IoT Composer: Composition and Deployment of IoT Applications

Krishna, Ajay; Pallec, Michel Le; Mateescu, Radu; Noirie, Ludovic; Salaün, Gwen

doi:10.1109/icse-companion.2019.00028

Cited by 29 publications

(29 citation statements)

References 8 publications

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“…To do so, they rely on stateful models of services, contextual information, a goal description and planning techniques in order to generate automatically a resulting composition of services. [8,9] proposes to model objects using behavioural models. An application is then described as a graph of connected objects by explicitly specifying bindings between objects.…”

Section: Related Workmentioning

confidence: 99%

MOZART: Design and Deployment of Advanced IoT Applications

Krishna

Pallec

Martínez

et al. 2020

Companion Proceedings of the Web Conference 2020

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Related Workmentioning

confidence: 99%

MOZART: Design and Deployment of Advanced IoT Applications

Krishna

Pallec

Martínez

et al. 2020

Companion Proceedings of the Web Conference 2020

Self Cite

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“…Each object must exhibit the actions it can execute as well as the order in which these actions must be triggered. Such a public interface can be described using an LTS, as proposed in [9,8], where labels on transitions correspond to these actions.…”

Section: Application To Composition Of Iot Objectsmentioning

confidence: 99%

Quantifying the Similarity of Non-bisimilar Labelled Transition Systems

Salaün

2020

Lecture Notes in Computer Science

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Equivalence checking is an established technique for automatically verifying that two behavioural models (Labelled Transition Systems, LTSs) are equivalent from the point of view of an external observer. When these models are not equivalent, the checker returns a Boolean result with a counterexample, which is a sequence of actions leading to a state where the equivalence relation is not satisfied. However, this counterexample does not give any indication of how far the two LTSs are one from another. One can wonder whether they are almost identical or totally different, which is quite different from a design or debugging point of view. In this paper, we present an approach for measuring the similarity between two LTS models. The set of metrics is computed automatically using a tool we implemented. Beyond presenting the foundations of the proposed solution, we will show how it can be applied to a concrete application domain for supporting the construction of IoT applications by composition of existing devices.

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“…Note that one could make use of these behavioural models for verification purposes as it is done for instance in [12,13], but this is not the goal of this work.…”

Section: Definition 3 (Ebm) An Enriched Behavioural Model Is Definedmentioning

confidence: 99%

F3ARIoT: A framework for autonomic resilience of IoT applications in the Fog

Ozeer

Letondeur²,

Salaün³

et al. 2020

Internet of Things

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Fog Computing is especially appealing to the Internet of Things (IoT) because it provides computing, storage, and communication resources at the edge of the network, near the physical world (PW). Thus, IoT located in the PW can have interesting properties such as low latencies, real-time operations, and data privacy. The Fog, however, is unstable because it is constituted of billions of devices in a dynamic environment. Moreover, the Fog is cyber-physical and devices are thus subjected to external PW conditions which increase the occurrence of failures. When failures occur in such an environment, the resulting consequences on the PW can be hazardous and costly. This paper presents F 3 ARIoT, a framework for autonomic resilience of IoT applications in the Fog. This framework recovers from failures as well as maintains consistency and safety with respect to the PW during the recovery procedure. F 3 ARIoT was implemented and evaluated on a smart home application. A performance evaluation showed that it has a negligible overhead and recovers from failures in a very short delay with respect to end-users.

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IoT Composer: Composition and Deployment of IoT Applications

Cited by 29 publications

References 8 publications

MOZART: Design and Deployment of Advanced IoT Applications

MOZART: Design and Deployment of Advanced IoT Applications

Quantifying the Similarity of Non-bisimilar Labelled Transition Systems

F3ARIoT: A framework for autonomic resilience of IoT applications in the Fog

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