Internet of Events for Healthcare Data: Automating Computational Placement in IoT Environments
This research project focuses on holistic stream processing for Internet of Things (IoT) applications.
Data analytics are automatically partitioned and deployed across heterogeneous platforms. These platforms (sensors, field gateways, clouds) then meet non-functional requirements:
energy
performance
security
The user gives a high-level declarative description of computation. This is in the form of Event Processing Language queries. These are compiled, optimised, and partitioned to meet the non-functional requirements. We use Database system techniques and cost models to meet the needs of IoT analytics.
We work with medical researchers on an application that uses wearable sensors. These sensors show the activity and glucose levels of type II diabetes patients. We analyse the measurements in near-real time to give short-term forecasts. The user receives personalised alerts. These prompt them to heighten their physical activity to raise their glucose levels. The app alerts the patient before their health is at risk.
Our research shows the potential for IoT management systems to automatically exploit fog/edge computing. This optimises non-functional requirements. Our system is also able to distribute stream processing computations across multiple platforms. This is doneautomatically. We use platform specific compilers and deployers.
