TY - GEN
T1 - BSNCloud
T2 - 15th International Conference on Body Area Networks, BodyNets 2020
AU - Li, Ming
AU - Enkoji, Ai
AU - Key, Matthew
AU - Marroquin, Aaron
AU - Prabhakaran, B.
N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Body sensor networks
KW - Cloud-assisted
KW - Wireless body area networks
UR - https://www.scopus.com/pages/publications/85098268206
U2 - 10.1007/978-3-030-64991-3_5
DO - 10.1007/978-3-030-64991-3_5
M3 - Conference contribution
SN - 9783030649906
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 59
EP - 73
BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health - 15th EAI International Conference, BODYNETS 2020, Proceedings
A2 - Alam, Muhammad Mahtab
A2 - Hämäläinen, Matti
A2 - Mucchi, Lorenzo
A2 - Niazi, Imran Khan
A2 - Le Moullec, Yannick
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 October 2020 through 21 October 2020
ER -