A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks

Abstract

The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users' context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.