The increasing of connected tiny, low-power, embedded devices, grouped into the generic definition of "Internet of Things" (IoT), raised remarkably the amount of in-situ collected data. However, at the time of writing, IoT devices have limited storage and computation resources if compared with a cloud computing or on-premises infrastructure. IoT devices often suffer for reduced connectivity due to the place of the deployment or other technical, environmental or economic reasons. In this work, we present the DagOn∗ workflow engine as a part of an IoT orchestration scenario oriented to operational environmental prediction. Our novel approach is devoted to join the two worlds of workflows, in which each task runs on a dynamically allocated computational infrastructure, with tiny jobs targeted to embedded devices hosting sensors and actuators. We show our preliminary results applied to a demonstration use case. We are confident that further development of the proposed technology will affect positively production applications for massive and geographically distributed data collection.

Internet of Things orchestration using DagOn∗ workflow engine

Di Luccio D.;Montella R.
2019

Abstract

The increasing of connected tiny, low-power, embedded devices, grouped into the generic definition of "Internet of Things" (IoT), raised remarkably the amount of in-situ collected data. However, at the time of writing, IoT devices have limited storage and computation resources if compared with a cloud computing or on-premises infrastructure. IoT devices often suffer for reduced connectivity due to the place of the deployment or other technical, environmental or economic reasons. In this work, we present the DagOn∗ workflow engine as a part of an IoT orchestration scenario oriented to operational environmental prediction. Our novel approach is devoted to join the two worlds of workflows, in which each task runs on a dynamically allocated computational infrastructure, with tiny jobs targeted to embedded devices hosting sensors and actuators. We show our preliminary results applied to a demonstration use case. We are confident that further development of the proposed technology will affect positively production applications for massive and geographically distributed data collection.
978-1-5386-4980-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/87432
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