Internet Of Things Research Articles

Low Power Gas Sensors: From Structure to Application.

Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal-organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.

Technological Unemployment in Terms of Global Labor Market Imbalances: A Bibliometric Analysis

The current society, known as the supersmart society or Society 5.0, emerged in response to the Fourth Industrial Revolution. also known as Industry 4.0 (I4.0), which arose with the development of different technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics (BDA), among others. Based on the report “Future of Jobs Report 2023” the most affectin g macro trend in driving business transformation is the increased adoption of new and frontier technologies with an 86.2% rate. I4.0 significantly changed production processes and manufacturing systems, transforming firms into smart factories. One of the consequences of the I4.0 revolution is technological unemployment (TU). This paper aims to conduct a comprehensive evaluation of the literature on Industry 4.0 (I4.0) and Technological Unemployment (TU) within the 2015-2024 timeframe, with a particular focus on their impact on labor market imbalances (LMI). Through a bibliometric analysis, the study will assess performance metrics, including the publication and citation performance of authors, institutions, countries, and journals. Additionally, the research will visualize and critically examine bibliometric citation outcomes, including cited references. To achieve this, we adopted a quantitative approach and employed a confirmatory-explicative research method to elucidate Technological Unemployment (TU) within the context of Industry 4.0 (I4.0) transformations and confirm its implications on Labor Market Imbalances (LMI). Bibliometric analysis was used to discern data patterns, trends, and relationships within the literature. Our findings revealed several significant trends, notably job polarization, income inequality, and TU. We determined that the effects of I4.0 on employment are not universally negative; in some cases, technological investments enhance employment rates by creating new professions and job opportunities. Our analysis also indicated that occupations requiring human judgment, decision-making, creativity, and innovation exhibit resilience to technological advancements.

Machine Learning in Health Anylitics and Patient Monitoring Solomon Kavuta and Mr Joel Mulepa Dmi St John The Baptist University, Lilongwe, Malawi

The integration of machine learning techniques in healthcare has revolutionized the way predictive analytics and patient monitoring are conducted. This transformation is reshaping traditional healthcare practices by harnessing the power of advanced computational methods to analyze vast amounts of patient data. Machine learning algorithms, which excel at detecting patterns and making predictions, leverage this data to forecast disease onset, progression, and treatment outcomes. This capability enables healthcare providers to adopt proactive measures, facilitating early intervention and the development of personalized treatment plans tailored to individual patients' needs. The potential of machine learning in healthcare is vast and multifaceted. By continuously analyzing real-time patient data streams from various sources such as electronic health records (EHRs), medical imaging, genomic information, and data from wearable devices, these algorithms can identify subtle changes in a patient's condition that may indicate the early stages of a disease or the risk of an adverse event. This continuous monitoring allows for timely interventions that can prevent complications, reduce the severity of diseases, and improve overall patient outcomes. For example, machine learning can help predict the likelihood of a patient developing conditions like diabetes or heart disease, allowing for lifestyle changes and medical interventions that can mitigate these risks before they become severe health issues. Moreover, machine learning facilitates remote patient monitoring, which has become increasingly important in the context of modern healthcare. With the advent of wearable devices and Internet of Things (IoT) sensors, it is now possible to collect and analyze health data outside of traditional clinical settings. These technologies enable healthcare providers to monitor patients in real-time, regardless of their location, ensuring continuous care and oversight. This is particularly beneficial for managing chronic diseases, where regular monitoring can significantly improve patient management and outcomes. For instance, remote monitoring of patients with heart conditions can detect early signs of arrhythmias or other issues, prompting immediate medical responses that can prevent more serious complications. The transformative impact of machine learning on healthcare delivery extends beyond patient monitoring and predictive analytics. It also includes enhancing diagnostic accuracy, optimizing treatment protocols, and streamlining administrative processes. By reducing the need for frequent hospital visits and enabling more efficient use of healthcare resources, machine learning can help lower healthcare costs while improving the quality of care provided to patients. Additionally, the ability to analyze large datasets quickly and accurately can aid in medical research, uncovering new insights into disease mechanisms and potential treatments.

Adaptive memory reservation strategy for heavy workloads in the Spark environment

The rise of the Internet of Things (IoT) and Industry 2.0 has spurred a growing need for extensive data computing, and Spark emerged as a promising Big Data platform, attributed to its distributed in-memory computing capabilities. However, practical heavy workloads often lead to memory bottleneck issues in the Spark platform. This results in resilient distributed datasets (RDD) eviction and, in extreme cases, violent memory contentions, causing a significant degradation in Spark computational efficiency. To tackle this issue, we propose an adaptive memory reservation (AMR) strategy in this article, specifically designed for heavy workloads in the Spark environment. Specifically, we model optimal task parallelism by minimizing the disparity between the number of tasks completed without blocking and the number completed in regular rounds. Optimal memory for task parallelism is determined to establish an efficient execution memory space for computational parallelism. Subsequently, through adaptive execution memory reservation and dynamic adjustments, such as compression or expansion based on task progress, the strategy ensures dynamic task parallelism in the Spark parallel computing process. Considering the cost of RDD cache location and real-time memory space usage, we select suitable storage locations for different RDD types to alleviate execution memory pressure. Finally, we conduct extensive laboratory experiments to validate the effectiveness of AMR. Results indicate that, compared to existing memory management solutions, AMR reduces the execution time by approximately 46.8%.

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Indoor Air Quality in an Orthopedic Hospital from Romania

Inside hospitals, there is a trend of increasing levels of air pollutants. However, only the indoor air quality in operating theaters is carefully monitored. Therefore, we set the goal of this study to evaluate the indoor air quality in areas of an orthopedics department and to compare the indoor air quality indices that characterize these areas. We used a monitoring system based on the Internet of Things with uRADMonitor model A3 sensors, with which we prospectively measured indoor air quality in the facilities of the orthopedic emergency hospital of Targu Mures in Romania, between 1 February 2023, and 31 January 2024. The primary target pollutants investigated in the emergency room, outpatient room and ward were carbon dioxide (CO2), nitrogen dioxide (NO2), volatile organic compounds (VOCs) and particles with a diameter smaller than 2.5 μm (PM2.5). We compared the effectiveness of the intervention for emergency rooms where air purifiers were working or not. The concentrations of CO2, VOCs and PM2.5 were significantly higher in the emergency room than in the outpatient room or ward. The indoor air quality was worst in winter, when the CO2, NO2 and VOC concentrations were at their highest. Air purifiers can help reduce the concentration of PM2.5 in emergency rooms. Medical staff and patients in orthopedic hospitals, especially in emergency rooms, are frequently exposed to polluted ambient air, which can affect their health. Orthopedic medical practice guidelines should address issues relating to the protection of personnel through the application of measures to improve indoor air quality.

Co-creating the future: participatory cities and digital governance.

The digital revolution, fuelled by advancements in social media, Big Data, the Internet of Things and Artificial Intelligence, is reshaping our urban landscapes into 'participatory cities'. These cities leverage digital technologies to foster citizen engagement, collaborative decision-making and community-driven urban development, thus unlocking new potentials while confronting emerging threats. Such technologies are empowering individuals and organizations in ways that were unimaginable just a few years ago. They do, however, introduce new risks and vulnerabilities that must be carefully managed. Hence, socio-technical innovation is urgently needed. In this connection, open-source technologies, participatory approaches and new forms of governance are becoming more popular and relevant. This theme issue looks into the tangible impacts of these technological advancements, with a focus on participatory cities. It aims to explain how digital tools are used in cities to tackle urban challenges, improve governance and promote sustainability. Through a collection of in-depth analyses, case studies and real-world examples, this issue seeks to offer a comprehensive understanding of the digital governance frameworks underpinning participatory cities. By offering a platform for multidisciplinary discourse, this theme issue endeavours to contribute to the broader narrative of shaping a more resilient, sustainable and democratic urban future in the digital age.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Retraction Note: Cataract eye detection by optik image analysis using encoder basis Boltzmann architecture integrated with internet of things and data mining

Retraction Note: Cataract eye detection by optik image analysis using encoder basis Boltzmann architecture integrated with internet of things and data mining

Enhancing brewery logistics with smart kegs: A simulation study

Background: Supply chain optimisation is critical for companies in today’s globalised world. Efficient supply chains ensure product quality and freshness in the food industry. For products like beer, efficiency extends beyond transportation and includes the management of logistics containers such as returnable kegs.Objectives: This study investigated the impact of tracking logistics data on the efficiency of container logistics in a small regional brewery. The focus was reducing fleet size, processing times and pick-up distances.Method: A simulation of a small brewery was used to analyse the impact of tracking logistics data. The study examined how kegs equipped with real-time tracking technology could improve logistics operations.Results: The simulation showed that tracking logistics data significantly improved logistics processes. It led to a reduction in the fleet size, turnaround times and pick-up distances for empty kegs. These improvements were attributed to better resource allocation and more efficient management of reusable containers.Conclusion: The study showed that smart kegs could reduce logistical inefficiencies in the brewing industry. The use of smart technologies in container logistics improves operational efficiency and sustainability.Contribution: This research provides insights into how smart technologies can transform traditional logistics models. The results suggest that smart containers can revolutionise supply chains with efficient resource allocation, real-time tracking and automation. This shift towards smart logistics aligns with the broader adoption of the Industrial Internet of Things, offering a blueprint for future innovations in supply chain management. The implications extend beyond brewing, highlighting the potential for greater sustainability and a positive reduction in environmental impact in global supply chains.

Neural networks empowered: a machine learning-enabled, Gyro mmID for enhanced virtual reality and motion tracking applications

Abstract With the emerging developments in millimeter-wave/5G technologies, the potential for wireless Internet of things devices to achieve widespread sensing, precise localization, and high data-rate communication systems becomes increasingly viable. The surge in interest surrounding virtual reality (VR) and augmented reality (AR) technologies is attributed to the vast array of applications they enable, ranging from surgical training to motion capture and daily interactions in VR spaces. To further elevate the user experience, and real-time and accurate orientation detection of the user, the authors proposes the utilization of a frequency-modulated continuous-wave (FMCW) radar system coupled with an ultra-low-power, sticker-like millimeter-wave identification (mmID). The mmID features four backscattering elements, multiplexed in amplitude, frequency, and spatial domains. This design utilizes the training of a supervised learning classification convolutional neural network, enabling accurate real-time three-axis orientation detection of the user. The proposed orientation detection system exhibits exceptional performance, achieving a noteworthy accuracy of 90.58% over three axes at a distance of 8 m. This high accuracy underscores the precision of the orientation detection system, particularly tailored for medium-range VR/AR applications. The integration of the FMCW-based mmID system with machine learning proves to be a promising advancement, contributing to the seamless and immersive interaction within virtual and augmented environments.

Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices

This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. A clone detection method is proposed to identify clone-caused vulnerabilities in IoT software. A hybrid solution combines syntactic and semantic analyses of the code. Based on the recovered code, an attributed abstract syntax tree is constructed for each code fragment. All nodes of the commonly used abstract syntax tree are proposed to be weighted with semantic attribute vectors. Each attributed tree is then encoded as a semantic vector using a Deep Graph Neural Network. Two graph networks are combined into a Siamese neural model, allowing training to generate semantic vectors and compare vector pairs within each training epoch. Semantic analysis is also applied to clones with low similarity metric values. This allows one to correct the similarity decision in the case of incorrect matching of functions at the syntactic level. To automate the search for clones, the BinDiff algorithm is added in the first stage to accurately select clone candidates. This has a positive impact on the ability to apply the proposed method to large sets of binary code. In an experimental study, the developed method—compared to BinDiff, Gemini, and Asteria tools—has demonstrated the highest efficiency.

Enhancing data management and real‐time decision making with IoT, cloud, and fog computing

AbstractThe convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques.

Leveraging Massive MIMO for Enhanced Energy Efficiency in High-Density IoT Networks

Maximizing energy efficiency (EE) in massive multiple-input multiple-output (MIMO) systems, while supporting the rapid expansion of Internet of Things (IoT) devices, is a critical challenge. In this paper, we delve into the intricate operations geared toward enhancing EE in such complex environments. To effectively support a multitude of IoT devices, we adopt a strategy of heavy reference signal (RS) reuse, and in this circumstance, we formulate the EE metrics and their corresponding inverses to determine pivotal operational parameters. These EE-centric parameters encompass factors such as the number of service antennas in the base station (BS), the number of IoT devices, and permissible coverage extents. Our objective is to calibrate these parameters to meet a predefined EE threshold, ensuring optimal system performance. Additionally, we recognize the indispensable role of Peak-to-Average Power Ratio (PAPR) reduction techniques, particularly in multicarrier systems, to further enhance EE. As such, we employ clipping-based PAPR reduction methods to mitigate signal distortions and bolster overall efficiency. Theoretical EE metrics are derived based on formulated signal-to-interference-plus-noise ratios (SINRs), yielding insightful closed-form expressions for the operational parameters. Leveraging two distinct EE metric models, we undertake parameter determinations, accounting for the levels of approximation. Intriguingly, our analysis reveals that even simplified models exhibit remarkable applicability in real-world scenarios, with a minimal margin of error. The results not only underscore the practical applicability of our theoretical constructs but also highlight the potential for significant EE enhancements in massive MIMO systems, thereby contributing to sustainable evolution in the IoT era.

Design of a Multiband Antenna Using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT Applications

ABSTRACTThe rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML).

IoT‐Based Hand Hygiene Compliance Monitoring System and Validation of Its Effectiveness in Hospital Environments

AbstractHealthcare‐associated infection (HAI) is the most common adverse medical event that affects patients. Internationally, healthcare workers (HCWs) are monitored for hand hygiene compliance to reduce HAI risk. While direct observation is considered the gold standard for monitoring, it has several disadvantages. To address this, the study focused on developing a comprehensive hand hygiene system that integrates the Internet of Things (IoT) hand hygiene with soap and water (HHW) and alcohol‐based formulation (HHA) monitoring, incorporates real‐time data visualization on a web interface to track HCWs' hand hygiene practices, and provides instant calculations of compliance and accuracy rates. This IoT system uses Bluetooth for HCW positioning and HHW detection, ultrasonic sensors for handwashing duration, and pressure sensors for HHA detection. Furthermore, a cloud server, database, and website are established to manage and display the data received by the IoT devices. To reduce HAI in Taiwan, hospitals must provide both HHW and HHA systems, and HCWs can choose either method when hand hygiene is necessary. The system achieved 72% accuracy in clinical practice within an adult intensive care unit (ICU).

Leveraging Industry 4.0 for marketing strategies in the medical device industry of emerging economies.

This research investigates the impact of the technologies through an integrated Fuzzy Analytic Hierarchy Process (F-AHP) and VIKOR model to determine their viability as a marketing strategy for business growth in the Prosthetics and Orthotics (P&O) medical device industry. Based on expert data, the study evaluates the integration of Industry 4.0 technologies and ranks them by priority for marketing performance: "Artificial Intelligence & Machine Learning" (0.3295), "Internet of Things" (0.4325), "Virtual Reality & Augmented Reality" (0.5000), and "Big Data" (0.5704). The findings confirm that the proposed model effectively Leverage the industry 4.0 technologies to enhance marketing strategies, using Pakistan as a case study. This research serves as a blueprint for addressing the integration of next-generation marketing tools, technological advancements, and business growth challenges in the P&O industry. It contributes to the growing body of knowledge on the role of technology in improving healthcare marketing and business performance in emerging markets. Future research should explore the broader impact of Industry 4.0 technologies on healthcare marketing, focusing on strategic decision-making, efficient marketing resource allocation, and performance measurement to ensure sustained success in the P&O medical device sector. The study concludes that prioritizing Industry 4.0 technologies, however, limitations such as sample size, data collection constraints, and model complexity may Leverage the generalizability of the results. Future research should address these limitations by expanding the scope of expert involvement and exploring the application of this model in other emerging economies, to further validate the findings and assess the broader implications for healthcare marketing strategies globally.

Development of an internet-of-things-based controlled-source ultrasonic audio frequency electromagnetic receiver

Abstract. Electromagnetic exploration, characterized by its low cost, wide applicability, and high operational efficiency, finds extensive applications in fields such as oil and gas exploration, mineral prospecting, and engineering geology. Traditional controlled-source electromagnetic detection methods are typically confined to operating frequencies below 250 kHz, resulting in insufficient detection accuracy for applications such as shallow- and intermediate-depth exploration, thereby constraining their performance in high-resolution imaging. To address these challenges, we propose a controlled-source ultrasonic audio frequency electromagnetic receive system based on the internet of things (IoT). We investigate cascaded digital filtering and sampling techniques to extend the receiver's sampling rate range, thereby elevating the operating frequency of controlled-source electromagnetic acquisition from the conventional maximum of 250 kHz to 1 MHz. The receiver achieves a sampling rate of up to 2.5 MHz, comprising three magnetic field measurement channels and two electric field measurement channels. The instrument is compact, lightweight, and capable of real-time data storage locally and real-time data transmission to an upper computer. Additionally, IoT technology is introduced, leading to the design of a cloud-based real-time remote control and data acquisition scheme. Experimental results demonstrate the stability of the instrument, meeting the requirements of field exploration.

Semantic web-assisted progress monitoring of crane operations in construction projects

The rising importance of cranes in modern construction has led to the need for more efficient crane monitoring and operational management. Previous studies have focused on acquiring crane monitoring data through Internet of Things (IoT) devices. However, they offer limited data reasoning capacity and only understand a few particular construction activities with distinct patterns. This restricts the applicability and generalisability of crane monitoring systems in real-world projects. Thus, this study proposes a semantic web-based method to enhance the reasoning of crane monitoring data by correlating as-is and as-planned information of crane operations from different IoT devices and information systems. The proposed method was validated through laboratory experiments, substantiating the potential for monitoring construction progress and enhancing crane utilisation.

6G Technology for Indoor Localization by Deep Learning with Attention Mechanism

This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively.

Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems.