Integration Of IoT Research Articles

Industrial Human Safety Detection System

Industrial workplaces can be hazardous environments where the safety and well-being of workers are paramount. To mitigate risks and ensure the safety of employees, a wide range of industrial human safety devices have been developed and implemented. This Research explores various safety devices designed to protect and enhance the safety of workers in industrial settings. The study examines devices such as personal protective equipment (PPE), machine guarding devices, fall protection gear, gas detection devices, and more. Through a comprehensive review of the current state of industrial safety technology, we assess the effectiveness, advantages, and limitations of these safety devices. We also discuss emerging trends in industrial safety technology, including the integration of IoT and AI. The findings presented in this Research will aid in understanding the critical role that these devices play in safeguarding industrial workers, and highlight the need for ongoing innovation in the field of industrial safety.. The real-time monitoring of human presence allows for prompt intervention and a more secure working environment . Furthermore, the automated safety measures based on human detection contribute to overall safety protocols and reduce the risk of injuries or mishaps. The successful implementation of this device demonstrates the efficiency and reliability of human detection using computer vision techniques. The device's ability to provide real-time monitoring and ensure immediate intervention significantly enhances workplace safety. It establishes a solid foundation for future integrating it with existing industrial control devices.

A Structural Equation Model Study for Adoption of Internet of Things for the Growth of Manufacturing Industries in Australia

This research study delves into the integration of IoT within the Australian manufacturing sector, analyzing five key factors influencing adoption and drawing comparisons with Rogers' innovation theory. Its objectives encompass assessing how competitiveness, regulatory support, management support, manufacturing growth, and cost savings affect IoT adoption. Methodologically, the research journey includes defining the problem, conducting a thorough literature review, crafting a research proposal, executing pilot and main studies, validating findings, and ultimately composing the final thesis. Quantitative data obtained from a large pool of over 500 respondents shed light on the positive impacts of competitiveness, regulatory support, and cost savings on IoT adoption. Additionally, the study employs Importance-Performance Map Analysis (IPMA) to underscore the significance of management support in successful IoT implementation. Despite facing limitations such as common method bias and constraints on generalizability, the research underscores the transformative potential of IoT in enhancing operational efficiency and decision-making within Australian manufacturing. Overall, the study's findings confirm the significant influence of key factors on IoT adoption, affirming its potential to drive competitiveness, regulatory compliance, cost efficiency, and growth within the Australian manufacturing landscape.

Analyzing Barriers to Internet of Things (IoT) Adoption in Humanitarian Logistics: An ISM–DEMATEL Approach

Background: Effective humanitarian logistics (HL) is essential in disaster response. The “Internet of Things” (IoT) holds potential to enhance the efficiency and efficacy of HL, yet adoption is slowed by numerous barriers. Methods: This study employs interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) to explore and classify barriers to IoT integration in HL. Results: A total of 12 barriers were identified, classified, and ranked according to their driving power and dependence. Key barriers include lack of standardization, organizational resistance, data quality issues, and legal challenges. Conclusions: Overcoming these barriers could significantly improve relief operations, reduce errors, and enhance decision-making processes in HL. This investigation is the first of its kind into IoT barriers in HL, laying the groundwork for further research and providing valuable insights for HL managers.

Artificial Intelligence Based CCTV-Accident Detection System

Urban infrastructure and public safety are severely hampered by traffic congestion and accidents. This research suggests an enhanced traffic light control system that makes use of deep learning methods and IoT integration to address these problems. The system computes vehicle density in real-time and indicates smart light conditions at traffic signals by combining the object identification algorithm of YOLOv3 with Internet of Things sensors. The secret to the system is the use of YOLOv3 for precise vehicle identification and categorization in video feeds recorded by security cameras. The technology can effectively identify cars, collisions, and violent incidents on the road by utilising YOLOv3. In order to enable quick emergency response, the system sends notification alerts via GSM connection to neighbouring police stations and hospitals upon detection. In addition, the suggested system uses Internet of Things devices to calculate the number of vehicles at crossings. Traffic signal timings can be dynamically adjusted thanks to this real-time data on vehicle flow, which improves traffic flow and lessens congestion. IoT integration also makes it possible for traffic signals to have smart light indication, which adjusts signal timings in response to current traffic circumstances to improve efficiency and safety.

Integration of Machine Learning and Iot based Multi-Layer Wireless Sensor Networks for Seamless Smart Home Automation

The integration of machine learning algorithms with Internet of Things -based multi-layer wireless sensor networks offers a transformative solution to modern smart home automation and security. This paper seeks to explore both the Sensor Data Processing and Machine Learning Components used. In essence, the machine learning model leverages aggregate sensor data to improve the efficiency and safety of modern living environments. Through attention to detail during the preprocessing and feature engineering of sensor data, the system analyzed learned patterns of household activity, including motion intensity, door/window interactions, and normal occupancy. Subsequently, the features were utilized as input variables in a number of models, including Convolutional Neural Networks , Support Vector Machines, Recurrent Neural Networks, and K-Nearest Neighbors. By simulating both vacation and occupant mode, the models processed the available data using to detect potential threats or other deficiencies in the patterns. Overall the results led by the model suggest that the Convolutional Neural Network model is the best amongst all of the aforementioned algorithms. It demonstrated the best accuracy out of all the other models due to the advanced image processing and facial recognition features, solidified by its 98.56% accuracy rate in recognizing a potential intruder. Other models were also effective or reasonably accurate, with the following results: SVMs – 95.45%; RNNs – 93.4%; and KNNs – 91.23%. The evaluation process also involves the calculation of precision, recall, and F1 score, which also indicates the overall strength of the model. The aggregation of these results implies a considerable potential for internet-of-things systems in combination with machine learning methods to cultivate smarter and safer living environments.

A Comparative Study on Machine Learning Models for Fault Detection and Classification in Manufacturing Integration of IoT and Artificial Intelligence in Industry 4.0 management

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

AbstractThis research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real‐time decisions by analyzing critical temperature and duration data essential for the low‐temperature long‐time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1‐score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300‐L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set‐point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set‐point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector.Practical ApplicationsThe article “Enhancing Low‐Temperature Long‐Time Milk Pasteurization Process with a C4.5 Algorithm‐Driven AIoT System for Real‐Time Decision‐Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts prevent downtime. The system's scalability supports production expansion with minimal resource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry.

Web-Based Integration of IoT and Artificial Intelligence for Monitoring Aquariums

Technology is now an indispensable part of work across all fields and locations. For instance, in fish cultivation and among ornamental fish enthusiasts, technology plays a pivotal role in regulating and notifying when ornamental fish's environmental conditions require attention. This includes monitoring and alerting for cleaning or feeding needs. Ornamental fish are particularly sensitive, often carrying a higher price tag. Due to their susceptibility to stress and their relatively costly nature, those involved in ornamental fish cultivation and collection frequently face significant losses. These losses can occur when fish are collected, raised, and fall ill or, in the worst-case scenario, die. In response to these challenges, technological innovations have emerged to simplify the monitoring of ornamental fish's environmental conditions. This is achieved through IoT-based solutions accessible anytime and anywhere via the web. These innovations incorporate three primary sensors: pH sensors, clarity sensors, and temperature sensors. Additionally, Thingsboard is used to facilitate real-time monitoring of sensor data. These aquarium monitoring systems for ornamental fish hold the potential to enhance fish welfare and health, mitigate risks, and provide hobbyists and professionals with a more effective and informed experience in maintaining ornamental fish. However, it's important to note that the success of this system relies heavily on the quality of hardware, meticulous software development, and a profound understanding of the specific needs of ornamental fish

Transforming the Energy Industry: A Comprehensive Analysis of Innovation with SAP S/4HANA Digital Platform

This research article explores the significant changes made in the energy industry and utilities through the adoption of SAP S/4HANA, a cutting-edge digital platform. The energy sector, facing challenges of sustainability, efficiency, and leveraging SAP S/4HANA to drive innovation, streamline operations, and enhance overall performance. The core of SAP S/4HANA lies in its status as a coordinated endeavor asset arranging (ERP) suite, consistently joining in-memory registering, constant examination, and high-level information handling capacities. This thorough analysis attempts to dive into the extraordinary effect of the SAP S/4HANA computerized stage, disentangling the complicated layers of development that are reshaping the energy area. The energy business, with its complex difficulties going from supportability worries to the complexities of developing administrative scenes, winds up at an essential point. The reception of SAP S/4HANA arises as an essential goal, giving an answer for smoothening out tasks, driving development, and hosting by and large execution. The article explores through the extraordinary force of in-memory registering, delineating how ongoing information handling enables energy organizations to speedily pursue informed choices. Generally, this complete examination fills in as a reference point in understanding how SAP S/4HANA isn't just a computerized stage but an extraordinary power, controlling the energy business towards a practical, effective, and mechanically progressed future. As the business keeps on developing, the beneficial interaction between SAP S/4HANA and the energy area vows to shape a story of advancement, versatility, and progress. Artificial intelligence, machine learning, and further advancements in IoT integration emerge as focal points, providing a glimpse into the evolving landscape of digital innovation in the energy sector. As the industry continues to evolve, the symbiosis between SAP S/4HANA and the energy sector promises to shape a narrative of innovation, resilience, and progress. The article navigates through the transformative power of in-memory computing, illustrating how real-time data processing empowers energy companies to make informed decisions promptly. This capability not only enhances operational visibility but also lays the foundation for data-driven decision-making in a sector where time is often the critical factor.

A Comprehensive Analysis and Visualization of Trends and Research Patterns in the Field of IoT Smart Cities

Every nation is interested in creating "smart cities," making this a hot topic requiring extensive scientific study. Given the current state of affairs, it is essential to conduct a systematic review to thoroughly understand the current research trends and patterns in this field. It is applied to a corpus of 9,131 papers published between 2010 and 2022 from the Scopus publications database to test the string's effectiveness. This research utilizes text mining and Latent Semantic Analysis (LSA) to delve into the current state-of-the-art study concerning IoT Smart cities research. KNIME was used to conduct the analysis. Predicting the study domain is done with the help of the K-means clustering method. Future researchers can build upon these patterns to strengthen security in various sectors. This report also reviews the history of smart city research, its current position, and its prospects. We help institutions and authors collaborate globally in science. This research analyses Smart City IoT integration trends and patterns in a graphical overview. According to the data, the identified areas are developing and need more research.

Advancements in apple disease classification: Machine learning models, IoT integration, and future prospects

<p>Apple orchards are of significant importance in the global agricultural sector, but they are vulnerable to a range of diseases that have the potential to cause diminished crop productivity and financial hardships. This manuscript investigates the utilization of machine learning methodologies, such as Logistic Regression, Neural Networks, and Random Forest, to classify three prevalent apple diseases: Blotch, Normal, and Rot Scab. The performance of these models is assessed using several assessment criteria, and confusion matrices are presented to aid in the prompt and precise detection of these diseases. This supports the implementation of efficient disease control strategies in apple orchards. By utilizing these ML models for the detection and treatment of diseases, not only augment agricultural productivity but also make a valuable contribution to sustainable agricultural practices by diminishing the necessity for excessive pesticide application. The experimental results indicates that Logistic Regression reflects the best performance as compared to other machine learning models taken into consideration using the different parameters. it obtained 90.6% of AUC and 65.7% of classification accuracy as compared to NN and Random Forest, which has achieved, 89.3%, 65.1%, 80.9% and 52.2.%, respectively.</p>

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

"Revolutionizing Lawn Care: AI-Driven Solar-Powered Humorless Grassland Mower With IoT Integration"

The primary objective of an AI-driven solar-powered humorless grassland mower with IoT integration is to provide an innovative, sustainable, and efficient solution for grassland maintenance, addressing the challenges of traditional lawn care methods while promoting environmental stewardship and technological advancement. Methods: Developing an AI-driven solar-powered humorless grassland mower with IoT integration involves integrating various technologies and methodologies such as AI Algorithm, Solar Power System Design, Robotics and Automation, IoT Integration, User Interface and Control System. Findings: It can vary depending on the specific objectives, implementation, and testing conducted. It can provide valuable insights into its performance, sustainability, reliability, and user acceptance, helping to inform further development and optimization efforts. Novelty: By introducing novel features and approaches in these areas, an AI-driven solar-powered grassland mower with IoT integration can offer unique capabilities and benefits that set it apart from conventional lawn care equipment and contribute to advancements in sustainable landscaping practices. This paper proposes an innovative AI-Driven Solar-Powered Humorless Grassland Mower with IoT Integration. Traditional manual lawn mowing not only demands labor but also contributes to environmental pollution through nonrenewable resource consumption. To address these challenges, our solution leverages advanced technologies to transform grassland upkeep. The mower is equipped with a range of IoT sensors including ultrasonic, proximity, GPS, and cameras. These sensors enable real-time data collection and decision-making, allowing the mower to adjust its mowing schedule based on forecasted data and navigate around obstacles. Furthermore, during periods of poor sunlight or low battery levels, the mower autonomously returns to its self-charging station for recharging. Data collected by the mower can be sent to the cloud for further analysis and storage. Users have the convenience of remotely controlling the mower through a smartphone app or web interface, enabling initiation, termination, or scheduling of mowing sessions from anywhere. Our AI-integrated IoT solution offers a sustainable and efficient approach to grassland maintenance, reducing labuor requirements and environmental impact while maximizing operational autonomy.

Analysis of Intelligent Water Management: Integration of IoT and AI in Water and Wastewater Treatment

Analysis of Intelligent Water Management: Integration of IoT and AI in Water and Wastewater Treatment

Supply Chain Innovative Practices and Customer Satisfaction: Insights from Manufacturing SMEs

Abstract In today’s highly competitive business landscape, SMEs face the challenges of meeting customer demands and expectations. To remain competitive, attract and satisfy customers, manufacturing SMEs must adopt innovative practices within their supply chain. This research investigates the influence of supply chain innovative practices (SCIPs) on customer satisfaction within the context of manufacturing SMEs. A cross-sectional questionnaire survey was utilised to collect the data from 172 customers of manufacturing SMEs in Mwanza, Tanzania to gain comprehensive insights into the influence of SCIPs on customer satisfaction. Descriptive statistics and binary logistic regression (BLR) were used for data analysis. The research findings revealed that manufacturing SMEs adopt SCIPs, including digitalization and IoT integration, vendor-managed inventory, customisation, Just-In-Time, collaborative relationships, and lean manufacturing to a moderate extent. Moreover, the findings demonstrate a significant and positive correlation between SCIPs and customer satisfaction. We conclude that SMEs that successfully adopt SCIPs experience higher levels of customer satisfaction, which, in turn, positively influences customer loyalty and brand reputation. Therefore, manufacturing SMEs can capitalise on customer satisfaction, loyalty, and sustainable growth by strategizing SCIPs along their supply chain. The study contributes to the existing knowledge by providing valuable insights into the importance of SCIPs in improving customer satisfaction in manufacturing SMEs.

Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services

The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing applications in cloud computing. This review focuses on AI-powered applications in distributed systems, conducting a thorough examination. Analyzing foundational studies and real-world applications, it extracts insights into the dynamic interplay between AI and distributed frameworks. Quantitative measures allow a nuanced comparison, revealing diverse contributions. The survey provides a broad overview of the state-of-the-art, spanning applications like performance optimization, security, and IoT integration. The ensuing discussion synthesizes comparative measures, significantly enhancing our understanding. Concluding with recommendations for future research and collaborations, it serves as a concise guide for professionals and researchers navigating the challenging landscape of AI-powered applications in distributed cloud computing platforms.

Integration of Computer Vision and IOT Into an Automatic Driving Assistance System for “Electric Vehicles”

Integration of Computer Vision and IOT Into an Automatic Driving Assistance System for “Electric Vehicles”

Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectives

The Intelligent Internet of Things (IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence (AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most of the state-of-the-art focus is on the topic independently, either on data science or IIoT. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT (IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions to the existing challenges.

Automated trusted collaborative processes through blockchain & IoT integration: The fraud detection case

Collaboration of business processes is essential for business-to-business (B2B) processes. Collaboration is interesting and important in connecting the digital context with the physical world (IoT) to feed processes with data or send data. However, it also presents multiple challenges, such as the lack of trust between participants with each other and additional privacy and security problems in the communicated data. Fraud detection is crucial for many type of organisations that deal with B2B transactions (banking, fintech, health, etc.) and are therefore exposed to a high risk of fraud. Fraud detection requires expensive professional investigations and intensive collaboration between processes of different organisations. This issue could be mitigated by effectively managing digital evidence, fostering trust and ensuring security for various stakeholders involved in the business processes. This paper proposes an approach to modelling and deploying any collaborative business process scenario, ensuring trust, security, and data privacy. Collaboration-level agreements are defined as a means to ensure trust, security, and data privacy. To accomplish this, our approach enables the automatic generation Smart Contract templates for the collaboration-level agreement specification involving different stakeholders in the collaboration. The Smart contracts are deployed in a Blockchain to ensure that the collaboration-level agreement conditions are signed by the parties. To validate the feasibility of our approach, a proof-of-concept for a fraud detection scenario is implemented, where different metrics are tested in relation to a set of threats and vulnerabilities.

Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.