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

Author

Listed:
  • TaeYoung Kim
  • JongBeom Lim

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.

Suggested Citation

  • TaeYoung Kim & JongBeom Lim, 2019. "An edge cloud–based body data sensing architecture for artificial intelligence computation," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719839014
    DOI: 10.1177/1550147719839014
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    Cited by:

    1. Dominika Cupkova & Erik Kajati & Jozef Mocnej & Peter Papcun & Jiri Koziorek & Iveta Zolotova, 2019. "Intelligent human-centric lighting for mental wellbeing improvement," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.

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