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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Enhanced Grid-Forming Inverter Control Through Integral Sliding Mode Control in a Cascaded Framework

Enhanced Grid-Forming Inverter Control Through Integral Slid...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Khaled Rayane Abdelhamid Rabhi Brahim Khalil Oubbati Mohammed Benzoubir University of Laghouat LACoSERE Lab Laghouat Algeria Department of Electrical and Computer Engineering Texas A and M University at Qatar Qatar Foundation Doha Qatar Laboratory of Modelisation Information and Systems–MIS University of Picardie Jules Verne

The microgrid (MG) relies heavily on the grid-forming inverter (GFI), making it a crucial component. Therefore, precise and adaptable control of the GFI is essential for microgrid operations due to its pivotal role in shaping voltage and regulating frequency. In this context, this paper introduces a Integral Sliding Mode Control (ISMC) based cascaded approach for controlling the GFI system. The primary goal of this control strategy is to produce a three-phase sinusoidal voltage with a specified amplitude while minimizing harmonic distortion in the output voltage under varying load conditions. The key advantage of the proposed GFI controller lies in its ability to deliver superior performance, characterized by low Total Harmonic Distortion (THD), rapid dynamic tracking capabilities, and robustness in the face of load fluctuations. A comparative assessment is conducted between the conventional repetitive controller and the newly proposed method, considering linear and nonlinear load scenarios. The simulation results provide strong validation for the effectiveness of the proposed approach.

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Reliability Evaluation and Improvement of Islanded Microgrid Considering Operation Failures of Power Electronic Equipment

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Journal of Modern Power systems and Clean Energy 2020年第1期8卷 111-123页

作者： Wen Zhong Lingfeng Wang Zhaoxi Liu Shiying Hou State Key Laboratory of Power Transmission Equipment&System Security and New Technology College of Electrical EngineeringChongqing UniversityChongqingChina Laboratory of Trustworthy Cyber-Physical Systems and Infrastructures Department of Electrical Engineering and Computer ScienceUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

With the high integration of power electronic technologies in microgrids,the reliability assessment considering power electronic devices has become a hot ***,so far no research has considered the impact of the operation failure probability of power electronic equipment on the overall reliability of the *** paper aims to construct a holistic operation failure rate model of power electronic systems based on the overall reliability assessment of islanded microgrid with high penetration of renewable energy sources(RESs).In addition,to improve the reliability of islanded microgrid,the conventional battery energy storage system(BESS)is replaced by the hybrid energy storage system(HESS).Based on the proposed model,the operation failure models for the power electronic modules in microgrid are built and tested,and then the sensitivity analysis is performed for exploring the influence of various factors on the reliability of the microgrid.

关键词： Reliability assessment reliability improvement operation failure rate power electronics islanded microgrid

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The Challenges of Music Deep Learning for Traditional Music 12

The Challenges of Music Deep Learning for Traditional Music

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12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Moysis, Lazaros Iliadis, Lazaros Alexios Sotiroudis, Sotirios P. Kokkinidis, Kostas Sarigiannidis, Panagiotis Nikolaidis, Spiridon Volos, Christos Boursianis, Achilles D. Babas, Dimitrios Papadopoulou, Maria S. Goudos, Sotirios K. Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits & Complexity School of Physics Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki54124 Greece University of Macedonia Department of Applied Informatics Thessaloniki Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece International Hellenic University Department of Information and Electronic Engineering Sindos57400 Greece

ISBN: (纸本)9798350321074

Numerous applications for music listeners, educators, DJs, and musicians have been created over the past decade as the field of Music Deep Learning has expanded. Evidently, the majority of works rely on training their architectures using well-established databases, which consist primarily of Western popular music genres. This creates a deficiency in applications focusing on traditional and regional music, which are vital to preserving culture. As an increasing number of research groups begin to develop works centered on traditional music, several practical challenges for applying deep learning arise. This work discusses these issues and the future of deep learning in the study of traditional music. © 2023 IEEE.

关键词： Deep learning

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A provable-secure and practical two-party distributed signing protocol for SM2 signature algorithm

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Frontiers of computer Science 2020年第3期14卷 203-215页

作者： Yudi ZHANG Debiao HE Mingwu ZHANG Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China School of Computer Sciences Hubei University of TechnologyWuhan 430068China Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTX 78249USA

Mobile devices are widely used for data access,communications and ***,storing a private key for signature and other cryptographic usage on a single mobile device can be challenging,due to its computational ***,a number of(t,n)threshold secret sharing schemes designed to minimize private key from leakage have been proposed in the ***,existing schemes generally suffer from key reconstruction *** this paper,we propose an efficient and secure two-party distributed signing protocol for the SM2 signature *** latter has been mandated by the Chinese government for all electronic commerce *** proposed protocol separates the private key to storage on two devices and can generate a valid signature without the need to reconstruct the entire private *** prove that our protocol is secure under nonstandard ***,we implement our protocol using MIRACL Cryptographic SDK to demonstrate that the protocol can be deployed in practice to prevent key disclosure.

关键词： SM2 signature two-party signing wireless environment provable security threshold secret sharing

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On Radically Expanding the Landscape of Potential Applications for Automated-Proof Methods

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SN computer Science 2021年第4期2卷 1-9页

作者： Uhlmann, Jeffrey Wang, Jie Department of Electrical Engineering and Computer Science University of Missouri 201 Naka Hall Columbia United States Laboratory for Analysis and Architecture of Systems French National Centre for Scientific Research Toulouse France

In this paper, we examine the potential of optimization-based computer-assisted proof methods to be applied much more widely than commonly recognized by engineers and computer scientists. More specifically, we contend that there are vast opportunities to derive valuable mathematical results and properties that may be narrow in scope, such as in highly specialized engineering control applications, that are presently overlooked, because they have characteristics atypical of those that are conventionally studied in the areas of pure and applied mathematics. As a concrete example, we demonstrate use of sum-of-squares (SOS) optimization for certifying polynomial nonnegativity as a part of a proposed dimension-pinning strategy to prove that the inverse of the relative gain array (RGA) of a d-dimensional positive-definite matrix is doubly stochastic for d≤ 4. However, it is not specifically this result and solution method that are of principal interest in this paper but rather how they illustrate the relevance of optimization-based proof techniques to engineering system design more broadly. We believe that our paper is the first to explicitly emphasize the fundamental distinction between methods that can be applied to prove results/properties over a fixed number of dimensions versus those that hold generally. The latter class of problems is the conventional domain of mathematicians, but the former is what we propose to be a fertile and largely unrecognized class of problems that are amenable to automated-proof technologies, e.g., as we demonstrate using our novel dimension-pinning approach. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Automated proofs Automated software methods computer-aided system design computer-assisted proofs Dimension pinning Inverse relative gain array IRGA Relative gain array RGA SOS proofs Sum-of-squares optimization

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Outage and DMT Analysis of Partition-based Schemes for RIS-aided MIMO Fading Channels

arXiv

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arXiv 2023年

作者： Nicolaides, Andreas Psomas, Constantinos Kraidy, Ghassan M. Yang, Sheng Krikidis, Ioannis The IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Norway The Laboratory of Signals and Systems CentraleSupélec Paris-Saclay University France

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side;this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface. © 2023, CC BY.

关键词： Fading channels

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Sailfish: A Dependency-Aware and Resource Efficient Scheduling for Low Latency in Clouds

Sailfish: A Dependency-Aware and Resource Efficient Scheduli...

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IEEE International Conference on Big Data

作者： Jinwei Liu Yingjie Lao Ying Mao Rajkumar Buyya Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Computer and Information Science Fordham University Bronx NY USA School of Computing and Information Systems The University of Melbourne Parkville VIC Australia

Efficiently scheduling jobs in clouds is critical for job performance, system throughput and resource utilization. The growing importance of parallel applications in clouds introduces challenges in scheduling data-parallel jobs. Production data-parallel jobs increasingly have complex dependency structure, i.e., complex task dependencies expressed as directed acyclic graphs (DAGs), and heterogeneous resource demands. NP-hard problems are introduced by relaxing either of these challenges (i.e., scheduling of homogeneous tasks with dependency constraints or independent and heterogeneous tasks) for scheduler design. It is challenging to design a scheduler for simultaneously achieving low latency and high resource utilization due to the complex dependency structure and job heterogeneity. In this paper, we propose Sailfish, a dependency-aware and resource efficient scheduling for low latency in clouds. Sailfish first uses the machine learning algorithm to classify jobs into two categories (high priority jobs and low priority jobs) based on the extracted features. Next, Sailfish splits the jobs into tasks and distributes the tasks to the master nodes based on the dependency of tasks and the load of master nodes. Then, Sailfish utilizes the dependency information of tasks to determine tasks’ priority, and packs tasks by leveraging the complementary of tasks’ requirements on different resource types and task dependency. Finally, the master nodes leverage the proposed mutual reinforcement algorithm to distribute tasks to workers in the system based on the resource demands of tasks, the available resources of workers and task dependency. Extensive experimental results based on a real cluster and experiments using real-world Amazon EC2 cloud service show that Sailfish can improve the average resource utilization (by up to 40%) and reduce the latency (the average job completion time) significantly (by up to 91%) compared to the existing schedulers.

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Variational Bayesian Learning based Joint Localization and Path Loss Exponent with Distance-dependent Noise in Wireless Sensor Network

arXiv

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arXiv 2024年

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua The Department of Electrical Engineering Anhui Polytechnic University Wuhu China The School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China The School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Taipa China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging non-convex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT

QoS Improvement using Federated Learning-based Position Pred...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Saniya Zafar Mohammed S. Alshehri Syed Aziz Shah Jawad Ahmad Adnan Zafar Aizaz Ahmad Khattak Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University KSA Research Centre for Intelligent Healthcare Coventry University UK Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism.

关键词： Wireless communication Urban areas Interference Quality of service Predictive models Prediction algorithms Probabilistic logic Internet of Things Long short term memory Edge computing

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