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Semantic Interoperability of Digital Twins in Smart Cities
Ahmed Shareef, Boris Tomaš, Neven Vrček
University of Zagreb
Faculty of Organization and Informatics
Pavlinska ul. 2, 42000, Varaždin
{ashareef, btomas, nvrcek}@foi.hr
Abstract. Semantic Interoperability of digital twins in
smart cities is crucial for improving the efficacy and
compatibility of digital twins' communication, which
will result in improved smart cities and lifestyles.
Without constant interoperability improvement, it will
be impossible to effectively communicate and share
information between digital twins. This paper
examines the possibility of interoperability
enhancement focusing Microsoft Azure Digital Twin
Definition Language (DTDL) and discusses future
research ideas that could contribute to the
enhancement of Azure DTDL.
Keywords. digital twin, interoperability, smart city,
azure dtdl
1 Introduction
Semantic interoperability refers to the ability of
different devices to communicate and exchange
information with each other in a meaningful way. In
other words, these systems can understand and
interpret the data they receive from each other, even if
they use different languages or formats to represent
those data. Achieving semantic interoperability is
essential to ensure that different systems can work
together seamlessly and efficiently, which is
particularly important in fields such as healthcare,
finance and government, where accurate and timely
data exchange is critical (Nilsson & Sandin, 2018).
Digital Twins of Smart Cities (DTSC) are virtual
replicas of physical cities that use data to simulate and
analyse the behaviour of the city in real time. These
digital models can be used to improve city operations,
improve urban planning, and improve the overall
quality of life of citizens. The digital twins of smart
cities rely on semantic interoperability to gather and
analyze data from various sources, such as sensors,
cameras, and other IoT devices, to provide a
comprehensive view of the city's activities (Deren et
al., 2021).
Semantic interoperability is a crucial component in
the development and implementation of DTSC, as it
enables the seamless exchange and analysis of data
from multiple sources, ultimately leading to more
efficient and sustainable urban environments.
By achieving semantic interoperability, digital
twins of smart cities can provide accurate and timely
insights into the functioning of a city, allowing
policymakers and urban planners to make informed
decisions and implement effective strategies for
sustainable development (Raghavan et al., 2020)(Yang
et al., 2021).
In addition to improving decision-making,
semantic interoperability also facilitates enhanced
collaboration between different stakeholders involved
in the development and management of smart cities.
With a shared understanding of the data and its
meaning, different departments and agencies can work
together more effectively towards common goals,
leading to greater efficiency and effectiveness in city
operations. Furthermore, semantic interoperability also
promotes transparency and accountability, as all
stakeholders have access to the same data and can work
together to ensure that the city is being managed in the
best possible way (Raghavan et al., 2020).
Semantic interoperability can enable the
automation of processes and workflows, reducing the
time and effort required to perform tasks (Raghavan et
al., 2020). By enabling different systems to work
together, semantic interoperability can help foster
innovation and the development of new solutions and
services (Yang et al., 2021).
The existing problems regarding semantic
interoperability in the context of smart cities and digital
twins include technical issues related to data collection,
retrieval, exchange, analysis, and processing, data
sovereignty issues, and the lack of a unified approach
to data exchange that takes into account the semantic
relationships between different systems and their data.
These challenges make it difficult to achieve seamless
data exchange and integration between different
systems and stakeholders in smart cities and digital
twins.
According to Lehtola et al (Lehtola et al., 2022) in
the context of smart cities and digital twins, there are
some existing problems regarding semantic
interoperability. These problems include the need for
semantic recognition of data pieces that represent
objects in the city, such as buildings. It also shows
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challenges of ensuring that different digital twins used
in the city are interoperable and can communicate with
each other effectively. Therefore the need for
standardization of data formats and communication
protocols to ensure interoperability between different
digital twins and smart city systems.
This paper focuses on achieving semantic
interoperability of digital twins in relation to DTDL
implementation while relying on an ontology-based
approach supported by MS Azure DTDL. The rest of
the paper is organized as follows: Section 2 describes
the current state of digital twins, smart cities, and the
challenges of achieving semantic interoperability.
Section 3 represents the reference model presented in
the paper for building an interoperable system of
systems supporting the implementation of smart cities
and digital twins. Section 4 uses the reference model as
a guide to demonstrate a framework for achieving
semantic interoperability in digital twins of smart
cities. Section 5 proposes future research ideas to
improve the interoperability. To conclude, Section 6
summarizes the key points of the article.
2 Background
2.1 Digital twins and smart cities
A digital twin is a virtual copy of a physical system,
which can be designed, tested, manufactured, and
applied in a virtual environment. It is a set of virtual
information constructs that are designed to fully
describe a potential or existing physical manufactured
product. In simpler terms, it is a digital representation
of a physical object that contains all the attributes and
behaviors of the real-life object through modeling and
data.
A smart city is a city that uses advanced
technologies, such as the Internet of Things (IoT),
artificial intelligence (AI), and data analytics, to
improve the quality of life for its citizens, enhance
sustainability, and optimize urban services. In the
context of the paper, a smart city is conceptualized as
a digital twin that includes the infrastructure, human
dynamics, spatial and temporal information flow, and
physical and virtual connectivity of a city.
According to Du et al (Du et al., 2020), using a
digital twin model to collect cognitive data and analyze
cognitive patterns under different information cues to
create a personalized information system that reduces
the risk of cognitive overload for people in a smart city.
The personalized information system will tailor
information based on the cognitive digital twin model
of each person. This model will tell the system what
information format to use, when to use it, and how to
use it. This will make information delivery in a smart
city more efficient and effective, making life better for
the people who live there and making the most of urban
services. So, the digital twin makes a smart city better
by making it possible to create a personalized
information system that reduces mental overload and
improves the way information is delivered.
The definition of "semantic interoperability" is the
use of the same language or framework between
different systems and devices so that they can
communicate with each other and share data. It is
important to use the same frameworks, ontologies, and
standards to make sure everything works together. But
there are still problems that need to be fixed in order to
make semantic IoT work better in smart cities (Nahhas,
2023).
2.2 Examples of existing architectures and
standards for data exchange in smart
cities and digital twins
The concept of "Common European data spaces", as in
Figure 1, which refers to a rich pool of data that varies
in accessibility and can flow freely across sectors and
countries while fully respecting the General Data
Protection Regulation (GDPR). It has several aspects
related to the management of data that need to be
considered in order to make smart cities and digital
twins a reality. These aspects include technical issues
related to collecting, retrieving, exchanging, analyzing,
and processing data, data sovereignty issues, and data
semantics, features, and metadata specifications. For
example, the European Union data strategy describes
the creation of ”data spaces”, and explicitly conceives
these as multiple interoperating data spaces for
different domains (Atkinson et al., 2022).
Figure 1. Common European data spaces
(Dataspaces, 2023)
Common European data spaces refer to a concept
of creating a shared platform for data exchange and
collaboration among different sectors and countries in
Europe. The Figure 1 shows a visual representation of
the Common European data spaces, highlighting the
different components involved in maintaining the flow
of information among them.
DPA stands for Data Privacy-preserving
Automation architecture. It is a backend computing
architecture proposed in the research paper (Xiao et al.,
2018) to facilitate online privacy-protection processing
automation and secure data privacy. The DPA
architecture can be seamlessly integrated with
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companies' principal application system in an
interruption-free manner, allowing for adaption to
flexible models and quality of service (QoS) guarantee
for cross-entity data exchange.
Figure 2. Examples of existing architectures and
standards for data exchange in smart cities and digital
twins (Xiao et al., 2018)
Data Privacy-Preserving Automation Architecture
(DPA) is designed to automate the process of
protecting data privacy while sharing information
across different entities. The goal is to create a
cooperative information sharing ecosystem that can
bridge the gaps between different domains and enable
advanced intelligence for smart cities (Xiao et al.,
2018).
2.3 Challenges of achieving semantic
interoperability
The main concern of the DT is the data and data
management systems. Much research has been
conducted focusing on the issue specifically based on
IoT sensors and its data collection. The main
technological infrastructure is indeed dedicated to
sensors and data management like FIWARE or Azure
DT (Sottet & Pruski, 2023).
However, ensuring data quality remains a
challenge, as the data collected from different sources
may vary in accuracy, completeness, and consistency.
Another challenge is the interoperability of different
data management infrastructures, which may use
different data formats and standards (Nilsson &
Sandin, 2018). Overcoming these challenges requires
collaboration between different stakeholders,
including government agencies, private companies,
and academic institutions, to establish common
standards and protocols for data collection,
management, and sharing. With the right approach,
semantic interoperability can unlock the full potential
of digital twins.
Considerably crucial issues include data security.
DTSC would remain to be attracted to hackers as it
surrounds by potential information from all around the
world. At the same time, DTSC is extremely hard to
secure as each point of vulnerability can open a gate to
overall DTSC (Raghavan et al., 2020).
Data analysis is essential as it enhances both
interoperability and DT autonomy generally. The
challenge arises from the data itself, which poses
problems during collection, cleaning, and structuring
before being fused for deeper meaning and used to
process intelligent tasks (Raghavan et al., 2020)(Boje
et al., 2020).
Without a doubt, implementing semantic
interoperability can be costly, especially for smaller
cities or organizations with limited resources (Yang et
al., 2021). However, financial perspective of
implementation is beyond the scope of this paper.
Rather, this research focuses on ontology of semantic
interoperability.
Semantic interoperability is important because it
makes it possible for different systems, such as DT and
non-DT systems like Computerized Maintenance
Management System (CMMS) or Enterprise Resource
Planning (ERP), to communicate and share
information in a useful way. It makes it possible to
combine data and models from different fields, which
is necessary for making decisions in asset maintenance
management that are based on DT. Without semantic
interoperability, people may not agree on what data and
models mean, which can lead to wrong interpretations
and bad decisions. Because of this, semantic
interoperability is a necessity for achieving efficient
and proactive maintenance strategies that can improve
the availability of assets and reduce downtime in a way
that is both cost-effective and time-efficient
(Ariansyah & Pardamean, 2022).
3 Reference Model
Atkinson et al (Atkinson et al., 2022) in their paper,
proposed a reference model (Figure 3) for building an
interoperable system of systems (SoS) that supports the
implementation of smart cities and digital twins. The
model consists of three components: Global Domain,
Operational SoS, and Consumer System. The Global
Domain refers to the overall environment in which the
SoS operates, including the physical and digital
infrastructure, policies, regulations, and stakeholders.
The Operational SoS represents the interconnected
systems that make up the smart city or digital twin,
such as sensors, data platforms, and analytics tools.
The Consumer System refers to the end-users who
interact with the SoS, such as citizens, businesses, and
government agencies. The proposed reference model
aims to address the technical, semantic, and
governance issues related to data management in smart
cities and digital twins. This includes issues such as
data collection, retrieval, exchange, analysis, and
processing, as well as data sovereignty, semantics,
features, and metadata specifications.
The authors Atkinson et al (Atkinson et al., 2022) also
noted that, while current projects and frameworks have
explored some of these issues and proposed solutions
for subsets of them, there is no overall framework that
connects all aspects in a unique model. The proposed
reference model builds on current experiences and
considers OGC standards and initiatives to provide
open solutions for specific parts of the framework.
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Figure 3. Scope Characterisation for SOS (Atkinson
et al., 2022)
Boje et al (Boje et al., 2020) discusses the use of
Building Information Modelling (BIM) and the
concept of a Construction Digital Twin to improve the
design, construction, and operation of buildings. While
BIM provides a standardised way to represent building
components and systems, it lacks semantic
completeness in areas such as control systems and
sensor networks. The authors propose a Construction
Digital Twin as a more holistic and process-oriented
approach that leverages the synchronicity of cyber-
physical bi-directional data flows.
The paper by J. B. Correia, M. Abel, and K. Becker
(Correia et al., 2023) suggests that semantic
interoperability can be applied in smart cities by
addressing the need for interoperability at all levels
(semantic, data, and others) to provide fully integrated
and optimized services for citizens. The interaction
between DTs from different subdomains (smart
buildings, urban planning) highlights the importance of
data management between DTs.
To ensure consistency and accuracy in data
interpretation, standardizing data models and
ontologies that can be applied across various domains
(Raghavan et al., 2020). This will also aid in the
integration of different systems and facilitate
communication between them.
Before expanding to larger areas, digital twin
implementations are tested and improved in pilot
projects (Chang & Jang, 2021).
4 Achieving Interoperability
There are numerous varieties of semantic
interoperability, each of which enables distinct actions
on the interoperability. This paper highlights on
DTDL-based ontology.
The DTDL-based ontology for smart cities
provides a modeling guideline for creating new
entities, using English terms, camel case syntax for
attribute names, and capital letters for entity type
names. It also allows describing relationships between
twins, which are digital representations of real-world
environments brought to life with real-time data from
sensors and other data sources.
The ontology is used to build Azure Digital Twins-
based solutions and bring them to life in a live
execution environment. The ontology is open source
and aims to drive openness and interoperability (Bloch
et al., n.d.).
4.1 Implementing DTDL Guideline
The guidelines for creating new entities in the DTDL-
based ontology for smart cities recommend checking if
the entity already exists in the repository before
creating a new one. The ontology uses English terms,
preferably American English, and camel case syntax
for attribute names. Entity type names must start with
a capital letter, for example, Streetlight. The ontology
also allows describing relationships between twins,
which are digital representations of real-world
environments brought to life with real-time data from
sensors and other data sources.
Telemetry refers to the data emitted by any digital
twin, be it a regular stream of sensor readings, a
computed stream of data, such as occupancy, or an
occasional alert or information message. The following
table details the properties that Telemetry may possess.
Figure 4. Telemetry Table (Douceur, 2023)
The following example demonstrates a simple
Telemetry definition for a currency using the double
data type, declared based on the properties described
above table.
Figure 5. Telemetry Definition
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This example illustrates the serialized Telemetry
data for the aforementioned Telemetry model
definition when JSON is utilized to serialize Telemetry
data.
Figure 6. JSON Serialized
The following example demonstrates a Telemetry
definition that includes a Currency semantic type and a
unit property.
Figure 7. Telemetry Definition
Figure 9 represents an example of telemetry definition
in DTDL that includes a currency semantic type and a
unit property. The example includes the following
properties: "@type": specifies the semantic type of the
telemetry, which in this case is "Currency". "name":
specifies the name of the telemetry, which in this case
is "eur". "schema": specifies the data type of the
telemetry, which in this case is "double". "unit":
specifies the unit of measurement for the telemetry,
which in this case is "EUR".
4.2 Authoring DTDL ontology
Microsoft's Azure Digital Twins' instructional
document (Learn, 2023a) describes ontology as a
collection of models that comprehensively describe a
particular domain, such as manufacturing, building
structures, IoT systems, smart cities, energy grids, and
web content, etc.
DTDL plays a significant role in the semantic
interoperability of digital twins of smart cities. DTDL
provides a common modeling language that enables
developers to describe twins in terms of the telemetry
they emit, the properties they report or synchronize,
and the commands they respond to (Berhane Russom,
2021)(Azure, 2021). DTDL also allows describing the
relationship between twins, which is essential for
interoperability and enabling data sharing between
multiple domains (Berhane Russom, 2021)(Azure,
2021). DTDL-based ontologies can be used to provide
a common representation of places, infrastructure, and
assets, which is paramount for interoperability and data
sharing (Berhane Russom, 2021)(Azure, 2021). The
Microsoft Digital Twins Definition Language (DTDL)
has a “context model” which is similar to the NGSI-LD
(Digital Twin Hub, 2023). Using DTDL to describe
any digital twin's abilities enables the platform and
solutions to leverage the semantics of each digital twin
(Alina Cartus et al., 2022). DTDL version 3 is being
developed, along with a new open-source parser that
supports v2 and v3 (Minguez Pablos (RIDO), 2023).
Therefore, DTDL is a crucial tool for achieving
semantic interoperability of digital twins of smart
cities.
As mentioned in the reference model section,
according to Atkinson et al (Atkinson et al., 2022),
there are key aspects which has to be addressed to
achieve interoperability. Microsoft Azure DTDL
reference model aims to address the technical,
semantic, and governance issues related to data
management in smart cities and digital twins. This
includes issues such as data collection, retrieval,
exchange, analysis, and processing, as well as data
sovereignty, semantics, features, and metadata
specifications. DTDL provides a common modeling
language that enables developers to describe twins in
terms of the telemetry they emit, the properties they
report or synchronize, and the commands they respond
to (Learn, 2023b)(Berhane Russom, 2021). DTDL also
allows describing the relationship between twins,
which is essential for interoperability and enabling data
sharing between multiple domains (Learn, 2023b).
5 Future Research
As DTDL is a language for describing digital twin
models of smart devices, assets, spaces, and more,
contributing to DTDL will enhance the semantic
interoperability of the digital twins. Future research
could propose new ways of using DTDL or suggest
improvements to the language. According to Cavalieri
et al (Cavalieri & Gambadoro, 2023) purpose of
proposing new ways of using DTDL is to enhance the
interoperability of Digital Twins through integration
into the OPC UA (Open Platform Communications
Unified Architecture) domain. The authors (Cavalieri
& Gambadoro, 2023) believe that interoperability
between Digital Twins and the OPC UA
communication standard should be enabled to meet the
main requirements of Industry 4.0.
6 Conclusion
Semantic interoperability certainly plays a significant
role in the implementation and integration of digital
twins in smart cities. It facilitates data exchange
between digital twins and enhances the analysis of
data. The review paper explored the topic of semantic
interoperability of digital twins in smart cities. It
highlights the importance of improving
interoperability for communication and data exchange
between different twins. Achieving semantic
interoperability is essential as it provides accurate and
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timely insights. And this leads to the existence of well-
functioning smart cities. The paper introduces
reference models that can be reliable in developing
interoperability between digital twins in smart cities. It
addressed technical, semantic, and governance issues
related to interoperability. Further, it discussed
challenges in achieving semantic interoperability. The
paper focuses on Microsoft Azure Digital Twin
Definition Language (DTDL) to enhance
interoperability. It highlights the DTDL and
demonstrates the practical application of DTDL in
achieving semantic interoperability. It discussed future
research ideas to further improve Azure DTDL.
Acknowledgments
This work has been supported by the Croatian Science
Foundation under the project IP-2019-04-4864.
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