Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation
It addresses wastewater flow estimation for IoT applications, but appears incremental as it builds on existing DL-based soft sensor approaches.
This study tackled the problem of wastewater flow estimation on resource-limited IoT devices by proposing an automated, end-to-end solution using a prototype IoT device, addressing gaps in datasets, toolchains, and hardware optimization.
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.