Visual Self-healing Modelling for Reliable Internet-of-Things Systems

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

João Pedro Dias (Author) FEUP; Bruno Lima (Author) FEUP; João Pascoal Faria (Author) FEUP; André Restivo (Author) FEUP; Hugo Sereno Ferreira (Author) FEUP

Conference proceedings International

Title: Computational Science - ICCS 2020 - 20th International Conference, Amsterdam, The Netherlands, June 3-5, 2020, Proceedings, Part V

Pages: 357-370

20th International Conference on Computational Science, ICCS 2020

3 June 2020 through 5 June 2020

Indexing

Scopus - 11 Citations

INSPEC

Other information

Authenticus ID: P-00S-A3D

DOI: 10.1007/978-3-030-50426-7_27

Abstract (EN): 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. © Springer Nature Switzerland AG 2020.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14