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.

翻译：软传感器在连接自主系统的物理与数字领域、增强传感器融合与感知能力方面至关重要。本研究并未将软传感器部署于云端，而是转向采用基于设备的软传感器，这有望显著提升效率并加强数据安全性。我们的方法通过将人工智能（AI）直接部署在无线传感器网络内的设备上，大幅提高了能源效率。此外，微控制器单元与现场可编程门阵列（FPGA）的协同整合，充分利用了后者快速的人工智能推理能力。来自实际应用案例的实证数据表明，基于FPGA的软传感器实现了1.04至12.04微秒的显著推理时间范围。这些令人信服的结果凸显了我们创新方法在执行实时推理任务方面的巨大潜力，从而为有效解决云端部署固有的延迟挑战提供了一个可行的替代方案。