1. Project
  2. FAR-EDGE

Factory Automation Edge Computing Operating System Reference Implementation

Factory Automation Edge Computing Operating System Reference Implementation
Summary

Despite the proclaimed benefits (i.e. scalability, reliability, cost-effectiveness) of Future Internet (FI) technologies (i.e. edge & cloud computing, IoT/CPS) for factory automation, their adoption from manufacturers remains low for various reasons, including technology issues (e.g., poor situation awareness, limited deployments, no standards-based reference implementations) and the lack of a smooth migration path from legacy systems.

FAR-EDGE is a joint effort of leading experts in manufacturing, industrial automation and FI technologies towards the smooth and wider adoption of virtualized factory automation solutions based on FI technologies. It will research a novel factory automation platform based on edge computing architectures and IoT/CPS technologies. FAR-EDGE will provide a reference implementation of emerging standards-based solutions for industrial automation, along with simulation services for validating automation architectures and production scheduling scenarios.

FAR-EDGE will lower the barriers for manufacturers to move towards Industrie 4.0, as a means of facilitating mass-customization and reshoring. Emphasis will be paid in the study of migration options from legacy centralized architectures, to emerging FAR-EDGE based ones.

FAR-EDGE will be validated in real-life plants (VOLVO, WHIRLPOOL) in the scope of user-driven scenarios (business-cases) for mass-customization and reshoring, where tangible improvements relating to reliability, productivity increase, quality cost, reduction in adaptation effort/costs will be measured and evaluated. Also, a wide range of migration scenarios will be evaluated in the scope of a CPS manufacturing testbed. FAR-EDGE will also establish a unique ecosystem for FI factory automation solutions, which will bring together the FoF and FI communities and will ensure sustainability of FAR-EDGE results.

More information
Website: http://www.faredge.eu
Duration: 36 months
Start date: 01-10-2016
End date: 30-09-2019
Number of participants: 12
Total budget - EC contribution: 4490194 Euro - 3992631 Euro
Call topic: Digital Automation - Novel architectures for factory automation based on CPS and IoT (FoF.2016.11-ii)
Instrument: Collaborative project (generic)
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Characteristics

Significant innovations, use case requirements and lessons learned - (4) view

    Significant innovations and achievements
    Comment:

    FAR-EDGE demostrates the feasibility and business value of Edge Computing (EC) applied to manufacturing, using Distributing Ledger Technology (DLT) as the key enabler. DLT allows several autonomous local processes to cooperate as peers in the scope of the same global process, the required state synchronization and common business loging being implemented by Smart Contracts. This approach, if applied correctly, results in totally decentralized and fail-safe CPS that are still easily monitored, controlled and managed centrally.

    Significance of the results for SMEs
    Comment:

    Adoption of new and disruptive technology is not an easy task for SME, due to lack of budget and of internal skills. To mitigate this problem, FAR-EDGE defines speficic migration strategies that may help SMEs plan their Industry 4.0 journey, with the support of FAR-EDGE assets.

    Lessons learned
    Comment:

    In FAR-EDGE, the value of DLT is in distributed consensus: enabling the intelligent edge nodes of a system to agree - or disagree - on state transitions, to the effect that any change in global state (e.g., the assignment of a task to a specific node) must be approved collectively; moreover, individual nodes may fail or go offline without compromising the system as a whole. However, standard DLT platforms also maintain an immutable log of transactions (the Blockchain) that is replicated on all nodes, which may represent a significant performance bottleneck in many real-world applications. What we learned from the FAR-EDGE experimentation is that, in most factory automation scenarios, the historical memory of any transaction may be cleared once consensus is safely reached on it - something that is not supported by any current DLT implementation. This form of short-lived state persistency is something that is probably worth experimenting with in the future.

    Specific use case requirements
    Comment:

    For being positively affected by the use of the FAR-EDGE Platform, use cases must have one or more of the following high-level requirements:

    • Distributed Automation: multiple (semi-)autonomous systems on the shopfloor having local resposibilities but global accountability.
    • Distributed Analytics: multiple local sources of raw data having high volume / velocity / variety characteristics (big data scenario).

Added value and impact - (3) view

Technologies and enablers - (8) view

    Information and communication technologies
    Comment:

    The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric).

      Programming Frameworks – Software Development Kits (SDKs)
        FIWARE
        Comment:

        We are considering some core GEs from FIWARE as candidate building blocks of our Edge Computing architecture. In particular, the Publish/Subscribe GE (Orion Context Broker implementation) is a good candidate as the northbound interface exposed by Edge Gateways – i.e., computing nodes aggregating a number of local edge nodes (field devices, smart factory equipment) and running local automation and/or analytics processes. We are considering to significantly extend the Publish/Subscribe GE by adding distributed computing capabilities: a data context that is replicated and kept in-sync across a number of GE instances (running anywhere on the network), using a Blockchain and smart contracts as the backing technology. FAR-EDGE will contribute its results, as open source software, to the FIWARE for Industry community.

      Architecture
      Comment: Relevant architectures for FAR-EDGE are Platform Industrie 4.0 (RAMI 4.0), Industrial Internet Consortium (IIRA), OpenFog Consortium (OpenFog RA). We will use the 3D coordinate system from RAMI 4.0 to map the elements of the FAR-EDGE Platform to concepts that are commonly understood and agreed on within the Industrie 4.0 community. We are also taking care that the FAR-EDGE Platform implementation will be compatible with future Industrie 4.0 compliant equipment – in particular I4.0 Components – by supporting OPC UA as the main protocol for field communication. Regarding IIRA and OpenFog RA, in the FAR-EDGE RA we are adopting a key architectural pattern included in both of them (with different names): a layered approach that leverages peer-to-peer communication in each layer to decentralize control. The most visible trait of the FAR-EDGE reference architecture is its partitioning into horizontal layers having different scopes – i.e., in bottom-up order: Fog (field, shopfloor), Edge (plant), Cloud (enterprise, supply chain). This pattern in inspired from a similar one in IIRA, called Layered Databus. However, in FAR-EDGE there is no “databus” included in each layer, but rather an additional layer called the Ledger, positioned between the Edge and the Cloud ones. Its role is to coordinate the execution of distributed processes running on Edge Gateways. The horizontal partitioning of the RA is driven by technical concerns (e.g., where software components are deployed, who are the stakeholders of their implementation). There is also a vertical partitioning, though, which is orthogonal to the horizontal one. This is a high-level functional decomposition which identifies three functional scopes: Automation, Virtualization and Analytics (more on that in the API section below).

Digitalisation pathways - (5) view

ICT performance characteristics - (12) view

    Data communication and interoperability
    Comment: According to the RAMI 4.0 architecture, the “standard” way for Industrie 4.0 platforms to integrate legacy equipment (or any other kind of legacy “object”, including software components) into will be to encapsulate them inside an ad-hoc Administration Shell wrapper, which will expose them as I4.0 Components. The I4.0 interface specification has not been published yet, but a key enabling technology will probably be OPC UA, used as both a communication protocol and a data meta-model. In FAR-EDGE, OPC UA will be one of the field communication technology supported.
      Integration level interoperability
      Comment: AutomationML, SenseML, OPC UA.
        Data/object model interoperability - Data exchange formats - APIs
        Comment: The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric). Following the high-level functional decomposition defined in the FAR-EDGE RA, three Open APIs will be exposed by the Cloud layer of the FAR-EDGE Platform: Automation, Virtualization and Analytics. Automation will provide endpoints to monitor, control and manage automation workflows deployed on the Edge layer. Virtualization will create hooks for external simulation tools that need to read and manage factory models deployed on the Cloud layer (that are kept in-sync with the real world by the lower layers). Analytics will allow to manage distributed data analysis processes running on the Edge layer, and to collect aggregated results.
        Connectivity & network interoperability – communication protocols
        Comment: The FAR-EDGE architecture defines a protocol abstraction layer (the “connectivity middleware” box in the diagram provided here – this name is provisional) to decouple the field from its Edge Computing infrastructure: this is where OPC UA compatibility is going to be introduced.

Standards, standardisation and regulation - (1) view

    Standards
    Comment: AutomationML, SenseML, OPC UA We expect OPC UA to gain more traction globally in smart factories, also independently from the standardization efforts of the Platform Industrie 4.0 initiative. It might indeed become, over time, the least common denominator of very diverse industrial systems all over the world.

Business model aspects - (2) view

Factory Automation Edge Computing Operating System Reference Implementation
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Mauro Isaja

Mauro Isaja
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