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SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

Computers, Materials & Continua 2024年第12期81卷 4625-4658页

作者： Naeem Ali Askar Adib Habbal Hassen Hamouda Abdullah Mohammad Alnajim Sheroz Khan Department of Computer Engineering Faculty of EngineeringKarabük UniversityKarabük78050Türkiye Department ofManagement Information Systems College of Business AdministrationMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

关键词： Energy efficient forwarding strategy health information system internet technologies IoHT people with disabilities reinforcement learning Q-learning

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Developing Multi-Agent Wide Area Damping Controller to Mitigate False Data Injection Attacks 24

Developing Multi-Agent Wide Area Damping Controller to Mitig...

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24th EEEIC International Conference on Environment and electrical engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Vavdareh, Masoud Babaei Ghafouri, Mohsen Ameli, Amir Concordia University Department of Information and Systems Engineering Montreal Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350355185

The effectiveness of wide-area damping controllers (WADCs) is significantly influenced by the integrity of the measurement data collected from phasor measurement units (PMUs). These damping controllers utilize PMU data to make decisions and transmit control commands to system actuators, such as synchronous generators. However, the incorporation of communication links for transmitting these signals exposes the power system to various cyber threats, such as false data injection attacks (FDIAs). On this basis, this study proposes two methods for defending against FDIAs: (i) the utilization of a neural network-based autoencoder as an effective attack detector for FDIAs, and (ii) the development of a multi-agent control strategy to mitigate these attacks. The performance of these proposed methods is analyzied using a two-area Kundur test system, compared against conventional data-driven attack detection methods and a global WADC technique. The findings indicate that the implementation of these methods significantly enhances defense capabilities against FDIA targeting wide-area measurement signals. © 2024 IEEE.

关键词： Phasor measurement units

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Development of A Deep Learning Hybrid Model for Classification of Plastic Waste Objects in the Coastal Environment

Development of A Deep Learning Hybrid Model for Classificati...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Djafar, Imran Indrabayu Syarif, Syafruddin Hasanuddin University Dipa Makassar University Doctoral Program of Electrical Engineering Department Makassar Indonesia Hasanuddin University Makassar Department of Informatics Indonesia Hasanuddin University Department of Electrical Engineering Makassar Indonesia

ISBN: (纸本)9798331513320

Plastic, glass, metal, and wood waste has become a global scourge and nightmare to the marine environment and the development of the coastal areas. This paper aims at developing a hybrid model of deep learning technique using Convolutional Neural Networks (CNN) and Vision transformers (ViT) in the performance of detecting underwater waste materials. The model is designed with the use of transfer learning and high-resolution imaging sourced from underwater drones to help overcome the challenges of underwater work such as poor visibility and insufficient ambient light. The local features are extracted using CNNs and the global spatial relationships of the images are attended to by the ViTs. The results achieved a waste detection accuracy rate of about 90%, which is effective even in difficult environmental conditions. The concept is for a waste management system where drones or underwater robotics systems can assist automated waste management, waste treatment processes, and aid marine renovation activities. © 2025 IEEE.

关键词： Convolutional neural networks

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Superiority of Strontium-Doped Barium Titanate as an Electron Transport for Perovskite Solar Cells for Enhanced Efficiency and Thermal Stability

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Transactions of Tianjin University 2025年第1期31卷 42-63页

作者： Minhaz Mahmood Mohammad Tariqul Islam Md Samiul Islam Sakhr M.Sultan Kashfia Noor M.A.Islam K.Sopian Abdullah Alghafis Mohd Adib Ibrahim K.Sobayel Department of Electrical Electronic and Systems EngineeringUniversiti Kebangsaan Malaysia(UKM)43600BangiMalaysia Solar Energy Research Institute(SERI) Universiti Kebangsaan Malaysia(UKM)43600BangiMalaysia Department of Electrical Engineering Faculty ofEngineeringUniversiti Malaya50603KualaLumpurMalaysia Department of Mechanical Engineering Universiti Teknologi Petronas32610Seri IskandarMalaysia Department of Mechanical Engineering College ofEngineeringQassim UniversityBuraydahSaudiArabia

This study presents a detailed comparative analysis of three electron transport layer(ETL)materials for perovskite solar cells(PSCs),namely titanium dioxide(TiO_(2)),barium titanate(BaTiO_(3)or BTO),and strontium-doped barium titan-ate(Ba_(1−x)Sr_(x)TiO_(3)or BST),and their impact on the quantum efficiency(QE)and power conversion efficiency(PCE)of CH_(3)NH_(3)PbI_(3)(MAPbI_(3))*** optimized structure demonstrates that devices utilizing BST as an ETL achieved the highest PCE of 29.85%,exhibiting superior thermal stability with the lowest temperature coefficient of−0.43%/*** temperature-induced degradation is comparable to that of commercially available silicon ***,BST-based ETLs show 29.50%and 26.48%higher PCE than those of TiO_(2)-based and BTO-based *** enhanced internal QE and favorable current density–voltage(J–V)characteristics of BST compared with those of TiO_(2)and BTO are attributed to its improved charge carrier separation,reduced recombination rates,and robust electrical characteristics under varied environmental ***,the electric field and generation rate of the BST-based ETLs show a more favorable distribution than those of the TiO_(2)-based and BTO-based *** findings provide significant insights into the role of different ETLs in enhancing QE,indicating that BST is a superior ETL that enhances both the efficiency and stability of *** study contributes to the understanding of how perovskite-structured ETLs can be used to design and optimize highly efficient and stable photovoltaic devices.

关键词： Perovskite solar cell Electron transport layer Light management Quantum efficiency SCAPS-1D Lumerical FDTD Barium titanate

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Cascaded Voltage and Power Control for Full-Bridge-Fed CLLC Resonant Converter to Expand Output Voltage Range

Cascaded Voltage and Power Control for Full-Bridge-Fed CLLC ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Chen, Hung-Chi Chang, Ya-Chun Lin, Jia-Liang Wu, Chih-Chiang Department of Electronics and Electrical Engineering Hsinchu Taiwan Institute of Electrical and Control Engineering Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350376067

In this paper, the cascaded voltage and power control is proposed to expand the output voltage range for full-bridge-fed CLLC resonant converter. In first, the CLLC resonant circuit is analyzed based on pulse frequency modulation (PFM) and pulse width modulation (PWM). The output voltage is sensed and the power is calculated to tune the switching period to achieve the wide output range. A 3.0kW prototype circuit is implemented to demonstrate the proposed control. © 2024 IEEE.

关键词： Pulse width modulation

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Latent Variable and Classification Performance Analysis of Bird-Drone Spectrograms with Elementary Autoencoder

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2024年 3卷 115-123页

作者： White, Daniel Jahangir, Mohammed Mishra, Amit Kumar Baker, Chris J. Antoniou, Michail University Of Birmingham School Of Electrical Electronic And Systems Engineering United Kingdom Aberystwyth University Department Of Computer Science United Kingdom

Deep learning with convolutional neural networks has been widely utilised in radar research concerning automatic target recognition. Maximising numerical metrics to gauge the performance of such algorithms does not necessarily correspond to model robustness against untested targets, nor does it lead to improved model interpretability. Approaches designed to explain the mechanisms behind the operation of a classifier on radar data are proliferating, but bring with them a significant computational and analysis overhead. This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation of a challenging dataset of urban bird and drone targets and subsequently if apparent quality of the representation via preservation of class labels leads to better classification performance after a separate supervised training stage. It is shown that a CAE that reduces the features output after each layer of the encoder gives rise to the best drone vs bird classifier. A clear connection between unsupervised evaluation via label preservation in the latent space and subsequent classification accuracy after supervised fine-tuning is shown, supporting further efforts to optimise radar data latent representations to enable optimal performance and model interpretability. © 2023 IEEE.

关键词： Drones

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