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Design and Implementation of e-Health System Based on Semantic Sensor Network Using IETF YANG.
1Department of Computer Engineering, Jeju National University, Jeju 63243, Korea. wenquan.jin@jejunu.ac.kr.
Sensors (Basel, Switzerland)
|February 21, 2018
Summary
This study introduces an e-Health system using Semantic Sensor Network (SSN) and IETF YANG modeling to solve device interoperability issues. The approach enables auto-configuration and semantic querying for diverse e-Health sensors.
Area of Science:
- Digital Health
- Biomedical Informatics
- Sensor Networks
Background:
- e-Health systems leverage Information and Communication Technologies (ICT) for remote healthcare delivery.
- Heterogeneous sensors in e-Health systems produce data in various formats, posing challenges for device interoperability and data normalization.
- Existing solutions often rely on manual programming, which is inefficient for integrating new devices.
Purpose of the Study:
- To propose an e-Health system addressing device interoperability using Semantic Sensor Network (SSN).
- To enable semantic interoperability and user-friendly data expression for diverse e-Health sensors.
- To facilitate auto-configuration and semantic querying within e-Health sensor networks.
Main Methods:
- Developed an e-Health system incorporating Semantic Sensor Network (SSN).
- Utilized IETF YANG for modeling semantic e-Health data and representing sensor information.
- Created a YANG-defined ontology for e-Health data to support various data formats and construct sensor meta-models.
Main Results:
- Achieved semantic interoperability between heterogeneous e-Health devices.
- Enabled user-friendly expression of sensing data through semantic modeling.
- Demonstrated the capability for auto-configuration of e-Health sensors.
- Facilitated semantic querying of the sensor network.
Conclusions:
- The proposed SSN-based e-Health system effectively addresses device interoperability challenges.
- IETF YANG modeling and a YANG-defined ontology provide a robust framework for semantic data interpretation.
- The approach enhances the flexibility and scalability of e-Health systems by supporting auto-configuration and semantic querying.
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