Learning Latent Correlation of Heterogeneous Sensors Using Attention based Temporal Convolutional Network

Internet of Things devices have various sensors. These sensors are responsible for sensing the environmental information around the device in many ways, and more sensors will be deployed as the device develops. However, as a result of multiple sensor devices performing sensing work together, the sensing cost increases. In order to prevent the increase in sensing costs caused by more and more sensors on mobile devices, we began to study how to reduce the sensor number and also complete the corresponding sensing functions. A latent correlation between sensor data is our first task in sensor replacement. Therefore, we propose the attention-based temporal convolutional network (ATT-TCN) to learn the latent correlation. The experimental verification is performed on the collected sensor data set, and the experimental results prove that our proposed model can learn the latent correlation between heterogeneous sensor well. Our proposed ATT-TCN has better performance on the data set than the basic TCN model.