Search Results (1,877)

In the modern era of industrial digitalization, the convergence of the Internet of Things (IoT), advanced data analysis, augmented reality (AR) and virtual reality (VR) is significantly transforming various industrial sectors. This research aimed to study and develop a proposal for an integrated system that combines IoT, data analysis, AR and VR for the monitoring and maintenance of industrial equipment. The importance of this research lies in its potential to contribute to the implementation of predictive maintenance solutions, which can significantly reduce machine downtime in an industrial environment and thus reduce or prevent operational failures. The central research question of this work was the following: how can the integration of IoT, data analysis and augmented and virtual reality contribute to optimizing industrial maintenance? We tested the combination of technologies to enable the creation of an effective predictive maintenance system, capable of alerting operators to anomalous conditions and providing detailed visual instructions for maintenance tasks. As a result, a prototype system was developed and tested, and it has shown the potential to evolve into a real system in an industrial environment. Full article

Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network’s operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs. Full article

Ultrasonic flowmeters are essential sensor devices widely used in remote metering systems, smart grids, and monitoring systems. In these environments, a low-power design is critical to maximize energy efficiency. Real-time data collection and remote consumption monitoring through remote metering significantly enhance network flexibility and efficiency. This paper proposes a low-complexity structure that ensures an accurate time-of-flight (ToF) estimation within an acceptable error range while reducing computational complexity. The proposed system utilizes Hilbert envelope detection and a differentiator-based parallel peak detector. It transmits and collects data through ultrasonic transmitter and receiver transducers and is designed for seamless integration as a node into wireless sensor networks (WSNs). The system can be involved in various IoT and industrial applications through high energy efficiency and real-time data transmission capabilities. The proposed structure was validated using the MATLAB software, with an LPG gas flowmeter as the medium. The results demonstrated a mean relative deviation of 5.07% across a flow velocity range of 0.1–1.7 m/s while reducing hardware complexity by 78.9% compared to the conventional FFT-based cross-correlation methods. This study presents a novel design integrating energy-efficient ultrasonic flowmeters into remote metering systems, smart grids, and industrial monitoring applications. Full article

The application of smart packaging technology in fruit and vegetable preservation has shown significant potential with the ongoing advancement of science and technology. Smart packaging leverages advanced sensors, smart materials, and Internet of Things (IoT) technologies to monitor and regulate the storage environment of fruits and vegetables in real time. This approach effectively extends shelf life, enhances food safety, and reduces food waste. The principle behind smart packaging involves real-time monitoring of environmental factors, such as temperature, humidity, and gas concentrations, with precise adjustments based on data analysis to ensure optimal storage conditions for fruits and vegetables. Smart packaging technologies encompass various functions, including antibacterial action, humidity regulation, and gas control. These functions enable the packaging to automatically adjust its internal environment according to the specific requirements of different fruits and vegetables, thereby slowing the growth of bacteria and mold, prolonging freshness, and retaining nutritional content. Despite its advantages, the widespread adoption of smart packaging technology faces several challenges, including high costs, limited material diversity and reliability, lack of standardization, and consumer acceptance. However, as technology matures, costs decrease, and degradable smart packaging materials are developed, smart packaging is expected to play a more prominent role in fruit and vegetable preservation. Future developments are likely to focus on material innovation, deeper integration of IoT and big data, and the promotion of environmentally sustainable packaging solutions, all of which will drive the fruit and vegetable preservation industry toward greater efficiency, intelligence, and sustainability. Full article

This article focuses on the integration of the Internet of Things (IoT) and the Internet of Robotic Things, representing a dynamic research area with significant potential for industrial applications. The Internet of Robotic Things (IoRT) integrates IoT technologies into robotic systems, enhancing their efficiency and autonomy. The article provides an overview of the technologies used in IoRT, including hardware components, communication technologies, and cloud services. It also explores IoRT applications in industries such as healthcare, agriculture, and more. The article discusses challenges and future research directions, including data security, energy efficiency, and ethical issues. The goal is to raise awareness of the importance of IoRT and demonstrate how this technology can bring significant benefits across various sectors. Full article

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations. Full article

This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average ${\mathrm{Accuracy}}_{pixel}$ of 96.4% and ${\mathrm{Accuracy}}_{logs}$ of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology’s success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors. Full article

The fourth industrial revolution has introduced a range of digital technologies (DTs) that possess the potential to significantly enhance the operations and competitiveness of heavy-construction firms. Grounded in the Technology–Organization–Environment (TOE) Framework, the Resource-Based View (RBV) and the Diffusion of Innovation Theory (DOI), this study investigates the relationship between the adoption of digital technologies and the competitive edge (CE) of heavy-engineering firms. Specifically, this research seeks to assess how the adoption of DTs impacts four critical competitive-edge metrics: efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4). A quantitative research approach was employed, using a structured questionnaire distributed to construction professionals in Lagos State, Nigeria. The principal results of the study revealed that firms adopting artificial intelligence (AI), cloud-based technology and the Internet of Things (IoT) exhibited significantly higher competitive-edge metrics compared to their counterparts. Notably, AI and cloud-based technology were found to have a particularly strong association with improved resource management, real-time monitoring, and decision-making processes. A major contribution of this research is the development of a DT-adoption model which can serve as a benchmarking tool for firms to assess their current adoption levels and identify areas for improvement. This model can also guide policymakers and regulators in developing strategies to encourage the integration of digital technologies within the heavy-construction industry. The originality of this study lies in its holistic approach, examining a broad spectrum of digital technologies and their collective impact on enhancing the competitive edge of construction firms. Full article

The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments. Full article

Heat-intensive industries (e.g., iron and steel, aluminum, cement) and explosive sectors (e.g., oil and gas, chemical, petrochemical) face challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies. Wireless solutions are favored in large facilities to reduce the costs and complexities of extensive wiring. However, conventional wireless devices powered by lithium batteries have limitations, including reduced lifespan in high-temperature environments and incompatibility with explosive atmospheres, leading to high maintenance costs. This paper presents a novel approach for energy-intensive and explosive industries, which represent over 40% of the gross production revenue (GPR) in several countries. The proposed solution uses residual heat to power ATEX-certified IIoT devices, eliminating the need for batteries and maintenance. These devices are designed for condition monitoring and predictive maintenance of rotating machinery, which is common in industrial settings. The study demonstrates the successful application of this technology, highlighting its potential to reduce costs and improve safety and efficiency in challenging industrial environments. Full article

The rapid advancement of industrial automation in the automotive manufacturing sector has heightened demand for welding quality, particularly in critical component welding, where traditional manual inspection methods are inefficient and prone to human error, leading to low defect recognition rates that fail to meet modern manufacturing standards. To address these challenges, an enhanced YOLOv8-based algorithm for steel defect detection, termed YOLOv8-WD (weld detection), was developed to improve accuracy and efficiency in identifying defects in steel. We implemented a novel data augmentation strategy with various image transformation techniques to enhance the model’s generalization across different welding scenarios. The Efficient Vision Transformer (EfficientViT) architecture was adopted to optimize feature representation and contextual understanding, improving detection accuracy. Additionally, we integrated the Convolution and Attention Fusion Module (CAFM) to effectively combine local and global features, enhancing the model’s ability to capture diverse feature scales. Dynamic convolution (DyConv) techniques were also employed to generate convolutional kernels based on input images, increasing model flexibility and efficiency. Through comprehensive optimization and tuning, our research achieved a mean average precision (map) at IoU 0.5 of 90.5% across multiple datasets, contributing to improved weld defect detection and offering a reliable automated inspection solution for the industry. Full article

Open innovation serves as a critical pathway for aligning Sustainable Business Models (SBMs) with the dual imperatives of sustainability and the digital economy. This editorial review synthesizes insights from theoretical frameworks, particularly the Resource-Based View (RBV) and Transaction Cost Theory (TCT), integrated with the Technology-Organization-Environment (TOE) framework to explore the mechanisms driving open innovation. Our editorial review highlights the key dimensions influencing open innovation: technology (digital platforms, emerging technologies like AI, IoT, and blockchain), organization (stakeholder collaboration, governance mechanisms), and environment (regulatory frameworks, market dynamics, and industrial spillovers). This unified framework offers actionable insights for policymakers to foster enabling ecosystems and for business leaders to adopt open innovation strategies for resource optimization and governance improvement. The review concludes that the RBV-TCT-TOE framework provides a generalizable and robust tool for understanding and advancing open innovation across industries and regions, bridging theoretical and practical dimensions to address the challenges of sustainability and digital transformation. Full article

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities. Full article