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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition

Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-S...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wu, Wenhan Hua, Yilei Zheng, Ce Wu, Shiqian Chen, Chen Lu, Aidong University of North Carolina at Charlotte Department of Computer Science United States School of Information Science and Engineering Wuhan University of Science and Technology China Center for Research in Computer Vision University of Central Florida United States

ISBN: (纸本)9798350313154

Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and reduce the demand for massive labeled training data. Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize a skeleton-based encoder-decoder transformer architecture to reconstruct the masked skeleton sequences. A novel masking strategy, named Spatial-Temporal Masking, is introduced in terms of both joint-level and frame-level for the skeleton sequence. This pre-training strategy makes the encoder output generalizable skeleton features with spatial and temporal dependencies. Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task. Extensive experiments show that our SkeletonMAE achieves remarkable performance and outperforms the state-of-the-art methods on both NTU RGB+D 60 and NTU RGB+D 120 datasets. © 2023 IEEE.

关键词： Learning systems

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

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Digital Communications and Networks 2023年第2期9卷 296-305页

作者： Jing Zhang Qichen Yan Xiaogang Zhu Keping Yu Department of Computer Science and Technology Xi'an University of Science and TechnologyXi'an710054China Department of Information Science and Technology Northwest UniversityXi’an 710127China School of Public Policy and Administration Nanchang UniversityNanchangJiangxi330031China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence ***,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not *** solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal ***,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position ***,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground *** them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,***,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

关键词： Crowd sensing Industrial Internet of things Safety monitoring Coal mine

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Developing a Fuzzy Theory-Based Professional Baseball Big Data System with an App 7

Developing a Fuzzy Theory-Based Professional Baseball Big Da...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Wang, Shih-Ming Hu, I-Hsiang Wu, Yi-Cheng Chen, Yin Fu, Lin-Jia Cheng Shiu University Department of Computer Science and Information Engineering Kaoshiung Taiwan University of Kaohsiung General Education Center of the Open Kaohsiung Taiwan

ISBN: (纸本)9798350307207

Amidst the rapid evolution of information technology and its implication with sports, the analysis of sports information has been increasingly complex. The burgeoning realm of sports big data, rooted in the vast expanse of the internet, is expanding rapidly. The Chinese Professional Baseball League (CPBL), standing as Taiwan's most popular professional baseball league and a prominent fixture in domestic sports, has attracted considerable attention regarding the prediction of games. However, the prediction results in baseball games remain a challenge. In this study, we employed a sophisticated fuzzy algorithm within an expert system analysis and used App Inventor to craft an interface tailored for predicting CPBL game results (win/loss). This mobile app interface empowers users to input pertinent data, encompassing details about teams and athletes to estimate probabilities of win or loss. Based on this approach, a professional baseball data inquiry mobile application (APP) was designed. This integration enables players and coaches to effortlessly access statistical insights on their smartphones or tablets at their convenience. © 2024 IEEE.

关键词： Baseball

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Avalanche-enabled optical modulation with single-photon intensities

Avalanche-enabled optical modulation with single-photon inte...

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2024 Frontiers in Optics, FiO 2024

作者： Sychev, Demid V. Chen, Peigang Yang, Morris Fruhling, Colton Lagutchev, Alexei Kildishev, Alexander V. Boltasseva, Alexandra Shalaev, Vladimir M. Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center National Quantum Information Science Research Center The U.S. Department of Energy Oak RidgeTN37931 United States

We demonstrate all-optical modulation of 1550-nm wavelength beam of 10mW power controlled by a single-photon intensity signal in an avalanche photodiode. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Light modulation

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Entanglement accelerates quantum simulation

arXiv

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arXiv 2024年

作者： Zhao, Qi Zhou, You Childs, Andrew M. QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Key Laboratory for Information Science of Electromagnetic Waves Ministry of Education Fudan University Shanghai200433 China Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

quantum entanglement is an essential feature of many-body systems that impacts both quantum information processing and fundamental physics. The growth of entanglement is a major challenge for classical simulation methods. In this work, we investigate the relationship between quantum entanglement and quantum simulation, showing that product-formula approximations can perform better for entangled systems. We establish a tighter upper bound for algorithmic error in terms of entanglement entropy and develop an adaptive simulation algorithm incorporating measurement gadgets to estimate the algorithmic error. This shows that entanglement is not only an obstacle to classical simulation, but also a feature that can accelerate quantum algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： quantum entanglement

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Performance comparison of different algorithms for land surface temperature (LST) retrieval from Landsat-8 and the use of deep neural network regression to enhance the LST result (Case Study: Dallas, Texas and surrounding areas) 8

Performance comparison of different algorithms for land surf...

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Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII 2024

作者： Ayudyanti, Amalia Gita Chen, Ying-Nong Jamaluddin, Ilham Center for Space and Remote Sensing Research National Central University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan

ISBN: (数字)9781510682757

ISBN: (纸本)9781510682740

Land Surface Temperature (LST) is important data for various fields, especially for monitoring global warming. Landsat-8 satellite imagery provides thermal data with a spatial resolution of 30m (resampled from 100m) as the main data for LST retrieval. This research aims to compare several LST retrieval algorithms such as LST retrieval using band 10, Single Channel Model (SCM), Qin's Split-Window Algorithm (Q-SWA), Sobrino's Split-Window Algorithm (S-SWA), and LST from the analysis ready data of Landsat 8 Level-2. The study focuses on Dallas, Texas, and surrounding areas in March 2023. We collected air temperature data from 20 U.S. Environmental Protection Agency (EPA) stations for indirect validation. Based on the results, the Q-SWA method outperformed other retrieval algorithms with mean absolute error (MAE) and root mean squared error (RMSE) were 0.683°C and 0.842°C, respectively. Given that Landsat-8's thermal band data is resampled from 100m to 30m, this research also explores enhancing the retrieved LST using deep neural network regression (DNNR). The best retrieved LST from the Q-SWA method served as the target data while several bands and spectral indices from Landsat-8 were used as the input for DNNR model. Due to the large scale of data, we randomly selected 10 million pixels and divided into 80% of training and 20% of testing data. The DNNR model achieved MAE of 1.022°C on the testing dataset. The enhanced LST from the DNNR model was also validated with the same air temperature validation data and achieved the MAE score of 1.037°C. Based on the visual comparison result, the DNNR model successfully enhanced the retrieved LST by providing more detailed results at the same 30m resolution and showing promising performance based on error metrics. This finding suggests the potential for using deep learning regression in LST downscaling to achieve better spatial resolution. © 2024 SPIE.

关键词： Landsat

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