Smart city position paper - GS1 standards perspective

Smart City
Interoperable Data-Driven Smart City: - Enabling standard
based data and service sharing platform for smart city ecosystem
Draft version 0.8
Korea

Introduction
According to the United Nations, currently, half of the world population lives in cities and it is forecasted
to reach two-thirds in 2050. This growth creates a huge challenge to resources and infrastructures of
urban areas. The concept of smart city is derived with the initiative to address those challenges posed
by urbanization with aim of enhancing the quality of urban life with reduced cost and resource
consumption. There is no absolute definition of a smart city with universally agreed definition, but it
involves the uses information and communication technologies (ICTs) along with different digital
technologies to address many of the long-term issues associated with city life.
It is clear that the first thing in starting smart
cities initiative is to identify and classify the
important stakeholders. In a more general term,
smart city stakeholders can be technically
categorized into four roles: citizens, service
providers, application developers, and
framework enablers. Without an active
participation of citizens, it is difficult to analyze
the needs and necessities of urban cities.
Service providers like city municipalities
embrace smart city services by sharing
historical and operational data. Application
developers can develop critical, predictive,
descriptive and prescriptive applications to help
citizens and service providers to make better
decisions. Framework enablers coordinates
data and service sharing by proposing
standards and tools that can be shared across
multiple applications.
In such organized smart cities, data will become
a big resource that can transform the capability
of a city, enabling the development of new
systems and services, and supporting informed
decisions. Therefore, for the sake of each
stakeholder’s benefit data must be open and
shared so that developers can use the data for
real time decision and strategists to develop
longer term plans to improve the safety and
quality of life for citizens.
Smart cities involve a wide range of services
including public transportation, safety, energy
distribution, and health care systems. However,
because these services are managed by
multiple independent sectors with no common
business process, data models, and service
interfaces, it hinders applications and users of
the service to properly discover and use these
services. It is only when cross-domain
applications are able to communicate and
discover that the full potential of a smart city can
be realized. This cannot be achieved without
openness and interoperability. ISO/IEC also
published a report emphasizing on “Open Data”
and how the value of data can be increased
when combination of various data sources are
used for analytics and to innovate new services.
In general, interoperability of organization across all sectors of city services can be achieved if the cities
are organized on the foundation of: globally unique identification of physical and digital entities; a
standardized interface to facilitate data and service sharing; a common framework for data model and
structure;
For a smart city ecosystem which contains heterogeneous things, devices, and services, identifying
each of them uniquely is the first step to interoperate and manage. In this regard, the globally unique
GS1 keys can be used to connect physical and digital objects with the associated information. EPCIS
can be used to create smart city data hubs to enable data sharing. By standardizing and modeling smart
city business processes with event and master data, a common understanding can be achieved on data
format and vocabularies. Through EPCIS standard capturing and querying interface, city applications
can easily interact to capture and get information from repositories. Furthermore, the competence of
EPCIS for smart city comes from its independent manageability (scalability). Each resource provider
(transportation, energy, etc.) can maintain their own EPCIS repositories and still be discovered and
accessed. GS1 Digital Link, ONS and DS play the role in discovering services and data sources.
Smart City Trends
The concept of smart city equipped with ICT technologies is emerging as a key strategy for solving
urban problems and improving cities functionalities. However smart city is not just a city that leverages
ICT technologies, but is an infrastructure for the complex ecosystem made up of many stakeholders

including citizens, governments, and industries. For this reason, various smart city concepts are
evolving, depending on the urban situation and the maturity of ICT technology. Two major concepts of
smart city are evolving as follows:
Open Data Hub for Smart City
Data – Intelligent City Platform (iCP) (Figure Credit: SmartCambridge)
The open data hub is a data infrastructure that can seamlessly collect and share public data along with
open data from a variety of data sources of the city. It is an effective way to promote civic-driven
innovation by encouraging openness in cities. The open data hub leverages underlying data
technologies and mechanism such as, identification standard for tracking and tracing of object in the
city, data model standards for the systematic labeling of data, data lakes to accommodate various types
of data, data license management mechanisms for protecting copyright of data, data governance
mechanisms for data quality management, data privacy and security technologies, and flexible interface
standards between data and services. For example, Milton Keynes City in the United Kingdom built a
data hub named MKDataHub in 2017, and presented several technologies for the data hubs such as
overall architecture, service model, and data governance. It currently holds approximately 714 data sets,
28 data owners, and 11 data licenses, and has become the reference model for other smart cities.
Smart City Living Lab
Concept of Living Lab (Figure Credit: Proseu / Stephan Köhler, ICLEI Europe)

The concept of Smart City Living Lab is a paradigm that brings government, citizen, and local industry
under one umbrella. The main purpose of smart city living lab is to provide a space and time that helps
everyone solve urban challenges. The living labs strategy has the advantage that ICT technology can
focus on consumers in the lives of citizens, rather than suppliers such as government and companies.
Although the application of living laboratories has been tried in various ways, it is commonly required
to prepare communication channels between participants and policies and regulations such as
regulatory sandbox. Examples of living labs attempted by many countries are shown in the following
table.
Country Project Title Description
South Korea Seongdaegol Living
Lab
Local residents are leading the use of appropriate technologies, such
as solar hot air heater installation, by forming a council to solve energy
problems.
United States Dr. Tong Louie
Living Lab
Development of products and environment to enhance the relationship
between housing and work space
Netherland Philips Home Lab Residential observation apartment to see how Phillips' new products
interact with people within two weeks
Norway Tennor Home of the
Future
Development of Smart IoT Home
Sweden E2Home In order to improve the quality of life for local residents, intelligent home
solutions are provided in actual largescale apartments to provide
feedback for the design of urban services.
Taiwan Suan-Lien Living
Lab
Demonstrate caring services for older people
Implementation Challenges
Fragmented data source in smart city
In the traditional city, data is fragmented. Organizations in different sectors (e.g. transportation, energy,
environment, safety, and administration) manage their own data independently. This created broken
city data which makes it hard to detect critical conditions based on various city services or discovering
any emerging trends by analyzing multiple data sources. The fragmented business process used by
each sector also incurs difficulties to understand the semantics of the data. It is not easy to search and
discover data sources since proper and standardized classification is not applied.
In most scenarios the fragmentation starts on how resources are identified. In the case where there is
no coordinator for allocation of IDs, those which are assigned locally are not guaranteed to be globally
unique. One solution could be to use UUID without any coordinator. But it is not possible to associate
semantics to such kind of ID allocation making it hard for classifications of resources and services by
using the IDs.
The other major fragmentation comes on how data are models with no common agreement on the
meaning of vocabularies. When each organization uses a solution of their own or selects any proprietary
solution, they either put extra effort to map terms from many sectors or every sector needs to ensure
common format when they bring their data in a sharable environment. The second approach is
preferable for obvious reasons. Moreover, in order to reduce the number APIs and facilitate discovery
of resources and services, a common interface should be designed and implemented.
Smart cities also need to have a marketplace where data is shared with legal and monetization
capabilities. Public or private organizations should be able to exercise control over their data by applying
for a license. On top of that, they need to be able to tag a price on the resource they are offering. In
terms of data protection, privacy and security should not be compromised. Data needs to be secured
and encrypted both during communication and storage. Users need to have strict permission control on
the type of data they are sharing and the users they are sharing with.
In most cases the current GS1 standard can be easily applied to smart cities to solve the issue of
interoperability. By analyzing existing smart city resources and services, appropriate types of GS1 ID

can be applied. For example, city resources, and services with characteristics of product and services
can be identified by GTIN. GLN can be assigned to city resources with characteristics of physical
locations such as buildings, bus stops, parks, and so on. GRAI can be assigned to shareable city
resources such as shard bicycles, and cars. City assets which are owned by the smart city can be
identified by GIAI. All other GS1 IDs such as GSRN, GDTI, and so on also can be used to identify city
resources.
With a proper extension and definition of GS1 data model and vocabularies for a smart city, Master and
Event data model of EPCIS can be applied in more effective and sustainable way since it allows data
to be stored and shared in a distributed manner. The open EPCIS capturing and accessing API can be
a good asset for smart cities also. Each silo organization can maintain their own independent EPCIS.
Since GS1 Digital Link, ONS and DS play the role in discovering services and data sources, all the
distributed EPCIS act as a virtually centralized system where any organization globally access
irrespective of the location.
Smart city service provisioning
Nowadays various city platforms and industry groups produce growing number of city services that are
associated with smart city resources. These city services are provisioned to improve citizen’s quality of
life and optimize city operations. Therefore, a versatile and scalable standard for service provisioning
is required in building smart city. The standard should be acceptable in different sectors of the smart
city and global industry groups.
Until now, most of city services are created and offered by city operators and provisioned from the city
platforms to citizens unidirectionally. Thus, there has been lack of consistent city service provisioning
system in which citizens and industries to create and provide city services also. Now, we are witnessing
the rise of new demand of bidirectional offering of smart city services. It should allow city stakeholders
to freely create and share their services utilizing open city resources. For city stakeholder’s active
participations and global service provisioning, leveraging global standards is required to identify city
resources, register city services, and discover city services.
The smart city can leverage current GS1 standards to realize the participatory and international service
provisioning. The GS1 ID can be applied to identify city resources. Since many smart city services are
associated with city resources, the GS1 ID of city resources can be used as key index of the service
provisioning. The Object Name Service (ONS) is a good candidate to enable city stakeholders to create,
manage, and share their services globally. Also, GS1 Digital Link is providing web-based service
connectivity enabling URL link promise to enhance the experiences for city stakeholders.
IoT is important in smart cities
While Internet of Things (IoT) plays an important role in enabling the connection of devices and
extraction of data for services in smart cities, it still faces various challenges. As an increasing influx of
companies invest in IoT solutions, fragmentation has become one of the biggest challenges in adopting
IoT technologies and applications. This fragmentation is shown in many aspects, one of which is the
uncertainty of interoperability across different communication networks and data streams. This lack of
common identifications (IDs), industry standards, and the absence of semantics in many IDs (e.g.
universally unique identifier (UUID)) can lead to significant associated adoption costs.
The necessity for IDs and standards can be seen in many examples like digital twin and IoT solutions.
In digital twin, there needs to be a common way of defining a digital replica of a living or non-living
physical entity. This includes Building Information Modeling (BIM) and Geographic Information System
(GIS), which need common standards of IDs, data, and service in order to structurally enable digital
twin for smart cities. The standards and IoT solutions like OCF, oneM2M, and FIWARE also lack
interoperable IDs and any ID governance schemes.
This is where GS1 standards can provide a significant help, by bringing a set of well-defined industry
standards with an extensive and highly customizable ID system. GS1 standards can support both the
design and implementation of services and infrastructures inside a smart city. Sharing the same well-
defined standards will resolve the interoperability issue and reduce fragmentation, which allows

potential customers in smart cities to easily procure and integrate solutions from different vendors.
Furthermore, GS1 standards can define common structures for the representations of any entities in a
smart city, which can greatly simplify the works necessary to enable smart cities’ data and services.
Data Governance in smart city
The United Nations Department of Economic and Social Affairs (UNDESA) has defined the data
governance as 1) The overall management of the availability, usability, integrity, and security of the
data employed in enterprises, 2) The practice of organizing and implementing policies, procedures, and
standards for the effective use of an organization's structured/unstructured data assets, 3) The
execution and enforcement of authority over the management of data assets and the performance of
data functions.
Data governance in smart cities is based on cooperation among city participants such as citizens,
governments, and industries. It deals with integrated policies and processes for the production,
utilization, sharing, data quality control, and security of urban public data. It is a smart city data synthesis
system that enables sustainable urban growth and citizens' quality of life by promoting and creating
new urban services.
Smart city data governance requires the social and technical consensus in various areas, the active
cooperation between cities and governments, data security and privacy, data-related law and strategy
creation at the government level, data ownership, and data market and pricing. In practice, we should
make efforts to various research challenges such as the ID governance for smart city resources, the
cooperation of urban data standardization, the sustainable data collection and quality control, and the
cooperation with other smart city alliances such as UTA(Urban Technology Alliance). Also,
developments of data producers and consumers, a citizen-participated data portal, smart city evaluation
indicators should be considered.
Smart city data governance is essential to define the concept and scope of data in order to consistently
and efficiently utilize data in various data-related industries. The plan for smart city governance should
be in consideration of the situation and circumstances of each country and city. The challenges for
smart city data governance are actively discussed at home and abroad, and the GS1 community should
also be actively involved.
Moving Forwards
Successful implementation of a smart city requires a collaborative atmosphere between citizens,
governments and industries. In addition, the principles for building open data infrastructure must be fully
followed as below.
1. Identification of urban resources
Identification schemes for the major physical and logical resources in the city should be provided. These
schemes not only clarify the data and service linkages between urban resources and citizens, but can
also be extended to the development of intercity services.
2. Data governance
Post-development management of smart cities is more important than design and implementation.
Many smart cities stopped after deployment due to improper management. To avoid this, design and
development of smart cities should be prepared with data quality management protocols and
communication channels for troubleshooting.
3. Data model standard
High quality data is a prerequisite for data-driven smart cities. To ensure the quality of urban data, it is
necessary to design a comprehensive data model according to the city's management process (Critical
Tracking Events) and provide exact context (Key Data Element) of each data.
These were areas of successful business in the GS1 community, for example, object identification in
complex logistic processes, flexible data governance between various business partners, and the

EPCGlobal Standard for exchanging data. We believe that the GS1 community and its standards will
play an important role in implementing data-driven smart cities.
Use Case Studies
Siehung city BIS integration (Korea)
Siehung smart city project is a joint research including Auto-ID Labs, KAIST with the objective of building
smart city data management and sharing platform.
The project proposed a GS1 identification system to harmonize the different local identification systems.
It uses EPCIS platform to create a smart city data hub for collecting data in the area of transportation,
environment, energy and welfare. Additionally, an interoperability mediation gateway has been
implemented to integrate the legacy system in the newly proposed GS1 standard based smart city
platform.
The data hubs are integrated with a digital twin of the city to provide a platform for new developers,
expertise, and designers to simulate and foresee any potential problems. The below figure shows a
demonstration of a bus information system integrated with the digital twin which accesses EPCIS.
Seokcohon Lake Park (Korea)
Songpa-gu District of Seoul and Auto-ID Labs, KAIST built citizen participation service platform which
is enabling and supporting citizen participatory service registration, management, and sharing. The
platform is deployed in Seokchon Lake Park area providing emergency service, facility information
service, tourist information service, social media service, and so on.
To realize the citizen and 3rd party company participation service provisioning, the platform leverages
GS1 standards involving GS1 identification standard, GS1 Digital Link, and GS1 Object Name Service
(ONS). Auto-ID Labs, KAIST has cooperated with the Korea Ministry of the Interior and Safety to encode
Korea's road name-based address system into the Global Location Number (GLN). In Seokchon Lake
along the 2.5km trail, Songpa-gu has installed 120 address signs with QR code that includes encoded
GLN in the GS1 Digital Link standard form. The GS1 Digital Link leads to the GS1 Object Name Service
(ONS) that registers, manages and shares services associated with a given GS1 Identification Key.
Through the GS1 standard based service sharing platform of Sonpa-gu, city stakeholders register their
services to ONS and citizens and tourist scan the GS1 Digital Link QR code of the address signs

installed in Seokchon Lake to access the services. Furthermore, the Korea Ministry of the Interior and
Safety plans to extend GS1 standard-based citizen participation service platform to public parks.
Korea-City GCP allocation (Korea)
GS1 publishes a set of standards consisting of Identification Keys for unique identification of products
and trading partners, a classification system for sharing of trade data, barcode symbologies and
electronic document formats. It is the most widely used of its kind in the world. GS1 Identification Keys,
one of the cornerstones of GS1 Standards, can be applied to trade items, objects and even locations
and provides uniqueness for each of the entities. Its applicability not only to physical things but also to
intangible ones such as service gives the keys a unique position to be adopted in almost any sector in
need of an identification system.
That is why Smart City initiatives should adopt GS1 Standards. A Smart City requires a data
identification system and a data classification system based on international standard to allow it to
manage and share data from different urban resources around the clock, and GS1 Standards can play
a pivotal role in the process. Use of GS1 Standards has another important advantage: global scalability
underpinned by sharing and linking of data among international cities.
Fully aware of such significance, the Korea Advanced Institute of Science and Technology (KAIST)
(also operating as Auto-ID Labs, KAIST) made a successful proposal of applying GS1 Standards to
Smart City initiatives in Korea which is part of the country’s strategic projects and is now working on
R&D activities as part of the project. Under the project, the KAIST work with GS1 Korea to allocate GS1
Company Prefixes to entities responsible for managing urban resources including central and local
government bodies, associations and industry bodies and ensure that GS1 Identification Keys and
classification numbers are correctly assigned to the right urban resources in the most effective manner
by issuing guidelines. In 2019-2020, GS1 Korea have provided GS1 company prefix for four Korean
cities of Daejeon, Busan, Siheung, and Songpa-gu District of Seoul. In addition, KAIST demonstrate
the robust applicability of GS1 Standards in Smart City initiatives by trying using it in a pilot city.
Contributors:
Daeyoung Kim, Director of Auto-ID Labs, KAIST
Yalew K. Tolcha, Auto-ID Labs, KAIST
Wondeuk Yoon, Auto-ID Labs, KAIST
Sangtae Kim, Auto-ID Labs, KAIST
Hoang Minh Nguyen, Auto-ID Labs, KAIST
Jiyong Han, Auto-ID Labs, KAIST
Heon-Bae Andrew Lee, GS1 Korea
Soyoun Park, GS1 Korea

Smart city position paper - GS1 standards perspective

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