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

arXiv: https://arxiv.org/abs/2407.05102 · HTML: https://mlsanity.com/paper/2407.05102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization](https://mlsanity.com/paper/2403.01922/) (2024)
- [On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data](https://mlsanity.com/paper/2311.15036/) (2023)

## Built on by (most cited first, 1 of 1)

- [Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management](https://mlsanity.com/paper/2508.13905/) (2025)

## Abstract (arXiv)

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.