IoT Technology Research Articles

Cybersecurity of Internet of Things in the health sector: Understanding the applicable legal framework

Although the digitalisation of healthcare is an ongoing process that dates back to more than two decades ago, it has gained more momentum with the COVID-19 pandemic. Recent developments in this sector include the adoption of wearable devices based on Internet of Things technology. The possibility of connecting devices that can work outside the physical boundaries of a hospital and follow patients at home, i.e. during their day-to-day life, has several obvious advantages. However, the digitalisation of the health sector through increased adoption of connected devices does not exclude vulnerabilities, particularly risks concerning the protection of patients’ data and the security of networks and information systems. Connected devices can gather, process, and store personal patient health data. Failure to safeguard the integrity and security of these data may affect the patients’ identity and finances and put their lives at risk. The presence of an IoT device in a healthcare setting may affect and reduce the level of network security of the overall system as it may provide an access point for an unlawful hacking attack. Although IoT technologies in the health sector are becoming increasingly pervasive, the European legal framework applicable to them is not clearly defined. This is extremely relevant in the case of cybersecurity, where the legal point of reference is the General Data Protection Regulation, addressing the measures and requirements applicable in case of data breaches, and the Medical Device Regulation, providing provisions focused on the security of data relevant to IoT defined as medical devices. The most recent interventions that address health data processing and cybersecurity are the proposed Cyber Resilience Act and the Health Data Space Regulation. The two acts provide measures and requirements applicable to IoT from two perspectives. Yet, they add complexities and some inconsistencies that may hamper the effectiveness of the overall cybersecurity framework.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

A cloud-based architecture for explainable Big Data analytics using self-structuring Artificial Intelligence

Big Data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big Data into this modern context. With these new manifestations of Big Data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big Data. In this paper we propose a cloud-based architecture for explainable Big Data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big Data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture.

Energy yield database management system based on solar photovoltaic cell using internet of things technology

This study investigates the performance of solar cell electric power generation, focusing on data collected from Prince of Songkla University, Surat Thani Campus, analyzing the Mean Squared Error (MSE) for each phase consisting of Phase A: 0.00133, Phase B: 0.00137, and Phase C: 0.00216. Through linear regression analysis, we establish the correlation between current and electrical energy in each phase, facilitating the prediction of photovoltaic power generation system performance and timely maintenance alerts. This predictive analysis aids in mitigating greenhouse gas emissions, preventing potential damages from short circuits, and ensuring system safety during installation. Our findings, which are accessible through a user-friendly dashboard interface, address a gap in previous studies by providing empirical data specifically from Prince of Songkla University, Surat Thani Campus, and by integrating IoT technology for real-time monitoring and analysis, underscoring the novelty of our approach.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries

This research article explores recent advances in IoT technology and its huge impact on various sectors. This research method uses a qualitative research approach involving in-depth interviews and thematic analysis. This research explores key IoT innovations such as wearable devices, smart medical devices, predictive maintenance systems, and IoT-based transportation solutions. The research findings highlight how these innovations are driving digital transformation across industries, leading to improved healthcare delivery, increased manufacturing efficiency, optimized transportation operations, sustainable agricultural practices, and personalized retail experiences. This study also aligns these empirical findings with theoretical frameworks regarding IoT, digital transformation, and industry-specific applications, emphasizing the strategic importance of leveraging IoT as a catalyst for innovation, competitiveness, and value creation. This research provides valuable insights for businesses, policymakers and researchers looking to leverage IoT technology to drive digital transformation and achieve sustainable growth across industries.

QoIoTX: A QoE-Aware IoT Service Discovery Framework

Quality of Experience QoE is a research area that evaluates performance based on subjective and objective metrics for the use of a product or a service. Currently, the substantial majority of QoE works mainly focus on multimedia services. However, the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE evaluation. With the evolution of new IoT technologies such as machine to machine communication and artificial intelligence, utilizing additional evaluation factors alongside the traditional ones is a crucial demand. This paper proposes QoIoTX, a QoE-based service discovery framework that can be utilized to evaluate the QoE of IoT service/application on the fly considering multiple parameters about data, network, users, and services. It extends the conventional QoE and goes beyond its legacy evaluation phases. Also, provides a novel alternative approach to evaluate the IoT services subjectively rather than using the traditional approaches that do not cope with the rapidly evolving services. Although each of the identified QoE factors is measured on a different scale and may involve different units of measurement, the proposed work can effectively evaluate the QoE through providing a single value that represents the acceptability degree of each service.

REAL TIME AIR QUALITY PREDICTION AND ANOMALY DETECTION

When it comes to addressing the growing concerns about environmental health and safety, real-time air quality forecast and anomaly detection are essential. This study describes a robust system that effectively tracks, analyses, and forecasts air quality parameters in real-time using a combination of cutting-edge sensors, sophisticated data analytics, and machine learning algorithms. Utilising a wide range of environmental sensors, the system continuously monitors pollutants such as carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter (PM2.5 and PM10), and ozone. Modern data analytics techniques are then used to process and analyse the gathered data. A machine learning platform at the centre of the system makes use of time-series forecasting methods to forecast air quality conditions. Additionally, it uses anomaly detection algorithms to identify anomalous trends or abrupt spikes in pollutant concentrations, which may indicate operational issues or environmental dangers. Environmental agencies and public health experts can take timely action and make informed judgements thanks to these skills. In addition, the system has an easy-to-use interface that provides both specialists and non-experts with real-time updates and visual representations of air quality statistics. Using IoT technology increases the system's expandability to larger regions and guarantees effective data flow. By providing consistent and accurate air quality information, this technique not only supports urgent mitigation measures but also facilitates strategic environmental and public health planning. Predictive analytics and proactive surveillance are intended to improve environmental governance and public health. Keywords—LSTM, Linear Regression and Minmax scalar.

ASSESSING THE IMPACT OF AI INTEGRATION ON ADVANCING CIRCULAR PRACTICES IN CONSTRUCTION

This study provides a thorough examination of the potential and problems associated with integrating artificial intelligence (AI) into the circular economy (CE) framework within Sri Lanka’s construction industry. The study uses approach that combines primary data obtained through a questionnaire survey involving several stakeholders with secondary data analysis from academic sources. The data were interpreted using descriptive and statistical analysis, such as Kendall’s Tau correlation and Pearson’s correlation. There is an optimistic view about AI’s potential advantages, including resource and energy conservation, even if the technology is still in its early integration phases. Nevertheless, there are still significant barriers to adoption, such as a lack of knowledge and reluctance to change. The study offers a conceptual framework for combining AI with CE principles, including IoT, computer vision, and machine learning technologies to enhance the Reduce, Reuse, and Recycle (3R) CE principles. This framework supports cooperative efforts, skill development, and policy development to support sustainable building practices in Sri Lanka.

A Water Resource Consumption Control Method for Energy Development Bases Based on IoT Technology and Energy–Water Correlation Law

Abstract To achieve the rational development and utilization of water resources in energy development bases and sustainable economic and social development, research is conducted on the control method of water resource consumption in energy development bases based on the law of energy–water correlation. Using Internet of Things technology to collect water resource consumption data from energy development bases, based on the results of data collection, taking Shanxi Coal Development Base as the research area, based on the objective relationship between energy and water, a multiregion input–output model and linear programming model are established, with objective functions and constraints set to achieve water resource consumption control in energy development bases. Under the dual constraint scenario of energy con-servation and water conservation, the optimization effect of the industrial structure of the water resource carrying capacity level in the research area is explored. The outcomes show that the water resource in the research area can bear 1,949.58 billion Yuan for 22.0336 million people. The water resources holding capacity is at the general level. Based on the limitations of energy consumption, raising the share of the mining sector and decreasing the share of the industrial and commercial service sectors can minimize the overall consumption of energy and water resources while ensuring the growth of the local economy. Reducing the extent of agricultural expansion supports both water conservation and regional economic growth because water resource consumption is constrained. A clear difference in the production and utilization effect of energy resource per unit will result from the direction difference in structural adjustment of the mining industry, which is based on the dual restrictions of energy–water resource.

Development and key technologies for architectural design and technical solutions of CIM platform: A review

The integration of building information modeling (BIM), geographic information systems (GIS), 5G, IOT, cloud computing, big data, artificial intelligence, and other technologies in urban management has become a new and rapidly developing trend in recent years. However, most of the existing research on the construction of city information modeling (CIM) platforms based on the integration of BIM and GIS technologies focuses on integration technologies, and there is a lack of theories, methods, and standards for further data analysis and mathematical modeling. This paper analyzes and summarizes the actual situation of the application of recent research papers in the BIM field and the application of CIM platform pilot projects, reviews the application of BIM and GIS technologies in smart cities, and discusses future trends. For example, the construction of a basic platform for smart cities requires the establishment of a three-dimensional visual CIM platform with functions such as the planning review, architectural design plan review, construction drawing review, completion acceptance filing. A smart city is built by analyzing the cyber security risks faced during the construction. The technical, management, and operational measures of the security system provide guidance and reference for the secure construction of smart cities. This involves promoting the reform of the construction work system driven by data resources obtained from government information, and the establishment a new digital government construction management system through the “separation of management and transportation.” These analyses highlight the evolution of the CIM platform construction. The results indicate that the use of key technology integration in the construction of a CIM platform requires a system theory that goes beyond integration technology and mathematical modeling methods for a detailed application. It involves analyzing the deep integration of key technologies and business processes in the CIM platform.

Smart Vehicle Parking Facility and Monitoring System Using ML

In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System designed to address the challenges of parking management without the need for sensors or IoT technology. This system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System implemented as a web application using HTML, CSS, and JavaScript, without the need for sensors or IoT technology. This web-based system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.

Developing a Nutritional Assessment Tool for Toddlers Using Anthropometry and IoT Technology

Data from the Indonesian Toddler Nutrition Status Survey (SSGBI) in 2021, the prevalence of stunting in Indonesia is still quite high, at 24.4%, which is equivalent to 5.33 million toddlers. This figure is still above the standard tolerated by WHO, which is below 20%. Therefore, efforts are needed to accelerate stunting reduction so that the prevalence of stunting in toddlers decreases to 19.4% by 2024. This research aims to develop a tool for measuring toddlers' height and weight to assess their nutritional status promptly, aiming to preemptively address any nutritional abnormalities and prevent exacerbation. Anthropometry serves as the primary method for assessing toddlers' nutritional status in this study. The tool's design incorporates the ESP32 as the main control unit, the HC-SR04 sensor for height measurements, and the HX711 module and loadcell sensor as weight sensors. Data from the sensor are transmitted from the ESP32 master to the ESP32 slave for processing, culminating in a nutritional status assessment. Notably, the tool boasts a minimal error rate of 0.18% for weight measurement with 99.82% accuracy and a 2.66% error rate for height measurement with 97.34% accuracy. Furthermore, the tool's integration with IoT technology offers additional advantages. It facilitates real-time data transmission and analysis, enabling healthcare professionals to promptly identify any nutritional issues in toddlers. This, in turn, allows for timely intervention and appropriate management strategies to prevent the development or exacerbation of stunting. Overall, the benefits of this research for the Anthropometry Stunting Monitor based on IoT are manifold. It enhances accuracy and efficiency in measuring toddlers' height and weight, enables early detection of stunting, and facilitates timely intervention to address nutritional abnormalities. This holds significant promise for improving pediatric healthcare outcomes and reducing the prevalence of stunting among children.

Smart Agriculture Management System: Integration of IoT Technology and Mobile Application for Enhanced Farming Practices

Abstract: This paper presents a collaborative effort between Computer Science and Electronics and Communication Engineering students aimed at developing a comprehensive smart agriculture management system. Traditional farming practices face challenges related to climate variability and resource optimization, highlighting the need for innovative solutions. Leveraging IoT technology and mobile application development, this project proposes a holistic approach to address these challenges. The system integrates wireless sensors, microcontrollers, cloud computing, and a user-friendly mobile application to enable real-time monitoring, control, and optimization of agricultural processes. The paper discusses the architecture, design, implementation, and performance evaluation of the Smart Agriculture Management System (SAMS), emphasizing its potential to revolutionize farming practices and enhance productivity.

Integrated Fault Detection System for Underground Lines Using ESP32 and LORA Communication Using IOT

Abstract: This paper introduces an advanced fault detection system tailored for underground line infrastructure, featuring the integration of ESP32 microcontroller technology and LORA communication protocols. The primary objective of this system is to enhance the reliability and efficiency of fault detection processes, crucial for maintaining the integrity of underground lines. At the core of this system lies the ESP32 microcontroller, serving as the central processing unit responsible for coordinating various functionalities. The detection mechanism incorporates six toggle switches strategically positioned along the underground lines, each assigned to monitor a specific location segment. In the event of a fault occurrence, the corresponding toggle switch is triggered, promptly initiating the fault detection process. Upon fault detection, the system seamlessly orchestrates the transmission of GPS location data to a LORA communication device. This data transmission mechanism ensures rapid dissemination of critical information regarding the fault's precise location. Additionally, an LED indicator is employed to provide immediate visual feedback, enabling quick identification of fault occurrences. Moreover, a LCD display unit offers realtime updates on the system's operational status, facilitating efficient monitoring and management. On the receiving end, the LORA communication device securely receives the transmitted data and forwards it to the cloud infrastructure using IOT technology. This cloud integration enables seamless data aggregation, storage, and analysis, empowering stakeholders with valuable insights into underground line health and performance.

Emergency Communication System for Hills and Forest Region Using IOT

This project presents an innovative approach to establishing robust communication systems in remote areas devoid of traditional network infrastructure. By harnessing the power of IoT and LoRa technology, the project proposes a Long Distance Communication System that not only facilitates connectivity but also supports critical emergency evacuation procedures. The cornerstone of this system is the LoRa Mesh Library, which enables a decentralized network topology, ensuring seamless communication even in the most challenging environments via ad hoc network. The project further explores QoS routing and traffic scheduling within 802.11 wireless mesh networks to guarantee consistent and quality communication links over long distances. Additionally, it integrates Secure IT, a security framework utilizing Firebase, Google Map, and Node.js, to provide real-time location-based alerts, enhancing the safety of individuals in emergency situations.Thisabstractencapsulatestheproject’smultifacetedapproach to overcoming the limitations of current communication infrastructures, offering a scalable, secure, and efficient solution that has the potential to revolutionize connectivity in isolated regions worldwide.

An IoT- based Measure Guage for Surveil of Water Quality by Parametric Quantity

Abstract: The document highlights the pressing need to address water-related challenges through innovative approaches, particularly by integrating IoT technology for water quality monitoring. Emphasizing the critical importance of safe drinking water, it proposes an IoT-based framework for continuous water quality assessment. This framework utilizes specific sensors to measure key parameters like turbidity, pH, and temperature, with microcontrollers processing the data for transmission to an Arduino Uno board. Real-time communication enables timely sharing of crucial information with stakeholders, facilitating proactive water resource management. The system determines water drinkability based on compiled data and provides a visual representation for easy interpretation by non-experts. Overall, the integration of IoT offers a comprehensive solution, ensuring safe water consumption and community engagement in water quality management, essential for securing sustainable water resources.

Smart Trashbin using Machine learning and Internet of Things

Health and hygiene are vital areas in the ever-expanding community that cannot be disregarded or compromised for the welfare of human communities. This idea uses the Internet of Things to separate biodegradable and non-biodegradable waste into smart dustbins. People can no longer separate their waste into biodegradable and non-biodegradable categories because of their hectic work schedules. By automatically identifying waste as biodegradable or non biodegradable, smart dustbins with image recognition technology improve waste management by encouraging effective waste segregation and lessening the load on landfills. The goal of an IOT and machine learning-based waste management system for a green, smart society is to use the newest IOT technologies to improve the efficiency of trash management from every apartment in the community.

IoT-based Pregnant Mother Contraction Monitoring System Design

Pregnancy is an important stage in a woman's life that requires special monitoring and care to ensure the well-being of both mother and fetus. The presence of uterine contractions is an im-portant indicator of imminent labour, and prompt monitoring is essential to spot difficulties and ensure a safe delivery. This study aims to track contractions in pregnant women to improve pre-natal and labour care. Real-time monitoring, recording and analysis of uterine contractions. This research is a type of RD research with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation. The result of this tool is that it can precisely track contraction activity and send information to the IoT network. Through an easy-to-use mobile app, this in-formation can be accessed by medical staff caring for pregnant women and pregnant women themselves. To provide continuous monitoring, provide early notification of alarming changes, and facilitate rapid medical response, this monitoring system utilizes IoT technology. This can ease the burden on medical institutions, lower the possibility of difficulties during labour, and provide a sense of security for pregnant women. As an innovative and effective tool, the Inter-net of Things-based Maternal Contraction Monitoring System is anticipated to improve prenatal and delivery healthcare, reduce maternal and infant mortality, and monitor contractions in preg-nant women. The healthcare of pregnant women around the world could be significantly im-proved with the successful implementation of this approach.

Smart Automated Liquid Sprinkler Robot Integrating IoT and Deep Learning for Precision Crop Management

Abstract: The Smart Automated Liquid Sprinkler (SALS) is a groundbreaking solution aimed at revolutionizing traditional watering systems through the integration of IoT technology. Its development addresses pressing challenges such as water scarcity and labor-intensive maintenance in outdoor environments. SALS combines advanced sensors to monitor humidity and weather conditions in real-time, ensuring precise watering tailored to the specific needs of plants while minimizing manual intervention. Furthermore, it incorporates obstacle detection and avoidance features, enhancing its efficiency and durability by mitigating the risk of accidents. By integrating deep learning models for rainfall prediction, SALS optimizes water usage preemptively, adjusting watering schedules to environmental conditions. Offering independent operation and a modular design, SALS provides flexibility to users, catering to gardens of all sizes and promoting greener and healthier environments. The longterm savings from reduced water consumption and labor costs make SALS a wise investment for homeowners, businesses, and municipalities, heralding a new era of efficiency and sustainability in outdoor maintenance