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.
@article{arxiv.2407.05102,
title = {Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation},
author = {Tianheng Ling and Chao Qian and Gregor Schiele},
journal= {arXiv preprint arXiv:2407.05102},
year = {2026}
}
Comments
2 pages, 3 figures, accepted by 2024 IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT)