An Adaptive Framework for Smart E-Health IoT Applications Using Asynchronous Data Under Edge Computing Services
Yufeng Lin, Jia Wang, Tony Sahama · 2022
This paper is concerned with adaptive features of smart e-Health Internet of Things (IoT) applications in the field of Ambient Assisted Living. To effectively utilise the embedded battery of smart sensors and communication resources, an adaptive sensing mechanism is proposed, based on the analysis of synchronous and asynchronous sensor data, characteristics extracted from the collected data, and the optimisation obtained by artificial intelligence. In this mechanism, initial sensor profiles are pre-set to collect various data in an e-Health sensor network. Within a pre-set period, the sampling data will be collected and sent to edge computing for analysis to extract the data characteristics of each sensor. The derived health data characteristics will be assessed as inputs of a neural network to determine how the pre-set parameters will be adjusted based on the required performance. In this paper, an adaptive framework will be proposed to facilitate e-Health smart IoT applications through the edge-IoT ecosystem.