A Comparative Analysis of Smart Cities Frameworks based on Data

Lifecycle Requirements

Claudia Roessing

1,2

and Markus Helfert

1,2

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.

1 INTRODUCTION

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).

212

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

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

respectively.

2 RESEARCH APPROACH

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.

3 LIMITATIONS OF DATA

LIFECYCLE MODELING

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,

2015).

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

213

Table 1: Data lifecycle limitations.

4 DATA LIFECYCLE

REQUIREMENTS

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

transformed.

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

activity.

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.

5 ANALYSIS OF SMART CITIES

FRAMEWORKS

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

model.

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

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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

support.

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

215

technology layer describes smart ICT infrastructure

and a smart environment that contains a network of

data sources.

6 ILLUSTRATIVE USE CASE

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.

Phases

Activities

Input

Output

Plan

Specification

Service

description

- Processing

plan

- Access

specification

- Definition of

roles

Collect

Collection

Set of data

values

Collected data

values

(Location,

date, IN,

OUT)

Storage

Archive

Backup

- Storage plan

- Data values

Storage data

Use

Manipulation

Retrieved

data values

Reports (csv,

pdf)

- Preparation

- Access

system

Presentation

Retrieved

data values

Disclosed data

(location, date,

IN,OUT)

Delete

Destruction

Processing

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.

7 DISCUSSION

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

entities.

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,

2018).

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

frameworks.

Overall, all these factors may result in a lack of

alignment between citizen's needs and smart city

implementation.

8 CONCLUSIONS

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

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between services, policies, and security requirements,

therefore it is necessary that frameworks reflect this

requirement.

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.

ACKNOWLEDGEMENTS

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

Research Centre for Software (www.lero.ie).

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