TY - JOUR
T1 - Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture
AU - Ribeiro Junior, Franklin M.
AU - Bianchi, Reinaldo A.C.
AU - Prati, Ronaldo C.
AU - Kolehmainen, Kari
AU - Soininen, Juha Pekka
AU - Kamienski, Carlos A.
N1 - Funding Information:
This work was jointly funded by the European Commission in Europe and MCTIC/RNP in Brazil [grant number EUB-02-2017 IoT Pilots call]; by the São Paulo Research Foundation - FAPESP [grant numbers 2018/25225-9 , 2019/07665-4 ]; by the Federal Institute of Education, Science and Technology of Maranhão (IFMA).
Publisher Copyright:
© 2022 IAgrE
PY - 2022/11
Y1 - 2022/11
N2 - 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 combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors.
AB - 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 combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors.
KW - Data reduction
KW - Internet of Things (IoT)
KW - Machine learning (ML)
KW - Smart agriculture
UR - http://www.scopus.com/inward/record.url?scp=85123707420&partnerID=8YFLogxK
U2 - 10.1016/j.biosystemseng.2021.12.021
DO - 10.1016/j.biosystemseng.2021.12.021
M3 - Article
AN - SCOPUS:85123707420
SN - 1537-5110
VL - 223
SP - 142
EP - 158
JO - Biosystems Engineering
JF - Biosystems Engineering
IS - Part B
ER -