INDUSTRIAL COMMUNICATIONFOR FACTORIES

BUILDING BLOCKS FOR A SECURE REAL-TIME COMMUNICATION AND COMPUTING INFRASTRUCTURE FOR INDUSTRY 4.0

WHITE PAPER

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IMPRINT

“Building Blocks for a Secure Real-Time Communication and Computing Infrastructure for Industry 4.0” White Paper

Version 1.0 (April 2018)

“Industrial Communication for Factories” (IC4F).

Published by the partners of the projectIndustrial Communication for Factories (IC4F).

Internet: www.ic4f.deE-mail: info@ic4f.de

EDITORIAL TEAM:

Erich Zielinski, Fraunhofer Heinrich Hertz Institute, Berlin, GermanyFelix Beierle, Technische Universität Berlin, GermanyHans-Werner Bitzer, Deutsche Telekom AG, Bonn, GermanyKnut Drachsler, GPS Gesellschaft für Produktionssysteme GmbH, Stuttgart, GermanyBernd Holfeld, Fraunhofer Heinrich Hertz Institute, Berlin, GermanyHarald Klaus, Deutsche Telekom AG, Bonn, GermanyMathias Mormul, Universität Stuttgart, GermanyAndreas Müller, Robert Bosch GmbH, Renningen, GermanyKaroline Saatkamp, Universität Stuttgart, GermanyChristian Schellenberger, Technische Universität Kaiserslautern, GermanyJulius Schulz-Zander, Fraunhofer Heinrich Hertz Institute, Berlin, GermanySlawomir Stanczak, Fraunhofer Heinrich Hertz Institute, Berlin, GermanyEdwin Sutedjo, Nokia Solutions and Networks, Munich, GermanyMatthias Wieland, Universität Stuttgart, GermanyAlexander Willner, Technische Universität Berlin, GermanyFlorian Zeiger, Siemens AG, Munich, GermanyMarc Zimmermann, Technische Universität Kaiserslautern, Germany

CONTACT:

Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHI, Einsteinufer 37, 10587 Berlin, Germany

Layout: LoeschHundLiepold Kommunikation GmbH

PICTURE CREDITS:

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EXECUTIVE SUMMARY

The flagship project “Industrial Communication for Factories”

(IC4F) is working on a secure communication and computing

infrastructure with real-time capabilities for Industry 4.0. The

project is part of the PAiCE program of the German Federal

Ministry for Economic Affairs and Energy (BMWi).

The IC4F project develops a RAMI 4.0-compliant reference

architecture for industrial communication. Specifically, on a

high abstraction level, IC4F considers two layers:

• The ICT infrastructure layer provides wireless or wired

access to all kinds of objects on the shop floor and con-

nects them with cloud resources in the different network

domains.

• The application and data layer includes factory applica-

tions, data models, data management, data analytics and

data visualizations, as well as artificial intelligence and

machine learning algorithms.

Moreover, an overarching security framework protects both

layers.

The IC4F project proposes building blocks, which allow

implementing Industry 4.0 use cases in an efficient and

flexible manner and to realize use case patterns with similar

requirements. These building blocks define packages of

functionalities to meet business needs. The building blocks

are described by templates, which include a description

of the functionality and context, exposed public interfaces,

interoperability, service parameters, and possible implemen-

tations. An Industry 4.0 ICT architecture can be built up from

a collection of interoperating building blocks.

In order to define building blocks, available and upcom-

ing technologies in the field of ICT, applications, and data

must be analyzed. This includes technology domains such

as cloud computing in an industrial environment, virtual-

ization and industrial edge computing, 5G radio and 5G

core network, big and fast data analytics, as well as artificial

intelligence and machine learning algorithms. The analysis

includes mechanisms for secure and reliable connectivity in

production, secure wireless communication and processes,

massive sensor data analysis, and (virtual) network elements

like secure gateways.

This whitepaper briefly outlines the following four specific

use cases and describes how they can be implemented

based on the technologies and the building block approach

developed by IC4F:

• Remote machine access

• Automated Guided Vehicles (AGVs)

• Massive wireless sensor networks

• Mobile operation & control with ultra-reliable machine

communication

During the project, the IC4F consortium will present a proof-

of-concept implementation in real-world industrial environ-

ments for relevant use cases, including the four use cases

mentioned above.

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1. INTRODUCTIONCONTENTS

German politics and leading industry associations togeth-

er with research and development from companies and

academia provide the foundation for concerted action in

digitizing the industrial production process. Combining the

organizational assets of all stakeholders will accelerate the

speed at which the goal of fully connected factories of the

future can be reached.

In addition to its global goal, the project was also devised

with Germany’s competitiveness in mind. Germany has a

unique landscape of small and medium-sized enterprises

(SMEs) that account for about 95 % of the entrepreneurial

forces. German companies are internationally recognized

for their innovative products and for their efficient use and

production of both tools and technologies for industrial pro-

duction worldwide. Accordingly, continuously improving and

enhancing Germany’s leading position in smart production

and cutting-edge products as well as the return of produc-

tion facilities from foreign sites are goals pursued by the

Industry 4.0 initiative that is governed by public and private

research projects.

Implementing the vision of Industry 4.0 requires a holistic

view of the underlying infrastructure – a type of industrial

Internet/Intranet – taking into account technical possibilities

adapted to industrial requirements. This new industrial com-

munication infrastructure that enables platforms and applica-

tions will become an important economic factor.

Just recently, the telecom industry radically transformed itself

by merging communication with information technologies.

Communication technology performance has increased

significantly over time, virtualization is reducing costs, and

the fifth generation of mobile networks (5G) is expected to

generate even more momentum.

The use of ICT technologies in the automation and manufac-

turing domain, including the required adaptations to industry

requirements, will bring tremendous benefits:

• Boost performance of production facilities thanks to tight

monitoring and configuration of equipment, e.g. condition

monitoring, predictive maintenance, digital twin of the

factory in real-time.

• Close alignment of production and business processes,

e.g., product customization and “hyper-personalization”

through flexible (re-)configuration of production facilities.

• Connectivity of all objects in a heterogeneous environment

and supporting both standardized and proprietary inter-

faces (interworking based on standards).

• Improved collaboration and increased confidence between

business partners along the value chain (e.g., suppliers,

distributors, tier-x) by quality-assured, secure and in-time

connectivity inside factories within and across factory

boundaries.

Convergence between Operational Technology (OT), Infor-

mation Technology (IT), and Communication Technologies

(CT) seems to be the way to reach the goals set for the

digitization of industrial production, and several initiatives

Executive Summary 3

1. Introduction 5

2. Analysis of Use Cases 8

3. Challenges and Requirements for the Building Block Approach 10

4. New Technologies and Functionalities for the Smart Factory 134.1. Cloud – Computing and Virtualization 134.2. 5G – The Communication Network for the Cloud Era 144.3. Data – Driving the Smart Factory 16 4.4. Security – Protecting the Smart Factory 18

5. Reference Architecture and Building Block Approach 205.1. The IC4F Reference Architecture 205.2. The IC4F Building Block Approach 23

6. Application of Building Blocks in Demo Scenarios 276.1. Remote Machine Access 286.2. Automated Guided Vehicles 286.3. Massive Wireless Sensor Networks 306.4. Mobile Cooperation and Control with Ultra-Reliable Machine Communication 31

7. About the IC4F Project 32

References, Abbreviations 34

76

worldwide have started to work towards this goal, e.g., the

Industrial Internet Consortium (IIC) or the Plattform Industrie 4.0.

The flagship project “Industrial Communication for Factories”

(IC4F), as part of the PAiCE program of the German Federal

Ministry for Economic Affairs and Energy (BMWi), is working

on a RAMI 4.0-compliant reference architecture for industrial

communication systems and is creating building blocks for

industrial communication systems that can be used in facto-

ries. The proposed building block approach addresses SMEs

as well as large enterprises, providing them with a basis to

develop tailor-made solutions for future Industry 4.0 use cas-

es. IC4F applies the proposed approach and validates tech-

nologies, along with interworking and integration in different

and representative demonstrators. Figure 1.1. visualizes the

focus and the goal of the IC4F project, i.e., the convergence

of OT, IT and CT.

The IC4F approach is based on a thorough analysis of in-

dustrial use cases from three main sources: the IC4F project

partners, the Industry 4.0 platform, and the workshops

conducted together with industrial application partners of

IC4F’s user forum. Use cases of interest are identified on the

basis of criteria such as “demanding industrial requirements”,

“clear request for new technologies”, and “enabling of new

business opportunities”.

As a result, the ICT and compute infrastructure have to meet

challenges and requirements along the following lines:

• Every object on the shop floor gets connected

• Objects become mobile – the shop floor goes wireless

• Artificial Intelligence (AI) in production

• Fast and reliable communication for machine and process

control – digital twin of the factory in real-time

• Automated deployment and operations

• Comprehensive and scalable secure communication and

data handling in industrial domains and processes

To implement use cases in these domains, we analyze avail-

able and upcoming technologies in the field of ICT, applica-

tions and data. In particular, cloud computing in an industrial

environment, virtualization and industrial edge computing,

5G (radio access and core network), analytics with big and

fast data, and AI technologies are investigated. Functionali-

ties, performance parameters and interfaces that enable new

use cases are emphasized.

The analysis also includes existing and innovative mecha-

nisms for secure connectivity in production, secure wireless

communication and secure processes as well as massive

sensor data analysis. In doing so, we take into account new

functionalities (e.g., role dependent and task-dependent

data handling, scalable security services) and dedicated (vir-

tual) network elements like security gateways and industrial

edge clouds.

Our aim is to establish an architecture that is able to de-

scribe the overall ICT and compute infrastructure for specific

use cases. To this end, we link the functionalities described

above to different levels of the architecture so that use case

patterns with similar requirements can be addressed. The

functionalities are viewed as building blocks on different ar-

chitectural levels and this is referred to as the IC4F building

block approach. The highest level (reference architecture) is

very similar to generally accepted approaches like RAMI 4.0

and IIRA. However, the building block approach enables the

step-by-step implementation of specific use cases.

Finally, we will present four examples of uses cases based on

the technologies and the building block approach assessed

by IC4F:

• Remote machine access

• Automated guided vehicles

• Massive wireless sensor networks

• Mobile operation & control with ultra-reliable machine

communication

The IC4F consortium is planning to implement a proof-of-

concept in an industrial environment for relevant use cases

including the four described above.

The overall picture of the addressed domains of industrial

communication is highlighted in Figure 1.2.

FIGURE 1.2.: HIGH-LEVEL REPRESENTATION OF THE COMMUNICATION DOMAINS ADDRESSED BY IC4F.

I C 4 FP R O J E C T

C T

MobilityCollaboration

SecurityPerformance,e.g., Real-Time

5G, TSN, Virtualization,...

I T

FlexibilityCost Reduction

SecurityFast Data, auto. Deployment

Mobile Edge Cloud

O T

EfficiencyConsistency, Continuity

SafetyDigital Twin in Real-Time

Industrial Edge Cloud

FIGURE 1.1.: FOCUS OF THE IC4F PROJECT.

I N T E R N E T

P U B L I C C L O U D

E N D - T O - E N D ( E 2 E )I N D U S T R I A L S L I C E Q o S V I A P U B L I C I N F R A S T R U C T U R E

M U LT I - O P E R AT O R E N V I R O N M E N T

FA C T O RY A

FA C T O RY B

R E M O T E O P E R AT I O N S C E N T E R

E N T E R P R I S E C L O U D

P U B L I C / P R I VAT E H Y B R I D C L O U D

FA C T O RY

M O B I L E D E V I C E S

E D G E C L O U D

N E W I I o T AUTHENTICATION M E C H A N I S M S

A P P L I C AT I O N P R O G R A M M I N G I N T E R FA C E ( E R P / M E S / P M / C I M / C A X )

A U T O M AT I O N G AT E WAY( O P E R AT I O N A L T E C H N O L O G Y )

P R I VAT E 4 G / 5 G B A S E S TAT I O N W I T H A L O C A L G AT E WAY, I N T E G R AT E D PA A S , I I o T P L AT F O R M A N D A N A LY T I C S

R E A L - T I M E R E M O T E M A I N T E N A N C E A N D C O N T R O L

P R I VAT E 4 G / 5 G L O C A L W I R E L E S S A C C E S S P O I N T

U N L I C E N S E D A N D S U B L I C E N S E D S P E C T R U M

I N T E G R AT E D H I G H A C C U R A C Y I N D O O R P O S I T I O N I N G ( H A I P )

P U B L I C 4 G / 5 G N E T W O R K C E RT I F I C AT E A U T H O R I T Y F O R I N D U S T R I A L C O M M U N I C AT I O N

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2. ANALYSIS OF USE CASES

A major objective of the IC4F project is to help enterpris-

es to implement the industrial use cases enabled by new

technologies. Many definitions for the term industrial use

case can be found in literature. We prefer the definition put

forward by Cockburn [1]: “A use case captures a contract

between the stakeholders of a system about its behavior and

describes the system’s behavior under various conditions as

it responds to a request from one of the stakeholder.”

We used the following sources as a basis for analyzing indus-

trial use cases:

• Use cases from the IC4F application partners and

associated partners.

• The IC4F User Forum, which includes more than 30 mem-

bers from academia and industry, for discussing Industry

4.0 use cases and future solutions.

• The Plattform Industrie 4.0 [2], which includes hundreds of

use cases that were filtered for analysis according to the

field of production and logistics.

For our use case analysis, we especially considered new,

innovative use cases representing a trend in the field of

Industry 4.0. We also included use cases with demanding

industrial requirements to communication technologies

beyond the state-of-the-art in our investigations. Based on

the key priorities mentioned by EFFRA [3], IC4F defined four

use case clusters in order to structure the use cases to be

analyzed (shown in Figure 2.1.):

• The “Value Chain Integration” cluster which includes

optimized processes and new business models along the

industrial value chain.

• The “Production Information Transparency” cluster which

focuses on the digital twin of processes and conditions in

the factory for improving productivity and efficiency.

• The “Versatile Production” cluster which deals with pro-

duction for user-specific products (e.g., lot size of one) and

products with a short lifecycle.

• The “Augmented Worker” cluster which supports humans

as actors in the field of production through assistance

systems.

As a result of the discussion with industrial users especially,

the use cases listed below that combine several charac-

teristics are expected to increase their performance in this

context or will only then be enabled:

• Use cases that include mobile smart objects which need

to exchange data with other objects and which cannot be

wired easily (e.g., transport vehicles, mobile robots, rotat-

ing machine components).

• Use cases that need real-time transfer of high data vol-

umes (e.g., acoustic or video data or data from a swarm of

numerous sensors) between different locations/companies.

• Use cases that need ultra-high reliable wireless data (safety

and low latency requirements).

• Use cases where wireless exchange of data at a high secu-

rity level plays an important role.

The analysis within IC4F provides a clear picture of the

concerns of stakeholders in future Industry 4.0 use cases

and alignes the results of IC4F’s work on architecture with

the relevant audience at SMEs and large enterprises. The

stakeholder concerns recorded also allow the IC4F project

to derive and prioritize requirements, directly influencing

the design of the IC4F implementations. Project results also

include prototypes showing the proof-of-concept in repre-

sentative real-world demonstrators. The IC4F demonstrators

focus on the clusters “Value Chain Integration”, “Production

Information Transparency”, and “Versatile Production” with

FIGURE 2.1.: FOUR USE CASE CLUSTERS WITH RELEVANCE TO NEW INDUSTRIAL COMMUNICATION TECHNOLOGIES.

clear mapping (cf. Section 6 for a more detailed description

of IC4F demonstrators):

• “Value Chain Integration” is represented by the

“Automated Guided Vehicles” use case.

• “Production Information Transparency” is represented by

the “Remote Machine Access” use case and the “Massive

Wireless Sensor Networks” use case.

• “Versatile Production” is represented by the “Mobile

Cooperation & Control with Ultra-Reliable Machine

Communication” use case.

VA L U E C H A I N I N T E G R AT I O N

V E R S AT I L E P R O D U C T I O N

P R O D U C T I O N I N F O R M AT I O N T R A N S PA R E N C Y

A U G M E N T E D W O R K E R

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3. CHALLENGES AND REQUIRE-MENTS FOR THE BUILDING BLOCK APPROACH

From the previous discussion of the use cases, it becomes

obvious that use cases come in different shapes and siz-

es. Likewise, there are also many different ways to tackle

underlying communication requirements. In this section, the

use cases will be revisited to identify common denominators

and a set of generic requirements that will drive technology

selection and architecture for industrial networks.

Everything becomes Connected

An essential property Industry 4.0 will be a new communica-

tion pattern. While a high degree of automation is already

state of the art in factories, Industry 4.0 adds the ability to

seamlessly exchange data between the factory network

and the rest of the enterprise. Ubiquitous connectivity and

easy data exchange and access will be established between

the internet, the intranet, and the shop floor. This will pave

the way for tighter integration between factory control and

business processes.

The Shop Floor goes Wireless

A close interlock between business and factory only makes

sense when the factory can adapt to different business

needs – also in the physical world. If, for instance, a new

product is to be launched, production will be executed by

flexible robots, creating a new production island on demand

rather than restructuring the entire static factory line.

To exploit the possibilities of seamless communication

between machine control and business processes, physical

flexibility on the shop floor is needed in order to allow for

the free flow of production equipment and material. From a

communication point of view, wireless connections should

be used to avoid the spatial constraints of fixed cabling.

High Bandwidth for Video

Outside the industrial context, the main performance charac-

teristic typically associated with a wireless network is band-

width, i.e., the amount of data transferred per time. While

use cases with high bandwidth requirements, such as video

surveillance, may also exist in a factory, bandwidth as such is

not expected to be a main driver in industrial networks.

High Device Density for Sensor Networks

One goal of the industrial factory network is the ability to

obtain deep insights into production processes by gather-

ing and analyzing data from many sensors. The number of

sensors that can be connected simultaneously is an impor-

tant performance parameter. The energy consumed by the

wireless connection should be minimized in order to enable

a long battery lifetime. This is where truly wireless sensors

without a wired power supply become feasible.

Fast and Reliable Communication for Machine Control

In factory automation, the amount of data to be transferred is

typically low, but the time between sending a message and

reception of the message (referred as latency) is of uttermost

importance. Predictability of latency allowing constant cycle

times within a production network is even more important

than low absolute latency. With higher, but predictable laten-

cy, a production process can still operate at a lower speed.

In the case of unpredictable latency, the entire production

could be disrupted resulting, for instance, in the need for a

machine safety stop. Low and predictable latency is addressed

by ultra-reliable low-latency communication. Besides factory

automation, tools that use Augmented Reality (AR) depend

heavily on low latency in order to achieve the targeted level

of usability and experience.

Hierarchical Infrastructure to Support Different Use Cases

In addition to wireless transmission, the timing requirements

of a use case must also include data processing. If a use case

requires low latency between event and action, process-

ing will have to be executed as close to wireless access as

possible. Collocation of access node and compute resource

is referred to as edge computing. It is used for communica-

tion and processing needs of objects connected to the same

edge computing instance, i.e., for a rather limited spatial

area only, such as a shop floor. Use cases that utilize data

from objects distributed over a larger spatial area require

can benefit from processing hierarchical cloud infrastructures.

In some use cases, both requirements may even co-exist.

Sensor data is utilized in edge computing to enable shop

floor automation, and the same data can be used together

with data from other shop floors, e.g., for analytics-based

process optimization in a central cloud. This means that a

factory network will consist of a hierarchy of compute re-

sources that are located so that the different needs in terms

of speed and spatial requirements can be covered.

Sharing Infrastructure between Use Cases and Tenants

In a real factory setup, several use cases owned and operat-

ed by different business entities and with different communi-

cation requirements will run on the same physical infrastruc-

ture. The difficulty in these multi-tenant scenarios is how to

optimize two contradicting properties. On the one hand, re-

sources should be pooled (“shared”) between different use

cases and tenants to enable the best-possible utilization of

resources. On the other hand, resources should be isolated

and dedicated to allow use case-specific optimizations and

ensure that resources are available when needed. The con-

cept of network slicing allows the virtual network embedding

in a common physical network.

Automated Deployment and Operation

Factory networks are complex. A manifold of use cases re-

sulting in different requirements, a rich choice of technology

options, and various possibilities for deploying these on a vir-

tualized hierarchical infrastructure will have to be considered.

Furthermore, an industrial network is not static.

1312

All of the above factors change over time and factory

networks need to adapt to these changes. A high degree

of automation is therefore a very important requirement for

the setup and operation of factory networks. The employed

ICT automation framework (not be confused with the

cyber-physical automation taking place on the shop floor)

must comprise deployable (“virtualized”) functions used to

build the factory network, a deployment system that pushes

these functions on the infrastructure, and an orchestration

framework that generates the required communication links

between these functions.

Security

In the context of Industry 4.0, security is becoming even

more important. In the past, automation networks were iso-

lated from the rest of the world, thus offering rather limited

points of attack. With the expansion of the Internet to the

cyber-physical domain, attack scenarios familiar from the In-

ternet are becoming relevant. An intruder does not have to

be inside the factory in order to launch an attack. Instead, a

hacker can launch the attack via a cloud system and corrupt

or even hijack a production environment from there. To pre-

vent scenarios like these, security must be an integral part of

an industrial network where communication only takes place

between verified identities and where end-to-end protection

is used. Furthermore, a fine granular access management

system is needed to limit access to resources to eligible

entities only.

Compatibility with Legacy and Heterogeneous

Environments

Although a consistent and uniform rollout of an industrial

network according to the described ideal requirements is

desirable, the reality is sure to be different. Existing equip-

ment, purchased before the dawn of Industry 4.0, will have

to continue to operate together with “native” Industry 4.0

equipment.

F U N C T I O N A L

C O N N E C T E V E RY T H I N G

W I R E L E S S A C C E S S

D I S T R I B U T I O N F O R L O C A L H I G H - S P E E D

C E N T R A L I Z AT I O N F O R G L O B A L S C A L E

O P E R AT I O N A L

A U T O M AT I O N

M U LT I - T E N A N C Y

S E C U R I T Y

C O M PAT I B I L I T Y

FIGURE 3.1.: MAIN FUNCTIONAL AND OPERATIONAL REQUIREMENTS FOR INDUSTRIAL NETWORKS

4. NEW TECHNOLOGIES AND FUNCTIONALITIES FOR THE SMART FACTORY

The IC4F project analyzes available and upcoming technolo-

gies in the field of ICT, applications, and data.

4.1. Cloud – Computing and Virtualization

Cloud Computing in Industrial Environments

The majority of the Industry 4.0 use cases [2] discussed aim

for flexible production and optimized efficiency through ad-

vanced data analytics, so that these use cases depend heav-

ily on cloud computing capabilities. Today, state-of-the-art

solutions connect machine data sources to industrial cloud

backend systems and much effort goes into establishing

communication solutions that follow standards that comply

with industrial requirements, e.g., OPC UA. Current research

is now exploring the integration of backend and edge-cloud

systems in an industrial context in order to enable seamless

interaction of on-site cloud deployments (e.g., industrial

edge clouds) and industrial backend cloud systems.

Virtualization & Industrial Edge Computing

Edge computing approaches in service provider infrastruc-

tures and IT/communication networks leverage the process-

ing power available at the edge of the network, e.g., by

providing processing power and/or storage close to the

edge of networks.

Mapping existing mobile edge computing approaches to

the industry domain reveals unanswered questions and chal-

lenges since edge computing resources in today’s approach-

es are still located away from production and shop floor

environments (introducing additional constraints with respect

to real-time requirements), or do not support the industrial

communication protocols required in OT.

The concept of an industrial edge cloud introduces a heter-

ogeneous resource pool for processing power and virtual-

ization (NFV, virtual networks, virtual working environments)

on the shop floor. Of course, the resource pool needs to

support key industry requirements, such as stringent QoS

requirements, redundancy concepts, safety features, or

industrial communication protocols. Industrial edge clouds

therefore allow for an efficient use of shared resources in OT

environments with a strong focus on safe, secure and reliable

industrial processes.

Physical resources are available to different stakeholders/

actors who can use their “own” virtual resources according

to the given agreements, but without interfering with other

actors’ resource assignments, and virtualization offers a suit-

able trade-off between resource pooling (shared use of phys-

ical resources) and isolation (stakeholders can use assigned

logical resources independent of each other). Mapping fea-

ture requests from Industry 4.0 use cases to industrial cloud

concepts shows that scenarios also foresee a service setup

across cloud instances, e.g., services, virtual tenant networks

or virtual work spaces connecting resources from industrial

edge clouds to “traditional” enterprise or public clouds.

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4.2. 5G – The Communication Network for the Cloud Era

Cellular mobile networks have been driven by the needs

of human communications evolving from voice and data

communication networks provided by 2G and 3G networks

towards the mobile web in LTE. While LTE already includes

some elements, such as narrowband communication, that

target communication between machines, 5G [4] is especial-

ly designed for the Internet of Things and to fulfill the need

of vertical industries. It consists of a 5G New Radio (NR)

interface and enhancements to the core network, needed

5GC. While 5G NR provides the technical basis in terms of

the performance needed for the wireless transmission link,

5GC with its service-based architecture enables the agile

and intent-driven deployment of the network according to

the requirements of specific use cases.

5G Performance

The performance of 5G systems can be summarized as

follows:

• eMBB - enhanced Mobile Broadband: data volumes reach

10 Tbps/km² and peak rates of 10 Gbps

• mMTC - massive Machine Type Communication: high IoT

device density of 1 million/km2 and optimized energy con-

sumption targeted at 10 % of LTE reference

• URLLC - ultra-reliable low-latency communication: one-way

latency below 1 ms, reliability of five 9’s and high mobility

5G New Radio – Design Principles

With regard to the wireless transmission of data, the above

goals for performance are to be achieved with the following

main technical design principles (among others):

• Increase overall wireless link capacity: New spectrum

options from approx. 400 MHz to 100 GHz in licensed

and unlicensed bands will be available and utilized by

ultra-small up to macro cells.

• Decrease latency: Very short packet lengths can be used.

• Increase reliability: The same data is submitted in a

redundant fashion using multiple channels (referred to as

diversity), utilizing, for instance, different frequency bands,

antennas or access points. The latter point is especially im-

portant with a view to reliability when devices are handed

over from one cell to another.

From an architectural point of view, the access points are

split into two components called Remote Unit (RU) and

Central Unit (CU). While the RUs hold the radio interface,

the CUs are responsible for controlling the radio resources

from several RUs. The CUs can be deployed as virtualized

functions on an industrial edge cloud, for instance, and in

this way complements the service-based architecture of the

5GC, which is explained below.

5G Core – Service-Based Architecture

The essence of 5G’s service based architecture (SBA) can be

described by the following three principles:

• Following the paradigm of software-defined networking

(SDN), network elements are completely decoupled into

software and hardware. The software parts are provided

in the form of virtualized network functions (VNFs) as part

of the network function virtualization (NFV) concept. They

are developed following cloud native design patterns

(like micro services or stateless operations) and are thus

well-suited for deployment on edge or central clouds (re-

ferred to as a 5G multi-layer cloud architecture).

• Dynamic interaction between network functions, which

replace the static point-to-point connections between net-

work elements in traditional networks, is achieved through

service-based interfaces that use HTTP 2.0 transport. The

new 5G Network Repository Function (NRF) takes care of

service registration and discovery.

• The network exposure functions offer Application Pro-

gramming Interfaces (APIs) that enable external entities,

like factory operators, to control and monitor network

policies on an individual device basis.

With the help of the above properties, it is possible to in-

stantiate a set of network functions to form a complete net-

work so that the requirements of a predefined use case can

be fulfilled. With this technique, known as network slicing,

different logical networks – extending from device to data

processing – can be deployed on top of one physical infra-

structure, with each slice optimized with respect to different

performance criteria such as latency or bandwidth.

Bridging the gap from use cases with real world require-

ments to a tailored connectivity service is achieved with the

help of Service Level Agreements (SLAs) that describe the

S L I C E # 1( e . g . , U R L L C )

S L I C E # 2( e . g . , e M B B )

S L I C E # N

S E R V I C E L E V E L A G R E E M E N T S

T O P O L O G Y, Q O S , R E L I A B I L I T Y

U S E C A S E S

R E A L W O R L DR E Q U I R E M E N T S

S E R V I C E L E V E L

D E F I N E

R E Q U E S T S V I RT U A L N E T W O R K

N E T W O R K L E V E L

I N S TA N T I AT E S S L I C E

R E S O U R C E L E V E L

O F F E R S R E S O U R C E S A N D F U N C T I O N S

V I RT U A L I Z E D R E S O U R C E S

A N D N E T W O R K F U N C T I O N S

S E R V I C E M A N A G E M E N T

FIGURE 4.1.: GENERATION OF NETWORK SLICES BASED ON USE CASE REQUIREMENTS

requirements of the use case in a formalized way. These

SLAs are passed to a service management entity that selects

appropriate resources from the resource pool and deploys

virtualized network functions. In this way, network slices

optimized for the respective use case can be generated in an

automated fashion (shown in Figure 4.1).

1716

4.3. Data – Driving the Smart Factory

Architecture for Smart Data

One major challenge is the implementation of an architec-

ture for big and fast data that enables all the steps in the

MAPE loop (Monitoring, Analysis, Planning, and Execution)

and that addresses the required latency and volume of data

processing. Furthermore, this architecture must be able to

support the different steps, which are to be executed to a

certain degree in the edge, in order to allow preprocessing.

In addition, the data from different edge environments in a

cloud architecture must be centrally aggregated. There are

existing architectures for centralized big data implementa-

tions, for instance, the Lambda and the Kappa architecture.

However, in the case of Industry 4.0, the focus of interest is

shifting from the notion of big data to the idea of distribut-

ed smart data. Cyber-physical systems typically use sensors

to obtain the situation, condition, and movement data of

artifacts (processes, machines, equipment, and products) on

the shop floor. This data can then be fed into an engine that

not only allows fast, real-time, streaming-based processing

but also stores relevant sensor data to consider the current

and historical digital representation of the given artifact. This

results in a data-driven production with learning capability, in

which observed behavior is used by prediction mechanisms.

Analytics in the Smart Factory

The future shop floor will contain a large range of sensors

ranging from temperature, humidity, audio, or light to video

and location data streams from moving vehicles or robots.

Such massive sensor networks act as enablers for a variety

of specific use cases or applications for controlling machines,

monitoring, anomaly detection, visualization, or long-term

data analysis. Different scenarios pose different requirements

for the building blocks of the system. Training machine

learning models on large amounts of collected sensor data is

a big data scenario, while video stream analysis from moving

robots poses low-latency requirements.

Artificial Intelligence and Machine Learning in the

Smart Factory

Modern communication networks and massively deployed

sensor networks, in particular, collect, generate and pro-

cess a huge amount of data. Reliable and efficient access

to this data in real-time will accelerate the advancement

of AI/ML technologies for use in the context of Industry

4.0. In addition to enabling new industrial applications and

businesses, these technologies will help to cope with the

hugely increased complexity of communication networks, for

instance, they will enhance their efficiency and robustness by

enabling new communication technologies and by making

the vision of self-organizing networks reality. ML technolo-

gies are expected to provide robust predictions that are not

only a basis for industrial applications, such as predictive

maintenance, but are also a key ingredient in the design of

ultra-reliable low-latency communication networks.

Since the importance of wireless communication for indus-

trial applications is constantly increasing, new AI/ML tech-

nologies will have to be developed for big data analytics

in wireless networks. These technologies need to take into

account the limitations of wireless networks (e.g., limited

bandwidth, severe limitations on battery capacity and com-

puting power, etc.) to fully exploit their inherent properties.

The main challenges posed by wireless networks include the

high mobility of mobile devices, which leads to changes in

network topology. In addition, noisy, capacity-limited wire-

less links are generally exposed to interference, making them

error-prone and unreliable.

The limitations of wireless networks together with the fact

that data is distributed at different geographical locations

call for the development of distributed AI/ML methods of

low-complexity for the efficient use of scarce wireless re-

sources. While being amenable to real-time implementation,

the methods envisioned will have to have good tracking ca-

pabilities and provide robust results based on relatively small

data sets and under strict latency constraints. In order to

achieve these goals, and also to meet the stringent require-

ments of many industrial applications, it is essential that the

rich structure of the wireless channel and the propagating

signals are exploited while the context information and ex-

pert knowledge is incorporated by devising hybrid-driven AI/

ML solutions that optimally combine data and model-based

approaches.

Building Blocks for Data Processing in Edge and

Cloud Computing

As a result, generic functionalities bundled as components

are needed for sensor data acquisition, data storage, data

analysis, data visualization, and industrial processing. Exe-

cution components are used to close the loop and feed the

results back to the shop floor. To connect the components,

sensors, and shop floor artifacts, a reliable and fast connec-

tion framework is needed. Based on the design paradigm of

edge computing, for fast communication between co-lo-

cated devices and to support analyses of data streams with

low-latency requirements, the components can be deployed

directly on an edge node. In order to cover the entire

cyber-physical system or for long-term analysis, a cloud com-

puting backend can fulfill the demands for higher disk space

and computing power.

1918

4.4. Security – Protecting the Smart Factory

Increased connectivity and in turn increased data processing

leads to new mobile and modular production methods that

have new security requirements. With these new approaches,

huge amounts of data will be transmitted over a wireless

connection and processed, for example, on the edge cloud.

Traditional security approaches, such as network layering

with firewalls, have to be adapted or completely replaced

with up-to-date security technologies like intrusion detection

and end-to-end encryption. OT security needs to address

requirements, such as real-time processing, long life cycles

and proprietary protocols. Security should no longer be seen

as an on-top option, but considered as soon as new systems

are planned in order to protect data, prevent incidents, and

improve the reliability of Industry 4.0 production processes.

That being said, however, new security approaches will have

to be compatible with old industrial systems. Three general

topics have been identified and will be described in detail:

• Secure Connectivity – end-to-end security in production

• Reliable Wireless Communication – protection for the

new medium

• Monitoring Processes – the use of edge cloud and

data analytics

Secure Connectivity – End-to-End Security in Production

Industry 4.0 production processes are becoming more and

more complex. Production plants are made up of modular

machines that can be rearranged individually and commu-

nicate with each other. Additionally, machines are able to

communicate with other Industry 4.0 assets. Due to these

new communication possibilities, the new security require-

ments mentioned earlier must be taken into consideration.

Secure end-to-end communication is needed for remote

access in order to load updates and read maintenance infor-

mation. A gateway is hence introduced to ensure a secure

connection between devices and remote operators. The

security gateway, which will be placed as a hardware trust

anchor, enables existing production facilities for Industry 4.0

applications. Devices will also require a mechanism so that

they can authenticate each other in order to start trusted

device-to-device communication.

Furthermore, a significant challenge is that most of the

Industrial IoT (IIoT) [5] infrastructure is designed for long

life cycles. This means that the components responsible for

system security must also be safe in the long term so that

facilities have to update or upgrade security mechanisms,

methods, and services in line with industry standards and

production processes.

Cloud-based security services as well as applications on the

gateway provide reliable access management by setting up

a role-based connection with requirement-specific restric-

tions for remote maintenance or control. This bridges differ-

ent wired and wireless network technologies and supports

different industrial application standards, such as OPC-UA

or MQTT. It also creates, manages, and distributes digital

identities by utilizing a public key infrastructure (PKI). Thanks

to digital identities, trusted nodes can be used in a massive

sensor network without any intrusion by malicious devices.

Reliable Wireless Communication – Protection

for the new Medium

In the future, wireless industrial communication could

increase, providing mobility and flexible ad-hoc commu-

nication between the machines themselves and between

machines and the Industry 4.0 product. In order to ensure

reliable and secure wireless communication, additional data

analysis and detection methods will be used.

First and foremost, a comprehensive authentication scheme

for devices and encryption of data ensures that data can-

not be altered or false data injected. However, a growing

number of wireless-enabled devices and wireless transmis-

sions will impact the stability and reliability of a wireless

connection. Simultaneous wireless transmissions especially

can cause interference and, accordingly, degrade transmis-

sion rates or even disrupt connections. To identify the root

cause of a wireless transmission disruption, classification can

be used to determine whether the interference was uninten-

tional or malicious. Classifying the interference allows the

appropriate measures to be selected, e.g., to either identify

a jamming device or perform radio resource management

in order to prevent a disruption or massive loss of perfor-

mance. In order to be able to switch off a malicious interfer-

ing device like a jammer, plant operators have to know the

precise location of the device. The operator can then either

turn the malicious device off or inform the authorities about

its existence and location. This kind of system can be also

used to identify machines and processes that interfere with

the radio channel, e.g., like frequency converters or welding

robots so that appropriate measures, such as EM shielding,

can be taken.

Monitoring Processes and Data Analytics

Massive sensor networks in Industry 4.0 production plants

constantly monitor the environment in order to detect anom-

alies or to identify attrition to support for instance predictive

maintenance. This, accordingly, generates huge amounts

of measured data for data analysis, i.e., making big data

analysis vital if the information is to be processed efficient-

ly. This means that distributed data storage is essential for

storing huge amounts of data. Furthermore, some informa-

tion needs to be processed as close as possible to the origin

to reduce latency, e.g., when near real-time requirements

are paramount. Moreover, in order to protect the data, the

system needs the capabilities for inherent encryption and

user management for access control.

Near real-time industrial data analytics may also rely on new

processing methods, e.g., by leveraging machine learning.

These new methods allow anomalies in production data to

be detected and can indicate machine manipulation or ma-

licious intrusion. Analysis of sensor data can also be used for

predictive maintenance in order to detect a machine failure

before it happens so that preemptive action can be taken.

Audio data, for example, can be used to listen to anomalies

that indicate failure in engines, bearings or shafts. The more

data is acquired, the more computing power will be needed.

Depending on latency and power requirements, the pro-

cessing units can be placed both on the edge of the network

(edge cloud) or centrally. The use of new detection methods

enables the detection of failures in hardware and software

that may be caused by wear and tear or attack. With the

factory now connected to the enterprise network or even the

Internet, new threats must be addressed which are familiar

from the Internet. A hacker could launch an attack from

cloud-based services or could hijack parts of the production

environment. This cannot be prevented if the attacker uses

zero-day or known exploits, but if a breach is detected, the

infected device can be excluded from communication in

order to protect the other devices from infection.

2120

5. REFERENCE ARCHITECTURE AND BUILDING BLOCK APPROACH

This section describes our approach to the IC4F reference

architecture. First of all, the layers of the architecture are de-

scribed. This is followed by how the architecture can be used

to realize real-world implementations (Section 5.1) using our

building block approach (Section 5.2).

5.1. The IC4F Reference Architecture

Industry 4.0 is bringing new business opportunities while

raising new challenges for the underlying ICT infrastructure

in the context of the factory of the future. The IC4F project

is examining the convergence of operational technology,

information technology, and communication technologies

in order to fulfill the requirements of the Industry 4.0 use

cases. To this end, IC4F takes a holistic view of the industrial

ICT infrastructure, applications, and data models. In par-

ticular, this approach goes beyond a pure physical view of

the communication infrastructure (box view), as it considers

higher layers and application frameworks. It also addresses

scenarios like cloud computing on the shop floor, 4G/5G in

the factory, and scalable fast data architectures for massive

sensor networks.

Consequently, the resulting IC4F reference architecture can

be described on a high abstraction level by two layers:

• The ICT infrastructure layer provides wireless or wired con-

nectivity to all objects on the shop floor and may connect

them with cloud resources in different network domains

• The application and data layer includes factory applica-

tions; modeling, management, analytics, and visualization

of data; as well as AI algorithms.

Both layers are complemented by security as well as man-

agement and control functions that are frameworks rather

than functions represented within a single layer.

The placement of the physical systems is especially impor-

tant with a view to security, availability and scalability. The

placement may range from close to the production process

on the shop floor, e.g., sensors that monitor the system state

or wireless URLLC connections for closed loop machine con-

trol, up to external partners along the value chain who may

be connected via public networks. Placement in this case

stems from the requirements of the use case (cf., chapter 3),

e.g., low latency requirements or specific security require-

ments in a certain network domain. Furthermore, use cases

that cover different position ranges may require specific solu-

tions, e.g., an edge cloud for low latency applications or a

security gateway for remote access via the public internet.

Figure 5.1 shows the different perspectives considered in

Industry 4.0 use cases. Based on the use case requirements,

the application and data as well as the underlying ICT infra-

structure can be defined and implemented. One objective of

the IC4F project is to capture architecture knowledge in the

different domains in building blocks that can then be reused

by enterprises to build their own architectures.

B U S I N E S S P R O C E S S E S

A P P L I C AT I O N D O M A I N , D ATA A N A LY T I C S

D ATA , D ATA M O D E L , S E R V I C E S

P L AT F O R M

M A N A G E M E N T & C O N T R O L

RE

QU

IRE

ME

NT

S F

RO

M U

SE

CA

SE

S

I C T I N F R A S T R U C T U R E

E D G E C L O U D

P R I VAT E

P U B L I C C L O U D

P U B L I C

W I R E D

W I R E L E S S

C O M P U T I N G N E T W O R K I N G S T O R A G E

APPLICATION LAYER

SECURITY

COMMUNICATION & COMPUTING INFRASTRUCTURE

ACCESS SUBSYSTEM

PLANT LEVEL

FA C T O RY C O M PA N Y VA L U E C H A I NS E N S O R S A C T U AT O R S

M A C H I N E E Q U I P M E N T

PRODUCTION CELL AND

L INE

B U I L D I N G B L O C K S F O R A S E C U R E R E A L - T I M E C O M M U N I C AT I O N A N D C O M P U T I N G I N F R A S T R U C T U R E I N I N D U S T RY 4 . 0

S E C U R I T Y

INT

ER

FA

CE

S A

T C

OM

PA

NY

BO

UN

DA

RIE

S

E N T E R P R I S E C L O U D ( P R I VAT E / H Y B R I D )

The IC4F approach corresponds clearly with existing frame-

works and reference architectures for communication and

Internet technologies (OSI model), for software architectures

(The Open Group Architectural Framework, TOGAF [6]), and

for the industrial context (RAMI 4.0 [7] and Industrial Internet

Reference Architecture (IIRA) [8]).

In RAMI 4.0, communication is one of the horizontal layers,

which is defined as the mechanism to exchange information

and to form an integrated physical asset. Accordingly, the

IC4F architecture may be viewed as one facet of the RAMI

4.0 cube (hierarchy levels IEC62264 / IEC61512) where

the ICT infrastructure layer corresponds to the RAMI 4.0

communication layer while the application and data layer

corresponds to the information layer. On the other hand,

the Industrial Internet Consortium (IIC) goes one step further

and extends its Industrial Internet Reference Architecture

(IIRA) with an industrial communication framework. In this

framework, communication is further split into several layers.

These layers are inspired by the OSI model. The IIC provides

a framework that can be used to structure Industry 4.0 topics.

The choice of Internet technology and the introduction of an

OSI-like communication model are important steps towards

practical implementations in all of the approaches. However,

RAMI 4.0 and IIRA models still lack important steps before

industrial use cases can be implemented:

• Each of the layers can be implemented using different

technology choices. Thus, the best technology needs to

be selected with regard to the use case requirements.

• The technologies selected need to be finally deployed on

a physical infrastructure to enable efficient implementation

of communication-driven factory applications.

FIGURE 5.1.: IC4F REFERENCE ARCHITECTURE FOR AN INDUSTRIAL ICT INFRASTRUCTURE, APPLICATION AND DATA

2322

Based on the analysis of industrial use cases and existing

technologies, the IC4F project addresses these points and

provides building blocks for solutions in a much finer gran-

ularity. This building block approach should help SMEs to

implement their use cases.

The overall IC4F approach to implement a specific use

case is depicted in Figure 5.2. Predefined building blocks

can be selected to create the architecture for different use

cases. These describe the functionalities required to meet

5.2. The IC4F Building Block Approach

The objective of the IC4F project is to define the reference

architecture and to provide building blocks to implement

Industry 4.0 use cases based on existing enterprise architec-

ture standards.

In conformity with the ISO/IEC/IEEE 42010:2011 stand-

ard, The Open Group Architecture Framework (TOGAF [9])

provides an Architecture Development Method (ADM) and

concepts for defining architectures for different perspectives

and for iteratively refining architecture building blocks to

form solution building blocks in order to implement a specif-

ic enterprise architecture. It is based on an iterative process

model supported by best practices and a reusable set of

existing architecture building blocks [10,11]. The IC4F ap-

proach applies to TOGAF because it addresses the different

architectures required not only for an enterprise architecture

but also for the factory. It also provides a practical and intu-

itive building block approach while the ADM, as a generic

framework, supports the development of a foundation

architecture made up of architecture building blocks that can

be reused in specific use cases. TOGAF therefore provides

methods and concepts that help us to achieve the overall

objective of a reference architecture with generic, reusable

building blocks.

R E F E R E N C EA R C H I T E C T U R E

C O N C E P T U A L A N D A R C H I T E C T U A L P E R S P E C T I V E

SOLUTION AND IMPLEMENTATION P E R S P E C T I V E

R E A LI M P L E M E N TAT I O N

D E S I G N PAT T E R N

B E S T P R A C T I C E S S Y S T E M D E S I G N I M P R O V E M E N T S F I E L D F E E D B A C K

T O B U I L DT O D E S I G NT O A R C H I T E C T

D ATA F L O W V I E W

N E T W O R K V I E W

O T H E R S

C O N D I T I O N M O N I T O R I N G

M O B I L E R O B O T I C S

O T H E R S

• C O M M O N T E R M I N O L O G Y A N D TA X O N O M Y

• F U T U R E T R E N D S

• B E S T P R A C T I C E T E M P L AT E S

• O V E R V I E W O F T E C H N O L O G Y B U I L D I N G B L O C K S

• S E R V I C E PA R A M E T E R S

• T E C H N O L O G Y R O A D M A P A N D M I G R AT I O N S T R AT E G I E S

• D E S I G N A N D I N T E G R AT I O N

• T E S T I N G P L A N

• I M P L E M E N TAT I O N D O C U M E N TAT I O N

INFORMATION SYSTEM(APPLICATION & DATA)

TECHNOLOGY(ICT INFRASTRUCTURE) S

EC

UR

ITY

A B B

A B B

A R C H I T E C T U R E B U I L -D I N G B L O C K S ( A B B s )

I C 4 F D E M O N S T R AT O R 1

I C 4 F D E M O N S T R AT O R 2

I C 4 F D E M O N S T R AT O R …

I C 4 F D E M O N S T R AT O R N

S O L U T I O N B U I L D I N G B L O C K S

FIGURE 5.2.: IC4F’S OVERALL BUILDING BLOCK APPROACH FOR IMPLEMENTING USE CASES

FIGURE 5.3.: USE OF TOGAF ARCHITECTURES TO SPECIFY THE IC4F REFERENCE ARCHITECTURE

The IC4F reference architecture based on TOGAF is de-

scribed below. Figure 5.3 depicts different architectures ad-

dressed by TOGAF and how the domains mainly addressed

by the IC4F project fit into these architectures. Based on the

TOGAF ADM, the business is first developed followed by the

data and application and finally the technology architecture.

These phases of the architecture development method are

used to define reusable architecture building blocks for the

different architectures and serves as a basis for implement-

ing specific use cases.

Architecture Building Blocks (Conceptual View)

Architecture building blocks (ABBs) define packages of func-

tionalities to meet business needs. Furthermore, building

blocks are described by templates which include a descrip-

tion of functionality and context, exposed public interfaces,

interoperability, service parameters, and possible implemen-

tations. and possible implementations. Use cases can be

built up from a collection of interoperating building blocks.

Therefore, interfaces and relations to other building blocks

need to be defined as well. Moreover, ABBs can be defined

at different levels of detail. Accordingly, depending on the

objective of the building block, both generic and refined

ABBs can be defined to facilitate the support of generic as

well as more specific functionalities.

TECHNOLOGY ARCHITECTURE

INFORMATION SYSTEM ARCHITECTURE

BUSINESS ARCHITECTURE

D ATAA P P L I C AT I O N

S E C U R I T Y

I N F R A S T R U C T U R E A N D H A R D WA R E

D E P L O Y M E N T C O M M U N I C AT I O N A N D C O M P U T E

the business needs in a vendor and product-independent

manner. Accordingly, these reusable architectural building

blocks can be used to design the solution for a specific use

case via the solution building blocks. The solution building

blocks implement the functionalities described by the archi-

tectural building blocks. In the IC4F project, demonstrating

specific use cases will be used to validate the IC4F reference

architecture.

2524

The purpose of generic architecture building blocks is to

provide an orientation within the framework and to under-

stand the related concepts for a certain use case. Since the

placement of ICT and application components plays an

important role in Industry 4.0 use cases, this placement con-

sideration must also be taken into account for the generic

architecture building blocks. Possible placement domains

are the machine, factory, enterprise, or public (open world)

level as shown in Figures 5.4. and 5.5. Each domain contains

generic functions, such as compute, storage, networking and

access. This references current operational domains such

as public cloud/networks, IT cloud network and shop floor/

OT networks. Today, these domains usually operate inde-

pendently. The IC4F project plans to investigate the seamless

use across domains, e.g., connectivity and QoS mechanisms

from the shop floor to remote sites, cloud resource access

FIGURE 5.4.: HIGH-LEVEL CONCEPT SHOWING AN EXAMPLE OF BUILDING BLOCKS FOR THE TECHNOLOGY ARCHITECTURE

FIGURE 5.5.: HIGH-LEVEL CONCEPT SHOWING AN EXAMPLE OF BUILDING BLOCKS FOR THE INFORMATION SYSTEM ARCHITECTURE

P U B L I C W I R E L E S S

C O N T R O L U N I T

F I E L D N E T W O R K

C O N T R O L U N I T

W I R E L E S S M O D E M

F I E L D N E T W O R K

I N D U S T R I A L W I R E L E S S

E N T E R P R I S E W I R E L E S S

E N T E R P R I S E N E T W O R K

E N T E R P R I S E C O M P U T E

E N T E R P R I S E S T O R A G E

I N D U S T R I A L N E T W O R K

I N D U S T R I A L C O M P U T E

P U B L I C N E T W O R K

P U B L I C C O M P U T E

T E C H N O L O G Y

INTERNET

OPEN WORLD

ENTERPRISE

FACTORY

WIRELESS CONNECTED MACHINE

WIRED CONNECTED MACHINE

P U B L I C S T O R A G E

P U B L I C W I R E D A C C E S S

LOGICAL COMMUNICATION PATH

from the edge to public nodes. The IC4F building blocks are

continuously advanced throughout the project. In particular,

the framework is extendable to consider future trends and

technologies.

When it comes to flexibility and dynamics in a distributed

end-to-end scenario/use case, two levels can be distin-

guished. On the communication infrastructure level, SDN

and NFV technologies allow for different optimized deploy-

ments for multiple distribution schemes according to chang-

ing needs and topologies. On the service and application

level, similar degrees of freedom and optimization potential

can be achieved with micro-services, modularized applica-

tions, and orchestration frameworks like TOSCA. Building

blocks are, for instance edge computing, Industrial wireless,

and (big) data analytics.

ICT infrastructures have traditionally been separated in vari-

ous physical areas like the field/machine, shop floor/factory,

enterprise and public area. In the past, different technolo-

gies, ecosystems, and business models have evolved along

these separation lines. In the IC4F project, we expect that

that these boundaries are successively breaking down and

that technologies from one area can be adapted and used in

other areas. One example of this, is the virtualization of com-

pute resources. In addition to making resources available for

multiple purposes, these are also interconnected across the

different areas. This means that there is a network of com-

puter resources available, ranging from local, enterprise wide

to public compute resources, that forms a seamless compute

cloud. Figure 5.4. shows how the different areas with various

computing, networking, storage, and wireless functions in

the domains could be interconnected. It provides a view of

the building blocks for more detailed solutions within the

overarching ICT infrastructure.

The application and data domain (see Figure 5.5.) con-

tains generic blocks that depict the logical data flow from

data producers, data distribution between the various user

applications, data management, data processing up to its

visualization. Unlike the ICT Infrastructure, where the focus is

more on the physical and virtual infrastructure, the emphasis

here is on the logical data flow.

There is a generic flow, i.e., data is generated, transport-

ed, processed, analyzed and then visualized somewhere or

further events are caused. Within this pattern, the data may

cross various areas, it may be processed and used at any

place, depending on the specific need. Furthermore, as data

has a tendency to grow along that flow line (i.e., replicating

and generating new data), a new need to manage data

arises in the respective area. This covers functions to store

data at the right place, transform it where needed, and make

it available when permitted. There are also area-specific

I N D U S T R I A L S E R V I C E S , E . G . M E S

B U S I N E S S S E R V I C E S , E . G . E R P

D ATA P R O D U C E R

A P P L I C AT I O N A N D D ATA

OPEN WORLD

ENTERPRISE (CENTRAL CLOUD)

FACTORY (EDGE CLOUD)

CONNECTED MACHINE

LOGICAL COMMUNICATION PATH

C E N T R A L D ATA M A N A G E M E N T

D ATA A N A LY T I C S

E D G E D ATA M A N A G E M E N T

M E S S A G I N G M I D D L E WA R E(PLATFORM SERVICE, NOT PART OF APPLICATION SERVICE)

D ATA V I S U A L I Z AT I O N

2726

services like Manufacturing Execution System (MES) for the

factory area and Enterprise Resource Planning (ERP) in the

enterprise area that utilize the data flow above. Along the

areas above, there is a correlation between the type of data

and services/applications running on top of a certain type of

ICT infrastructure. In the past, these were hard boundaries.

The IC4F project is investigating what needs to be done in

order to establish communication across these boundaries in

a controlled and defined way.

Refined architecture building blocks can be defined to

meet specific use case requirements, following the generic

building blocks approach. The refinement is based on an

iterative process of selecting appropriate building blocks

for a specific use case. With refined architecture building

blocks, technology choices, interworking, solution integra-

tion/interfacing, and migration strategies can be considered

FIGURE 5.6.: EXAMPLES OF ARCHITECTURE BUILDING BLOCKS AT DIFFERENT LEVELS (MARKED IN BLUE AND ORANGE)

and visualized. Figure 5.6. shows examples of functionalities

(building blocks) which are possible choices for the archi-

tecture of particular use cases. In this figure, blue building

blocks represent generic building blocks and orange blocks

represent the more specific refined building blocks. Inde-

pendent of their refinement level, these building blocks are

vendor and product independent.

Solution Building Blocks (Solution/Instantiation View)

The solution building blocks (SBBs) represent vendor-specific

deployable/executable components related to the archi-

tecture building blocks. The SBBs provide the performance

details required for the implementation of specific use cases.

In the IC4F project, SBBs are generated within the scope of

the selected IC4F demonstrators selected. However, these

are vendor and use-case specific and consequently do not

embody a general view.

T E C H N O L O G Y A R C H I T E C T U R EI N F O R M AT I O N S Y S T E M A R C H I T E C T U R E S E C U R I T Y

D ATA S T O R A G E & M G M T.

D ATA A N A LY T I C S

C L O U D O R C H E S T R AT I O N

W I R E D N E T W O R K

W I R E L E S S C O N N E C T I V I T Y

BIG DATA ANALYTICS/BATCH

PROCESSING

RELATIONAL DATABASE

MGMT. SYSTEM

TOSCA APPLICATION DEPLOYMENT

& MGMT. ENGINE

TOSCA CLOUD SERVICE TEMPLATE

TOSCA APPLICATION &

MODELING TOOL

NOSQL DATABASE

MGMT. SYSTEM

NEWSQL DATABASE

MGMT. SYSTEM

TIME SERIES DATABASE

MGMT. SYSTEM

COMPLEX EVENT PROCESSING

STREAM ANALYTICS

T C O S S M A RT C A R D

R O L E M A N A G E M E N T

C E RT I F I C AT E / K E Y M A N A G E M E N T

P U B L I C K E Y I N F R A S T R U C T U R E

P R O F I N E T

S E R C O S

E T H E R N E T

M P L S

T S ND ATA L A K E

W I R E L E S S H A RT

W L A N

M U LT E F I R E

4 G - LT E

5 G - N E W R A D I O

S E C U R E G AT E WAY

6. APPLICATION OF BUILDING BLOCKS IN DEMO SCENARIOS

In order to validate the reference architecture and building

block approach outlined in the previous section and to show

its practical relevance, it is essential that some real-world

examples are considered along with how this approach can

be used to implement concrete use cases and applications.

To this end, four different use cases, which are outlined in

Section 2, are briefly discussed. Specifically, the four different

use case are a subset of the uses cases which will be shown

through ten advanced demonstrators.

2928

S E C U R I T Y G W A N A LY T I C S V I S U A L I Z AT I O N

4 G – M O D E M

R E M O T E S E R V I C E C E N T E R

FA C T O RY

M A C H I N E

S E C U R I T Y G W S E N S O R / A C T U AT O R / C O N T R O L L E R

P U B L I C 4 GN E T W O R K

FIGURE 6.1.: BASIC SETUP OF “REMOTE MACHINE ACCESS” USE CASE INCLUDING SELECTED BUILDING BLOCKS FOR IMPLEMENTING SUCH A SCENARIO

FIGURE 6.2.: BASIC SETUP OF THE “AUTOMATED GUIDED VEHICLES” USE CASE INCLUDING SELECTED BUILDING BLOCKS FOR IMPLEMENTING SUCH A SCENARIO

6.2. Automated Guided Vehicles

Automated guided vehicles (AGVs) that take care of the

flow of goods and material in a factory in an autonomous

manner are considered as another relevant example. Due

to their mobility, wireless connectivity is a natural choice for

such devices. In the simplest case, this connection can be

used to transmit new tasks or to retrieve status information.

However, as more and more reliable and powerful wireless

technologies become available, advanced functionalities

may be implemented. One example could be to offload a lot

of the intelligence that is traditionally contained in the AGV

U R L L C - M O D E M

D E V I C E - T O - D E V I C E C O M M U N I C AT I O N

O N B O A R D G W

4 G - M O D E M L O C AT I O N TA G S

A C T U AT O R

S E N S O R

D ATA V I S U A L I Z AT I O N

L O C AT I O N B E A C O N S

Q R C O D E

T O S C A O R - C H E S T R AT I O N

M Q T T B R O K E R

D ATA S T R E A M P R O C E S S I N G

T R A N S P O RT P R O T O C O L S

D ATA M A N A G E M E N T

N E T O R K M A N A G E M E N T

I T D ATA S T O R A G E

L O C AT I O NA N A LY T I C S

V I S U A LA N A LY T I C S

A N O M A LYA N A LY T I C S

T O S C A D E V I C E M O D E L I N G

I N D U S T R I A L E D G E C L O U D

GRAND MASTER CLOCK (PTP)

I T E D G E C L O U D

U R L L C S Y S T E M

4 G S Y S T E M

E N T E R P R I S E N E T W O R K

A C T U AT O R

S E N S O R

4 G - M O D E M

U R L L C - M O D E MO N B O A R D G W

A G V 1

I C T I N F R A S T R U C T U R E

P L AT- F O R M

A P P L I C AT I O N S / S E R V I C E S

FA C T O RY S U P P O RT S Y S T E M

WA R E H O U S E D I G I TA L T W I N

A G V 2

L O C AT I O N TA G S

itself (e.g., video processing for recognizing the environment

or analytics functionality) to an edge cloud. Likewise, AGVs

could communicate directly with each other, e.g., via direct

device-to-device communication, in order to jointly collabo-

rate in a swarm-like manner so that more complex or difficult

tasks can be managed than by a single AGV, such as joint

lifting of heavy goods. Moreover, localization technologies

6.1. Remote Machine Access

In some situations, it may be helpful to remotely connect

to a certain machine or component, for example, in case of

malfunctions or for remote maintenance. As the supplier of

such a machine or component does not necessarily know in

advance where his equipment will ultimately be used and

what communication infrastructure will be available, the eas-

iest and presumably most generic way to implement remote

access is via a cellular 4G network. However, this generally

poses security challenges, because this kind of “bypass” to

a public network infrastructure may vitiate any local security

mechanisms in place and hence lead to a potential security

threat. One possible way to address this challenge is to care-

fully monitor, control, and log the traffic that goes from and

to a remotely connected machine or component, for exam-

ple, via a dedicated security gateway. The principle setup of

such a system, including selected building blocks outlined in

the previous section for implementing this kind of use case,

are depicted in Figure 6.1.

integrated into the wireless infrastructure could be used to

assist in positioning an AGV on the factory floor as well as

to the current destination. A likely architecture, including cer-

tain building blocks that are required to build such a system,

is shown in Figure 6.2.

3130

6.3. Massive Wireless Sensor Networks

A wide variety of different sensors may be deployed in a

factory to implement functions, such as condition monitor-

ing, predictive maintenance or to detect anomalies. In many

cases, it makes sense to connect these sensors wirelessly as

this facilitates easy retrofit solutions, so that existing ma-

chines can also be easily “upgraded” simply by integrating

additional sensors. Moreover, this can reduce maintenance

and installation work and improve usability. In fact, we envi-

sion that in future hundreds or thousands of sensors may be

deployed in a factory, leading to a potentially significant ac-

WIRELESS SENSOR WIRELESS SENSOR

S E N S O R4 G - M O D E M S E N S O R4 G - M O D E M

APPLICATION / SERVICES

ICT INFRASTRUCTURE

FACTORY SUPPORT SYSTEM

B A C K E N D D ATA A G G R E G AT I O N

4 G S Y S T E M

I N D U S T R I A L E D G E C L O U D

I T D ATA C E N T E R C L O U D

E N T E R P R I S E N E T W O R K

I T E D G E C L O U D

L O C A L D ATA A G G R E G AT I O N

A N O M A LY D E T E C T I O N

FIGURE 6.3.: BASIC SETUP OF THE “MASSIVE WIRELESS SENSOR NETWORK” USE CASE INCLUDING SELECTED BUILDING BLOCKS FOR IMPLEMENTING SUCH A SCENARIO

6.4. Mobile Cooperation and Control with Ultra-Reliable Machine Communication

As a last example, we are considering a mobile control panel

that can be used to configure or monitor a machine. Such

control panels typically also have safety-critical functions,

e.g., an emergency stop button. Most panels currently have

wired connections due to the demanding reliability and

latency constraints of the safety-critical functions. However,

FIGURE 6.4.: BASIC SETUP OF USE CASE “MOBILE COOPERATION & CONTROL WITH ULTRA-RELIABLE MACHINE COMMUNICATION” INCLUDING SELECTED BUILDING BLOCKS FOR IMPLEMENTING SUCH A SCENARIO

APPLICATION / SERVICES

ICT INFRASTRUCTURE

S A F E T Y C O N T R O L L E R

I N D U S T R I A L G W5 G - S Y S T E M

USER EQUIPMENT

C O N T R O L PA N E L5 G - M O D E M

FACTORY SUPPORT SYSTEM

cumulated data rate. However, it is not necessary to transmit

every sensor value to the cloud since much of the data may

be redundant or correlated and since adequate actions may

only have to be carried out locally. Therefore, one promising

approach is to have some local pre-processing/pre-aggre-

gation, for example, in an edge cloud, and to forward only

the pre-processed data to an actual backend cloud. One

major challenge in this respect is how distributed processing

with potential instances in the end devices, the edge cloud

and the backend cloud can be properly orchestrated and

deployed. A likely architecture of this use case, including

selected building blocks, is shown in Figure 6.3.

with new wireless technologies, such as 5G with its ultra-reli-

able and low-latency communication, a wireless connection

becomes possible, for example, in combination with appro-

priate safety protocols such as PROFIsafe. To this end, the

mobile control panel must be connected to a 5G network via

a 5G modem and a suitable gateway to communicate with

the machine control unit. Figure 6.4 depicts a possible setup

of such a system using ABBs.

3332

7. ABOUT IC4F

The flagship project “Industrial Communication for Factories”

(IC4F) aims to develop secure, robust, and real-time commu-

nication solutions for the manufacturing industry. Throughout

the project, the IC4F partners develop building blocks for a

trusted industrial communication and computing infrastruc-

ture based on an open cross-domain architecture that allows

modular expansion for new applications and communication

technologies. Key technologies include 5G, multi-access

edge computing, cloud computing, virtualization, and

industrial monitoring and analytics. The building blocks are

designed to enable users to select the appropriate ICT tech-

nologies, according to the new Industry 4.0 requirements

and the specific migration approach.

The IC4F reference architecture will provide a validated

approach for defining Industry 4.0 communication systems

in a variety of factory ecosystems. Accordingly, IC4F involves

relevant stakeholders along the value chain and brings

together the expertise from different specialist disciplines.

The project is supported by the German Federal Ministry of

Economic Affairs and Energy (BMWi).

3534

REFERENCES, ABBREVIATIONSReferences

[1] Cockburn, Alistair, “Writing effective Use Cases”, Addi-son-Wesley, 2001

[2] Anwendungsbeispiele der Plattform Indutrie 4.0, http://www.plattform-i40.de/I40/Navigation/Karte/SiteGlobals/Forms/Formulare/karte-anwendungsbeispiele-formular.html

[3] EFFRA: Factories 4.0 and Beyond, Recom-mendations for the work programme 18-19-20 of the FoF PPP under Horizon 2020, Version: v30 – Date: 12/09/2016

[4] NGMN Alliance (2014), 5G White Paper -Executive Version[5] Jeschke, S., Brecher, C., Song, H., & Rawat, D. B. (2017), Industrial Internet of Things, Cham: Springer International Publishing, https://doi.org/10.1007/978-3-319-42559-7 (Last retrieved on March 15, 2018)[6] Open Group, TOGAF standard, http://www.opengroup.org/

subjectareas/enterprise/togaf/[7] Deutsches Institut für Normung (2016), Referenzarchitektur-

modell Industrie 4.0 (RAMI4.0)[8] Industrial Internet Consortium (2015),Industrial Internet

Reference Architecture, http://www.iiconsortium.org/IIC_PUB_G1_V1.80_2017-01-31.pdf

[9] http://pubs.opengroup.org/architecture/togaf8-doc/arch/chap32.html

[10] http://pubs.opengroup.org/architecture/togaf9-doc/arch/chap37.html

[11] http://pubs.opengroup.org/architecture/togaf9-doc/arch/chap37.html#tag_37_03

Abbreviations

2G/3G/4G/5G 2nd/3rd/4th/5th Generation Mobile Network3GPP 3rd Generation Partnership Project5GC 5G CoreABB Architecture Building BlockADM Architecture Development MethodAGV Automated Guided VehicleAI Artificial IntelligenceAPI Application Programming InterfaceAR Augmented RealityCT Communication TechnologyCU Central UniteMBB Enhanced Mobile Broadband ERP Enterprise Resource PlanningGW GatewayIC4F Industrial Communication for FactoriesIIC Industrial Internet ConsortiumIIoT Industrial Internet of ThingsIIRA Industrial Internet Reference ArchitectureIoT Internet of ThingsIT Information TechnologyKPI Key Performance IndicatorLTE Long Term EvolutionLTE-A Long Term Evolution-AdvancedMAPE Monitoring, Analysis, Planning, and ExecutionMES Management Execution SystemML Machine LearningMQTT Message Queue Telemetry TransportMTC Machine-Type CommunicationNFV Network Function VirtualizationNR New RadioNRF Network Repository FunctionOSI Open Systems InterconnectionOT Operational TechnologyPKI Public Key Infrastructure PaaS Platform as a ServiceQoS Quality of ServiceRAMI 4.0 Reference Architecture Model Industry 4.0RU Remote UnitSDN Software-defined NetworkTOGAF The Open Group Architectural FrameworkTSN Time Sensitive NetworkURLLC Ultra Reliable Low Latency CommunicationVM Virtual MachineVNF Virtual Network FunctionVR Virtual RealityWLAN Wireless Local Area Network