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... abstract syntax is provided by the developed metamodel (see Figure 1). The main element of this metamodel is "Device". A Device represents a physical device of the system (a sensor, a router, a mobile, etc.) and is characterized by a unique identifier (name), its description and optionally an icon. Any device instantiated in an application will have a location (Location), so that the device will be located by its Uniform Resource Identifier (URI). The devices can connect and communicate with each other through the services (Service) offered and required by each of these devices. And depending on the context (ContextService) in which the system is integrated it will be possible to establish connections between different devices without having to change the deployment of ...
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... Integrating access management in smart cities in digital twins so that it can be applied in the built environment has proven to be a difficult task to date.To preserve the safety and identity of their physical twin, digital twins must be safe. Methodologies and data models are required to ensure effective protection across platforms, domains and scales [4,5]. Access management approaches ensure that only relevant users who are correctly identified can access and utilize resources [6,7]. ...
To achieve the potential of smart cities, there is a strong requirement to use a set of useful, but still accessible services within smart city systems. Interoperability challenges and roadblocks for software developers and integrators are well-known consequences of fragmented semantics across different contexts. Furthermore, in the smart city context, there is a need to ensure the security and identity of real-world services operating on this data through the adoption of access control principles (authorization and authentication). The use of ontologies to unify the diverse semantics of multiple domains is one strategy that has had considerable success in the past. This paper describes an access management ontology in smart cities developed to enable the interoperability of physical built environment assets, sensing and actuation devices and current built environment services with existing security standards, digital twin and Building Information Model (BIM) datasets. It also provides interoperability between user interfaces and the actors who use them in the context of establishing an access management in smart cities framework for the built environment. This has been validated through its implementation in the Cardiff Urban Sustainability Platform (CUSP), deployed to manage a variety of smart services on a university campus. This validation has successfully shown the ability of the ontology to function as intended in the context of a digital twin, thereby offering single sign-on and suitable access control.
... In particular, model-driven engineering (MDE) [10] techniques are widely used to represent complex systems through abstract models. Examples exist also in the smart city context (e.g., see [11,12]). Several European projects promoting KPIs definition and monitoring have been funded (e.g., see [7,13]). However, there is still an important cornerstone missing, namely a comprehensive methodology supporting (i) systematic and uniform modeling of smart cities and KPIs, (ii) automatic KPIs measurement on top of candidate smart cities, and (iii) intuitive representation and visualization of assessed KPIs. ...
... Regarding the information about the green areas, we gain the TotalGreenArea data by the service Atlante Statistico dei Comuni designed here as an instance of open data. 11 The data about RealTime-TransportMonitoring is modeled in a data package called TransportMonitoring. The provider of this information is the stakeholder instance GSSI since it is the institute in charge of developing real-time transport monitoring systems and technologies. ...
... MDE can be helpful, and it has been already used in this domain. In [11], the authors present a DSL to model Smart City Systems (SCSs). They face with the high heterogeneity of services, devices, and communication protocols that can help in creating a SCS. ...
Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: ( i ) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and ( ii ) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.
... On the one hand, Web services and APIs, i.e., the most common way to specify open services in the SC domain, are widely adopted to build new applications on top of open data, IoT, and cloud services. On the other hand, model-based approaches are exploited in the SC domain, i.e., to represent complex systems through abstract models [5]. However, despite the huge availability of SC services and models, they are currently not well-connected, which would be required to reach the notion of a digital twin [6]. ...
The automatic Key Performance Indicators (KPIs) assessment for smart cities is challenging, since the input parameters needed for the KPIs calculations are highly dynamic and change with different frequencies. Moreover, they are provided by heterogeneous data sources (e.g., IoT infrastructures, Web Services, open repositories), with different access protocol. Open services are widely adopted in this area on top of open data, IoT, and cloud services. However, KPIs assessment frameworks based on smart city models are currently decoupled from open services. This limits the possibility of having runtime up-to-date data for KPIs assessment and synchronized reports. Thus, this paper presents a generic service-oriented middleware that connects open services and runtime models, applied to a model-based KPIs assessment framework for smart cities. It enables a continuous monitoring of the KPIs’ input parameters provided by open services, automating the data acquisition process and the continuous KPIs evaluation. Experiment shows how the evolved framework enables a continuous KPIs evaluation, by drastically decreasing (88%) the latency compared to its baseline.
... Authors in [22] present a domain-specific language that can be used to model the structural and behavioral views of a smart city application. A metamodel was developed using EMF and the graphical interface of the DSL was generated using GMF. ...
Developing Internet of Things (IoT) systems has to cope with several challenges mainly because of the heterogeneity of the involved sub-systems and components. With the aim of conceiving languages and tools supporting the development of IoT systems, this paper presents the results of the study, which has been conducted to understand the current state of the art of existing platforms, and in particular low-code ones, for developing IoT systems. By analyzing sixteen platforms, a corresponding set of features has been identified to represent the functionalities and the services that each analyzed platform can support. We also identify the limitations of already existing approaches and discuss possible ways to improve and address them in the future.
This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.
Industries like telecommunications, medical, automotive, military, avionics, and aerospace use complex real-time systems. Specification and Description Language (SDL) is one of the leading domain specific languages that is formally defined by international standards and well established in describing such systems. To check system properties abstracted model of the system is prepared in selected modeling language. We use Spin (Simple Promela Interpreter) model checker that is one of the leading tools for verification of complex concurrent and reactive systems. This paper focuses on modeling the SDL timer construct. It is one of the SDL constructs that is not easily modeled with Promela, but is present in many SDL systems. After an overview of the related work we propose a new Discrete Time Model for Promela (DTMP) that is seamlessly integrated in our framework for modeling SDL systems and can be used with the mainstream version of the Spin tool. To the best of our knowledge, this is not possible with the existing solutions. We describe how DTMP can be used to model SDL systems that use timers. Experimental results demonstrate its applicability to non-SDL systems with Fischer’s mutual exclusion protocol and the Parallel Acknowledgment with Retransmission that were used in prior studies. We compare state-space requirements with one of the existing solutions DT Promela and DT Spin. With that, virtues and shortcomings of this high-level solution are exposed. We have shown that DTMP is effective when an extensive range of timer expiration values are used, which is usually the case in real-life SDL systems.
