Smart City Architecture Development Methodology (SCADM): A Meta-Analysis Using SOA-EA and SoS Approach

Abstract

Architecture and methodology development for smart city are still being carried out together in clarifying the scope of smart city. This is because the application of Enterprise Architecture (EA) still does not accommodate its characteristics as a form of System of System. This study discusses the EA research overview on smart city design and the gaps in EA implementation for smart city architecture development. This research is intended to create a smart city architecture development methodology as a System of System for reference architecture with the collaboration of several systems. The system is an element of smart city designed and developed by the leaders of each coordinated system. In the end, this methodology can form the basis for building and coordinating the development of a collaborative smart city by several actors.

Keywords

smart city gap analysis architecture service-oriented enterprise

Introduction

Smart City, also known as an information city, a digital city, and a virtual city, is expected to overcome the current and future complex challenges in increasing resource efficiency, reducing emissions, sustaining health care services for the aging, empowering youth, and integrating minorities (Clohessy, 2014). The reference architecture has become an essential discipline in the design of Information Technology (IT) systems of the company. The discipline required in the architectural development of the smart city observation approach is to use Enterprise Architecture (EA). In the last few years, several studies on the development of smart city architecture have been published. However, the development of smart city architecture as a reference is still constrained. This is indicated by the continued development of smart city research with the EA and Service-Oriented Architecture (SOA) approaches (Zhang et al., 2007) for references of architecture that should be emphasized in the opportunities and potency of integration and sustainability, which give empowerment to the citizenship (Lubis, Fauzi, et al., 2018; Lubis & Maulana, 2010). Some researchers cite the characteristics of the system in the enterprise and the System on Smart City as different characteristics (Anthopoulos & Vakali, 2012; Skilton, 2016; Sobczak, 2017). The purpose of smart city architecture is architecture in business, IT architecture, data architecture, and performance architecture as well as architectural contexts in the area of service computing. Smart City can be seen as a collection of collaboration and the integration of several systems. Some researchers mentioned that Smart City is a form of System of System (SoS; Elshenawy et al., 2017; Skilton, 2016). Smart City, in an SoS perspective, does not recognize business processes that cross systems but between these systems, which will require service modes with each other. Thus, Smart City needs to be seen as a service-oriented system to fulfill the SoS perspective (Blackstock, 2014; Clement et al., 2017), though there is no formal definition on the proper evolution that should be taken by the initiator for the infrastructure, both physical and digital (Lubis & Maulana, 2010).

To understand the user perception regarding the implementation of Smart City as the requirement of social needs, not just of a political campaign, this study utilizes gap analysis, which is a business management technique that requires an assessment of the difference between the best and actual results of a business effort. It also includes the recommendations for steps that can be taken to fill gaps by measuring the amount of time, money, and resources needed to fulfill potentiality and achieve desired conditions. The main reason that gap analysis is important for government in implementing the smart city concept is the fact that the gap between expectations and user experience causes customer dissatisfaction and distrust. In a coordinated manner, the distance between perception and reality should be narrowed to elevate the user experience to new heights and, thus, increase satisfaction. This study discusses the gap between EA and smart city architecture with the systematic literature review method and the smart city architecture development methodology as a service-oriented SOSs with the meta-analysis method. The approach taken will be influenced by the disciplines of service-oriented architecture, EA, and system engineering. The problem definitions in this study are:

Research Question 1: What are the results of the EA perspective research in Smart City design?

Research Question 2: How to form a gap analysis using EA in developing Smart City?

Research Question 3: What components are needed in the formulation of service-oriented architecture design methodologies?

Research Question 4: How is the smart city architecture design methodology based on the results of previous studies?

Literature Review

Based on the study of Smart City using the EA approach, which was conducted using the systematic literature review method, the results of research distribution were obtained based on concepts and perspectives. The main concerns of research on EA and Framework EA’s perspective for Smart City are the business and IT domains.

EA Perspective

View is a representation of several things that are considered and what is produced from a perspective (Inversini & Perroud, 2013). Viewpoint (perspective) is the definition from the point of view of the presentation (view) produced (Inversini & Perroud, 2013). There are four perspectives in EA (Land et al., 2009): Business view, IT view, Governance view, and Security view.

Business view

The key features to manage complexity, increase efficiency, reduce operation cost, and enhance quality of life are related to the collaboration between competitiveness, citizen satisfaction, human capital, and sustainability (Kumar & Krishna, 2015), which can be seen from the studies discussed in Table 1.

Table 1.

Business View.

Approach	Discussion
System Engineering	Smart city Ecosystem (Lnenicka et al., 2018), Boundary of Organization and Enterprise (Magalhães & Proper, 2017), System of System (Anthopoulos & Vakali, 2012), Ecosystem of Interconnected Market Place (Strasser et al., 2016), macro systems including interconnected and overlapping functions to micro applications within the city (Patrick Rau, 2015)
Benefit	Business Benefit (Abramowicz et al., 2016)
Business Architecture	Goal, Organization Unit (Bezbradica & Bastidas, 2015)
Stakeholder	Network of stakeholder (Gołuchowski & Linger, 2017)
Business Model	Business Model Canvas IoT (Kralewski, 2016)

Note. IoT = Internet of things.

The business perspective is influenced by the system that is defined, the solution for which is based on the interaction between components (Endrei et al., 2004). Systems defined in a business perspective are business-oriented systems. Some definitions of the system or systems in Smart City are designated differently in previous research literature. Some of these definitions were as follows:

Smart City as an ecosystem has a need for the involvement and different roles of several stakeholders in shaping processes and services (Lnenicka et al., 2018).

Organization is a combination of several enterprises for resources or activities to achieve goals or multiple objectives (Magalhães & Proper, 2017).

Joint systems and large-scale distributed systems where components are complex (Skilton, 2016).

Ecosystems compose a fragmented market of services that are interrelated (Strasser et al., 2016).

Smart City operates by taking into account differences in coverage of macro-system operations including the same connectivity and functions of city applications (Patrick Rau, 2015).

The system of a system in a digital ecosystem can be a network of devices, networks of objects, relationships, and services that describe the digital world and several other forms of work (Skilton, 2016). This network, if it is intended as a design, can represent each domain needed to describe the system from various perspectives.

Information technology view

The IT domain approach is in application architecture, data architecture, and technology architecture. To have a responsive solution to the trend and demand changes, the integration should balance between profitable and nonprofitable service to increase the agility and reliability (Al-Jaroodi & Mohamed, 2018; Hashemi & Hashemi, 2012). This can be seen in Table 2.

Table 2.

Information Technology View.

Approach	Result
Application	The energy management platform (Abramowicz et al., 2016), Cultivate resilient smart Objects for Sustainable city applications (Goldsteen et al., 2015), Architecture: Application Portfolio, Interface Catalog (Gołuchowski & Linger, 2017), Application: 6 smart city component (Anthopoulos, 2017), an application of Architecture-Oriented systems (R. Chen et al., 2016), PartiCity 3-tier software architecture (Foth et al., 2015), Autonomic Service Bus Architecture (Andrea & Marco, 2012).
Data	Big and Open Link Data (Lnenicka et al., 2018), Data Architecture: Data Entity, Application Data Architecture: Data Entity, Application (Gołuchowski & Linger, 2017), Data Architecture: Data flow model, Indicative open data multi-tier architecture (Anthopoulos, 2017).
Technology	Technology Architecture: Technology Standard (Gołuchowski & Linger, 2017), Network of IT System (Gołuchowski & Linger, 2017), Technology Architecture: Network types in smart city, A representative data center architecture, Indicative cloud computing architecture (Anthopoulos, 2017), i9ITS Architecture (Barbosa et al., 2016), Layered Architecture of IoT4S (Fazio et al., 2014), Smart city G-Cloud platform (V. Clohessy, 2014), Autonomic Service Bus Architecture (Andrea & Marco, 2012), The multi-tier architecture of a digital city (Anthopoulos & Vakali, 2012).

Note. IoT = Internet of things; IT = Information technology.

Governance view

Critical infrastructure must effectively connect the physical and digital world to provide self-monitoring and self-response systems to present inspiration, culture sharing, and knowledge motivation (Incki & Aria, 2018; Nam & Pardo, 2011). Research on the development of smart city architecture governance can be seen in Table 3.

Table 3.

Governance View.

Approach	Result
IT Management	Consent Management: Management, automatic consent, and auditing (Goldsteen et al., 2015), High Level Architecture for IT Service Management (Grant & Collins, 2016)
IT Standard	Smart city: Governing a Smart city standard (Anthopoulos, 2017), Information exchange standards (Anthopoulos & Vakali, 2012)

Note. IT = Information technology.

Security view

In the security view, the results of research from a security perspective show several studies on cyber security, IoT, and the security requirement. The results can be seen in Table 4. The competitive pressure and advantages have resulted in various organizations becoming more mature and enhancing their decision-making processes, but often neglecting the transparency and security in each phase of the information system (Lubis, Kusumasari, & Hakim, 2018; Oliveira et al., 2012).

Table 4.

Security View.

Perspective	Result
Cyber Security Architecture Framework (Nguyen et al., 2017)	Assessment Model, Questionnaire support diagram, Logical Design and Business Diagram of CSAF Assessment Service
IOT Security Frameworks (Rebollo et al., 2012)	Summary of Comparison of IOT Security Framework
The three-layer security requirements analysis process (T. Li et al., 2016)	The Three Layer Analysis Framework, Meta-model of the three-layer security requirements Framework, An excerpt of the three-layer requirements model of the smart grid scenario, the three-layer security requirements analysis process, case study

Note. CSAF = Cyber security architecture framework; IoT = Internet of things.

Gap Analysis

For efficient data management, we need to respect the diversity of data provided, most different formats, and the fact that data from billions of devices will contain noise (Incki & Aria, 2018; Schleicher et al., 2016). Gap analysis aims to determine EA capability to meet architectural design needs as a reference in developing Smart City architecture. The analysis is presented in Table 5.

Table 5.

Gap Analysis.

No.	Aspects	Enterprise architecture	Smart city architecture
1	System, Principle	Single System	- System of System: Joint systems and large-scale distributed systems where components are complex themselves (Skilton, 2016) - The System of a System in a digital ecosystem can be a network of devices, networks of objects, relationships, and services that describe the digital world and several other forms of work (Skilton, 2016)
		Internal company	Digital service chain (Štěpánek et al., 2017) or network of several stakeholders and systems (Foth et al., 2015; Gołuchowski & Linger, 2017; Patrick Rau, 2015; Strasser et al., 2016)
		One hierarchy for the system	Architectural hierarchy or different perspective on EA (Sandoval-Almazán et al., 2017; Sobczak, 2017)
		There is no need for collaboration of external stakeholders in building EA	Collaboration of several stakeholders is needed from different systems in building Smart City architecture (Foth et al., 2015; Gołuchowski & Linger, 2017; Sandoval-Almazán et al., 2017; Sobczak, 2017; Štěpánek et al., 2017; Zhu & Zuo, 2013)
		Enterprise Model: Enterprise as a system on several frameworks	Smart City architecture as a System of Systems is above the hierarchy of EA needs shown in the following research: Macro, meso, and micro (Sobczak, 2017) Long-term Framework, regional Framework, and particular Framework (Anthopoulos & Vakali, 2012)
2	Model	The main models are built primarily based on business processes and services	Service-oriented models from different system stakeholders (Anthopoulos, 2017; Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) and business processes are at different hierarchical architecture that is in the program (Sandoval-Almazán et al., 2017) or in regional/particular (Anthopoulos & Vakali, 2012) and micro (Sobczak, 2017)
3	Perspective	Business view: Business Architecture IT View: Application architecture, data architecture, and technology architecture that includes network and data centers Governance View Security View	System Architecture: Application layer (Basic Services and Application Services), Platform Layer (Software Platforms, Data Center, and Data Processing), Transmission Layer (Optical Transmission and Communication and Network Equipment), and Sensor Layer (Sensors, RFID, Two Dimensional Codes, GPS, and Cameras; Zhu & Zuo, 2013)
			Architecture Layer: Natural environment, Hard infrastructure (Non-ICT Based), Hard Infrastructure (ICT Based), Smart Services and Soft Infrastructure (Anthopoulos, 2017)
			EA Layer Model: Service Applications, Cloud, Fog, and Smart Bricks (Schirmer et al., 2016).
			Layer consists of interface, service observation service, sensor manager, and sensing infrastructure (Fazio et al., 2014)
4	Framework	Helping architects to design company IT systems	Helping architects to design smart cities (Anthopoulos, 2017)
		EA is an enterprise system architecture	Smart City architecture is a multi-service architecture (Anthopoulos, 2017; Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) from an IoT-based service computing system (Zhu & Zuo, 2013)
		Information system architecture as EA domain	Smart City architecture is a platform that meets service from multi-information systems

Note. EA = Enterprise architecture; IoT = Internet of things; RFID = Radio frequency identification; GPS = Global positioning system; IT = Information technology; ICT = Information and communication technology.

Based on the results of the analysis, EA needs to be further developed because it does not meet the needs of the engineering framework of the SOSs architecture due to the following:

EA has different characteristics,

EA requires the needs of a service-oriented model,

EA has a broader perspective to IoT infrastructure, and

EA is a collaborative architecture of multisystems as an element of SOSs.

Service-Oriented Architecture–Enterprise Architecture (SOA-EA) Methodology

SOA and EA have the same scope, which is the enterprise scope. This enterprise scope has the same architectural structure. The following is a comparison of the architectural structure of EA and SOA (Table 6).

Table 6.

EA and SOA (Rosen, 2008).

EA	SOA
Business Architecture: Business Model	Business Model
Information Architecture	Common Semantics and Data
Technology Architecture	Service Bus
Application Architecture	Enterprise Business ProcessIntegration Service (data and application) based on Service

Note. EA = Enterprise architecture; SOA = Service-oriented architecture.

There are fundamental differences between architecture and development (Rosen, 2008), where the architecture team is responsible for understanding the big picture of a system. While the development team is responsible for the implementation and installation of individual services, it also focuses on maximizing SOA’s value in providing enterprise solutions (Rosen, 2008). Technical development aims to provide services in the form of implementing specific business functions efficiently and effectively including from the perspective of IT systems (Rosen, 2008). SOA is the best way to minimize and manage the impact of changes. The modular application of attention to data, applications, technology, and other layers will provide flexibility in managing impacts in the form of isolation and minimization (Rosen, 2008). However, framework is a tool that can be used to obtain data in the form of a consistent structure so that it can be managed and improved. The framework will answer the question of “what?” in the terminology of SOA management practices. The methodology will show “how” in practice terminology (Sweeney, 2010). In general, the process of implementing SOA-EA flow (Sweeney, 2010) is as follows:

Corporate Strategy

Business Unit Planning

SOA Initiation

SOA Project

SOA Implementation.

SoS

The attributes of SoS include a large scale and a distribution network that forms a complex system (Jamshidi, 2009). In other words, the SoS is an integrated system with a large scale that is heterogeneous and stands alone, working with its own operations but working together with others to achieve the same goal. The basic model of the SoS can be seen in Figure 1. Elements in the SoS are independent systems themselves. Each element and the SoS also has its own property, with the most common system elements leading to smart governance, smart living, smart economy, smart mobility, smart people, and smart environment with the highlighted dimension of the rapid advancement of information and communication technology (ITC) to create sustainability of development through innovative ways of communication (Giffinger et al., 2007). Therefore, the implementation of the smart city concept often emerges as the solution in advanced communities that are ready to focus on a digital nervous system, intelligent responsiveness, and optimization of every level of integration as an SoS (Ratti, 2014). Characteristics, architecture, and system dynamics become critical aspects chosen for development methods that must be known and related to each other. Comparison of SoS and non-SoS properties as an elaboration would associate with a centralized methodology or a noncentralized methodology. The methodology will help as an attempt to perform system engineering. Comparison of SoS and non-SoS engineering is shown in Table 7. From the table, it can be seen that SoS engineering requires a different approach in the development of different SOSs (Jamshidi, 2009).

Figure 1.

SoS-based model (Jamshidi, 2009).

Table 7.

Difference in Engineering Between Non-SoS and SoS (Jamshidi, 2009).

Non-SoS Engineering	SoS Engineering
Non-SoS can be produced repeatedly	There are no similar SoS
Non-SoS can be made based on predetermined specifications	SoS continues to grow to increase its own complexity
Non-SoS has clear limitation	SoS has unclear limits
Unwanted conditions can be removed at the time of non-SoS realization	New conditions assessed for utility and their feasibility in SoS evolution
Integrating external agents	The SoS carries out its own integration and integrates
Development always ends for each instance of non-SoS realization	The development of SoS is required as the repeated cycle for improvement
Non-SoS development ends when unwanted possibilities and internal conflicts are removed	SoS depend on internal cooperation and internal competition to trigger their evolution

Note. SoS = System of system.

Synthesis of the Study

In this section, synthesis is carried out to get the results of the research. Our aim was to synthesize the methodology using the SOA, EA, and system engineering approaches. From several studies, the SOA approach was obtained from the SOA Methodology and SOA-EA Methodology. These two methodologies are service-oriented architecture development methodologies. EA uses methodologies with approaches such as The Open Group Architecture Frameworn (TOGAF) and Enterprise Architecture Planning (EAP). These two methodologies are used with consideration of convenience in order to have a higher value of use. The system engineering approach is used to explore the SoS. The three approaches are used to obtain the architecture methodology, which can be seen in Table 8. The results of the formulation are Initiation, Strategy and Goal; Principle; Value Architecture; Organization/Constituent; Service Channel Architecture; Process Domain; Domain Service; Service Integration Architecture; Service Bus; Data Center Architecture; Network Architecture; Sensor Architecture; Security Architecture; and Evaluation.

Table 8.

Mapping SoS SOA Methodology and SoS Domain.

SoS design (Jamshidi, 2009)	Enterprise architecture planning (Minoli, 2008)	SOA methodology (Minoli, 2008)	SOA-EA layer (Rosen, 2008)	Synthesis methodology
Analyze	Layer 1: Planning Initiation	SOA Reference Architecture Definition	Corporate Goal/Strategy	Initiation, Strategy and Goal
				Principle
	Layer 2: Business Modeling	Architecture Business Definition	Business Unit Plans	Smart City Model (Business Model)
	Layer 2: Current System & Technology	Service Identification	Business Architecture	Organization/Constituent
		Definition of Semantic Information Model		Service Channel Architecture
		Service Specification		Process Domain
				Service Domain
Synthesis	Layer 3: Data Architecture	Design and Service Implementation	Enterprise SOA Reference Architecture	Service Integration Architecture
	Layer 4: Application Architecture	Service Solution Implementation	Enterprise SOA Reference Architecture
				Service Bus
	Layer 5: Technology Architecture			Data Center Architecture
			Enterprise SOA Platform Architecture	Network Architecture
				Sensor Architecture
	Layer 6: Implementation/Migration Plan			Security Architecture
Evaluation				Evaluation

Note. SoS = System of system; EA = Enterprise architecture; SOA = Service-oriented architecture.

Research Methodology

The research methodology used in this study is a systematic literature review and meta-analysis. The systematic literature review methodology has seven stages, namely, (a) problem definition search and framework concepts, (b) selection of work teams, (c) search strategies, (d) search process, (e) conformity and coding, (f) assessment quality, (g) synthesis and exposure. The results of the synthesis carried out are discussed below. Understanding the importance of collecting, storing, and retrieving effective data and providing efficient network resources can provide a high level of architecture for smart cities (Bawany & Shamsi, 2015; Naranjo et al., 2019). However, widespread artificial intelligence, as well as the ownership of personal data within government or private companies, can reduce citizen awareness of the production of negative feedback rings on all systems as a system process (Giffinger et al., 2007). The gap analysis is a method of assessing the difference in performance between business information systems or software applications and the resources to determine whether business requirements are met accordingly.

Discussion

Analysis Design

At this stage, the methodology, which consists of several phase components, is formulated as in the following:

L: Phase

L1: SoS Initiation, Strategy and Goal

L2: Principle

L3: Smart City Model

L4: Organization/Constituent

L5: Service Channel Architecture

L6: Process Domain

L7: Service Domain

L8: Service Integration (Data/Information) Architecture

L9: Service Integration: (Application/Platform) Architecture

L10: Service Bus/Distributed System Architecture

L11: Data Center Architecture

L12: Network Architecture

L13: Sensor Architecture

L14: Security Architecture

L15: Evaluation and Measurement

Previous samples, which discuss the methodology of smart city architecture, have been collected. The results of the study form the basis for the codification in Table 5. The component of the phase relies on the presence or absence of a component based on the existing phase sequence (L1-L15, where L is the number of layers of each publication). The sample column is the code for each publication in the sample. The value of each component is the value at which the phase of architecture is the nominal scale in accordance with the number of phases. Our analysis collected 30 papers as samples to analyze methodological generalizations. This methodology is part of the framework for compiling architecture. This is important because the characteristics of the system as a SOSs have different characteristics from ordinary systems. Service computing systems can be tangible in a single system and SOSs. The methodology needed for the characteristics of the SOSs is predicted to differ from a single system.

In research, the specific methods and procedures used by researchers are systematically reflected in research design, sample design, data collection, data analysis, interpretation of data, and so on. A literature review is a method of strategies and procedures to identify, record, understand, and prove meaning and convey information about topics of interest (Onwuegbuzie & Frels, 2016). In contrast, research questions are the attempts from researchers to answer the phenomenon using search methods of alternative perspective or domain. In most cases, research questions arise from previous literature because they represent a narrowing of goal data, which in turn reflects gaps in the current knowledge base of certain topics. Even when the research questions come from practical experience, it is always best for researchers to study the literature not only to place the research questions in context, but also to examine whether the research questions were not addressed by one or more other researchers (Onwuegbuzie & Frels, 2016).

Given the fact that there is no universally accepted approach to research, the research strategy should include (a) the logic of the research and its various justifications, and (b) specific action plans and problems that must be driven by research (Malthus, 2017). To have a good concept, the definition should be clear and complete, and there must be guidance to lead researchers in delivering the outputs, as well as a comprehensive literature review to provide the framework for the research. Therefore, the boundaries in this research are related to the artifact to be produced, which has been defined as the enterprise architecture utilization from relevant perspectives and evaluation of its implementation (Dawe & Paradice, 2016). In short, we hold SoS as a total holistic approach that allows inter-process and intra-process consisting of government to government, citizen to citizen, and citizen to government in various sectors such as energy and utilities, education, economic development, transportation, public safety, social services, health care, and other ICT-related systems to create unified information and optimize the engagement (Makhdum & Mian, 2012). The Table 9 will be normalized according to the number of layers of each sample with a maximum value of 1. After normalization, L1 to L15 will be the nominal value data for statistical processing.

Table 9.

Codification of Previous Research Results.

No.	Sample	L	L1	L2	L3	L4	L5	L6	L7	L8	L9	L10	L11	L12	L13	L14	L15
1	(Lnenicka et al., 2018)	4	0	0	1	0	0	1	0	3	1	0	4	4	0	0	0
2	(Gołuchowski & Linger, 2017)	4	1	2	1	3	4	4	4	4	4	0	4	0	0	0	0
3	(Sobczak, 2017)	5	2	1	2	3	4	4	4	4	4	4	4	4	4	0	5
4	(Anthopoulos, 2017)	6	1	0	2	2	2	2	2	3	4	4	5	5	5	3	6
5	(Mamkaitis et al., 2016)	3	1	1	2	0	0	0	0	2	2	0	2	2	0	0	3
6	(McGinley & Nakata, 2015)	6	1	0	3	1	3	3	3	4	4	4	5	6	5	6	0
7	(Kakarontzas et al., 2014)	3	1	2	2	0	0	0	0	3	3	0	3	3	0	0	0
8	(Zhu & Zuo, 2013)	6	0	0	1	0	0	0	0	3	2	0	3	4	5	0	6
9	(Khandokar et al., 2016)	3	1	0	2	1	2	2	2	3	3	0	0	0	0	0	0
10	(Rebollo et al., 2012)	5	0	0	0	0	1	0	0	0	2	0	0	3	4	5	0
11	(Orlowski et al., n.d.)	4	1	0	2	0	0	2	0	3	3	0	0	4	0	0	0
12	(N. Chen & Du, 2015)	4	1	0	2	0	0	3	0	4	4	0	4	0	4	0	0
13	(Anthopoulos & Vakali, 2012)	4	0	0	0	0	1	1	1	2	3	0	0	0	4	0	0
14	(Elshenawy et al., 2017)	4	0	0	1	1	1	0	0	3	4	4	2	2	0	0	0
15	(Barbosa et al., 2016)	5	1	0	2	0	2	2	2	3	2	0	4	4	5	0	0
16	(Krylovskiy et al., 2015)	5	0	0	1	0	2	0	0	3	4	4	0	5	0	0	0
17	(Jokinen et al., 2016)	3	1	1	0	0	2	0	2	3	3	0	0	0	0	0	0
18	(Clement et al., 2017)	2	0	0	1	0	1	1	1	2	3	0	0	4	4	2	0
19	(Al-hader et al., 2009)	4	0	0	1	0	1	1	2	1	3	4	4	4	0	2	0
20	(Fu et al., 2015)	5	0	0	1	3	2	0	3	4	5	0	0	0	0	0	0
21	(Rolim et al., 2015)	6	0	0	1	0	0	2	2	3	5	0	0	4	0	0	6
22	(Xiong et al., 2014)	6	0	0	1	0	0	0	2	3	4	5	6	6	0	0	0
23	(Schirmer et al., 2016)	5	0	2	1	0	0	0	0	4	3	0	5	0	0	0	0
24	(Kirwan, 2015)	4	1	0	0	0	0	0	0	2	2	0	3	0	4	0	0
25	(Fazio et al., 2014)	3	0	0	1	1	3	0	0	2	2	0	2	2	0	0	0
26	(Andrea & Marco, 2012)	4	0	0	1	0	0	0	1	2	3	0	0	0	4	0	0
27	(Hoon et al., 2013)	3	0	0	1	0	0	0	1	2	3	0	0	3	0	0	0
28	(Jin et al., 2013)	5	1	0	2	3	0	4	5	5	5	0	5	5	5	0	0
29	(Falconer & Mitchell, 2012)	5	0	0	1	0	0	0	0	2	3	0	0	4	5	0	0
30	(Blackstock, 2014)	3	1	0	2	2	0	0	0	3	3	0	0	0	0	0	0

Statistical Analysis

We process the data through three type analyses, which are

Reliability analysis, to test the normal distribution of sample data,

Cluster analysis, to group based on the proximity of each phase component, and

Correlation analysis, to find out the correlation of each phase component in a cluster (group).

We show reliability analysis of the data in Table 10, where Cronbach’s alpha is .623, that is, the data above are normally distributed so that they can be tested using parametric testing. Therefore, the execution can take a different direction in terms of the effect due to the large process that should consider a distributed system, personal data protection as the policy compliance as the center point of SoS (G. Li et al., 2010; Lubis & Kartiwi, 2014; Rosmaini et al., 2017).

Table 10.

ANOVA Test Summary.

Reliability statistics
Cronbach’s alpha	Cronbach’s alpha based on standardized items	N of items
.623	.699	15

The following analysis uses clustering analysis with the K-means test. Based on the test results on several iterations, the cluster obtained by all phase components is obtained with two clusters; see Table 11.

Table 11.

Cluster Result.

	Cluster
	1	2
L1: SoS Initiation, Strategy and Goal	0.04	0.14
L2: Principle	0.00	0.09
L3: SoS Value Architecture	0.30	0.32
L4: Organization Team/Constituent	0.04	0.17
L5: SoS Service Channel Architecture	0.30	0.25
L6: SoS Process Domain	0.25	0.24
L7: SoS Service Domain	0.25	0.29
L8: Service Integration (Data/Information) Architecture	0.52	0.72
L9: Service Integration: (Application/Platform) Architecture	0.79	0.78
L10: Service Bus/Distributed System Architecture	0.17	0.20
L11: Data Center Architecture	0.21	0.52
L12: Network Architecture	1.10	0.52
L13: Sensor Architecture	1.16	0.33
L14: Security Architecture	0.75	0.04
L15: Evaluation and Measurement	0.00	0.19

Note. SoS = System of System.

Because these data are clustered into two, the phase is 2, which is the last analysis that can be done. A correlation analysis is in the form of bivariate correlation analysis. These results are shown in Tables 12 and 13. Thus, the table represents the proximity of the phase components of each cluster. Significant correlation is obtained from the Pearson correlation value at the level of .05 and .01. Significant correlations obtained in Cluster 1 and Cluster 2 are L1-L2, L2-L8, L4-L8, L5-L6, L12-L14, and L13-L14. Significance correlation at level 0.01 in Cluster 1 is L1-L3.

Table 12.

Value of Pearson Correlation Cluster 1.

	L1	L2	L3	L4	L7	L8	L10	L11	L15
L1	1	.452*	.544**	.358	.266	.547**	–.123	.203	.166
L2	.452*	1	.18	.093	.118	.404*	–.166	.341	.065
L3	.544**	.18	1	.252	.031	.553**	–.064	.148	.074
L4	.358	.093	.252	1	.562**	.443*	.067	.117	.059
L7	.266	.118	.031	.562**	1	.338	.103	.106	.016
L8	.547**	.404*	.553**	.443*	.338	1	–.25	.122	–.173
L10	–.123	–.166	–.064	.067	.103	–.25	1	.285	.128
L11	.203	.341	.148	.117	.106	.122	.285	1	.083
L15	.166	.065	.074	.059	.016	–.173	.128	.083	1

Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed).

Table 13.

Value of Pearson Correlation Cluster 2.

	L5	L6	L9	L12	L13	L14
L5	1	.391*	.25	–.059	–.036	.162
L6	.391*	1	.298	.08	.27	.109
L9	.25	.298	1	.073	.037	.067
L12	–.059	.08	.073	1	.246	.458*
L13	–.036	.27	.037	.246	1	.455*
L14	.162	.109	.067	.458*	.455*	1

Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed).

Table 14.

Phases Explanation.

Phase	Step	Description
1. Planning and Data Architecture	1. Initiation, Strategy and Goal	Initiation: Defining the plan for initiating smart city development in the form of activity planning, scope, and teams involved in the formulation of smart city developmentStrategy: Formulate steps and responsible parties in the team involved in smart city developmentGoal: Formulating the objectives of implementing smart city that are measurable and can meet the aspects of smart city development goals
	2. Architecture Principle	The principle is a reference in designing and implementing architecture in building smart cities
	3. Smart City Model (Value chain model)	The Smart City model is an overview model of Smart City that will be built on the values desired by the Smart City model
	4. Service Domain	Defining the service domain that will be the coverage of the smart city that will be built (target and existing)
	5. Constituent/Organization	Defining the user or entity that provides and uses the services declared on the previous item (target and existing)
	6. Data/Information Architecture	Defining the data that are owned and the data relations that are owned between constituents based on service domain needs (target and existing)
	7. Service Bus	Designing of service interaction and data with the use of service bus technology (target and existing)
	8. Data Center Architecture	Defining data centers owned by constituents or data centers in smart cities themselves (target and existing)
	9. Evaluation (Gap Analysis)	Identify smart city development compared with the target and existing conditions
Infrastructure Architecture	Service channel architecture	Perform composition services according to service requirements on a type of channel such as web, mobile, or other
	Process Domain	Documentation and process design for services that have been defined in constituents and smart cities themselves
	Application/Platform Architecture	Designing platform architecture and applications needed by smart cities
	Network Architecture	Defining and designing networks involved in smart cities
	Sensor Architecture	Defining and designing sensors involved in smart cities
	Security Architecture	Defining and designing security needs in smart cities

Interpretation of Results

The results of the interpretation of the above analysis are presented as follows:

Data are normally distributed.

There are two clusters.

The phase does not need to be analyzed for proximity because there are only two clusters in the following order: 2-1.

The correlation seen is to look at the correlation of each component in the cluster to help formulate the meta-model as a service-based computing artifact.

Modeling

Modeling that can be formulated based on statistical analysis consists of three phases, that is,

Data Planning and Architecture,

Infrastructure Architecture (applications, networks, and sensors) and

Security Architecture.

This can be seen in detail in Figure 2.

Figure 2.

Definition of architectural development methodology.

The phases and stages are explained as follows:

Providing city services through the concept of smart city in challenging times puts pressures on the application system due to societies in transition, stakeholders in government, and social or economic opportunities as government becomes service provider by aligning business strategy and IT architecture (Giffinger et al., 2007). Relationships based on correlation analysis affect the making of meta-model-based computing services as follows.

Figure 3.

Smart city base-model architecture pattern.

The Smart City model is an aggregation of several services provided and used by constituents with physical forms that are data and service channels as constituent interfaces with support for processes, applications/platforms, networks, sensors, and security. Because businesses typically require the use of embedded functions in stand-alone applications that may have been developed over different time periods using different technologies, it is necessary to integrate stand-alone applications (Mehta et al., 2006).

Conclusion

The biggest research area in the implementation of EA in the smart city field is in the development of the framework and perspective for smart city architecture. The gap analysis becomes the fundamental building block due to the importance in identifying the distance between the expectation and the realization of the smart city concept. The statistical analysis used the clustering technique to present the different perspectives in the EA as one concept often utilized by smart city providers in the planning phase to ensure the alignment between strategic planning and the resources or the assets that implementers have. Based on this in-depth study on the Business and IT perspective for smart city architecture, the following conclusions can be made:

There is a need to define the methodology for smart city architecture above the development of EA.

Based on the perspective analysis, there are clear differences in the need for IoT-based architectural levels for service-oriented needs.

The methodology and architecture of EA is currently still not accommodating the development of IoT technology.

Based on the meta-analysis discussed above, the methodology formulation can be used as a development of smart city architecture as a form of SoS. This methodology can be used to create smart city architecture with characteristics of an SoS based on computational services. This methodology accommodates the design of smart city models, data architectures, service bus architectures, data center architectures, service channel architectures, application architectures or platforms, network architectures, and security architectures.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Muharman Lubis

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