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Doctoral symposium: Automating computational placement in IoT environments

Lookup NU author(s): Peter Michalak, Dr Sarah Heaps, Professor Michael Trenell, Professor Paul Watson

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Abstract

© 2016 ACM. The growth in the number of Internet of Things (IoT) devices and applications, and an increase in the capabilities of sensors creates an opportunity to optimise IoT applications by partitioning the computation across all components in the processing chain: sensors, field gateways and clouds. This can be done to optimise a range of factors including performance, energy and cost. This paper presents an overview of an optimiser designed to achieve this. It takes as input a high-level, declarative description of the computation, along with a set of non-functional requirements. From this it aims to generate the best deployment plan. The main use case, described in the paper is the use of wearable sensors for the real-time monitoring of the activity and glucose levels of type II diabetes patients. This paper describes the architecture of the optimiser, gives an example of an energy-based cost model, and shows how the approach applies to the diabetes use case.


Publication metadata

Author(s): Michalák P, Heaps S, Trenell M, Watson P

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: DEBS 2016 - Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

Year of Conference: 2016

Pages: 434-437

Online publication date: 20/06/2016

Acceptance date: 02/04/2016

Publisher: Association for Computing Machinery, Inc

URL: https://doi.org/10.1145/2933267.2933435

DOI: 10.1145/2933267.2933435

Library holdings: Search Newcastle University Library for this item

ISBN: 9781450340212


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