A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.

The idea of a smart city is to connect physical objects or things with sensors, software, electronics, and Internet connectivity for data communication through the Internet of Things (IoT) devices. IoT enhances productivity and efficacy intelligently using remote management, but the risk of security and privacy increases. Cyber threats are advancing day by day, causing insufficient measures of security and confidentiality. As the hackers use the Internet, several IoT vulnerabilities are introduced, demanding new security measures in the IoT devices of the smart city. The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. The output of machine learning could be used to detect anomalies in IoT‐network systems. This paper employed several machine learning classifiers and a deep learning model for intrusion detection using seven datasets of the TON_IoT telemetry dataset. The proposed IDS achieved an accuracy of 99.7% using Thermostat, GPS Tracker, Garage Door, and Modbus datasets via voting classifier.

The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies.

This study investigates the performance of different numerical techniques, modeling, and simulation in solving complex problems. The study found that the Finite Element Method was found to be the most precise numerical approach for simulating the behavior of structures under loading conditions, the Finite Difference Method was found to be the most efficient numerical technique for simulating fluid flow and heat transfer problems, and the Boundary Element Method was found to be the most effective numerical technique for solving problems involving singularities, such as those found in acoustics and electromagnetics. The mathematical model established in this research was able to effectively forecast the behaviors of the system under different conditions, with an error of less than 5%. The physical model established in this research was able to replicate the behavior of the system under different conditions, with an error of less than 2%. The employment of multi-physics or multi-scale modeling was found to be effective in overcoming the limitations of traditional numerical techniques. The results of this research have significant effects for the field of numerical techniques, modeling and simulation, and can be used to guide engineers and researchers in choosing the most appropriate numerical technique for their specific problem or application.

This study used a technology–organization–environment (TOE) framework as the primary analytical tool to explore the burgeoning capabilities of blockchain technology in the area of 5G ceramic antenna development. A causal loop diagram (CLD) analysis is used to further clarify the complex dynamics and feedback mechanisms, and the impact of blockchain on the design, production, and deployment phases of ceramic antennas, which play a pivotal role in the development of 5G communications, is studied. We found that blockchain’s unique features, including its immutable ledger and decentralized architecture, have the potential to significantly improve the transparency, security, and efficiency of the ceramic antenna manufacturing process. Technology (T), organization (O), and environment (E) were used as the top factors, and the subfactors of TOE were selected and analyzed using the Analytic Hierarchy Process (AHP) by CLD. The AHP analysis was used to evaluate the relative importance of various internal and external factors affecting the adoption of blockchain technology. The integration of the TOE framework with AHP and CLD provides a comprehensive analytical tool that enhances the understanding of the complex dynamics in the 5G ceramic antenna manufacturing process. This methodological approach not only clarifies the interactions between technological, organizational, and environmental factors but also facilitates strategic decision-making through a structured evaluation of these factors. The AHP analysis showed that technical factors are the most important in the TOE analysis of 5G ceramic antenna manufacturing, with a weight of 0.427, which indicates the important role of technical factors in the development of ceramic antenna production. In addition, environmental and organizational factors were given weights of 0.302 and 0.271, respectively, confirming the importance of technological innovation and internal process optimization. In the subfactor of Technology (T), ‘Blockchain Technology’ has the highest ranking among the subfactors, with a global weight value of 0.129, emphasizing the importance of blockchain technology. This study explored the technical and organizational complexities of introducing blockchain technology into the 5G ceramic antenna manufacturing industry and, through an in-depth investigation of the potential benefits of such integration, it aims to propose new approaches to improve quality control and manufacturing efficiency. The research findings aim to contribute to the sustainable growth of the telecommunications industry by providing strategic recommendations for the application of blockchain technology in the production of 5G ceramic antennas.