Abstract
IoT development is complex. To reduce this complexity, IoT platforms provide a set of resources and functionalities to enable application development and support its execution. In this work, we present a human-centered approach for requirements elicitation and mapping them to application resources in IoT platforms, using empathy, definition and ideation methods. A previous study by the authors has identified 11 categories of resources provided by 47 IoT platforms to developers in their application layers. From this set, 6 categories were selected for this work: schedulers and triggers, message and notification triggers, big data and analytics, artificial intelligence and machine learning, dashboards, and services. We invited 18 members of 8 projects for a workshop and divided them in 4 teams, according their project areas, which are: Industry 4.0 (6 participants), Environmental Disasters (4 participants), Environmental Management (3 participants) and Pollution (5 participants). We divided the workshop in 3 phases: warm-up, with user journey mapping, requirements identification using “how might we” questions as a trigger and requirements clustering the questions by the 6 selected categories of resources or an extra category named “others” for those which could not be related to any previous category. Our contribution for the IoT application development is an approach for turning easier requirements elicitation using DT techniques, covering the stages of empathise, definition and ideation, with well-available materials and considering the resources present at application layer of IoT platforms.
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Grant #2018/23052-0, São Paulo Research Foundation (FAPESP). Grant #2017/50343-2, São Paulo Research Foundation (FAPESP).
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Lima Dantas, D., Filgueiras, L.V.L., Brandão, A.A.F., Machado Domingues, M.C., Ferreira, M.R. (2020). Detecting IoT Applications Opportunities and Requirements Elicitation: A Design Thinking Based Approach. In: Streitz, N., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2020. Lecture Notes in Computer Science(), vol 12203. Springer, Cham. https://doi.org/10.1007/978-3-030-50344-4_7
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