5,252 research outputs found
Designing the Health-related Internet of Things: Ethical Principles and Guidelines
The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols
The Internet of Hackable Things
The Internet of Things makes possible to connect each everyday object to the
Internet, making computing pervasive like never before. From a security and
privacy perspective, this tsunami of connectivity represents a disaster, which
makes each object remotely hackable. We claim that, in order to tackle this
issue, we need to address a new challenge in security: education
The applicability of the Internet of Things (IoT) between fundamental rights to health and to privacy
This work aims to study main insecurities and uncertainties regarding to IoT, verifying its impact to the exercise of the fundamental rights to healthcare and to privacy. Its specific objectives are: i) to present promises of IoT to healthcare and treatments; ii) to expose risks and uncertainties identified with IoT until the present moment; iii) to analyze ethical and legal principles (mainly in Brazil) concerning to IoT uses. Its main hypothesis is that healthcare can be revolutionarily improved with IoT, but despite of all of that revolution in good practices, good technologies of security, securitized by public policies and legal practices, have also to be implemented and improved by scholars, jurists and politicians. Methodology: hypothetical-deductive method of research, with a qualitative and transdisciplinar method of approach, and a bibliographical research technique. Results: IoT/IoMT presents a great potential of actualization of the fundamental right to health, but the security of the collection and storage of sensitive data should be the first concern in the development of systems involving such technologies, since there is an immense potential of disrespect to the fundamental right to the privacy of individuals from their use, not only by private third parties, but also, by the State
Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review
Internet of Things (IoT) is an evolution of the Internet and has been gaining increased
attention from researchers in both academic and industrial environments. Successive technological
enhancements make the development of intelligent systems with a high capacity for communication
and data collection possible, providing several opportunities for numerous IoT applications,
particularly healthcare systems. Despite all the advantages, there are still several open issues
that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information
security, and privacy. IoT provides important characteristics to healthcare systems, such as availability,
mobility, and scalability, that o er an architectural basis for numerous high technological healthcare
applications, such as real-time patient monitoring, environmental and indoor quality monitoring,
and ubiquitous and pervasive information access that benefits health professionals and patients.
The constant scientific innovations make it possible to develop IoT devices through countless services
for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced
living environments (ELEs). This paper reviews the current state of the art on IoT architectures for
ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities,
open-source platforms, and operating systems. Furthermore, this document synthesizes the existing
body of knowledge and identifies common threads and gaps that open up new significant and
challenging future research directions.info:eu-repo/semantics/publishedVersio
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
With the advent of the IoT, AI, ML, and DL algorithms, the landscape of
data-driven medical applications has emerged as a promising avenue for
designing robust and scalable diagnostic and prognostic models from medical
data. This has gained a lot of attention from both academia and industry,
leading to significant improvements in healthcare quality. However, the
adoption of AI-driven medical applications still faces tough challenges,
including meeting security, privacy, and quality of service (QoS) standards.
Recent developments in \ac{FL} have made it possible to train complex
machine-learned models in a distributed manner and have become an active
research domain, particularly processing the medical data at the edge of the
network in a decentralized way to preserve privacy and address security
concerns. To this end, in this paper, we explore the present and future of FL
technology in medical applications where data sharing is a significant
challenge. We delve into the current research trends and their outcomes,
unravelling the complexities of designing reliable and scalable \ac{FL} models.
Our paper outlines the fundamental statistical issues in FL, tackles
device-related problems, addresses security challenges, and navigates the
complexity of privacy concerns, all while highlighting its transformative
potential in the medical field. Our study primarily focuses on medical
applications of \ac{FL}, particularly in the context of global cancer
diagnosis. We highlight the potential of FL to enable computer-aided diagnosis
tools that address this challenge with greater effectiveness than traditional
data-driven methods. We hope that this comprehensive review will serve as a
checkpoint for the field, summarizing the current state-of-the-art and
identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa
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