The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote di- agnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbe- havior analysis. Moreover, falsified or modified context generates a high percentage of false positives and, in some cases, causes a by-pass in misbehavior detection. Relying on the recent pow- erful consolidation of Blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure mis- behavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through the integration of Blockchain technology based on Ethereum smart contract environment. Fur- thermore, the effectiveness of the proposed model is bench- marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, ex- haustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

The personal health information (PHI) is an activity among the health-care providers and the patients in terms of managing the data which is sensitive to the parties. The PHI data have been maintained by multiple health-care providers, thus resulting in separated data. Moreover, the PHI data are stored in the provider’s database, hence the patients have no authority to manage their own information. Therefore, in this article, we propose a conceptual model for managing the PHI data which is derived from several health-care providers by relying on the blockchain technology in the peer-to-peer overlay network. In addition, we elaborate the security analysis that might be occurring in the proposed model. By leveraging on our model, it allows the patients and the providers to collect effectively the PHI data onto a single view as well guarantee of data integrity. The blockchain offers an immutable of the data record without having to trust a third party. The experimental results show that the proposed approach is promising to be developed due to the high success rate in terms of data dissemination.

Virtual reality (VR) has emerged as a transformative technology in education, enabling immersive simulations that foster student engagement and improve knowledge retention. This study presents a bibliometric analysis of global trends, research patterns, and challenges related to the integration of VR in educational contexts. Data were collected from the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Emerging Sources Citation Index (ESCI) within the Web of Science Core Collection, including only articles also indexed in Scopus to ensure high-quality and comprehensive coverage. Of the 1220 articles published between 2010 and 2024, 97 met the inclusion criteria based on relevance, accessibility, and methodological rigor. We performed keyword analysis, citation analysis, and thematic mapping using RStudio and the Bibliometrix package. The findings indicate that the United States, China, and the United Kingdom are the most active contributors to VR research in education. VR has been applied across diverse learning modalities, enriching both formal and informal educational environments. However, its widespread adoption faces several barriers, including high implementation costs, health and safety concerns, limited curricular integration, and insufficient educator training. Proposed solutions include strategic partnerships with technology providers and the use of cloud-based infrastructures to reduce costs and complexity. This study offers actionable insights for educators, policymakers, and developers, providing a robust, data-driven foundation for the effective and equitable integration of VR in education.

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence model without exposing the UE’s valuable data to the public, or to the model providers. Furthermore, the blockchain is an immutable data approach that can be leveraged for FL across 5G ENs and beyond. The recorded transactions cannot be altered maliciously, and they remain unchanged by design. We further propose a dynamic authentication protocol for UE to interact with a diverse base station. We apply blockchain as a reward mechanism in FL to enable computational offloading in wireless networks. Additionally, we implement and investigate blockchain technology for FL in 5G UE.

The shared storage is essential in the decentralized system. A straightforward storage model with guaranteed privacy protection on the peer-to-peer network is a challenge in the blockchain technology. The decentralized storage system should provide the privacy for the parties since it contains numerous data that are sensitive and dangerous if misused by maliciously. In this paper, we present a model for shared storage on a blockchain network which allows the authorized parties to access the data on storage without having to reveal their identity. Ring signatures combined with several protocols are implemented to disguise the signer identity thereby the observer is unlikely to determine the identity of the parties. We apply our proposed scheme in the healthcare domain, namely, decentralized personal health information (PHI). In addition, we present a dilemma to improve performance in a decentralized system.