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Sensor faults can produce abnormal and spurious observations in the solar insecticidal lamp Internet of Things (SIL-IoTs) system. Early detection and identification of the sensor node&#x2019;s abnormality are critical to ensure the SIL-IoTs system&#x2019;s reliability. In this study, we propose a lightweight separable 1D convolution neural network that can be implemented in SIL-IoTs nodes to identify sensor faults, reduce detecting delay, and decrease data transmission. However, the reliability of data acquired by sensors is decreased because a SIL-IoTs node releases high voltage pulse discharge (a kind of electromagnetic interference) when pests collide with its metal mesh. This kind of data fluctuation impacts fault diagnosis accuracy. Consequently, fault-related feature maps and temporal signals are characterized via a novel time and channel attention module (TCAM) method, which contributes to separating electromagnetic interference noise from sensor faults of SIL-IoTs nodes. A real-world testbed is applied to validate the effectiveness of the proposed method on sensor fault diagnosis in the SIL-IoTs system. Experimental results demonstrate that the proposed method can detect four typical sensor faults with the best trade-off between accuracy (99.9&#x0025; average accuracy and 97.6&#x0025; average F1-score) and efficiency (351 KB inference model size and 4.33 W average energy consumption on Raspberry Pi).

SA1D-CNN: A Separable and Attention Based Lightweight Sensor Fault Diagnosis Method for Solar Insecticidal Lamp Internet of Things