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Sensors (Basel). 2018 Dec 2;18(12). pii: E4226. doi: 10.3390/s18124226.

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications.

Author information

1
Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea. sajjad@hufs.ac.kr.
2
Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea. aslam.jarwar@hufs.ac.kr.
3
Department of Information and Communications Engineering, Hankuk University of Foreign Studies, Seoul 02450, Korea. iychong@hufs.ac.kr.

Abstract

In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

KEYWORDS:

Internet of Things (IoT); IoT analytics; Web of Objects (WoO); microservices; semantic ontology

PMID:
30513822
DOI:
10.3390/s18124226
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