In this study, an IoT framework with a four-layer and blockchain storage
system has been developed for the secure and high-accuracy operation of
IoT assets with distributed-based learning systems. In the second part
of the study, firstly, an IoT device was developed for processing nodes
that collect data at endpoints. This IoT end device is responsible for
transmitting temperature and humidity data to the upper layer. In the
second step, edge devices were developed to transfer the collected node
data to the server. These edge devices are responsible for both
transferring the data to the server and transmitting the learning
results to the nodes. In the last step, the data collected in the center
is learned with the LSTM learning algorithm and six different order
classes are decided. In addition, it is explained how the processing
data is stored in the blockchain. As a result, it is predicted that the
proposed secure and blockchain-based IoT framework will contribute to
the literature with its contribution as follows.
• It has been shown that distributed learning-based systems will provide
higher learning performance by combining datasets on the central server
and updating their weights.
• It has been shown that preferring edge devices in distributed
IoT-based systems will use network traffic more efficiently.
• It has been observed that the transmission of data transfer with smart
contract and storage in blockchain-based systems ensure data integrity
and security.
• In IoT systems with high data security, solution-specific development
of edge and node devices has enabled the use of faster and more secure
cryptographic methods.
In the second part of the study, the technical details of the artificial
intelligence-based and secure IoT infrastructure are explained. In this
section, structures such as the IoT device developed, the general
working architecture of the study, the prepared blockchain and smart
contract structure, and the developed machine learning model are
explained. In the third part, there are the findings obtained from the
study. In this section, the network traffic measurements of the
developed infrastructure, the speed and resource usage measurements of
the infrastructure are clearly shown. In the fourth chapter, the results
obtained from the study are explained.
ARTIFICIAL INTELLIGENCE BASED AND SECURED IOT FRAMEWORK
In this study, it has been tried to develop a blockchain-based
distributed edge computing backbone. The study will be examined under
three main headings in itself. The first part of the developed backbone
consists of the technical features and visuals of the IoT card, which
provides data from the environment. In the second part, the details of
the designed edge computing structure and artificial intelligence
processes will be discussed. In the last part, how the blockchain
technology is integrated into the first and second parts will be
explained.
Designed IoT Device: IoT_TH
We chose to develop the IoT card ourselves, which we will use within the
layered architecture of the infrastructure we will develop. We placed
sensors on the IoT card we developed to collect the temperature and
humidity data of the environment. We named the card we developed using
the initials of this environment data and the abbreviation of the
concept of internet of things, IoT_TH. The purpose of designing this
card is to measure the temperature and humidity values of the
environment where the card is located and to send the values obtained
to the remote server via the Wi-Fi module. ESP8266 is used as hardware,
MCU and Wi-Fi module. DHT11 temperature and humidity sensor is used as
the sensor. There is a Relay and a Buzzer on the board to activate when
necessary. The reason for placing a relay on the board is to give this
board the ability to control motors and similar equipment. The developed
board is fed with 5V power. The relay and buzzer on the board work with
5V. The DHT11 sensor works with 3.3V, which is the same value as the
ESP8266. There is 1 test pin on the PCB board to measure the total
consumed current and voltage. With this pin, it is possible to measure
how much power the card consumes in which situations. With the SS34
protection diode, when reverse polarity is applied to the board, damage
to the circuit elements is prevented.
Under normal circumstances, when the location of the card changes or the
SSID and password information of the Wi-Fi device to which it will be
connected changes, the card must be reprogrammed and the SSID and
password information must be reconfigured as hardware code. However, we
can easily do this by dynamically adjusting the Wi-Fi configurations of
the card through the switch we placed on the card. Thus, when the
location of the card is changed, the information of the new Wi-Fi device
to be connected is entered with the Wi-Fi configuration button and it
can be easily adapted to the new location. Thanks to these features, the
card has a more effective quality. The developed card is an ergonomic
card with dimensions of 70mm - 30mm - 18mm. The internal structure,
connection diagram and design of the developed card are shown in Figure
1.