Abstract: Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.

Abstract: The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

Abstract: The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms.

Abstract: Ultimate goal of next generation Vehicle-to-everything (V2X) communication systems is enabling accident-free cooperative automated driving that uses the available roadway efficiently. To achieve this goal, the communication system will need to enable a diverse set of use cases, each with a specific set of requirements. We discuss the main use case categories, analyze their requirements, and compare them against the capabilities of currently available communication technologies. Based on the analysis, we identify a gap and point out towards possible system design for 5G V2X that could close the gap. Furthermore, we discuss an architecture of the 5G V2X radio access network that incorporates diverse communication technologies, including current and cellular systems in centimeter wave and millimeter wave, IEEE 802.11p and vehicular visible light communications. Finally, we discuss the role of future 5G V2X systems in enabling more efficient vehicular transportation: from improved traffic flow through reduced inter-vehicle spacing on highways and coordinated intersections in cities (the cheapest way to increasing the road capacity), to automated smart parking (no more visits to the parking!), ultimately enabling seamless end-to-end personal mobility.

Abstract: This book discusses challenges and solutions for the required information processing and management within the context of multi-disciplinary engineering of production systems. The authors consider methods, architectures, and technologies applicable in use cases according to the viewpoints of product engineering and production system engineering, and regarding the triangle of (1) product to be produced by a (2) production process executed on (3) a production system resource. With this book industrial production systems engineering researchers will get a better understanding of the challenges and requirements of multi-disciplinary engineering that will guide them in future research and development activities. Engineers and managers from engineering domains will be able to get a better understanding of the benefits and limitations of applicable methods, architectures, and technologies for selected use cases. IT researchers will be enabled to identify research issues related to the development of new methods, architectures, and technologies for multi-disciplinary engineering, pushing forward the current state of the art.

Abstract: Future automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or bane - it greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cybersecurity becomes important as additional concern because attacks are now much more likely and severe. Unfortunately, there is lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To remediate this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. The application of a combined safety and security pattern engineering workflow is shown and demonstrated by an automotive use case scenario.

Abstract: System requirements specification describes technical concerns of a system and is used throughout the project life-cycle. Requirements specification helps sharing the system vision among its stakeholders, as well facilitating the communication, project management and system development processes. For an effective communication, everyone communicates by means of a common language, and natural language provides the foundations for such language. Although natural language is the most common and preferred form of requirements representation, it also exhibits intrinsic characteristics that often present themselves as the root cause of many requirements quality problems, such as incorrectness, inconsistency, incompleteness and ambiguousness.This paper presents the RSL (short name for "Requirements Specification Language") which is a language to improve the production of requirements specifications in a more systematic, rigorous and consistent way. RSL includes constructs logically arranged into views according to the specific requirement engineering concerns they address. These constructs are defined as linguistic patterns and are represented textually by multiple linguistic styles. Due to space constraints, this paper focuses only on its business level constructs and views, namely on glossary terms, stakeholders, business goals, processes, events and flows. RSL can be used and applied by different types of users such as requirement engineers, business analysts, or domain experts. They can produce system requirements specifications with RSL at different level of detail, considering different writing styles and different types of requirements (e.g., business goals, system goals, functional requirements, quality requirements, constraints, user stories, and use cases). In addition, they can use other types of constructs (e.g., terms, stakeholders, actors, data entities) that, in spite of not being requirements, are important to complement and enrich the specification of such requirements. Based on a simple running example, we also show how RSL users (i.e., requirements engineers and business analysts) can produce requirements specifications in a more systematic and rigorous way.

Abstract: This paper describes an eight-step approach for defining the behaviors of CubeSats that begins with mission requirements and ends with a functional architecture modeled as an activity hierarchy using the Object Management Group's (OMG) Systems Modeling Language (SysML). This approach could be applied to other satellite development efforts but the emphasis here is on CubeSats because of their historically high mission failure rate and the rapid growth in the number of these missions over the last few years. In addition, this approach complements the International Council on Systems Engineering's (INCOSE) Space Systems Working Group's (SSWG) efforts to develop a CubeSat Reference Model. This approach provides a repeatable, generalized method for CubeSat development teams to follow that incorporates standard systems engineering practices such as: a top-down approach, requirements analysis, use case development, and functional analysis. This effort uses a Model-Based Systems Engineering (MBSE) approach. Some of the benefits of using an MBSE approach over a traditional document-based approach are: enhanced communications, reduced development risk, improved quality, and enhanced knowledge transfer [1]. Systems engineering artifacts produced using this approach, such as definitions of the mission domain elements, requirements, use cases, and activities, are captured in a system model which serves as a single-source-of-truth for members of the CubeSat development team. Examples are provided throughout the paper which illustrates the application of this approach to a CubeSat development effort. Since most space missions are concerned with the generation or flow of information, the examples focus on requirements to collect and distribute mission data ending with a definition of the required system functionality to satisfy those requirements.

Abstract: In this paper, we present SeMFIS - a flexible engineering platform for semantic annotations of conceptual models. Conceptual models have in the past been used for many purposes in the context of information systems' engineering. These purposes include for example the elicitation of requirements, the simulation of the behavior of future information systems, the generation of code or the interaction with information systems through models at runtime. Semantic annotations of conceptual models constitute a recently established approach for dynamically extending the semantic representation and semantic analysis scope of conceptual modeling languages. Thereby, elements in conceptual models are linked to concepts in ontologies via anno- tations. Thus, additional knowledge aspects can be represented without modifications of the modeling languages. These aspects can then be analyzed using queries, specifically designed algorithms or external tools and services. At its core, SeMFIS provides a set of meta models for visually representing ontologies and semantic annotations as models. In addition, the tool contains an analysis component, a web service interface, and an import/export component to query and exchange model information. SeM- FIS has been implemented using the freely available ADOxx meta modeling platform. It can thus be easily added to the large variety of other modeling methods based on this platform or used as an additional service for other tools. We present the main features of SeMFIS and briefly discuss use cases where it has been applied. SeMFIS is freely available via the OMiLAB.org website at http://www.omilab.org/web/semfis.

Abstract: Requirements captured by requirements engineers (REs) are commonly inconsistent with their client's intended requirements and are often error prone. There is limited tool support providing end-to-end support between the REs and their client for the validation and improvement of these requirements. We have developed an automated tool called MaramaAIC (Automated Inconsistency Checker) to address these problems. MaramaAIC provides automated requirements traceability and visual support to identify and highlight inconsistency, incorrectness and incompleteness in captured requirements. MaramaAIC provides an end-to-end rapid prototyping approach together with a patterns library that helps to capture requirements and check the consistency of requirements that have been expressed in textual natural language requirements and then extracted to semi-formal abstract interactions, essential use cases (EUCs) and user interface prototype models. It helps engineers to validate the correctness and completeness of the EUCs modelled requirements by comparing them to "best-practice" templates and generates an abstract prototype in the form of essential user interface prototype models and concrete User Interface views in the form of HTML. We describe its design and implementation together with results of evaluating our tool's efficacy and performance, and user perception of the tool's usability and its strengths and weaknesses via a substantial usability study. We also present a qualitative study on the effectiveness of the tool's end-to-end rapid prototyping approach in improving dialogue between the RE and the client as well as improving the quality of the requirements.

Abstract: Within the current organization of design activities it is difficult to fully realize the potential of BIM (Building Information Modelling). In BIM processes, it is necessary to identify and exchange the specific information that is relevant in the exchange information requirements of each project. The concept of level of detail of the information is a tool for describing and quantifying the information that should be exchanged. But the existing definitions of level of detail of information are not easily usable. Precise definitions of methodology, tools and principles are needed to redefine this concept and to use it in order to define the elements of information that are relevant to be exchanged. The authors propose the use of System Engineering and Requirements Engineering to define BIM uses and the relevant level of detail of information and its modelling concerned by each BIM use. The authors first explain why, in this context, the existing definitions of LOD, which can mean level of detail, level of development, level of definition, etc., are not sufficient and not always coherent. They demonstrate through real use cases that System Engineering and Requirement Engineering are a part of this methodology and propose, using formalisms, to describe each BIM use in detail. The authors apply two different approaches to defining the relevant information to each BIM use. Using a top-down conceptual approach they show that Level Of Detail (LOD) is a crucial element in defining the content of a BIM use. They then verify the practicality of their proposal using a bottom-up approach based on three use cases (acoustic studies, safety audit, sizing drainage system). These cases studies allow the authors to address different kinds of systems and objects within a whole infrastructure project. They represent domain use cases (acoustic and drainage) and coordination use cases (safety audit). This part of the work is based on the L2 project in Marseille, which is a Public Private Partnership for expressways. As a result, a methodology is proposed both to redefine the level of detail of information concept and to describe how this concept is used to complete the BIM uses definition. The system engineering and requirement engineering methods are partially adapted to infrastructure projects. These elements facilitate the description of BIM uses expected in BIM Execution Plans. It was necessary to change the definitions of LOD in order to make them compatible with the methods of requirement management. Design activities should be reorganized toward the common goal of meeting project needs and requirements. The authors demonstrate that the best way to meet requirements is to define BIM uses using a new definition of the level of detail of information. It has first to consider requirements that have to be reached through BIM and then define the relevant level of detail of information, using the concept of abstraction of reality. To do it well, applying system engineering and requirement engineering, adapted to infrastructure project, is required. To validate this proposal on an entire project, more use cases will be tested in further work.

Abstract: Providing proper support for debugging models at model-level is one of the main barriers to a broader adoption of Model Driven Development (MDD). In this paper, we focus on the use of MDD for the development of real-time embedded systems (RTE). We introduce a new platform-independent approach to implement model-level debuggers. We describe how to realize support for model-level debugging entirely in terms of the modeling language and show how to implement this support in terms of a model-to-model transformation. Key advantages of the approach over existing work are that (1) it does not require a program debugger for the code generated from the model, and that (2) any changes to, e.g., the code generator, the target language, or the hardware platform leave the debugger completely unaffected. We also describe an implementation of the approach in the context of Papyrus-RT, an open source MDD tool based on the modeling language UML-RT. We summarize the results of the use of our model-based debugger on several use cases to determine its overhead in terms of size and performance. Despite being a prototype, the performance overhead is in the order of microseconds, while the size overhead is comparable with that of GDB, the GNU Debugger.

Abstract: Cyber-Physical Production Systems (CPPS) foster new processes and production methods for reducing “time to market”, waste and failures, as well as improving quality and cost effectiveness. However, changes cannot be restricted to the technological side. An increasing share of services is offered with these systems in order to deliver new customized functions and other benefits. This trend has led to the introduction of Product Service Systems (PSS) as a promising framework describing the integrated development, realization and offering of specific product-service bundles as a solution. The integration of both CPPS and PSS concepts is becoming relevant for industry, because data monitoring, storage and processing allow creating a higher service layer able to deliver production systems with new “intelligent” behaviors and communicating capabilities. In this chapter, we use the term Cyber-physical Product-Service Systems (CPSS) for such an integrated approach. It gives a definition of CPS-based PSS and unveils the state-of-the-art for both concepts with major research issues for their integration. The evolution from products to solutions through servitization is shown, as well as the hardware, software, and service elements of CPSS, requiring an alignment of CPPS and service lifecycle models. Based on industrial use cases, this chapter also deals with challenges for engineering CPS-based PSS in terms of complexity, end user involvement with information exchange among stakeholders and linking views of multiple disciplines (mechanical engineering, information systems, service science etc.). This leads to implications for engineering processes, particularly cross-domain Requirements Engineering and design but also servitized Business Models enabled by CPS.

Abstract: Context Service-oriented computing and context-aware computing are two consolidated paradigms that are changing the way of providing and consuming software services. Whilst service-oriented computing is based on service-oriented architectures for providing flexible software services, context-aware computing articulates different phases of a context life cycle for changing the behavior of such services. The synergy between both paradigms provides the context to this study. Objective This study analyzes the current state of the art of context models, specifically: (1) which are these proposals and how are they related; (2) what are their structural characteristics; (3) what context information is the most addressed; and (4) what are their most consolidated definitions. Given their dominance on the field, the study focuses on ontology-based approaches. Method We conducted a systematic mapping by establishing a review protocol that integrates automatic and manual searches from different sources. We applied a rigorous method to elicit the keywords from the research questions and selection criteria to retrieve the papers to evaluate. Results Overall, 138 primary studies were selected to answer our research questions. These proposals were studied in depth by analyzing: 1) distribution along time and their relationships; 2) size correlated with the number of classes and levels of the context model, and coverage of the definitions provided as indicator of quality provided; 3) most addressed context information; 4) most consolidated definitions of context information. Conclusions The contribution of this survey is to make available a unified and consolidated body of knowledge on context for service-oriented computing that could be instantiated and used as starting point in a variety of use cases. This sweeping view on the anatomy of context models may help avoiding the postulation of new proposals not aligned with the current research.

Abstract: Gamification has been applied in software engineering contexts, and more recently in requirements engineering with the purpose of improving the motivation and engagement of people performing specific engineering tasks. But often an objective evaluation that the resulting gamified tasks successfully meet the intended goal is missing. On the other hand, current practices in designing gamified processes seem to rest on a try, test and learn approach, rather than on first principles design methods. Thus empirical evaluation should play an even more important role.We combined gamification and automated reasoning techniques to support collaborative requirements prioritization in software evolution. A first prototype has been evaluated in the context of three industrial use cases. To further investigate the impact of specific game elements, namely point-based elements, we performed a quasi-experiment comparing two versions of the tool, with and without pointsification. We present the results from these two empirical evaluations, and discuss lessons learned.

Abstract: Complex, cyber-physical systems must be founded on a digital blueprint that provides the most accurate representation of the system by federating information from engineering models across multiple enterprise repositories. This blueprint would serve as the digital surrogate of the system and evolve as the actual system matures across its lifecycle, from conception and design to production and operations. This paper presents a graph-based approach for realizing the digital blueprint, which we refer to as the Total System Model. The paper is divided into five parts. Part 1 provides an introduction to use cases for model-based systems engineering. Part 2 introduces graph concepts for the Total System Model. Part 3 provides a demonstration of the graph-based approach using Syndeia software as a representative application. Part 4 provides a summary of this paper, and Part 5 lays out potential directions for future work.

Abstract: Introducing a microservice architecture is a complex task, requiring many design decisions regarding system architecture, organizational structure, and system infrastructure. Decision models have been successfully used in other domains for design space exploration, decision making and decision documentation. In this paper, we investigate the use of decision models for microservice architecture. As a first step, we identified areas of microservice design and created decision models for some of the identified areas. We then used the created models as part of a technical action research (TAR) process with partner companies to identify important stakeholders and use cases for decision models in this context, as well as to identify requirements on decision model elements and presentation. Results indicate that practitioners perceive decision models for microservices to be useful. Challenges include the large number of interlinked knowledge areas, the need for context-specific adaptations, and the need for processes to manage the decision space over time.

Abstract: Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the Inferior-Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. Main results: The combined use of different HPC fabrics demonstrated that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments. Our performance analysis shows clearly that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance: The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

Abstract: Advanced analytics solutions are becoming widespread in business organizations. While data scientists create, implement, or apply machine learning algorithms, business stakeholders need the ultimate solution to gain competitive advantage and performance improvement. How can one, systematically, elicit analytical requirements? How can one design the analytics system for addressing such requirement? How can one assure the alignment between data analytics solutions and business strategies? How can one codify and represent analytics know-how in terms of design patterns? This paper has two contributions. First, it introduces a conceptual modeling framework for addressing those challenges. Second, it assesses the potential use cases and limitations of the framework by applying it to two case studies.

Abstract: Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.

Abstract: This paper presents a novel cognitive management architecture developed within the H2020 CogNet project to manage 5G networks. We also present the instantiation of this architecture for two Operator use cases, namely ‘SLA enforcement’ and ‘Mobile Quality Predictor’. The SLA enforcement use case tackles the SLA management with machine learning techniques, precisely, LSTM (Long Short Term Memory). The second use case, Mobile Quality Predictor, proposes a framework using machine learning to enable an accurate bandwidth prediction for each mobile subscriber in real-time. A problem statement, stakeholders, an instantiation of the cognitive management architecture, a related work as well as an evaluation results are presented for each use case.

Abstract: Business process models (BPMs) describe tasks achieved by business workers to reach business goals. With the advancements in the Information Technology (IT), the use of software systems as a support for the business process becomes increasingly dominant and critical. Ideally, software design starts with acquiring knowledge from the BPMs to understand the current context (as-is-system), to outline the target system (to-be-system) and to identify the user requirements (URs) that fulfill business goals. Nevertheless, often the modeling of business processes (BPs) and of ISs are carried out separately. This leads to a misalignment between them. Although, years of research have gone into seeking tools that support business process aligned with IS, the transition of business process to IS modeling is still highly manual and the alignment of BPMs and information system models (ISMs) is still far from reach.To bridge the gap between BPMs and ISMs we propose, in this paper, a MDA approach named BPMN2UC to generate UML use cases (UCs) that represent URs towards the IS. To do this, we first carry out pre-treatments on the BPMN model to allow for a quasi-automatic UC generation. Then, we propose a set of transformation rules based on the semantics of BPMN and UML modeling elements. In addition to UC diagram, we generate also a textual description for each generated complex UC in order to help the system designer to understand its scenarios. To improve our approach, we apply it on a case study. Also, our approach is implemented with EMF tools and our transformation rules are specified by ATLAS Transformation Language (ATL) for generating the UC diagram and Acceleo Templates principle to generate the textual descriptions of complex UCs.

Abstract: The efficiency of computing resources utilization by user applications can be analyzed in various ways. The JobDigest approach based on system monitoring was developed in Moscow State University and is currently used in everyday practice of the largest Russian supercomputing center of Moscow State University. The approach features application behavior analysis for every job run on HPC system providing: the set of dynamic application characteristics - time series of values representing utilization of CPU, memory, network, storage, etc. with diagrams and heat maps; the integral characteristics representing average utilization rates; job tagging and categorization with means of informing system administrators and managers on suspicious or abnormal applications. The paper describes the approach principles and workflow, it also demonstrates JobDigest use cases and positioning of the proposed techniques in the set of tools and methods that are used in the MSU HPC Center to ensure its 24/7 efficient and productive functioning.

Abstract: Context and motivation: Product Line Engineering (PLE) is increasingly common practice in industry to develop complex systems for multiple customers with varying needs. In many business contexts, use cases are central development artifacts for requirements engineering and system testing. In such contexts, use case configurators can play a significant role to capture variable and common requirements in Product Line (PL) use case models and to generate Product Specific (PS) use case models for each new customer in a product family. Question/Problem: Although considerable research has been devoted to use case configurators, little attention has been paid to supporting the incremental reconfiguration of use case models with evolving configuration decisions. Principal ideas/results: We propose, apply, and assess an incremental reconfiguration approach to support evolving configuration decisions in PL use case models. PS use case models are incrementally reconfigured by focusing only on the changed decisions and their side effects. In our prior work, we proposed and applied Product line Use case modeling Method (PUM) to support variability modeling in PL use case diagrams and specifications. We also developed a use case configurator, PUMConf, which interactively collects configuration decisions from analysts to generate PS use case models from PL models. Our approach is built on top of PUM and PUMConf. Contributions: We provide fully automated tool support for incremental configuration as an extension of PUMConf. Our approach has been evaluated in an industrial case study in the automotive domain, which provided evidence it is practical and beneficial.

Abstract: Presently, one of the critical challenges for mobile operators is how to implement in the near future a 5G network. This network must support a variety of use cases each of them with different requirements. 5G use cases are so diverse and challenging that the 5G networks must be customizable for the broad range of individual scenarios. We analyze in this paper the steps, objectives, architecture and specific requirements necessary for development and implementation of a Smart City Use Case in a 5G mobile network's operator. We consider that this use case will be the first that will use the 5G network, deployed by Orange Romania, in the near future.