# On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data

arXiv: https://arxiv.org/abs/2311.15036 · HTML: https://mlsanity.com/paper/2311.15036/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Toward smart composites: small-scale, untethered prediction and control for soft sensor/actuator systems](https://mlsanity.com/paper/2205.10940/) (2022)

## Built on by (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)
- [Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation](https://mlsanity.com/paper/2407.05102/) (2024)

## Abstract (arXiv)

Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network. Furthermore, the synergistic integration of the Microcontroller Unit and Field-Programmable Gate Array (FPGA) leverages the rapid AI inference capabilities of the latter. Empirical evidence from our real-world use case demonstrates that FPGA-based soft sensors achieve inference times ranging remarkably from 1.04 to 12.04 microseconds. These compelling results highlight the considerable potential of our innovative approach for executing real-time inference tasks efficiently, thereby presenting a feasible alternative that effectively addresses the latency challenges intrinsic to Cloud-based deployments.