Reaching a sufficient number of data sets, learning past experiences
from many systems and using this experience in instant or future
predictions are among the capabilities of artificial intelligence. The
horizontal and vertical growth of industrial systems and the transfer of
experience from each location to all other locations increase the
quality of the process. However, the rapid growth of IoT (Internet of
Things) and OT (Operational Technology) assets in recent years raises
questions about data integrity, confidentiality and accessibility. It
deploys edge computing and blockchain-based solutions for data security
and secure transmission in the IoT ecosystem. In this study, a
four-layer IoT ecosystem network is proposed that combines the learning
capabilities of artificial intelligence-based systems used in different
locations and offers a blockchain-based storage system for data
security. These layers consist of node layer, edge layer, decision
layer, and training and blockchain layer, respectively. The lowest
layer, the node layer, is responsible for collecting the temperature and
humidity values in different locations with the developed node devices
in order to evaluate them. The data generated in the node devices is
transferred to the communicating edge device in the edge layer. The edge
layer collects the data from the nodes in the edge system and transfers
it to the server centrally. The training and blockchain layer provide
the collection of data from edge devices, training the artificial
intelligence model and transferring the weights to the decision layer.
At the same time, the blockchain-based storage system works at this
layer to securely store the processed data. As a result, with this
study, it is aimed to develop a framework for transferring the local
learning experiences of distributed IoT devices to all IoT devices and
for the secure storage of data.