Smart Health in the 6G Era: Bringing Security to Future Smart Health Services

Batista, Edgar; López-Aguilar, Pablo; Solanas, Agustí

doi:10.1109/mcom.019.2300122

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…The advancement of wireless communication networks has had a profound effect on the healthcare industry. The shift from 4G to 5G initiated a new era in digital healthcare [7], facilitating the emergence of the Intelligent Internet of Healthcare Things (IIoHT) or Internet of Medical Things (IoMT). This technology transition has created new possibilities in the healthcare sector, enhancing the efficiency, resilience, sustainability, affordability, and widespread availability of services.…”

Section: Healthcare In 6gmentioning

confidence: 99%

“…This technology transition has created new possibilities in the healthcare sector, enhancing the efficiency, resilience, sustainability, affordability, and widespread availability of services. The expected shift to 6G is poised to profoundly transform healthcare [2,7]. 6G will be a comprehensive system that will combine sensors, mobile communications, and processing capacity to effectively link virtual and actual environments.…”

Section: Healthcare In 6gmentioning

confidence: 99%

“…Patient privacy is impacted by the increased connectivity and data sharing in 6G-enabled healthcare. 6G technology promises to make healthcare more efficient, robust, sustainable, and ubiquitous, yet increased communication raises patient privacy concerns [7,13]. Security and privacy concerns for 6G are being actively considered from the very beginning, with the identification of particular security requirements, highlighting of security challenges, and identification of gaps for future research.…”

Section: Privacy Concernsmentioning

confidence: 99%

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Adaptive Security in 6G for Sustainable Healthcare

Ahmad,

Harjula

2024

Communications in Computer and Information Science

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Abstract6G will fulfill the requirements of future digital healthcare systems through emerging decentralized computing and secure communications technologies. Digital healthcare solutions employ numerous low-power and resource-constrained connected things, such as the Internet of Medical Things (IoMT). However, the current digital healthcare solutions will face two major challenges. First, the proposed solutions are based on the traditional IoT-Cloud model that will experience latency and reliability challenges to meet the expectations and requirements of digital healthcare, while potentially inflicting heavy network load. Second, the existing digital healthcare solutions will face security challenges due to the inherent limitations of IoMT caused by the lack of resources for proper security in those devices. Therefore, in this research, we present a decentralized adaptive security architecture for the successful deployment of digital healthcare. The proposed architecture leverages the edge-cloud continuum to meet the performance, efficiency, and reliability requirements. It can adapt the security solution at run-time to meet the limited capacity of IoMT devices without compromising the security of critical data. Finally, the research outlines comprehensive methodologies for validating the proposed security architecture.

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Section: Healthcare In 6gmentioning

confidence: 99%

Section: Healthcare In 6gmentioning

confidence: 99%

Section: Privacy Concernsmentioning

confidence: 99%

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Adaptive Security in 6G for Sustainable Healthcare

Ahmad,

Harjula

2024

Communications in Computer and Information Science

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“…In the contemporary post-pandemic landscape, research by Vázquez-Ingelmo et al [106] and Batista et al [107], among others, has shown a trend towards more integrated, intelligent, and user-centric medical diagnostic systems. Vázquez-Ingelmo et al presented the CARTIER-IA platform, which enhances the usability of medical data management by integrating various types of data and enabling the application of AI algorithms through a web application, catering to diverse roles in medical research and practice.…”

Section: Advanced Medical Imaging and Diagnosis Systems: Algorithms A...mentioning

confidence: 99%

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Triantafyllopoulos,

Paxinou,

Feretzakis

et al. 2024

Future Internet

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The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI’s current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview.

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“…6G networks will have a larger attack surface than the previous generation of networks because they will use a wider range of frequencies and technologies [ 53 , 54 ]. However, in contrast, it will make the 6G network more complex than previous network generations, which makes it more difficult to secure them, as there will be more potential vulnerabilities to exploit [ 55 ]. As 6G networks become more sophisticated, new attack vectors will emerge.…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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Smart Health in the 6G Era: Bringing Security to Future Smart Health Services

Cited by 12 publications

References 13 publications

Adaptive Security in 6G for Sustainable Healthcare

Adaptive Security in 6G for Sustainable Healthcare

Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

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