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Lookup NU author(s): Lauren Roberts,
Dr Sarah Heaps,
Professor Mike TrenellORCiD,
Professor Darren Wilkinson,
Professor Paul WatsonORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2018.
For re-use rights please refer to the publisher's terms and conditions.
© 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.
Author(s): Roberts L, Michalák P, Heaps S, Trenell M, Wilkinson D, Watson P
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2018 IEEE 14th International Conference on e-Science (e-Science)
Year of Conference: 2018
Online publication date: 27/12/2018
Acceptance date: 02/04/2018
Date deposited: 06/03/2019
Library holdings: Search Newcastle University Library for this item