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An Adaptive Cognitive sensor Node for ECG Monitoring in the Internet of Medical Things

IEEE ACCESS 2022年 10卷 1688-1705页

作者： Scrugli, Matteo A. Loi, Daniela Raffo, Luigi Meloni, Paolo Univ Cagliari Dept Elect & Elect Engn DIEE I-0923 Cagliari Italy

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edge-computing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.

关键词： Electrocardiography Monitoring Task analysis sensors Neural networks Deep learning Convolutional neural networks Adaptive system health information management Internet of Things low power electronics neural network remote sensing runtime wearable sensors

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Analysis and Modelling of Meteorological sensory Signals for One-Hour-Ahead Wind Speed Forecasting using Dynamic Neural networks 30

Analysis and Modelling of Meteorological Sensory Signals for...

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30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Ozbilge, Emre Kirsal, Yonal Uluslararasi Kibris Univ Bilgisayar Muhendisligi TR-10 Mersin Turkey Uluslararasi Kibris Univ Bilgisayar Muhendisligi TR-10 Kuzey Kibris Mersin Turkey Lefke Avrupa Univ Elekt & Elekt Muhendisligi TR-10 Kuzey Kibris Mersin Turkey

ISBN: (纸本)9781665450928

Predicting the expected wind speed can help to estimate the wind power generation. Hence, it is important to obtain forecasting as much as close to actual wind speed. One of the importance of estimating wind power generation is to inform the consumers of the corresponding wind farm area to control their electricity consumption. To model and predict the wind speed, it is also required to include other weather-based factors such as relative humidity, temperature, and so on due to their strong relationship between wind speed. Therefore, in this paper, the first meteorological sensors' data are analysed by computing the mutual information score of the sensors and as a result of this irrelevant sensors' data are removed from the system inputs. Then, the sensors' data were reduced by compressing these data by using principal component analysis. After obtaining the final processed input features, four different dynamic neural networks are used to model the temporal relationship between processed sensors' data and one-hour-ahead wind speed. Consequently, the time delay radial basis function network outperformed other dynamic networks.

关键词： wind speed forecasting dynamic neural network artificial intelligent meteorological sensory signals intelligent systems feature selection

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Research on Transmission Line Ice-Cover Monitoring and De-Icing Based on Convolutional Neural Network and Stress Wave Signals 4

Research on Transmission Line Ice-Cover Monitoring and De-Ic...

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4th International conference on Energy Engineering and Power Systems, EEPS 2024

作者： Yan, Jianfeng Shi, Xishuang Guo, Zhuangjun Wei, Hai Zhu, Yimin Wuzhou Bureau of China Southern Power Grid Uhv Transmission Company Guangxi Wuzhou543000 China Hangzhou Yiyi Information Technology Co. Ltd Zhejiang Hangzhou311121 China

ISBN: (纸本)9798350366914

This study develops an intelligent de-icing system for transmission lines using stress wave signal analysis and convolutional neural network (CNN) modeling. The system integrates real-time monitoring with advanced predictive analytics to accurately identify and classify different ice cover conditions. Key components include stress wave sensors, a central processing unit, and a CNN model for predictive analytics. The system employs both mechanical and thermal de-icing methods to efficiently remove ice and maintain the stability of power transmission. Continuous feedback and adjustment mechanisms ensure optimal performance under varying environmental conditions. This approach enhances the reliability and safety of power networks by providing an effective solution for managing ice accumulation on transmission lines. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Adaptive Butterfly Optimizer based Feature Selection for Protecting the Data in Industry based WSN

Enhanced Adaptive Butterfly Optimizer based Feature Selectio...

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2024 International conference on intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Dontu, Sravanthi Vallabhaneni, Rohith Addula, Santosh Reddy Kumar Pareek, Piyush Hussein, Ramy Riad University of the Cumberlands Department of Information Technology Williamsburg United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350360660

Industry 4.0 relies heavily on networked systems, facilitating seamless communication between machines, sensors, devices, and humans in real-time. This interconnectedness, coupled with data transparency, fosters comprehensive insights and analytics, facilitating informed decision-making. However, the increasing reliance on wireless sensor networks (WSNs) within Industry 4.0 exposes them to heightened cyber threats. As these networks continuously gather data and refine processes, they become prime targets for cybercriminals. Robust cybersecurity measures are imperative to safeguard sensitive data, particularly with the proliferation of connected devices. To effectively mitigate cybersecurity risks in Industry 4.0 WSNs, this paper proposes a predictive methodology. This approach employs a multi-criteria framework to enhance WSN cybersecurity, leveraging deep-learning algorithms to prioritize threats based on severity and contextual factors. Notably, the study introduces an Enhanced Adaptive Butterfly Optimization Algorithm (EABOA) for feature selection, addressing shortcomings of existing methods such as sluggish convergence, susceptibility to local optima, and lack of population diversity. Furthermore, the paper presents a novel adaptive fragrance model to improve algorithmic performance. Incorporating Lévy flight to facilitate escaping local optima, this model exhibits both high-frequency short-step hopping and low-frequency long-step walking, enhancing convergence speed and accuracy. Comparative analysis demonstrates superior performance of the proposed framework over benchmark representations. This system offers a smart prioritization approach crucial for efficiently identifying and mitigating high-risk breaches. Experimental evaluation attests to its efficacy, achieving an accuracy of 94.76%, precision of 86%, recall of 80%, and an F-score of 80.06%. © 2024 IEEE.

关键词： Wireless sensor networks

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Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation 8

Challenges and Opportunities of Using Data Fusion Methods fo...

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8th International conference on Control, Decision and information Technologies (CoDIT)

作者： Guido, Giuseppe Haghshenas, Sina Shaffiee Vitale, Alessandro Astarita, Vittorio Univ Calabria Dept Civil Engn Via Bucci I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9781665496070

Collection and analysis of traffic data are the most critical challenges in traffic control in transportation networks. The dramatic growth of new technologies such as tiny devices equipped with elements of computing, sensors, and actuators in the smart environment of the internet of things (IoT) has led to the collection of large volumes of data. Identifying and combining data obtained from various smart devices plays a key role in data processing. Hence, data fusion is required to collect and extract usable data from numerous sources. This study discusses the challenges and opportunities of using data fusion to estimate travel time under wireless sensor networks (WSNs) in the intelligent Transportation Systems (ITS). For this purpose, the three main categories of data fusion, namely the probability-based method, the artificial intelligence-based method, and the evidence theory-based method, were considered. Consequently, discussing the advantages and drawbacks of data fusion techniques can give researchers a better perspective of travel time estimation with better accuracy.

关键词： Performance evaluation Wireless sensor networks Urban areas Data integration Estimation Transportation Traffic control

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An Efficient and intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization 2

An Efficient and Intelligent IoT-Based Security Model for En...

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2nd IEEE International conference on networks, Multimedia and information Technology, NMITCON 2024

作者： Gangadharaswamy, H.B. Kumar, Narendra Sharmila, N. Shilpa, M. Mallikarjunaswamy, S. Shivaji, R. Basavaraju, N.M. Balachandra, P. Komala, M. Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Rns Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python's image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it's automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs. © 2024 IEEE.

关键词： Cloud upload Data storage efficiency IoT-based Smart Security Solution (IoT-SSS) Motion detection Power consumption

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23rd International Symposium on intelligent Data Analysis, IDA 2025

23rd International Symposium on Intelligent Data Analysis, I...

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23rd International Symposium on intelligent Data Analysis, IDA 2025

ISBN: (纸本)9783031913976

The proceedings contain 35 papers. The special focus in this conference is on intelligent Data Analysis. The topics include: Development of Models to Quantify Training Load in Outdoor Running Using Inertial sensors;estimating the Learning Capacity of Bacterial Metabolic networks;semi-supervised Learning with Pairwise Instance Comparisons for Medical Instance Classification;local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition;siamCircle: Trajectory Representation Learning in Free Settings;synthetic Tabular Data Detection in the Wild;assessing the Graph Structure Learning in Graph Deviation networks;the When and How of Target Variable Transformations;Balancing Performance and Scalability of Demand Forecasting ML Models;balancing Global Importance and Source Proximity for Personalized Recommendations Using Random Walk Length;counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data;BOWSA: A Contribution of Sensitivity Analysis to Improve Bayesian Optimization for Parameter Tuning;overfitting in Combined Algorithm Selection and Hyperparameter Optimization;local Subgroup Discovery on Attributed Network Graphs;imposing Constraints in Probabilistic Circuits via Gradient Optimization;improving Next Tokens via Second-to-Last Predictions with Generate and Refine;detection of Large Language Model Contamination with Tabular Data;Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM;make Literature-Based Discovery Great Again Through Reproducible Pipelines;extracting information in a Low-Resource Setting: Case Study on Bioinformatics Workflows;Vocabulary Quality in NLP Datasets: An Autoencoder-Based Framework Across Domains and Languages;expertise Prediction of Tetris Players Using Eye Tracking information;integrating Inverse and Forward Modeling for Sparse Temporal Data from sensor networks;bridging Spatial and Temporal Contexts: Sparse Transfer Learning;meta-learning and Data Au

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Neural Network Technology of Mine Recognition Based on Data From Magnetic Field sensors 12

Neural Network Technology of Mine Recognition Based on Data ...

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12th IEEE International conference on intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Peleshchak, Roman Lytvyn, Vasyl Peleshchak, Ivan Voloshyn, Serhii Lviv Polytechnic National University Department of Information Systems and Networks Lviv79000 Ukraine

ISBN: (纸本)9798350358056

Mine detection is an ongoing and growing problem that affects many people around the world due to the enormous danger that mines pose to humans. Mine detection occurs using various methods that use active sensors, including ultra-wideband (UWB) radar. Although active landmine sensors are often used for performance reasons, they can cause unintended landmine detonations because they operate on transmitted and reflected electromagnetic signals. Another way to detect mines without detonating mechanisms is the use of passive detectors. In this article, an optimal neural network structure with Adam's optimization algorithm is developed for effective mine classification based on magnetic field sensor data. This approach made it possible to increase the accuracy of mine detection to 99.23 percent. © 2023 IEEE.

关键词： Adam's optimization algorithm data preprocessing data visualization deep neural network passive mine detection

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An Overview of Localization Techniques in Underwater Wireless sensor networks 3

An Overview of Localization Techniques in Underwater Wireles...

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3rd International conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Bagadi, Kalapraveen Ravikumar, C.V. Sathish, K. Vellore Institute Technology School of Electronics Engineering Vellore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Researchers in the arena of Underwater Wireless sensor networks (UWSNs) and their applications-challenges has been a major source of inspiration for many research areas in the Telecommunications industry. For the collection and distribution of wireless data, UWSN is the most important network. Due to the fact that most sensors are located in outlying areas, recent developments in UWSN technology still face significant challenges. sensor modules in Wireless sensor networks must be identified and their proper location determined in order to determine the accuracy of the information being processed. In this paper, we go into great detail about the WS networks process, various localization techniques, and the wide variety of various uses for Underwater Wireless sensor networks in the real-time environment. The use of wireless sensor networks for location determination. © 2022 IEEE.

关键词： Wireless sensor networks

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Adaptive Spider Monkey Optimization Algorithm Based Clustering and Routing for Cognitive Radio sensor networks

Adaptive Spider Monkey Optimization Algorithm Based Clusteri...

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International conference on 6G Communications Networking and Signal processing, SGCNSP 2023

作者： Nijaguna, G.S. Babu, J. Ananda Ramya, R. Saravanan, V. Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Department of ISE Malnad College of Engineering Hassan India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai Thandalam India

ISBN: (纸本)9789819778751

Cognitive radio sensor networks (CRSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve network lifetime. In many CRSN applications, node mobility poses challenges due to changing network topology. In this research, the Adaptive Spider Monkey Optimization (ASMO) algorithm is proposed for obtaining the energy efficient Cluster Head (CH) selection and routing in CRSN. The consumption of energy in CRSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based clustering and routing approach is developed for designing the optimal transmission path by CH to destination. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network lifetime (NL), and end-to-end delay (EED) are employed to evaluate suggested methodology. Comparative analysis shows that the ASMO methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms

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