RSS Uni. Porto Visually-defined Real-Time Orchestration of IoT Systems

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Title: Visually-defined Real-Time Orchestration of IoT Systems
Abstract: In this work, we propose a method for extending Node-RED to allow the automatic decomposition and partitioning of the system towards higher decentralization. We provide a custom firmware for constrained devices to expose their resources, as well as new nodes and modifications in the Node-RED engine that allow automatic orchestration of tasks. The firmware is responsible for low-level management of health and capabilities, as well as executing MicroPython scripts on demand. Node-RED then takes advantage of this firmware by (1) providing a device registry allowing devices to announce themselves, (2) generating MicroPython code from dynamic analysis of flow and nodes, and (3) automatically (re-)assigning nodes to devices based on pre-specified properties and priorities. A mechanism to automatically detect abnormal run-time conditions and provide dynamic self-adaptation was also explored. Our solution was tested using synthetic home automation scenarios, where several experiments were conducted with both virtual and physical devices. We then exhaustively measured each scenario to allow further understanding of our proposal and how it impacts the system's resiliency, efficiency, and elasticity.​



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Title: Visually-defined Real-Time Orchestration of IoT Systems Abstract: In this work, we propose a method for extending Node-RED to allow the automatic decomposition and partitioning of the system towards higher decentralization. We provide a custom firmware for constrained devices to expose their resources, as well as new nodes and modifications in the Node-RED engine that allow automatic orchestration of tasks. The firmware is responsible for low-level management of health and capabilities, as well as executing MicroPython scripts on demand. Node-RED then takes advantage of this firmware by (1) providing a device registry allowing devices to announce themselves, (2) generating MicroPython code from dynamic analysis of flow and nodes, and (3) automatically (re-)assigning nodes to devices based on pre-specified properties and priorities. A mechanism to automatically detect abnormal run-time conditions and provide dynamic self-adaptation was also explored. Our solution was tested using synthetic home automation scenarios, where several experiments were conducted with both virtual and physical devices. We then exhaustively measured each scenario to allow further understanding of our proposal and how it impacts the system's resiliency, efficiency, and elasticity.



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