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The Internet of Things (IoT) represents a powerful new paradigm for connecting and communicating with the world around us. It has the potential to transform the way we live, work, and interact with our surroundings. IoT devices are transmitting information over the Internet, most of them with different data formats, despite they may be communicating similar concepts. This often leads to data incompatibilities and makes it difficult to extract the knowledge underlying that data. Because of the heterogeneity of IoT devices and data, interoperability is a challenge, and efforts are underway to overcome this through research and standardization. While data collection and monitoring in IoT systems are becoming more prevalent, contextualizing the data and taking appropriate actions to address issues in the monitored environment is still an ongoing concern. Context Awareness is a highly relevant topic in IoT, as it aims to provide a deeper understanding of the data collected and enable more informed decision-making. In this paper, we propose a semantic ontology designed to monitor global entities in the IoT. By leveraging semantic definitions, it enables end-users to model the entire process from detection to action, including context-aware rules for taking appropriate actions. The advantages of using semantic definitions include more accurate and consistent data interpretation, which improves the overall monitoring process and enables more effective decision-making based on the collected insights. Our proposal includes semantic models for defining the entities responsible for monitoring and executing actions, as well as the elements that need to be considered for an effective monitoring process. Additionally, we provide a new definition for the components known as gateways, which enable the connection and communication between devices and the Internet. Finally, we show the benefits of our ontology by applying it to a critical infrastructure domain where a rapid response is vital to prevent accidents and malfunction of the entities. This work is partially funded by Industrial Doctorates from Generalitat de Catalunya (2019 DI 001), the SUDOQU project, PID2021-126436OB-C21 from MCIN/AEI, 10.13039/501100011033, FEDER, UE, and the Grup de Recerca Consolidat IMP, 2021 SGR-01252. |
