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Real-Time Monitoring and Control systems Using IoT-Integrated Wireless sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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Wearable Electrodermal Activity sensor for Real-Time Stress Detection Using Machine Learning

Wearable Electrodermal Activity Sensor for Real-Time Stress ...

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18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Santos, Salvador Sousa, Joana Ferreira, João NOS Inovação Rua Actor António Silva Lisbon Portugal

This paper discusses the design and implementation of a wearable electrodermal activity (EDA) sensor intended to detect subtle changes in skin conductivity, which are indicative of emotional states such as stress and anxiety, thus monitoring stress and arousal levels through advanced machine learning techniques. The device incorporates innovative conductive lycra combined with silver-silver chloride (Ag/AgCl) electrodes, enabling optimal skin contact and enhancing signal reliability. This integration allows for effective measurement of EDA. Utilizing the XGBoost algorithm, our machine learning model was trained on the ASCERTAIN dataset, achieving an overall accuracy of approximately 77% in predicting levels of arousal. While the model exhibited some challenges in predicting intermediate arousal states, it demonstrated strong precision and recall for extreme levels of arousal, underscoring its potential applications in mental health monitoring and human-computer interaction. The capabilities of this wearable technology for continuous and long-term health monitoring pave the way for further research into stress assessment and the understanding of emotional responses, emphasizing its relevance in enhancing psychological well-being. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Silver halides

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Collaborative Perception in Multi-Robot systems: Case Studies in Household Cleaning and Warehouse Operations 6

Collaborative Perception in Multi-Robot Systems: Case Studie...

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6th International conference on Robotics and Computer Vision, ICRCV 2024

作者： Nair, Bharath Rajiv Columbia University Dept. of Mechanical Engineering New York United States

ISBN: (纸本)9798331527426

This paper explores the paradigm of Collaborative Perception (CP), where multiple robots and sensors in the environment share and integrate sensor data to construct a comprehensive representation of the surroundings. By aggregating data from various sensors and utilizing advanced algorithms, the collaborative perception framework improves task efficiency, coverage, and safety. Two case studies are presented to showcase the benefits of collaborative perception in multi-robot systems. The first case study illustrates the benefits and advantages of using CP for the task of household cleaning with a team of cleaning robots. The second case study performs a comparative analysis of the performance of CP versus Standalone Perception (SP) for Autonomous Mobile Robots operating in a warehouse environment. The case studies validate the effectiveness of CP in enhancing multi-robot coordination, task completion, and overall system performance and its potential to impact operations in other applications as well. Future investigations will focus on optimizing the framework and validating its performance through empirical testing. © 2024 IEEE.

关键词： Multipurpose robots

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advanced hydrogen sensitive material WO3 and its optical sensing properties 12

Advanced hydrogen sensitive material WO3 and its optical sen...

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conference on advanced sensor systems and applications XII

作者： Zhou, Yuchen Yang, Minghong Hu, Wenbin Dai, Jixiang Natl Engn Res Ctr Fiber Opt Sensing Technol & Net Wuhan Peoples R China

ISBN: (纸本)9781510657090;9781510657083

The correlation between reflected light intensity and hydrogen concentration is the basis of the end-face type hydrogen sensor with WO3 coating. However, the changes of optical properties of WO3 under different hydrogen concentrations have not been fully investigated, and its hydrogen sensitivity mechanism is still unclear, which restricts the improvement and optimization of hydrogen sensing performance. Therefore, the relationship between hydrogen concentration and Refractive Index (RI) of WO3 is established in this paper to explore the hydrogen sensitive mechanism. The experimental results show that the RI of WO3 changes when hydrogen is introduced, and the RI varies under different hydrogen concentration. Since there is a certain mathematical relationship between the RI value and the reflectivity of the films, the RI changes measured by ellipsometer is further verified by the intensity-tracing experiments results. Hence, we can clarify that the hydrogen sensitivity mechanism of WO3 is related to its RI changes under different hydrogen concentration, which will provide a basis for the further research and applications.

关键词： WO3 Gasochromic effect the Refractive Index Optical fiber hydrogen sensor

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Towards Industry 4.0: An IoT-Enabled Data-Driven Architecture for Predictive Maintenance in Pharmaceutical Manufacturing 1

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International conference on advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Manchadi, Oumaima Ben-BOUAZZA, Fatima-Ezzahraa Dehbi, Zineb El Otmani Said, Zakaria Jioudi, Bassma Clinical and Medical Sciences and Biomedical Engineering Laboratory Mohammed VI University of Sciences and Health Casablanca Morocco Mohammed VI University of Sciences and Health Casablanca Morocco Laboratory of Genomics Bioinformatics and Digital Health Mohammed VI University of Sciences and Health Casablanca Morocco LaMSN La Maison des Sciences Numériques Saint-Denis France Mathematical Analysis and Applications Laboratory University Sidi Mohamed Ben Abdellah Fes Morocco

ISBN: (数字)9783031523854

ISBN: (纸本)9783031523847

In recent years, there has been a surge in the adoption of Predictive Maintenance (PdM) systems. This is primarily due to their ability to restrict maintenance expenses, reduce unscheduled downtime, and enhance the dependability of equipment. The implementation of Predictive Maintenance (PdM) systems holds significant importance in the pharmaceutical industry, where equipment downtime can have severe implications for product quality and patient safety. The present study puts forth a system architecture for PdM in the pharmaceutical sector, with a particular emphasis on the cyclotron as a case study. Several novel characteristics distinguish our predictive maintenance architecture from prior techniques. We start with advanced machine learning techniques that can handle complex and dynamic cyclotron data patterns. Second, we combine realtime sensor data from many sources for precise health monitoring and fault diagnosis. Our design also uses a data-driven prognostic model to predict breakdowns and suggest maintenance. These innovative parts create a full, proactive cyclotron maintenance system. The implementation of a PdM system has the potential to enhance operational efficiency, ensure safety, and improve product quality by anticipating equipment malfunctions prior to their occurrence. The proposed architecture for predictive maintenance makes a significant contribution to the field of cyclotron maintenance. By leveraging advanced machine learning techniques, real-time sensor data integration, and proactive fault prediction, our solution addresses the critical challenges associated with ensuring cyclotrons operate without interruption. Our research opens up novel possibilities for optimizing predictive maintenance for complex, high-stakes systems such as cyclotrons. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Research on Task Scheduling Methods for Intelligent Connected Vehicles with End-to-End Delay Constraints 22

Research on Task Scheduling Methods for Intelligent Connecte...

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22nd IEEE International conference on Industrial Informatics, INDIN 2024

作者： Yang, Shichun Chen, Wenlong Zhang, Boao Duan, Qianlong Hao, Bide Yan, Xiaoyu School of Transportation Science and Engineering Bei Hang University Beijing China Research Institute of Aero-Engine Bei Hang University Beijing China

ISBN: (纸本)9798331527471

With the rapidly growing Intelligent Connected Vehicle (ICV) industry, modern advanced Driver Assistance systems (ADAS) integrate critical and non-critical software functions on the same hardware platform. These functions have complex timing requirements and associated dependencies. To ensure the real-time safety of critical automotive applications, complex task chains with dependencies generated by automotive applications (e.g. sensor data acquisition, data processing, and control execution) must meet the worst end-to-end latency. The resulting task scheduling problem requires the assignment of tasks to available cores for execution and the generation of static schedules that satisfy the timing constraints of the tasks and the end-to-end latency of the task chain. In this paper, a task scheduling and task mapping method based on an intelligent heuristic algorithm is proposed for the ADAS platform, and simulated scheduling based on the EDF algorithm. The optimal task scheduling scheme is selected by calculating the fitness function by the intelligent heuristic algorithm, and the simulated annealing algorithm (SA) is selected as the benchmark for the experiment. The experimental results show that the grey wolf optimizer converges faster and finds a better scheduling scheme, which reduces the total time of scheduling tasks. © 2024 IEEE.

关键词： advanced driver assistance systems

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Rydberg Atomic Receiver for AM, FM, and Phase Shift Keying Data Transmission 3

Rydberg Atomic Receiver for AM, FM, and Phase Shift Keying D...

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2025 conference on Quantum Sensing, Imaging, and Precision Metrology

作者： Wang, He Boone, Carl T. Iyanu, Gabe H. Aerosp Corp El Segundo CA 90245 USA

ISBN: (数字)9781510685338

ISBN: (纸本)9781510685321;9781510685338

Recently, Rydberg atoms have attracted increasing interests for applications as electric field sensors, RF spectrometers, and microwave receivers. In this paper, we present experimental studies of a cesium (Cs) Rydberg atomic microwave receiver and its temporal response characteristics to amplitude modulation (AM), frequency modulation (FM), and binary phase shift keying (BPSK) modulation. The receiver is associated with the Cs Rydberg transitions of n(2)D(5/2) - (n+1)P-2(3/2) at various frequencies, which is generated by double- resonance laser excitations in a room temperature Cs vapor cell. We use the Electromagnetically Induced Transparency (EIT) and Autler-Townes (AT) effects to directly receive modulated microwave signals and recover the data encoded in the carriers. We demonstrated AM and FM digital data transmissions at data rates up to 1 Mbps and BPSK modulation and demodulation with a clock frequency of 250 kHz. Modulation depths up to 90% are experimentally observed and studied as a function of the modulation clock frequency. We also perform modeling to show that Cs atoms have accessible Rydberg transitions at frequencies covering the range from below GHz to THz for wideband microwave receiver applications across RF systems.

关键词： Quantum sensing Rydberg sensor Rydberg receiver

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Assessing Switch Weld Quality with 3D Sensing and Machine Learning 3

Assessing Switch Weld Quality with 3D Sensing and Machine Le...

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Multimodal Sensing and Artificial Intelligence: Technologies and applications iii 2023

作者： Patruno, Cosimo Nitti, Massimiliano Cardellicchio, Angelo Mosca, Nicola Di Summa, Maria Renò, Vito Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - CNR Amendola 122 D/O Bari70126 Italy

ISBN: (纸本)9781510664517

This paper presents a preliminary study for evaluating the quality of welds in thermomagnetic switches using 3D sensing and machine learning techniques. A 3D sensor based on laser triangulation is used to gather the point cloud of the component. The point cloud is then processed to extract hand-crafted signatures for binary classification: defective or non-defective component. Features such as Gaussian and mean curvatures, density, and quadric surface properties, are used for building these significant signatures. Different machine learning models, including decision trees, Support Vector Machines, k-nearest neighbors, random forests, ensemble classifiers, and Artificial Neural Networks, are trained using the built signatures to classify the weld as defective or non-defective. Preliminary results on actual data achieve high classification accuracy (>84%) on all the tested models. © 2023 SPIE.

关键词： Welds

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International conference on Decision Aid Sciences and applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar 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

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Universal design of an Embedded Hardware for Educational robotics based on Atmega328P 3

Universal design of an Embedded Hardware for Educational rob...

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3rd International conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： Miri, Mohammed El Khaldi, Said El Driouch, Ismael Elkhaldi, Loukmane Abdelmalek Essaadi University Experimentation and Modeling in Mechanics and Energy Systems Team National School of Applied Sciences ENSAH Al hoceima Morocco Abdelmalek Essaadi University Sciences and Advanced Technologies National School of Applied Sciences ENSATE Tetouan Morocco

ISBN: (纸本)9798331522056

This paper presents the design and development of an universal and cost-effective embedded hardware platform for educational robotics applications. The core of the system utilizes the popular Atmega328P microcontroller, known for its robustness and affordability. The design prioritizes minimalism principles while maintaining essential functionalities for basic robot control and sensors integration. The hardware system includes four stepper motor drivers, four H-bridge motor drivers, a 16-channel PWM controller and GPIO connections for sensors and user command. The software design provides a versatile framework for controlling motors, reading sensor data, and enabling communication between devices, making the platform easy to program and customize. Our design makes the system ideal for educational and research settings, allowing users to explore robotics concepts without the constraints of complex and expensive hardware. © 2024 IEEE.

关键词： Stepping motors

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