A component based approach for the Web of Things

Ruppen, Andreas; Pasquier, Jacques; Meyer, Sonja; Rüedlinger, Alexander

doi:10.1145/2834791.2834792

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…To finish, the work is extended with concrete and abstract syntax to perform a Model‐to‐Model (M2M) transformation for automatically generating WoT Servients 28 . Other papers that propose a metamodel for the Web of Things are, 29 which explores a metamodel focused on representing devices as virtual entities that offer services and Reference 30, that proposes a metamodel for context information.…”

Section: Literature Reviewmentioning

confidence: 99%

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Llopis,

Fernández‐García,

Criado

et al. 2024

Softw Pract Exp

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The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

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Section: Literature Reviewmentioning

confidence: 99%

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

Llopis,

Fernández‐García,

Criado

et al. 2024

Softw Pract Exp

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“…In [39] a component-based approach for the Web of Things was presented. They defined a Model Driven Development process to model Web of Things (WoT) systems by using model-driven techniques such as meta-modelling and model transformations.…”

Section: A Low Level Iot Simulation Environmentsmentioning

confidence: 99%

SimulateIoT: Domain Specific Language to Design, Code Generation and Execute IoT Simulation Environments

et al. 2021

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Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Modeldriven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.INDEX TERMS IoT systems, IoT simulation, fog computing, model-driven development, model to text transformation, data analysis.

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“…The authors of [25] and [26] introduce ubiquitous BPMN (uBPMN)-a BPMN extension whose main goal is the modeling of IoT-related devices and activities. In [27], the principle of model-driven architecture is applied to IoT. In [28], the IoT interoperability is discussed and four layers are defined: technical, syntactical, semantic, and organizational.…”

Section: Related Workmentioning

confidence: 99%

Conceptual Modeling of IoT Ecosystems: A Business-Oriented Approach

Andreev

Ilchev

Chikalanov

et al. 2018

SJC

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In this paper, a new approach to the conceptual modeling of IoT ecosystems is presented. Taking into consideration the business processes of an organization, this approach allows the creation of models which integrate IoT-devices and IoT-governed activities into the business flow of the company. The main purpose of this research is to help organizations make faster and more efficient choices based on formalized input/output from their IoT-resources and networks. The use of modeling and formal description is necessary in order to include IoT in ERP-systems, so that IoT is considered in the overall strategic planning and decision making on all levels.

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A component based approach for the Web of Things

Cited by 6 publications

References 16 publications

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

SimulateIoT: Domain Specific Language to Design, Code Generation and Execute IoT Simulation Environments

Conceptual Modeling of IoT Ecosystems: A Business-Oriented Approach

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