Sharing Knowledge to Promote Proactive Multi-environments in the WoT

Abstract

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.