Assessing Energy Consumption in Data Acquisition from Smart Wearable Sensors in IoT-Based Health Applications.

Smart wearable devices for patient monitoring rely on batteries as energy-source for capturing vital signs, processing information locally, and transmitting data. The advantages of such solutions are providing mobility to users, connectivity to send data constantly, and low cost. These devices are wireless and must be tiny to be carried comfortably by the users. This fact restricts energy autonomy and requires frequent replacement or recharge of batteries. The highest energy cost is commonly attributed to transmissions in wireless devices, and several studies focused on communication and routing protocols to enhance energy efficiency in such solutions. However, researchers should give more attention to data acquisition of physiological sensors regarding energy efficiency in such solutions. In this preliminary study, we present the effects of a self-adaptive algorithm on the energy consumption of popular wearable physiological sensors. Our prototype is composed of an oximeter and a temperature sensor. Our experiments demonstrate that the self-adaptive procedure can save up to 80% energy consumption regarding the oximeter when monitoring stable patients at low risk and 51% in unstable patients. In addition, the temperature sensor can reach 97% of energy savings in the self-adaptive mode. The sensors’ data acquisition can present a superior energy cost than radio transmissions on such devices. In future work, we will explore the potential benefits of the algorithm in all main activities of our monitoring device.