A Model-Driven Methodology to Accelerate Software Engineering in the Internet of Things

Berrouyne, Imad; Adda, Mehdi; Mottu, Jean-Marie; Tisi, Massimo

doi:10.1109/jiot.2022.3170500

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…In Reference 16, as the existing MDE proposals are for modeling the internal behavior of things, that is, how devices manage data generation, the authors propose an MDE approach for modeling the networking, thus solving the existing interoperability problem regarding IoT communications due to the heterogeneity of devices. The proposal includes the generation of network artifacts using ThingML.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed data model is used to improve a Deep Learning model from a The Deep Learning model used for analyzing the proposal of this paper was trained using a set of observations from a Smart Home scenario. 14 As not all the devices from the dataset have a natural language sentence associated, the attributes from the proposed data model are merged to build a sentence that can help in the training process (16). The sentence represents the place where the device is deployed (title from Location) if the device has to be reachable (ttlStatus from TimeToLive), the operation that we want to perform (DataSchema) and the kind of operation that we want (InteractionAffordance).…”

Section: Transformermentioning

confidence: 99%

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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%

Section: Transformermentioning

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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“…Thus, in IoT, network resources are dynamically allocated using application programming interfaces (APIs). An integrated MDE methodology to design and deploy a network of things was presented in (Berrouyne et al, 2022). The use of this methodology may provide the abstraction of the heterogeneous things and the control of these things as well as a mechanism to define constraints on the network.…”

Section: Mde Studies For Iot Applicationsmentioning

confidence: 99%

Toplu Taşıma İçin Nesnelerin İnterneti Yazılımlarının Modellenmesi

ARSLAN,

KARDAŞ

2023

Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi

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The Internet of Things (IoT) is a very promising domain and it is one of the leading technologies used in the public transportation in recent years. However, in addition to the heterogeneity and high complexity problems which are usually observed in the development of IoT systems, the specific needs of public transportation domain make the construction of such systems even harder for the public transportation. This paper proposes the use of a domain-specific modelling language (DSML), called DSML4PT, to facilitate the design and implementation of IoT-based public transportation systems. A metamodel is introduced that enables modeling IoT-based applications according to the different viewpoints and leads to the model-driven engineering of such applications for different IoT-based public transportation platforms. Furthermore, originated from this metamodel, design and implementation of the DSML4PT language with including its syntax and semantics definitions are all discussed in this paper. Use of this DSML supports both the design of the IoT-based public transportation software graphically and the automatic generation of the code required for the implementation. Based on the conducted case study, it has been observed that 80% of a public transportation application can be generated only with using DSML4PT.

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Models2Code: Autonomous model‐based generation to expedite the engineering process

Paniagua,

Caso

2024

Systems Engineering

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Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

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A Model-Driven Methodology to Accelerate Software Engineering in the Internet of Things

Cited by 12 publications

References 49 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

Toplu Taşıma İçin Nesnelerin İnterneti Yazılımlarının Modellenmesi

Models2Code: Autonomous model‐based generation to expedite the engineering process

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