Context predictor based sparse sensing technique and smart transmission architecture for IoT enabled remote health monitoring applications.

In hyperconnectivity scenario, managing the amount of data acquired from sensors in the Body Area Networks (BANs) is one of the major issues. In this paper we propose an on-chip context predictor based sparse sensing technology with smart transmission architecture which makes use of confidence interval calculation from the features that present in the data, thereby achieving statistical guarantee. The proposed architecture uses intelligent sparse sensing, which eradicates the collection of redundant data, thereby reducing the amount of data generated. For the performance analysis, we considered ECG data acquisition and transmission system. The proposed architecture when applied on the data collected from 10 patients reduces the duty cycle of the sensing unit to 27.99%, by achieving an energy saving of 72% and the mean deviation of sampled data from the original data is 2%.