Improving the Energy Efficiency of Wearable Computing Units Using on Sensor Fifo Memory

International audienceProliferation of wearable devices with wide spectrum of sensing capabilities together with commercial availability has increased the applicability of ambient intelligence concepts in practical system designs. Being wearable enforces extra constraints in terms of form factor and weight that limit the computational properties and the battery lifetime. There has been increasingly many number of studies for the energy efficiency of embedded and mobile hardware platforms. Due to the known techniques, increasing the energy consumption of an embedded system inherently requires some components to go into the low energy modes with a certain pattern, which in turn entails performance penalties at the application level. Existing solutions for increasing energy efficiency mainly focus only on a certain component of the system, such as hardware, networking firmware and try to achieve energy efficiency without considering the state the application is dynamically in. In this study, the critical balance between energy efficiency and application performance is handled. Application feedback is merged with energy efficiency and according to the application performance, duty cycle mechanism can be configured dynamically. A memory unit (FIFO) of the sensing component is also involved into the dynamic sleep scheduling mechanism in order to process latest sampled data while microprocessor and radio module of the sensor devices are in sleep mode. In this context, one of the fundamental implementations of ambient application which is based on triaxial accelerometer signal, pedometer is performed. Experiments realized on the dataset proved that it exists an interval where energy efficiency is obtained without degrading application performance under critical level and also usage of FIFO showed a significant impact on application performance and energy gain