A lightweight and cost effective edge intelligence architecture based on containerization technology

The integration of Cloud computing and Internet of Things led to rapid growth in the edge computing field. This would not be achievable without combining the data centers' managing systems with much more restrained technologies. One significantly effective and lightweight solution to this issue is presented by the Docker technology. It is able to manage virtualization process and can therefore be used to distribute, deploy and manage cloud and edge applications assigned into the clusters. In our study, this technology was represented by the Raspberry Pi devices, which are convenient thanks to their low cost, robust applicability and lightweight nature. Our application scenario focuses on identification of the human activities. In this paper, we suggest and evaluate an architecture on the basis of the distributed edge/cloud integration paradigm. We explain all of its advantages which lie in the combination of affordability and several other benefits provided by the fact that data processing is conducted by the edge devices instead of the central server. To recognize and identify human activity, the Regularized Extreme Leaning Machine (RELM) was engaged in our architecture. Our study presents detailed information about our use case scenario and the experimental simulation we performed.