Research Article
BSNCloud: Cloud-Centered Wireless Body Sensor Data Collection, Streaming, and Analytics System
@INPROCEEDINGS{10.1007/978-3-030-64991-3_5, author={Ming Li and Ai Enkoji and Matthew Key and Aaron Marroquin and B. Prabhakaran}, title={BSNCloud: Cloud-Centered Wireless Body Sensor Data Collection, Streaming, and Analytics System}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health. 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings}, proceedings_a={BODYNETS}, year={2020}, month={12}, keywords={Body sensor networks Cloud-assisted Wireless body area networks}, doi={10.1007/978-3-030-64991-3_5} }- Ming Li
Ai Enkoji
Matthew Key
Aaron Marroquin
B. Prabhakaran
Year: 2020
BSNCloud: Cloud-Centered Wireless Body Sensor Data Collection, Streaming, and Analytics System
BODYNETS
Springer
DOI: 10.1007/978-3-030-64991-3_5
Abstract
Cloud-assisted body area networks have been the focus of researchers in past years as a response to the development of robust wireless body area networks (WBANs). While software such as Signal Processing in Node Environment (SPINE) provide Application Programming Interfaces (APIs) to manage heterogeneous biomedical sensor networks, others have focused on developing tools that address the issue of sensor connection/control, data receiving, and visualization. However, existing software tools lack sufficient flexibility, scalability, and support for complicated biomedical systems. In this paper, BSNCloud, a cloud-centered heterogeneous and comprehensive wireless body sensor data collection, streaming, and analytics framework is proposed. The system combines the sensor control and data aggregator event detection, real-time data analysis, visualization, and streaming into one Android App and incorporated four key components in the cloud server: data repository, algorithm repository, machine learning engine, and web portal. A prototype has been implemented with preliminary performance evaluation. Results show that the system is promising in its full utilization of the high performance computing power as well as the large volume storage capacity.
