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Kalman Filters in IoT: A Bibliometric Analysis

Kalman Filters in IoT: A Bibliometric Analysis

引用

international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Obaideen, Khaled AlShabi, Mohammad Gadsden, Stephen Andrew Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies RISE P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical and Nuclear Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Mechanical Engineering McMaster University HamiltonONL8S 4L8 Canada

ISBN: (纸本)9789819747832

This bibliometric analysis focuses on the evolution and trends of the Kalman Filters (KFs) studies in the Internet of Things (IoT) from the year 2009 to 2023. This being a data-intensive study, it uses the information from major academic databases, which it adapts to explore key terms, publication patterns, and the interdisciplinary nature of the research. The paper comes out with a sharp increase of the research in 2015, which corresponds to growing of the research interest to the application of KFs for IoT. Key points of the study accentuate the role of KFs in IoT, especially in respect of the betterment of the systems for indoor positioning, global positioning, and sensor data fusion. This software proves the KFs are in the spotlight in IoT by helping to improve localization accuracy and data processing. The research highlights progress in filtering methods, for example, extended and unscented Kalman filters, evidently to improve state estimation and predictive analytics in dynamic contexts. Furthermore, KFs research scope includes novel areas such as machine learning and deep learning, which indicates the possibility of using this technology as a tool for advancing IoT. The rapid growth of technology in this area also presents challenges, a part of those are the data privacy and security problems in complex IoT environments. The paper emphasizes the major part KFs are playing in driving IoT technology and also stresses the type of interdisciplinary studies that are needed to navigate the changing landscape of IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Extended Kalman filters

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4th international conference on Optical and Wireless technologies, OWT 2020

4th International Conference on Optical and Wireless Technol...

引用

4th international conference on Optical and Wireless technologies, OWT 2020

ISBN: (纸本)9789811628177

The proceedings contain 62 papers. The special focus in this conference is on Optical and Wireless technologies. The topics include: Performance Evaluation of Routing Protocols in MANETs with Variation in Pause Time;three-Qubit Implementation of Quantum Fourier Transform for Shor’s Algorithm;Performance Enhancement of Surface Plasmon Resonance (SPR) Structure Using a Sinusoidal Diffraction Grating;wide-Band Meander Line Antenna for Ka Band Application;design of Silicon-on-Insulator Based Mode Splitters with Asymmetrical Variation of Slots;human Body Monitoring Wearable Antenna;on Design of Airborne Radomes with Brent’s Method for Radiation Pattern Peak Detection;Wideband Bandpass Substrate Integrated Waveguide (SIW) Filter for C Band Application;Design of Polarized 2 × 2 MIMO Antenna Using Partially Stepped Ground;study of Microstrip Antenna Geometry: Effect of Antenna Geometry on Antenna Parameters—A Comprehensive Review;Simulation Studies on Force sensor Using PDMS Coated Fiber Bragg Grating for Robot-Assisted Surgery;design and Analysis of E Shaped Microstrip Patch Antenna with Defected Ground Structure for Improvement of Gain and Bandwidth;microwave Imaging Breast Cancer Detection Techniques: A Brief Review;Implementation of XOR Gate Using a Nonlinear Polarization Rotation in Highly Nonlinear Fiber;designing of Hybrid Photonic Crystal Fiber for Better Filter Characteristics Using Gallium Nitride;Silicon-On-Insulator Photonics Waveguide Design for Near-IR Evanescent Field-Based Blood sensor;analysis of Upper Aortic Blood Vessels as a Data Communication Channel;a Ground Plane Modified Broadband Circularly Polarized Patch Antenna for Wireless applications;Deterministic Two Qubit iSWAP Gate Using a Resonator as Coupler;Miniaturization and Gain Enhancement of Rectangular Patch Antenna Using CSRR.

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Cost Effective loT-Based Smart System for Avoidance of Obstacle and Intruder Detection

Cost Effective loT-Based Smart System for Avoidance of Obsta...

引用

Intelligent Cybernetics Technology & applications (ICICyTA), international conference on

作者： Md. Shadman Sakib Talukdar Mahiyan Rahman Takbir Imam Tajnoor Hossain Amrit Md Mahin Rahman Mohammad Noor Nabi Computer Science & Engineering Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This study introduces a cost-effective integrated system that significantly enhances the safety and security of autonomous vehicles by merging obstacle avoidance and intru-sion detection functionalities through Internet of Things (loT) technologies. The system employs ultrasonic sensors for accurate obstacle detection, with an Arduino Uno and Raspberry Pi 3 handling data processing and control tasks. Facial recognition technology is incorporated to monitor and identify unauthorized individuals in real time. This integration allows the vehicle to autonomously navigate while also securing itself against potential intruders, all achieved with readily available and affordable components. The system demonstrates strong performance in detecting and avoiding obstacles in straightforward scenarios, although accuracy may diminish in more complex environments. The facial recognition component achieves consistent and re-liable results under controlled conditions. By combining these technologies in a budget-friendly solution, this research offers practical advancements in intelligent and secure autonomous vehicle systems, suitable for applications in various domains including robotics and smart vehicle technology.

关键词： Accuracy Navigation Face recognition Intrusion detection sensor systems Real-time systems Security Collision avoidance Autonomous vehicles Testing

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Smart Agriculture Using Modern technologies

Smart Agriculture Using Modern Technologies

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： N Shanmugasundaram G Santhip Kumar S Sankaralingam S Vishal N Kamaleswaran Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

Agriculture is a critical resource for survival of all living things on earth. Agriculture also provides most of the food and fabrics to the world. Environmental changes have impacted today’s agriculture and it has slowed down the productivity of crops. Shortage of water and pests are the major issue that mitigates the yield in agriculture. To sort out these issues, experts and researchers have proposed new processes and adoption of new technologies like Internet of Things (IoT), Smart sensors, Wireless sensor Networks (WSN), Image processing, Data analytics, Artificial Intelligence (AI) and Machine learning (ML) in agriculture in the last decade to make agriculture smart, easy and maximize the production with minimum resources. This paper studies the impact of new technologies and its applications in modern agriculture.

关键词： Smart agriculture Productivity Wireless sensor networks Image processing Process control Machine learning Agriculture

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Dynamic Threshold-Based Anomaly Detection in Photovoltaic Generation Time Series Using Statistical Methods

Dynamic Threshold-Based Anomaly Detection in Photovoltaic Ge...

引用

Intelligent Data Science technologies and applications (IDSTA), international conference on

作者： Michelle Melo Cavalcante João Lucas de Souza Silva Tárcio André Dos Santos Barros FEEC/LESF-MV University of Campinas (Unicamp) Campinas Brazil

ISBN: (数字)9798350354751

ISBN: (纸本)9798350354768

The efficient operation of photovoltaic (PV) plants requires continuous monitoring to identify and correct anomalies that may affect the performance and lifespan of the equipment. However, some challenges include defining which data can be collected and useful from the installation to identify anomalies, as well as which models can be applied. Therefore, this paper proposes an approach for anomaly detection in PV generation using dynamic threshold techniques based on descriptive statistics. The methodology involves monthly analysis of AC power characteristic curves and irradiance normalization, applying interquartile ranges and standard deviations to identify anomalies. AC power was chosen as it is the inverter output and, therefore, more easily obtained data. The methodology’s implementation is validated using real PV inverter data to identify anomalous behaviors. Additionally, the data used are generally available in PV plants without the need for additional sensors. Therefore, this approach provides an effective tool for predictive maintenance and optimization of PV systems.

关键词： Temperature sensors Photovoltaic systems Temperature measurement Temperature distribution Time series analysis sensor systems and applications Inverters Monitoring Anomaly detection Intelligent sensors

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Fast Dynamic Calibration of Millimeter-Wave Radar External Parameters: Research and applications

Fast Dynamic Calibration of Millimeter-Wave Radar External P...

引用

sensors and Information Technology (ICSI), international conference on

作者： Jie Yang Xiaohui Li Xianjin He Qin Xia Juncheng Huang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rapid development of intelligent connected technologies, the application of millimeter-wave radar in roadside perception systems has become increasingly widespread [1], [2]. Known for its excellent anti-interference capabilities and resistance to adverse weather conditions, millimeter-wave radar has become a key sensor in these systems [3]. However, the accuracy of radar data is still limited by the calibration of the coordinate system, especially in dynamic environments [4]. Traditional calibration methods rely on manual adjustments, which are inefficient [5]. This paper proposes an automated dynamic millimeter-wave radar calibration method based on deep reinforcement learning, combined with multi-sensor data fusion, to address the high-precision calibration issue between the radar coordinate system and the world coordinate system in dynamic environments. Experiments on open roads show that both the new method and the method incorporating deep reinforcement learning (DQN) significantly improve calibration performance. The calibration accuracy increased by 11.11% and 48.15%, respectively, compared to traditional methods, while the calibration speed was also greatly improved. The proposed method effectively reduces human intervention, lowers system deployment costs, and demonstrates strong scalability, making it highly valuable for practical applications in intelligent connected vehicles and roadside perception systems.

关键词： Accuracy Roads Manuals Millimeter wave radar sensor fusion Deep reinforcement learning Calibration Trajectory Vehicle dynamics Optimization

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Multifunctional, IoT-Enabled, Smart Portable Enclosures

Multifunctional, IoT-Enabled, Smart Portable Enclosures

引用

international conference on Engineering and Emerging technologies (ICEET)

作者： Mike HM Teodorescu

ISBN: (数字)9798350316926

ISBN: (纸本)9798350316933

This is a short perspective paper on an emerging topic. Portable enclosures with controlled atmosphere are useful in field surgery, disaster response, biotechnology, and other applications. Blending these technologies with AI and IoT capabilities could lead to the next generation of such devices. For example, future surgical drapes and enclosures may benefit from the use of IoT sensors and control. IoT connection to a remote center for operation monitoring and control could support decision making and analytics. Further, adding remote AI capabilities to predict conditions as they arise could open new applications, such as dynamic decision-making for casualty management based on clinical progression.

关键词： Patents Technological innovation Decision making Surgery sensor systems Artificial intelligence Task analysis

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Bibliometric review of smart sensor for health monitoring

Bibliometric review of smart sensor for health monitoring

引用

international conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)

作者： Christian V. Halim Murti Marinah Dinda A. I. Shafira Muhammad Y. Dwiyantara Imam Abadi Department of Physics Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9781837242863

Smart sensors and medical health technologies are becoming increasingly popular due to their potential for real-time, non-invasive health monitoring and diagnostics. With the integration of Artificial Intelligence (AI), these devices can track vital signs and important biomarkers, supporting the management of chronic diseases. This study employs bibliometric analysis using tools like VOSviewer to map research trends in this domain, identifying collaborations, emerging keywords, and contributions from institutions and countries. Findings indicate a significant increase in publications and citations, especially after 2010, highlighting influential journals such as IEEE sensors Journal. The primary focus is on health monitoring applications and advancements in sensor technology, with an increasing use of terms like "machine learning" and "data analytics." These findings provide insights into future research directions and technological innovations that impact medical health applications. It is recommended that future development integrate advanced data processing techniques and apply sensors in infrastructure and smart city environments, aiming to create systems that are more responsive, efficient, and sustainable.

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Cluster Head Selection Algorithm using Machine Learning

Cluster Head Selection Algorithm using Machine Learning

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： M.S. Nidhya E. Gurumoorthi M. Babu A.C. Charumathi Mohd Naved Balajee Maram School of Computer Applications Dayananda Sagar University Bangalore School of Engineering Malla Reddy University Dept of AI & DS Chennai Institute of Technology college Chennai Dept of Information Technology Sona College of technology Dept of Business Analytics Jaipuria Institute of Management Noida School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Wireless sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies.

关键词： Wireless sensor networks Machine learning algorithms Smart cities Heuristic algorithms Supervised learning Clustering algorithms Magnetic heads Energy efficiency Stability analysis Unsupervised learning

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INSPIRE PROJECT: INTEGRATED technologies FOR SMART BUILDINGS AND PREDICTIVE MAINTENANCE 7

INSPIRE PROJECT: INTEGRATED TECHNOLOGIES FOR SMART BUILDINGS...

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7th international conference on Smart City applications, SCA 2022

作者： Raco, F. Balzani, M. Planu, F. Cittadino, A. University of Ferrara Department of Architecture DIAPReM/TekneHub Ferrara Italy ACSoftware Italy

Applying integrated digital technologies for the management and maintenance of the existing built heritage appears to be one of the main current challenges for the definition and application of digitisation protocols for the construction supply chain. Key enabling technologies, collaborative platforms, Big Data management and information integration in a BIM environment are areas of increasing experimentation. In the field of intervention on the built heritage, it is the boundaries and opportunities offered by the integration of many different information sources that constitutes the main challenge. Furthermore, the study of the accessibility and usability of data and information from sources such as the three-dimensional terrestrial survey, existing databases, sensor networks, and satellite technologies make it possible to investigate both different ways of data modelling, even with a view to the development of predictive algorithms, and of visualisation and information management. The study illustrates part of the results of the InSPiRE project, an industrial research project financed with European structural funds and carried out in a public-private partnership by four universities and public research bodies, an innovation centre and six companies, SMEs, large enterprises, and start-ups. Specifically, the project highlights the growing importance of BIM-based modelling as a tool to lead users, both experts and non-experts, through the multiple information paths resulting from the relation between data and metadata. © 2022 international Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Information management

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