Osmotic Collaborative Computing for Machine Learning and Cybersecurity Applications in Industrial IoT Networks and Cyber Physical Systems with Gaussian Mixture Models

To implement machine learning algorithms and other useful algorithms in industrial Internet of Things (IIoT), new computing approaches are needed to prevent costs associated with having to install state of the art edge analytic devices. A suitable approach may include collaborative edge computing using available, resource-constrained IoT edge analytic hardware. In this paper, collaborative computing method is used to construct a popular and very useful waveform for IoT analytics, the Gaussian Mixture Model (GMM). GMM parameters are learned in the cloud, but the GMMs are constructed at the IIoT edge layer. GMMs are constructed using C28x, a ubiquitous, low-cost, embedded digital signal processor (DSP) that is widely available in many pre-existing IIoT infrastructures and in many edge analytic devices. Several GMMs including 2-GMM and 3-GMMs are constructed using the C28x DSP and Embedded C to show that GMM designs could be achieved in form of an osmotic microservice from the IIoT edge to the IIoT fog layer. Designed GMMs are evaluated using their differential and zero-crossings and are found to satisfy important waveform design criteria. At the fog layer, constructed GMMs are then applied for novelty detection, an IIoT cybersecurity and fault-monitoring application and are found to be able to detect anomalies in IIoT machine data using Hampel identifier, 3-Sigma rule, and the Boxplot rule. The osmotic collaborative computing method advocated in this paper will be crucial in ensuring the possibility of shifting many complex applications such as novelty detection and other machine learning based cybersecurity applications to edges of large scale IoT networks using low-cost widely available DSPs.