Energy-Efficient IoT-Health Monitoring System using Approximate Computing

Abstract

Wireless Body Sensor Nodes (WBSN) are frequently used for real time IoT-based health monitoring of patients outside the hospital environment. These WBSNs involve bio-sensors to capture signals from a patient’s body and wireless transmitters to transmit the collected signals to a server located in private/public cloud in real time. These WBSNs include hardware for processing of signals before being transmitted to the cloud. Simultaneous occurrence of all these processes inside energy constrained WBSNs results in considerable amount of power consumption, thus limiting their operational lifetime. Due to the inherent error-resilience in signal processing algorithms, most of these data reaching the servers are redundant in nature and hence of not much clinical importance. Transmission and storage of these excess data result in inefficient usages of transmission bandwidth and storage capabilities. In this paper, we develop a real time encoding scheme that performs iterative thresholding and approximation of wavelet coefficients for sparse encoding of bio-signals (ECG signals), thereby reducing the energy and bandwidth consumption of the WBSN. The encoding scheme compresses bio-signals (ECG signals), while still maintaining the clinically important features. We optimize various process parameters to model a low power hardware prototype for the implementation of our algorithm on a real time microcontroller based IoT platform that operates as an end-to-end WBSN system in real time. Experimental results show a system-level energy improvement of 96% with a negligible impact on signal quality (2%).