Context Awareness in Systems with Limited Resources

Abstract

Mobile embedded systems often have strong limitations regarding available resources. In this paper we propose a statistical approach which could scale down to microcontrollers with scarce resources, to model simple contexts based on raw sensor data. As a case study, two experiments are provided where statistical modeling techniques were applied to learn and recognize different contexts, based on accelerometer data. We furthermore point out applications that utilize contextual information for power savings in mobile embedded systems