Authors
Franklin M Ribeiro Junior, Reinaldo AC Bianchi, Ronaldo C Prati, Kari Kolehmainen, Juha-Pekka Soininen, Carlos A Kamienski
Publication date
2022/11/1
Journal
Biosystems Engineering
Volume
223
Pages
142-158
Publisher
Academic Press
Description
Highlights
- It is a challenge to manage a massive amount of data generated by sensors in IoT.
- Combining machine learning with data compression results in a larger data reduction.
- Depending on the context, the fog needs to decide which classifier should use.
Smart agriculture applications that analyse and manage agricultural yield using IoT systems may suffer from intermittent operation due to cloud disconnections commonly occurring in rural areas. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, the fog needs to send a high volume of data to the cloud and this can cause link congestion with unusable data traffic. Here we propose an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods. Sixteen techniques for data reduction are investigated; eight machine learning (ML) methods …
Total citations
Scholar articles
FMR Junior, RAC Bianchi, RC Prati, K Kolehmainen… - Biosystems Engineering, 2022