Paper Info
Title
Towards Cognitive IoT: Autonomous Prediction Model Selection for Solar-Powered Nodes
Abstract
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.
Year
DOI
Venue
2018
10.1109/ICIOT.2018.00023
2018 IEEE International Congress on Internet of Things (ICIOT)
Keywords
Field
DocType
Cognitive Internet of Things (CIoT),cognitive device management,solar powered devices,energy efficiency,machine learning,predictive model selection
Energy management,Computer science,Generalization,Efficient energy use,Bootstrapping,Model selection,Computer network,Predictive modelling,Cognition,Management system,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-7245-7
2
0.38
References 
Authors
11
2
Name
Order
Citations
PageRank
Anders Eivind Braten1273.87
Frank Alexander Kraemer226221.13