In-Network Processing Acoustic Data for Anomaly Detection in Smart Factory

TL;DR

This paper proposes an in-network processing scheme for acoustic data-based anomaly detection in smart factories, enabling faster separation of audio sources and more efficient use of resources, thereby improving real-time responsiveness.

Contribution

It introduces a distributed microservice-based processing scheme for audio source separation in IoT-enabled factories, reducing delay and resource consumption compared to traditional centralized methods.

Findings

Data separation is 43.75% faster with the proposed scheme.

The distributed approach uses fewer total computing resources.

Numerical simulations validate the scheme's advantages over benchmarks.

Abstract

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.