Fog computing architectures for healthcare
| Autor: | Vladimir Stantchev, Lisardo Prieto González, Corvin Jaedicke, Johannes Schubert |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Sociology and Political Science
Knowledge representation and reasoning Computer Networks and Communications Computer science business.industry Communication Distributed computing Reliability (computer networking) 020206 networking & telecommunications Context (language use) Cloud computing 02 engineering and technology Application layer World Wide Web Philosophy 020204 information systems 0202 electrical engineering electronic engineering information engineering Wireless Layer (object-oriented design) business Data transmission |
| Zdroj: | Journal of Information, Communication and Ethics in Society. 14:334-349 |
| ISSN: | 1477-996X |
| DOI: | 10.1108/jices-05-2016-0014 |
| Popis: | Purpose The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments. |
| Databáze: | OpenAIRE |
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