The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

The rapid advancement of technology has substantially impacted numerous sectors, including the banking industry. It is now apparent that the banking industry is affected by the innovative role that the Internet of Things (IoT) plays, which affects a multitude of operations and services. This study uses a quantitative approach and PLS-SEM to investigate the widespread impact of Internet of Things (IoT) technology on the Excel Performance of Islamic Banks in the UAE. The study integrates the Resource-Based View (RBV), Islamic Banking Theories, and Fraud Triangle Theory to create a complete framework. The research's reliability is supported by a sample of 407 replies from 504 participants. The findings strongly support the hypotheses that IoT integration improves Islamic banking performance in the UAE, such as data analytics, customer service, automation systems, fraud detection capabilities, and asset-backed finance, while aligning with Sharia Principles and improving risk-sharing mechanisms. However, the influence of IoT on escalating fraudulent activities and hence negatively impacting performance was not proven. The study emphasizes the importance of IoT in improving operational efficiency and customer satisfaction in Islamic banks in the UAE.

Under its Vision 2030, Saudi Arabia has heavily invested in mega construction projects such as NEOM and QIDDIYA, where logistics resilience is critical due to environmental, infrastructural, and operational uncertainties. This study proposes an IoT-enabled framework for managing project disruptions via real-time sensing, environmental stress indicators, and predictive modeling. Using a dataset of 1,000 logistics events, multiple statistical and machine learning models were applied to evaluate the impact of IoT deployment on delivery delays caused by disruptions, cost variability, and environmental stress. The findings indicate that IoT integration significantly reduces delay volatility and enhances cost predictability. However, disruptions like traffic and weather partially attenuate these benefits. Environmental stress had a minor but consistent influence on logistics risk exposure. A mediation analysis revealed no significant indirect effect of IoT on delay through environmental stress, suggesting a more direct intervention pathway. A decision dashboard is also proposed to visualize delay triggers and automate risk signaling. Theoretical implications extend the Unified Theory of Acceptance and Use of Technology (UTAUT) to construction logistics, emphasizing real-time sensing as the key performance enabler. Managerial implications suggest full IoT adoption in NEOM and QIDDIYA logistics and real-time SCM dashboarding. Future research may incorporate ensemble learning and edge-based IoT to improve predictive capacity. This study demonstrates the feasibility and strategic value of IoT-based disruption control in transforming mega-infrastructure supply chains.

The growing importance of digital technologies in industrial environments has increased academic and managerial interest in understanding how Internet of Things (IoT) adoption can improve sustainability performance. Industrial firms are under rising pressure to enhance resource efficiency, reduce environmental impact, and support long-term sustainable operations. Despite the expanding relevance of IoT in industrial transformation, limited empirical research has examined its effect on sustainability performance through renewable energy and under the influence of green logistics capability. Grounded in the Resource-Based View, this study investigates the impact of IoT adoption on sustainability performance in the industrial sector, while examining the mediating role of renewable energy and the moderating role of green logistics capability. A quantitative approach was adopted, and data were collected from industrial firms using a structured questionnaire. Partial Least Squares Structural Equation Modeling was employed to test the proposed hypotheses. The findings reveal that IoT adoption has a significant positive effect on sustainability performance. Renewable energy was found to mediate this relationship, while green logistics capability strengthened the positive influence of IoT adoption on sustainability performance. The study contributes to the literature by presenting an integrated model that connects digital adoption, renewable energy, green logistics capability, and sustainability performance.

Internet of Things (IoT) and Internet of Medical Things (IoMT) networks provide a massive amount of data. These types of data need a protection level, such as an intrusion detection framework. Deep learning models become a powerful tool for this purpose. Therefore, this work proposes an intrusion detection framework based on a deep learning technique which employs TabTransformer and self-attention mechanisms to imprison intricate dependencies among tabular features and detect abnormal attack behaviors. Precisely, each numerical feature is mapped into a learnable embedding vector and augmented with positional encodings to preserve feature identity and inter-feature relationships within the embedding space. The main task for the proposed model is to achieve binary classification tasks the model should classify the traffic data as either normal or abnormal. Furthermore, the model utilized a benchmark dataset such as the CICIoMT2024. Furthermore, this type of dataset faces issues, such as imbalance. So, the system integrates SMOTE-based data balancing, Stratified K-Fold Cross-Validation, and threshold optimization to ensure fairness and reproducibility to accomplish a binary classification task. As a consequence, experiments on the CICIoMT2024 dataset yield superior results, achieving a mean accuracy of 99.85. Through SHAP-based interpretability, key features influencing model predictions are identified, confirming the framework’s transparency, robustness, and suitability for real-world ARP intrusion detection.

Smart home technologies have revolutionized modern living by enhancing convenience, efficiency, and security. In contrast, many interconnected devices introduce significant security and privacy challenges. This comprehensive review investigates the integration of blockchain technology as a robust solution for secure access control in smart home environments. The decentralized and tamper-resistant nature of blockchain technology effectively solves important problems, including device authentication, data integrity, and access management, through the use of cryptography and distributed ledgers. The study synthesizes findings from 52 research papers, categorizing them into three thematic areas: blockchain in access control systems, its applications in IoT, and specific implementations for smart homes. It highlights the transformative potential of blockchain in mitigating vulnerabilities inherent in centralized systems, fostering trust, and enhancing security frameworks. Despite its promising applications, challenges such as scalability, interoperability, and energy consumption persist, warranting further research. This paper stresses the necessity of collaboration to tackle these limitations and enhance blockchain-based access control solutions for smart homes, setting the stage for more secure and user-focused smart environments.

The increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.

The world of the Internet of Things (IoT), even though it is continuously morphing as a fresh paradigm at the intersection of technology and education, is still struggling with several difficulties that prevent its absorption into the e-learning platforms of higher education institutions (HEIs). The breadth of Internet of Things implementation in developing countries, particularly Jordan, Malaysia, Iran, Saudi Arabia, Iraq, and Bangladesh, remains behind, even though industrialized nations have made significant advancements in their utilization of IoT, with the United Kingdom, the United States of America, China, and Japan acting as prominent examples. In the realm of research that focuses on the progression of the IOT integration into the e-learning systems of economically challenged countries, there is a substantial disparity. In particular, the focus of this research is on Jordan to shed light on the primary variables that are either facilitating or hindering the adoption of the IoT within the e-learning sector of Jordan's HEIs. A comprehensive analysis of previous research has been undertaken as a first stage in this investigation. The goal of this analysis is to identify important factors that are involved in the process of IOT adoption. Following that, we used an inferential technique, collecting data from 306 respondents who were enrolled in Jordanian higher education institutions. During our investigation, we discovered that the rate of the IOT integration was significantly influenced by factors such as accessibility, usability, technical assistance, and individual capabilities. In addition, our findings suggest that factors such as attitude, behavior, financial preparedness, dependability, and training have a substantial impact on the adoption of the IOT. On the other hand, the study seemed to indicate that characteristics such as class capacity, awareness, system resources, and course design had a minor influence on the adoption rates inside HEIs. In conclusion, this study provides tangible suggestions to strengthen the integration of the IoT inside Jordanian HEIs. These recommendations provide significant insights that can be used by policy architects, government entities, and higher education institutions to overcome the challenges that relate to the deployment of IoT in the higher learning sector.

This research aims to investigate the relationships between Big Data and Internet of Things (IoT) adoption and employee behavior in the chemical manufacturing industry, specifically focusing on the mediating role of organizational innovation. The research methodology employs a quantitative approach that involves employee surveys, statistical analysis, and mediation testing. The primary findings reveal that Big Data adoption significantly enhances Organizational Innovation, contributing positively to Organizational Citizenship Behavior (OCB) among employees. Conversely, IoT adoption has a significant positive impact on Organizational Innovation but does not directly influence OCB. The relationship between IoT adoption and OCB is mediated by Organizational Innovation, highlighting the pivotal role of innovation as an intermediary in influencing employee behavior. The practical implications of this research suggest that organizations in the chemical manufacturing industry should strategically integrate Big Data and IoT technologies to foster innovation and elevate OCB. Leadership support and employee training are crucial. Study limitations include industry specificity, self-reported data, and static analysis. Future research should diversify samples and use longitudinal methods. Recommendations: embrace tech with innovation focus, train leaders, and deepen understanding of tech, innovation, and behavior.

IoT devices with weak security designs are a serious threat to organizations. They are the building blocks of Botnets, the platforms that launch organized attacks that are capable of shutting down an entire infrastructure. Researchers have been developing IDS solutions that can counter such threats, often by employing innovation from other disciplines like artificial intelligence and machine learning. One of the issues that may be encountered when machine learning is used is dataset purity. Since they are not captured from perfect environments, datasets may contain data that could affect the machine learning process, negatively. Algorithms already exist for such problems. Repeated Edited Nearest Neighbor (RENN), Encoding Length (Explore), and Decremental Reduction Optimization Procedure 5 (DROP5) algorithm can filter noises out of datasets. They also provide other benefits such as instance reduction which could help reduce larger Botnet datasets, without sacrificing their quality. Three datasets were chosen in this study to construct an IDS: IoTID20, N-BaIoT and MedBIoT. The filtering algorithms, RENN, Explore, and DROP5 were used on them to filter noise and reduce instances. Noise was also injected and filtered again to assess the resilience of these filters. Then feature optimizations were used to shrink the dataset features. Finally, machine learning was applied on the processed dataset and the resulting IDS was evaluated with the standard supervised learning metrics: Accuracy, Precision, Recall, Specificity, F-Score and G-Mean. Results showed that RENN and DROP5 filtering delivered excellent results. DROP5, in particular, managed to reduce the dataset substantially without sacrificing accuracy. However, when noise got injected, the DROP5 accuracy went down and could not keep up. Of the three dataset, N-BaIoT delivers the best accuracy overall across the learning techniques.