Ericsson Business Review, Issue 4, 2015
Manufacturing reengineered:
robots, 5G and
the Industrial IoT
Dust off your conveyor belt – the Industrial Internet of Things is
coming. But realizing a new era of productivity and efficiency will
only be possible with the right business models – and the right
communications technologies.
“In terms
of mobile
communication,
each Industrial
IoT use case
has specific
requirements”
Industrial applications are at the heart of the Internet of Things (IoT) [1]. By enabling the physical execution of industrial processes to be observed, monitored, controlled and automated in
the digital domain, the Industrial IoT will deliver
an important leap in productivity and trigger economic growth.
This potential is reflected in the large number
of global initiatives already tackling various aspects of the Industrial IoT concept. For example,
Industrie 4.0 [2] is a German initiative created in
2013 to help maintain the competitive edge of the
country’s industry. The objective is to integrate
the IoT into industrial production, and in particular, to create networks that incorporate the entire
manufacturing process, thereby converting factories into a smart environment.
Another example is the Industrial Internet Consortium (iic) [3], which was founded in March
2014 and has two main goals: to create new industry use cases and test-beds for real-world applications; and to influence the global development standards process for internet and industrial systems.
This article investigates how mobile communication is a key enabler for the Industrial IoT. It
presents the communication requirements of specific Industrial IoT use cases, outlines potential
business models for ict players and identifies a
series of success factors that these players need
to address in order to thrive in the Industrial IoT
ecosystem. Finally, it offers a real-world case study
that shows the potential of Industrial IoT solutions to reengineer the manufacturing industry.
TH E MO B I LE COM MU N I C ATI O N R EQU I R E M E NTS O F
INDUSTRIAL IOT USE CASES
At its most fundamental level, the Industrial IoT
is about automation. This automation is usually
divided into:
▶ FACTORY AUTOMATION: the automation of operations in the production of items such as electronics, cars and appliances.
▶ PROCESS AUTOMATION: automatically steered and
controlled processes based on continuous data
gathering and analysis. Examples include processes in oil refineries, paper mills and power plants.
Within these two categories, there is a plethora of Industrial IoT use cases. In turn, these use
cases constitute a subset of the total IoT use cases [1][4].
The Industrial IoT use cases where wireless
communication can play a significant role include:
▶ CELL AUTOMATION: devices in an assembly line
and control units communicate wirelessly with
high enough reliability and low enough latency to
enable flexible and highly efficient production.
▶ AUTOMATED GUIDED VEHICLES: unmanned vehicles autonomously move around safely and transfer goods in, for example, a factory or container
harbor by communicating reliably with each other and a central controller.
▶ PROCESS AUTOMATION: a high number of lowmaintenance sensors and actuators spread out
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Ericsson Business Review, Issue 4, 2015
▶
over a wider area communicate wirelessly with
observation and control units for industrial processes.
▶ LOGISTICS TRANSPORT TRACKING: the ability to
track the flow of goods throughout a supply chain
(from raw material to delivery).
▶ COMPONENT STOCK TRACKING: the ability to track
components in stock (e.g. in a warehouse) through
a very high number of low–cost, low-maintenance
devices sporadically sending id, sensor and location data to a central controller.
▶ REMOTE ASSISTANCE: an expert remotely supports an operator via high-definition, two-way
augmented reality video using very high data rates
and low latency.
▶ AUGMENTED REALITY: a live direct or indirect view
of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video and graphics.
▶ REMOTE ROBOT CONTROL: remote control of a robot in order to fulfill operations such as measurement, digging and manipulation of hazardous materials.
In terms of mobile communication, each of the
above Industrial IoT use cases has specific requirements in terms of data rates, latency, reliability, density of connections, coverage etc. Table
1 lists the most stringent requirements and the
preferred cellular access technology solution for
each use case.
It should be noted that several Industrial IoT
use cases can be addressed through today’s lte
access technology. Moreover, 5G will help address
the remaining use cases by providing adequate
technology solutions for sustaining extreme reliability and ultra-low latency.
“At present, the
penetration of
ICT players into
the Industrial IoT
mobile communication ecosystem
is low”
Today, the wireless and wired access technology landscape is very fragmented, and there is no
single dominant global technology. Some access
technologies currently in use include the ieee
families (802.15, 802.11), Wirelesshart, Bluetooth, low power wide area technologies, profinet and Ethernet. Some of these technologies will
continue to operate in the Industrial IoT in the
coming years, with cellular access technologies
providing complementary or replacement solutions.
POTENTIAL ICT PLAYER BUSINESS MODELS
At present, the penetration of ict players (e.g. ict
providers and/or operators) into the Industrial
IoT mobile communication ecosystem is low.
However, a number of potential business models
are open:
1.LOCAL BASIC ACCESS PROVIDER: the ict player connects machines in a factory to a local gateway or
hub.
2.R ELIABLE AND LOCAL BASIC ACCESS PROVIDER: an
enhancement of the previous model, which introduces more stringent connectivity requirements
driven by the needs of the system.
3.LOCAL SYSTEMS INTEGRATOR: the ict player integrates wired, wireless access, data processing, security and Enterprise Resource Planning (erp)/
manufacturing execution systems (mes) infrastructure.
4.G LOBAL CELLULAR NETWORK ACCESS AND CLOUD
PROVIDER: the ict player extends basic and reliable connectivity to a global macro network outside a factory. The global access will enable further possibilities such as industrial cloud servic-
Use case
Most challenging
requirements
Value
Cellular access
technology
Cell automation
Latency
Reliability
0.5ms
99.9999999
5G (uMTC1)
Automated guided
vehicle
Mobility
Reliability
10m/s
99.99999
LTE, 5G
Process automation
Reliability
99.9999999
LTE, 5G
(mMTC, uMTC)
Logistics transportation
tracking
Numb. devices2
Coverage
100000/sqkm
Global
LTE
Components
tracking
Numb. devices2
Mobility
1000000/sqkm
Static
LTE
Remote assistance
Reliability
99.999%
5G (uMTC)
Augmented reality
Data rate
10Gbps
5G (xMBB)
Remote robot control
Reliability
99.999%
5G (uMTC)
Table 1: Requirements and access technology for Industrial IoT use cases.
xMBB (Extreme Mobile
Broadband), uMTC (Ultrareliable Machine Type
Communication) and
mMTC (Massive MTC) are
the main 5G services [1].
2
The device density should
be seen as indicative (and
upper-bound), since it
might vary enormously
from one case to another.
The ITU-R IMT-2020
recommendation was
used as a reference; see
ITU Radiocommunication
Sector ITU-R, “Framework
and overall objectives of
the future development of
IMT for 2020 and beyond,”
Recommendation ITU-R
M.2083, September 2015.
1
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Ericsson Business Review, Issue 4, 2015
▶
es, remote data processing and centralization of
data processing across distributed factories.
5.I NDUSTRIAL IOT ICT SYSTEM PROVIDER: a combination of the previous two models. The ict player
automates, digitalizes and connects the full industrial cycle, from raw components to product
servicing and recycling.
In general, a desirable and effective business
model will address end-to-end integration by
combining service enablement, cloud, security
and connectivity infrastructure.
SUCCESS FACTORS
Whatever business model an ict player decides
to adopt in the Industrial IoT, effectively addressing the following factors will be crucial in determining their success (or failure):
▶
MANAGING A COMPLEX ECOSYSTEM
▶
ANALYZING AND QUANTIFYING CORRECTLY
The sheer diversity of Industrial IoT use cases, as
outlined above, represents a challenge as well as
an opportunity. There is no such one-size-fits-all
business model, and it is therefore important to
develop a common platform for common functions, where specific solutions are supported by
building use case-specific modules on top.
A further factor that impacts every business model is understanding the true potential volume of
Industrial IoT solutions. Predictions often vary
from two- to three-digit potential growth – for
instance, Verizon data shows 204 percent yearon-year growth in the number of IoT connections
in the manufacturing sector [5], while the estimated market volume of factory automation solutions was eur 76 billion in 2014, including automation equipment and control systems [6].
These values have to be treated with caution,
partly due to the difficulty of making such an assessment, and more importantly, due to the lack
of calibration of results among analysts to ensure
the same criteria are considered for the market
volume estimation model.
▶
SECURING SPECTRUM
All Industrial IoT business models ultimately depend on the spectrum regime. This regime will ultimately set the boundaries for the performance
of wireless technology solutions. In particular,
technology performance is a function of spectrum.
○ Exclusive spectrum (licensed or licensedshared) offers full control, reliability and predictability. This is a prerequisite to guarantee ultrareliable communication for factory cell automation, for example.
○ Non-exclusive spectrum (unlicensed) is the other extreme offering, but makes it challenging to
provide guarantees of reliable service delivery
over longer periods.
The relation between business model and access technology is therefore of a second order of
dependency compared with the relation between
business model and spectrum. In fact, the first relation can be considered close to agnostic for most
Industrial IoT use cases.
▶
CREATING TRUST
Information security demands in the Industrial
IoT are much higher than for consumer products.
In the Industrial IoT, multiple actors in the supply chain should be able to share data with each
other in a controlled and secure environment.
This differs from the traditional enterprise situation, in which data is to be secured within a single company and information sharing is limited.
To realize any of the proposed use cases for the
Industrial IoT, information sharing must therefore fulfill certain criteria:
○ As large amounts of data are shared, it must be
possible to control and limit data access. Access
control must be highly automated and the focus
should be the metadata level where access is controlled based on data about the data, rather than
the data itself.
○ As data will be a crucial part of operations for
many companies, it has to be available to all parties in the value chain at all times. This puts high
requirements on the infrastructure, both on a local scale in the factory as well as on a global scale.
○ All parties should be able to trust the data being shared. The system must guarantee that the
data has not been manipulated and that the sender and receiver are who they claim to be. This task
is made more complex by the facts that participants in the value chain are constantly changing,
and that the bar to join the chain should be flexible.
CASE STUDY: MOBILE CLOUD ROBOTICS
The following case study summarizes a mobile
cloud robotics (mcr) demonstration built by Ericsson with two external partners in Tuscany, Italy. The objective was to show how lean production in manufacturing can be achieved by combining robotics with cloud and mobile communication.
Connecting robots and moving their intelligence to the cloud will lead to the development
of smart robot systems with unlimited computing capacity, while individual robots will need
much less hardware and software. This will significantly drive down the cost of industrial robots.
In addition, any upgrade of an mcr solution can
be accomplished with minimal costs by re-planning and re-programming the management system or the intelligence in the cloud, rather than
by re-programming individual robots or reinstalling automated production lines, as happens
in current systems. Consequently, mcr can
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Ericsson Business Review, Issue 4, 2015
“Information
security
demands in
the Industrial
IoT are much
higher than
for consumer
products”
Smart facility
managing service
Cloud management
service
Data center
5G
Remote
control
Cloud
network
Work cell 2
Collision
avoidance in
real time
Robot navigation
service
Robot 2
host
host
Two possible routes:
clockwise and
counter-clockwise
Robot
Robot data
processing service
Work cell 1
Robot 1
Warehouse
Figure 1: A mobile cloud robotics demonstration.
enable more efficient manufacturing and lean production.
In mcr, robots only include low-level controls,
sensors and actuators. In contrast to classical robots, control services for mcr robots are put in
the cloud, running on dedicated hosts or datacenters. The connection between mcr robots and
the cloud is provided through the mobile network,
or through Wi-Fi when needed.
mcr services can be divided between time-critical and non-time-critical. The cloud management service has to locate time-critical services,
such as navigation and sensor data processing,
close to the radio base station serving the working area in order to keep latency low and to guarantee stability and a proper reaction time. Nontime-critical services, like the smart facility management system controlling the plant, can be located remotely, since their actions do not affect
real-time behaviors.
The mcr concept was validated with a demonstration reproducing a logistics use case in industry. In the demonstration, the robots shuttle materials between workcells and warehouses in any
flexible sequence, carrying materials to the right
place at the right time.
The smart facility management system dynamically assigns tasks to the robots and controls their
execution. The robots do not need guides on the
floor, and avoid collisions with people and objects
thanks to remote real-time processing of lidar
sensor data, images and odometry offered by the
data processing and navigation services.
The mcr robots can be monitored through an
app running on a tablet. Using the app, personnel can request services and monitor their status,
as well as receive information on how to interact
with incoming robots. The optimal strategy for
managing the plant and the fleet of robots is decided autonomously by the smart facility management system.
The conceptual ideal behind the demonstration
can be easily extended to a modern factory typically covering an area of several square kilometers. With a typical radius in the order of tens to
hundreds of meters in manufacturing plants, several radio cells are required to operate simultaneously to serve the whole campus. At the same
time, industrial applications need seamless, reliable and fast connectivity between the cloud and
each individual robot in order to support high
bandwidth and low latency.
Cellular technologies such as 4G are suitable to
meet these requirements, since they can guarantee a smooth, seamless and lossless handover
when robots move between radio cells and, at the
same time, deliver good control over interfering
signals from other machines and devices. With
the advent of 5G, an even broader range of
The order of things
A scenario in which all objects (both
humans and machines) are uniquely addressable and communicate via a wire or
wirelessly via a network is often referred
to as the Internet of Things. Some define
it as the network of networks, in particular
networks of cyber-physical systems (CPSs).
CPSs occur when physical industrial objects
(e.g. sensors or actuators) are extended
with a digital representation. The physical
execution of industrial processes can then
be observed, monitored, controlled and
automated in the digital domain, while
sensing and actuation happens in the physical world.
Two key technical components enable
CPSs: embedded computing and communication. Digital processors have been
embedded at all levels of industrial systems
for many years. However, new communication capabilities enable the interconnection
of many distributed processors and the
possibility to move digital observation and
control from a local level to a system-wide
and global level.
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Ericsson Business Review, Issue 4, 2015
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ERICSSON
ABOUT THE AUTHORS
▶ AFIF OSSEIRAN is Director of
Radio Communications at the CTO office,
Ericsson. He holds a doctorate degree
from KTH Royal Institute of Technology in
Stockholm, Sweden, and master’s degrees
from École Polytechnique de Montreal,
Canada, and Université de Rennes-I and
INSA Rennes, France. Between 2012 and
2014, Osseiran managed METIS, the
European Union’s flagship 5G project.
requirements (in particular reliability) will be addressed, unleashing many possible applications.
CONCLUSION
The Industrial IoT is where the worlds of communications and automation come together,
opening up new frontiers for industrial productivity and efficiency. From a mobile communications perspective, the Industrial IoT consists of a
wide range of requirements that reflect the diverse use cases that can be addressed.
A number of potential Industrial IoT business
models are open to ict players (e.g. ict providers and/or operators), ranging from local basic
access provider to an Industrial IoT ict system
provider. A desirable and effective business model would address end-to-end integration by combining service enablement, cloud, security and
connectivity infrastructure.
However, managing a complex ecosystem, analyzing and quantifying correctly, securing spectrum and creating trust will be essential – whatever model is chosen. ●
▶ JOACHIM SACHS is a Principal
Researcher at Ericsson Research. He is
currently focusing on M2M
communication and 5G system design.
Sachs received diploma and doctorate
degrees from Aachen University, Germany
and Technical University of Berlin,
Germany respectively. Since 1995 he has
been active in the IEEE and the VDE.
▶ MARZIO PULERI received an
MSc degree in electronic engineering
from the Sapienza University of Rome in
1992, and has worked at Ericsson
Telecomunicazioni S.p.A since 1993. He
joined the Ericsson research group in Pisa
in 2008, where his research interests
include micro­electronics, artificial
intelligence and robotics.
▶ REFERENCES
[1] A. Osseiran, J. Monserrat and P. Marsch, 5G Mobile and Wireless Communications Technology, Cambridge University Press, 2016 (forthcoming)
[2] Industrie 4.0, Zukunftsprojekt Industrie 4.0, accessed December 2015, available at: http://www.bmbf.de/de/9072.php
[3] Industrial Internet Consortium, accessed December 2015, available at: http://www.iiconsortium.org/
[4] A. Osseiran, “Five alive! 5G beyond the hype”, Ericsson Business Review, June 2014, available at:
http://www.ericsson.com/res/thecompany/docs/publications/business-review/2014/five-alive-5g-beyond-the-hype.pdf
[5] Verizon, State of the Market: The Internet of Things 2015, February 2015, available at:
http://www.verizonenterprise.com/resources/reports/rp_state-of-market-the-market-the-internet-of-things-2015_en_xg.pdf
[6] Berg Insight, Industrial Automation and Wireless IoT, May 2015, available at: http://www.berginsight.com/ShowReport.aspx?m_m=3&id=207
▶ ADDITIONAL
CONTRIBUTORS
Mark Mowlér, Sebastian Elmgren,
Kristoffer Gramnaes, Aulis Koivisto,
Roberto Sabella and Konstantin Zervas.
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© Ericsson AB 2015