Integration Of Internet Of Things Research Articles

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Smart Wet and Dry Waste Management System Using IoT

Abstract: Waste management is one of the important aspects when it comes to avoiding diseases caused by unsegregated waste. Many people have lesser knowledge about what is wet and dry waste. The escalating global waste generation hasled to a pressing need for innovative waste management solutions that are efficient, sustainable, and technologically advanced. This abstract introduces a "Smart Dry and Wet Waste Management System" that leverages the Internet of Things (IoT) to revolutionize waste management practices. Traditional waste management systems often lack real-time monitoring and optimization capabilities, resultingin inefficiencies, environmental concerns, and increased operational costs. The proposed system aims to address these challenges by integrating IoT technologies, sensor networks, and data analytics into waste collection and disposal processes.The main objective is to automate the segregation system thereby removing the human intervention. The hardware components such as Arduino, sensors, LCD are used to build the system. The integration of IoT in waste management contributes to a cleaner environment, reduced operational costs, and increased overall efficiency. By preventing overflows, minimizing unnecessary collection trips, and promoting recycling practices, the system aligns with sustainable waste management goals. Moreover, the collected data can be analyzed over time to identify longterm trends, enabling continuous optimization of waste management strategies..

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

Impact of Internet of Things (IoT) on Enhancing Transparency and Efficiency in Bangladesh's Agri-food Supply Chain

Background: The agri-food supply chain in Bangladesh is crucial for the nation's sustenance and economic stability but faces challenges like lack of transparency, inefficiencies, and information asymmetry. The integration of Internet of Things (IoT) technologies offers transformative potential, enhancing the Transparent Physical and Information Flow (PHF) and thereby influencing the Transparency in the Agri-food Supply Chain (TASC).   Objective: This study aims to investigate the impact of IoT integration on PHF and its subsequent effect on TASC in Bangladesh, framed around two core hypotheses: the significant influence of IoT on PHF and the consequent impact of an IoT-enhanced PHF on TASC.   Methods: Employing a comprehensive methodological framework, the study utilized IBM SPSS for exploratory factor analysis and IBM AMOS for confirmatory factor analysis (CFA) and structural equation modeling (SEM) to assess the relationships between IoT technologies, PHF, and TASC. The analysis was based on data collected from 400 respondents in Bangladesh's agriculture sector.   Results and Conclusion: The findings validate both hypotheses, demonstrating a significant and positive impact of IoT technologies on PHF and, subsequently, on TASC. The results highlight the crucial role of IoT in enhancing supply chain transparency and operational efficiency.   Implications of the Research: This research provides empirical evidence on the effectiveness of IoT technologies in addressing transparency issues within the agri-food supply chain. It underscores the potential of IoT to foster improved decision-making, efficiency, and consumer trust in the agricultural sector, particularly in developing contexts like Bangladesh.   Originality/Value: This study contributes original insights into the role of IoT technologies in enhancing the agri-food supply chain's transparency and efficiency. By focusing on the context of Bangladesh, it offers valuable implications for policymakers, industry practitioners, and researchers, emphasizing the transformative potential of IoT in agricultural supply chain management.

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

The Smart Agriculture System Using IOT and ML

Numerous countries have abundant resources, including land, rivers, groundwater, the environment, with agriculture serving as the primary source of income for many people in country. Nonetheless, resource shortage has grown in recent decades, particularly for groundwater and river water, so remote control smart irrigation system is developed to avoid access use and loss of water in the agriculture. A lack of information about the best use of available resources leads to increased resource consumption and worse crop yields. Choosing crops that are unsuited for the soil or climate reduces both quality and quantity of crop. As a result, system that recommends appropriate crops and fertilizers based on soil NPK values, soil colour, Season, PH, Rainfall, temperature etc has been developed. Fires are one of the most destructive global disasters, thus early detection is necessary to avoid the damages in agriculture. To reduce losses, an automated system capable of early fire detection through alarm systems and prompt extinguishing procedures through the roof structure pipeline in the farm is developed. Another use of that same pipeline is for spraying the pesticides and fertilizers. A potential resolution to address all these challenges involves the development and implementation of an intelligent agricultural system integrating Internet of Things (IOT) and Machine Learning technologies.

Enterprise Architecture for Efficient Integration of IoT Lighting System in Smart City Framework

This research investigates the influence of enterprise architecture design in integrating Internet of Things (IoT)-based street lighting systems into an innovative city framework, emphasizing the importance of efficient lighting infrastructure as a fundamental component of a creative urban ecosystem. With a focus on building an architectural model that supports the integration of IoT street lighting with other components of a smart city, this research addresses the knowledge gap in optimizing enterprise architecture design for integration efficiency, considering technological complexity and interoperability needs between systems. The methodology applied involved an in-depth analysis of the architectural components essential to facilitate the integration of IoT-based street lighting within the more extensive intelligent city infrastructure. The findings of this study show that a well-structured enterprise architecture model can significantly improve operational efficiency, reduce energy consumption, and provide a rich source of data for strategic decision-making regarding the management and maintenance of city infrastructure. Furthermore, these results emphasize the importance of an adaptive and unified architecture design, which not only improves the functionality of the lighting system but also strengthens the synergy between IoT technologies and innovative city operations. These discoveries have a wide range of repercussions and implications, offering new insights into designing enterprise architectures that can support the transition to more efficient and sustainable smart cities, thereby improving the quality of service for citizens.

Enhancing Sustainability: Exploring IoT Integration in Renewable Energy Infrastructure

The management and utilization of sustainable resources have undergone a significant transformation as a result of the integration of Internet of Things (IoT) technology into the renewable energy sector. IoT has simplified the optimization of renewable energy sources, including solar, wind, and hydropower, by leveraging sensor technology, data analytics, and connectivity. This optimization has resulted in major advances in energy production efficiency, equipment maintenance procedures, and the smooth integration of renewable energy into established power grids. Operators can optimize energy output while reducing waste by adjusting operations in real time through continuous monitoring and analysis of data gathered from IoT-enabled sensors. Moreover, by predicting and fixing possible problems before they arise, predictive maintenance algorithms significantly contribute to increased equipment reliability by lowering downtime and operating expenses. Furthermore, IoT systems allow for dynamic modifications to energy flow, guaranteeing the stability and dependability of the integration of renewable energy sources into the current power networks. This research investigates the use of IoT technology in the renewable energy sector in detail, exploring its several uses and assessing how it affects industry standards, sustainability goals, and the overall energy environment.

Design of an Iterative Method for Dynamic Resource Management in 5G Networks with IoT Integration Operations

In the realm of fifth-generation (5G) wireless networks, the escalating demands for high-speed, reliable, and efficient communication are paramount, especially with the widespread deployment of Internet of Things (IoT) devices & scenarios. Despite the advancements in 5G technologies, existing network management strategies often fall short in addressing the dynamic nature of network conditions, the heterogeneity of IoT device requirements, and the need for stringent privacy measures. These limitations underscore the necessity for innovative approaches that can adapt in real-time to varying demands while ensuring optimal network performance and user privacy. This paper introduces a suite of machine learning models designed to enhance the efficiency and reliability of 5G networks, catering specifically to the diverse needs of IoT applications. At the forefront, DynamicSlicerNet, a deep reinforcement learning-based model, dynamically slices 5G network resources tailored to IoT devices' requirements, addressing device mobility, application demands, and network congestion. This model demonstrates a substantial reduction in latency by up to 30% and improvement in reliability by up to 20%, outperforming static resource allocation methods. Further enhancing edge computing capabilities, FedEdgeAI leverages federated learning to train models directly on edge devices, a move that not only slashes latency by minimizing data transmission to centralized servers but also fortifies data privacy. Experimental evaluations highlight FedEdgeAI's efficacy in maintaining model accuracy while halving communication overhead. PredictiveNetCare, employing time-series analysis and anomaly detection, anticipates network failures, facilitating preemptive maintenance strategies. This predictive approach has shown a marked precision over 90% in identifying potential disruptions, significantly reducing maintenance-related downtime by 30% and bolstering network reliability by 15%. OptiAllocRL and AdaptiveQoSDL, both harnessing reinforcement and deep learning techniques, respectively, optimize resource allocation and manage Quality of Service (QoS) parameters adaptively. OptiAllocRL's strategy results in a 40% latency reduction and a 25% throughput increase, while AdaptiveQoSDL minimizes packet loss by up to 50% and enhances end-to-end delay by up to 35%, ensuring high QoS levels under fluctuating network conditions. This comprehensive approach sets a new benchmark for future 5G network management, paving the way for a more connected, efficient, and secure digital world.  

The Use of the Internet of Things in the Distribution Logistics of Consumables

This research focuses on integrating Internet of Things (IoT) technologies in the field of distribution logistics of consumables, specifically on the optimization of dispensing through vending machines in a corporate environment. The primary goal was to identify IoT-based vending machine solutions that increase the work efficiency and cost-effectiveness of consumables distribution. In the first part, this paper focuses on the definition of IoT, and its current trends and development over time. With the help of the authors, we define the key aspects of its use in practice, and we also focus on establishing critical security points. We evaluate its impact on the industry, where there is currently a great pressure for automation and robotization of production, as a result of which the labor market is experiencing a major transformation of job positions from executive to technologically creative ones. Using the performance measurement method, this study established key performance indicators (KPIs), which we then collected and, through thorough research, determined the basis for setting the future state. Using the method of technological innovation, we prepared prototypes of the vending machine, which were then placed at the customer’s location for a 3-month trial period using the method of technological demonstration. During the trial period, key performance indicators were collected again using the performance measurement method, and we then evaluated them using the comparison method. The results demonstrate a significant reduction in the cost of consumables and a simultaneous increase in employee efficiency. These results underline the potential of IoT technologies in the field of distribution logistics for consumables. The research points to the advantages and perspectives of using the Internet of Things in the business environment, especially in the field of distribution logistics.

Unraveling the Dynamics of User Acceptance on the Internet of Things: A Systematic Literature Review on the Theories and Elements of Acceptance and Adoption

The Internet of Things (IoT) connects various devices to the internet, enabling seamless communication and coordination. Its sensor and action capabilities aid in early fire detection and response. This article investigates the theory and elements of Internet of Things (IoT) acceptance and acceptance to see how IoT can assist in monitoring and controlling fire safety management. This study uses a systematic literature review (SLR) methodology to search, identify, appraise, synthesize, analyze, and summarize studies on the Internet of Things (IoT). The study highlights the importance of theoretical frameworks like TPB, TAM, UTAUT, and TOE in understanding the Internet of Things (IoT) acceptance and adoption. TPB focuses on attitudes, subjective standards, and perceived behavioral control, TAM emphasizes ease of use, UTAUT focuses on performance expectancy, effort expectancy, social influence, and facilitating conditions, and TOE examines organizational, technological, and environmental aspects affecting commercial IoT adoption. These frameworks provide actionable insights for successful IoT implementation and integration in individual and organizational settings.

Transforming Poultry Management through the Fusion of Cutting-Edge Technologies: A Robust Approach to Secure Poultry Insight with Integration of IoT and Blockchain

This paper focuses on the integration of Internet of Things (IoT) and blockchain technology modernizing the poultry industry. Production of poultry is highly dependent on environmental parameters like temperature, humidity, air, and lighting to maintain and boost the production of poultry. Monitoring several environmental parameters is crucial for a large poultry farm, especially in the traditional way. Recently, the adoption of IoT and Blockchain has been used for monitoring and maintaining the farm automatically. At first, the temperature is monitored using IoT-based sensors. All the details of the registered users are stored in the blockchain to provide security for their details. Since storing the entire supply chain data on Blockchain is very costly, distributed off-chain file storage IPFS is used. Keywords—poultry, blockchain, IoT , Environmental monitoring

Beyond Automation: Exploring the Synergy of Cloud, AI, Machine Learning, and IoT for Intelligent Systems

In the rapidly evolving landscape of Industry 4.0 and beyond, the amalgamation of Cloud Computing, Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has emerged as a transformative force capable of elevating traditional automation to intelligent systems. This research paper delves into the profound potential of synergizing these advanced technologies, aiming to surpass the limitations of rule-based automation and foster a new era of adaptability, efficiency, and innovation.
 The study begins by articulating the escalating demand for intelligent systems that can dynamically respond to complex and ever-changing environments. The integration of Cloud, AI, ML, and IoT is posited as a solution to the constraints of conventional automation, offering the ability to process vast datasets, make informed decisions, and continuously learn from interactions.
 A comprehensive review of existing approaches and related works forms the foundation of this research. The analysis encompasses diverse applications, ranging from smart manufacturing to healthcare, showcasing the ways in which individual technologies have been leveraged. By scrutinizing these approaches, the study aims to distill the strengths and weaknesses, paving the way for a novel methodology that harnesses their collective power.
 Identifying the limitations of current approaches, such as scalability challenges, real-time processing bottlenecks, and interoperability issues, serves as a critical precursor to the proposed methodology. The paper presents a holistic strategy that intricately weaves together Cloud, AI, ML, and IoT into a unified framework. The architectural design, data flow, and interaction mechanisms are elucidated to demonstrate how this synergy can overcome existing challenges, providing adaptability and innovation in diverse domains.
 Empirical results derived from the implementation of the proposed methodology are presented and rigorously analyzed in the Results and Discussion section. Performance metrics, efficiency gains, and the impact on decision-making processes are thoroughly examined. Real-world case studies exemplify the effectiveness of the integrated approach, offering tangible evidence of its potential applications.
 Concluding remarks encapsulate the key findings, emphasizing the significance of the research in shaping the trajectory of intelligent systems. The broader implications of the proposed methodology across various industries are discussed, and avenues for future work are suggested. As technologies continue to evolve, the proposed methodology serves as a foundation for ongoing exploration, adaptation, and integration with emerging technologies.
 In essence, this research paper offers a detailed exploration into the synergy of Cloud, AI, ML, and IoT, paving the way for a new era of intelligent systems that transcend the limitations of traditional automation, fostering adaptability, efficiency, and innovation in an ever-changing technological landscape.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Assessing Real-Time Health Impacts of outdoor Air Pollution through IoT Integration

Air pollution constitutes a significant global challenge in both public health and the environment, particularly for countries undergoing industrialization and transitioning from low- to middle-income economies. This study aims to investigate the feasibility and effectiveness of a real-time air quality prediction system based on data collected from Internet of Things (IoT) sensors to help people and public institutions track and manage atmospheric pollution. The primary objective of this study was to investigate whether an IoT-based approach can provide accurate and continuous real-time air quality forecasting. The standard dataset provided by the Indian government was analyzed using regression, traditional Long-Short-Term Memory (LTSM), and bidirectional LSTM (BLSTM) models to evaluate their performance on multivariate air quality features. The results show that the proposed BLSTM model outperformed the other models in minimizing RMSE errors and avoiding overfitting.

Sustainable manufacturing through empowering machine tools with IoT innovations: a literature review

In the 21st century, sustainability takes the front seat as one of the main objectives while designing and developing new technologies. Machine tools have significant contributions in fuelling all four industrial revolutions. One of the major stepping stones to the fifth Industrial Revolution is making these machine tools sustainable. In this paper, we review the major developments driven by the integration of internet of things (IoT) in the field of machine tools which contribute to achieving a sustainable future. This integration of IoT in machine tools is also significant in contributing to different sustainable development goals of the United Nations. Through this work, we aim to reach a better understanding of current improvements in machine tool operations and ecosystem achieved through IoT innovations. The future research directions that are opening in this domain and their scope and limitations are also covered.

A sustainable blue economy: blockchain and internet of things integration in tourism villages

Coastal areas have a wealth of marine and river fisheries resources that have great potential to be developed. This potential is utilized by various parties to make utilization efforts including fishing activities; conservation; ports; and marine cultivation; and coastal and aquatic tourism. Problems that currently occur in coastal areas include: (a) illegal fishing, (b) damage to ecosystems in coastal and marine areas, (c) not optimal utilization, (d) unsustainable development, (e) leakage of development due to corruption and injustice. The research methodology used is descriptive and qualitative research with framework analysis, which means framework analysis techniques. This analysis technique is carried out by conceptualizing the system through a structure that describes the connection between one variable and another in a systematic and detailed manner. The findings of this study provide innovative solutions for the application of financial and marketing technology through blockchain and the Internet of Things (IoT) can increase the productivity of fishermen, internal control, financial transactions, and reliable marketing strategies in helping business entities run their business processes. The management of regional potential independently and integrated can be collaborated through community empowerment and the role of village government through village-owned enterprises (BUMD) based on Sharia cooperatives in villages in coastal areas of Indonesia. So that the productivity of fishermen and government agencies in tourist villages can increase.

Smart Shopping Trolley Using Internet of Thing (IOT)

Abstract: Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today's life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds .The integration of Internet of Things (IoT) technology into shopping trolleys presents a significant advancement in the retail industry, revolutionizing the shopping experience for consumers. The proposed SST utilizes a network of sensors and connectivity solutions to enable real-time monitoring, inventory management, and personalized services. Through IoT integration, the SST offers features such as automatic item detection, seamless checkout processes, and personalized recommendations based on individual shopping habits. Moreover, the system empowers retailers with tools for efficient inventory management, reducing stock outs, and analysing consumer behaviour for targeted marketing strategies. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.

Integrating IoT in pediatric dental health: A data-driven approach to early prevention and education

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.