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Soft sensors are crucial in bridging autonomous systems' physical and digitalrealms, enhancing sensor fusion and perception. Instead of deploying softsensors on the Cloud, this study shift towards employing on-device softsensors, promising heightened efficiency and bolstering data security. Ourapproach substantially improves energy efficiency by deploying ArtificialIntelligence (AI) directly on devices within a wireless sensor network.Furthermore, the synergistic integration of the Microcontroller Unit andField-Programmable Gate Array (FPGA) leverages the rapid AI inferencecapabilities of the latter. Empirical evidence from our real-world use casedemonstrates that FPGA-based soft sensors achieve inference times rangingremarkably from 1.04 to 12.04 microseconds. These compelling results highlightthe considerable potential of our innovative approach for executing real-timeinference tasks efficiently, thereby presenting a feasible alternative thateffectively addresses the latency challenges intrinsic to Cloud-baseddeployments.

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