An edge cloud–based body data sensing architecture for artificial intelligence computation

TaeYoung Kim and JongBeom Lim

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 4, 1550147719839014

Abstract: As various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present an edge cloud–based body data sensing architecture for artificial intelligence computation. The main rationale for designing the edge cloud–based sensing architecture is as follows. By analyzing physical body data on the edge cloud computing system, we can identify the relationship between body activities and health conditions for persons. In addition, we can support real-time applications without catastrophic failures by our efficient and stable implementation of the sensing architecture. Our cloud storage architecture is designed to support both stateful and stateless applications, which are compatible with traditional infrastructures and provide server consolidation with a CPU-aware scheduling of virtual machines. Performance results show that our edge cloud–based architecture outperforms the previous architecture in terms of failures, processing time, and scalability.

Keywords: Internet of things; cloud computing; sensor network; big data; artificial intelligence (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147719839014 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719839014

DOI: 10.1177/1550147719839014

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().