Towards Efficient Information Retrieval in Internet of Things Environments Via Machine Learning Approaches

The Internet of Things (IoT) brings countless real-world items together, producing a great deal of data. The IoT efficiently retrieves information from diverse data sources to achieve intelligent applications. In this regard, IoT systems can benefit from Machine Learning (ML) approaches in improving information retrieval. In this paper, advanced ML methods are thoroughly examined for this specific purpose, considering the unique constraints that IoT data present, such as its variety, capacity to accommodate a considerable amount of data, and constant change. The study covers both unsupervised and supervised learning approaches, emphasizing the critical role ML has in dealing with IoT data. Deep learning architectures model practical features of the unstructured data handled by natural language processing. This review discusses reinforcement learning methods applied to IoT environments, while ensemble learning methods are explored in seeking enhanced information retrieval performance and robustness. Further, we present real case studies that indicate how ML has been useful in rich domains such as health informatics and smart cities. Finally, the review discusses future directions, contemplating a scenario where ML will keep shaping IoT information retrieval.