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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Lukas Sohlbach René Harmann Fernando M. Quintana Heng Wei Zhang Fernando Perez-Peña Karsten Schmidt Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt/Main Germany Department of Informatics and Technology Engineering School of Engineering University of Cá diz Cádiz Spain Department of Automation Electronics and Computer Architecture and Networks School of Engineering University of Cádiz Cádiz Spain

Dielectric Elastomer Actuators (DEAs) can be seen as flexible capacitors, where an incompressible elastomer is sandwiched between two compliant electrodes. From the elastomer non-linear and time-dependent phenomena arise during the activation of the actuator. This results in the need for appropriate control methods, especially when they are used in the field of soft robots. Therefore, this paper presents a feedforward controller which is based on Spiking Neural networks (SNNs). To the best of our knowledge this is the first time DEAs and SNNs are combined to make a step towards true soft robots. It was demonstrated that it is necessary to include feedback into the control scheme. Also, it was found that in the context of a SpiNN-3 development board as a neuromorphic hardware platform, neuron encoding is the most promising encoding method. This also implies that a classification approach is preferable to a regression approach.

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Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems

Energy-Constrained Task Scheduling in Heterogeneous Distribu...

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International Conference on computer Supported Cooperative Work in Design

作者： Chen Chen Jie Zhu Haiping Huang Yingmeng Gao Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.

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Bringing human perception to validate weather measurements in Smart City: Human-Techno Centric Approach 8

Bringing human perception to validate weather measurements i...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Founoun, Adnane El Ghazouani, Laila Haqiq, Abdelkrim Hayar, Aawatif Radoine, Hassan Hassan First University of Settat Faculty of Sciences and Techniques Computer Networks Mobility and Modeling Laboratory: IR2M Settat26000 Morocco School of Architecture Planning and Design Mohammed Vi Polytechnic University Benguerir Morocco Ritm Lab R&d Centre Hassan Ii University Ced ENSEM/TICDev Casablanca Morocco

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

To provide different services to smart cities, many new approaches were used, including the strategic approach and the techno-human-centric approach. When it combines both the human aspect through its sensitivities and the technical aspect of data management to enable the city's stakeholders to act better. Indeed, a new governance framework is possible through the introduction of human intelligence to answer specific questions. It's about putting citizens at the center of the smart city issue while putting a coalition with new technologies. In this paper, we suggest validation of weather data, temperature, and wind, according to citizens' perceptions through a gaming scenario. The use of this mobile platform will assist stakeholders towards a roadmap for future urban design. This will be done by building up a database of citizens' perceptions and appreciations. Also, the wide range of sites of interest will allow an urban promotion and accompany the cultural life of the city. This will be possible by inviting subscribers to go and visit these places subject to promotions. © 2022 IEEE.

关键词： Climate change

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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Evaluating the Inertia of the Jordanian Power Grid

SSRN

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SSRN 2023年

作者： Alnawafah, Hamza Sarrias-Mena, Raúl Harb, Ahmad Fernández-Ramírez, Luis M. Llorens-Iborra, Francisco Department of Electrical Engineering Higher Technical School of Engineering of Algeciras University of Cadiz Algeciras11202 Spain Department of Engineering in Automation Electronics and Computer Architecture & Networks University of Cadiz Higher Technical School of Engineering of Algeciras Algeciras11202 Spain Amman Jordan

The increasing penetration of renewable energy sources in power grids has resulted in the need for a comprehensive evaluation of their impact on the dynamic behavior of the power system, including its inertia. This study aims to evaluate the inertia of the current Jordanian power system at different penetration levels of renewable energy sources. The study was conducted using DIgSILENT PowerFactory simulation software, and the results were analyzed to determine the effect of renewable energy penetration on the inertia of the power system. The results of this study provide valuable information for the development of control strategies for integrating renewable energy sources into the Jordanian power system, ensuring stability and reliability in the power system operation. The findings of this study contribute to the understanding of the impact of renewable energy sources on power system inertia, and support the development of renewable energy integration strategies. © 2023, The Authors. All rights reserved.

关键词： computer software

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Performance analysis of the Raft consensus algorithm on Hyperledger Fabric and Ethereum on cloud

Performance analysis of the Raft consensus algorithm on Hype...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： João H. F. Battisti Vitor E. Batista Guilherme P. Koslovski Maurício A. Pillon Charles C. Miers Marco A. Marques Marcos Simplício Diego Kreutz Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil Laboratory of Computer Networks and Architecture (LARC) Universidade de São Paulo (USP) Brazil Graduate Program in Software Engineering Federal University of Pampa (Unipampa) Brazil

The use of private or consortium blockchains in organizations’ applications is growing. A relevant aspect of blockchains is the choice of consensus mechanism. This decision delimits which blockchain solutions are suitable for the private scenario. Once the consensus mechanism is chosen, more than one blockchain may be enabled. However, this decision making is not trivial and requires detailed experimental indicators about algorithms and blockchains performance. In this context, we provide a comprehensive performance analysis of the Raft consensus mechanism based on its implementation in Hyperledger Fabric and Ethereum blockchain solutions. We performed our experiments on an OpenStack private cloud using each blockchain developer’s default settings for virtual machines. Our findings show how the implementation of each solution can impact the application’s performance under certain conditions.

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Enhancing the Uplink of Cell-Free Massive MIMO Through Prioritized Sampling and Personalized Federated Deep Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mendoza, Charmae Franchesca Kaneko, Megumi Rupp, Markus Schwarz, Stefan Technische Universität Wien Institute of Telecommunications Sustainable Radio Access Networks Christian Doppler Laboratory Digital Twin Assisted AI Vienna1040 Austria National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Department of Computer Science University of Tokyo Tokyo Japan Technische Universität Wien Institute of Telecommunications Vienna1040 Austria

Effective power control is key to solving the inter-user interference problem that degrades performance in cell-free massive multiple-input multiple-output (MIMO) systems. Motivated by its ability to operate online and model-free, without relying on training datasets, we leverage deep reinforcement learning (DRL) for uplink power control, aiming to maximize the guaranteed rate. We propose a fully centralized single-agent framework and two distributed schemes that employ several agents for improved scalability, leveraging prioritized experience replay to enable fast adaptation to the dynamic changes of the wireless environment. We investigate the performance of two multi-agent system architectures: (1) centralized training, decentralized execution (CTDE), where each agent forwards its RL experience to a central trainer, and (2) personalized federated learning (FedPer), where the training is performed locally at each agent, and only the base layer of the local deep neural network (DNN) model is forwarded periodically for aggregation at a server. We focus on the realistic scenario of dynamic device (de-)activation, combined with user mobility. Numerical evaluations demonstrate that the proposed FedPer with prioritized sampling achieves near-optimal rate and power performance while incurring the least amount of communication overhead. © 2015 IEEE.

关键词： Deep reinforcement learning

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Latency-aware Partial Task Offloading in Collaborative Edge Computing

Latency-aware Partial Task Offloading in Collaborative Edge ...

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International Conference on computer Supported Cooperative Work in Design

作者： Yingmeng Gao Jie Zhu Haiping Huang Chen Chen Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing China Jiangsu High Technology Reasearch Key Laboratory of Wireless Sensor Networks Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency.

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Cluster-Based Interference-Free MAC Protocol with Load Aware in Software Defined VANET

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China Communications 2020年第12期17卷 217-234页

作者： Yangshui Gao Tao Luo Xinxin He Zhilong Zhang Beijing Laboratory of Advanced Information Networks Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Mathematics and Computer Dali UniversityDali 671003China

In order to reduce the interference,a novel,cluster-based medium access control(MAC)protocol with load aware for VANETs is proposed in this ***,all vehicles on roads are grouped into stable clusters in the light of their direction,number of neighbors,link reliability,and traffic *** utilizing the advantages of centralized control in software defined VANETs(SDVN),cluster stability can be maintained in ***,a contention-free MAC mechanism composed of inter-cluster multi-channel allocation and intra-cluster dynamic TDMA frame allocation is proposed to prevent co-channel interference and hidden terminal *** results show that the proposed protocol outperforms some existing protocols in cluster stability,delivery ratio,throughput and delay performance.

关键词： SDVN MAC co-channel interference non-safety services cluster

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