Research Article
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
@INPROCEEDINGS{10.1007/978-3-031-63992-0_36, author={Tianheng Ling and Chao Qian and Gregor Schiele}, title={On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part II}, proceedings_a={MOBIQUITOUS PART 2}, year={2024}, month={7}, keywords={On-Device AI Soft Sensors Real-Time Inference FPGA-Based Inference}, doi={10.1007/978-3-031-63992-0_36} }- Tianheng Ling
Chao Qian
Gregor Schiele
Year: 2024
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
MOBIQUITOUS PART 2
Springer
DOI: 10.1007/978-3-031-63992-0_36
Abstract
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(\mu )s. 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.
