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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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Cybernation and Computation (CYBERCOM), International conference on

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

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

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..

关键词： Wireless sensor networks Energy consumption Machine learning algorithms Accuracy Smart cities Predictive models Control systems Real-time systems Reliability Long short term memory

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Improving Human Activity Recognition Through Multisensor Data Fusion Techniques in Wireless sensor Networks

Improving Human Activity Recognition Through Multisensor Dat...

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Computing and Machine Intelligence (ICMI), International conference on

作者： Rasha S. Gargees Department of Computer Science Central Michigan University Michigan USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Wireless sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Support vector machines Wireless communication Wireless sensor networks Accuracy Machine learning algorithms Refining Data integration

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Hospital Bed Vacancy Detection using Depthwise Separable CNN

Hospital Bed Vacancy Detection using Depthwise Separable CNN

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advanced Computing Technologies (ICoACT), International conference on

作者： R Balaji V Deepan S Faiz J Mythili Satyanarayana Murthy Polisetty Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Bank of America United States

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The rising demand for real-time hospital bed man-agement in healthcare institutions requires creative solutions to improve patient care and resource allocation. This research introduces an IoT-enabled system for detecting hospital bed vacancies utilising a Depthwise Separable Convolutional Neural Network (DSCNN). The proposed system utilises IoT sensors to perpetually monitor bed occupancy, guaranteeing precise and prompt updates to medical personnel. The DSCNN is utilised to diminish computing complexity while preserving high detection accuracy, attaining a notable accuracy of 92.4%. The model's performance is assessed in comparison to traditional CNNs, revealing notable enhancements in both speed and accuracy, with accelerated convergence times enabled by the Adam optimiser. The system guarantees minimal latency in hospital bed detection, rendering it suitable for real-time applications on resource-limited devices. This system provides a scalable and effective method for optimising hospital resource management, especially during high demand or emergencies.

关键词： Accuracy Hospitals Rendering (computer graphics) Real-time systems sensor systems sensors Internet of Things Convolutional neural networks Resource management Personnel

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FishTankv2 Insights: Proactive Environmental Management with Predictive Analytics

FishTankv2 Insights: Proactive Environmental Management with...

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International conference on Computational Intelligence and applications (ICCIA)

作者： William P. Rey School of Information Technology Mapua University Makati City Philippines

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

This study presents FishTankv2, an advanced Internet of Things (IoT) based smart aquarium management system designed for freshwater fish enthusiasts. FishTankv2 integrates a range of sensors to monitor critical water parameters such as ammonia, nitrite, nitrate, dissolved oxygen, pH levels, and temperature. These enhancements provide users with comprehensive insights into their aquarium's health, facilitating proactive maintenance and care. The system features a mobile application that allows users to remotely monitor and control their aquariums, offering functionalities such as real-time parameter tracking, automated feeding, and maintenance reminders. A key innovation in FishTankv2 is the incorporation of predictive analytics, which utilizes machine learning algorithms to forecast potential issues based on historical data. This enables timely interventions to prevent adverse conditions, ensuring a stable and healthy environment for aquatic life. User feedback collected through surveys and usability testing indicates high satisfaction with the system's intuitive interface, comprehensive monitoring capabilities, and predictive alert features. Comparative analysis shows that FishTankv2 outperforms many existing smart aquarium systems in terms of sensor integration, predictive analytics, and overall user experience. The study concludes that FishTankv2 effectively meets its objectives of enhancing aquarium management through advanced technology, making it a valuable tool for both novice and experienced aquarium enthusiasts. Future developments will focus on further refining predictive models, expanding device compatibility, and adding more automation features to maintain its competitive edge.

关键词： Temperature sensors Temperature measurement Water quality Predictive models Fish sensor systems Maintenance Predictive analytics Intelligent sensors Testing

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Multi-threshold Hysteresis-Based Congestion Control for UAV-Based Detection sensor Network 22nd

Multi-threshold Hysteresis-Based Congestion Control for UAV...

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22nd International conference on Next Generation Teletraffic and Wired/Wireless advanced Networks and systems, NEW2AN 2022

作者： Aliyu, Bashir Mokrov, Evgeny Samouylov, Konstantin 6 Miklukho-Maklaya st. Moscow117198 Russia Institute of Informatics Problems Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow119333 Russia

ISBN: (纸本)9783031302572

Nowadays, unmanned aerial vehicles (UAV) are considered for a variety of different applications. One of the important ones is automatic collection of sensory data. In this work, we study the use of UAV-based for secure remote monitoring network enabled via LoRaWAN lower-power wide area (LPWA) network. In the considered use-case the UAVs carry sensors collecting data and LoRa modules to transmit collected data to the stationary gateway. The work focuses on congestion control on the gateway. Specifically, we propose a multi-threshold hysteresis-based control mechanism to alleviate system overload by dropping some of the data send by the sensors. The considered system is modeled using the queuing system with multiple thresholds. The work studies time-dependent characteristics of the system: average time the system spends in overload and reduced load states. Our results demonstrate that using hysteresis control allows for the system to accept priority data traffic even under relatively high system load. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wide area networks

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Features of Information Support for Safe Maneuvering in Different Road Conditions in an Unmanned Semi-trailer Road Train with a Traffic Control System 11

Features of Information Support for Safe Maneuvering in Diff...

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11th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems - Technology and applications (IDAACS)

作者： Piskachov, Oleksandr Kolisnyk, Maryna Natl Aerosp Univ KhAI Dept Comp Syst Networks & Cybersecur Kharkiv Ukraine

ISBN: (纸本)9781665426053

One of the main areas of development of ground-based unmanned vehicles (UMV), such as unmanned semi-trailer road train, is the creation of methods and systems for their control, providing the required maneuverability. UMV include such systems: cruise control, directional stability when forward and backward movement, collision avoidance when maneuvering and parking and others. The mathematical apparatus and models necessary for the creation of such systems and control of the UMV in reverse mode to the selected point based on the sensors measured parameters are proposed. The paper obtained a mathematical model of the movement of a road train with fixed wheels of a semitrailer in reverse in a given directionThe schemes for constructing such systems and special tools of measurement of the required parameters for control are considered. The results of theoretical studies of the developed UMV models with possible control systems were presented and analysed.

关键词： truck semi-trailer sensor folding angle steering angle of road train reversing

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MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments

MRS Modular UAV Hardware Platforms for Supporting Research i...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Hert, Daniel Baca, Tomas Petracek, Pavel Kratky, Vit Spurny, Vojtech Petrlik, Matej Vrba, Matous Zaitlik, David Stoudek, Pavel Walter, Viktor Stepan, Petr Horyna, Jiri Pritzl, Vaclav Silano, Giuseppe Licea, Daniel Bonilla Stibinger, Petr Penicka, Robert Nascimento, Tiago Saska, Martin Czech Tech Univ Fac Elect Engn Prague Czech Republic

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications. The proposed UAV design is highly modular in terms of used actuators, sensor configurations, and even UAV frames. This allows to achieve, with minimal effort, a proper experimental setup for single, as well as, multi-robot scenarios. Presented platforms are intended to facilitate the transition from simulations, and simplified laboratory experiments, into the deployment of aerial robots into uncertain and hard-to-model real-world conditions. We present mechanical designs, electric configurations, and dynamic models of the UAVs, followed by numerous recommendations and technical details required for building such a fully autonomous UAV system for experimental verification of scientific achievements. To show strength and high variability of the proposed system, we present results of tens of completely different real-robot experiments in various environments using distinct actuator and sensory configurations.

关键词： Actuators Buildings Autonomous aerial vehicles Robot sensing systems Hardware Indoor environment Vehicle dynamics

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A Lamb wave based liquid sensor for biomedical applications

A Lamb wave based liquid sensor for biomedical applications

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Smart systems Integration (SSI)

作者： Widesh Gahar Johan Meyer Paddy French Alper Sisman Erdal Korkmaz Microelectronics - Bioelectronics Delft University of Technology Delft Netherlands Research Group Smart Sensor Systems The Hague University of Applied Science Delft Netherlands

ISBN: (数字)9798350388770

ISBN: (纸本)9798350388787

This study presents an AlN based Lamb wave (A0 mode) liquid sensing device that can be used for biomedical applications. The Lamb wave device features a 1.5 μm composite membrane consisting of a 500 nm LPCVD SiN and a 1 μm of a c-axis oriented AlN film. A 45° rotated design was also considered for this project to reduce the reflections from the edges towards the output IDT. A liquid testing experiment involving IPA, DI water, and D-PBS was performed to see if the devices were able to differentiate between these liquids. The results showed that the fabricated Lamb wave devices exhibited sensitivity to mass loading and were able to distinguish between the liquids based on their phase, frequency, and gain characteristics. Notably, devices with the rotated design have shown a substantial increase in resonance by 15 dB, as well as enhanced sensitivity, when compared to the devices with the normal design. Furthermore, the devices featuring the normal design had a Q factor of 450, whereas devices with the rotated design exhibited a Q factor of 680, indicating superior performance of the latter. These findings suggest that a Lamb wave device with the 45° rotated IDT design holds considerable potential for liquid sensing, particularly in biomedical applications.

关键词： Q-factor Performance evaluation Liquids Sensitivity Resonant frequency Resonance Reflection sensors iii-V semiconductor materials Aluminum nitride

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Recent advances in photoelectrochemical detection of hydrogen sulfide

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2025年第7期20卷

作者： Chen, Fang Wei, Xiuhua Hao, Yuanqiang Wan, Yikun Zhang, Yintang Xu, Maotian Shangqiu Normal Univ Coll Chem & Chem Engn Shangqiu 476000 Peoples R China Hunan Univ Sci & Technol Sch Chem & Chem Engn Xiangtan 411201 Peoples R China Guizhou Phosphate Chem Grp Co Ltd Guiyang Peoples R China

Hydrogen sulfide (H2S), as a biologically significant gaseous signaling molecule and environmental pollutant, has garnered extensive attention due to its crucial roles in various physiological processes and environmental systems. Photoelectrochemical (PEC) sensing has emerged as a promising analytical strategy for H2S detection owing to its high sensitivity, excellent selectivity, and minimal background interference, achieved through effectively coupling specific analyte recognition events with photoinduced electrochemical signals. This review systematically summarizes recent advances in PEC sensing technologies for H2S detection, categorized into three main strategies: (i) PEC sensors based on the intrinsic hole-scavenging capability of H2S;(ii) PEC platforms constructed via the in situ generation of metal sulfides (such as CdS, Bi2S3, Ag2S, and CuxS);and (iii) PEC sensors incorporating small organic molecular probes specifically responsive to H2S. Detailed discussions include detection principles, sensor configurations, material components, and analytical performance of various PEC sensing systems. Finally, perspectives are provided regarding future opportunities and challenges, including the exploration of novel metal sulfide semiconductors, development of advanced single-atom catalysts for precise recognition of H2S, and further optimization of multifunctional organic PEC probes to extend practical applications in environmental monitoring, biomedical diagnostics, and food safety analyses.

关键词： Hydrogen sulfide Photoelectrochemical sensor Review

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Efficient Implementation of Multi-sensor Adaptive Birth Samplers for Labeled Random Finite Set Tracking

Efficient Implementation of Multi-sensor Adaptive Birth Samp...

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International conference on Information Fusion

作者： Jennifer Bondarchuk Anthony Trezza Donald J. Bucci Advanced Technology Laboratories Lockheed Martin Cherry Hill NJ USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Adaptive track initiation remains a crucial component of many modern multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A naive construction of this adaptive birth set density results in an exponential number of newborn components in the number of sensors. A truncation procedure was provided that leverages a Gibbs sampler to truncate the birth density, reducing the complexity to quadratic in the number of sensors. However, only a limited discussion has been provided on additional algorithmic techniques that can be employed to substantially reduce the complexity in practical tracking applications. In this paper, we propose five efficiency enhancements for the labeled random finite sets multi-sensor adaptive birth procedure. Simulation results are provided to demonstrate their computational benefits and show that they result in a negligible change to the multi-target tracking performance.

关键词： Pediatrics Runtime Filters Adaptive systems Density measurement Simulation Benchmark testing sensors Complexity theory Clutter

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