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 data analysis within networks, laying the groundwork for a scalable cloud-assisted infrastructure. However, recent work in cloud-assisted architectures have revealed several issues, specifically pertaining to applications in the biomedical field. Data-reliability and context aware adaptations are paramount to the success of biomedical applications, due to the field’s data quality needs when seeking in-depth analyses of the data sets. In addition, the cloud server must have a way to organize heterogeneous biomedical body sensor data and perform different types of biomedical body sensor research. The software infrastructure presented in this paper proposes several feedback mechanisms built off of dynamic variables within the system including data importance, data quality and network layout in order to provide researchers an optimal quality of service. The implementation of a domain specific language (DSL) will enable diverse biomedical data processing operations. Furthermore, a robust set of APIs will give researchers the ability to build flexible and unique biomedical applications.
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This work was supported by the United States National Science Foundation, CNS division (Award No. 1626586) and NSF of China (Grant No. 61305087).
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Reeves, J., Moreno, C., Li, M., Hu, C., Prabhakaran, B. (2019). Data Reliability-Aware and Cloud-Assisted Software Infrastructure for Body Area Networks. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_23
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