A Comparative Analysis of Smart Cities Frameworks based on Data
Lifecycle Requirements
Claudia Roessing
and Markus Helfert
Innovation Value Institute, Maynooth University, Maynooth, Ireland
Lero, The Science Foundation Ireland Research Centre for Software, Ireland
Keywords: Data Lifecycle, Data Lifecycle Requirements, Smart City Framework.
Abstract: Citizens migrate from rural areas to urban centres in search of better living conditions. The rural-urban
migration combined with rapid population growth lead to overpopulation, which consequently creates
challenges to cities in the use and reallocation of their resources. Smart cities have emerged as an opportunity
to assist cities to overcome these difficulties with the usage of information and communication technology
(ICT) to improve the lifestyle of their citizens. However, maintenance of a smart city is a difficult task. In this
multi-stakeholder system, services from different domains are offered to citizens, which collect data from
different sources with different formats that need to be in compliance with regulations, privacy, and security
requirements. Therefore, a data lifecycle plays a vital role as a data management framework as a means of
reducing the complexity of their ecosystems to assist align their objectives and services offered to the citizens.
Prior researches have stated a need for improvement in this framework modelling. The aim of this paper is to
address this gap and define data lifecycle requirements which will be used to analyse a selection of smart
cities architecture frameworks.
Over the years, the population has been growing and
moving from rural areas to cities in search of
improvement on their living standards, thus leading
to several challenges for governments to manage
cities (Albino et al., 2015). There are several factors
that motivate the migration of people from rural areas
to cities, for example, the opportunity to find a better
livelihood, climate variability, access to basic
services and infrastructure (Manzi, 2016; Haoyang et
al., 2019). Rural migration combined with growth
population cause overpopulation of cities, impact on
urban development, sustainability, pollution and
cause a reduction in agricultural production
(Fernandez-Anez et al., 2018; Manzi, 2016). It also
has an impact on health, social infrastructures, and
housing sector that cannot keep up with high demand
and often resulting in informal growth of urban
settlements (Mahbubur Rahman et al., 2019). The
concept of a smart city has appeared as an opportunity
to improve the quality of life of its citizens using
information and communication technology by
offering better quality services and at the same time
transforming cities into more sustainable ones (Lim
et al., 2018; Pérez-Delhoyo et al., 2016; Rabelo et al.,
2017). Digital transformation has brought several
opportunities for services and infrastructure
management, however, these opportunities bring
challenges in several aspects (Lnenicka and
Komarkova, 2019). The implementation and
maintenance of a smart city is a complex task due to
its specific characteristics. A smart city is made of
heterogeneous technologies and data, several
domains which are composed of multi-stakeholders,
which in the end needs to achieve goals and
objectives having a focus on citizens (Albino et al.,
2015; Siddiqa et al., 2016). And in order to provide
all services and products to citizens, a city must be in
compliance with regulations, security, and privacy
requirements (Liu et al., 2017). Therefore, the use of
a data lifecycle is necessary to assist to integrate
processes, people, and systems in a smart city, as well
as the use of enterprise architectures.
A data lifecycle is a framework that contains
phases and activities that data has to go through from
its creation, processing, archival, and/or disposal in
order to prepare data for relevant users meeting
specific requirements for quality and security (Arass
et al., 2017; Sinaeepourfard et al., 2016).
Roessing, C. and Helfert, M.
A Comparative Analysis of Smart Cities Frameworks based on Data Lifecycle Requirements.
DOI: 10.5220/0010479302120219
In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 212-219
ISBN: 978-989-758-512-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Enterprise architecture is a conceptual blueprint
that captures the essence of business, IT, and its
evolution. It is used to align IT infrastructure with the
business goals of organizations (Lankhorst, 2017).
Enterprise architecture has been used to model smart
cities, in order to reduce complexity and to tackle
challenges faced by this ecosystem which has to
integrate different components, moreover to assist in
the communication between stakeholders (Guo and
Gao, 2020). Several enterprise architecture
frameworks have been developed over the years.
Some have been developed for specific domains and
others in a more generalized way, causing similarities
and disparities between them (Urbaczewski and
Mrdalj, 2006).
This paper explores the limitations of data
lifecycle modelling and defines a set of requirements
in order to bridge the gap regarding a lack of formal
specification of this framework in the smart city
domain. Moreover, this study provides some
comparison on a selected number of smart cities
frameworks based on data lifecycle requirements
defined by the authors.
This paper is structured as follows: Section 2
provides the research approach followed by this
study. In section 3, a background of data lifecycle
modelling is presented. Section 4 defines the
requirements of data lifecycle models. The
selection and comparative analysis of smart cities
frameworks are conducted in Section 5. Section 6
presents an illustrative use case followed by a
discussion and conclusions in sections 7 and 8
One of the goals of this article is to analyse a selected
number of smart city frameworks based on data
lifecycle requirements. With this in mind, a literary
review was conducted to define necessary
requirements to model a data lifecycle.
For this review, the authors adopted a
methodology proposed by Webster and Watson
(2002). Firstly, data sources were defined from where
relevant studies were going to be collected (Springer
Link, Google Scholar, IEEE Xplore, Web of
Science). Secondly, we defined keywords to be used
as search strings in each library database provided.
The keywords used were: data life cycle and data
lifecycle requirements. Thirdly, a screening phase
was conducted, where duplicate articles were
excluded, and abstracts of remained articles were
revised to remove ones that were not relevant to this
study. In the final step, 27 articles were selected out
of a total of 97.
In the next section, this study provides a
background on data lifecycles modelling.
Despite advances in the area of data management,
representation of life cycles is still being made in a
generic way. Even with the changing role of data in
organizations, the framework is modelled from a high
abstract point of view, and not representing reality,
but an ideal situation, showing data as unproblematic
(Carlson, 2014; Cox and Tam, 2018; Pouchard,
Due to the complexity related to data management
in a smart city, it is paramount that models show data
transformation throughout the process, from its
collection, processing, and service delivery to end-
user, also showing various stakeholders involved in a
process (Ball, 2012). Another drawback in the
representation of a process occurs in the acquisition
of data since it only allows collection at beginning of
a process, thus not allowing a new acquisition in case
of any error in data previously collected (Pouchard,
2015). Thus, there are only a few models that make it
possible to return to an earlier stage if necessary. To
take better advantage of an immense amount of data
to which organizations have access, it is essential that
they create value from this data, so that they can offer
better services and products to end-users.
The purpose of lifecycles is to provide
information to interested parties for those who can
make decisions, and because of that, it is a relevant
tool to use it. And in order to provide information to
stakeholders, data lifecycles need to be updated to
assist stakeholders to make the best decisions (Plale
and Kouper, 2017).
As stated previously, prior studies recognize
models' limitations, identifying that they provide an
unrealistic point of view when managing data and
only a few models recognize this flaw and try to
circumvent it (Cox and Tam, 2018). Overall, the
studies provide valuable insights into data lifecycle’s
limitations (see Table 1) and strengths but also bring
attention to gaps and a necessity for change in the data
management field.
A Comparative Analysis of Smart Cities Frameworks based on Data Lifecycle Requirements
Table 1: Data lifecycle limitations.
This section defines requirements that a data lifecycle
should have, in order to enhance the way data is
modelled and meet researchers and practitioner’s
needs. Modelling requirements were identified during
a literature review (see Appendix A), which showed
a lack of formal specification for the framework
modelling in the smart city domain. Some problems
related to this lack of standardization were also
identified in the literature (Cox and Tam, 2018).
1. Phase - represents all steps that data needs to go
through to achieve a specific outcome.
2. Activities - processes that are conducted in each
stage to prepare data for the next stage or to a
final objective.
3. Data input - data used in a stage to be
4. Data output - it represents data that has been
transformed from a previous stage and is going
to be used in the next one or it is the final output
if a life cycle has reached its end.
5. Role - actor responsible to conduct a phase or
6. Pre and post requirement (phase quality) - it is
used to know if activities of a phase have been
performed with success, in other words, if data
has achieved the goal of a phase, therefore it can
proceed to the next one, otherwise, it has to be
processed again. These requirements are related
to the quality of each phase or activity.
7. Relationship between phases in order to
process data for a specific purpose it is
necessary to know the order and relationship
between phases of a cycle.
8. Variation driver - it is composed of relevant
aspects related to data such as regulations,
lifespan, category, and sensitivity. Data can be
classified into different categories based on their
type of sensitivity. In order to process data, it is
necessary that phases and activities be in
compliance with regulations. It is also necessary
to take into account the lifespan of data because
it specifies how long data can be used and
stored. These aspects influence the choice of
lifecycle activities.
As stated previously, enterprise architecture is a
strategic instrument to organizations, and it can be
used to guide organizations to go from a current state
to a future one (Lankhorst, 2017).
This section provides a high-level analysis of five
smart city frameworks which selection criteria were
to be composed of at least three layers including
information or data layer and their respective
descriptions. The frameworks are analysed using
concept centric approach proposed by Webster and
Watson (2002) based on data lifecycle requirements
defined in the previous section. The selected
frameworks are: open geospatial consortium, smart
city reference architecture meta-model, Nora, ICT
architecture, and government enterprise architecture
for Big and Open Linked Data analytics. The
frameworks are presented and analysed below.
The Open Geospatial Consortium (Open
Geospatial Consortium, 2015) developed a smart city
spatial information framework. The framework uses
viewpoints based on ISO/IEC 10746, information
technology open distributed processing reference
The work emphasizes the importance of location
in order to organize smart city services. The
framework provides a high-level view of components
and it is composed of four layers, application,
business, data, and sensing layers. It also contains a
security system, cloud-hosted resources, and a list of
stakeholders. As it is a high-level structure, it does not
detail the applications involved, only the application
SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems
domain. The structure also does not show
relationships between entities, as well as there is a
lack of goals and objectives. Data entities are divided
into domains and are stored in an urban/municipal
database. The business layer shows that analytics and
models are used for visualization and decision
The Smart City Reference Architecture Meta-
Model (smartCityRA) developed by Abu-Matar
(2016) emerged to supply the need for heterogeneous
ecosystem design. It provides a new approach to
design heterogeneous ecosystems like smart cities.
The framework consists of building blocks that
highlight intra and inter views relationships.
SmartCityRA follows ISO / IEC / IEEE 42010.2011
to describe terms of models, views, and viewpoints.
The reference architecture was developed in a
modular way, thus allowing its extension according
to domain experts’ needs. The meta-model of the
framework consists of eight views that are unified by
capability view, which represents business
requirements provided by a smart city project. The
views are capability, participation, place, services,
data, application, infrastructure, and business
process. The model provides relationships between
views. The capability view can represent the goals of
a city, however is modelled in higher abstraction. The
framework does not provide objectives either.
NORA is the Dutch Government Reference
Architecture (SmartCities, 2011). The framework
gets requirements from Europe, Dutch Government,
companies, and citizens, which are used to build
architecture. Company, information, and technical
are the architecture domains that describe who, what,
and how for each domain. Maintenance/control and
security are also described as domains. Company
architecture defines an organization,
services/products, and processes. Information defines
employees/software, message/data, and information
exchange. The technical architecture identifies
technical components, data storage, and network. The
framework is used to create an initial step in the e-
government that is to create reusable e-government
assets. The model is used as a primary reference
architecture used in new ICT projects. It also provides
design principles at different levels of organization,
process, information, and technology. Further on, the
framework focus on domain-specific reference
architectures for various aspects of the Netherlands
(municipalities, provinces, and water control boards).
ICT Architecture (SmartCities, 2011) provides a
simplified architecture metamodel, which is based on
TOGAF. The architecture metamodel is presented in
two parts. The framework contains seven domains,
governance, business, information systems, and
technology are the layers of the architecture. The
model also defines characteristics for architecture
domains related to interoperability, service
orientation, and information security. The
governance domain at the top defines business goals,
strategic drivers, business principles and guidelines,
management models, compliance to laws,
regulations, and standards. This information is the
basis for developing an organization's architecture.
Service orientation is considered as one domain and
it flows from top to bottom, which emphasizes
enterprise vision for service orientation. Thus
allowing reusability and the ability to exchange
architecture components without causing a disruption
to service. Dependencies between domains are
represented too. The metamodel states an alignment
between the scope of requirements and
implementation of an enterprise architecture. The
metamodel is presented in order to provide guidance
to e-government stakeholders regarding
recommendations to design ICT architectures.
Government Enterprise Architecture for Big and
Open Linked Data Analytics (Lnenicka et al., 2017)
developed a conceptual framework focusing on big
and open linked data analytics requirements in order
to guide developers and designers to create
government enterprise architectures in smart cities.
Before defining a framework, the study presents
requirements and their relationships in a smart city
ecosystem. The framework consists of four layers
’business, application, data, and technology
architectures. Security and privacy + interoperability
+ evaluation and monitoring occur in all architecture
layers. Data flows from bottom to top while service
provisionary occurs from top to bottom. Business
architecture defines e-government and governance
architecture and open government processes. The
application identifies smart application services. Data
architecture is composed of programming models for
analytics that contain batch and stream processing
layers. Followed by data API and other interfaces.
The last component of this layer is related to data
storage, distributed and scalable databases that
contain historical and real time data. Last, the
Table 2: Analysis of smart city frameworks.
A Comparative Analysis of Smart Cities Frameworks based on Data Lifecycle Requirements
technology layer describes smart ICT infrastructure
and a smart environment that contains a network of
data sources.
In order to facilitate the proposed approach, this study
will use Footfall counter as a use case. This service is
offered in some cities and it aims to collect pedestrian
counting in certain locations through the use of
sensors. It is used for purpose of knowing the traffic
pattern of pedestrians and data is mainly used for
tourism, retail development, events, just to name a
few. This use case is based on real information.
Data lifecycle requirements applied to this use
case can be seen below.
Table 3: Use case Data lifecycle requirements.
- Processing
- Access
- Definition of
Set of data
Collected data
date, IN,
- Storage plan
- Data values
Storage data
data values
Reports (csv,
- Preparation
- Access
data values
Disclosed data
(location, date,
plan to
destroy data
Set of
destroyed data
Variation driver:
- regulations: as no personal data is collected,
there is no specific regulation that the organization
needs to be in compliance with.
- lifespan: the organization has decided to keep
data for 10 years.
- category and sensitivity: data collected in this
service is classified as public.
Relationship between phases: Phases are
conducted in a sequential order (Plan, Collect,
Storage, Use, Share, Delete).
Pre and Post requirements: these requirements are
verified during each phase and activity to know if
inputs and outputs have been met.
Role: programme manager, system users.
The frameworks analysed in section 5 show existing
variations in their modelling. The analysis showed
that none of the frameworks meet all requirements
defined in this study (see Table 2). Another important
point was a lack of connection between layers and
entities with the exception of smartCityRA (Abu-
Matar, 2016) and Government Enterprise
Architecture for Big and Open Linked Data Analytics
(Lnenicka et al., 2017) models. Furthermore, the
majority of models do not take into account
regulations, data category, sensitive data, and data
lifespan, which are necessary components to define
activities that will be conducted in a data lifecycle.
Entity role is defined in all models, however, it does
not show a relationship between them and other
Due to the characteristics of a smart city, which is
an integration of several components and having data
as its main resource, it is relevant to show how entities
of a model connect and data flows. The alignment of
objectives with policy, regulations, business, and
technical approaches are necessary, but these aspects
are not reflected in the analysed frameworks
(National Institute of Standards and Technology,
It is also important to emphasize the importance
of classifying data so that it can be processed properly
and this was another aspect absent from the analysed
Overall, all these factors may result in a lack of
alignment between citizen's needs and smart city
Smart cities have emerged as a solution to various
challenges found in today's cities, it is a solution-
focused on its citizens and mainly to improve their
lives and there are many challenges to implement it.
Due to its unique characteristics as multi-stakeholder,
citizen-centric, data-centric, each smart city has its
own implementation and particularity, leading to
variations in its enterprise architecture models. The
implementation of a smart city requires an alignment
SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems
between services, policies, and security requirements,
therefore it is necessary that frameworks reflect this
Data is considerably important in a smart city,
however over the years, it has not been modelled
adequately in order to provide essential information
regarding some concerns, for instance, how data is
being collected, processed, reused, and stored. Data is
used to generate information and knowledge which
are used in the decision making and to offer better
services to citizens, therefore improvements are
needed in its modelling.
The use case provided showed how the
application of data lifecycle requirements can assist
decision makers to have a holistic view of data
processing to offer services to citizens. Sensitive data
were not processed in the example, however, usage of
data lifecycle requirements proved beneficial to have
a better view of the process, especially when sensitive
data are processed and also shared with third parties.
Therefore, data lifecycle requirements can assist
organizations to align services, regulations, and
security requirements and moreover to assist in
process improvements.
This paper analysed selected frameworks based
on data lifecycle requirements, the investigation has
shown limitations present in the modelling of smart
cities using enterprise architectures. A natural
progression of this work is to analyse how to integrate
data lifecycle requirements identified in this work and
their connections in a smart city framework and to
conduct case studies to validate the findings.
This work was supported by the Science Foundation
Ireland grant “13/RC/2094” and co-funded under the
European Regional Development Fund through the
Southern & Eastern Regional Operational
Programme to Lero, the Science Foundation Ireland
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