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arXiv · 2407.05102

Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation

Tianheng Ling·Chao Qian·Gregor Schiele
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Abstract

Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of reliability and energy efficiency. However, its application in the field of wastewater flow estimation remains underexplored due to: (1) a lack of available datasets, (2) inconvenient toolchains for on-device AI model development and deployment, and (3) hardware platforms designed for general DL purposes rather than being optimized for energy-efficient soft sensor applications. This study addresses these gaps by proposing an automated, end-to-end solution for wastewater flow estimation using a prototype IoT device.

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Tianheng Ling, Chao Qian, Gregor Schiele. 2026-04-21. Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation. https://doi.org/10.1109/aiot63253.2024.00055

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