3,973 research outputs found
From access and integration to mining of secure genomic data sets across the grid
The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled Microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS
Self-managed cells and their federation
Future e-Health systems will consist of low-power, on-body wireless sensors attached to mobile users that interact with a ubiquitous computing environment. This kind of system needs to be able to configure itself with little or no user input; more importantly, it is required to adapt autonomously to changes such as user movement, device failure, the addition or loss of services, and proximity to other such systems. This extended abstract describes the basic architecture of a Self-Managed Cell (SMC) to address these requirements, and discusses various forms of federation between/among SMCs. This structure is motivated by a typical e-Health scenario
Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture
Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient
means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering
intelligent decision-making and a wide range of Artificial Intelligence (AI)
services across major corporations such as Google, Walmart, and AirBnb. KGs
complement Machine Learning (ML) algorithms by providing data context and
semantics, thereby enabling further inference and question-answering
capabilities. The integration of KGs with neuronal learning (e.g., Large
Language Models (LLMs)) is currently a topic of active research, commonly named
neuro-symbolic AI. Despite the numerous benefits that can be accomplished with
KG-based AI, its growing ubiquity within online services may result in the loss
of self-determination for citizens as a fundamental societal issue. The more we
rely on these technologies, which are often centralised, the less citizens will
be able to determine their own destinies. To counter this threat, AI
regulation, such as the European Union (EU) AI Act, is being proposed in
certain regions. The regulation sets what technologists need to do, leading to
questions concerning: How can the output of AI systems be trusted? What is
needed to ensure that the data fuelling and the inner workings of these
artefacts are transparent? How can AI be made accountable for its
decision-making? This paper conceptualises the foundational topics and research
pillars to support KG-based AI for self-determination. Drawing upon this
conceptual framework, challenges and opportunities for citizen
self-determination are illustrated and analysed in a real-world scenario. As a
result, we propose a research agenda aimed at accomplishing the recommended
objectives
Federated Embedded Systems â a review of the literature in related fields
This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous
computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways
Smart and secure medical device gateway for managing patient recovery
Patients recuperating from orthopedic surgery require frequent monitoring and hospital visits with a wealth of personal
medical data generated both on and off-site, making it challenging to maintain records. This paper discusses a secure
blockchain-based data management software to enable safe remote access without compromising patient information. The
BlockTrack software developed at our group will be customized to interface with modules for orthopedic recuperation
monitoring. Modules can consist of ultrasonic bone health monitoring sensors, connected to relay nodes that can transmit
patient data to the BlockTrack mobile app, which then intercepts the information to be stored securely on a cloud-based
Blockchain network. Each record will have a unique ID enabled by Blockchain, for secure access and review of patient
information by other parties, including doctors and pharmacists. Key findings are discussed with a goal to further develop
this solution
Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration
As the integration of Internet of Things devices with cloud computing
proliferates, the paramount importance of privacy preservation comes to the
forefront. This survey paper meticulously explores the landscape of privacy
issues in the dynamic intersection of IoT and cloud systems. The comprehensive
literature review synthesizes existing research, illuminating key challenges
and discerning emerging trends in privacy preserving techniques. The
categorization of diverse approaches unveils a nuanced understanding of
encryption techniques, anonymization strategies, access control mechanisms, and
the burgeoning integration of artificial intelligence. Notable trends include
the infusion of machine learning for dynamic anonymization, homomorphic
encryption for secure computation, and AI-driven access control systems. The
culmination of this survey contributes a holistic view, laying the groundwork
for understanding the multifaceted strategies employed in securing sensitive
data within IoT-based cloud environments. The insights garnered from this
survey provide a valuable resource for researchers, practitioners, and
policymakers navigating the complex terrain of privacy preservation in the
evolving landscape of IoT and cloud computingComment: 33 page
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