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iSEE:A Semantic Sensors Selection System for Healthcare
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The massive use of Internet-based connectivity of devices such as smartphones and sensors has led to the emergence of Internet of Things(IoT). Healthcare is one of the areas that IoT-based applications deployment is becoming more successful. However, the deployment of IoT in healthcare faces one major challenge, the selection of IoT devices by stakeholders (for example, patients, caregivers, health professionals and other government agencies) given an amount of available IoT devices based on a disease(for ex-ample, Asthma) or various healthcare scenarios (for example, disease management, prevention and rehabilitation). Since healthcare stakeholders currently do not have enough knowledge about IoT, the IoT devices selection process has to proceed in a way that it allows users to have more detailed information about IoT devices for example, Quality of Service (QoS) parameters, cost, availability(manufacturer), device placement and associated disease. To address this challenge, this thesis work proposes, develops and validates a novel Semantic sEnsor sElection system(iSEE) for healthcare. This thesis also develops iSEE system prototype and Smart Healthcare Ontology(SHO). A Java application is built to allow users for querying our developed SHO in an efficient way.The iSEE system is evaluated based on query response time and the result-set for the queries. Further, we evaluate SHO using Competency Questions(CQs). The conducted evaluations show that our iSEE system can be used efficiently to support stakeholders within the healthcare domain.

Place, publisher, year, edition, pages
2016. , 88 p.
Keyword [en]
IoT, Healthcare, Semantic Web and Ontology
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ltu:diva-59635OAI: oai:DiVA.org:ltu-59635DiVA: diva2:1033985
Educational program
Computer Science and Engineering, master's level
Supervisors
Examiners
Available from: 2016-11-01 Created: 2016-10-10 Last updated: 2016-11-01Bibliographically approved

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