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

2024/07/06 by Tianheng Ling, Chao Qian, Ling, Tianheng +3 · 1 citation
Computer Science · Environmental Science · #Artificial Intelligence (cs.AI) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #Water Quality Monitoring Technologies #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2407.05102

openalex publication_date 2024/07/06 · openalex created_date 2024/07/10 · openalex updated_date 2026/07/28

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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