Problem Detection in the Edge of IoT Applications.

Iván Bernabé Sánchez; Alberto Fernández; Holger Billhardt; Sascha Ossowski

doi:10.9781/ijimai.2023.07.007

Authors

Iván Bernabé Sánchez Universidad Rey Juan Carlos
Alberto Fernández Universidad Rey Juan Carlos
Holger Billhardt Universidad Rey Juan Carlos
Sascha Ossowski Universidad Rey Juan Carlos

DOI:

https://doi.org/10.9781/ijimai.2023.07.007

Keywords:

Complex Event Processing, Intelligent Agents, Internet of things, Ontologies

Supporting Agencies

This work has been supported by grant VAE: TED2021-131295B-C33 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”, by grant COSASS: PID2021-123673OB-C32 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by the AGROBOTS Project of Universidad Rey Juan Carlos funded by the Community of Madrid, Spain. Iván Bernabé has been funded by the Spanish Ministry of Universities through a grant related to the Requalification of the Spanish University System 2021–23 by the University Carlos III of Madrid.

Abstract

Due to technological advances, Internet of Things (IoT) systems are becoming increasingly complex. They are characterized by being multi-device and geographically distributed, which increases the possibility of errors of different types. In such systems, errors can occur anywhere at any time and fault tolerance becomes an essential characteristic to make them robust and reliable. This paper presents a framework to manage and detect errors and malfunctions of the devices that compose an IoT system. The proposed solution approach takes into account both, simple devices such as sensors or actuators, as well as computationally intensive devices which are distributed geographically. It uses knowledge graphs to model the devices, the system’s topology, the software deployed on each device and the relationships between the different elements. The proposed framework retrieves information from log messages and processes this information automatically to detect anomalous situations or malfunctions that may affect the IoT system. This work also presents the ECO ontology to organize the IoT system information.

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