A systematic literature review on AI in IoT systems: Tasks, applications, and deployment

Abstract

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) systems has garnered considerable attention for its ability to enhance efficiency, functionality, and decision making. To drive further research and practical applications, it is essential to gain a deeper understanding of the different roles of AI in IoT systems. In this systematic literature review, we analyze 103 articles describing Artificial Intelligence of Things (AIoT) systems found in three databases, i.e. Scopus, IEEE Xplore, and Web of Science. For each article, we examined the tasks for which AI was used, the input and output data, the application domain, the maturity level of the system, the AI methods used, and where the AI components were deployed. As a result, we identified six general tasks of AI in IoT systems, and thirteen subtasks, the most frequent being prediction, object and event recognition, and operational decision-making. Moreover, we conclude that most AI components in IoT systems process numeric data as input and that healthcare is the most common application domain followed by farming and transportation. Our analysis further revealed that most AIoT systems are in early development stages not validated in real environments. We also identified that Convolutional Neural Networks is the most frequently employed AI method, with supervised learning being the dominant approach. Additionally, we found that both AI deployment, either in the cloud or at the edge, are frequent, but that hybrid deployment is not that common. Finally, we identified key gaps in current AIoT research and based on this, we suggest directions for future research.