TY - GEN
T1 - IoT Cooking Workflows for End-Users
T2 - A Comparison Between Behaviour Trees and the DX-MAN Model
AU - Ventirozos, Filippos
AU - Batista-Navarro, Riza Theresa
AU - Clinch, Sarah
AU - Arellanes Molina, Damian
N1 - Funding Information:
This research is supported by ARM Ltd and the UK EPSRC under grant number EP/S513842/1 (studentship 2109081).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/12/20
Y1 - 2021/12/20
N2 - 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.
AB - 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.
KW - Behavior Trees
KW - DX-MAN model
KW - End-User Development
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85123999883&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/87d0cb23-f480-33aa-b5ab-d7ff675e564f/
U2 - 10.1109/models-c53483.2021.00057
DO - 10.1109/models-c53483.2021.00057
M3 - Conference contribution
SN - 9781665424844
T3 - ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
SP - 341
EP - 350
BT - 24th International Conference on Model-Driven Engineering Languages and Systems MODELS 2021
PB - IEEE Computer Society
CY - California
ER -