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Journal articles on the topic 'IoT visualization'
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Mykhailo, Lukianets, and Sulema Yevgeniya. "REAL-TIME DATA VISUALIZATION FOR IOT NETWORK SYSTEMS: CHALLENGES AND STRATEGIES FOR PERFORMANCE OPTIMIZATION." System technologies 5, no. 148 (2024): 52–61. http://dx.doi.org/10.34185/1562-9945-5-148-2023-05.
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Real-time data visualization has become an essential tool for decision-making systems in various industries, including finance, healthcare, IoT, and manufacturing. Real-time data visualization enables organizations to monitor and analyze data as it is generated, providing real-time insights into critical business operations. However, real-time data visualization poses several challenges, including performance, data quality, and visualization complexity. This paper will explore the importance of real-time data visualization in IoT network systems, and the challenges associated with it. Specifically, the paper will discuss the challenges of real-time data visualization and ideas to increase performance. The paper will also provide a comprehensive analysis of the impact of real-time data visualization on IoT network and decision-making systems, highlighting its benefits and potential drawbacks. The paper will begin by discussing the importance of real-time data visualization in IoT network systems, highlighting its role in providing timely insights into critical operations. It will then delve into the challenges associated with real-time data visualization, including data quality, visualization complexity, and performance. The paper will provide a detailed analysis of each challenge, outlining the potential impact on real-time data visualization systems and deci-sion-making processes. The paper will also explore ideas to increase performance in real-time data visualization, including implementing high-performance computing infrastructure, op-timizing data processing and analysis, using caching techniques, using visualization techniques optimized for performance, implementing data compression, and using real-time analytics. The paper will provide a comprehensive analysis of each idea, outlining its potential impact on real-time data visualization systems' performance and overall effective-ness. Finally, the paper will conclude by highlighting the importance of real-time data visualization in IoT network systems and the need to address the challenges associated with it. The paper will also provide recommendations about how to implement real-time data visualization systems, outlining key considerations and best practices to ensure successful implementation and optimal performance.
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ЛУК’ЯНЕЦЬ, МИХАЙЛО, та ЄВГЕНІЯ СУЛЕМА. "МЕТОД ВІДОБРАЖЕННЯ ТОЧКОВИХ ТЕМПОРАЛЬНИХ МУЛЬТИМОДАЛЬНИХ ПОТОКОВИХ ДАНИХ ПРИСТРОЇВ IOT ЗІ ЗБЕРЕЖЕННЯМ СТАБІЛЬНОСТІ ВІДОБРАЖЕННЯ ПОПЕРЕДНЬО ВІЗУАЛІЗОВАНИХ ДАНИХ". Herald of Khmelnytskyi National University. Technical sciences 347, № 1 (2025): 245–50. https://doi.org/10.31891/2307-5732-2025-347-32.
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This paper presents a method for visualizing temporal multimodal streaming data from Internet of Things (IoT) devices, ensuring the stability of previously visualized data. The growing volume of real-time IoT data presents challenges in visualization and analysis, requiring efficient techniques for handling large datasets in dynamic environments. The goal of this research is to reduce system load without losing the context of the data being visualized. The proposed method uses downsampling techniques to reduce the volume of displayed data while maintaining visualization stability by limiting the time window for data resampling. This method optimizes system resources, ensuring that users can interact with real-time data without cognitive overload, even when processing large volumes of data continuously. Moreover, the research shows that the proposed method can handle the challenges of real-time data streams effectively, providing a better user experience by preventing cognitive overload. Users are presented with only the most relevant data, ensuring that the visualization remains clear, stable, and easy to interpret. The paper demonstrates the effectiveness of the method through an implementation using the SciChart library for .NET, showcasing its ability to reduce system resource consumption and improve real-time data representation. The results reveal that the proposed approach significantly reduces CPU and GPU load, making it suitable for real-time IoT data visualization, especially in resource-constrained environments. In conclusion, this method offers a scalable and efficient solution for visualizing temporal multimodal IoT data, balancing the need to reduce system load while maintaining stable, clear, and meaningful visualizations. This approach ensures that real-time data remains accessible and interpretable, even when large amounts of data are continuously processed.
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Likhitkar, Swapnil, Jatin Gereja, Nikhil Sonwane, and Savita Pawar. "PLANT VISUALIZATION USING IOT AND AR." International Journal of Engineering Applied Sciences and Technology 04, no. 12 (2020): 235–39. http://dx.doi.org/10.33564/ijeast.2020.v04i12.037.
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Phupattanasilp, Pilaiwan, and Sheau-Ru Tong. "Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming." Sustainability 11, no. 9 (2019): 2658. http://dx.doi.org/10.3390/su11092658.
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Benefitted by the Internet of Things (IoT), visualization capabilities facilitate the improvement of precision farming, especially in dynamic indoor planting. However, conventional IoT data visualization is usually carried out in offsite and textual environments, i.e., text and number, which do not promote a user’s sensorial perception and interaction. This paper introduces the use of augmented reality (AR) as a support to IoT data visualization, called AR-IoT. The AR-IoT system superimposes IoT data directly onto real-world objects and enhances object interaction. As a case study, this system is applied to crop monitoring. Multi-camera, a non-destructive and low-cost imaging platform of the IoT, is connected to the internet and integrated into the system to measure the three-dimensional (3D) coordinates of objects. The relationships among accuracy, object coordinates, augmented information (e.g., virtual objects), and object interaction are investigated. The proposed system shows a great potential to integrate IoT data with AR resolution, which will effectively contribute to updating precision agricultural techniques in an environmentally sustainable manner.
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Surya Kumara, I. Made, I. Gusti Made Ngurah Desnanjaya, and Kannan Nataraj. "Enhancing 3D building visualization and real-time monitoring in construction through IFC and IoT integration." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2233–41. https://doi.org/10.11591/eei.v14i3.9263.
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The integration of industry foundation classes (IFC) and internet of thing (IoT) addresses a key challenge in construction: real-time data visualization on specific building storeys. Traditional methods often struggle with data integration and timely monitoring. This study introduces a web-based platform that combines three-dimensional (3D) technology, IFC models, and IoT sensors to enhance visualization and monitoring in construction projects. Unlike prior approaches that focus on static visualization or lack real-time IoT integration, this platform delivers dynamic, storey -specific updates, enabling real-time monitoring of critical building parameters. A case study showed that file size significantly impacted loading speed, ranging from 0.17 kB/ms (97.3 kB model in 572 ms) to 11.72 kB/ms (7.2 MB model in 629 ms). Despite a slight drop in frame rate from 60 to 55 frames per second (FPS), the system maintained smooth user interactions. Memory usage increased from 180 MB to 314 MB to handle complex 3D models and IoT data in real time. These findings demonstrate that integrating IFC with IoT enhances data visualization, providing more efficient decision-making tools for construction stakeholders and improving on-site coordination and resource management.
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Susanto Putro, Adi Nugroho, and Agung Nugroho. "Mapping the Journey of Internet of Things (IoT) Research: A Bibliometric Analysis of Technology Advancements and Research Focus." West Science Interdisciplinary Studies 1, no. 08 (2023): 564–75. http://dx.doi.org/10.58812/wsis.v1i08.181.
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The Internet of Things (IoT) has ushered in a new era of connectivity, transforming industries and societies across the globe. This research delves into the IoT research landscape through a comprehensive bibliometric analysis, unveiling technological trends and research focuses. The methodology encompasses data collection, preprocessing, advanced bibliometric techniques, and visualization tools. Results highlight key contributors, collaborative networks, research themes, and influential works. Through analysis and visualization, we gain insights into the trajectory of IoT research, its multidisciplinary nature, and the evolving challenges it addresses. This study serves as a compass for researchers, policymakers, and practitioners navigating the intricate IoT ecosystem.
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Lavalle, Ana, Miguel A. Teruel, Alejandro Maté, and Juan Trujillo. "Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices." Sustainability 12, no. 14 (2020): 5595. http://dx.doi.org/10.3390/su12145595.
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Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.
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Vera-Piazzini, Ofelia, Massimiliano Scarpa, and Fabio Peron. "Building Energy Simulation and Monitoring: A Review of Graphical Data Representation." Energies 16, no. 1 (2022): 390. http://dx.doi.org/10.3390/en16010390.
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Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building analysis: simulation and monitoring. Likewise, data visualizations are categorized according to goals, level of detail and target users. Visualization tools published in the scientific literature, as well as those currently used in the IoT platforms and visualization software, were analyzed. This overview can be used as a starting point when choosing the most efficient data visualization for a specific type of building energy analysis.
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Yang, M. "An IoT platform for smart metal forming." Journal of Physics: Conference Series 2878, no. 1 (2024): 012030. http://dx.doi.org/10.1088/1742-6596/2878/1/012030.
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Abstract Manufacturers in the metal forming industry are paying attention on smart manufacturing with the industrial internet of things (IIoT). An innovation has been occurring by changing metal forming processes from analog to digital by utilizing servo press machine and IT technology for improving efficiency and worth of the mass production manufacturing technology and of ability for the SDGs. This paper aims to introduce initiatives to survey of the current situation and formulation of strategies on the digital transformation in metal forming promoted by the Sokeizai Centre in Japan. An IoT platform for smart metal forming which has been developed based on the strategies is also introduced. The platform consists of sensor system for visualization of forming processes, sensor data wireless communication using local 5G and data assimilation with process simulation. Several sensing systems developed for process visualization are introduced.
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Singh,, Rajat R., Mr Anway A. .Lande, Mr Kunal S. Gadhawe,, Gauri S. Ujawane, Keya S. Gawai,, and Kunal S. Yende. "Real-Time Atmospheric Parameters Versatile Monitoring and Display System: A General Overview." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02434.
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This paper presents the development of a Real-Time Atmospheric Parameter Versatile Display and Monitoring System to monitor critical environmental parameters such as temperature, humidity, air pressure, and air quality index. The system employs IoT-based sensors and cloud computing for data acquisition, storage, and visualization. The real-time data display enables users to make informed decisions regarding environmental conditions. Case studies and standard reference values have been utilized to validate the system's efficiency. The paper also includes graphical representations for data interpretation. Keywords: Atmospheric Monitoring, IoT, Real-Time Display, Environmental Parameters, Data Visualization
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Prabha, B., Rashmi Shahu, Satish V. T., et al. "IoT based Data-Driven Methodology for Real Time Production Optimization and Supply Chain Visibility in Smart Manufacturing and Logistics." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 22 (May 9, 2025): 926–47. https://doi.org/10.37394/23207.2025.22.78.
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This research looks at how data methods and IoT technologies can be used effectively for planning and improving supply chain transparency. It seeks to explain the importance of measurements namely cycle time, lead time, on-time delivery, inventory turns, and fill rate. Analyzing the company objectives and requirements based on the identified ones, the study underscores the paramount importance of KPI visualization in helping the users comprehend organizational processes and seek improvement. The study also explores how the effectiveness of the IoT infrastructure is assessed and how the IoT devices are chosen and subsequently deployed for strategic purposes and the building of real-time data acquisition systems. In addition, the article also covers the approaches with regard to data acquisition and assimilation; more focus is given to the understanding of the performance of the machine, conditions of the environment, and the logistical aspects by means of data visualization of the IoT. The study also emphasizes data quality governance mechanisms to ensure the accuracy and comprehensiveness of IoT data, and thus make people more confident in data reliability.
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Girhepunje, Bhuvan. "Smart Garbage Monitoring System Using IoT." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2347–51. https://doi.org/10.22214/ijraset.2025.70543.
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Abstract: This paper presents a smart garbage monitoring system leveraging Internet of Things (IoT) technology to optimize waste management in urban areas. The system is designed to detect the waste level in garbage bins using ultrasonic sensors and communicate real-time data to municipal authorities via the Blynk IoT platform. The proposed solution addresses inefficient waste collection processes and helps maintain hygienic urban environments by ensuring bins are emptied before overflowing. Key components include Arduino Uno, ultrasonic sensors, GSM modules, and IoT-based data visualization through Blynk. The methodology encompasses three phases: sensing and data acquisition, wireless transmission using GSM, and real-time visualization via mobile application. This system reduces manual monitoring efforts, facilitates timely garbage collection, and supports scalable implementation across cities. Results show consistent bin-level detection, reliable SMS alerts to the control center, and seamless IoT integration, making the prototype an effective model for smart city waste management initiatives.
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Sulistyo, Joko, Angga Tegar Setiawan, and Isa Setiyasah Toha. "Development of cloud visualization a machining manufacturing system shop floor." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1005–15. https://doi.org/10.11591/ijeecs.v33.i2.pp1005-1015.
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The industry is currently experiencing the fourth industrial revolution, characterized by the automation of cyber physical systems and advanced connectivity through the internet of things (IoT). This revolution enables real-time monitoring of machines status on the shop floor by leveraging cyber-physical and IoT technologies. This paper describes the results of research that develops IoT and cloud-based visualization for a machining manufacturing system shop floor. Our proposed solution involves an internet of things device equipped with two current sensors to detect machine and spindle current. The sensor connected to an Arduino Nano, which is then connected to Wemos D1 for wireless transmission of data to the cloud. The cloud has been developed to store data and provide visualization applications, in the form of machines layout map to monitor machines conditions in the form of machines ON, machines OFF, spindles ON and spindles OFF in real time.
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Waleed, Muhammad, Tariq Kamal, Tai-Won Um, Abdul Hafeez, Bilal Habib, and Knud Erik Skouby. "Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges." Sensors 23, no. 15 (2023): 6760. http://dx.doi.org/10.3390/s23156760.
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The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications.
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Kurniawan, Andi. "A Survey Paper: Implementation of the Internet of Things in Industry." ITEJ (Information Technology Engineering Journals) 5, no. 1 (2020): 37–50. http://dx.doi.org/10.24235/itej.v5i1.42.
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The use of the Internet of Things (IoT) has been widely developed in various fields of technology application. Using IoT can make it easier to build robust industrial systems and applications and provide visualization for the safety of industrial employees by taking advantage of the increasing number of radio frequencies, wireless devices, mobile devices and sensors. In understanding the development of IoT in the industry, the paper reviews the latest IoT research, a survey that compares the use of IoT from several existing papers, the main applications of IoT in the industry, and identifies trends and research challenges. The main contribution of this survey paper is to systematically summarize the current state of IoT in the industry.
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Sulistyo, Joko, Angga Tegar Setiawan, and Isa Setiyasah Toha. "Development of cloud visualization a machining manufacturing system shop floor." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1005. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1005-1015.
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<p><span>The industry is currently experiencing the fourth industrial revolution, characterized by the automation of cyber physical systems and advanced connectivity through the internet of things (IoT). This revolution enables real-time monitoring of machines status on the shop floor by leveraging cyber-physical and IoT technologies. This paper describes the results of research that develops IoT and cloud-based visualization for a machining manufacturing system shop floor. Our proposed solution involves an internet of things device equipped with two current sensors to detect machine and spindle current. The sensor connected to an Arduino Nano, which is then connected to Wemos D1 for wireless transmission of data to the cloud. The cloud has been developed to store data and provide visualization applications, in the form of machines layout map to monitor machines conditions in the form of machines ON, machines OFF, spindles ON and spindles OFF in real time.</span></p>
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Fernando, Xavier, and George Lăzăroiu. "Energy-Efficient Industrial Internet of Things in Green 6G Networks." Applied Sciences 14, no. 18 (2024): 8558. http://dx.doi.org/10.3390/app14188558.
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The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.
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Lavalle, Ana, Miguel A. Teruel, Alejandro Maté, and Juan Trujillo. "Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production." Sensors 20, no. 16 (2020): 4556. http://dx.doi.org/10.3390/s20164556.
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Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.
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Shao, Cuili, Yonggang Yang, Sapna Juneja, and Tamizharasi GSeetharam. "IoT data visualization for business intelligence in corporate finance." Information Processing & Management 59, no. 1 (2022): 102736. http://dx.doi.org/10.1016/j.ipm.2021.102736.
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Sun, Hanjie. "Interactive Knowledge Visualization Based on IoT and Augmented Reality." Journal of Sensors 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/7921550.
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In order to solve the integration value of information technology and education, it is mainly reflected in the container for storing and disseminating information, the problem is that learners lack the proper self-learning ability, and the author proposes an interactive knowledge visualization system based on the Internet of Things and augmented reality technology. According to the applicable characteristics of augmented reality technology applied to IoT data presentation and interaction, the method can analyze and describe its application possibility. In the design of interactive electronic technology computer-aided teaching system based on NET platform, the system hardware structure consists of user interface layer, business selection layer, and data management layer. Users such as teachers and students enter their identity information at the user interface layer to log in to the system and enter the business selection layer and click the corresponding program according to their application requirements, the service selection layer transmits the user’s selection instruction to the data management layer, and the data management layer selects the corresponding resources according to the user’s needs and feeds it back to the user. The interaction of the system is mainly reflected in interactive teaching and information interaction, and interactive teaching is reflected in the online teaching of teachers and students. Information interaction is embodied in the information transmission of the system information interaction model. Experimental results show that after applying the system, the number of students with high self-efficacy increased from 11 to 21 and the proportion increased from 21.4% to 34.8%. The system designed by the author has strong antipressure ability, can respond to the application instructions of a large number of users in real time, and has a good interactive teaching effect, which improves the self-efficacy of students.
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Suwondo, Nanang, and Al Faris Habibullah. "The IoT for Visualization of RC Circuits Transient Phenomena." Bincang Sains dan Teknologi 1, no. 02 (2022): 57–62. http://dx.doi.org/10.56741/bst.v1i02.148.
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The development of a device for visualizing the transient phenomena of RC circuits on smartphones has been carried out, utilizing the IoT platform of NodeMCU as a solution to restrictions on attendance in laboratory during COVID-19 pandemic. The quantity displayed is the voltage and electric current during charging or discharging capacitor. NodeMCU is coupled with a CD4066 to switch the process, and a 74HC4051 multiplexer to select the voltage or current value of capacitor. At the end there is an RC circuit connected to the switch and the multiplexer and also a voltage source. User commands are sent from the smartphone to the NodeMCU via the Blynk server. The software consists of a program uploaded in NodeMCU and a Blynk graphic interface arranged on smartphone. The system can work quite well, including controlling the charging and discharging capacitor. The visual appearance of voltage and current on smartphone screen quite similar with those of theory.
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Zhang, Jing. "Research on Visualization Management of Human Resources Based on Big Data Neural Network Technology." Review of Computer Engineering Studies 9, no. 2 (2022): 79–81. http://dx.doi.org/10.18280/rces.090206.
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Nowadays, the number of internet of things (IoT) connected devices continues to increase exponentially. However, the core underlying wireless network technologies that enable IoT devices to achieve such growth and wide applications face numerous challenging deployment requirements such as operating range, power consumption, and cost. Low-power wide-area network (LPWAN) technologies enable long-distance, low-power, and low data transmission at a low cost. These new wireless technologies shape the IoT ecosystem due to their wide applications. This study aims to review the various features and analyze the performances of the leading LPWANs, namely LoRa, SigFox, NB-IoT, and Weightless. Initially, precise descriptions of their underlying technologies and various applications were provided. Then, several challenges facing the LPWANs and potential solutions were outlined. Finally, the study analyzed their performance against several specifications, including frequency range, Bandwidth, Modulation, and so on. The outcome shows that these technologies are more efficient than the short and medium-range IoT technologies, particularly regarding power, range, and cost. The study’s findings are hoped to provide a guide and eliminate LPWAN technology selection issues.
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Leal Sobral, Victor Ariel, Jacob Nelson, Loza Asmare, et al. "A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application." Smart Cities 6, no. 3 (2023): 1416–34. http://dx.doi.org/10.3390/smartcities6030068.
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Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.
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Liang, Jing. "Analyzing biomechanical force characteristics in sports performance monitoring using biochemical sensors and internet of things devices." Molecular & Cellular Biomechanics 22, no. 2 (2025): 727. https://doi.org/10.62617/mcb727.
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This study explores the application of Internet of Things (IoT) devices and biochemical sensors in sports performance monitoring, focusing on the biomechanical force characteristics of athletes to address limitations in traditional methods, such as limited data types, poor real-time accuracy, and insufficient visualization. Emphasizing mechanobiological principles, the analysis targets key force-producing regions of the body—such as the feet, legs, and torso—to optimize energy efficiency, motion precision, and overall athletic performance. Biochemical sensors were employed to monitor real-time biomechanical and physiological data, while IoT devices ensured accurate data transmission, visualization, and feedback. Data accuracy was enhanced through methods such as zero correction, timestamp synchronization, and Kalman filtering, while data transmission efficiency was optimized using a lossless compression algorithm, hierarchical structuring, the MQTT protocol, and encryption via the AES algorithm. Data organization utilized a star-structured MySQL database with composite indexing for swift access. Analytical tools such as the Apriori algorithm for data correlation, linear discriminant analysis for feature extraction, and multi-source data fusion enabled detailed visualization of performance metrics. Experimental applications in football and sprinting demonstrated the effectiveness of IoT-based monitoring. Football experiments captured multi-dimensional data on technical characteristics, while sprint tests recorded precise performance metrics, including real-time speed profiling and timing accuracy. For instance, in a 100-meter sprint test, an IoT system measured an athlete's performance at 12.54 seconds with 100% accuracy, surpassing manual timing methods. These findings highlight the transformative potential of IoT devices and biochemical sensors in sports analytics, offering enhanced accuracy, real-time tracking, and actionable insights to refine athletic performance and decision-making.
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Dang, Chi Van, Khoat Duc Nguyen, Hieu Dao, and Luc The Nguyen. "Apply Matlab in Thingspeak Server to build the system measure and analyze data using IoT Gateway technology." Journal of Mining and Earth Sciences 61, no. 5 (2020): 88–95. http://dx.doi.org/10.46326/jmes.2020.61(5).10.
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ThingSpeak is an open Internet of Things (IoT) platform with MATLAB® analytics that enables the collection and storage of sensor data in the cloud and development of IoT applications. The ThingSpeak IoT platform provides applications that allow data analysis and visualization in MATLAB. With MATLAB® analysis in ThingSpeak, MATLAB code can be executed to perform preprocessing, visualization, filtering, data analysis, and for object modeling applications. This paper presents researches on Matlab application in Thingspeak Server to build data measurement and analysis system using IoT LoRa Gateway technology. The research contents include suggestions on device configuration for the system, programming the Arduino board and LoRa Shield to collect measurement data from sensor nodes and communicate by LoRa waves to the LoRa Gateway. The LoRa Gateway will send data to Web Server based on Thingspeak's Cloud Service platform using MQTT (Message Queing Telemetry Transport). Thingspeak's Matlab interface will display online and store values from the sensor nodes. The system is integrated and tested on temperature and humidity monitoring model, evaluated for the results with the required accuracy. The research results allow the deployment of IoT Gateway system in practice for online measurement, analysis and data processing applications that require the use of algorithms and code generation in Matlab using Web Server.
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Singh, Rajesh, Anita Gehlot, Mamoon Rashid, et al. "Cloud Server and Internet of Things Assisted System for Stress Monitoring." Electronics 10, no. 24 (2021): 3133. http://dx.doi.org/10.3390/electronics10243133.
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Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.
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Dost, Muhammad Khan. "Data Streaming of Healthcare from Internet of Things (IoTs) using Big Data Analytics." Global Social Sciences Review 4, no. 1 (2019): 287–95. https://doi.org/10.5281/zenodo.4362047.
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The present study aims at the concept of the IoTs (IoT) and its relation with the healthcare sector. Nowadays, IoT is the main focus of researchers and scientists while this concept illustrates the data stream generated from IoT devices in massive amounts like big data with a continuous stream that requires its proper handling. This study aims at the analytical processing of big datasets having a medical history of patients and their diseases. The data cleansing is applied before going through the analytics phase due to the existence of some noisy and missing data. The analytics of data identified that what events are happening while the mining approaches identified why and how events are happening. Together, both phases help in data analytics and mining. Finally, the analytics and visualization led to the decision making and its results depict the effectiveness and efficiency of the proposed framework for data analytics in IoT
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ANDRIOAIA, DRAGOS-ALEXANDRU, GEORGE CULEA, and PETRU-GABRIEL PUIU. "ENVIRONMENTAL TEMPERATURE AND HUMIDITY MONITORING SYSTEM USING RASPBERRY PI 4 AND THINGSPEACK." Journal of Engineering Studies and Research 27, no. 3 (2022): 20–23. http://dx.doi.org/10.29081/jesr.v27i3.283.
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In recent years, IoT platforms have become increasingly used due to their untapped potential. This paper aims to create an IoT system to monitor temperature and humidity in an enclosure The Raspberry Pi 4 SBC (Single-Board Computer) development board and ThingSpeak cloud platform will be used to make this system. Data from the DHT11 humidity and temperature sensor will be collected by the Raspberry PI 4 SBC development board, which will transmit it via the WiFi connection to the IoT ThingSpeak platform cloud for further analysis. The IoT ThingSpeak platform provides data storage, processing and visualization services.
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Villa, Valentina, Berardo Naticchia, Giulia Bruno, Khurshid Aliev, Paolo Piantanida, and Dario Antonelli. "IoT Open-Source Architecture for the Maintenance of Building Facilities." Applied Sciences 11, no. 12 (2021): 5374. http://dx.doi.org/10.3390/app11125374.
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The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.
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Moens, Pieter, Vincent Bracke, Colin Soete, et al. "Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications." Sensors 20, no. 15 (2020): 4308. http://dx.doi.org/10.3390/s20154308.
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The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.
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Josephine D.C, Jullie. "Accessible Carbon Emission Assessment System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47722.
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Abstract - This paper presents an integrated system for real-time carbon emission tracking and management utilizing IoT, AI/ML, and interactive technologies. IoT-based sensors continuously monitor data from electricity consumption, fuel usage, and transportation activities to ensure accurate and up-to date carbon footprint calculations. Based on these inputs, the system computes emissions in real-time and applies machine learning regression models to predict future carbon outputs. Personalized carbon reduction strategies are generated, tailored to each user’s activities. An intuitive data visualization module displays emission trends through graphs, heatmaps, and comparative reports, simplifying analysis and promoting awareness. To further enhance user engagement and foster sustainable behavior, a gamification layer incorporating points, badges, and leaderboards is integrated. Together, these modules form a dynamic, data-drivenplatform that empowers users to monitor, predict, and actively reduce their carbon footprint. Key Words - Carbon Emission Tracking, IoT, Machine Learning, Carbon Prediction, Sustainability, Data Visualization, Gamification, User Engagement
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Ali, Zulfiqar, Azhar Mahmood, Shaheen Khatoon, et al. "A Generic Internet of Things (IoT) Middleware for Smart City Applications." Sustainability 15, no. 1 (2022): 743. http://dx.doi.org/10.3390/su15010743.
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The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA.
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Patil, Shivani, Menachem Domb, and Sujata Joshi. "Smart Manufacturing Using Embedded IoT, Data Mining, and Machine Learning." International Journal of Advanced Engineering and Management Research 09, no. 04 (2024): 45–58. http://dx.doi.org/10.51505/ijaemr.2024.9404.
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The Internet of Things (IoT) facilitates objects' connection to the Internet and data exchange. The IoT and Big- data analytics revolutionize the industrial sector by offering real-time data visualization, tracking, and building predictive models for future actions using machine learning. It helps make an operation process more efficient and provides insight into the production process, increasing productivity and lowering costs. The data from IoT devices can be mined and visualized through advanced analytics tools. Thus, manufacturers will have a comprehensive picture of the production processes. This paper uses Data Mining (DM), Big Data Analytics, and IoT to monitor operations and determine predictive models for applying actions in the manufacturing Industry.
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Alarcón-Santos, Emilio-Antonio, Luis G. Montané-Jiménez, and José-Guillermo Hernández-Calderón. "Integration of IoT and Data Visualization for Personalized Diabetes Management." Avances en Interacción Humano-Computadora 9, no. 1 (2024): 249–52. https://doi.org/10.47756/aihc.y9i1.178.
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This paper presents a framework for developing diabetes management systems, emphasizing the importance of data collection, storage, analysis, and visualization. It discusses two main methods of data entry: manual and automated, each with its advantages and disadvantages. Data visualization is highlighted as a crucial component, enabling users to interpret their health information clearly and understandably, using graphs and tables that facilitate the identification of trends and patterns. Additionally, the document addresses security and privacy challenges in data storage, both locally and in the cloud. Data analysis allows for generating personalized recommendations that help patients manage their condition more effectively. Overall, the document underscores the need for intuitive and accessible interfaces that enhance user experience and promote proactive diabetes management.
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Safari Bazargani, Jalal, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "A Survey of GIS and IoT Integration: Applications and Architecture." Applied Sciences 11, no. 21 (2021): 10365. http://dx.doi.org/10.3390/app112110365.
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IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.
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Protogerou, Aikaterini, Evangelos V. Kopsacheilis, Asterios Mpatziakas, et al. "Time Series Network Data Enabling Distributed Intelligence—A Holistic IoT Security Platform Solution." Electronics 11, no. 4 (2022): 529. http://dx.doi.org/10.3390/electronics11040529.
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The Internet of Things (IoT) encompasses multiple fast-emerging technologies controlling and connecting millions of new devices every day in several application domains. The increased number of interconnected IoT devices, their limited computational power, and the evolving sophistication of cyber security threats, results in increased security challenges for the IoT ecosystem. The diversity of IoT devices, and the variety of QoS requirements among several domains of IoT application, impose considerable challenges in designing and implementing a robust IoT security solution. The aim of this paper is to present an efficient, robust, and easy-to-use system, for IoT cyber security operators. Following a by-design security approach, the proposed system is a platform comprising four distinct yet cooperating components; a distributed AI-enhanced detection of potential threats and anomalies mechanisms, an AI-based generation of effective mitigation strategies according to the severity of detected threats, a system for the verification of SDN routing decisions along with network- and resource-related policies, and a comprehensive and intuitive security status visualization and analysis. The distributed anomaly detection scheme implementing multiple AI-powered agents is deployed across the IoT network nodes aiming to efficiently monitor the entire network infrastructure. Network traffic data are fed to the AI agents, which process consecutive traffic samples from the network in a time series analysis manner, where consecutive time windows framing the traffic of the surrounding nodes are processed by a graph neural network algorithm. Any detected anomalies are handled by a mitigation engine employing a distributed neural network algorithm, which exploits the recorded anomalous events and deploys appropriate responses for optimal threat mitigation. The implemented platform also includes the hypothesis testing module, and a multi-objective optimization tool for the quick verification of routing decisions. The system incorporates visualization and analytics functionality and a customizable user interface.
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Natarajan Sankaran. "Enhancing IoT edge intelligence: Machine learning-driven visualization for smart cities decision-making." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1680–91. https://doi.org/10.30574/wjarr.2023.19.2.1685.
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Revolutionizing data processing, security and real-time decision making, the move to IoT edge intelligence is advancing the state of the art in how we approach these and all challenges of modern business. Latency, bandwidth constraints, security vulnerability are the traditional pain points of traditional cloud-based service models, edge computing is a critical solution. The IoT systems can be made more responsive, better able to utilize resources more effectively, and more secure by way of integrating ML driven visualization and edge AI strategies. Nevertheless, there are still some challenges about this such as scaling, data privacy, and computational efficiency. These risks can be mitigated with the solutions like federated learning, blockchain integration and then the anomaly detection, and all that data can actually flow seamlessly and securely. Edge AI takes the best of centralized cloud along with cost efficiency of distributed systems and results in reducing dependence on centralized cloud infrastructure, and optimizing data processing by doing the computation locally to lower latency and save bandwidth. Furthermore, ML based visualization tools help in making IoT applications efficient for smart cities, health-care and industrial automation domains. Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training. It is expected that later IoT edge intelligence can be advanced further from emerging technology such as quantum computing and AI driven automation. Such advancements will enable more scalable, secure and efficient processing frameworks that would lead to making intelligent, autonomous decisioning in the real time environment. As organizations adopt the edge AI solutions, it is important to address their current limitations and exploit the future innovation for the further growth and efficiency of IoT ecosystems.
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Imankulova, B., S. Alpar, and S. Amanzholova. "DATA SECURITY, MODELING AND VISUALIZATION OF DATA FROM IOT DEVICES." Scientific Journal of Astana IT University 10 (June 30, 2022): 107–18. http://dx.doi.org/10.37943/acwt2121.
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The article describes the IoT infrastructure, the hardware of the IoT system, considers the issue of security of the chosen LoRa data transmission technology. Data was received from sensors for gas, temperature and humidity, atmospheric pressure, as well as the location of the end device. At the same time, the standardized security features of the selected LoRa technology for transmitting data from sensors to the server were investigated. The article deals with LoRa bi-directional secure communication line, the security function requires devices/end devices to be configured through the LoRa gateway. Security research is devoted to the development of a security mechanism to increase its resilience. The payload was formed with a hash of the last bytes, and the entire payload was encrypted with AES for integrity and confidentiality. A method for assessing and visualizing atmospheric air pollution is given on the example of the city of Almaty, Kazakhstan. The process of numerical modeling of the study of emissions of harmful substances into the atmosphere is based on a mathematical model formed by the system of Navier-Stokes equations, consisting of the continuity equation, as well as the equations of motion and the k-epsilon turbulence model. To test the numerical methods for processing mixing and chemical reactions, a test problem was chosen – a jet in a transverse flow. Three-dimensional numerical simulation has been implemented. The use of the Internet of Things (IoT) and the acquisition of big data made it possible to simultaneously observe the concentrations of several pollutants in the atmosphere, calculate this concentration and analyze the state of the surface air layer. Modeling allows forecasting the possible concentration of pollutants in certain areas at certain times of the year.
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Ji, Wen, Jingce Xu, Hexiang Qiao, Mengdi Zhou, and Bing Liang. "Visual IoT: Enabling Internet of Things Visualization in Smart Cities." IEEE Network 33, no. 2 (2019): 102–10. http://dx.doi.org/10.1109/mnet.2019.1800258.
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Wang, Q., S. Gong, and YF Fu. "Power Visualization System Based on IoT and eSIM Card Technology." Journal of Physics: Conference Series 2476, no. 1 (2023): 012086. http://dx.doi.org/10.1088/1742-6596/2476/1/012086.
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Abstract To improve the quality and efficiency of power system management in power transmission and transformation equipment, a visualization system based on IoT and eSIM card technology is designed. The scheme adopts RFID, network development, .NET technology, MSSQL and related technologies and tools to realize the basic functions of the life-cycle management system of power transmission and transformation equipment. At the same time, the On air card writing of eSIM card is used to directly correct the communication parameter settings of the terminal, and the dialing and positioning information are integrated into a module. The visual component uses the latest Web development technology to design and complete the view editor function. Finally, the visual monitoring and data presentation functions of the equipment are tested based on the scenario design, and the results show that the system can achieve real-time monitoring and centralized management under the IoT environment, and comprehensively improves the intelligent level of the power system.
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Sensors, Journal of. "Retracted: Interactive Knowledge Visualization Based on IoT and Augmented Reality." Journal of Sensors 2023 (October 18, 2023): 1. http://dx.doi.org/10.1155/2023/9806940.
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Koch, Jacob, Jess Kropczynski, Janette Perez-Jimenez, and Joseph S. Johnson. "Transforming bridges into smart infrastructure: a data-driven approach to monitoring bats and promoting sustainable coexistence." Information Research an international electronic journal 30, iConf (2025): 189–202. https://doi.org/10.47989/ir30iconf47542.
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Introduction. Smart infrastructure development often prioritizes human needs such as safety and efficiency, overlooking the potential to facilitate coexistence with wildlife. This paper addresses this gap by demonstrating innovative use of internet of things (IoT) technology and data visualization for ecological monitoring. Method. We repurposed commercially available wildlife trail cameras to collect data on bats roosting in bridges, despite commercial devices not being built for this application. Furthermore, we developed a map- and cloud-based dashboard to integrate this ecological data with existing bridge metadata, transforming raw sensor data into actionable insights for transportation authorities. Analysis. We explore the ability of the dashboard to meet decision-making needs and offer design implications to further advance wildlife-inclusive smart infrastructure efforts. Results. Our findings demonstrate the efficacy of using IoT devices for wildlife monitoring and the power of data visualization in informing conservation efforts within smart infrastructure development. Conclusions. This study showcases the potential of IoT-enabled cameras and a user-friendly dashboard, BatMap, for effective bat monitoring and informed decision-making in infrastructure projects.
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Nikolskiy, Serhiy, and Iryna Klymenko. "EMBEDDED IOT PLATFORM FOR REMOTE TRAFFIC CONTROL IN SMART CITY IOT INFRASTRUCTURE." Measuring Equipment and Metrology 84, no. 3 (2023): 31–38. http://dx.doi.org/10.23939/istcmtm2023.03.031.
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A justified hybrid multilevel approach to IoT infrastructure implementation is set to facilitate the achievement of a scalable IoT (Internet of Things) infrastructure, incorporating integration into cloud technologies and services. The localization of hardware-software traffic management means at the lower level of the IoT infrastructure, close to data collection devices, ensures the generation of control influences in real-time, and relieves communication channels at the higher levels of the IoT infrastructure architecture. A technology for generating control influences for remote traffic management is proposed, which is based on the developed AT command system for deploying a web server and generating web pages using the capabilities of the embedded IoT platform on modern microcontrollers. The proposed technology allows for the formation of control influences in real-time, in an easily comprehensible textual format, using a web interface in the local domain of the IoT infrastructure. It also enables the visualization of information on remote displays and information boards, as well as on displays integrated into automotive equipment. The proposed technology can be used to inform road traffic participants about critical situations and can be embedded in smart traffic lights within remote traffic management systems or used to implement virtual traffic lights.
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Sandhya, M. Rajibul Alam Sanjana M. Suraj Pratap Dutta. "SMART AMBULANCE MANAGEMENT SYSTEM – AI AND HUMAN INTERFACE TECHNOLOGY IN SMART CITIES USING AI & MACHINE LEARNING." International Journal For Technological Research In Engineering 11, no. 5 (2024): 45–48. https://doi.org/10.5281/zenodo.10464231.
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This project introduces an fresh approach leveraging IoT technology and ML models to revolutionize patient monitoring in ambulances. A wearable health monitoring device equipped with IoT capabilities tracks vital signs, including body heat, pulse rate, and breathing patterns during various activities. Machine learning algorithms analyse collected data to accurately identify patient activities. Additionally, an intuitive application interfaces with the IoT device, allowing real-time data visualization and analysis for healthcare providers. The project aims to improve emergency medical response by delivering personalized, accurate patient data, enhancing decision-making, and potentially transforming the landscape of ambulance-based healthcare services.
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x, Jayakrishnan, and Rinsa Rees. "IoT-Driven Smart Weather Monitoring System with Global Data Access and Real-Time Visualization." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1665–68. https://doi.org/10.21275/sr25417194734.
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Khujamatov, Halimjon, Khaleel Ahmad, Nargiza Usmanova, Jamshid Khoshimov, Mai Alduailij, and Mona Alduailij. "Fog Computing Capabilities for Big Data Provisioning: Visualization Scenario." Sustainability 14, no. 13 (2022): 8070. http://dx.doi.org/10.3390/su14138070.
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With the development of Internet technologies, huge amounts of data are collected from various sources, and used ‘anytime, anywhere’ to enrich and change the life of the whole of society, attract ways to do business, and better perceive people’s lives. Those datasets, called ‘big data’, need to be processed, stored, or retrieved, and special tools were developed to analyze this big data. At the same time, the ever-increasing development of the Internet of Things (IoT) requires IoT devices to be mobile, with adequate data processing performance. The new fog computing paradigm makes computing resources more accessible, and provides a flexible environment that will be widely used in next-generation networks, vehicles, etc., demonstrating enhanced capabilities and optimizing resources. This paper is devoted to analyzing fog computing capabilities for big data provisioning, while considering this technology’s different architectural and functional aspects. The analysis includes exploring the protocols suitable for fog computing by implementing an experimental fog computing network and assessing its capabilities for providing big data, originating from both a real-time stream and batch data, with appropriate visualization of big data processing.
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Monroy, Martín Emilo, Gabriel Elías Chanchí, and Manuel Alejandro Ospina. "IoT system development for heart rhythm monitoring and cardiovascular risk estimation." Eastern-European Journal of Enterprise Technologies 1, no. 2 (127) (2024): 54–65. http://dx.doi.org/10.15587/1729-4061.2024.299068.
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The research focuses on addressing the global issue of cardiovascular diseases. The key variable under consideration for predicting cardiovascular diseases is heart rate variability (HRV). Leveraging the widespread adoption of IoT in various applications, particularly in the health sector, the study proposes the design and implementation of an IoT system for HRV monitoring. The research unfolded in four methodological phases: exploration and selection of technologies, definition of the IoT architecture, development of the prototype, and verification of its functionality. The implemented IoT system adheres to the conventional 4-layer IoT architecture: capture, storage, analysis, and visualization. Heart rate data is periodically acquired using a heart rate sensor and an Arduino-compatible board. The storage layer employs a non-relational database to store the captured data. The analysis layer extracts metrics related to HRV (High: RR <750 ms, Moderate: RR 750–900 ms, Low: RR >900 ms) by applying and delivering quantitative results from clustering algorithms such as machine learning models to evaluate data distribution. Risk levels indicate specific patient metrics. Thus, a 75-year-old patient exhibits an average HR of 75.56, Avg. RR of 795.42, falling into Cluster 1 with a risk value of 1.0. Similar detailed metrics and risk stratifications are presented for patients aged 68, 46, 37, and 18, demonstrating the system's robustness and efficacy in assessing cardiovascular risk. The visualization layer enables real-time observation of physiological variables, risk metrics, and results from data analytics models. The distinctive features of the results lie in the portability advantages of the IoT system, utilizing free hardware and software tools. This facilitates easy replication and utilization of the proposed system in medical campaigns, specifically for the early detection of cardiac conditions. The portable IoT system, leveraging free tools, enhances predictive capabilities for early cardiovascular risk detection globally
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Joseph,, B., P. Gayathri, A. .Vamshi, B. .Prabhas, and G. .Sridhar. "IOT Based Smart Energy Meter." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43659.
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This paper proposes an IoT based smart energy meter designed to monitor and manage electricity consumption efficiently. The system utilizes a combination of sensors and IoT technology to provide real-time monitoring of energy usage. It integrates an energy measurement module, a microcontroller Arduino UNO, ESP8266 Wi-Fi module, and a cloud platform ThingSpeak for data visualization and analysis. Key electrical parameters such as voltage, current, power, power factor, frequency and energy are measured and transmitted to the cloud for remote access. The smart energy meter enables users to monitor their energy consumption from anywhere, helping to optimize energy usage, reduce costs, and contribute to sustainability. This approach offers an affordable and scalable solution for intelligent energy management in residential and industrial settings. Keywords: IoT, smart energy meter, energy optimization, load management.
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Smt, Ambikatai Vamanrao Mittapally M.TECH-CSE MIE. "BIGDATA WITH INTERNET OF THINGS(IOT)." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY` 6, no. 6 (2017): 21–25. https://doi.org/10.5281/zenodo.802789.
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<strong>Big data</strong> is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.
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Liu, Renjun. "Three-dimensional visualization design strategies for urban smart venues under the internet of things." Computer Science and Information Systems, no. 00 (2025): 37. https://doi.org/10.2298/csis241122037l.
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With the increasing demand for smart venue management and data visualization, existing three-dimensional (3D) visualization technologies face challenges in meeting the requirements for efficient, real-time, and multifunctional data presentation. This study systematically compares and analyzes various 3D visualization methods, exploring their application effectiveness in smart venues to provide a reference for technology selection and optimization. Firstly, based on Building Information Modeling (BIM), Geographic Information System (GIS), and Internet of Things (IoT) technologies, this study delves into the principles and concepts of 3D architectural visualization. Meanwhile, it conducts a comprehensive analysis of common 3D visualization technologies. Secondly, using Cesium rendering technology, the study refines surface data for smart venues and performs detailed comparisons with Digital Twins (DTs), BIM, and Octree technologies. Finally, performance indicators like model response time, rendering speed, and frame rate are evaluated under different environments. The results reveal that in IoT environments, the combination of databases and browsers remarkably affects 3D visualization rendering performance. When using the My Structured Query Language (MySQL) database and the Chrome browser, Cesium achieves the best performance, with a model compression size of 5612 KB. It outperforms Unity (6021 KB), Three.js (5720 KB), and Octree (6754 KB). With the PostgreSQL database and Chrome browser, Cesium demonstrates strong lightweight performance with a model compression size of 13,991 KB. Under varying hardware conditions, rendering speed and response time improve significantly with advancements in processor and Graphics Processing Unit (GPU) performance. For instance, Cesium's rendering speed increases from 24 frames per second (FPS) on a Core i3 processor to 34 FPS on a Core i7 processor. Performance differences are observed among methods in response time, rendering speed, and user interaction experience, with Cesium outperforming others across multiple performance indicators. Overall, Cesium rendering technology demonstrates exceptional performance in 3D visualization for smart venues, surpassing other common 3D visualization technologies. The Cesium-based smart venue visualization system functions effectively, meeting practical requirements and contributing to improved user experience, optimized data presentation, and enhanced venue management.