Integrating internet of things for performance enhancement of hot air dryer

The research here investigates how Internet of Things (IoT) technologies can be integrated into responsive architecture in order to create sustainable and energy-efficient smart buildings. The main issue being solved is that there is no clearly defined framework for integrating IoT in responsive architectural design that makes it challenging to attain real-time adaptability, reduce energy consumption, and enhance the user experience. The research aims to evaluate existing IoT implementations in architecture by analyzing real-world case studies and developing a conceptual framework that articulates IoT's impact on building sustainability and efficiency. In an attempt to realize this goal, the research explores the following hypotheses: IoT integration greatly lowers a building's energy use. IoT-enabled buildings are more flexible and more satisfactory to users than conventional architectural buildings. An IoT-responsive architecture framework that is well designed can be employed as a scalable model for future smart city infrastructure. Through IoT-enabled building case studies, it has been found that IoT deployment results in a 30% reduction in energy consumption and substantial improvement in occupant comfort and operational efficiency. Keeping these findings in perspective, the research proposes a holistic framework with specific guidance to architects and urban planners to design more adaptive, efficient, and user-friendly buildings.

Purpose Integrating Internet of Things (IoT) networks with distributed ledger technology (DLT) and artificial intelligence (AI) presents critical challenges, particularly related to latency, scalability, hardware constraints and data security. Efficient data ingestion and validation are essential to enable real-time AI processing. The main contribution of this paper is the proposal of the Energy consensus algorithm, designed to minimize both latency and energy consumption in such environments. Design/methodology/approach Energy is a consensus algorithm tailored for public directed acyclic graph-based DLTs in IoT contexts. It introduces a flexible transaction validation mechanism that reduces or bypasses Proof of Work requirements. The algorithm’s performance is experimentally compared with IOTA under varying payload conditions. Findings Results show that Energy significantly reduces latency and energy consumption, especially for small payloads, which are common in IoT applications. These findings demonstrate Energy’s ability to enhance transaction efficiency and support real-time AI model updates based on verified IoT data streams. Research limitations/implications Future work should investigate the scalability of Energy in larger and more heterogeneous IoT ecosystems, as well as its compatibility with different AI frameworks. Evaluating its performance under diverse network conditions and hardware setups would further strengthen the generalizability of the results. Practical implications The Energy algorithm enables continuous AI model updates while ensuring data integrity, traceability and low latency. Its adaptability makes it a suitable solution for large-scale IoT deployments requiring secure and efficient data processing. Originality/value This paper presents a novel consensus algorithm that bridges the requirements of IoT, DLT and AI, with a particular focus on improving latency and energy efficiency. Energy offers a robust approach for optimizing data flow and transaction processing in real-time, AI-driven IoT systems.

Internet of Things (IoT) technology is something that is equipped with sensors and software so that it can send data over a network without human interaction. As a result, the Internet of Things can improve connectivity by connecting many devices via the internet, facilitating human interaction with machines. This research was conducted with the aim of increasing efficiency, reliability and innovation in the teaching and learning process. By using IoT, this research focuses on developing interactive and personalized learning media to increase students' understanding of technology. In addition, this research aims to improve teachers' abilities in using information technology to help students learn to use it. In conducting this research, researchers used quantitative methods in carrying out the research. The data obtained by the researcher was obtained through distributing questionnaires presented by the researcher via a goggle from application. The distribution of this questionnaire is carried out by researchers online, and then the results of the distribution of this questionnaire will be processed using an SPSS application. From this research, researchers can conclude that the integration of Internet of Things (IoT) technology can increase classroom management efficiency. With the help of the Internet of Things (IoT), teachers can monitor and manage classes more efficiently and intelligently by collecting data such as student activity, room temperature, and other information. Thus, IoT allows teachers to make smarter and strategic decisions about classroom management. Based on the results of this research, the impact of integrating IoT technology can provide benefits for teachers and students. Developing digital skills is one of the benefits of integrating IoT technology. By learning how to use Internet of Things technology in learning, teachers can improve their digital skills and increase their ability to use technology to improve the efficiency and quality of education.

Thin-layer drying kinetics of lignite during hot air forced convection

The integration of the Internet of Things (IoT) with Machine Learning (ML) is a transformative advancement that is revolutionizing the way data-driven decision-making occurs across various industries. IoT systems comprise interconnected devices that collect and transmit vast amounts of real-time data from sensors, machines, and appliances. However, merely collecting data is not sufficient; the real value lies in the analysis and interpretation of this data to generate actionable insights. This is where ML comes into play. ML techniques allow systems to learn from the data generated by IoT devices, enabling predictive analysis, automation, and enhanced decision-making processes. This integration of IoT and ML is paving the way for smarter, more efficient systems that can be applied in a wide array of fields such as healthcare, manufacturing, transportation, home automation, and smart cities. For instance, in healthcare, wearable IoT devices track vital health statistics like heart rate and blood pressure, while ML algorithms process these data in real-time to detect anomalies, predict potential health risks, and provide healthcare professionals with alerts for timely interventions. Similarly, in manufacturing, IoT devices collect sensor data from machines, which is analyzed by ML algorithms to predict maintenance needs, preventing costly breakdowns and improving operational efficiency. The sheer scale and complexity of data produced by IoT devices pose significant challenges for traditional data processing methods. ML algorithms are essential for managing and extracting value from this data, as they can handle large datasets, identify patterns, and make predictions in a scalable manner. By utilizing ML models such as deep learning, reinforcement learning, and clustering techniques, IoT systems are capable of adapting to changing environments, learning from their surroundings, and making intelligent decisions without human intervention. This paper will review the various ways ML can be leveraged within IoT systems to provide scalable, intelligent decision-making processes for analyzing the vast amounts of data produced by IoT devices. It will examine key use cases across different sectors where the integration of ML and IoT has shown significant promise. Specific case studies will be highlighted, including healthcare, where ML models enhance the monitoring and prediction of patient health; industrial IoT (IIoT), where predictive maintenance and anomaly detection improve operational efficiency; and smart cities, where ML-optimized IoT systems are used to manage traffic flow, energy consumption, and public services. By exploring these case studies, this paper aims to demonstrate the immense potential of integrating IoT with ML. It will also examine the challenges that arise in implementing such systems, including issues of scalability, data privacy, and security, and discuss potential solutions to these challenges. The paper will conclude with insights into the future of IoT-ML integration and how these technologies can continue to evolve to create even more intelligent, autonomous, and efficient systems across a broad range of industries.

Heat transfer characteristics on lignite thin-layer during hot air forced convective drying

This paper explores the integration of IoT devices, data analytics, and education techniques to enhance pediatric dental health outcomes. By leveraging real-time data collection, analysis, and personalized interventions, IoT can empower both caregivers and children to adopt proactive dental hygiene practices. This comprehensive approach not only improves oral health but also establishes lifelong habits for overall wellness. Pediatric dental health is a vital but often overlooked component of overall well-being. Despite its significance, it frequently lacks the attention it deserves. Integrating Internet of Things (IoT) technologies into pediatric dental care presents an opportunity for substantial improvement in early prevention and education strategies. This comprehensive approach not only enhances oral health but also establishes lifelong habits conducive to overall wellness. Pediatric dental health is a crucial determinant of overall well-being, yet it frequently remains overshadowed by other health priorities. Addressing pediatric dental health requires proactive measures, including early prevention and education strategies. The integration of Internet of Things (IoT) technologies presents a promising avenue to revolutionize pediatric dental care and enhance health outcomes. This paper delves into the potential of IoT devices, data analytics, and education techniques in improving pediatric dental health. By harnessing real-time data collection, analysis, and personalized interventions, IoT empowers caregivers and children to adopt proactive dental hygiene practices. This holistic approach not only enhances oral health but also fosters the development of lifelong habits conducive to overall wellness. Through a comprehensive examination of IoT integration, this paper underscores the transformative impact it can have on pediatric dental health, emphasizing the importance of prioritizing innovative approaches to address this critical aspect of childhood well-being.

In this digital era, integrating Internet of Things (IoT) technology with mobile applications has revolutionised various industries, including weather monitoring. This project presents the development of a weather monitoring app that harnesses the power of IoT and Kotlin programming language. The app utilizes a weather API to obtain real-time weather data and seamlessly communicates with an IoT device, enabling users to access and display weather information conveniently. project begins with a detailed analysis of requirements, outlining the desired features and functionalities of the app. The weather API selection process is discussed, and the chosen API is integrated into the Kotlin app to fetch weather data. Additionally, location services are implemented to offer location-based weather updates. An IoT device, such as a Raspberry Pi or Arduino, is selected for displaying the weather data. The IoT device is connected to the internet and programmed to fetch weather data from the app. Communication protocols between the Android app and the IoT device are established, ensuring smooth data exchange. The app's user interface is designed to provide an intuitive experience for users, with weather data and location information prominently displayed. Error handling mechanisms are implemented to deal with potential API downtimes or communication failures between the app and the IoT device. Through comprehensive testing, the app's functionality, performance, and reliability are assessed. Security measures are implemented to safeguard user data and privacy during data transmission. The result is a robust and user-friendly weather-monitoring app that seamlessly integrates IoT technology with Kotlin's efficiency and flexibility. The app empowers users to access real-time weather updates, enhancing their preparedness for weather changes and enabling informed decision-making for various outdoor activities. With the proliferation of IoT and its impact on various sectors, this project serves as a valuable example of leveraging IoT and Kotlin to develop innovative mobile applications for everyday use. Keywords – Weather Monitoring App, Kotlin, IoT, Real- time Weather Data, Weather API

The proliferation of Internet of Things (IoT) technology has significantly enhanced smart healthcare systems, enabling the collection and processing of vast healthcare datasets such as electronic medical records (EMRs) and remote health monitoring (RHM) data. However, this rapid expansion has also introduced critical challenges related to data security, privacy, and system reliability. To address these challenges, we propose a retrieval integrity verification and multi-system data interoperability mechanism for a Blockchain Oracle in smart healthcare with IoT Integration (RIVMD-BO). The mechanism uses the cuckoo filter technology to effectively reduce the computational complexity and ensures the authenticity and integrity of data transmission and use through data retrieval integrity verification. The experimental results and security analysis show that the proposed method can improve system performance while ensuring security.

Objective: This study examines the impact of integrating Internet of Things (IoT) technologies and sophisticated analytics on supply chain efficiency within the automobile sector. It examines the alleviation of issues including data security, difficulties of system integration, and deficiencies in workforce skills to attain best operational results. Through this research we aim to achieve the sustainable development goals (SDGs) of 8 and 9, which are Decent Work and Economic Growth and Industry, Innovation and Infrastructure. It builds resilient infrastructure for the industry using innovative technologies like IoT and analytics and as a result achieving decent work and economic growth. Design/methodology/approach: The study used structural equation modeling (SEM) to examine data gathered from 150 automotive OEMs and their tier one supplier companies in and around Chennai, India. The research utilizes frameworks such as the Technology Acceptance Model (TAM) and the Theory of Constraints (TOC) to elucidate the connections among IoT problems, predictive analytics, and supply chain performance. Results and Discussion: The results show that predictive analytics enhances supply chain transparency, reducing inefficiencies, and thereby achieving accurate demand forecasting. Addressing the issues of IoT-related risks and operational inefficiencies significantly enhances supply chain performance metrics. Real-time data monitoring and strategic problem resolution were found to be essential for the successful integration of IoT and analytics. Practical implications: The research offers practical techniques for the use of IoT and analytics, highlighting real-time monitoring and data-driven decision-making to enhance supply chain responsiveness and efficiency. This research addresses IoT-related difficulties, enhancing academic debate on digital transformation in supply chains and providing a practical framework for the successful utilization of IoT and analytics. This study offers an effective framework for organizations in the automotive industry to successfully implement IoT and analytics by addressing key challenges. Originality/value: By focusing on the mediating role of recognizing and mitigating challenges in IoT and analytics adoption, this research contributes to the academic discourse on digital transformation in supply chains. It provides actionable insights for practitioners aiming to optimize supply chain operations through advanced technological solutions. The findings point out the importance of implementing proactive, data-driven strategies and fostering real-time visibility to achieve substantial gains in supply chain efficiency and responsiveness.

INTRODUCTION: Prioritizing sustainability initiatives is crucial for retail chains as they integrate Internet of Things (IoT) technologies to drive environmental responsibility. Retail chains have responsibility to establish environmental stewardship when they globally expand in terms of operations, supply chain and offerings. By prioritizing the initiatives retail chains can reduce impacts on environment, resource waster and mitigate risks related to that with the help of concepts like IoT. OBJECTIVES: This paper aims to explore how IoT can aid in sustainable practices, mitigate risks, and enhance efficiency while addressing challenges, ultimately providing insights for retail chains to prioritize sustainability in the IoT context. METHODS: The research employs a qualitative approach, focusing on in-depth case studies and analysis of industry reports and literature to explore IoT-driven sustainability initiatives in retail chains. It includes a diverse sample of retail chains, such as supermarkets and fashion retail, selected based on data availability related to their use of IoT for sustainability. The study involves descriptive analysis to present an overview of these initiatives and competitive analysis to identify sustainability leaders and areas for improvement. However, limitations include potential data availability issues and reliance on publicly available sources, with findings reflecting data up to the 2018-2021 timeframe. RESULTS: The results highlight significant sustainability benefits achieved through IoT integration in various retail chain types. Case studies, such as Sainsbury's and Coca-Cola, demonstrate waste reduction and sustainable practices. Examples from Nordstrom and 7-Eleven showcase energy efficiency improvements. The versatility of IoT technologies across supermarkets, department stores, and convenience stores emphasizes the transformative power of IoT in driving sustainability in the retail industry. The study proposes a prioritization approach, considering key metrics and leveraging frameworks like the Triple Bottom Line, Life Cycle Sustainability Assessment, and Sustainability Framework for effective decision-making and goal alignment in IoT-driven sustainability initiatives. CONCLUSION: In conclusion, this paper highlights the substantial potential of prioritizing IoT-driven sustainability initiatives in retail chains for positive environmental, social, and economic outcomes. Through case studies, the diverse applications of IoT, such as food waste reduction and energy-efficient lighting, demonstrate tangible benefits. The trend towards sustainable sourcing and materials is evident across various retail chain types. The discussion underscores the need for a systematic approach, utilizing frameworks like the Triple Bottom Line, to align with strategic objectives and optimize resources.

Evaluation on the air-borne ultrasound-assisted hot air convection thin-layer drying performance of municipal sewage sludge

The internet of things (IoT) has seen immense growth in a couple of decades. Many data centers are producing bulk data daily, which is needed to be handled properly. The extent of produced data is constantly growing. Storing data on local IoT devices is not recommended because of the availability of limited storage space, less security, and high energy consumption. The IoT integration with cloud computing requires service provision with other attributes like efficiency, high performance, scalability, reliability, and ubiquity. For accomplishing such attributions, research vision and business are anticipated to merge IoT and cloud computing concepts to enable everything as a service model to encompass new capabilities of cognitive IoT and functionalities. This manuscript describes IoT-cloud computing-based smart infrastructure and presents different techniques for preventing challenges for integrating cloud computing and IoT.

Indonesia is the fourth most populous country in the world, which is significant with energy consumption. Currently, Indonesia is heavily dependent on fossil fuels to its energy needs, but continued reliance on these fuels could lead to depletion. To overcome this problem, biogas is considered as an alternative energy source for cooking and electricity, especially from waste such as cow dung. This research provides an overview of biogas production from small cattle farms in Indonesia, with a focus on cow dung as a valuable resource. It covers factors that increase biogas production, multiple digesters, purification techniques, and integrates Internet of Things (IoT) technologies. Articles for this study were selected using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method from reputable journals indexed in Scopus and PubMed. Sustainable biogas from cattle farms offers energy generation using abundant cow dung. Optimizing production involves adjusting raw materials, temperature, pH, C/N ratio and HRT. Different types of digesters have unique advantages. Purification techniques such as water scrubbing, PSA, etc. increase methane production. Integrating IoT provides monitoring and optimization. Biogas production has enormous potential for renewable energy, requiring the use and application of efficient techniques, digester types, purification, and IoT integration for a greener future.

Heat transfer characteristics and drying kinetics of hematite thin layer during hot air convection