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Computing and information Technology (ICCIT-1441), 2020 International conference on

作者： Ahmed Subhi Abdalkafor Salah A. Aliesawi College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent years, Wireless sensor Network (WSN) has played a broad role in many vital applications that are used to monitor environmental and physical events. One of the major issues in WSN is the overlapping region of sensing bands, which occurs due to densely and randomly deployed nodes in the target environment. The data aggregation technique is used as a solution that processes overlapping regions. This technique manages and redirects these data to the sink node through a multi-hop network. Therefore, the data transmission number and the duration toward the sink node are reduced, which fixes the overlapping region issue. However, using data aggregation techniques limited network performance due to energy consumption. The objective of this paper is to address this limitation by employing intelligent algorithms to enhance the operation strategies of data aggregation techniques. A protocol based on the Hierarchical Agglomerative Clustering (HAC), Self-Organized Map (SOM), and Enhanced Radial Basis Function (ERBF) neural network is proposed in order to accomplish this goal. The clustered SOM and RBF-based intelligent Protocol (CSRP) is the name of this proposed protocol. When compared to pertinent recent studies, the suggested protocol produced the best results in terms of the accuracy of the data arriving at the base station.

关键词： Training Wireless sensor networks Base stations Protocols Accuracy Artificial neural networks Spread spectrum communication Radial basis function networks Data aggregation sensors

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A Comparative Study of Validation Methods for sensor-Based Human Activity Recognition Using Deep Learning Models 10

A Comparative Study of Validation Methods for Sensor-Based H...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

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8th International conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

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Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices 2nd

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozo...

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2nd International conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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On Integrating Robotic Data with GIS Tools in a Cloud Environment (Application Paper)

On Integrating Robotic Data with GIS Tools in a Cloud Enviro...

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Workshops of the EDBT/ICDT 2025 Joint conference, EDBT/ICDT-WS 2025

作者： Wrembel, Robert Kasprzyk, Jean-Paul Billen, Roland Bimonte, Sandro d'Orazio, Laurent Sacharidis, Dimitris Skrzypczyński, Piotr Université de Liège Liège Belgium INRAE Clermont-Ferrand France Université de Rennes Rennes France Université Libre de Bruxelles Bruxelles Belgium Poznan University of Technology Poznań Poland

Merging robotic technologies, sensor networks, and Geographic information Systems (GIS) offers significant potential across various domains, including agriculture and urban planning. However, a critical challenge lies in the lack of interoperability between data generated by these technologies and existing GIS tools. The EU-funded GIS4IoRT project addresses this gap by developing a plug-and-play and cloud-based middleware. This middleware facilitates seamless integration and visualization of multi-dimensional and multi-modal data within GIS environments. Key GIS4IoRT components include: a middleware architecture, a scalable cloud-based infrastructure, real-time robot querying capabilities, data quality assurance, spatio-temporal query support within the cloud, integration with GIS tools, and adherence to relevant standards. The middleware supports diverse data types, including LiDaR, imagery, and sensor data. This paper (1) presents an initial data integration architecture specifically designed for the sustainable architecture domain, (2) outlines the challenges encountered in designing such an architecture, and (3) explores novel data processing paradigms enabled by the architecture. © 2025 Copyright © 2025 for this paper by its authors.

关键词： data integration geographical information system images LiDaR robots sensors sustainable agriculture

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Optimizing Human Motion Posture Detection and Humanized Computing for Smart Health Applications

Optimizing Human Motion Posture Detection and Humanized Comp...

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Digital Analysis and processing, intelligent Computation (DAPIC), International conference on

作者： Weiwei Wang Jingshan Zhang School of Sports Medicine and Rehabilitation North Sichuan Medical College Nanchong Sichuan China Department of Sports Humanities and Social Sciences Sichuan Sports College Chengdu Sichuan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Existing human motion detection technologies have problems such as insufficient accuracy and poor real-time performance, which makes it difficult to meet the needs of health management. This paper proposes a human motion posture detection and humanized calculation method based on multi-sensor fusion and LSTM. This paper first deploys IMU sensors at key parts of the human body, and uses visual sensors to capture the three-dimensional posture information of human motion, and obtains physiological data such as heart rate and number of steps through wearable devices such as smart bracelets. Secondly, the IMU data is denoised and calibrated to eliminate sensor errors. Next, the fused data is trained using a long short-term memory network (LSTM). The rationality of the motion posture is then analyzed, and the user's exercise intensity and health status are evaluated based on the motion data and physiological data. In terms of posture recognition accuracy, the average value of this method is 92.61 %. In terms of abnormal posture detection rate, the detection rate of some data points of this method is close to 100%. At the same time, the maximum recognition time of this method is only 789 milliseconds, and the minimum recognition time is as low as 503 milliseconds, which is better than the traditional method. The optimized human motion posture detection and humanized calculation method proposed in this paper realizes high-precision detection of human motion and intelligent health management through multi-source data fusion, LSTM construction, human kinematics analysis and personalized health assessment.

关键词： Training Accuracy Soft sensors Data integration Real-time systems Physiology Data models Long short term memory intelligent sensors Wearable sensors

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Method for Distributed Node Metric Measurement in Wireless sensor networks Based on Multiple Attributes

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International Journal of High Speed Electronics and Systems 2025年第3期34卷

作者： Liu, Tao Zhang, Chunwei School of Information Technology JiangSu Open University Jiangsu Province Nanjing210036 China Intelligent Engineering College Nanjing Vocational Institute of Railway Technology Jiangsu Province Nanjing210036 China

Traditional sensors require large power inputs and accuracy cannot be guaranteed. In this study, we introduce a revolutionary technique to enhance the accuracy of node measurements in Wi-Fi sensor networks (WSNs) while minimizing power consumption. Our approach focuses on a mixture of key attributes, including overall strength performance, landmark strength, and node geolocation. We have set quantitative measures for these attributes, which is the foundation of our sizing method. The basic framework for evaluating node allocation indicators is superior, mainly based on the multifaceted attributes of individual nodes and adjacent nodes, promoting selection in data transmission and processing. This framework has a powerful statistical collection mechanism that can accumulate every intrinsic attribute, including external attributes such as overall power performance, signal energy, and geographic region. Using these collected records, our techniques include selecting methods for metric calculations. It is worth noting that we have merged a set of adaptive strategies that can dynamically adjust measurement parameters based on discovered community conditions. The adaptability of this strategy ensures normal operational performance in various community states, achieving a balance between measurement accuracy and energy conservation. This method ensures a significant improvement in the operational performance of wireless sensor networks and has broad applicability in the field of reliable high-intensity sensor networks. © 2025 World Scientific Publishing Company.

关键词： sensor nodes

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Analysis of sensor Technologies for Structural Health Monitoring in Civil Engineering Applications

Analysis of Sensor Technologies for Structural Health Monito...

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Automation and Computation (AUTOCOM), International conference on

作者： Trapty Agarwal Ayush Gandhi Yogomaya Mohapatra Jagtej Singh Dimple Bahri V. Venkata Ramesh Reddy Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Sponsored Research Presidency University Bangalore Karnataka India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Civil engineering is considered complete with structural health monitoring SHM, a continuous process in which structures are tested for their health and integrity, ensuring they remain safe and reliable. Recent advancements in sensor technologies for SHM have improved the empirical side of structural performance. The advancement of sensor technologies for SHM is largely related to implementing wireless and smart sensors. It is easily fixable and provides real-time information on the strain, temperature, vibration, etc. Wireless and smart sensors can also be deployed in difficult-to-access or non-safe areas, allowing for an increased size of monitored structures. In addition, the emergence of sensor technologies has resulted in smart algorithms and data analytics methods suitable for SHM. Such algorithms process huge amounts of data and detect potential structural problems or anomalies before any expected maintenance intervention.

关键词： Temperature sensors Wireless communication Temperature measurement Vibrations Wireless sensor networks Real-time systems Maintenance Monitoring Civil engineering intelligent sensors

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Distributed-Robust Source Localization in Wireless Acoustic sensor networks

Distributed-Robust Source Localization in Wireless Acoustic ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xu Wang De Hu Qintuya Si College of Computer Science Inner Mongolia University Hohhot China College of Electronic and Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose a distributed-robust sound source localization (D-R-SSL) method in wireless acoustic sensor networks (WASNs). Specifically, the space is first divided into small equal-sized grids, and the local probability of each grid containing the source is calculated by using intra-node time difference of arrivals (TDoAs). Next, we allocate weights for local probabilities from different nodes, and develop a distributed weight calculation strategy which is robust against the TDoA outliers. Then, the source position is derived from the weighted summation of local probabilities. In order to improve the SSL efficiency, we further present a fast implementation approach (named as F-D-R-SSL). Finally, we also analyse the computational complexity and communication loads of the proposed methods. Simulation and real-world experiments demonstrate that the F-D-R-SSL is as powerful or even stronger than state-of-the-art non-grid SSLs.

关键词： Location awareness Wireless communication Wireless sensor networks Time difference of arrival Probability Acoustic sensors Signal processing Robustness Computational complexity Speech processing

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Real-Time Monitoring of Structural Health with Wireless sensor networks in IoT Infrastructure

Real-Time Monitoring of Structural Health with Wireless Sens...

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Automation and Computation (AUTOCOM), International conference on

作者： Bharat Bhushan V. Pushparajesh J Bethanney Janney Girija Shankar Sahoo Prateek Aggarwal Dibyasundar Das Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Cyber Security Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Wireless sensor networks for structural health monitoring of infrastructure are now becoming an IoT. It provides the ability to collect and analyze survivability data in real-time to enable continuous structural health monitoring for buildings, bridges, and other critical infrastructure. The purpose of this abstract is to introduce Real-time Monitoring with Wireless sensor networks in IoT Infrastructure. Wireless sensor networks are used in structural health monitoring. They are typically composed of many distributed, cheap sensors embedded in the structure that communicate wirelessly to a central monitoring station. Those sensors, including temperature, strain and vibration, are critical in detecting potential structural problems. This data gets transmitted to a cloud for processing, which provides indications of an unusual characteristic or predictive failure. Compared to the traditional methods, there are several advantages of applying WSN for real-time structural health monitoring. Lead leads provide real-time updates on the condition of a bridge that allows for continuous monitoring, with advanced sensors pinpointed through its structure. It will assist in keeping the infrastructure safe and affordable by guaranteeing rapid time-to-resolution. In addition, WSNs have low implementation costs and do not interfere too much with the structure of large structures.

关键词： Bridges Vibrations Temperature sensors Wireless sensor networks Costs Buildings sensor phenomena and characterization Real-time systems Internet of Things Monitoring

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