Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures.
CITATION STYLE
Dias, J. P., Lima, B., Faria, J. P., Restivo, A., & Ferreira, H. S. (2020). Visual self-healing modelling for reliable internet-of-things systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12141 LNCS, pp. 357–370). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50426-7_27
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