Pattern Based Integration of Internet of Things Systems

Tekinerdoğan, Bedir; Köksal, Ömer

doi:10.1007/978-3-319-94370-1_2

Cited by 15 publications

(15 citation statements)

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“…A key characteristic of a digital twin is that it is connected to a physical entity which is typically established by the use of real-time data using sensors. Digital twins are made possible through the integration of various technologies such as Internet of Things [ 3 , 4 , 5 ], artificial intelligence, machine learning, and data science, which enable living digital simulation models to be created that reflect the changes of the physical counterparts. Hereby, a digital twin continuously learns and updates itself using data from sensors or external entities.…”

Section: Introductionmentioning

confidence: 99%

Systems Architecture Design Pattern Catalog for Developing Digital Twins

Tekinerdoğan

Verdouw

2020

Sensors

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A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twin-based Internet of Things (IoT) systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns are described using the well-known documentation template and support the various phases in the systems engineering life cycle process. For illustrating the application of digital twin patterns, we adopt a case study in the agriculture and food domain.

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Section: Introductionmentioning

confidence: 99%

Systems Architecture Design Pattern Catalog for Developing Digital Twins

Tekinerdoğan

Verdouw

2020

Sensors

Self Cite

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“…An IoT platform component (or artifact) is a combination of software and hardware to address the service(s) required by the IoT solution, usually organized according to the generic layered architecture (device, network, session, cloud and application layers), which is supported by management and security services, as explained in [198]. Figure 3.2 illustrates the four main components of an IoT system: the IoT device, including sensor networks, the middleware (IoT gateway), the cloud services (IaaS, PaaS, SaaS and data storage) and the IoT application.…”

Section: Enabling Technologiesmentioning

confidence: 99%

“…Heterogeneous devices usually adopt different communication protocols, affecting the integration of data provided by these devices. IoT platforms use middleware solutions to develop IoT gateways, which "acts as a portal layer between two elements of one or multiple IoT systems, allowing them to share information by communicating between the adopted IoT protocols" [198].…”

Section: Using Middleware For Context and Service Brokersmentioning

confidence: 99%

“…One Publisher can write to many Topics and one Subscriber can read from many Topics. Unlike the event-based pattern, the subscribers in this pattern are all interested in a type of event happening without knowing the publisher of the event" [198].…”

Section: Using Middleware For Context and Service Brokersmentioning

confidence: 99%

“…49 https://kafka.apache.org/ 50 https://github.com/rabbitmq 51 https://www.mulesoft.com/ 52 https://github.com/stormmq [198] To tackle scalability and resilience, some IoT gateways can support scaling both in horizontal, i.e., scaling within a single broker, and in vertical, i.e., scaling through interlinked broker clusters, where each cluster can be organized as a hierarchy with a head broker and edge brokers. The most advanced gateway deployment patterns for integration are (1) multi-protocol web services and (2) intelligent gateways [198]. IoT Hub is an example of an intelligent gateway that supports MQTT, AMQP and HTTPS 53 .…”

Section: Using Middleware For Context and Service Brokersmentioning

confidence: 99%

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Semantic model-driven development for IoT interoperability of emergency services

Moreira¹

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Disaster Risk Reduction (DRR) is a systematic approach to analyze potential disasters and reduce their occurrence rate and possible impact. The main DRR component is an Early Warning System (EWS), which is a distributed information system that is able to monitor the physical world and issue warnings if abnormal situations occur. EWSs that use Internet-of-Things (IoT) technologies, so called IoT EWS, are suitable to realize (near) real-time data acquisition, risk detection and message brokering between data sources and information receivers, comprising both humans (e.g., emergency managers) and machines (e.g., sirens). Over the last years, numerous IoT EWSs were developed to monitor different types of hazards. Multi-hazard EWSs are a special class of EWS that can detect different types of situations and, if necessary, react to them. Multi-hazard EWSs require integration of existing EWSs and seamless integration with new EWSs. Interoperability of EWS components is necessary for effective integration, e.g., so that sensors, devices and platforms work with each other and with other EWSs. Although IoT technologies offer possibilities to improve the EWS efficiency and effectiveness, this potential can only be exploited if interoperability challenges are addressed at all levels. In this thesis, we focus on how to improve the semantic interoperability of IoT EWSs. Semantic interoperability refers to the ability of two or more EWSs (or EWS components) to share data elements in a prescribed format (syntax) and precise unambiguous meaning (semantics). From a literature review on semantic IoT EWS approaches, we selected the three major challenges that need to be addressed together: 1) semantic integration of a variety of data sources that make use of different standards, ontologies and data models; 2) near-real-time processing in time-and safety-critical applications; and 3) data analysis for effective situation awareness and decision support. This thesis introduces the "SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS" (SEMIoTICS) framework, which is a holistic approach for semantic IoT EWS. SEMIoTICS consists of a semantic modeldriven architecture that facilitates the application of data representations, model transformations and distributed software components. SEMIoTICS is a framework that can be used to develop interoperable IoT EWSs for different domains, enabling an IoT EWS to act as a cloud-based semantic broker for situation-aware decision support. SEMIoTICS is leveraged by the adoption of ontology-driven conceptual modelling for situation-aware applications, covering both EWS design-time (specification and implementation) and runtime. Furthermore, the SEMIoTICS guides the application of the Findable, Accessible, Interoperable and Reusable (FAIR) data principles, in which the role of standardization is emphasized. SEMIoTICS was validated in the context of the H2020 INTER-IoT project, in which a semantic interoperable IoT EWS was developed to detect accident risks with trucks that deli...

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Data Management in Precision Agriculture

Teki̇nerdoğan

2023

Encyclopedia of Smart Agriculture Technologies

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Pattern Based Integration of Internet of Things Systems

Cited by 15 publications

References 15 publications

Systems Architecture Design Pattern Catalog for Developing Digital Twins

Systems Architecture Design Pattern Catalog for Developing Digital Twins

Semantic model-driven development for IoT interoperability of emergency services

Data Management in Precision Agriculture

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