The healthcare industry has experienced remarkable growth in data generation and revenue, making it the most rapidly expanding sector. To enhance security measures, our study explores the adoption of blockchain technology, leveraging its various aspects such as decentralization, consortiums, Ethereum, and Hyperledger. This paper proposes a Secured Healthcare Framework utilizing blockchain technology, with a focus on securing Electronic Health Records (EHR) through smart contracts. This approach ensures end-to-end security and non-repudiation. By integrating IoT devices such as RFID and Arduino, our method not only enhances security but also stream-lines data management within healthcare settings. The results demonstrate significant efficacy in the secure transmission and management of patient medical records. Moreover, experimental findings indicate an 89.88 % difference in latency between the MetaMask and Ganache environments, and a 13.04 % difference in gas usage for transactions in the Remix and Ganache environments. These findings highlight the potential of our proposed framework to improve security and efficiency in healthcare data management.

Recently, underwater wireless sensor networks (UWSNs) have been employed in the marine environment to forecast landslides by determining the amount of water and soil conditions, such as soil salinity, wetness, and movement. It is used in a variety of applications, including data collecting, disaster prediction, resource inquiry, marine surveillance, and so on. Energy efficiency becomes a challenge in UWSN since the nodes function on inherent energy and it can be difficult to exchange the power supply. This study focuses on developing an energy-efficient metaheuristic cluster-based routing protocol for UWSN, known as the EEMCBR-UWSN approach. The primary goal of the EEMCBR-UWSN technology is to improve energy efficiency through clustering and routing procedures. EEMCBR-UWSN technique uses a two-stage process. Initially, the spotted hyena optimization algorithm-based clustering (SHOA-C) method was used to arrange nodes in UWSN and pick appropriate cluster heads. The SHOA-C approach creates a fitness function for selecting CHs based on energy usage. Furthermore, the tumbleweed optimization algorithm-based routing (TWOA-R) approach was employed in the second stage to find the best routes in the UWSN. For route selection, the TWOA-R approach creates a fitness function based on energy, distance, and node degree. The simulation results were suggested that the EEMCBR-UWSN method outperforms traditional techniques by effectively enhancing the cluster head selection and routing processes.

The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet of Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.

Abstract This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

Modern cloud computing strategies greatly reduce investment in infrastructure and data maintenance costs across startup companies and large organizations. This is mainly because of the emergence of the cloud-based IoT paradigm, which allows IoT-based devices to directly upload the data acquired from the environments to the remote cloud and allows the owner of those resources to manage their data through cloud-based applications. However, there are many challenges associated with this kind of data being outsourced, and the cloud server is entirely responsible for securing that data. Since these cloud servers cannot always be fully trusted, securing the deletion of unwanted sensitive data stored in the cloud to prevent data leaking is another challenging issue. As a result, most of the existing technology allows coarse-grained deletion of data on cloud servers. Consequently, cloud data security is jeopardized and new technological development in the cloud environment is hampered. This work implements a Cloud-based, Scalable, Secure unique data Deletion and Verification policy (CSSDV) that uses Ciphertext-Attribute-based encryption to enable fine-grained secure data deletion and data verification. We confirm the CSSDV scheme’s security under the standard model and demonstrate its accuracy and efficacy through theoretical analysis and extensive numerical experiment data. The experimental results for eleven attribute each with different sizes demonstrate the superiority of our algorithm in accessing and deleting the files in a secure and scalable manner.

Abstract Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.