QoS-aware edge server placement for collaborative predictive maintenance in industrial internet of things.

Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive maintenance (PdM) plays a crucial role within the Industrial Internet of Things (IIoT) by estimating the remaining useful life (RUL) of machines so that proactive maintenance measures can be taken to mitigate potential failures and minimize disruptions. RUL estimation is enabled by gathering and processing the data sensed by sensors mounted on and around the machines at the central server (base station or cloud) after analyzing the failure patterns. However, this approach imposes a significant load on network bandwidth and leads to poor response time for the monitoring system because a large volume of sensed data has to be transmitted to the central server for processing. Moreover, due to the singularity of the computing resource, many problems, such as inefficient resource utilization, frequent offloading, single-point failure, etc., have become major challenges. To address these issues, this article proposes an edge computing-enabled predictive maintenance framework called "Collaborative Predictive Maintenance Framework" (CollabRULe), which first identifies the optimal locations for edge server placement in the deployment region by considering the QoS parameters, such as energy, delay and connectivity. Then, it uses federated learning for predictive maintenance by estimating the RUL of the machines. Simulation results show that the proposed mechanism effectively minimizes the overall network energy consumption and end-to-end delay by ≈ 60 % and ≈ 35 % , respectively, as compared to state-of-the-art approaches. Furthermore, it shows significant improvement in the accuracy of RUL prediction as compared to its counterparts. [ABSTRACT FROM AUTHOR]