditional high-temperature solid-state *** results of Rietveld refinement,energy dispersive spectroscopy(EDS) spectrum and elemental mapping demonstrate that Eu^(3+) successfully replaces the Gd^(3+) sites and distributes uniformly in the particles of *** luminescence properties of Sr_(2)GdSbO_(6):Eu_(3+)phosphors were investigated in *** emission spectra of the strongest emission peak is the ^(5)D_(0)→^(7)F_(1)(593 nm) transition,which can emit orange-red light under393 nm *** the doping concentration of Eu3+ions is x=0.2,the luminescence intensity of the phosphors reaches the *** detailed mechanism of concentration quenching is attributed to dipole-dipole *** thermal stability values of Sr_(2)GdSbO_(6):0.2Eu^(3+) phosphors are 87%,82% and114% under 393,467 and 527 nm excitations,*** causes of the abnormal thermal quenching under 527 nm excitation were *** on the abnormal thermal quenching under527 nm excitation,the optical thermometry properties of Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors were investigated by fluorescence intensity ratio(FIR) technique,and appreciable relative sensitivity was *** results suggest that Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors can be potentially applied to w-LEDs and optical thermometers.

关键词： Sr_(2)GdSbO_(6):Eu^(3+) Phosphors Luminescence properties Abnormal thermal quenching Rare earths

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi department of Software engineering International Islamic UniversityIslamabad25000Pakistan information Technology department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia department of Computer Science University of PeshawarPeshawar25121Pakistan department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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Science China(information Sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of Cyber engineering Xidian University School of Computer Science and Technology Xidian University department of Computer and information Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of Computer Science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer engineering KIIT Deemed to be UniversityBhubaneshwar 751024India department of Computer Science and engineering Indian Institute of Information Technology SuratSurat 394190India department of Computer Science and engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of information and Communication engineering Harbin Engineering UniversityHarbin150000China department of Electrical and Computer engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators