DDFlow

Noor, Joseph; Tseng, Hsiao-Yun; García, Luis Antonio Ribot; Srivastava, Mani

doi:10.1145/3302505.3310079

Cited by 16 publications

(1 citation statement)

References 15 publications

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“…The runtime interface utilises available resources to dynamically scale and map an IoT application similar to EdgeProg [ 22 ]. While DDFlow can be used for multi-modal sensing, the library is very high level and does not currently have the capabilities to facilitate high throughput application pipelines [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

REIP: A Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping

Piadyk

Steers

Mydlarz

et al. 2022

Sensors

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Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We demonstrate the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.

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Section: Related Workmentioning

confidence: 99%