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RLR: Joint Reinforcement Learning and Attraction Reward for Mobile Charger in Wireless Rechargeable Sensor Networks

IEEE Internet of Things Journal 2023年第18期10卷 16107-16120页

作者： Shang, Cuijuan Chang, Chih-Yung Liao, Wen-Hwa Roy, Diptendu Sinha Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Tamkang University Department of Computer Science and Information Engineering Taipei25137 Taiwan National Taipei University of Business Institute of Information and Decision Sciences Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

Advances in wireless charging technology give great new opportunities for extending the lifetime of a wireless sensor network (WSN) which is an important infrastructure of IoT. However, the existing greedy algorithms lacked learning from the experiences of energy dissipation trends. Unlike the existing studies, this article proposes a reinforcement learning approach, called reinforcement learning recharging (RLR), for mobile charger to learn the trends of WSNs, including the energy consumption of the sensors, the recharging cost as well as the coverage benefit, aiming to maximize the coverage contribution of the recharged WSN. The proposed RLR mainly consists of three modules, including sensor energy management (SEM), charger location update (CLP), and charger reinforcement learning (CRL) modules. In the SEM module, each sensor manages its energy and calculates its threshold for the recharging request in a distributed manner. The CLP module adopts the quorum system to ensure effective communication between sensors and the mobile charger. Meanwhile, the CRL module employs attraction rewards to reflect the coverage benefit and penalties of waiting time raised due to charger movement and recharging other sensors. As a result, the charger accumulates the learning experiences from the Q -Table such that it is able to execute the appropriate actions of charging or moving in a manner of state management. Performance results show that the proposed RLR outperforms the existing recharging mechanisms in terms of charging waiting time of sensors, the energy usage efficiency of the mobile charger, as well as the coverage contribution of the given sensor network. © 2014 IEEE.

关键词： Reinforcement learning

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Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality

Digital Twin-Based 3D Map Management for Edge-Assisted Mobil...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhou, Conghao Gao, Jie Li, Mushu Cheng, Nan Shen, Xuemin Sherman Zhuang, Weihua University of Waterloo Department of Electrical and Computer Engineering Canada Carleton University School of Information Technology Canada Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Canada Xidian University School of Telecommunication Engineering Xi'an China

ISBN: (纸本)9798350345384

In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to update the 3D map. To address the challenges of the dynamic uplink data rate and the time-varying pose of the MAR device, we propose a digital twin (DT)-based approach to 3D map management. First, a DT is created for the MAR device, which emulates 3D map management based on predicting subsequent camera frames. Second, a model-based reinforcement learning (MBRL) algorithm is developed, utilizing the data collected from both the actual and the emulated data to manage the 3D map. With extensive emulated data provided by the DT, the MBRL algorithm can quickly provide an adaptive map management policy in a highly dynamic environment. Simulation results demonstrate that the proposed DT-based 3D map management outperforms benchmark schemes by achieving lower pose estimation uncertainty and higher data efficiency in dynamic environments. © 2023 IEEE.

关键词： Cameras

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Deploying Healthcare Monitoring System For Elderly Patient Care using IoT and Neural Network Techniques 3

Deploying Healthcare Monitoring System For Elderly Patient C...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Garg, Neha Sethuraman, Ravikumar Ghosh, Soumi Kumar, A Hemantha Kolluri, Raju Bennett University School of Computer Science Engineering and Technology Greater Noida India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science & Engineering Chennai India Maharaja Agrasen Institute of Technology Department of Information Technology Delhi India Annamacharya Institute of Technology and Sciences Department of Mechanical Engineer India Narasaraopet Engineering College Department of Electronics and Communication Engineering Narasaraopet India

ISBN: (纸本)9798350384598

Utilizing Internet of Things (IoT) technology in the healthcare sector can revolutionize the care provided to individuals. This study illustrates the application of IoT in ensuring continuous healthcare for elderly patients, particularly focusing on scenarios like epidemiology in rehabilitation centers, intensive care units (ICU), and home health care. The research integrates a variety of sensors, machine learning algorithms, and cloud computing to establish a healthcare system driven by IoT. With a participant pool of 50 individuals, including both healthy adults and those with various ailments, the study employs Short-Term Memory (LSTM), decision trees, and Artificial Neural Network (ANN) models to analyze sensor data, specifically temperature and heart rate measurements. The primary objective is to identify uncertainties and facilitate timely interventions. Results indicate that ANN surpasses LSTM and tree models in parameter estimation, displaying superior accuracy and F1 scores in anomaly detection while minimizing false negatives. This use of predictive ability has the chance of reforming treatment through early detection and administration of treatments. This research, in short, exemplifies the importance of integrating prognostic analytics in health systems that support IoT;in other words, it enables continuous monitoring, forecasting, and access to care requirements concerning changing forms of illness. From a scientific perspective, this work is seen as being proactive because it adopts green medical materials and uses IoT-based methods so that control performance may be improved for the patients. © 2024 IEEE.

关键词： health care IoT machine learning performance metrics security

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Advanced Flood Prediction at Forest with Rainfall Data Using Various Machine Learning Algorithms 3

Advanced Flood Prediction at Forest with Rainfall Data Using...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Saravanan, M.S. Sivashankar, S. Rajesh, A. Ibrahim, Masrullizam Mat Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science Engineering Tamilnadu Chennai India Gla University Mathura India Faculty of Electronics and Computer Technology and Engineering Melaka Malaysia

ISBN: (纸本)9798331518592

The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. Two Classification algorithms are used to achieve the maximum accuracy namely K-Nearest Neighbour with a sample size=5 and Logistic Regression with a sample size=5 for continues iterations. The work focused towards comparison of K-Nearest Neighbour and logistic regression, which has confidential and forecast the standards from the rainfall statistics to produce estimated accuracy with K-nearest neighbour has higher accuracy by comparing with Logistic Regression accuracy. It has a high accuracy of 50.35%, in comparison with the Logistic Regression algorithm 45.96%. The significant values have been statistically defined with the value of (p © 2024 IEEE.

关键词： Adversarial machine learning

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis 4

Clustering the Various Categorical Data: An Exploration of A...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kumar, Raman Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita School of Computing Department of Computer Science and Engineering India Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350335750

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm. © 2023 IEEE.

关键词： K-means clustering

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Smart Contract-Driven Pandemic Management Using Blockchain Technology and Artificial Intelligence 24

Smart Contract-Driven Pandemic Management Using Blockchain T...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Easwaramoorthy, Sathishkumar Veerappampalayam Subramani, Neelakandan Hoong, Angela Lee Siew Sunway University School of Engineering and Technology Selangor Darul Ehsan47500 Malaysia R.M.K Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331530631

The COVID-19 epidemic has negatively affected features of human beings and diverse sectors, particularly the healthcare industry. This modality led to the formation of novel life patterns that people need to face to reduce the spreading of the pandemic by promising social distance, amongst others. For that reason, investigators have discovered several deep learning (DL) and machine learning (ML) based techniques to quickly diagnose COVID-19 patients using X-ray images. ML-based approaches can decrease costs and take less time for treatment. Nevertheless, maintaining patient privacy poses challenges inside such third-person-controlled models, potentially decreasing to protect patients from possible discomfort and disgrace. However, BC technology provides the potential to safely store complex HealthCare data anonymously, without needing third-person interference. In this study, we introduced a Leveraging Blockchain Technology and Artificial Intelligence using a Smart Contract-Driven COVID-19 Pandemic Detection (LBCTAI-SCCPD) model. The main aim of the LBCTAI-SCCPD model is to investigate the absence or presence of COVID-19 from medical images. To accomplish that, the LBCTAI-SCCPD model is a blockchain-based COVID-19 recognition infrastructure, which contains a smart contracting method for uploading COVID-19-positive case-oriented information with blockchain. At the initial step, the LBCTAI-SCCPD technique employs an adaptive median filtering (AMF) approach to pre-process the input image. In addition, the complex patterns and features in the images can be derived from CapsNet model. Furthermore, the radial basis function neural network (RBFNN) model can be used for a precise COVID-19 process of classification. Lastly, the dung beetle optimization (DBO) algorithm is used for optimal hyperparameter selection of the RBFNN model. To demonstrate the better performance of the LBCTAI-SCCPD technique, a sequence of simulations is executed on the benchmark dataset. The stimulat

关键词： COVID-19

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IoT and AI-Driven Conceptual Model of Museum Ecosystem

IoT and AI-Driven Conceptual Model of Museum Ecosystem

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Puspasari, Shinta Siradjuddin, Indah Agustien Rachmansyah Fahtu Rahman, M. Ade Haversyalapa, Ditho Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia Universitas Trunojoyo Faculty of Engineering Department of Informatics Madura Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Computer System Palembang Indonesia

ISBN: (纸本)9798350381290

Museum ecosystems need digital transformation to provide value added to museums. This study discusses the digital museum ecosystem conceptual model based on artificial intelligence (AI) technology and the Internet of Things (IoT). The new conceptual model integrates three elements of the museum ecosystem, people, collections & infrastructure, and museum services collaborating and communicating to achieve outputs in supporting the achievement of museum organizational goals through IoT and AI-based digital technology. The conceptual model describes various forms of implementing IoT and AI in the museum ecosystem for museum management along with the value added or results generated for museum management. The development of this ecosystem conceptual model into a physical museum has an impact on optimizing museum performance by providing value added in the form of convenience and prosperity for users and various museum stakeholders, especially in Society 5.0. © 2023 IEEE.

关键词： Internet of things

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Aligning Transregional Neural Dynamics with Transformer-based Variational Autoencoders 46

Aligning Transregional Neural Dynamics with Transformer-base...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wu, Shenghui Zhang, Xiang Huang, Yifan Wang, Yiwen The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The University of Hong Kong Medicine School Department of Orthopaedics and Traumatology Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

ISBN: (纸本)9798350371499

Spike prediction models can reveal how different brain regions communicate via neural spiking activity. Meanwhile, low-dimensional latent variables have been widely used to describe the evolvement of neural activities in single-region analysis. However, correlations of these latent dynamics for transregional neural activity have rarely been studied. Here, we propose a unified architecture to analyze and exploit the correlation of latent dynamics between two cortical areas for spike prediction from upstream to downstream areas. The method is validated on neural population activity from the medial prefrontal cortex (mPFC) and the primary motor cortex (M1) of a Sprague Dawley rat during the two-lever discrimination task. We separately train two Transformer-based variational autoencoders (tVAEs) for mPFC and M1 neurons to extract the latent variables from their self-history spike trains. Then, we align the latent variables from the two brain regions with a regression model. By cascading the tVAE encoder for mPFC neurons and the tVAE decoder for M1 neurons through aligned latent variables, we achieve the prediction from mPFC spike train history to M1 future neural activity. The results show that the tVAEs can extract latent dynamics from the mPFC and M1 spike ensembles that resemble behavioral trajectories. We also demonstrate that the mPFC and M1 neural activity have shared latent dynamics and can be linearly aligned for spike prediction. Consequently, our method can be applied to study the evolving relationship of transregional latent dynamics and contribute to the design of future neural prostheses. © 2024 IEEE.

关键词： Population dynamics

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Adaptive Interference Detection and Removal in SAR Images Based on the Order-2 Tracy-Widom Distribution 2

Adaptive Interference Detection and Removal in SAR Images Ba...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Huizhang Chen, Chengzhi Xi, Feng Lang, Ping Yin, Junjun Yang, Jian Nanjing University of Science and Technology Department of Electronic Engineering Nanjing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

This paper proposes a novel eigenvalue-based detector for adaptive detection of radio interference in synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in single-look-complex SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block's covariance matrix, and compares the largest eigenvalue with a threshold to determine the presence of interference under the criteria of constant false alarm rate. The threshold is determined by the order-2 Tracy-Widom distribution of the largest eigenvalue for each image block under the assumption of image homogeneity. The proposed detector is integrated with our previously proposed block subspace filter for adaptive interference detection and removal. Experimental results on Sentinel-1 double-polarization and Gaofen-3 HV-polarization images demonstrate the effectiveness of the proposed method on adaptive detection and suppression of interference artefacts in SAR images. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Soft,full Wheatstone bridge 3D pressure sensors for cardiovascular monitoring

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npj Flexible Electronics 2024年第1期8卷 910-917页

作者： Yoonseok Park Haiwen Luan Kyeongha Kwon Ted S.Chung Seyong Oh Jae-Young Yoo Gooyoon Chung Junha Kim Suhyeon Kim Sung Soo Kwak Junhwan Choi Hoang-Phuong Phan Seonggwang Yoo Hyoyoung Jeong Jaeho Shin Sang Min Won Hong-Joon Yoon Yei Hwan Jung John A.Rogers Department of Advanced Materials Engineering for Information and Electronics Integrated Education Institute for Frontier Science&Technology(BK21 Four)Kyung Hee UniversityYongin-siGyeonggi-do 17104Republic of Korea Department of Mechanical and Aerospace Engineering University of California San DiegoLa JollaCA 92093USA School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea Department of Mechanical Engineering Columbia UniversityNew YorkNYUSA Division of Electrical Engineering Hanyang University ERICAAnsan 15588Republic of Korea Department of Semiconductor Convergence Engineering Sungkyunkwan UniversitySuwonRepublic of Korea Biomedical Research Division Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Chemical Engineering Dankook UniversityYonginGyeonggi-do 16890Republic of Korea School of Mechanical and Manufacturing Engineering Tyree Institute of Health EngineeringUniversity of New South WalesSydneyNSW 2052Australia Querrey Simpson Institute for Bioelectronics Northwestern University EvanstonEvanstonIL 60208USA Department of Electrical and Computer Engineering University of California DavisDavisCA 95616USA Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon-siGyeonggi-do 16419Republic of Korea Department of Electronic Engineering Gachon UniversitySeongnamsiGyeonggi-do 13120Republic of Korea Department of Electronic Engineering Hanyang UniversitySeoulRepublic of Korea Department of Materials Science and Engineering Biomedical EngineeringNeurological SurgeryChemistryMechanical EngineeringElectrical Engineering and Computer Science Simpson Querrey Institute and Feinberg Medical SchoolCenter for Bio-Integrated ElectronicsNorthwestern University EvanstonEvanstonIL 60208USA

Variations in parameters associated with the ambient environment can introduce noise in soft,body-worn *** example,many piezoresistive pressure sensors exhibit a high degree of sensitivity to fluctuations in temperature,thereby requiring active compensation *** research presented here addresses this challenge with a multilayered 3D microsystem design that integrates four piezoresistive sensors in a full-Wheatstone bridge *** optimized layout of the sensors relative to the neutral mechanical plane leads to both an insensitivity to temperature and an increased sensitivity to pressure,relative to previously reported devices that rely on similar operating *** this 3D pressure sensor into a soft,flexible electronics platform yields a system capable of real-time,wireless measurements from the surface of the *** above the radial and carotid arteries yields high-quality waveforms associated with pulsatile blood flow,with quantitative correlations to blood *** results establish the materials and engineering aspects of a technology with broad potential in remote health monitoring.

关键词： Wheat bridge thereby

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