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

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.

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“…For instance, if the user would be exercising, higher heart rate does not have to mean that he is angry, so with more types of user data, we could provide more accurate light condition knowing what is happening with the user in the environment. The wearables can be highly diverse, so utilizing paradigms like the edge computing 24 and cloud computing 25 would facilitate the management process. Besides, we would like to take into consideration emotion and mood in the group of people interacting with our system, because it is possible that more than one person could be present within sight of the camera which means they would also be detected.…”

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