This article presents the benefits of introducing novel solutions for the virtualization of the factory automation pyramid such as edge computing and blockchain technology and introduces the FAR-EDGE Reference Architecture (RA) that is aligned and exploit concepts from other RAs and standards. This article was first published on LinkedIn network by the FAR-EDGE consortium member John Soldatos and are available here.
FAR-EDGE (FACTORY AUTOMATION EDGE COMPUTING OPERATING SYSTEM REFERENCE IMPLEMENTATION), is an EU project funded by the European Commission under its H2020 framework programme (grant agreement No. 723094). The project is a joint effort of leaders in industrial automation, cyber-physical systems (CPS) for manufacturing and the Industrial Internet-of-Things (IIoT) towards providing a novel edge computing solution for the virtualization of the factory automation pyramid.
The vision of decentralizing factory automation is not a new one. Rather, for over a decade, several initiatives have introduced decentralized factory automation solutions based on various technologies including intelligent agents and Service Oriented Architectures (SOA). These background initiatives produced proof-of-concept implementations that highlighted the benefits of decentralized automation in terms of flexibility, yet they are still not being widely deployed in manufacturing plants.
With the advent of the Industrie 4.0 and the Industrial Internet of Things (IIoT), such solutions are revisited in the light of the integration of Cyber-Physical Systems with cloud computing infrastructures. Several cloud-based solutions are already deployed in factories, which leverage the capacity and scalability of the cloud, while fostering supply chain collaboration and virtual manufacturing chains. However, early implementations have also revealed the limitations of the cloud in terms of efficient bandwidth usage and its ability to support real-time operations, including operations close to the field. In order to alleviate these limitations edge computing architectures have recently introduced. Edge computing architectures introduce layers of edge nodes between the field and the cloud, as a means of:
- Saving bandwidth and storage.
- Enabling low-latency and proximity processing (i.e. processing close to the field).
- Providing enhanced scalability based on decentralized data storage and processing.
- Supporting shopfloor isolation and privacy-friendliness, including increased security and protection of manufacturing datasets.
These benefits make edge computing suitable for specific classes of use cases in factories, including:
- Large scale distributed applications, typically applications that involve multiple plants or factories, which collect and process data from numerous distributed systems and devices at scale.
- Nearly real-time applications, which need to analyze data close to the field or even control Cyber-Physical Systems such as smat machines and industrial robots. A special class of such real-time applications involves edge analytics applications.
As a result, the application of edge computing for factory automation is extremely promising, since it can support decentralized factory automation in a way that supports real-time interactions and analytics at scale. FAR-EDGE researches and explores the application of the edge computing paradigm in factory automation, through designing and implementing reference implementations in-line with recent standards for edge computing in industrial automation applications.
FAR-EDGE is not the sole effort that focuses on a general-purpose architecture and on an associated reference implementation of an edge computing platform for factory automation. Acknowledging the benefits of edge computing for factory automation in the Industrie4.0 era, several other initiatives are exploring similar directions. These include:
- Standards Development Organizations (SDOs): SDOs such as the OpenFog Consortium and the Industrial Internet Consortium (IIC) have produced Reference Architectures (RA) for IIoT. The RA of the OpenFog Consortium prescribes a high-level architecture for internet of things systems, which covers industial IoT use cases. On the other hand, the RA of the IIC outlines the structuring principles of systems for industrial applications. The IIC RA is not limited to edge computing, but rather based on edge computing principles in terms of its implementation. It addresses a wide range of industrial use cases in multiple sectors, including manufacturing in general and factory automation in particular. These RAs have been very recently released and reference implementations for them are still in their early stages.
- Reference Implementations:A reference implementation of the IIC RA’s edge computing functionalities for factory automation is provided as part of IIC’s edge intelligence testbed. This testbed provides a proof-of-concept implementation of edge computing functionalities on the plantfloor. The focus of the testbed is on configurable edge computing environments, which enable the development and testing of leading edge systems and algorithms for edge analytics. Moreover, Dell-EMC has recently announced the EdgeX Foundry framework, which is a vendor-neutral open source project hosted by the Linux Foundation building a common open framework for Industrial IoT edge computing. The focus of this product is on tiered edge computing architectures in the Industrial IoT market, including factory automation solutions. The framework is influenced by the above-listed reference architectures and is expected to be released later in 2017.
The FAR-EDGE architecture is aligned to the IIC RA, while exploiting concepts from other RAs and standards such as the OpenFog RA and RAMI 4.0 (Reference Architecture Model Industrie 4.0). The project will be providing one of the world’s first reference implementation of edge computing for factory automation, similar to IIC’s edge intelligence testbed and the EdgeX foundry. Note however, that FAR-EDGE will be exclusively focused on factory automation, rather than being inspired from a broader set of industrial use cases, as is the case with the rest reference implementations. Furthermore, FAR-EDGE will offer a host of functionalities that are not addressed by other implementations, such as IEC 61499 compliant automation and simulation in collaboration with the H2020 DAEDALUS project.
Beyond its functional uniqueness, FAR-EDGE is also unique from a research perspective. In particular, the project will research the applicability of disruptive distributed computing technologies for IIoT (i.e. the blockchain) as a means of configuring and deploying edge analytics and automation functionalities in a scalable and secure way. In particular, FAR-EDGE will provide a proof-of-concept implementation of a blockchain mechanism for sharing state and reconfiguring analytics rules and automation workflows in factories. This is among the project’s unique research contributions, which essentially sets it apart from other edge computing efforts worldwide.
In the coming weeks we will be publishing FAR-EDGEs reference architecture, including both edge computing and blockchain aspects. A LinkedIn post with a high-level overview of the architecture will follow.