Internet Of Things Sensors Research Articles

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important. This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.

Flood Prediction System Using IOT & Artificial Neural Network

Floods pose significant challenges as one of nature's most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface.

The role of technology in supply chain risk management: Innovations and challenges in logistics

The review delves into the pivotal role of technology in revolutionizing supply chain risk management practices within the logistics sector. The paper explores the myriad of risks faced by modern supply chains, ranging from natural disasters and geopolitical tensions to cyber threats and disruptions in global trade patterns. It investigates how technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics are reshaping traditional risk management approaches by providing real-time visibility, data-driven insights, and proactive mitigation strategies. Through a comprehensive analysis, the study examines the transformative potential of these technologies in enhancing supply chain resilience, agility, and responsiveness to unforeseen disruptions. It highlights the benefits of leveraging blockchain technology for secure and transparent supply chain transactions, IoT sensors for real-time monitoring of goods in transit, AI algorithms for predictive risk modeling, and predictive analytics for identifying and mitigating potential disruptions before they escalate. Furthermore, the paper delves into the challenges and complexities associated with adopting and integrating these technologies into existing supply chain processes, including interoperability issues, data privacy concerns, and the need for specialized expertise. By providing practical insights, case studies, and best practices, the study aims to empower logistics professionals, policymakers, and industry stakeholders to harness the full potential of technology-driven solutions in managing supply chain risks effectively and ensuring business continuity in an increasingly uncertain and volatile global environment.

A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications.

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

Autonomous Healthcare Systems: Deep Learning-Based IoT Solutions for Continuous Monitoring and Adaptive Treatment

Autonomous healthcare systems are a big change in the way medicine is done. They use deep learning algorithms and Internet of Things (IoT) devices to keep an eye on patients all the time and change their treatment as needed. This new way of doing things could change the way patients are cared for by giving real-time information and personalized treatments. With the help of deep learning, these systems can look at huge amounts of data produced by IoT sensors, like those in medical implants and smart tech, to spot small changes in health and spot problems before they get worse.Autonomous healthcare systems are based on their ability to constantly gather and analyze data from a variety of sources, such as vital signs, biological markers, and patient-reported complaints. Deep learning algorithms are very important to this process because they can find complicated patterns and connections in the data. These algorithms can get useful information from raw sensor data by using methods like CNN, MobileNet, and InceptionV3. This algorithm lets healthcare professionals move quickly and proactively.Also, independent healthcare systems are made to change based on the wants and interests of each patient by using personalized treatment plans. These systems can improve results and patient happiness by constantly checking how patients respond to actions and making changes to treatment plans on the fly. Adding IoT devices also makes it easier for patients and healthcare workers to talk to each other, allowing for distant discussions and quick solutions. The automated healthcare systems are a revolutionary way to provide medical care. They use deep learning-based IoT solutions to keep an eye on patients all the time and adjust their treatment as needed. These systems might be able to improve patient results, lower healthcare costs, and raise general quality of life by using the power of data-driven insights and individual actions

KRISHI MITRA – A SMART FARMING SYSTEM THROUGH AI

The concept of smart agriculture is still in its infancy, since Internet of Things (IOT) sensors may gather data about agricultural fields and behave accordingly based on human input. The development of a smart agriculture system that takes advantage of cutting edge technologies like Arduino, IOT, and wireless sensor networks is suggested in this paper. The goal of the article is to employ automation and emerging technologies, such as IOT and smart agriculture. One of the main ways to increase the production of productive crops is to monitor the surrounding conditions. This paper's highlight is the development of a system that uses sensors utilizing an Arduino board to monitor temperature, humidity, moisture, and even the movement of animals that could kill crops in agricultural fields. If there is any disparity, the system will be notified.

To enhance customer (or patient) experience based on IoT analytical study through technology (IT) transformation for E-healthcare

Over the past few decades, there were drastic changes in information technology across all the domains except healthcare. A new era of Information Communication Technology (ICT) has grown to higher potential by implementing eHealth systems to improve healthcare quality and simplify the problems and challenges health professionals and customer/patient have faced. The purpose of this qualitative, multiple case study was to explore information technology transformation for eHealth, which provides a broad scope to pursue research on how Information Technology (IT) strategy and implementation in eHealth using the latest technologies strategies to implement advanced eHealth systems. These strategies and implementation can provide cost-efficient and effective services to customer/patient instantly. E-Health combines medical information, public health, healthcare services, data delivery, and data security strategies through the internet using the latest web/mobile-based and big data technologies by providing simplified and instant access to customer/patient information. Due to the continuous and advanced technology transformation phase, healthcare industries are still migrating their legacy and outdated eHealth systems and applications to provide a better scope of experiences to their customer/patient. In this research, achieving effective and efficient experience is generated through IoT Sensors as it gets generalized into several different areas of classifications specific to the latest information technology trends, such as the role of smart portal technologies, cloud migration, patient data privacy and security, electronic health records (EHR), cloud infrastructures, internet of things (IoT), big data, and deep learning strategies, eHealth usability, and accessibility.

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors.

Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review.

The Internet of Things (IoT) is a critical component of smart cities and a key contributor to the achievement of the United Nations Sustainable Development Goal (UNSDG) 11: Sustainable Cities and Communities. The IoT is an infrastructure that enables devices to communicate with each other over the Internet, providing critical components for smart cities, such as data collection, generation, processing, analysis, and application handling. IoT-based applications can promote sustainable urban development. Many studies demonstrate how the IoT can improve smart cities' sustainable development. This systematic literature review provides valuable insights into the utilization of the IoT in the context of smart cities, with a particular focus on its implications for sustainable urban development. Based on an analysis of 73 publications, we discuss the role of IoT in the sustainable development of smart cities, focusing on smart communities, smart transportation, disaster management, privacy and security, and emerging applications. In each domain, we have detailed the attributes of IoT sensors. In addition, we have examined various communication technologies and protocols suitable for transmitting sensor-generated data. We have also presented the methods for analyzing and integrating these data within the IoT application layer. Finally, we identify research gaps in the literature, highlighting areas that require further investigation.

THE ROLE OF TECHNOLOGY IN SUPPLY CHAIN RISK MANAGEMENT: INNOVATIONS AND CHALLENGES IN LOGISTICS

The review delves into the pivotal role of technology in revolutionizing supply chain risk management practices within the logistics sector. The paper explores the myriad of risks faced by modern supply chains, ranging from natural disasters and geopolitical tensions to cyber threats and disruptions in global trade patterns. It investigates how technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and predictive analytics are reshaping traditional risk management approaches by providing real-time visibility, data-driven insights, and proactive mitigation strategies. Through a comprehensive analysis, the study examines the transformative potential of these technologies in enhancing supply chain resilience, agility, and responsiveness to unforeseen disruptions. It highlights the benefits of leveraging blockchain technology for secure and transparent supply chain transactions, IoT sensors for real-time monitoring of goods in transit, AI algorithms for predictive risk modeling, and predictive analytics for identifying and mitigating potential disruptions before they escalate. Furthermore, the paper delves into the challenges and complexities associated with adopting and integrating these technologies into existing supply chain processes, including interoperability issues, data privacy concerns, and the need for specialized expertise. By providing practical insights, case studies, and best practices, the study aims to empower logistics professionals, policymakers, and industry stakeholders to harness the full potential of technology-driven solutions in managing supply chain risks effectively and ensuring business continuity in an increasingly uncertain and volatile global environment.
 Keywords: Technology, Supply Chain, Risk Management, Innovations, Challenges, Logistics.

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

AbstractWith the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

ADVANCING FOOD SAFETY THROUGH IOT: REAL-TIME MONITORING AND CONTROL SYSTEMS

This paper proposes the development of an Internet of Things (IoT)-based real-time monitoring and control system for food safety management. By leveraging IoT sensors and devices throughout the food production and distribution process, this system aims to continuously monitor food safety parameters, providing immediate data to prevent contamination and ensure compliance with safety standards. In response to the critical need for enhanced food safety measures, this concept paper proposes the implementation of IoT (Internet of Things) technology to advance food safety through real-time monitoring and control systems. The global food industry faces significant challenges in ensuring food safety throughout the supply chain, including inefficiencies, delays in response to potential hazards, and increased risks of contamination. Traditional methods of monitoring and controlling food safety often lack real-time capabilities and are prone to human error. The proposed solution focuses on introducing an IoT-based system for real-time monitoring of critical food safety parameters such as temperature, humidity, and the presence of pathogens. This system will leverage a combination of sensor technologies, data analytics, and communication protocols to continuously monitor environmental conditions and potential hazards throughout the food supply chain. Key components of the proposed system include IoT sensors and devices, a centralized monitoring platform, data transmission infrastructure, and integration with existing food safety management systems. Advanced data analytics techniques, including machine learning algorithms, will be employed to analyze collected data, identify patterns, trends, and anomalies, and develop predictive models to anticipate potential hazards. The implementation strategy outlined in this concept paper includes comprehensive assessment and planning, deployment of IoT sensors, establishment of data collection and transmission infrastructure, data analysis and insights, integration with existing systems, and training and capacity building. By following this strategy, stakeholders can proactively manage food safety risks and ensure the integrity and safety of the food supply chain. Advancing food safety through IoT-based real-time monitoring and control systems holds great promise in revolutionizing the way food safety is managed, safeguarding public health, enhancing consumer trust, and ensuring a safer and more reliable food supply for consumers worldwide.
 Keywords: Food Safety, Real Time, Monitoring, IoT.

ADVANCING FOOD SAFETY THROUGH IOT: REAL-TIME MONITORING AND CONTROL SYSTEMS

This paper proposes the development of an Internet of Things (IoT)-based real-time monitoring and control system for food safety management. By leveraging IoT sensors and devices throughout the food production and distribution process, this system aims to continuously monitor food safety parameters, providing immediate data to prevent contamination and ensure compliance with safety standards. In response to the critical need for enhanced food safety measures, this concept paper proposes the implementation of IoT (Internet of Things) technology to advance food safety through real-time monitoring and control systems. The global food industry faces significant challenges in ensuring food safety throughout the supply chain, including inefficiencies, delays in response to potential hazards, and increased risks of contamination. Traditional methods of monitoring and controlling food safety often lack real-time capabilities and are prone to human error. The proposed solution focuses on introducing an IoT-based system for real-time monitoring of critical food safety parameters such as temperature, humidity, and the presence of pathogens. This system will leverage a combination of sensor technologies, data analytics, and communication protocols to continuously monitor environmental conditions and potential hazards throughout the food supply chain. Key components of the proposed system include IoT sensors and devices, a centralized monitoring platform, data transmission infrastructure, and integration with existing food safety management systems. Advanced data analytics techniques, including machine learning algorithms, will be employed to analyze collected data, identify patterns, trends, and anomalies, and develop predictive models to anticipate potential hazards. The implementation strategy outlined in this concept paper includes comprehensive assessment and planning, deployment of IoT sensors, establishment of data collection and transmission infrastructure, data analysis and insights, integration with existing systems, and training and capacity building. By following this strategy, stakeholders can proactively manage food safety risks and ensure the integrity and safety of the food supply chain. Advancing food safety through IoT-based real-time monitoring and control systems holds great promise in revolutionizing the way food safety is managed, safeguarding public health, enhancing consumer trust, and ensuring a safer and more reliable food supply for consumers worldwide.
 Keywords: Food Safety, Monitoring, Real Time, Authority, Nigeria.

Anomaly detection in IoT-based healthcare: machine learning for enhanced security

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

Predicting Desired Fertigation for Rose Using Internet of Things Sensors and Time-Series Model

Greenhouse provides opportunities to have big yield effectively and efficiently. However, many resources are required, such as fertigation, a kind of solution of nutrient. Resources supply is essential to cultivate crops. Inadequate supply will hinder plant growth whereas the surplus results in waste. In this paper, we are especially interested in the fertigation supply. Further, excess fertigation leads to drainage which is difficult to purify and threatens the environment. To address this challenge, we aim to predict the desired amount of fertigation. To achieve this objective, we first establish a prototype to record the climate conditions inside a rose greenhouse using Internet of Things sensors. Simultaneously, the desired fertigation amount is obtained with the help of weight scale and historical data of fertigation supply and drainage. Second, a method is proposed to predict the desired fertigation by taking the sensors’ data as input, with a time-series model. Extensive experimental results suggest the potential of our objective and method. To be specific, our method achieves an average MAE 0.032 in the validation datasets.

Low-Cost Internet of Things Solution for Building Information Modeling Level 3B—Monitoring, Analysis and Management

The integration of the Internet of Things (IoT) and Building Information Modeling (BIM) is progressing. The use of microcontrollers and sensors in buildings is described as a level 3B maturity in the use of BIM. Design companies, contractors and building operators can use IoT solutions to monitor, analyze or manage processes. As a rule, solutions based on original Arduino boards are quite an expensive investment. The aim of this research was to find a low-cost IoT solution for monitoring, analysis and management, and integrate it with a BIM model. In the present study, an inexpensive NodeMCU microcontroller and a temperature and pressure sensor were used to study the thermal comfort of users in a single-family home. During the summer season, analysis of the monitored temperature can contribute to installation (HVAC) or retrofit work (for energy efficiency). The article presents a low-cost solution for studying the thermal comfort of users using a digital twin built-in BIM. Data obtained from sensors can support both the design and management processes. The main contribution of the article enables the design, construction and use of low-cost circuits (15.57 USD) even in small developments (single-family houses, semi-detached houses, terraced houses, atrium buildings). Combining IoT sensor telemetry with BIM (maturity level 3C) is a challenge that organizations will face in the near future.

Optimization of Stable Energy PV Systems Using the Internet of Things (IoT)

The modern power grid faces rapid growth in load demand due to industrialization, leading to an unregulated environment and increasing adoption of renewable energy sources, which presents technical challenges, particularly in terms of stability. Hydrogen conversion technology revolutionizes clean electricity storage with renewable energy, and solar hydrogen is now available in autonomous solar systems. The efficiency of solar photovoltaic systems is closely related to using digital electronic maximum peak power tracking (MPPT) technology. The Internet of Things (IoT) is crucial for performance monitoring and real-time control of PV systems, enhancing the understanding of real-time operating parameters. IoT and wireless sensor networks for distributed solar energy devices and joint building design are essential for developing the photovoltaic construction industry. In this paper, the monitoring system that has been proposed offers a potentially effective solution for the intelligent remote and real-time monitoring of solar photovoltaic (PV) systems. It demonstrated a high level of accuracy, reaching 98.49%, and can transmit graphical representations to a smartphone application within a time frame of 52.34 seconds. Consequently, the battery's longevity was extended, energy consumption was diminished, and the quality of service (QoS) for real-time applications inside the Internet of Things (IoT) was enhanced.