Towards Cognitive IoT: Autonomous Prediction Model Selection for Solar-Powered Nodes

摘要

The future devices connected to the IoT must have the ability to solve their tasks intelligently, optimize their operations and adapt to changes autonomously. Machine learning, executed as part of device management, is the key to such intelligence. We discuss an evolution of device management systems, that also takes meta-knowledge into account, i.e., has the ability to manage the learning in the system. This is a step towards the vision of Cognitive IoT. As one important function for device management, we identified the selection among various prediction models. As an example, we use autonomous energy management of solar-powered sensor devices in an environment with volatile weather and seasonal changes. We investigate an algorithm that selects among a set of prediction models based on historic performance. Our results show that even though a classical machine learning model is able to learn quickly in some periods, it has trouble generalizing well over the data in other periods, compared to a simple physical model. We show that providing a system with the ability to select among a set of predictors can mitigate the bootstrapping problem for constrained devices and also help them stay in operation in periods when training data is missing.