Internet of Things
The Power of the IoT Platform
Thomas Ochs
1
and Ute Riemann
2
1
Villeroy & Boch, P.O. Box 11 20, 66688 Mettlach, Germany
2
SAP Deutschland SE & Co. KG, Hasso Plattner Ring 7, 69190 Walldorf, Germany
Keywords: Internet of Things, IoT Platform, Self-Governed Pattern Analysis, De-Central Intelligence, Operational
Excellence, User-Oriented Products.
Abstract: According to Forbes Magazine (August 18, 2014), the Internet of Things (IoT) takes over Big Data as the
most hyped technology. As already well-known the IoT can be characterized by its elements and paradigms
(Atzori et al, 2010). The tight integration of the physical and digital worlds enables companies using
sensors, software, machine-to-machine learning and other technologies to gather and analyse data from
physical objects or other large data streams and sharing this information across platforms in order to
develop a common operating picture. (Gubi et al, 2013). If we look towards the promising value IoT is an
umbrella for covering various value aspects related operational excellence and new business opportunities
(Xiaocong & Jidong, 2010). Having stated that, we would like to focus on the envisions of an IoT value in
which digital and physical entities are linked, by means of a single IT platform to enable a whole new class
of products and services (Bröring et al, 2017). We believe that once issues such as the security issue are
covered, a single and comprehensive IoT IT platform is THE unique element serving not only as an enabler
for an IoT ecosystem (Bröring et al, 2017) but combines two previously separated worlds: it expands the
value reach of the IoT for process excellence as well as for new business opportunities and new intelligent
products.
1 INTRODUCTION
The IoT is a network of uniquely identifiable
“things” using the internet and emerging
technologies (Miorandi, 2012), (McKinsey, 2013)
communicating without human interaction using IP
connectivity (Meadon, 2013), (Lopez Research,
2013). The IoT refers to the network of networks
encompassing IP connected processes, and devices
(Karimi, 2013) to enable new cyber-physical
transformation of everyday objects into smart
objects that can understand and react to their
environment enabling novel computing applications
(Kortuem et al, 2010) and forming pervasive
computing environments. (Meiser 1991). As these
IoT- enabled objects share information about their
condition and surrounding environment with people,
software systems and other machines (Lopez
Research, 2015) new modes of collaboration and
intelligence are fostered - a trend that we call “smart
business.” (Harbor-Research, 2013)
In this article we aim at providing a view on the
IoT fundamentals following a focused view on the
value of the IoT for the business combined with a
use-case from the manufacturing area to underline
our basic assumption that the single IT platform is
the unique key and value of the IoT (Roussos &
Kostakos, 2004), (Dobson et al. 2006), (Elmenreich
et al., 2009) driving a transformation in operational
excellence as well in product design practices that
focus on a certain product intelligence, real-time
contextually rich decisions, event-analysis and broad
access to data.
The transfer of the ability to communicate onto
“things” leads to an „intelligence charging” of
previously stupid objects to an autonomous
communication. Consequently, the objects drop off
from the pure repetitive tasks towards an own level
of “intelligence” enabling a continuous adaptation
towards its environment. With the convergence of
the physical & virtual worlds a continuous data flow
and information enrichment is enabled and
autonomous, pattern-based reactions become
possible. This end-to-end principle in network
operations (Saltzer, Reed, & Clark, 1994) then
allows decentral decision-making and optimizing
procedures. This technological enablement leads to a
284
Ochs, T. and Riemann, U.
Internet of Things - The Power of the IoT Platform.
DOI: 10.5220/0006301702840294
In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 284-294
ISBN: 978-989-758-245-5
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

paradigm shift within operational excellence and
business development due to the provisioning of one
single content centric IT platform (Jacobson et al,
2009) facilitating a spontaneous, self-governed and
adaptive communication with the ability of an
autonomous analysis of patterns and to derive
appropriate measure decentral.
2 INTERNET OF THINGS -
FUNDAMENTALS
The IoT idea is not new but it only recently became
relevant to the practical world, because of the
progress made in hardware development in the last
decade. (Fleisch, 2010)
In 1999 Kevin Ashton had the idea of the IoT at
MITs AutoID lab (Ashton, 2009): „The Internet of
Things enables us to track and count everything, and
greatly reduce waste, loss and cost. We would know
when things needed replacing, repairing or recalling,
and whether they were fresh or past their best,
without the limitations of human-entered data.” His
basic idea was to empower computers with their
own means of gathering information using RFID and
sensor technology (Michahelles et al., 2007),
(Akyilidiz et al, 2004), (Roussos & Kostakos, 2009),
Murphy & Butler, 2006) to observe, identify and
understand the world—without the limitations of
human-entered data.”(Lopez Research, 2013a) For
this article we would consider the following
perspective on IoT:
 Global network interconnecting smart
objects by means of extended Internet
technologies. (Ma, 2011)
 Set of supporting technologies necessary to
realize such a vision (including, Ad Hoc
Networks. (Blazevic, Buttyan, Capkun,
Giordano, Hubaux, & Le Boudec, 2001).
 Ensemble of applications and services
leveraging such technologies to open new
business and market opportunities. (Azori,
Iera, & Morabito, 2010), (Strategy, I. T. U,
2005)
2.1 IoT Core Elements
The IoT infrastructure comprises existing and
involving internet and network developments with a
specific object-identification, sensors and connection
capability as the basis of independent cooperative
services and applications. They are characterized by
a high degree of autonomous data capture, event
transfer, network connectivity and interoperability.
The core elements of the IoT are (e.g. SAP 2014,
Cisco, 2012, LNS Research, 2015):
 Things: IoT offers a multitude of new
opportunities to develop new products and
services of higher relevance, context-aware,
helping people and machines make more
relevant and valuable decisions and be more
efficient by linking computer networks,
sensors, actuators, machines, and devices.
(Da Xu, He, & Li, 2014)
 Network Infrastructure: IoT devices
communicate with networks via the network
layer which provides the physical
infrastructure for transporting sensor/device
data over wireless and wired
telecommunications networks. (Zanella,
Bui, Castellani, Vangelista, & Zorzi, 2014)
 Communication Services: The ubiquitous
and inexpensive communication services
form the backbones for transporting
sensor/device data to collection and
analytics systems. (Iera, Floerkemeier,
Mitsugi, & Morabito, 2010).
 Big Data Analytics: The set of data
generated by IoT devices will need to be
collected, stored and analysed as a key
source for patter analysis and decision
making. (Chen, Mao, & Liu, 2014).
2.2 IoT Enablers
If we take a look at the value the IoT adds for both
businesses and consumers (…) we can state that
every business process in essentially every industry
is affected. (Fleisch, 2010) A number of significant
enablers have come together to enable the rise of the
IoT promising many opportunities and benefits
(Kranenburg & Bassi, 2012):
 Technology: The drop of costs for the
necessary IoT technology components is
fundamental. In almost all applications, low-
cost data communication links (both short
distance and long distance) are essential e.g.
RFID tags and other hardware to make IoT
tracking practical for low-value, high-
volume items in package delivery and
retailing, inexpensive, low-cost battery
power to keep distributed sensors and active
tags operating.
 Intellectual Property, Security, Privacy
and Confidentiality: IoT enabled products
and services not only have to create a
compelling value proposition for data. The
way how and where the data are used have
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285

to be appropriately protected as privacy
breaching attacks pose considerable
challenges in the development and
deployment of IoT applications. (Ukil,
2014)
 Business Organization and Culture: Due
to the combination of physical and digital
worlds the IoT challenges conventional
notions of organizational responsibilities. In
an IoT world, IT is embedded and directly
affects the business metrics against which
the operations are measured. This leads to
the need of a closer alignment of business
and IT. Additionally there is the need for an
enhanced data-driven decision making
within the business, as well as the ability to
adapt their organizations to new processes
and business models. (Dijkman. Sprenkels,
Peeters, & Janssen, 2015)
 Public Policy: Certain IoT applications
cannot proceed without regulatory approval.
As technology is evolving rapidly, the
policy frame needs to be updated and clear.
In addition, regulators must establish rules
about liability. Policy makers also often
have a role to play in shaping market rules
that affect IoT adoption.
2.3 IoT Benefits
As companies increase in operational optimization
maturity, the IoT offers improved capabilities to
serve customer needs and a subsequent increase in
financial and operational performance.
The IoT impacts and revolutionizes the business
and offers benefits as it helps to gain efficiencies,
harness intelligence from a wide range of
equipment, improve operations and increase
customer satisfaction. While there are many ways
that the IoT could impact the business, we would
like to highlight the three major benefits categories:
(Harbor Research, 2013)
 Communication: The IoT communicates
information to people and systems, such as
state and health of equipment (e.g. it’s on or
off, charged, full or empty). The key benefit
is the ability to continuously and seamlessly
communicate with an improved end-to-end
interaction. (Suraki, Suraki, & Nejati, 2012)
 Control / Automation: In a connected world
based on an open IoT environment, a
business will have visibility into a device’s
condition enabling pattern analysis and self-
adapting and self-learning capabilities.
(Kishore,Preuveneers, & Berbers, 2014).
 Cost Savings / Increase Revenue: Companies
will adopt the IoT to save money and
increase revenue. (Färe, & Grosskopf,
2012), (Insights, O. E. C. D., & Revenue, I.
The Internet of things) With new sensor
information, the IoT minimizes equipment
failure and allow the business to perform a
planned maintenance. (Wilson, &
Rosenbaum, 2005), or measure items (Swan,
2012). Additionally the connected supply
chain enables the understanding of the
customers demand at a very early stage by
connecting its demand and behaviour
providing products and services faster and
thus generate the new revenue streams
earlier.
2.4 IoT Challenges
Beyond the benefits, there are a number of (…)
challenges ahead (Haller et al, 2008) to be
considered in order to realize the IoT value. These
challenges are:
 Architectual: IoT encompasses a wide range of
technologies with an increasing number of
smart interconnected devices involved.
(Uckelmann, Harrison, & Michahelles, 2011)
The communications among these devices are
expected to happen anytime, anywhere for
any related services in a wireless, autonomic,
and ad hoc manner becoming more mobile,
decentralized. The requirement towards an
IoT architecture is therefore to support data
integrations over different environments to
provide the appropriate service (Shang,
Zhang, & Chen, 2012). This has to be
supported by modular and interoperable
components managing volumes of data from
various sources and determine relevant
features, to interpret data, show their
relationships, and support decision-making.
A heterogeneous and open architecture have
to be in place following standards without
fixed, end-to-end solutions.
 Hardware: IoT is enabled by smart objects
devices with enhanced inter-device
communication leading to smart systems and
the creation of new services. Therefore,
hardware researches are focusing on
designing wireless identifiable systems with
low size, low cost sufficient functionality.
(Lanzisera, 2014)
 Privacy and Security: Compared with
traditional networks the security and privacy
issues of IoT become more prominent
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because of the combinations of things,
services, and networks, security of IoT needs
to cover more management objects and levels
than traditional network security (Ning,
2013) and privacy. Especially for privacy
there are a partial solution with Privacy
Enhancing Technologies (PET) is presented
by The Privacy Coach. (Broenink, van
Kranenburg, et al., 2011)
 Standards: Standards play an important role
in forming IoT as they are essential to allow
all actors to equally access and use. As global
standards are more relevant than local
agreements the developments and
coordination of standards and proposals will
promote a further enhancement and
development of the entire IoT infrastructure
and applications, services, and devices.
(Främling, & Maharjan, 2013)
 Business: For a professional usage within the
business mature IoT applications to reduce
the risk of failure is are fundamental
prerequisite. In addition, since the IoT is an
investment and challenging the traditional
business models the IoT applications have be
profitable for the business. (Dijkman,
Sprenkels, Peeters, & Janssen 2015), (Li &
Xu, 2013) This is what the IoT applications
are perceived as a help for the business and to
seamlessly transform the organization into a
networked (business) model. (Nold & van
Kranenburg, 2010), (van Kranenburg, 2011)
2.5 Security as a Key Issue on IoT
While foster on a large network of integrated and
interoperable devices there are new challenges
simply because the IoT offers a larger attack surface
and bigger consequences.
 Larger Attack Surface: systems in the past
largely ran deep in secured data centers,
operated by dedicated teams. Now, once they
become IoT devices they may have a
questionable security – are potentially
physically accessible – and are connected
over public networks to a cloud environment.
 Bigger Consequences: In the past system
were largely administrative. Now the IoT
solutions will take actions in the real world,
reacting to events from the real world i.e.
scheduling maintenance, issuing purchase
orders, creating transactions, etc. Potentially
actions back to actuators and relays: close
doors, shut down production lines, start a
heating system, manage street lighting, etc.
The basic attack form is to provide input into a
system leading to an unexpected state desired by the
attacker. In the normal state systems (computer
programs, applications, end-to-end solutions) allow
for input, which is processed and leads to a
predictable output. A malicious input can be
appended to otherwise legitimate requests to affect
an unexpected outcome (buffer overflows, SQL
injection, etc.) Malicious input can be submitted
where the system cannot distinguish between
legitimate and malicious input: impersonation
attacks or “spoofing”.
To enable the business to generate the value that
is inherent in the IoT, it is a must to ensure an
adequate security. This IoT security needs to be
“things-driven”:
 Machine focused interaction & patterns with
deceiving machines
 Standard and limited selection of
communication with consistent
communication intervals, numerous protocols
(Zigbee, BT, Sigfox, etc.) and valid fields and
fixed or semi fixed geo-locations (current use
cases)
 Identity management and provisioning of
‘Things’
 Data driven offering – Security is Key: Any
IoT solution is predicated on the ‘data
economy’. If the data is compromised and
cannot be ‘trusted’ then the entire IoT value
proposition is at risk.
Having stated these key principles of a machine-
driven IoT security concept, we would like to outline
the key elements of a IoT security concept:
 Physical security: the physical security of
IoT devices must be ensured. IoT devices
should not be easily removed, lost or stolen.
Only the required external ports and
connectors should be used on IoT devices.
IoT devices must not be easily
disassembled. In cases where IoT devices
are not able to be physically secured, it
MUST be ensured that the device does not
contain any data of value or the data is
sufficiently secured on the IoT device itself.
 Secure configuration: IoT devices must be
in a secure default state. This includes
secure booting and the secure default state.
Operating system or firmware updates for
the IoT device must be kept up-to-date.
Additionally, only signed and trusted
updates need to be used for the device. IoT
devices should use a trusted time server and
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ensure the use of proper time. IoT devices
must use whitelisting approach for
firewalling and only required network traffic
should be allowed. Other security
requirements such as passwords, logging,
authentication and access control must
follow related security policies and
directives.
 Security of data at rest: Data which is
classified as non-public data and stored on
the device must be encrypted on the IoT
device using approved encryption methods.
If data is not stored on the IoT device,
encryption may not be required.
In addition to the pure security, compliance is a
further degree of security, that needs to be achieved
and secured:
 Compliance to Cloud Infrastructure
Security Directive: this includes securing
network communications from the IoT
device to the cloud by using secure
protocols such as TLS. Transport encryption
as well as identity verification must be
ensured.
 Compliance to secure product
development standards: Web services and
data processing services must not be
susceptible to XSS, SQL injection or CSRF.
Services should not be vulnerable to
overflows, DoS attacks and fuzzing attacks.
Authentication must be done in a secure
way, using certificates, SAMLv2 or oAuth if
possible. Compliance to a password policy
must be ensured.
 Compliance of data storage and data
processing with laws and regulations:
storage and processing of IoT device data
must ensure compliance with data protection
laws and regulations. All governmental or
industry certificates for IoT devices and
related data (Laws, regulations, standards,
Tüv, GS, etc.) must be followed.
3 VALUE
Having outlined the core elements we can define
three fundamental steps on the way towards IoT-
driven value and benefit realization:
 Things to Insight: using the technology to
provide insights with real-time data
allowing to act in the moment with data
driven decisions and further more to
transform data into meaningful insights and
strategy. (Liu & Zhou, 2012)
 Insight to Action evolving the business
processes (Del Giudice, 2016) and the
business model (Li & Xu, 2013) towards the
IoT with simple, streamlined and informing
processes to make the business more
efficient and effective in the business
network.
 Action to Outcomes: with the technological
basis new business opportunities, sources of
value and ecosystem advantage can be
achieved. (Waltari & Kangasharju, 2016)
Following we would like to give a practical
example for the value and benefits generated in
regards to operational excellence within a
manufacturing environment.
3.1 Example: IoT Operational
Excellence Driven by IoT
As stated in various articles, the IoT promises
significant value for the manufacturing are and
production processes. (Tao, Zuo, Da Xu & Zhang,
2014), (Zhang, Zhao, Sun, Wang, & Si 2012),
Houyou, Huth, Kloukinas, Trsek, & Rotondi, 2012),
(Bi, Da Xu, & Wang, 2014)
Since the manufacturing ecosystem becomes
more complex (Leminen, Westerlund, Rajahonka, &
Siuruainen, 2012). it was important for Villeroy &
Boch (V&B) to modernize their production
processes with the ability to access, analyze, and
manage vast volumes of data exceeding while
keeping the capability of traditional data processing
(Borkar, Carey & Li, 2012). This increases the
production efficiency leading to an improved
productivity and better production quality (Manyika
et al, 2011) addressing the ultimate target to improve
the overall operational efficiency. In that context IoT
presents an extraordinary opportunity (Vailaya,
2012), as it promises – and as we later see kept the
promise – new and valuable production process
insights shifting from a production that relies on
representative heuristics towards a fact-driven
production with manufacturing intelligence.
Villeroy & Boch (V&B) is an innovative
company with a time-honored tradition and one of
the most important brands in the ceramics industry.
Since its origins over 265 years ago, the company
has developed into an international lifestyle brand.
Currently, V&B is represented in 125 countries
around the world and 14 production facilities
worldwide with a focus on business activities in the
company divisions ‘Bathroom and Wellness’ and
‘Tableware’.
To stay successful in their competitive markets
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the decision has been taken by V&B to implement a
comprehensive IoT-based solution for producing
faster and smarter (Heesen, 2016) with the use of
available technologies to strengthen their
competiveness. (Brown, Chui, & Manyika, 2011)
One key target was the improvement of the
product quality and yield (the amount of output per
unit of input) (Misra, 1975). Given the number and
complexity of production activities that influence the
yield in the ceramics, V&B needs a sophisticated
approach to diagnose and correct process flaws. For
this, a data-driven platform to process and analyze a
complex dataset (Ward & Barker 2013), was crucial
for the entire project.
The idea for achieving that target of yield
reduction was to sense all unmatched processing
data in real-time, combine them with production
patterns and match them with predictive analytics to
understand the relevant parameters and its
correlations that are critical to the production
process. (Kannengiesser, & Weichhart, 2015) This
allows the steering of the processes on a detailed
level helping to unlock the value of previously
unused data and analyze the data in a way that
hidden insights become transparent. The value was
to gain the ability of a deep dive into process data,
identifying unknown patterns and relationships
among all process steps, provide transparency on
factors and effects on yield and thus allowing work-
in-process decisions. With this intelligent, forward-
looking, anticipatory, and actionable information in
place better (decentral) decisions become possible
while opening the door for an optimization potential
beyond the limits given by the former infrastructure.
With the provisioning of an IoT-ready infrastructure
a barely tapped pool of information and knowledge
now leads to fact-driven actions within the
production for enhanced quality and reduced cost.
The precise goal of the yield database project
was to identify quality issues within the
manufacturing process reducing product quality
issues. The target was to ensure a production process
that covers “Right at first Time”, meaning a final
yield (= production without mistakes and any post
processing). This requires not only a process
monitoring but as well a monitoring of the used
ingredients and of the environmental related
variables to ensure the high quality of the final
product addressing an unexplained variability within
this production processes creating within product
quality negatively affecting the production
efficiency and cost.
To take advantage of IoT to significantly
increase the yield, V&B segments the entire
production process into clusters of closely related
production activities. For each cluster, real-time
machine data per each process step, the materials
used, and other environmental data are collected and
gathered in a central IT platform. (Cai, Da Xu, Xu,
Xie, Qin, & Jiang, 2014). Next step was to apply
various forms of statistical analysis to the raw data
to determine interdependencies among the different
process parameters (upstream and downstream) and
their impact on yield. In addition to the already
existing capturing of production data, sensor-
generated data were seamlessly added to the analysis
process. With this abundance of real-time shop-floor
data in addition to the historical data and the
capability to conduct sophisticated statistical
assessments, another level of transparency will be
achieved. The ability to capture new data from
sources like machines and production units, directly
generated from the origin through sensors, the new
approach provides reliable ground-level information
on what’s really happening at each moment of the
production. In real time (Zhang, Zhang, Wang, Sun,
Si, & Yang, 2015) and with the benefit of advanced
predictive models this approach provides the
capability for a quicker response to the signs of
defects within the production while having insights
about quality issues during the production process,
allowing an appropriate production adaptation just in
time, if necessary.
An IoT driven production has many advantages
as a data - and fact-driven approach enables a deep
dive into historical and real time process data,
identifying patterns and relationships among process
steps and the related data, and then either optimizing
the entire production process, the factors that prove
to have the greatest effect on e.g. yield or deciding
on the entire production processing at a very early
stage. For this the link to dedicated real-time
production data is a novelty and surplus, since the
historical data are already captured. Linking
previously isolated data sets of historical data and
real-time data, ultimately gains the capability to
conduct sophisticated assessments revealing
important insights in real life.
Hearing and seeing what happens within the
production at the very moment it occurs, is just the
first step. Being able to add value to these decisive
moments with advanced analytics is where it gets
interesting. Therefore, these data need to be
transformed into information and knowledge
meaning that the noise and the irrelevant
information, need to be filtered and the relevant
information need to put into a context, so that
business can react upon it. These context data can be
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289

many things, e.g. production machine breakdown,
walking around on the shop floor, getting feedback
from quality inspections regarding the used raw
materials, weather conditions. Merging the real time
machine data and the environmental manufacturing
data (Qu, Lei, Wang, Nie, Chen, & Huang, 2016)
provide the context required to understand the
production environment entirely and to know what is
the best action to be taken in these production
circumstances. Being able to contextualize the
insights into real-time actions is the point where
analytics really pay off. While turning insights into
actions the information turns from a “nice-to-have”
into a “need-to-have”. To comprehensively address
the complexity of the project three main dimensions,
have to be considered:
 Technological Dimension with a suitable IT
infrastructure and robust, actionable data to
achieve the benefits proposed by IoT
(Gerhardt, Griffin & Klemann, 2012) the IT
infrastructure and data have to be in place.
This infrastructure allows to continuously
pull data and information from different
sources within the production and link them
together. In parallel, a rigorous process to
ensure the data’s quality was implemented. In
that sense, it is most important to consider
that all measures rely on normative data,
stable measurements, and processes. Further,
a robust data set that encompasses all
necessary inputs was set up as it is the very
foundation of any analytics. To ensure robust
data that facilitate analytics are available,
these data have to carefully extracted,
validated, and visualized to ensure that they
examine complete information rather than
relying on aggregate data sets that capture
averages or on a sampling of inputs, since
such methods can lead to false positives or
missed patterns.
 Organizational Dimension for a company-
wide support addressing the new insights to
manageable actions. The use IoT deserves an
organizational adjustment, making sure that
the generated knowledge leads to the
appropriate consequences within the
production process. To avoid that the
investment taken gets lost on the level of
tactical responsibility, a top management
sponsorship needs to be secured making sure
that the results gathered are adequately used
within the manufacturing. To not only
generate the knowledge but to make the
knowledge “understandable” for the target
audience, the new role of a data scientist
emerged with the key responsibility to do the
mining of the information and to generate the
business-oriented knowledge base. This data
scientist needs to be located in the respective
business department to establish the link
between methodology and business expertise
as the analysis shall focus on solving
important business issues and changing
employee behavior. Still a non-answered
question is, if the methodological competence
center shall be established either within the
corporate IT or be positioned as a new
corporate analytics unit.
 Knowledge Dimension addressing the need of
well-trained staff. Having a professional
production organization in place that has
worked within a certain methodology for
quite some time, the appliance of IoT does
not only reveal opportunities but leads to a
change in the knowledge required of the team
to overcome the traditional and well-
established production handling. In addition
to the introduction of the new role of the data
scientist, existing roles in the production need
to change as well. Even though it remains
true that an experienced production
professional will more quickly sense what
will work than a junior, we need to accept
that in a production environment as complex
as today’s, there is so much information to
process before one can make an effective
decision, that just being imaginative and
following a ‘gut feeling’ no longer suffices.
The new way of manufacturing requires an
understanding of the power of data and
analytical capabilities to truly grasp what to
accomplish with this data set. (Miragliotta &
Shrouf, 2012) This does mean that
manufacturers need to ask what business
problems they are trying to solve and which
critical decisions is taken automatically or
has to be initiated.
These dimensions allow not only the description
of the complexity but serves as a guideline to
understand the impact of the project as well as the
changes and challenge.
The IoT era has only just emerged at V&B but
the initial project phase has already showed a great
pay-off whilst V&B is at the beginning of being able
to make the prediction with a higher confidence at a
level of insight and detail that was not possible
before. By identifying the key influencing factors
and their correlation impact for production failure,
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the analysis significantly helped to prevent yield loss
early in the production process. The unified IT
platform to prepare, visualize, and share the data and
insights were one of the cornerstones of success.
The critical first step to capitalize on IoT was to
consider how much data are at the companies’
disposal. As `real-time’ projects can only react as
fast as the slowest system in the architecture, it is a
challenge struggling with the legacy architecture
filled with informational silos where data are only
refreshed once every few hours, which makes it
impossible to “listen” at decisive moments when
they actually happen.
A further challenge is the data collection itself.
Previously, V&B has collected vast troves of
process data but used them only for tracking
purposes, not as a basis for improving operational
excellence. Therefore, it was necessary to invest in
the systems and skills to allow the optimization of
existing process information. Additionally, the need
to rigorously assess production data even though
they are less than complete as of today need to be
overcome by applying traditional master data
cleansing and harmonization approaches and to
reconcile data inconsistencies and information gaps.
Nevertheless, the IoT-based production is a critical
tool for realizing improvements in yield, particularly
in any manufacturing environment in which process
complexity, process variability, and capacity
restraints are present.
4 CONCLUSIONS
IoT is enabled by smart devices, technologies and
processes that are seamlessly connected. By taking
advantage of the large volumes of data that are
produced by sensors the IoT platform is a key
enabler for scenarios seen in Industry 4.0 and
Product-as-a-Service business transformations
impacting operational efficiencies and new business
models. The IoT platform delivers a functionality for
building unique and differentiated IoT Applications.
The value of the IoT is the provisioning of
common and integrative digital platform covering
intelligent services and interoperable interfaces in
order to support flexible and networked business
environments to allow smart embedded devices
working together seamlessly. The centralized
production steering paradigm will be overcome
towards a control systems will give way to
decentralized intelligence. The IoT vision is not
limited to operational excellence but incorporates as
well the integration across core functions. With this
high level of integration and visibility across
business processes, connected with new
technologies will enable not only greater operational
efficiency, but as well responsive manufacturing,
and improved product design and thus link
previously disparate business areas with a common
IT source. The IoT and its underlying technologies
will not only optimize the processes of companies, it
will also open new opportunities and transform the
way companies interact with customers, suppliers,
employees and governments. As seen in the
example, Big Data and analytic capabilities are big
differentiators and in this sense Big Data and IoT are
symbiotic partners.
Based on a yield database project at V&B this
example provides a practical insight on the path
forward from a subjective rating of production
parameters towards an objective and fact-based
steering-competency. The challenges listed in the
theoretical part have been approved, e.g. with the
required the investment in systems is essential to
generate the required process information, e.g. new
sensor data, indexing data from multiple sources, so
that they can be analyzed more easily and the
organizational challenges as the required skill set
needs to be in place. The staff has to be trained on
spotting patterns and drawing actionable insights
from information. The employees need to be able to
understand the analysis revealed by a number of
previously unseen sensitivities.
Figure 1: Benefits of a comprehensive IoT platform.
While the IoT can in many ways lead to
operational excellence, they conversely make the
business far more complex. The level of complexity
is immense, because it not only concerns isolated
smart devices, but IT environments have to be
considered, including various other smart devices,
machines and IT systems, which are interacting
across organizational boundaries. We propose the
following key characteristics that outline the value
of a comprehensive IoT IT platform:
Connectivity, interoperability, and
comprehensiveness are key enablers. When used
Internet of Things - The Power of the IoT Platform
291

effectively, IoT can not only significantly improve
operations and margins but also reduce cost, helping
companies to make better decisions by using
accurate, reliable, and scientific information to
analyze risk, optimize processes, and predict failure.
Furthermore the IoT platform is a basic technology
driving the implementation of IoT-centric business
applications built around event-driven architecture
and IoT data, instead of business applications built
around traditional master data leading to the
development of intuitive, and radically user-oriented
products turning the technological growth into
business value.
REFERENCES
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., &
Cayirci, E. (2002). Wireless sensor networks: a
survey. Computer networks, 38(4), 393-422.
Ashton, K. (2009): That ‘Internet of Things’ Thing, in:
RFID Journal.
Atzori, L., Iera, A., Morabito, G. (2010). The Internet of
Things: a survey. In Computer Networks, 54, pp
2787–2805.
Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of things
for enterprise systems of modern manufacturing. IEEE
Transactions on Industrial Informatics, 10(2), 1537-
1546.
Blackstock, M., & Lea, R. (2014, October). IoT
interoperability: A hub-based approach. In Internet of
Things (IOT), 2014 International Conference on the
(pp. 79-84). IEEE.
Blazevic, L., Buttyan, L., Capkun, S., Giordano, S.,
Hubaux, J. P., & Le Boudec, J. Y. (2001). Self
organization in mobile ad hoc networks: the approach
of Terminodes. IEEE Communications Magazine,
39(6), 166-174.
Borkar V., Carey, M.J. & Li, C. (2012): Inside Big Data
Management, Ogres, Onions, or Parfaits?,
EDBT/ICDT 2012 Joint Conference Berlin German.
Broenink, G., Hoepman, J. H., Hof, C. V. T., Van
Kranenburg, R., Smits, D., & Wisman, T. (2010). The
privacy coach: Supporting customer privacy in the
internet of things. arXiv preprint arXiv:1001.4459.
Bröring, A., Schmid, S., Schindhelm, C. K., Khelil, A.,
Kaebisch, S., Kramer, D., ... & Galway, N. U. I.
Enabling IoT Ecosystems through Platform
Interoperability. IEEE Software, forthcoming, 2017.
Brown, B., Chui, M. & Manyika, J. (2011): Are you
Ready for the era of ‘Big Data’?, McKinsey Quarterly,
McKinsey Global Institute, October 2011.
Cai, H., Da Xu, L., Xu, B., Xie, C., Qin, S., & Jiang, L.
(2014). IoT-based configurable information service
platform for product lifecycle management. IEEE
Transactions on Industrial Informatics, 10(2), 1558-
1567.
Carlini, S. (2016). The Drivers and Benefits of Edge
Computing. Schneider Electric–Data Center Science
Center, 8.
Chen, Y., et al. (2014). Time-reversal wireless paradigm
for green internet of things: An overview. IEEE
Internet of Things Journal, 1(1), 81-98.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: a survey.
Mobile Networks and Applications, 19(2), 171-209.
Cisco (2012): The Internet of Everything How More
Relevant and Valuable Connections Will Change the
World, Retrieved February 1, 2016 from
http://www.cisco.com/c/dam/en_us/about/ac79/docs/in
nov/IoE.pdf.
CMI (2015): Things on the Internet to 'The Internet of
Things' - Market Overview, Competitive Market
Intelligence, January 2015.
Cohen, R. B. (2016). Case Studies on How the Internet of
Things (Iot) Helps Firms Optimize Their Digital
Performance and How Iot Will Have a Large
Economic Impact in the Future (Presentation Slides).
Available at SSRN 2775301.
Da Xu, L., He, W., & Li, S. (2014). Internet of things in
industries: A survey. IEEE Transactions on Industrial
Informatics, 10(4), 2233-2243.
Del Giudice, M. (2016). Discovering the Internet of
Things (IoT) within the business process management:
a literature review on technological revitalization.
Business Process Management Journal, 22(2), 263-
270.
Dijkman, R. M., Sprenkels, B., Peeters, T., & Janssen, A.
(2015). Business models for the Internet of Things.
International Journal of Information Management,
35(6), 672-678.
Dobson, S., Denazis, S., Fernández, A., Gaïti, D.,
Gelenbe, E., Massacci, F., ... & Zambonelli, F. (2006).
A survey of autonomic communications. ACM
Transactions on Autonomous and Adaptive Systems
(TAAS), 1(2), 223-259.
Elmenreich, W., D’Souza, R., Bettstetter, C., & de Meer,
H. (2009, December). A survey of models and design
methods for self-organizing networked systems. In
International Workshop on Self-Organizing Systems
(pp. 37-49). Springer Berlin Heidelberg.
Färe, R., & Grosskopf, S. (2012). Cost and revenue
constrained production. Springer Science & Business
Media.
Fleisch, E. (2010): What is the internet of things? An
economic perspective, in: Economics, Management,
and Financial Markets, 2010.
Främling, K., & Maharjan, M. (2013). Standardized
communication between intelligent products for the
IoT. IFAC Proceedings Volumes, 46(7), 157-162.
Gerhardt, B., Griffin, K. & Klemann, R. (2012):
Unlocking Value in the Fragmented World of Big
Data Analytics, Cisco Internet Business Solutions
Group, June 2012. http://www.cisco.com/web/about/
ac79/docs/sp/Information-Infomediaries.pdf.
Gubi, J., et al (2013): Internet of Things (IoT): A vision,
architectural elements, and future directions, in:
Elsevier, Future Generation Computer Systems,
Volume 29, Issue 7, September 2013, pp 1645–1660.
IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security
292

Haller, S., et al (2008): The Internet of Things in an
Enterprise Context, in: Future Internet – FIS 2008,
Volume 5468 of the series Lecture Notes in Computer
Science pp 14-28.
Harbor Research (2013): IoT Market Opps, Paper 2013.
Heesen, B. (2016): Effective Strategy Execution,
Management for Professionals, Springer Verlag Berlin
Heidelberg, 2016.
Houyou, A. M., Huth, H. P., Kloukinas, C., Trsek, H., &
Rotondi, D. (2012, September). Agile manufacturing:
General challenges and an IoT@ Work perspective. In
Proceedings of 2012 IEEE 17th International
Conference on Emerging Technologies & Factory
Automation (ETFA 2012) (pp. 1-7). IEEE.
IDC (2014): IDC FutureScape: Worldwide Internet of
Things 2015 Predictions, IDC Analyze the Future,
December 3, 2014.
Iera, A., Floerkemeier, C., Mitsugi, J., & Morabito, G.
(2010). The internet of things [guest editorial]. IEEE
Wireless Communications, 17(6), 8-9.
Insights, O. E. C. D., & Revenue, I. The Internet of things.
Jacobson, V., Smetters, D. K., Thornton, J. D., Plass, M.
F., Briggs, N. H., & Braynard, R. L. (2009,
December). Networking named content. In
Proceedings of the 5th international conference on
Emerging networking experiments and technologies
(pp. 1-12). ACM.
Kannengiesser, U., & Weichhart, G. (2015). Designing
and executing interoperable IoT manufacturing
systems. Enterprise Interoperability: Interoperability
for Agility, Resilience and Plasticity of Collaborations
(I-ESA 14 Proceedings), 229.
Karimi, K. (2013): The Building Blocks Needed To Make
Internet of Things (IoT) Happen, White Paper,
Freescale Semiconductor Inc., 9/25/2013.
Kishore Ramakrishnan, A., Preuveneers, D., & Berbers,
Y. (2014). Enabling self-learning in dynamic and open
IoT environments. Procedia Computer Science, 32,
207-214.
Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012,
December). Future internet: the internet of things
architecture, possible applications and key challenges.
In Frontiers of Information Technology (FIT), 2012
10th International Conference on (pp. 257-260). IEEE.
Kortuem, G., et al (2010): Smart objects as building
blocks for the Internet of things, in: Internet
Computing, IEEE (Volume: 14, Issue: 1), pp 44-51.
Lanzisera, S., Weber, A. R., Liao, A., Pajak, D., & Meier,
A. K. (2014). Communicating power supplies:
Bringing the internet to the ubiquitous energy
gateways of electronic devices. IEEE Internet of
Things Journal, 1(2), 153-160.
Leminen, S., Westerlund, M., Rajahonka, M., &
Siuruainen, R. (2012). Towards iot ecosystems and
business models. In Internet of Things, Smart Spaces,
and Next Generation Networking (pp. 15-26). Springer
Berlin Heidelberg.
Li, F., Vögler, M., Claeßens, M., & Dustdar, S. (2013,
June). Efficient and Scalable IoT Service Delivery on
Cloud. In IEEE CLOUD (pp. 740-747).
Li, H., & Xu, Z. Z. (2013). Research on business model of
Internet of Things based on MOP. In International
Asia Conference on Industrial Engineering and
Management Innovation (IEMI2012) Proceedings (pp.
1131-1138). Springer Berlin Heidelberg.
Liu, Y., & Zhou, G. (2012, January). Key technologies
and applications of internet of things. In Intelligent
Computation Technology and Automation (ICICTA),
2012 Fifth International Conference on (pp. 197-200).
IEEE.
LNS Research (2015): Revolutionize your Manufacturing
System Manufacturing Architecture with IIoT & Big
Data, 2015.
Lopez Research (2013a): An Introduction to the Internet
of Things, White Paper published by Cisco, Aug 25,
2013.
Lopez Research (2013b): 4 Things Your Business Should
Know About Internet of Things, December 10, 2013,
Retrieved February 15, 2016, from:
http://www.lopezresearch.com/2013/12/10/4-things-
your-business-should-know-about-internet-of-things/
Ma, H. D. (2011). Internet of things: Objectives and
scientific challenges. Journal of Computer science and
Technology, 26(6), 919-924.
MacManus (2010): Chinese Premier Talks Up Internet of
Things, Richard MacManus, ReadWrite, January 19,
2010.
Manyika, J. et al (2011): "Big data: The next frontier for
innovation, competition, and productivity", McKinsey
Global Institute, http://www.mckinsey.com/~/media/
McKinsey/dotcom/Insights%20and%20pubs/MGI/Res
earch/Technology%20and%20Innovation/Big%20Dat
a/MGI_big_data_full_report.ashx.
McKinsey (2013): The Internet of Things and the Future
of Manufacturing, McKinsey & Company, Interview
June 2013, Retrieved February 15, 2016, from:
http://www.mckinsey.com/insights/business_technolo
gy/the_internet_of_things_and_the_future_of_manufa
cturing.
McKinsey (2015): The Internet of Things: Mapping the
value beyond the hype, McKinsey Global Institute,
June 2015.
Michahelles, F., Thiesse, F., Schmidt, A., & Williams, J.
R. (2007). Pervasive RFID and near field
communication technology. IEEE Pervasive
Computing, 6(3), 94-96.
Miorandi, D., et al (2012): Internet of things: Vision,
applications and research challenges, Survey paper in:
Ad Hoc Networks, Elsevier, Volume 10, Issue 7,
September 2012, pp 1497–1516.
Miragliotta, G., & Shrouf, F. (2012, September). Using
Internet of Things to improve eco-efficiency in
manufacturing: a review on available knowledge and a
framework for IoT adoption. In IFIP International
Conference on Advances in Production Management
Systems (pp. 96-102). Springer Berlin Heidelberg.
Misra, R. B. (1975). Optimum production lot size model
for a system with deteriorating inventory. The
International Journal of Production Research, 13(5),
495-505.
Internet of Things - The Power of the IoT Platform
293

Murphy, M., & Butler, J. Proactive computing: RFID &
sensor networks, Final Report on the Conference
organised by DG Information Society and Media,
Networks and Communication Technologies
Directorate, March 2006.
Ning, H., Liu, H., & Yang, L. T. (2013). Cyberentity
security in the Internet of Things. Computer, (4), 46-
53.
Nold, C., & van Kranenburg, R. (2010). Situated
technologies pamphlets 8: The internet of people for a
post-oil world. The Architectural League of New
York, New York.
Qu, T., Lei, S. P., Wang, Z. Z., Nie, D. X., Chen, X., &
Huang, G. Q. (2016). IoT-based real-time production
logistics synchronization system under smart cloud
manufacturing. The International Journal of Advanced
Manufacturing Technology, 84(1-4), 147-164.
Roussos, G., & Kostakos, V. (2009). RFID in pervasive
computing: state-of-the-art and outlook. Pervasive and
Mobile Computing, 5(1), 110-131.
SAP SE (2014): SAP Solutions for the Internet of Things
– Built for Your Business.
Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984). End-to-
end arguments in system design. ACM Transactions
on Computer Systems (TOCS), 2(4), 277-288.
Shang, X., Zhang, R., & Chen, Y. (2012). Internet of
things (IoT) service architecture and its application in
e-commerce. Journal of Electronic Commerce in
Organizations (JECO), 10(3), 44-55.
Suraki, M. Y., Suraki, M. Y., & Nejati, O. (2012,
October). Benefit of internet of things to improve
business interaction with depression prevention and
treatment. In e-Health Networking, Applications and
Services (Healthcom), 2012 IEEE 14th International
Conference on (pp. 403-406). IEEE.
Strategy, I. T. U., & Unit, P. (2005). ITU Internet Reports
2005: The internet of things. Geneva: International
Telecommunication Union (ITU).
Swan, M. (2012): Sensor Mania! The Internet of Things,
Wearable Computing, Objective Metrics, and the
Quantified Self 2.0, in: Journal of Sensor and Actuator
Networks — Open Access Journal, Volume 1, Journal
1.
Tao, F., Zuo, Y., Da Xu, L., & Zhang, L. (2014). IoT-
based intelligent perception and access of
manufacturing resource toward cloud manufacturing.
IEEE Transactions on Industrial Informatics, 10(2),
1547-1557.
Uckelmann, D., Harrison, M., & Michahelles, F. (2011).
An architectural approach towards the future internet
of things. In Architecting the internet of things (pp. 1-
24). Springer Berlin Heidelberg.
Ukil, A., Bandyopadhyay, S., & Pal, A. (2014, April). Iot-
privacy: To be private or not to be private. In
Computer Communications Workshops (INFOCOM
WKSHPS), 2014 IEEE Conference on (pp. 123-124).
IEEE.
Van Kranenburg, R., Anzelmo, E., Bassi, A., Caprio, D.,
Dodson, S., & Ratto, M. (2011). The internet of
things. A critique of ambient technology and the all-
seeing network of RFID, Network Notebooks, 2.
Van Kranenburg, R., & Bassi, A. (2012). IoT challenges.
Communications in Mobile Computing, 1(1), 1.
Vailaya, A. (2012): What’s All the Buzz Around “Big
Data?”, IEEE Women in Engineering Magazine,
December 2012, pp 24-31.
Verizon (2015): State of the Market THE INTERNET OF
THINGS, Discover how IoT is transforming business
results, 2015.
Ward, J. S. & Barker, A. (2013): Undefined By Data: A
Survey of Big Data Definitions, In Proceedings of
IEEE CloudCom 2013, pp 647-654, IEEE Computer
Society, December 2013.
Waltari, O., & Kangasharju, J. (2016, January). Content-
centric networking in the internet of things. In 2016
13th IEEE Annual Consumer Communications &
Networking Conference (CCNC) (pp. 73-78). IEEE.
Weiser, M. (1991). The computer for the 21st century.
Scientific american, 265(3), 94-104.
Weiser, M. (1993). Some computer science issues in
ubiquitous computing. Communications of the ACM,
36(7), 75-84.
Wilson, Ch. & Rosenbaum, St. (2005): Categories of
Return on Investment and Their Practical /
Implications, in: Cost-Justifying Usability: An Update
for the Internet Age, Second Edition, by Randolph G.
Bias, Deborah J. Mayhew, pp 215 – 256.
Xiaocong, Q., & Jidong, Z. (2010, November). Study on
the structure of “Internet of Things (IOT)” business
operation support platform. In Communication
Technology (ICCT), 2010 12th IEEE International
Conference on (pp. 1068-1071). IEEE.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., &
Zorzi, M. (2014). Internet of things for smart cities.
IEEE Internet of Things Journal, 1(1), 22-32.
Zhang, Y. F., Zhao, X. B., Sun, S. D., Wang, J. Q., & Si,
S. B. (2012). Implementing method and key
technologies for IoT-based manufacturing execution
system. Computer Integrated Manufacturing Systems,
18(12), 2634-2642.
Zhang, Y., Zhang, G., Wang, J., Sun, S., Si, S., & Yang,
T. (2015). Real-time information capturing and
integration framework of the internet of manufacturing
things. International Journal of Computer Integrated
Manufacturing, 28(8), 811-822.
IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security
294