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

Smart City Architecture Development Methodology (SCADM): A Meta-Analysis Using SOA-EA and SoS... 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 well as architectural contexts in the area of service comput- Introduction ing. Smart City can be seen as a collection of collaboration Smart City, also known as an information city, a digital city, and the integration of several systems. Some researchers and a virtual city, is expected to overcome the current and mentioned that Smart City is a form of System of System future complex challenges in increasing resource efficiency, (SoS; Elshenawy et al., 2017; Skilton, 2016). Smart City, in reducing emissions, sustaining health care services for an SoS perspective, does not recognize business processes the aging, empowering youth, and integrating minorities that cross systems but between these systems, which will (Clohessy, 2014). The reference architecture has become an require service modes with each other. Thus, Smart City essential discipline in the design of Information Technology needs to be seen as a service-oriented system to fulfill the (IT) systems of the company. The discipline required in the SoS perspective (Blackstock, 2014; Clement et al., 2017), architectural development of the smart city observation though there is no formal definition on the proper evolution approach is to use Enterprise Architecture (EA). In the last that should be taken by the initiator for the infrastructure, few years, several studies on the development of smart city both physical and digital (Lubis & Maulana, 2010). architecture have been published. However, the development To understand the user perception regarding the imple- of smart city architecture as a reference is still constrained. mentation of Smart City as the requirement of social needs, This is indicated by the continued development of smart city not just of a political campaign, this study utilizes gap analy- research with the EA and Service-Oriented Architecture sis, which is a business management technique that requires (SOA) approaches ( (Zhang et al., 2007)) for references of an assessment of the difference between the best and actual architecture that should be emphasized in the opportunities results of a business effort. It also includes the recommenda- and potency of integration and sustainability, which give tions for steps that can be taken to fill gaps by measuring the empowerment to the citizenship (Lubis, Fauzi, et al., 2018; amount of time, money, and resources needed to fulfill Lubis & Maulana, 2010). Some researchers cite the charac- teristics of the system in the enterprise and the System on Telkom University, Bandung, Indonesia Smart City as different characteristics (Anthopoulos & Corresponding Author: Vakali, 2012; Skilton, 2016; Sobczak, 2017). The purpose of Yuli Adam Prasetyo, Telkom University, Jalan Telekomunikasi No. 1, smart city architecture is architecture in business, IT archi- Bandung 40257, West Java, Indonesia. tecture, data architecture, and performance architecture as Email: adam@telkomuniversity.ac.id Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open 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. potentiality and achieve desired conditions. The main reason EA (Land et al., 2009): Business view, IT view, Governance that gap analysis is important for government in implement- view, and Security view. ing the smart city concept is the fact that the gap between expectations and user experience causes customer dissatis- Business view. The key features to manage complexity, faction and distrust. In a coordinated manner, the distance increase efficiency, reduce operation cost, and enhance qual- between perception and reality should be narrowed to elevate ity of life are related to the collaboration between competi- the user experience to new heights and, thus, increase satis- tiveness, citizen satisfaction, human capital, and sustainability faction. This study discusses the gap between EA and smart (Kumar & Krishna, 2015), which can be seen from the stud- city architecture with the systematic literature review method ies discussed in Table 1. and the smart city architecture development methodology as The business perspective is influenced by the system that a service-oriented SOSs with the meta-analysis method. The is defined, the solution for which is based on the interaction approach taken will be influenced by the disciplines of ser- between components (Endrei et al., 2004). Systems defined vice-oriented architecture, EA, and system engineering. The in a business perspective are business-oriented systems. problem definitions in this study are: Some definitions of the system or systems in Smart City are designated differently in previous research literature. Some Research Question 1: What are the results of the EA per- of these definitions were as follows: spective research in Smart City design? Research Question 2: How to form a gap analysis using •• Smart City as an ecosystem has a need for the involve- EA in developing Smart City? ment and different roles of several stakeholders in Research Question 3: What components are needed in shaping processes and services (Lnenicka et al., the formulation of service-oriented architecture design 2018). methodologies? •• Organization is a combination of several enterprises Research Question 4: How is the smart city architecture for resources or activities to achieve goals or multiple design methodology based on the results of previous objectives (Magalhães & Proper, 2017). studies? •• Joint systems and large-scale distributed systems where components are complex (Skilton, 2016). •• Ecosystems compose a fragmented market of services Literature Review that are interrelated (Strasser et al., 2016). Based on the study of Smart City using the EA approach, •• Smart City operates by taking into account differences which was conducted using the systematic literature review in coverage of macro-system operations including the method, the results of research distribution were obtained same connectivity and functions of city applications based on concepts and perspectives. The main concerns of (Patrick Rau, 2015). research on EA and Framework EA’s perspective for Smart City are the business and IT domains. The system of a system in a digital ecosystem can be a net- work of devices, networks of objects, relationships, and ser- vices that describe the digital world and several other forms EA Perspective of work (Skilton, 2016). This network, if it is intended as a View is a representation of several things that are considered design, can represent each domain needed to describe the and what is produced from a perspective (Inversini & system from various perspectives. Perroud, 2013). Viewpoint (perspective) is the definition from the point of view of the presentation (view) produced Information technology view. The IT domain approach is (Inversini & Perroud, 2013). There are four perspectives in in application architecture, data architecture, and technology Prasetyo and Lubis 3 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. 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. Table 4. Security View. Perspective Result Cyber Security Architecture Assessment Model, Questionnaire support diagram, Logical Design and Business Framework (Nguyen et al., 2017) Diagram of CSAF Assessment Service IOT Security Frameworks (Rebollo Summary of Comparison of IOT Security Framework et al., 2012) The three-layer security The Three Layer Analysis Framework, Meta-model of the three-layer security requirements analysis process requirements Framework, An excerpt of the three-layer requirements model of the (T. Li et al., 2016) smart grid scenario, the three-layer security requirements analysis process, case study Note. CSAF = Cyber security architecture framework; IoT = Internet of things. architecture. To have a responsive solution to the trend and security, IoT, and the security requirement. The results can demand changes, the integration should balance between be seen in Table 4. The competitive pressure and advantages profitable and nonprofitable service to increase the agility have resulted in various organizations becoming more and reliability (Al-Jaroodi & Mohamed, 2018; Hashemi & mature and enhancing their decision-making processes, but Hashemi, 2012). This can be seen in Table 2. often neglecting the transparency and security in each phase of the information system (Lubis, Kusumasari, & Hakim, Governance view. Critical infrastructure must effectively con- 2018; Oliveira et al., 2012). nect the physical and digital world to provide self-monitor- ing and self-response systems to present inspiration, culture Gap Analysis sharing, and knowledge motivation (Incki & Aria, 2018; Nam & Pardo, 2011). Research on the development of smart For efficient data management, we need to respect the diver- city architecture governance can be seen in Table 3. sity of data provided, most different formats, and the fact that data from billions of devices will contain noise (Incki & Security view. In the security view, the results of research Aria, 2018; Schleicher et al., 2016). Gap analysis aims to from a security perspective show several studies on cyber determine EA capability to meet architectural design needs 4 SAGE Open Table 5. Gap Analysis. No. Aspects Enterprise architecture Smart city architecture 1 System, Single System -• System of System: Joint systems and large-scale distributed systems Principle 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 several stakeholders is needed from different systems in collaboration of external building Smart City architecture (Foth et al., 2015; Gołuchowski & Linger, stakeholders in building EA 2017; Sandoval-Almazán et al., 2017; Sobczak, 2017; Štěpánek et al., 2017; Zhu & Zuo, 2013) Enterprise Model: Enterprise Smart City architecture as a System of Systems is above the hierarchy of EA as a system on several needs shown in the following research: frameworks •• Macro, meso, and micro (Sobczak, 2017) •• Long-term Framework, regional Framework, and particular Framework (Anthopoulos & Vakali, 2012) 2 Model The main models are built Service-oriented models from different system stakeholders (Anthopoulos, primarily based on business 2017; Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) and business processes and services 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 System Architecture: Application layer (Basic Services and Application Architecture Services), Platform Layer (Software Platforms, Data Center, and Data •• IT View: Application Processing), Transmission Layer (Optical Transmission and Communication architecture, data and Network Equipment), and Sensor Layer (Sensors, RFID, Two architecture, and Dimensional Codes, GPS, and Cameras; Zhu & Zuo, 2013) technology architecture Architecture Layer: Natural environment, Hard infrastructure (Non- that includes network and ICT Based), Hard Infrastructure (ICT Based), Smart Services and Soft data centers Infrastructure (Anthopoulos, 2017) •• Governance View EA Layer Model: Service Applications, Cloud, Fog, and Smart Bricks •• Security View (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 Helping architects to design smart cities (Anthopoulos, 2017) company IT systems EA is an enterprise system Smart City architecture is a multi-service architecture (Anthopoulos, 2017; architecture Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) from an IoT-based service computing system (Zhu & Zuo, 2013) Information system Smart City architecture is a platform that meets service from multi- architecture as EA domain 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. as a reference in developing Smart City architecture. The 4. EA is a collaborative architecture of multisystems as analysis is presented in Table 5. an element of SOSs. Based on the results of the analysis, EA needs to be fur- ther developed because it does not meet the needs of the Service-Oriented Architecture–Enterprise engineering framework of the SOSs architecture due to the Architecture (SOA-EA) Methodology following: SOA and EA have the same scope, which is the enterprise 1. EA has different characteristics, scope. This enterprise scope has the same architectural struc- 2. EA requires the needs of a service-oriented model, ture. The following is a comparison of the architectural struc- 3. EA has a broader perspective to IoT infrastructure, and ture of EA and SOA (Table 6). Prasetyo and Lubis 5 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 Process Integration Service (data and application) based on Service Note. EA = Enterprise architecture; SOA = Service-oriented architecture. Figure 1. SoS-based model (Jamshidi, 2009). Note. SoS = System of system. There are fundamental differences between architecture (Sweeney, 2010). In general, the process of implementing and development (Rosen, 2008), where the architecture team SOA-EA flow (Sweeney, 2010) is as follows: is responsible for understanding the big picture of a system. While the development team is responsible for the imple- 1. Corporate Strategy mentation and installation of individual services, it also 2. Business Unit Planning focuses on maximizing SOA’s value in providing enterprise 3. SOA Initiation solutions (Rosen, 2008). Technical development aims to pro- 4. SOA Project vide services in the form of implementing specific business 5. SOA Implementation. functions efficiently and effectively including from the per- spective of IT systems (Rosen, 2008). SOA is the best way to SoS minimize and manage the impact of changes. The modular application of attention to data, applications, technology, and The attributes of SoS include a large scale and a distribution other layers will provide flexibility in managing impacts in network that forms a complex system (Jamshidi, 2009). In the form of isolation and minimization (Rosen, 2008). other words, the SoS is an integrated system with a large However, framework is a tool that can be used to obtain data scale that is heterogeneous and stands alone, working with in the form of a consistent structure so that it can be managed its own operations but working together with others to and improved. The framework will answer the question of achieve the same goal. The basic model of the SoS can be “what?” in the terminology of SOA management practices. seen in Figure 1. Elements in the SoS are independent sys- The methodology will show “how” in practice terminology tems themselves. Each element and the SoS also has its own 6 SAGE Open 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 New conditions assessed for utility and their feasibility realization in SoS evolution Integrating external agents The SoS carries out its own integration and integrates Development always ends for each instance of non-SoS The development of SoS is required as the repeated realization cycle for improvement Non-SoS development ends when unwanted possibilities and SoS depend on internal cooperation and internal internal conflicts are removed competition to trigger their evolution Note. SoS = System of system. property, with the most common system elements leading to Bus; Data Center Architecture; Network Architecture; Sensor smart governance, smart living, smart economy, smart mobil- Architecture; Security Architecture; and Evaluation. ity, smart people, and smart environment with the highlighted dimension of the rapid advancement of information and com- Research Methodology munication technology (ITC) to create sustainability of devel- opment through innovative ways of communication (Giffinger The research methodology used in this study is a systematic et al., 2007). Therefore, the implementation of the smart city literature review and meta-analysis. The systematic literature concept often emerges as the solution in advanced communi- review methodology has seven stages, namely, (a) problem ties that are ready to focus on a digital nervous system, intelli- definition search and framework concepts, (b) selection of gent responsiveness, and optimization of every level of work teams, (c) search strategies, (d) search process, (e) con- integration as an SoS (Ratti, 2014). Characteristics, architec- formity and coding, (f) assessment quality, (g) synthesis and ture, and system dynamics become critical aspects chosen for exposure. The results of the synthesis carried out are dis- development methods that must be known and related to each cussed below. Understanding the importance of collecting, other. Comparison of SoS and non-SoS properties as an elabo- storing, and retrieving effective data and providing efficient ration would associate with a centralized methodology or a network resources can provide a high level of architecture noncentralized methodology. The methodology will help as an for smart cities (Bawany & Shamsi, 2015; Naranjo et al., attempt to perform system engineering. Comparison of SoS 2019). However, widespread artificial intelligence, as well as and non-SoS engineering is shown in Table 7. From the table, the ownership of personal data within government or private it can be seen that SoS engineering requires a different approach companies, can reduce citizen awareness of the production in the development of different SOSs (Jamshidi, 2009). 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 Synthesis of the Study information systems or software applications and the In this section, synthesis is carried out to get the results of the resources to determine whether business requirements are research. Our aim was to synthesize the methodology using met accordingly. the SOA, EA, and system engineering approaches. From sev- eral studies, the SOA approach was obtained from the SOA Discussion Methodology and SOA-EA Methodology. These two meth- odologies are service-oriented architecture development Analysis Design methodologies. EA uses methodologies with approaches such as The Open Group Architecture Frameworn (TOGAF) and At this stage, the methodology, which consists of several Enterprise Architecture Planning (EAP). These two method- phase components, is formulated as in the following: ologies are used with consideration of convenience in order to have a higher value of use. The system engineering approach L: Phase is used to explore the SoS. The three approaches are used to L1: SoS Initiation, Strategy and Goal obtain the architecture methodology, which can be seen in L2: Principle Table 8. The results of the formulation are Initiation, Strategy L3: Smart City Model and Goal; Principle; Value Architecture; Organization/ L4: Organization/Constituent Constituent; Service Channel Architecture; Process Domain; L5: Service Channel Architecture Domain Service; Service Integration Architecture; Service L6: Process Domain Prasetyo and Lubis 7 Table 8. Mapping SoS SOA Methodology and SoS Domain. SoS design Enterprise architecture SOA methodology SOA-EA layer (Jamshidi, 2009) planning (Minoli, 2008) (Minoli, 2008) (Rosen, 2008) Synthesis methodology Analyze Layer 1: Planning Initiation SOA Reference Corporate Goal/ Initiation, Strategy and Architecture Definition Strategy Goal Principle Layer 2: Business Modeling Architecture Business Business Unit Smart City Model Definition Plans (Business Model) Layer 2: Current System & Service Identification Business Organization/Constituent Technology Architecture Definition of Semantic Service Channel Information Model Architecture Service Specification Process Domain Service Domain Synthesis Layer 3: Data Architecture Design and Service Enterprise SOA Service Integration Implementation Reference Architecture Architecture Layer 4: Application Service Solution Architecture Implementation Service Bus Layer 5: Technology Data Center Architecture Architecture Enterprise Network Architecture SOA Platform Architecture Sensor Architecture Layer 6: Implementation/ Security Architecture Migration Plan Evaluation Evaluation Note. SoS = System of system; EA = Enterprise architecture; SOA = Service-oriented architecture. L7: Service Domain The methodology needed for the characteristics of the SOSs L8: Service Integration (Data/Information) Architecture is predicted to differ from a single system. L9: Service Integration: (Application/Platform) In research, the specific methods and procedures used by Architecture researchers are systematically reflected in research design, L10: Service Bus/Distributed System Architecture sample design, data collection, data analysis, interpretation of L11: Data Center Architecture data, and so on. A literature review is a method of strategies L12: Network Architecture and procedures to identify, record, understand, and prove L13: Sensor Architecture meaning and convey information about topics of interest L14: Security Architecture (Onwuegbuzie & Frels, 2016). In contrast, research questions L15: Evaluation and Measurement are the attempts from researchers to answer the phenomenon using search methods of alternative perspective or domain. In Previous samples, which discuss the methodology of smart most cases, research questions arise from previous literature city architecture, have been collected. The results of the study because they represent a narrowing of goal data, which in turn form the basis for the codification in Table 5. The component reflects gaps in the current knowledge base of certain topics. of the phase relies on the presence or absence of a component Even when the research questions come from practical experi- based on the existing phase sequence (L1-L15, where L is the ence, it is always best for researchers to study the literature not number of layers of each publication). The sample column is only to place the research questions in context, but also to the code for each publication in the sample. The value of each examine whether the research questions were not addressed by component is the value at which the phase of architecture is one or more other researchers (Onwuegbuzie & Frels, 2016). the nominal scale in accordance with the number of phases. Given the fact that there is no universally accepted Our analysis collected 30 papers as samples to analyze approach to research, the research strategy should include (a) methodological generalizations. This methodology is part the logic of the research and its various justifications, and of the framework for compiling architecture. This is impor- (b) specific action plans and problems that must be driven by tant because the characteristics of the system as a SOSs have research (Malthus, 2017). To have a good concept, the defi- different characteristics from ordinary systems. Service com- nition should be clear and complete, and there must be guid- puting systems can be tangible in a single system and SOSs. ance to lead researchers in delivering the outputs, as well as 8 SAGE Open 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 Table 10. ANOVA Test Summary. Reliability statistics Cronbach’s alpha Cronbach’s alpha based on standardized items N of items .623 .699 15 a comprehensive literature review to provide the framework layers of each sample with a maximum value of 1. After for the research. Therefore, the boundaries in this research normalization, L1 to L15 will be the nominal value data for are related to the artifact to be produced, which has been statistical processing. defined as the enterprise architecture utilization from rele- vant perspectives and evaluation of its implementation Statistical Analysis (Dawe & Paradice, 2016). In short, we hold SoS as a total holistic approach that allows inter-process and intra-pro- We process the data through three type analyses, which are cess consisting of government to government, citizen to citizen, and citizen to government in various sectors such 1. Reliability analysis, to test the normal distribution of as energy and utilities, education, economic development, sample data, transportation, public safety, social services, health care, and 2. Cluster analysis, to group based on the proximity of other ICT-related systems to create unified information and each phase component, and optimize the engagement (Makhdum & Mian, 2012). The 3. Correlation analysis, to find out the correlation of Table 9 will be normalized according to the number of each phase component in a cluster (group). Prasetyo and Lubis 9 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. 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. We show reliability analysis of the data in Table 10, where Cronbach’s alpha is .623, that is, the data above are normally L5 L6 L9 L12 L13 L14 distributed so that they can be tested using parametric test- L5 1 .391* .25 –.059 –.036 .162 ing. Therefore, the execution can take a different direction in L6 .391* 1 .298 .08 .27 .109 terms of the effect due to the large process that should con- L9 .25 .298 1 .073 .037 .067 sider a distributed system, personal data protection as the L12 –.059 .08 .073 1 .246 .458* policy compliance as the center point of SoS (G. Li et al., L13 –.036 .27 .037 .246 1 .455* 2010; Lubis & Kartiwi, 2014; Rosmaini et al., 2017). L14 .162 .109 .067 .458* .455* 1 The following analysis uses clustering analysis with the K-means test. Based on the test results on several iterations, *Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed). the cluster obtained by all phase components is obtained with two clusters; see Table 11. Because these data are clustered into two, the phase is 2, Significant correlation is obtained from the Pearson correla- which is the last analysis that can be done. A correlation anal- tion value at the level of .05 and .01. Significant correlations ysis is in the form of bivariate correlation analysis. These obtained in Cluster 1 and Cluster 2 are L1-L2, L2-L8, L4-L8, results are shown in Tables 12 and 13. Thus, the table repre- L5-L6, L12-L14, and L13-L14. Significance correlation at sents the proximity of the phase components of each cluster. level 0.01 in Cluster 1 is L1-L3. 10 SAGE Open Figure 2. Definition of architectural development methodology. Providing city services through the concept of smart city Interpretation of Results in challenging times puts pressures on the application system The results of the interpretation of the above analysis are pre- due to societies in transition, stakeholders in government, sented as follows: and social or economic opportunities as government becomes service provider by aligning business strategy and IT archi- 1. Data are normally distributed. tecture (Giffinger et al., 2007). Relationships based on cor- 2. There are two clusters. relation analysis affect the making of meta-model-based 3. The phase does not need to be analyzed for proximity computing services as follows. because there are only two clusters in the following The Smart City model is an aggregation of several ser- order: 2-1. vices provided and used by constituents with physical 4. The correlation seen is to look at the correlation of forms that are data and service channels as constituent each component in the cluster to help formulate the interfaces with support for processes, applications/plat- meta-model as a service-based computing artifact. forms, networks, sensors, and security. Because businesses typically require the use of embedded functions in stand- alone applications that may have been developed over dif- Modeling ferent time periods using different technologies, it is Modeling that can be formulated based on statistical analysis necessary to integrate stand-alone applications (Mehta consists of three phases, that is, et al., 2006). 1. Data Planning and Architecture, Conclusion 2. Infrastructure Architecture (applications, networks, and sensors) and The biggest research area in the implementation of EA in the 3. Security Architecture. smart city field is in the development of the framework and perspective for smart city architecture. The gap analysis This can be seen in detail in Figure 2. becomes the fundamental building block due to the impor- The phases and stages are explained as follows: tance in identifying the distance between the expectation and Prasetyo and Lubis 11 Figure 3. Smart city base-model architecture pattern. Table 14. Phases Explanation. Phase Step Description 1. Planning 1. Initiation, Strategy and Initiation: Defining the plan for initiating smart city development in the form of and Data Goal activity planning, scope, and teams involved in the formulation of smart city Architecture development Strategy: Formulate steps and responsible parties in the team involved in smart city development Goal: 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 The Smart City model is an overview model of Smart City that will be built on the (Value chain model) 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/ Defining the user or entity that provides and uses the services declared on the Organization previous item (target and existing) 6. Data/Information Defining the data that are owned and the data relations that are owned between Architecture 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 Defining data centers owned by constituents or data centers in smart cities Architecture themselves (target and existing) 9. Evaluation Identify smart city development compared with the target and existing conditions (Gap Analysis) Infrastructure Service channel Perform composition services according to service requirements on a type of Architecture architecture 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 Designing platform architecture and applications needed by smart cities Architecture 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 the realization of the smart city concept. The statistical anal- •• There is a need to define the methodology for smart ysis used the clustering technique to present the different city architecture above the development of EA. perspectives in the EA as one concept often utilized by smart •• Based on the perspective analysis, there are clear dif- city providers in the planning phase to ensure the alignment ferences in the need for IoT-based architectural levels between strategic planning and the resources or the assets for service-oriented needs. that implementers have. Based on this in-depth study on the •• The methodology and architecture of EA is currently Business and IT perspective for smart city architecture, the still not accommodating the development of IoT following conclusions can be made: technology. 12 SAGE Open Based on the meta-analysis discussed above, the methodol- on Web Intelligence and Smart Sensing (pp. 1–2). Association for Computing Machinery. ogy formulation can be used as a development of smart city Chen, N., & Du, W. (2015, June 19–21). Spatial-temporal based architecture as a form of SoS. This methodology can be used integrated management for smart city: Framework, key to create smart city architecture with characteristics of an techniques and implementation [Conference session]. 23rd SoS based on computational services. This methodology International Conference on Geoinformatics, Wuhan, China. accommodates the design of smart city models, data archi- Chen, R., Sun, S.-P., & Chao, W. S. (2016, November 12–13). tectures, service bus architectures, data center architectures, Architecture-oriented design method for smart tourism inno- service channel architectures, application architectures or vative service systems [Conference session]. International platforms, network architectures, and security architectures. Conference on Advanced Materials for Science and Engineering, Tainan, Italy. Declaration of Conflicting Interests Clement, S. J., McKee, D. W., & Xu, J. (2017, April 6–7). Service- oriented reference architecture for smart cities [Conference The author(s) declared no potential conflicts of interest with respect session]. IEEE Symposium on Service-Oriented System to the research, authorship, and/or publication of this article. Engineering, San Francisco, CA, United States. Clohessy, T. A. (2014, December 8–11). Smart city as a service Funding (SCaaS): A future roadmap for e-government smart city cloud The author(s) received no financial support for the research, author- computing initiatives [Conference session]. IEEE/ACM 7th ship, and/or publication of this article. International Conference on Utility and Cloud Computing, London, England. ORCID iD Dawe, S. N., & Paradice, D. (2016, December 11–14). 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Smart City Architecture Development Methodology (SCADM): A Meta-Analysis Using SOA-EA and SoS Approach

Prasetyo, Yuli Adam; Lubis, Muharman
SAGE Open , Volume OnlineFirst: 1 – May 26, 2020
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2158-2440
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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 well as architectural contexts in the area of service comput- Introduction ing. Smart City can be seen as a collection of collaboration Smart City, also known as an information city, a digital city, and the integration of several systems. Some researchers and a virtual city, is expected to overcome the current and mentioned that Smart City is a form of System of System future complex challenges in increasing resource efficiency, (SoS; Elshenawy et al., 2017; Skilton, 2016). Smart City, in reducing emissions, sustaining health care services for an SoS perspective, does not recognize business processes the aging, empowering youth, and integrating minorities that cross systems but between these systems, which will (Clohessy, 2014). The reference architecture has become an require service modes with each other. Thus, Smart City essential discipline in the design of Information Technology needs to be seen as a service-oriented system to fulfill the (IT) systems of the company. The discipline required in the SoS perspective (Blackstock, 2014; Clement et al., 2017), architectural development of the smart city observation though there is no formal definition on the proper evolution approach is to use Enterprise Architecture (EA). In the last that should be taken by the initiator for the infrastructure, few years, several studies on the development of smart city both physical and digital (Lubis & Maulana, 2010). architecture have been published. However, the development To understand the user perception regarding the imple- of smart city architecture as a reference is still constrained. mentation of Smart City as the requirement of social needs, This is indicated by the continued development of smart city not just of a political campaign, this study utilizes gap analy- research with the EA and Service-Oriented Architecture sis, which is a business management technique that requires (SOA) approaches ( (Zhang et al., 2007)) for references of an assessment of the difference between the best and actual architecture that should be emphasized in the opportunities results of a business effort. It also includes the recommenda- and potency of integration and sustainability, which give tions for steps that can be taken to fill gaps by measuring the empowerment to the citizenship (Lubis, Fauzi, et al., 2018; amount of time, money, and resources needed to fulfill Lubis & Maulana, 2010). Some researchers cite the charac- teristics of the system in the enterprise and the System on Telkom University, Bandung, Indonesia Smart City as different characteristics (Anthopoulos & Corresponding Author: Vakali, 2012; Skilton, 2016; Sobczak, 2017). The purpose of Yuli Adam Prasetyo, Telkom University, Jalan Telekomunikasi No. 1, smart city architecture is architecture in business, IT archi- Bandung 40257, West Java, Indonesia. tecture, data architecture, and performance architecture as Email: adam@telkomuniversity.ac.id Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open 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. potentiality and achieve desired conditions. The main reason EA (Land et al., 2009): Business view, IT view, Governance that gap analysis is important for government in implement- view, and Security view. ing the smart city concept is the fact that the gap between expectations and user experience causes customer dissatis- Business view. The key features to manage complexity, faction and distrust. In a coordinated manner, the distance increase efficiency, reduce operation cost, and enhance qual- between perception and reality should be narrowed to elevate ity of life are related to the collaboration between competi- the user experience to new heights and, thus, increase satis- tiveness, citizen satisfaction, human capital, and sustainability faction. This study discusses the gap between EA and smart (Kumar & Krishna, 2015), which can be seen from the stud- city architecture with the systematic literature review method ies discussed in Table 1. and the smart city architecture development methodology as The business perspective is influenced by the system that a service-oriented SOSs with the meta-analysis method. The is defined, the solution for which is based on the interaction approach taken will be influenced by the disciplines of ser- between components (Endrei et al., 2004). Systems defined vice-oriented architecture, EA, and system engineering. The in a business perspective are business-oriented systems. problem definitions in this study are: Some definitions of the system or systems in Smart City are designated differently in previous research literature. Some Research Question 1: What are the results of the EA per- of these definitions were as follows: spective research in Smart City design? Research Question 2: How to form a gap analysis using •• Smart City as an ecosystem has a need for the involve- EA in developing Smart City? ment and different roles of several stakeholders in Research Question 3: What components are needed in shaping processes and services (Lnenicka et al., the formulation of service-oriented architecture design 2018). methodologies? •• Organization is a combination of several enterprises Research Question 4: How is the smart city architecture for resources or activities to achieve goals or multiple design methodology based on the results of previous objectives (Magalhães & Proper, 2017). studies? •• Joint systems and large-scale distributed systems where components are complex (Skilton, 2016). •• Ecosystems compose a fragmented market of services Literature Review that are interrelated (Strasser et al., 2016). Based on the study of Smart City using the EA approach, •• Smart City operates by taking into account differences which was conducted using the systematic literature review in coverage of macro-system operations including the method, the results of research distribution were obtained same connectivity and functions of city applications based on concepts and perspectives. The main concerns of (Patrick Rau, 2015). research on EA and Framework EA’s perspective for Smart City are the business and IT domains. The system of a system in a digital ecosystem can be a net- work of devices, networks of objects, relationships, and ser- vices that describe the digital world and several other forms EA Perspective of work (Skilton, 2016). This network, if it is intended as a View is a representation of several things that are considered design, can represent each domain needed to describe the and what is produced from a perspective (Inversini & system from various perspectives. Perroud, 2013). Viewpoint (perspective) is the definition from the point of view of the presentation (view) produced Information technology view. The IT domain approach is (Inversini & Perroud, 2013). There are four perspectives in in application architecture, data architecture, and technology Prasetyo and Lubis 3 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. 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. Table 4. Security View. Perspective Result Cyber Security Architecture Assessment Model, Questionnaire support diagram, Logical Design and Business Framework (Nguyen et al., 2017) Diagram of CSAF Assessment Service IOT Security Frameworks (Rebollo Summary of Comparison of IOT Security Framework et al., 2012) The three-layer security The Three Layer Analysis Framework, Meta-model of the three-layer security requirements analysis process requirements Framework, An excerpt of the three-layer requirements model of the (T. Li et al., 2016) smart grid scenario, the three-layer security requirements analysis process, case study Note. CSAF = Cyber security architecture framework; IoT = Internet of things. architecture. To have a responsive solution to the trend and security, IoT, and the security requirement. The results can demand changes, the integration should balance between be seen in Table 4. The competitive pressure and advantages profitable and nonprofitable service to increase the agility have resulted in various organizations becoming more and reliability (Al-Jaroodi & Mohamed, 2018; Hashemi & mature and enhancing their decision-making processes, but Hashemi, 2012). This can be seen in Table 2. often neglecting the transparency and security in each phase of the information system (Lubis, Kusumasari, & Hakim, Governance view. Critical infrastructure must effectively con- 2018; Oliveira et al., 2012). nect the physical and digital world to provide self-monitor- ing and self-response systems to present inspiration, culture Gap Analysis sharing, and knowledge motivation (Incki & Aria, 2018; Nam & Pardo, 2011). Research on the development of smart For efficient data management, we need to respect the diver- city architecture governance can be seen in Table 3. sity of data provided, most different formats, and the fact that data from billions of devices will contain noise (Incki & Security view. In the security view, the results of research Aria, 2018; Schleicher et al., 2016). Gap analysis aims to from a security perspective show several studies on cyber determine EA capability to meet architectural design needs 4 SAGE Open Table 5. Gap Analysis. No. Aspects Enterprise architecture Smart city architecture 1 System, Single System -• System of System: Joint systems and large-scale distributed systems Principle 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 several stakeholders is needed from different systems in collaboration of external building Smart City architecture (Foth et al., 2015; Gołuchowski & Linger, stakeholders in building EA 2017; Sandoval-Almazán et al., 2017; Sobczak, 2017; Štěpánek et al., 2017; Zhu & Zuo, 2013) Enterprise Model: Enterprise Smart City architecture as a System of Systems is above the hierarchy of EA as a system on several needs shown in the following research: frameworks •• Macro, meso, and micro (Sobczak, 2017) •• Long-term Framework, regional Framework, and particular Framework (Anthopoulos & Vakali, 2012) 2 Model The main models are built Service-oriented models from different system stakeholders (Anthopoulos, primarily based on business 2017; Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) and business processes and services 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 System Architecture: Application layer (Basic Services and Application Architecture Services), Platform Layer (Software Platforms, Data Center, and Data •• IT View: Application Processing), Transmission Layer (Optical Transmission and Communication architecture, data and Network Equipment), and Sensor Layer (Sensors, RFID, Two architecture, and Dimensional Codes, GPS, and Cameras; Zhu & Zuo, 2013) technology architecture Architecture Layer: Natural environment, Hard infrastructure (Non- that includes network and ICT Based), Hard Infrastructure (ICT Based), Smart Services and Soft data centers Infrastructure (Anthopoulos, 2017) •• Governance View EA Layer Model: Service Applications, Cloud, Fog, and Smart Bricks •• Security View (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 Helping architects to design smart cities (Anthopoulos, 2017) company IT systems EA is an enterprise system Smart City architecture is a multi-service architecture (Anthopoulos, 2017; architecture Fazio et al., 2014; Patrick Rau, 2015; Zhu & Zuo, 2013) from an IoT-based service computing system (Zhu & Zuo, 2013) Information system Smart City architecture is a platform that meets service from multi- architecture as EA domain 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. as a reference in developing Smart City architecture. The 4. EA is a collaborative architecture of multisystems as analysis is presented in Table 5. an element of SOSs. Based on the results of the analysis, EA needs to be fur- ther developed because it does not meet the needs of the Service-Oriented Architecture–Enterprise engineering framework of the SOSs architecture due to the Architecture (SOA-EA) Methodology following: SOA and EA have the same scope, which is the enterprise 1. EA has different characteristics, scope. This enterprise scope has the same architectural struc- 2. EA requires the needs of a service-oriented model, ture. The following is a comparison of the architectural struc- 3. EA has a broader perspective to IoT infrastructure, and ture of EA and SOA (Table 6). Prasetyo and Lubis 5 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 Process Integration Service (data and application) based on Service Note. EA = Enterprise architecture; SOA = Service-oriented architecture. Figure 1. SoS-based model (Jamshidi, 2009). Note. SoS = System of system. There are fundamental differences between architecture (Sweeney, 2010). In general, the process of implementing and development (Rosen, 2008), where the architecture team SOA-EA flow (Sweeney, 2010) is as follows: is responsible for understanding the big picture of a system. While the development team is responsible for the imple- 1. Corporate Strategy mentation and installation of individual services, it also 2. Business Unit Planning focuses on maximizing SOA’s value in providing enterprise 3. SOA Initiation solutions (Rosen, 2008). Technical development aims to pro- 4. SOA Project vide services in the form of implementing specific business 5. SOA Implementation. functions efficiently and effectively including from the per- spective of IT systems (Rosen, 2008). SOA is the best way to SoS minimize and manage the impact of changes. The modular application of attention to data, applications, technology, and The attributes of SoS include a large scale and a distribution other layers will provide flexibility in managing impacts in network that forms a complex system (Jamshidi, 2009). In the form of isolation and minimization (Rosen, 2008). other words, the SoS is an integrated system with a large However, framework is a tool that can be used to obtain data scale that is heterogeneous and stands alone, working with in the form of a consistent structure so that it can be managed its own operations but working together with others to and improved. The framework will answer the question of achieve the same goal. The basic model of the SoS can be “what?” in the terminology of SOA management practices. seen in Figure 1. Elements in the SoS are independent sys- The methodology will show “how” in practice terminology tems themselves. Each element and the SoS also has its own 6 SAGE Open 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 New conditions assessed for utility and their feasibility realization in SoS evolution Integrating external agents The SoS carries out its own integration and integrates Development always ends for each instance of non-SoS The development of SoS is required as the repeated realization cycle for improvement Non-SoS development ends when unwanted possibilities and SoS depend on internal cooperation and internal internal conflicts are removed competition to trigger their evolution Note. SoS = System of system. property, with the most common system elements leading to Bus; Data Center Architecture; Network Architecture; Sensor smart governance, smart living, smart economy, smart mobil- Architecture; Security Architecture; and Evaluation. ity, smart people, and smart environment with the highlighted dimension of the rapid advancement of information and com- Research Methodology munication technology (ITC) to create sustainability of devel- opment through innovative ways of communication (Giffinger The research methodology used in this study is a systematic et al., 2007). Therefore, the implementation of the smart city literature review and meta-analysis. The systematic literature concept often emerges as the solution in advanced communi- review methodology has seven stages, namely, (a) problem ties that are ready to focus on a digital nervous system, intelli- definition search and framework concepts, (b) selection of gent responsiveness, and optimization of every level of work teams, (c) search strategies, (d) search process, (e) con- integration as an SoS (Ratti, 2014). Characteristics, architec- formity and coding, (f) assessment quality, (g) synthesis and ture, and system dynamics become critical aspects chosen for exposure. The results of the synthesis carried out are dis- development methods that must be known and related to each cussed below. Understanding the importance of collecting, other. Comparison of SoS and non-SoS properties as an elabo- storing, and retrieving effective data and providing efficient ration would associate with a centralized methodology or a network resources can provide a high level of architecture noncentralized methodology. The methodology will help as an for smart cities (Bawany & Shamsi, 2015; Naranjo et al., attempt to perform system engineering. Comparison of SoS 2019). However, widespread artificial intelligence, as well as and non-SoS engineering is shown in Table 7. From the table, the ownership of personal data within government or private it can be seen that SoS engineering requires a different approach companies, can reduce citizen awareness of the production in the development of different SOSs (Jamshidi, 2009). 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 Synthesis of the Study information systems or software applications and the In this section, synthesis is carried out to get the results of the resources to determine whether business requirements are research. Our aim was to synthesize the methodology using met accordingly. the SOA, EA, and system engineering approaches. From sev- eral studies, the SOA approach was obtained from the SOA Discussion Methodology and SOA-EA Methodology. These two meth- odologies are service-oriented architecture development Analysis Design methodologies. EA uses methodologies with approaches such as The Open Group Architecture Frameworn (TOGAF) and At this stage, the methodology, which consists of several Enterprise Architecture Planning (EAP). These two method- phase components, is formulated as in the following: ologies are used with consideration of convenience in order to have a higher value of use. The system engineering approach L: Phase is used to explore the SoS. The three approaches are used to L1: SoS Initiation, Strategy and Goal obtain the architecture methodology, which can be seen in L2: Principle Table 8. The results of the formulation are Initiation, Strategy L3: Smart City Model and Goal; Principle; Value Architecture; Organization/ L4: Organization/Constituent Constituent; Service Channel Architecture; Process Domain; L5: Service Channel Architecture Domain Service; Service Integration Architecture; Service L6: Process Domain Prasetyo and Lubis 7 Table 8. Mapping SoS SOA Methodology and SoS Domain. SoS design Enterprise architecture SOA methodology SOA-EA layer (Jamshidi, 2009) planning (Minoli, 2008) (Minoli, 2008) (Rosen, 2008) Synthesis methodology Analyze Layer 1: Planning Initiation SOA Reference Corporate Goal/ Initiation, Strategy and Architecture Definition Strategy Goal Principle Layer 2: Business Modeling Architecture Business Business Unit Smart City Model Definition Plans (Business Model) Layer 2: Current System & Service Identification Business Organization/Constituent Technology Architecture Definition of Semantic Service Channel Information Model Architecture Service Specification Process Domain Service Domain Synthesis Layer 3: Data Architecture Design and Service Enterprise SOA Service Integration Implementation Reference Architecture Architecture Layer 4: Application Service Solution Architecture Implementation Service Bus Layer 5: Technology Data Center Architecture Architecture Enterprise Network Architecture SOA Platform Architecture Sensor Architecture Layer 6: Implementation/ Security Architecture Migration Plan Evaluation Evaluation Note. SoS = System of system; EA = Enterprise architecture; SOA = Service-oriented architecture. L7: Service Domain The methodology needed for the characteristics of the SOSs L8: Service Integration (Data/Information) Architecture is predicted to differ from a single system. L9: Service Integration: (Application/Platform) In research, the specific methods and procedures used by Architecture researchers are systematically reflected in research design, L10: Service Bus/Distributed System Architecture sample design, data collection, data analysis, interpretation of L11: Data Center Architecture data, and so on. A literature review is a method of strategies L12: Network Architecture and procedures to identify, record, understand, and prove L13: Sensor Architecture meaning and convey information about topics of interest L14: Security Architecture (Onwuegbuzie & Frels, 2016). In contrast, research questions L15: Evaluation and Measurement are the attempts from researchers to answer the phenomenon using search methods of alternative perspective or domain. In Previous samples, which discuss the methodology of smart most cases, research questions arise from previous literature city architecture, have been collected. The results of the study because they represent a narrowing of goal data, which in turn form the basis for the codification in Table 5. The component reflects gaps in the current knowledge base of certain topics. of the phase relies on the presence or absence of a component Even when the research questions come from practical experi- based on the existing phase sequence (L1-L15, where L is the ence, it is always best for researchers to study the literature not number of layers of each publication). The sample column is only to place the research questions in context, but also to the code for each publication in the sample. The value of each examine whether the research questions were not addressed by component is the value at which the phase of architecture is one or more other researchers (Onwuegbuzie & Frels, 2016). the nominal scale in accordance with the number of phases. Given the fact that there is no universally accepted Our analysis collected 30 papers as samples to analyze approach to research, the research strategy should include (a) methodological generalizations. This methodology is part the logic of the research and its various justifications, and of the framework for compiling architecture. This is impor- (b) specific action plans and problems that must be driven by tant because the characteristics of the system as a SOSs have research (Malthus, 2017). To have a good concept, the defi- different characteristics from ordinary systems. Service com- nition should be clear and complete, and there must be guid- puting systems can be tangible in a single system and SOSs. ance to lead researchers in delivering the outputs, as well as 8 SAGE Open 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 Table 10. ANOVA Test Summary. Reliability statistics Cronbach’s alpha Cronbach’s alpha based on standardized items N of items .623 .699 15 a comprehensive literature review to provide the framework layers of each sample with a maximum value of 1. After for the research. Therefore, the boundaries in this research normalization, L1 to L15 will be the nominal value data for are related to the artifact to be produced, which has been statistical processing. defined as the enterprise architecture utilization from rele- vant perspectives and evaluation of its implementation Statistical Analysis (Dawe & Paradice, 2016). In short, we hold SoS as a total holistic approach that allows inter-process and intra-pro- We process the data through three type analyses, which are cess consisting of government to government, citizen to citizen, and citizen to government in various sectors such 1. Reliability analysis, to test the normal distribution of as energy and utilities, education, economic development, sample data, transportation, public safety, social services, health care, and 2. Cluster analysis, to group based on the proximity of other ICT-related systems to create unified information and each phase component, and optimize the engagement (Makhdum & Mian, 2012). The 3. Correlation analysis, to find out the correlation of Table 9 will be normalized according to the number of each phase component in a cluster (group). Prasetyo and Lubis 9 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. 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. We show reliability analysis of the data in Table 10, where Cronbach’s alpha is .623, that is, the data above are normally L5 L6 L9 L12 L13 L14 distributed so that they can be tested using parametric test- L5 1 .391* .25 –.059 –.036 .162 ing. Therefore, the execution can take a different direction in L6 .391* 1 .298 .08 .27 .109 terms of the effect due to the large process that should con- L9 .25 .298 1 .073 .037 .067 sider a distributed system, personal data protection as the L12 –.059 .08 .073 1 .246 .458* policy compliance as the center point of SoS (G. Li et al., L13 –.036 .27 .037 .246 1 .455* 2010; Lubis & Kartiwi, 2014; Rosmaini et al., 2017). L14 .162 .109 .067 .458* .455* 1 The following analysis uses clustering analysis with the K-means test. Based on the test results on several iterations, *Correlation is significant at the .05 level (two-tailed). ** Correlation is significant at the .01 level (two-tailed). the cluster obtained by all phase components is obtained with two clusters; see Table 11. Because these data are clustered into two, the phase is 2, Significant correlation is obtained from the Pearson correla- which is the last analysis that can be done. A correlation anal- tion value at the level of .05 and .01. Significant correlations ysis is in the form of bivariate correlation analysis. These obtained in Cluster 1 and Cluster 2 are L1-L2, L2-L8, L4-L8, results are shown in Tables 12 and 13. Thus, the table repre- L5-L6, L12-L14, and L13-L14. Significance correlation at sents the proximity of the phase components of each cluster. level 0.01 in Cluster 1 is L1-L3. 10 SAGE Open Figure 2. Definition of architectural development methodology. Providing city services through the concept of smart city Interpretation of Results in challenging times puts pressures on the application system The results of the interpretation of the above analysis are pre- due to societies in transition, stakeholders in government, sented as follows: and social or economic opportunities as government becomes service provider by aligning business strategy and IT archi- 1. Data are normally distributed. tecture (Giffinger et al., 2007). Relationships based on cor- 2. There are two clusters. relation analysis affect the making of meta-model-based 3. The phase does not need to be analyzed for proximity computing services as follows. because there are only two clusters in the following The Smart City model is an aggregation of several ser- order: 2-1. vices provided and used by constituents with physical 4. The correlation seen is to look at the correlation of forms that are data and service channels as constituent each component in the cluster to help formulate the interfaces with support for processes, applications/plat- meta-model as a service-based computing artifact. forms, networks, sensors, and security. Because businesses typically require the use of embedded functions in stand- alone applications that may have been developed over dif- Modeling ferent time periods using different technologies, it is Modeling that can be formulated based on statistical analysis necessary to integrate stand-alone applications (Mehta consists of three phases, that is, et al., 2006). 1. Data Planning and Architecture, Conclusion 2. Infrastructure Architecture (applications, networks, and sensors) and The biggest research area in the implementation of EA in the 3. Security Architecture. smart city field is in the development of the framework and perspective for smart city architecture. The gap analysis This can be seen in detail in Figure 2. becomes the fundamental building block due to the impor- The phases and stages are explained as follows: tance in identifying the distance between the expectation and Prasetyo and Lubis 11 Figure 3. Smart city base-model architecture pattern. Table 14. Phases Explanation. Phase Step Description 1. Planning 1. Initiation, Strategy and Initiation: Defining the plan for initiating smart city development in the form of and Data Goal activity planning, scope, and teams involved in the formulation of smart city Architecture development Strategy: Formulate steps and responsible parties in the team involved in smart city development Goal: 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 The Smart City model is an overview model of Smart City that will be built on the (Value chain model) 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/ Defining the user or entity that provides and uses the services declared on the Organization previous item (target and existing) 6. Data/Information Defining the data that are owned and the data relations that are owned between Architecture 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 Defining data centers owned by constituents or data centers in smart cities Architecture themselves (target and existing) 9. Evaluation Identify smart city development compared with the target and existing conditions (Gap Analysis) Infrastructure Service channel Perform composition services according to service requirements on a type of Architecture architecture 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 Designing platform architecture and applications needed by smart cities Architecture 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 the realization of the smart city concept. The statistical anal- •• There is a need to define the methodology for smart ysis used the clustering technique to present the different city architecture above the development of EA. perspectives in the EA as one concept often utilized by smart •• Based on the perspective analysis, there are clear dif- city providers in the planning phase to ensure the alignment ferences in the need for IoT-based architectural levels between strategic planning and the resources or the assets for service-oriented needs. that implementers have. Based on this in-depth study on the •• The methodology and architecture of EA is currently Business and IT perspective for smart city architecture, the still not accommodating the development of IoT following conclusions can be made: technology. 12 SAGE Open Based on the meta-analysis discussed above, the methodol- on Web Intelligence and Smart Sensing (pp. 1–2). Association for Computing Machinery. ogy formulation can be used as a development of smart city Chen, N., & Du, W. (2015, June 19–21). Spatial-temporal based architecture as a form of SoS. This methodology can be used integrated management for smart city: Framework, key to create smart city architecture with characteristics of an techniques and implementation [Conference session]. 23rd SoS based on computational services. This methodology International Conference on Geoinformatics, Wuhan, China. accommodates the design of smart city models, data archi- Chen, R., Sun, S.-P., & Chao, W. S. (2016, November 12–13). tectures, service bus architectures, data center architectures, Architecture-oriented design method for smart tourism inno- service channel architectures, application architectures or vative service systems [Conference session]. International platforms, network architectures, and security architectures. Conference on Advanced Materials for Science and Engineering, Tainan, Italy. Declaration of Conflicting Interests Clement, S. J., McKee, D. W., & Xu, J. (2017, April 6–7). Service- oriented reference architecture for smart cities [Conference The author(s) declared no potential conflicts of interest with respect session]. IEEE Symposium on Service-Oriented System to the research, authorship, and/or publication of this article. Engineering, San Francisco, CA, United States. Clohessy, T. A. (2014, December 8–11). Smart city as a service Funding (SCaaS): A future roadmap for e-government smart city cloud The author(s) received no financial support for the research, author- computing initiatives [Conference session]. IEEE/ACM 7th ship, and/or publication of this article. International Conference on Utility and Cloud Computing, London, England. ORCID iD Dawe, S. N., & Paradice, D. (2016, December 11–14). 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Journal

SAGE Open – SAGE

Published: May 26, 2020

Keywords: smart city; gap analysis; architecture; service-oriented enterprise

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