Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/147792
Title: A model-based infrastructure for the specification and runtime execution of self-adaptive IoT architectures
Author: Alfonso, Ivan  
Garcés, Kelly  
Castro, Harold  
Cabot, Jordi  
Others: Universidad de los Andes
Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Catalan Institution for Research and Advanced Studies (ICREA)
Citation: Alfonso, I., Garcés, K., Castro, H. & Cabot, J. (2023). A model-based infrastructure for the specification and runtime execution of self-adaptive IoT architectures. Computing, 1-24. doi: 10.1007/s00607-022-01145-7
Abstract: To meet increasingly restrictive requirements and improve quality of service (QoS), Internet of Things (IoT) systems have embraced multi-layered architectures leveraging edge and fog computing. However, the dynamic and changing IoT environment can impact QoS due to unexpected events. Therefore, proactive evolution and adaptation of the IoT system becomes a necessity and concern. In this paper, we present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems. Our proposal comprises the design of a domain-specific language (DSL) for the specification of such architectures, and a runtime framework to support the system behaviuor and its self-adaptation at runtime. The code for the deployment of the IoT system and the execution of the runtime framework is automatically produced by our prototype code generator. Moreover, we also show and validate the extensibility of such DSL by applying it to the domain of underground mining. The complete infrastructure (modeling tool, generator and runtime components) is available in a online open source repository.
Keywords: domain-specific language
internet of things
self-adaptive system
edge computing
fog computing
DOI: https://doi.org/10.1007/s00607-022-01145-7
Document type: info:eu-repo/semantics/article
Version: info:eu-repo/semantics/publishedVersion
Issue Date: 9-Feb-2023
Publication license: https://creativecommons.org/licenses/by/4.0/  
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