Digital Twin Architecture and Standards
surveillance and protection. 6 Industrial IoT
providers
must
convince
existing
stakeholders that their intellectual property
is safe. This requires a holistic cybersecurity
solution that addresses the various security
and privacy risks at all abstraction levels, 7
enabling the next generation of industrial
processing and service. Industrial raw
measurements are created independent of
hosted services, making it challenging to
collect and process the inputs. Initial raw
process data ownership is controlled by
organizations, not individuals.
An industrial process may be orchestrated
by a single control system, but the assets
performing the work are selected with a best
of breed strategy. Process plant design is
guided in part by requirements for
manufacturing precision and the cost of the
individual workflow elements, bringing
many different vendors into the solution
space. Each asset vendor has unique subject
matter expertise for their equipment,
making them the best analyst of the related
data. Traditionally, analysis is performed
only when there is a process issue where
temporary service access to the data is
allowed close to the site.
This increases the complexity of negotiations
for who benefits from monetizing the data,
especially when industrial activity and
intellectual property can be revealed simply
by the characteristics and timing of the
measurements. Industrial installations can
have multiple vendors each with their own
data representations and legacy technology
stacks. Many of these concerns can be
addressed by using digital twins in the ways
we propose.
B ROWNFIELD P ERSPECTIVE
Traditional industry is characterized by
plants where the equipment is installed,
configured and operated for years, even
decades. These legacies cannot be forgotten
or discarded but instead need to be
integrated with new technologies. Industrial
IoT market growth will accelerate only if
there is business value for both the
consumers and suppliers of products and
services. Legacy devices may encounter
system security challenges because they are
usually deployed in places without rigorous
Industrial IoT promises to increase scalability
for process plant services by reducing the
need to be on site. This is made possible by
data collection using access from a remote
location, potentially transferring the
relevant measurements to the cloud. The
dominant approach of aggregating all the
data to a single datacenter can significantly
6
Stojmenovic, I., Wen, S., Huang, X., and Luan, H. 2015. An overview of Fog computing and its security issues. Concurrency
Computat.: Pract. Exper., DOI= http://dx.doi.org/10.1002/cpe.3485.
7
Sadeghi, A.R., Wachsmann, C. and Waidner, M. 2015. Security and Privacy Challenges in Industrial Internet of Things. In
Proceedings of the 52nd Annual Design Automation Conference (San Francisco, June 07 – 11, 2015). DAC ‘15. ACM, New York,
NY, Article 54. DOI= http://dx.doi.org/10.1145/2744769.2747942.
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