Low-Latency Anomaly Detection on the Edge-Cloud Continuum for Industry 4.0 Applications: the SEAWALL Case Study

Abstract

Several emerging Industry 4.0 applications related to the monitoring and fault diagnostic of critical equipment introduce strict bounds on the latency of the data processing. Edge computing has emerged as a viable approach to mitigate the latency by offloading tasks to nodes nearby the data sources; at the same time, few industrial case studies have been reported so far. In this paper, we describe the design, implementation and evaluation of the SEAWALL platform for the heterogeneous data acquisition and low-latency processing in Industry 4.0 scenarios. The framework has been developed within the homonymous project founded by the Italian BIREX industrial consortium and involving both academic and industrial partners. The proposed framework supports data collection from heterogeneous production line machines mapped to different IoT protocols. In addition, it enables the seamless orchestration of workloads in the edge-cloud continuum so that the latency of the alerting service is minimized requirement of the processing task is continuously met, while taking into account the constrained resources of the edge servers. We evaluate the SEAWALL framework in a small-case industrial testbed and quantify the performance gain provided by the dynamic workload allocation on the continuum.