Dispersed Automation for Industrial Internet of Things

Abstract

This position article presents dispersed automation, a novel approach for reprogramming the omnipresent industrial Internet of Things (IIoT) devices in critical infrastructure for the co-execution of general-purpose computation workloads in an optimal and reliable manner. The key observation is that IIoT devices are available in large quantities in critical infrastructure. However, these devices are often underutilized due to the conservative allocation of resources, static task assignment, and lack of exploitation of parallelism. Dispersed automation gives new life to IIoT devices, some of them as powerful as general-purpose computers, and makes them accessible for the co-execution of various computational workloads, while dynamically adapting to different applications and operational environments. Two key contributions make this possible: the use of a domain-specific language and on-channel computation. We present dispersed automation as an enabling technology for the implementation of future smart and adaptive IIoT systems. Note to Practitioners —In this article, we explain our concept and vision on how programs can be created and designed in a way that makes it possible to run them on IIoT networks with a greater amount of flexibility than we have today. With this technology, it will be possible for programs to adapt to changes in their operation, and to changes in the network environment, in order to better utilize the available hardware resources. This means that a dispersed automation system can continue its operation in certain situations where a traditionally deployed system would fail due to insufficient resources (e.g., memory, processing speed). It also means that the software can make better use of the underlying hardware whenever a special task needs to be carried out (e.g., a “heavy” diagnostic process). We show some experimental results of using our approach based on simulation. The way forward is to implement and evaluate this technology in an industrial setting.