Cloud-Edge Collaboration Framework for IoT data analytics
Jaewon Moon, Sangyeon Cho, Seung Woo Kum, Sang-Won Lee · 2018
In recent years, big data analysis from Internet of Things (IoT) has been getting more attention. Several cloud platforms have provided machine learning service with a pre-trained model to understand IoT data. However, it is necessary to transfer personal data in order to use the cloud service, and network problems might be preventing the customer from getting analysis results at an appropriate time. To overcome these problems, data and analysis task are moving to the edge platform. However, most edge devices do not have enough capacity to process and train large amounts of data. In this paper, we propose a new framework structure that can analyze IoT data by distributing analysis role. The proposed framework is designed to maximize the resources of the cloud to generate the model and to use the model at the edge to enable immediate and instantaneous actuator operation. And we also present a case study to verify this framework.