MediAlly: A provenance-aware remote health monitoring middleware

This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitoring. Data is collected using physiological sensors and transported back to the middleware using a smart phone. The key to MediAlly´s energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring (ATDM) paradigm, where data collection episodes are triggered only when the subject is determined to possess a specified context. MediAlly supports the on-demand collection of contextual provenance using a novel low-overhead provenance collection sub-system. The behaviour of this sub-system is configured using an application-defined context composition graph. The resulting provenance stream provides valuable insight while interpreting the `episodic´ sensor data streams. The paper also describes our prototype implementation of MediAlly using commercially available devices.