The Internet of Things enhances the quality of life by automating tasks and streamlining human-device interactions. However, manual device management remains time-consuming, especially in multiple or new environments that demand new settings and interactions. Learning systems aid in automating task management, but their learning times hinder personalization and struggle when the system has to interact with multiple IoT environments, impacting user experience. This paper aims to optimize knowledge sharing for IoT environments, proposing a framework that utilizes recommender systems to find optimal and reusable configurations among IoT environments and users. To that end, this work leverages teacher-student relationships in Knowledge Distillation, facilitating knowledge sharing and enhancing knowledge reuse in learning models. In addition, real-time processing eliminates training time. This approach achieves a remarkable 93.15% accuracy.

The main goal of the Web of Things (WoT) is to improve people’s quality of life by automating tasks and simplifying human–device interactions with ubiquitous systems. However, the management of devices still has to be done manually, which wastes a lot of time as their number increases. Thus, the expected benefits are not achieved. This management overhead is even greater when users change environments, new devices are added, or existing devices are modified. All this requires time-consuming customization of configurations and interactions. To facilitate this, learning systems help manage automation tasks. However, these require extensive learning times to achieve customization and cannot manage multiple environments so new approaches are needed to manage multiple environments dynamically. This work focuses on knowledge distillation and teacher–student relationships to transfer knowledge between IoT environments in a model-agnostic manner, allowing users to share their knowledge each time they encounter a new environment. This work allowed us to eliminate training times and achieve an average accuracy of 94.70%, making model automation effective from the acquisition in proactive WoT multi-environments.

The Internet of Things (IoT) is more present in our daily lives than ever before, turning everyday physical objects into smart devices. However, these devices often need excessive human interaction before reaching their best performance, making them time-consuming and reducing their usability. Nowadays, Artificial Intelligence (AI) techniques are being used to process data and to find ways to automate different behaviours. However, achieving learning models capable of handling any situation is a challenging task, worsened by time training restrictions. This paper proposes a Federated Learning solution to manage different IoT environments and provide accurate predictions, based on the user’s preferences. To improve the coexistence between devices and users, this approach makes use of other users’ previous behaviours in similar environments, and proposes predictions for newcomers to the federation. Also, for existing participants, it provides a closer personalization, immediate availability and prevents most manual interactions. The approach has been tested with synthetic and real data and identifies the actions to be performed with 94% accuracy on regular users.