Fog computing architectures for healthcare: Wireless performance and semantic opportunities. Issue 4 (14th November 2016)

• Record Type: Journal Article
• Authors: Prieto González, Lisardo; Jaedicke, Corvin; Schubert, Johannes; Stantchev, Vladimir
• Is Part Of: Journal of information, communication & ethics in society. Volume 14:Issue 4(2016)
• Page Start: 334
• Page End: 349
• Publication Date: 2016-11-14
• Subjects: Healthcare -- Internet of Things -- Cloud computing -- Ontologies -- Fog computing -- Knowledge representation; Information technology -- Moral and ethical aspects -- Periodicals; Information society -- Moral and ethical aspects -- Periodicals; Information superhighway -- Social aspects -- Periodicals; Digital media -- Social aspects -- Periodicals; Digital communications -- Social aspects -- Periodicals; Computer networks -- Social aspects -- Periodicals; Computer security -- Periodicals; 303.4833
• DOI: 10.1108/JICES-05-2016-0014
• Languages: English
• ISSNs: 1477-996X

Abstract

Abstract : 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.