IoT Cooking Workflows for End Users : A Comparison Between Behaviour Trees and the DX-MAN Model

Ventirozos, Filippos and Batista-Navarro, Riza Theresa and Clinch, Sarah and Arellanes, Damian (2021) IoT Cooking Workflows for End Users : A Comparison Between Behaviour Trees and the DX-MAN Model. In: 22nd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion :. IEEE, JPN, pp. 341-350. ISBN 9781665424851

[thumbnail of IoT-Cooking-Workflows]
Text (IoT-Cooking-Workflows)
IoT_Cooking_Workflows.pdf - Accepted Version

Download (485kB)

Abstract

A kitchen underpinned by the Internet of Things (IoT) requires the management of complex procedural processes. This is due to the fact that when supporting an end-user in the preparation of even only one dish, various devices may need to coordinate with each other. Additionally, it is challenging— yet desirable—to enable an end-user to program their kitchen devices according to their preferred behaviour and to allow them to visualise and track their cooking workflows. In this paper, we compared two semantic representations, namely, Behaviour Trees and the DX-MAN model. We analysed these representations based on their suitability for a range of end-users (i.e., novice to experienced). The methodology required the analysis of smart kitchen user requirements, from which we inferred that the main architectural requirements for IoT cooking workflows are variability and compositionality. Guided by the user requirements, we examined various scenarios and analysed workflow complexity and feasibility for each representation. On the one hand, we found that execution complexity tends to be higher on Behaviour Trees. However, due to their fallback node, they provide more transparency on how to recover from unprecedented circumstances. On the other hand, parameter complexity tends to be somewhat higher for the DX-MAN model. Nevertheless, the DX-MAN model can be favourable due to its compositionality aspect and the ease of visualisation it can offer.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
159351
Deposited By:
Deposited On:
07 Sep 2021 12:50
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Dec 2024 00:47