RIS-aided smart manufacturing: information transmission and machine health monitoring

This paper proposes a novel industrial Internet-of-Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilises reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size (K) and the transmit power (P). We observe that the accuracy of detecting MHCs does not change significantly with K and P , implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. By contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: i) reasonably high data rate for primary transmission and ii) high accuracy for secondary MHC information.

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