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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

IEEE Transactions on Automation Science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

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Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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Teaching IoT in Both Physical and Virtual Environments

Teaching IoT in Both Physical and Virtual Environments

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Arnold, William Fontaine, J. Griggs, S. Huff, G. Johnson, D. Linares, C. Patel, S. Reader, J. Roman, J. Sawaqed, Y. Yadav, R. Edmund, Lucas Mallory, James R. National Technical Institute for the Deaf Rochester Institute of Technology Computing and Information Technology Division Education and Research Methods Division United States

The growing field of the Internet of Things (IoT) is valuable for Engineering and Engineering technology students to know. Due to COVID and often limited resources, this can be a difficult topic to teach. The authors pioneered a way to implement the same IoT systems both with physical devices and a Virtual Machine (VM) environment using a Raspberry Pi with servos, buttons, and lights. The VM used the Quick Emulator (QEMU) on the Ubuntu Linux platform. QEMU is a type 2 hypervisor that runs within the user space and performs virtual hardware emulation. The authors developed educational activities which allowed AAS/AOS level students to implement a Raspberry Pi lab using both physical and virtual devices including servos, buttons, and lights. The trials and tribulations will be shared as well as the successes with this student lab project implementation. © American Society for Engineering Education, 2023.

关键词： Students

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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An Improved Depth Separable Convolutional Neural Networks for Efficient and Accurate Melanoma Detection 5

An Improved Depth Separable Convolutional Neural Networks fo...

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5th International Conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： He, Yichen Toe, Teoh Teik Harbin Institute of Technology Faculty of Computing Harbin China Nanyang Technological University Division of Information Technology Singapore

ISBN: (纸本)9798350338492

In recent years, Convolutional Neural Networks (CNNs) have achieved significant growth and outstanding success in the field of image classification. This paper explores the performance of a deep separable convolutional neural network optimized based on Google's proposal, applied to the task of melanoma classification. In addition, several machine learning models, including k-nearest neighbors (K-NN), Gradient Boosting Decision Trees (GBDT), and Adaptive Boosting (AdaBoost), were constructed to evaluate the models from multiple dimensions for industrialisation. In addition to comparing model performance, this paper also innovatively introduces the concept of 'model cost-effectiveness', which provides reference recommendations for model selection in related field tasks considering the future deployment and application scenarios of the models. The experimental results show that the improved deep separable convolutional neural network we constructed performs more comprehensively and favorably on different indicators, while traditional learning models consume less computing and human resources. © 2023 IEEE.

关键词： Convolutional neural networks

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Al-Sabri, Raeed Gao, Jianliang Chen, Jiamin Oloulade, Babatounde Moctard Wu, Zhenpeng Abdullah, Monir Hu, Xiaohua Central South University School of Computer Science and Engineering Changsha China College of Science and Engineering Hamad Bin Khlifa University Division of Information and Computing Technology Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University United States

ISBN: (纸本)9798350386226

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework's superior performance compared to state-of-the-art models. © 2024 IEEE.

关键词： Contrastive Learning

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A Lightweight Committee-Based Approach for Privacy-Preserving Federated Learning 22

A Lightweight Committee-Based Approach for Privacy-Preservin...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Bentafat, Elmahdi Lasla, Noureddine Albaseer, Abdullatif Abdallah, Mohamed College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar Algiers Algeria

ISBN: (纸本)9798331508050

Despite its advantages for privacy-preserving data-driven modeling, federated learning is vulnerable to privacy breaches, as demonstrated by recent attacks on its privacy properties. It has been proven that sharing the weights alone is insufficient to protect the underlying data. In this work, we provide a solution for sharing the aggregated weights with a central server while safeguarding the privacy of individual client weights. Our solution introduces a decentralized committee election mechanism, eliminating the need for a trusted party. The election phase is based on verifiable random functions (VRFs), whereas the aggregation phase is based on Elliptic curve cryptography and multi-party secret-sharing schemes. Our experimental results show that our solution outperforms the proposed solutions in terms of communication and computation costs. Overall, our approach offers a robust solution for privacy-preserving federated learning without compromising its accuracy and without relying on a third party. © 2025 IEEE.

关键词： Sensitive data

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OpenPLC and lib61850 Smart Grid Testbed: Performance Evaluation and Analysis of GOOSE Communication

OpenPLC and lib61850 Smart Grid Testbed: Performance Evaluat...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Elmasry, Ahmed Albaseer, Abdullatif Abdallah, Mohamed College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar

ISBN: (纸本)9798350335590

Datasets are essential for training machine learning algorithms and ensuring accurate classifications. However, obtaining such datasets, especially those relating to IEC61850 GOOSE messages in smart grids, poses a significant challenge for researchers. Privacy regulations and potential misuse of industrial data have compounded this problem, necessitating alternative methods for generating datasets. In this paper, we detail the design and implementation of a simulation testbed that uses MATLAB/Simulink, OpenPLC and the lib61850 library. This testbed simulates a smart grid system and generates IEC61850 GOOSE messages for dataset creation. We assess the testbed performance using a simplified smart grid model with multiple protection relays, also known as Intelligent Electronic Devices (IEDs), enabling us to measure the response time of the protection system under various fault conditions. The testbed is flexible, allowing for design alterations and expansions, which aids researchers in generating diverse datasets to represent various smart grid networks. This flexibility will increase the availability of valuable datasets and enable the broader application of machine-learning techniques in this domain. © 2023 IEEE.

关键词： Smart power grids

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Mitigating IEC-60870-5-104 Vulnerabilities: Anomaly Detection in Smart Grid based on LSTM Autoencoder

Mitigating IEC-60870-5-104 Vulnerabilities: Anomaly Detectio...

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Sathar, Sajath Al-Kuwari, Saif Albaseer, Abdullatif Qaraqe, Marwa Abdallah, Mohamed College of Science and Engineering Division of Information and Computing Technology Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798350335590

Advanced information Communication technology (ICT) is used in smart grid systems to introduce intelligence and efficiency, potentially outperforming conventional power systems. A fundamental component of a smart grid system is the Smart Meters (SMs), which are integrated with billing utilities, such as national control centers (NCC), and advanced metering infrastructure (AMI). However, like most emerging technologies, some security vulnerabilities and attacks were found. In this paper, we address such vulnerabilities, specifically associated with SMs, that occur when energy consumption is reported to the billing system, specifically through the IEC-60870-5-104(IEC-104) protocol. Since existing datasets do not include sufficient data related to such vulnerabilities, especially in SM with IEC-104 protocol communication, we developed a testbed with a virtual environment and generated a dataset with and without attack vectors. We then proposed a novel anomaly detection algorithm based on LSTM autoencoder, which combines the functional benefits of LSTM and the deep learning of autoencoders. The model's performance is evaluated against two popular attacks, MITM and Replay, and our result shows that the replay attack is harder to find since the attack is executed without data alteration. © 2023 IEEE.

关键词： Smart meters

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AttDCT: Attention-Based Deep Learning Approach for Time Series Classification in the DCT Domain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1626-1638页

作者： Haboub, Amine Baali, Hamza Bouzerdoum, Abdesselam Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Ar Rayyan Qatar University of Adelaide School of Electrical and Mechanical Engineering Australia Hamad Bin Khalifa University Ar Rayyan Qatar

This paper proposes a new deep learning framework for time series classification in the discrete cosine transform (DCT) domain with spectral enhancement and self-attention mechanisms. The time series signal is first partitioned into discrete segments. Each segment is rearranged into a matrix using a sliding window. The signal matrix is then transformed to spectral coefficients using a two-dimensional (2D) DCT. This is followed by logarithmic contrast enhancement and spectral normalization to enhance the DCT coefficients. The resulting enhanced coefficient matrix serves as input to a deep neural network architecture comprising a self-attention layer, a multi-layer convolutional neural network (CNN), and a fully connected multilayer perceptron (MLP) for classification. The AttDCT CNN model is evaluated and benchmarked on thirteen different time series classification problems. The experimental results show that the proposed model outperforms state-of-the-art deep learning methods by an average of 2.1% in classification accuracy. It achieves higher classification accuracy on ten of the problems and similar results on the remaining three. © 2020 IEEE.

关键词： Convolutional neural networks

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