Integration Of Internet Of Things Research Articles

Optimising Business Operations: Integrating Internet of Things (IoT) with Management Information Systems (MIS)

The current landscape of business operations is largely dominated by the integration of the Internet of Things (IoT) with Management Information Systems (MIS), which results in higher efficiency and innovative solutions for common and complex organisational challenges [1] [2]. Owing to this, the following research paper aims to explore the multifaceted benefits and challenges associated with this integration. Alongside this, it seeks to provide insights for not just future research within the field, but also for practitioners and scholars alike. The study begins with a comprehensive introduction to the relevance of IoT and MIS, highlighting their evolution and the complexities that may arise in their integration. After this, it moves further, delving into the technological foundations of IoT, and examining its core components, architecture, and emerging trends. Discussing the theoretical frameworks that surround information systems, it then seeks to illustrate both historical and contemporary integration issues and conducts a deep-seated analysis of current research on IoT- MIS Integration, extrapolating on the advantages, risks, and tangible benefits. The qualitative research is backed up by real-world case studies of well-known companies such as GE and Walmart, which present a strong insight into successful IoT-MIS integration and the challenges encountered during implementation. These examples reveal key takeaways, best practices, and strategic recommendations for businesses looking to harness the full potential of this integration. Lastly, strategic recommendations are presented, including best practices for effective integration and mitigation strategies for associated risks. The paper eventually concludes by outlining future research directions, identifying emerging trends in IoT and MIS integration, and formulating relevant research questions. With the aforementioned, the paper aims to contribute to the overall comprehension of IoT-MIS integration and presents a balanced view of its potential usability in modern-day content. Furthermore, the findings thus attained offer a valuable guide tool for firms willing to navigate the complexities of digital transformation, allowing them to emphasize the quintessential importance of strategic planning, investment in technology, and a commitment to robust security measures. Thus, this study serves as a roadmap for organizations which aspire to thrive in an increasingly interconnected and data-centric business environment.

Revolutionizing Freight Management: A Synergistic Approach Using IoT and Blockchain Technologies

This article explores the transformative potential of integrating Internet of Things (IoT) and blockchain technologies in freight tracking and management. Traditional freight tracking methods often suffer from inaccuracies, delays, and lack of transparency, leading to inefficiencies across the supply chain. Integrating IoT devices, such as GPS trackers, RFID tags, and condition monitoring sensors, with blockchain's secure and immutable ledger system offers a promising solution to these challenges. This article examines the current state of freight tracking, the fundamentals of IoT and blockchain technologies, and their combined application in creating real-time, secure, and transparent freight management systems. It discusses the benefits of this integrated approach, including enhanced visibility, improved efficiency, reduced disputes, and more agile supply chain operations. The article also addresses the challenges and considerations in implementing such systems, including technical hurdles, data privacy concerns, scalability issues, and regulatory requirements. Finally, it explores future prospects and research directions, such as the integration with AI and machine learning and the potential for expanding applications beyond tracking to areas like autonomous logistics. Through a comprehensive analysis of current implementations and future possibilities, this article provides valuable insights into how IoT-blockchain integration is poised to revolutionize the freight industry, offering a roadmap for more efficient, secure, and transparent global supply chains.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Application of Internet of Things (IoT) in Energy Infrastructure: Lessons for the Future of Operations and Maintenance

The application of the Internet of Things (IoT) in energy infrastructure is revolutionizing operations and maintenance practices, driving efficiencies, and enhancing sustainability across the sector. This paper explores the transformative impact of IoT technologies on energy infrastructure, focusing on how they facilitate real-time monitoring, predictive maintenance, and data-driven decision-making. By leveraging IoT devices and systems, energy operators can optimize performance, reduce operational costs, and minimize downtime, ultimately contributing to more resilient and sustainable energy systems. The discussion highlights key IoT applications within energy infrastructure, including smart grids, renewable energy systems, and conventional power plants. Smart sensors and devices enable continuous data collection, providing insights into equipment performance, energy consumption, and environmental conditions. Predictive analytics, powered by IoT data, allows operators to anticipate equipment failures and conduct maintenance proactively, thereby extending asset life and improving reliability. Additionally, the abstract addresses the challenges associated with IoT integration in energy infrastructure, such as cybersecurity risks, data management issues, and the need for interoperability among diverse systems. It emphasizes the importance of establishing robust security protocols to protect sensitive data and ensure the integrity of IoT networks. Furthermore, the role of regulatory frameworks and industry standards in promoting the safe and effective deployment of IoT technologies is discussed. The paper also examines case studies of successful IoT implementations in energy infrastructure, showcasing the lessons learned from these initiatives. These examples illustrate the potential for IoT to enhance operational efficiency, reduce greenhouse gas emissions, and support the transition to cleaner energy sources. In conclusion, the abstract underscores that the adoption of IoT technologies in energy infrastructure is not just a trend but a necessity for the future of operations and maintenance. As the energy sector continues to evolve, embracing IoT will be crucial for achieving sustainability goals and meeting the growing demands for reliable energy supply.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

Fog Computing: A Systematic Review of Architecture, IoT Integration, Algorithms and Research Challenges with Insights into Cloud Computing Integration

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

Design and Build Automatic Fan Control Based Internet of Things

This research aims to create and develop an automatic fan control system based on the Internet of Things (IoT) that can regulate the room temperature well. The NodeMCU ESP8266 serve as a microcontroller for this system. The DHT22 sensor detects temperature, the L298N Driver Motor controls the speed of the 12V DC fan, and the 16x2 LCD displays temperature information in realtime. When the room temperature exceeds the threshold, the fan will automatically turn on and off when the temperature returns to normal. The Blynk app also allows users to monitor and control fans through smartphones. It allows users to manually control the fan and monitor it remotely. The results of the study show that this system works well and improves user comfort by managing the room temperature. The system also offers high flexibility with IoT integration, allowing for further development for wider applications in home automation and energy management.

Literature Mapping on IoT Integration and Smart City: Bibliometric Analysis and Socio-Economic Impact

The integration of the Internet of Things (IoT) in smart cities has become a transformative force, revolutionizing urban development and management. This study provides an in-depth analysis of the socio-economic impacts of IoT integration in smart cities, examining its influence on economic growth, job creation, public service optimization, social inequality, privacy, security, and environmental sustainability. Through a comprehensive literature review and bibliometric analysis, we highlight the opportunities and challenges that IoT technologies present in the urban context. While IoT fosters economic growth and improves service efficiency, concerns related to data privacy, security, and the digital divide pose significant challenges. Additionally, IoT's role in promoting environmental sustainability is emphasized, as smart cities increasingly adopt technology to monitor resources and reduce carbon emissions. This study underscores the importance of addressing these socio-economic factors to ensure the equitable and sustainable development of smart cities, thereby maximizing the benefits of IoT technologies while mitigating potential risks.

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System

ABSTRACT The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient’s large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL‐MDTC and CSO-CLSTM-DD-SHS respectively.

RANCANG BANGUN ALAT PEMOTONG DAUN BAWANG BERBASIS INTERNET OF THINGS (IOT)

The utilization of Internet of Things (IoT) technology in the agriculture sector continues to evolve, including in the automation of agricultural processing tools. This research aims to design and develop an IoT-based scallion cutting tool integrated with the Blynk application, which functions to control and manage the device in real-time. This tool is equipped with key components such as a cutting motor, sensors, and a microcontroller, all controlled via the IoT network. The Blynk application allows users to adjust cutting parameters, such as motor speed, and receive information about the tool's status. Testing has shown that this tool can operate automatically according to settings provided through the Blynk app, producing consistent scallion cuts that meet the requirements. The implementation of this technology is expected to enhance efficiency, reduce manual labor and time, and improve the productivity and quality of scallion cutting. With the integration of IoT and the Blynk application, this scallion cutting tool offers an innovative solution to support the modernization of the agricultural industry, particularly in scallion processin.

A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques

Climate change poses a threat to the global ecosystem. Many countries adopt various approaches, including ecosystem-based adaptation (EbA), to address this problem. However, the assessment of the effectiveness of the EbA interventions is conducted manually, is resource-intensive, and is focused on short-term outputs. These limitations underscore a critical gap: the need for a comprehensive, automated system that enables long-term monitoring and predictive analysis. This study aimed to address this gap by developing an innovative framework that integrates Internet of Things (IoT) devices and machine learning (ML) algorithms to continuously monitor weather, hydrological, environmental, and other variables. We conducted a thorough analysis to design an appropriate framework. In addition, to obtain the relevant information and data, we conducted interviews with the local community and collected secondary data from various sources. The proposed framework consists of five layers: (i) EbA interventions; (ii) IoT-based key performance indicators (KPI) for monitoring and evaluation (M&E); (iii) primary data collection; (iv) data storage; and (v) application. As a proof of concept, we developed a system that supports early flood and drought alerts while simultaneously providing long-term evaluations of the effectiveness of EbA strategies. The developed system consists of IoT devices and a web application integrated with machine learning (ML). We set up and tested the IoT devices before deploying them in the study area. The devices capture data for two primary purposes: (1) short-term: flood detection and alerting, and (2) long-term: drought prediction and evaluation of EbA effectiveness through continuous data analysis. This research represents a significant advancement in the automation and long-term assessment of climate adaptation measures, offering a scalable and effective solution to disaster risk reduction.

Evaluation of the Impact of the Internet of Things on Postal Service Efficiency in Slovakia

This paper investigates the impact of technological advancements, particularly the integration of the Internet of Things (IoT), on the efficiency and productivity of the postal service industry in Slovakia. By employing the Cobb–Douglas production function, we analyze the relationship between key inputs—capital and labor—and output for six major postal service providers: Slovak Post, Packeta, DHL Express, DPD, TNT Express, and GLS. Using data from 2017 to 2021, this study quantifies the elasticities of capital and labor to assess how IoT adoption influences operational performance. The regression analysis reveals significant variations in input contributions across companies, highlighting the diverse effects of IoT integration. Findings suggest that while some companies benefit more from capital investments in technology, others rely heavily on labor efficiency. These insights offer valuable implications for policymakers and industry stakeholders aiming to optimize resource allocation and enhance productivity through technological innovation.

Smart Benefits Distribution : Leveraging IoT and Data Analytics for Streamlined Office Efficiency

This article explores the transformative potential of integrating Internet of Things (IoT) devices and advanced data mining techniques in benefits distribution systems. Faced with challenges such as long wait times, inefficient processes, and resource mismanagement, government agencies and private organizations are turning to these technologies to revolutionize service delivery. The article discusses how IoT-enabled smart kiosks, occupancy sensors, and process automation tools can streamline operations and improve client experiences. It further examines how data mining techniques, including predictive analytics, clustering, and anomaly detection, can enhance decision-making and operational efficiency. Through a case study and analysis of benefits and challenges, the article demonstrates how this integrated approach can lead to reduced wait times, improved staff productivity, enhanced fraud detection, and significant cost savings, ultimately transforming the landscape of benefits distribution to be more efficient, responsive, and beneficiary-centric.

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention.

The challenge of preventing in-patient falls remains one of the most critical concerns in health care. This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS

This systematic review explores advancements and challenges in photovoltaic thermal (PVT) systems, focusing on efficiency improvements, cooling mechanisms, material innovations, Internet of Things (IoT) integration, and cost and environmental considerations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 45 articles were analyzed to provide a comprehensive understanding of current trends and future directions in PVT technology. Findings reveal that cooling mechanisms, particularly liquid-based and nanofluid-based systems, are essential for maintaining high PVT efficiency under diverse environmental conditions. Material advancements, including phase change materials (PCMs) and nanotechnology, have enhanced thermal management and energy storage capabilities, yet their high costs and environmental impacts remain significant barriers to broader adoption. IoT and smart grid integration have transformed PVT system functionality by enabling real-time monitoring, predictive maintenance, and energy flow adjustments within connected energy networks. However, persistent challenges—including high initial investment costs, environmental concerns related to materials, and the need for adaptable designs—highlight areas for future research. Advancements in adaptive materials, sustainable cooling solutions, and digital automation are essential for developing cost-effective, resilient PVT systems that contribute to global renewable energy goals. This review provides a foundation for future research to address the identified challenges and maximize the potential of PVT systems in sustainable energy production.

Exploring The Impact of Internet of Things (Iot) Technologies on Tax Compliance: Opportunities, Challenges and Policy Implications

The proliferation of Internet of Things (IoT) technologies has revolutionized various aspects of modern life, including business operations and government services. This research paper delves into the intersection of IoT technologies and tax compliance, aiming to elucidate the opportunities, challenges, and policy implications arising from their integration. Through a comprehensive review and analysis of existing literature, this paper seeks to provide insights into the transformative potential of IoT in tax compliance, while also highlighting potential hurdles and offering policy recommendations. IoT technologies offer a myriad of opportunities to enhance tax compliance processes for both taxpayers and tax authorities. The ability of IoT devices to collect real-time data and automate various tasks can streamline recordkeeping, improve accuracy in reporting, and enable proactive compliance measures. For taxpayers, IoT-enabled systems can simplify tax documentation, minimize errors, and provide timely reminders for compliance deadlines. Likewise, tax authorities can leverage IoT data for better monitoring, enforcement, and risk assessment, thus enhancing overall tax administration efficiency. However, the integration of IoT in tax compliance also presents notable challenges. Privacy and data security concerns emerge as paramount considerations, given the sensitive nature of financial information involved in taxation. The vast amount of data generated by IoT devices also raises questions regarding data governance, ownership, and liability. Moreover, the digital divide may exacerbate existing disparities in access to IoT technologies, potentially marginalizing certain segments of the population and hindering equitable tax compliance. In light of these opportunities and challenges, policymakers must carefully design regulations and guidelines to harness the benefits of IoT while mitigating associated risks. Clear standards for data protection, encryption, and user consent are essential to safeguard taxpayer privacy and instill trust in IoT-enabled tax compliance systems. Additionally, implementation of measures to bridge the digital divide and ensure equitable access to IoT technologies, thereby promoting inclusivity in tax compliance efforts. Furthermore, tax authorities need to develop robust frameworks for data governance, encompassing data collection, storage, sharing, and usage. Collaboration between public and private sectors is crucial to foster innovation in IoT-based tax compliance solutions while adhering to regulatory requirements and ethical principles. In conclusion, the integration of IoT technologies in tax compliance holds immense potential to revolutionize the way in which taxes administered and enforced. However, realizing this potential necessitates a holistic approach that addresses technical, legal, and socio-economic considerations. By embracing innovation while safeguarding privacy and inclusivity, governments can harness the transformative power of IoT to foster more efficient, transparent, and equitable tax systems.

Internet of Things and the Evolution of Human 2.0: An Analytical Study on Enhancing Human Capabilities

The Internet of Things (IoT) technology has introduced new tools for enhancing human capabilities, promising improved quality of life and professional efficiency. The concept of "Augmented Human" (AH) refers to using technology to enhance and improve natural human abilities, whether physical or cognitive, through devices and technologies that boost sensory perception, physical strength, and cognitive performance. However, the integration of IoT with human augmentation technology has expanded the scope of IoT-related challenges beyond current boundaries. So far, these potential challenges associated with IoT-enabled human augmentation have not investigated. Therefore, this article aims to introduce readers to the concept of human augmentation (AH) and to summarize the key research challenges posed by such systems, thereby encouraging readers' interest in this topic. The article explores emerging IoT applications in human augmentation, along with devices and design principles, connectivity requirements, and security aspects. Keywords: Internet of Things, human capabilities, flexible hybrid electronics.

New paradigms shift in buildings: experimental application of Digital Twin for safety and well-being

Abstract Enhancing durability and service life of buildings and components is pivotal for sustainable development. It constitutes an opportunity to reduce energy consumption, carbon emissions and life cycle impact of buildings in a climate neutral perspective. Issues strongly introduced and set as priorities by EU policies that highlight the urgent need to tackle them also through deeply heritage renovation and digital transformation in the building sector. Digitalization is a cornerstone of this transformation, instrumental in facilitating and enabling it sustainably. The Key Enabling Technologies (KETs) are directly associated with new technological potential and emerging technologies. Digital Twin (DT) approach appears alongside these. Its experimentation and widespread application are gaining prominence in innovating Life Cycle Management (LCM) and sustainability practices of buildings. In this scenario the paper explores the role and potentialities of DT approach presenting the experimental application of the DT4SEM. A digital infrastructure that, by integrating Internet of Things (IoT), synchronizes a physical building with its virtual counterpart. The two realities (physical and virtual) remain interconnected through the mutual exchange of data, both in real-time and asynchronously enabling proactive monitoring and analysis of seismic behaviour. The experimental setup involves simultaneously Big Data analytics, simulation tools and deploying sensors within the sample building. Data is then fed into the virtual DT model, allowing for continuous comparison and analysis to detect anomalies and predict potential risks. This approach facilitates enhanced decision-making, performance optimization and sustainability improvements across the building’s lifecycle: a new vision for built environment management that evolves from a process resulting in a sequence of operative phases into a complex digital infrastructure. The design of the DT4SEM and the current application for seismic monitoring in buildings, results from a collaborative effort involving the academic spinoff BIG srl, the startup Sysdev, the company Berna Engineering srl, and ACCA Software spa.

Rancang Bangun Alat Otomatisasi Pengomposan dari Sampah Organik Berbasis Internet of Things

Organic waste management in Indonesia is currently suboptimal. Approximately 33.7% of waste is not properly managed, and 56% of the total waste consists of organic waste, most of which comes from households. Composting is an effective method for managing organic waste. However, many people are still reluctant to convert organic waste into compost due to the lengthy process, which typically takes 2 to 3 months. This study proposes an IoT-based composting automation system with the goal of accelerating the composting process without the use of additional microorganisms, producing both dry and liquid compost. The system method involves integrating Internet of Things (IoT) technology with sensors: DHT22 for temperature monitoring, soil moisture sensors for measuring soil moisture, MQ-4 sensors for detecting methane gas, pH sensors for measuring soil acidity, and water level sensors for detecting the height of liquid compost. Data collected from these sensors is used to automatically control the organic waste mixing process until all composting specifications are met. The results indicate that this system effectively accelerates the composting process to 24 days for dry compost and 19 days for liquid compost without the need for additional microorganisms, while producing compost of high quality according to the SNI-19-7030-2004 specifications.

Efficient wastewater management for smart cities using Internet of Things (IoT) and blockchain technology

Access to clean and sufficient water is considered a basic right for humans and other living organisms sharing the environment. However, factors like rapid urban growth, climate change, population increase, and the overproduction of industrial and agricultural goods have contributed to water shortages and quality problems in many communities worldwide. Digital technologies, such as blockchain, machine learning, and the Internet of Things (IoT), offer opportunities to develop new solutions for creating smart and sustainable environments. In this paper, we introduce a unified water management system (IB-WMS) using IoT and blockchain technologies to monitor water quality, level, temperature, pressure, and consumption. This system is designed to be reliable, scalable, and transparent due to the integration of blockchain and IoT. Simulations indicate that the IB-WMS system achieves 89% efficiency, a 90% wastewater reuse rate, and a 95% water recycling rate.