EnergyEfficient IoTHealth Monitoring System using Approximate Computing Abstract   Wireless Body Sensor Nodes (WBSN) are frequently used for real time IoTbased health monitoring of patients outside the hospital environment. These WBSNs involve biosensors to capture signals from a patientxe2x80x99s body and wireless transmitters to transmit the collected signals to a server located in privatepublic 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 errorresilience 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 biosignals (ECG signals), thereby reducing the energy and bandwidth consumption of the WBSN. The encoding scheme compresses biosignals (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 endtoend WBSN system in real time. Experimental results show a systemlevel energy improvement of 96% with a negligible impact on signal quality (2%).

A Wireless Body Area Network for Remote Observation of Physiological Signals The objective of this work is to describe the design process of a wireless body area network (WBAN) for the remote observation of multiple physiological signals from a patient. Various sensors such as temperature, heart rate monitor utilizing electrocardiography, and accelerometer to detect fall and seizure conditions were integrated in the WBAN. Sensed data is wirelessly transmitted to the central control unit (CCU) that is associated with a remote base station. For benchmarking, medically certified sensors were employed to validate wearable sensors data. The sensor information can be ported in the cloud environment using CCUbased gateway with Global System for Mobile communication (GSM) modem capability. This mechanism is facilitating remote access to sensors information. To connect Radio Frequency (RF) units wirelessly, Zigbee mesh topology was adopted. In this way, they can be remotely overseen, managed and controlled by assigned staff. The presented prototype featuring the desired WBAN system performance was evaluated with different human postures and moving scenarios.