Automating the placement of time series models for IoT healthcare applications

Roberts, L; Michalák, P; Heaps, S; Trenell, M; Wilkinson, D; Watson, P

doi:10.1109/eScience.2018.00056

Automating the placement of time series models for IoT healthcare applications

Lookup NU author(s): Lauren Roberts, Dr Peter Michalak, Dr Sarah Heaps, Professor Mike Trenell ORCiD, Professor Darren Wilkinson, Professor Paul Watson ORCiD

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2018.

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Abstract

© 2018 IEEE.There has been a dramatic growth in the number and range of Internet of Things (IoT) sensors that generate healthcare data. These sensors stream high-dimensional time series data that must be analysed in order to provide the insights into medical conditions that can improve patient healthcare. This raises both statistical and computational challenges, including where to deploy the streaming data analytics, given that a typical healthcare IoT system will combine a highly diverse set of components with very varied computational characteristics, e.g. sensors, mobile phones and clouds. Different partitionings of the analytics across these components can dramatically affect key factors such as the battery life of the sensors, and the overall performance. In this work we describe a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth. We illustrate this using our implementation of a statistical model predicting the glucose levels of type II diabetes patients in order to reduce the risk of hyperglycaemia.