  Modern manufacturing is now deeply integrating new technologies such as 5G,
Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a
new level -- Smart Factory. Autonomic anomaly detection (e.g., malfunctioning
machines and hazard situations) in a factory hall is on the list and expects to
be realized with massive IoT sensor deployments. In this paper, we consider
acoustic data-based anomaly detection, which is widely used in factories
because sound information reflects richer internal states while videos cannot;
besides, the capital investment of an audio system is more economically
friendly. However, a unique challenge of using audio data is that sounds are
mixed when collecting thus source data separation is inevitable. A traditional
way transfers audio data all to a centralized point for separation.
Nevertheless, such a centralized manner (i.e., data transferring and then
analyzing) may delay prompt reactions to critical anomalies. We demonstrate
that this job can be transformed into an in-network processing scheme and thus
further accelerated. Specifically, we propose a progressive processing scheme
where data separation jobs are distributed as microservices on intermediate
nodes in parallel with data forwarding. Therefore, collected audio data can be
separated 43.75% faster with even less total computing resources. This solution
is comprehensively evaluated with numerical simulations, compared with
benchmark solutions, and results justify its advantages.
