Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures

The use of machine learning modeling techniques enables smart IoT applications in geo-distributed infrastructures such as in the areas of Industry 4.0, smart cities, autonomous driving, and telemedicine. The data for these models is continuously emitted by sensor-equipped devices. It is usually unlabeled and commonly has dynamically-changing data distribution, which impedes the learning process. However, many critical applications such as telemedicine require highly accurate models and human supervision. Therefore, online supervised learning is often utilized, but its application remains challenging as it requires continuous labeling by experts, which is expensive. To reduce the cost, active learning (AL) strategies are used for efficient data selection and labeling. In this paper we propose a novel AL framework for IoT applications, which employs data selection strategies throughout the multiple layers of distributed IoT architectures. This enables an improved utilization of the available resources and reduces costs. The results from the evaluation using classification and regression tasks and synthetic as well as real-world datasets in multiple settings show that the use of multilayer AL can significantly reduce communication, expert costs, and energy, without a loss in model performance. We believe that this study motivates the development of new techniques that employ selective sampling strategies on data streams to optimize the resource usage in IoT architectures.