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Digital transformation, powered by technologies like AI, IoT, and big data, is reshaping industries and societies at an unprecedented pace. While these innovations promise smarter energy management, precision agriculture, and efficient resource utilization, they also introduce serious environmental challenges. This paper examines the dual impact of digital technologies, highlighting key threats such as rising energy consumption, growing e-waste, and the increased extraction of raw materials. By synthesizing the existing literature, this study highlights mitigation strategies that include adopting energy-efficient practices, integrating renewable energy, and implementing circular economy principles. It emphasizes the need for a balanced approach—making the most of technological advances while protecting the environment. By identifying gaps in the current research, this paper also suggests future areas to explore to ensure that digital progress does not come at the expense of our planet. This review advocates for an integrated strategy to achieve sustainable digital growth aligned with global climate goals. Full article

The Fourth Industrial Revolution (Industry 4.0) has profoundly transformed industries worldwide through the integration of advanced digital technologies, including artificial intelligence, digital twins, building information modeling (BIM), and the Internet of Things (IoT). The Architecture, Construction, and Engineering (ACE) sectors are increasingly adopting these innovations to meet the evolving demands of the global market. Within this dynamic context, Saudi Arabia has emerged as a front-runner and significant investor in this sector, as evidenced by the launch of ambitious mega-projects such as NEOM and The Line. These developments prompt valuable discussions about the readiness of graduates to adapt to rapid technological advancements and meet the current demands of the Saudi market. Although numerous studies have explored this issue, the Saudi context presents unique challenges and opportunities due to the accelerated pace of change within the ACE sectors, driven by the goals of Vision 2030. For this reason, this paper aims to address this gap by exploring the readiness of architectural programs in the context of Saudi Arabia to meet the demands of Industry 4.0. To achieve this, a comprehensive literature review was conducted, developing an analytical framework. Subsequently, a multiple-cases approach was employed, with an overall top-level discussion on the undergraduate architecture program subjects available in the five regions in Saudi Arabia. A combination of field observations, domain expertise, and evidence-based coding methods was employed to develop the SWOT analysis. The SWOT framework was utilized to identify key strengths, weaknesses, opportunities, and threats within the current academic programs. The findings were then analyzed in a comprehensive discussion, highlighting necessary transformations in existing programs. The methodology employed in our study involves prolonged engagement and persistent observation to enhance the quality and credibility of the discussion. This paper serves as a roadmap for guiding future educational reforms and aligning architectural education with emerging industry demands and technological advancements in the field. Four key themes are essential for aligning architectural education with Industry 4.0: sustainability in the built environment, innovation and creativity, digital applications in the built environment, and entrepreneurship and leadership in venture engineering. It also strongly emphasized sustainability courses and noted notable deficiencies in preparing students for a digitally driven professional landscape. For example, the average program comprises 162 credit hours and 58 courses, with only six related to Industry 4.0. The top five institutions offering Industry 4.0 courses ranked from highest to lowest are ARCH-U11, ARCH-U8, ARCH-U3, ARCH-U4, and ARCH-U15. ARCH-U11 offers the most Industry 4.0 courses, totaling 15, which account for 26.8% of its courses and 15% of its credit hours, in contrast to ARCH-U20, which offers no courses. The novelty of this research lies in its comprehensive analysis of the readiness of architecture program curricula from 20 Saudi universities to meet the requirements of Industry 4.0. Importantly, these findings support previous studies that established guidelines that mandate the inclusion of sustainability, innovation, and digital skills in architectural education programs. Contribution to the knowledge and findings is valuable for educational institutions, policymakers, and industry leaders, offering insights into evolving architectural education to meet future industry demands and foster technological innovation and sustainable development. Moreover, it provides actionable recommendations for curriculum development in alignment with Vision 2030. Contrary to expectations, findings show that lower-ranked universities offer more Industry 4.0-related courses than higher-ranked ones, emphasizing the need to align university evaluation standards with labor market demands. Full article

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs. Full article

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article

The occurrence of the fourth industrial revolution (Industry 4.0) has led many industries to the path of adopting new technologies. Such technologies include blockchain, artificial intelligence (AI), and the Internet of Things (IoT). Blockchain creates the opportunity to access data and information in a decentralized manner, resulting in increased customer satisfaction. This study develops a smart newsvendor model of the food industry with consignment contracts and blockchain technology. Under a consignment policy, the central division (manufacturer) can utilize the retailer’s warehouse for storage. The producer may also have the opportunity to share the holding cost with retailers without losing the ownership of products. The main contribution of this study is to analyze the profitability of the retailing and supply chain when the blockchain technology is implemented by the food industry. Moreover, a thorough investigation of profit and loss is conducted under a consignment contract when uncertain demand is encountered. This study mainly concerns perishable food items, and increasing volatility in market demand. Two cases of probabilistic uncertainty are considered, including uniform and normal distribution. The key investigations of this study are presented in terms of (a) the effect of adopting blockchain on market demand for the food industry, (b) analysis of company profitability for perishable food items and demand uncertainty, and (c) the effect of the consignment contract under blockchain technology in the food industry. Finally, this research develops an optimization tool to numerically analyze the effect of several factors of the blockchain technology on demand. Moreover, the optimal values of the design variables and the resulting maximum profitability provide valuable insights that support industry in formulating effective policies and making informed strategic decisions. Full article

As Internet of Things (IoT) technology continues to advance, there is a growing awareness of IoT security within the industry. Quantum communication technology can potentially significantly improve the communication security of IoT devices. Based on semi-quantum cryptography and utilizing single photons, this paper introduces two semi-quantum secure direct communication (SQSDC) protocols for use in smart door locks. Protocol 1 is more efficient, and the efficiency analysis shows that the communication efficiency is as high as 28.57%. Security analysis demonstrates the asymptotic security of the protocols, effectively resisting intercept–measure–resend attacks and entangle–measure attacks from potential eavesdroppers. The extended SQSDC protocol (protocol 2) builds upon protocol 1 by enabling a single qubit to transmit two bits of information, resulting in a double efficiency outcome. Full article

Buildings are responsible for approximately 40% of global energy consumption, putting pressure on the construction industry to mitigate its environmental impact. Therefore, there is an urgent need for innovative solutions to reduce power consumption, particularly in lighting systems. This study’s primary objective was to investigate novel integrated lighting solutions that significantly reduce energy use, as well as to explore their enhancement through Building Information Modelling (BIM) and the Internet of Things (IoT) to improve energy efficiency further and reduce the carbon footprint of buildings. Hence, this literature review examined energy-saving actions, retrofitting practices and interventions across a range of multi-use buildings worldwide, focusing on research from 2019 to 2024. The review was conducted using Scopus and Web of Science databases, with inclusion criteria limited to original research. The objective was to diagnose the goals being undertaken and ultimately validate new actions and contributions to minimise energy consumption. After applying eligibility criteria, 48 studies were included in the review. First, daylight harvesting and retrofitting solutions were examined using the latest technologies and external shading. The review indicates a lack of proper coordination between daylight and electrical lighting, resulting in energy inefficiency. Secondly, it reviews how the integration of BIM facilitates the design process, providing a complete overview of all the building variables, thus improving indoor daylight performance and proper lighting with energy analysis. Lastly, the review addresses the role of the Internet of Things (IoT) in providing real-time data from sensor networks, allowing for continuous monitoring of building conditions. This systematic literature review explores the integration of these fields to address the urgent need for innovative strategies and sustainability in the built environment. Furthermore, it thoroughly analyses the current state of the art, identifying best practices, emerging trends and concrete insight for architects, engineers and researchers. The goal is to promote the widespread adoption of low-carbon systems and encourage collaboration among industry professionals and researchers to advance sustainable building design. Ultimately, a new parametric design framework is proposed, consisting of five iterative phases that cover all design stages. This framework is further enhanced by integrating BIM and IoT, which can be used together to plan, reconfigure, and optimise the building’s performance. Full article

In various types of industrial applications, such as reverse engineering, machine operation, technical metrology, or modern factory maintenance, it is important to have systems that enable the quick and easy scanning of selected mechanical parts. This study presents the design process and analysis of a low-cost, 3D scanning system which can be used in industrial applications. The system collects point cloud data using an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. Communication with the system is enabled through a serial interface and a dedicated window application, allowing users to monitor and adjust scanning parameters. The output data in the form of a point cloud are saved in a text file in the scanner’s controller memory and then sent wirelessly to an external device, e.g., cloud and/or a diagnostic controller. The electronic system is equipped with a radio module that can be used to communicate with other devices in line with the idea of the Internet of Things and the concept of Industry 4.0. The results of the study are based on the accuracy of the three-dimensional digitization of the tested object and on the determination of the average measurement uncertainty. Full article

The growth of the Internet of Things (IoT) and its integration with Industry 4.0 and 5.0 are generating new security challenges. One of the key elements of IoT systems is effective anomaly detection, which identifies abnormal behavior in devices or entire systems. This paper presents a comprehensive overview of existing methods for anomaly detection in IoT networks using machine learning (ML). A detailed analysis of various ML algorithms, both supervised (e.g., Random Forest, Gradient Boosting, SVM) and unsupervised (e.g., Isolation Forest, Autoencoder), was conducted. The results of tests conducted on popular datasets (IoT-23 and CICIoT-2023) were collected and analyzed in detail. The performance of the selected algorithms was evaluated using commonly used metrics (Accuracy, Precision, Recall, F1-score). The experimental results showed that the Random Forest and Autoencoder methods are highly effective in detecting anomalies. The article highlights the importance of appropriate data preprocessing to improve detection accuracy. Furthermore, the limitations of a centralized machine learning approach in the context of distributed IoT networks are discussed. The article also presents potential directions for future research in the field of anomaly detection in the IoT. Full article

In order to realise the efficient maintenance of industrial machines, Small and Medium-sized Enterprises (SMEs) need a system that utilises digital technology to handle everything from data collection to the visualisation of the collected data in an integrated manner. In this paper, an integrated monitoring platform using external sensor devices is proposed and implemented for the purpose of preventive maintenance of industrial machines. The proposed system performs edge processing to calculate features effective for monitoring on the sensor device, collects only the obtained features, and visualises them on a web server. In order to determine the features required by edge processing, a cycle waveform cut-out algorithm was proposed. As an evaluation experiment, the proposed system was used to detect the loosening of bolts on the support side of a ball screw. The results of the analysis showed that the dispersion value immediately after the start of uniform motion from the right end to the left end was valid, so the system was implemented as edge processing in the sensor device. In wireless transmission experiments on a testbed, an average of 20 consecutive cycles were used to achieve a 99.9% correct response rate and high detection accuracy, demonstrating the usefulness of the proposed system. Full article

This paper investigates the contribution of the various cybersecurity domains to the United Nations’ Sustainable Development Goals (SDGs), emphasizing the critical role of cybersecurity in advancing sustainable economic growth and resilient IoT infrastructure. The paper also examines specific use cases on how cybersecurity measures and practices can contribute to achieving SDG 8 and SDG 9 focused on decent work and economic growth and industry, innovations, and infrastructure. In the context of SDG 8 the use case of a smart agriculture network was examined, whereas for SDG 9, the use case focuses on a smart factory processing raw materials. An analysis of the prioritization of the several cybersecurity domains following the MoSCoW method is also presented. This paper offers valuable insights and guidance for enhancing corporate resilience and economic benefits in the Internet of Things (IoT) aligning with the SDGs and contributing to a more sustainable and resilient future for the IoT. Full article

A novel tracking and monitoring system for ISO 668 dry containers was realized by the ESA-funded “5G SENSOR@SEA” project, integrating 5G cellular technologies for massive Internet of Things with a GEO satellite-optimized backhauling link. The scope is the development of monitoring and tracking new services for multimodal container shipping. With the cooperation of four industrial partners and a telecommunication research center, the so-called “5GT System” was designed, developed, tested and validated up to field trials. Several modules of the system were designed, built and finally installed on the ship and in the teleport: the container tracking devices placed on the containers, the NB-IoT cellular network with optimized satellite backhauling, the Ku-band satellite terminals and the maritime service platform based on the OneM2M standard. The field trial conducted during the intercontinental liner voyage of a container ship showed primary technical achievements, including fair switching between terrestrial and satellite networks, reduction in packet loss in the open sea scenario and seamless integration of the BLE mesh network over the container tracking devices as NB-IoT/BLE LE Mesh gateways. Full article

The hospitality business model has undergone profound changes influenced by successive waves of technological advances and platforms. This paper explores how different waves of technologies and platforms are shaping the evolution of the hospitality business model. Employing a systematic literature review, this research analyzes how platform innovation has driven significant shifts in the industry. The analysis yielded four key findings. Firstly, the incorporation of digital technologies resulted in a radical transformation of conventional business models, prompting substantial alterations in marketing strategies, distribution processes, and consumer behavior patterns. Secondly, the industry has undergone five significant technological revolutions, evolving from steam-powered transportation to the integration of artificial intelligence (AI) and the Internet of Things (IoT), thereby driving a shift towards innovation-centric business models. Thirdly, the advent of digital platforms, most notably online travel agencies (OTAs), posed a challenge to traditional hospitality models by introducing personalized experiences and disrupting established market norms. While digital platforms offer advantages such as enhanced convenience and customization, they also present challenges, including intensified competition and declining profit margins. Nonetheless, technological advancements such as AI, virtual reality (VR), and augmented reality (AR) present a potential avenue for redefining value propositions and enhancing customer experiences. Fourth, the future of the hospitality business model is poised for further disruption, influenced by the emergence of new virtual platforms and services. This could potentially result in a reshaping of the industry’s value chain and value proposition. Full article

European policies on climate change (CC), food security (FS), and biodiversity (BD) represent the EU’s commitment to a sustainable agri-food system, highlighting the interdependence between environmental health and food security. By analyzing key drivers and indicators, the present study evaluates the effectiveness of existing measures and identifies gaps in the policy framework. A Scoping Group activity facilitated dialogue between policymakers, industry, and farmer representatives to gather feedback and strengthen the data–policy link. The results highlight progress in areas such as promoting sustainable agriculture and biodiversity, while pointing out unresolved issues like the challenges faced by smallholder farmers. The study emphasizes the need for real-time monitoring tools and tailored solutions to address the complexities of the agri-food system. It also encourages the integration of emerging technologies, such as IoT and AI, to enhance the sustainability of agricultural practices. Ultimately, the findings call for a landscape-specific approach to maximize biodiversity gains, mitigate climate impacts, and ensure food security within the broader context of the EU’s ecological and socio-economic challenges. Full article

The Internet of Things (IoT) has emerged as a transformative technology with a variety of applications across various industries. However, the development of IoT systems is hindered by challenges such as interoperability, system complexity, and the need for streamlined development and maintenance processes. In this study, we introduce a robust architecture grounded in discrete event system specification (DEVS) as a model-driven development solution to overcome these obstacles. Our proposed architecture utilizes the publish/subscribe paradigm, and it also adds to the robustness of the proposed solution with the incorporation of the Brooks–Iyengar algorithm to enhance fault tolerance against unreliable sensor readings. We detail the DEVS specification that is used to define this architecture and validate its effectiveness through a detailed home automation case study that integrates multiple sensors and actuators. Full article