Name
Abstract | ||
|---|---|---|
Device management can enhance large-scale deployments of IoT nodes in non-stationary environments by supporting prediction and planning of their energy budget. This increases their ability for perpetual operation and is a step towards maintenance-free IoT. In this paper we consider how to accelerate the collection of relevant training data for nodes that are introduced into an existing deployment to increase the accuracy of their predictions. In particular, we investigate how nodes powered by solar energy can learn their energy intake faster and more accurately by using data from selected nodes that are working in similar conditions. We explore an architecture that utilizes different training data selection policies to manage the learning processes. For validation, we perform a case study to explore how nodes with correlated data can contribute to the learning process of other nodes. The obtained results indicate that this approach improves the accuracy of the predictions of a new node by 14%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IOTSMS48152.2019.8939220 | 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) |
Keywords | Field | DocType |
Cognitive device management,autonomous operation,adaptive energy management,solar powered devices,energy harvesting,machine learning,training data selection | Training set,Architecture,Energy budget,Software deployment,Computer science,Internet of Things,Computer network,Energy harvesting,Solar energy,Correlation,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-7281-2950-1 | 0 | 0.34 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anders Eivind Braten | 1 | 27 | 3.87 |
| Frank Alexander Kraemer | 2 | 262 | 21.13 |
| David Palma | 3 | 72 | 8.58 |