• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.955-963
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• 2022

The Internet of Things (IoT), combined with various technologies, is rapidly becoming an integral part of our daily life. While it is rapidly taking root in society, security considerations are relatively insufficient, making it a major target for cyber attacks. Since all devices in the IoT environment are connected to the Internet and are closely used in daily life, the damage caused by cyber attacks is also serious. Therefore, encryption communication using a network security protocol must be considered for a service in a more secure IoT environment. A representative network security protocol includes TLS (Transport Layer Protocol) defined by the IETF. This paper analyzes the performance measurement results for TLS version 1.2 and version 1.3 in an IoT device open platform environment to predict the load of TLS, a representative network security protocol, in IoT devices with limited resource characteristics. In addition, by analyzing the performance of each major cryptographic algorithm in version 1.3, we intend to present a standard for setting appropriate network security protocol properties according to IoT device specifications.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.17-23
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• 2022

NB-IoT and LTE Cat.M1 based on LPWA(Low Power Wide Area) are commercialized and serviced by mobile carriers. As the demand for IoT devices is increased, the number of subscribers to these services is also increasing. In the beginning of service, there was no issue that eNB capacity for NB-IoT and LTE Cat.M1. However, as the number of subscribers increases, there is an issue that the eNB capacity for these service is insufficient. Active UE capacity issue may cause overload by continuous increase and temporary increase. In this paper, we propose a solution to solve the problem of LTE RRC(Radio Resource Control) Active UE capacity shortage and base station overload caused by the increase of NB-IoT and LTE Cat.M1 UE in same eNB. The proposed solution can increase a cell capacity without cell division and additional eNB, and can also improve the service quality of these UEs.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.41-47
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• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.131-138
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• 2023

This paper is related to performance improvement of Image-to-Image translation using Relativistic Average Patch GAN and Residual in Residual Dense Block. The purpose of this paper is to improve performance through technical improvements in three aspects to compensate for the shortcomings of the previous pix2pix, a type of Image-to-Image translation. First, unlike the previous pix2pix constructor, it enables deeper learning by using Residual in Residual Block in the part of encoding the input image. Second, since we use a loss function based on Relativistic Average Patch GAN to predict how real the original image is compared to the generated image, both of these images affect adversarial generative learning. Finally, the generator is pre-trained to prevent the discriminator from being learned prematurely. According to the proposed method, it was possible to generate images superior to the previous pix2pix by more than 13% on average at the aspect of FID.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.33-40
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• 2023

This study was aimed at examining the effects of aroma blending oil inhalation on academic stress and class concentration in nursing students. The research design was a nonequivalent placebo control group nonsynchronized. The subjects of the study were 24 students in the treatment group and 24 placebo control group. Data collection was from November 4, 2022 to December 3, 2022, and the data were analyzed chi-square test, independent t-test, paired t-test using the SPSS 23.0 Program. The treatment group inhaled aroma blending oil for 10 days showed a significant decrease in academic stress (t=-8.79, p<.001) and a significant increase in class concentration (t=24.44, p<.001).

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.1-6
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• 2023

One of the economical ways to satisfy the quality of service desired by the user in a web service environment is to adjust the size of the object. To this end, this study finds the maximum size of objects that satisfy this constraint when the mean response time is given below an arbitrary threshold for quality of service. It can be inferred that in the steady state of system, the mean response time in the deterministic model by using the round-robin will be the same as that of the queueing model following the general distribution. Based on this, analytical formulas and procedures for finding the maximum object size are obtained. As a service distribution of web traffic, the Pareto distribution is appropriate, so the maximum object size is computed by applying the M/G(Pareto)/1 model and the M/G/1/PS model using exponential distribution as computational experience. Performance evaluation through numerical calculation shows that as the shape parameter in the Pareto distribution increases, the M/G(Pareto)/1 model and M/G/1/PS model have the same maximum object size. The results of this study can be used to environments where objects can be sized for economical web service control.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.45-53
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• 2023

This study is a nonequivalent placebo control group pretest-posttest design to confirm the effects of auricular pressure therapy on perceived stress and sleep quality for nursing students. The subjects of the study were selected from K College, 30 people in the treatment group and 30 people in the control group, a total of 60 people. Data were collected from March 2 to April 26, 2022. Auricular pressure was applied to the treatment group (shenmen, heart, thalamus), and the placebo control group was applied to the auricle (hip, knee, ankle) in the same method during the same period. Data were analyzed independent t-test repeated measures ANOVA using the SPSS 23.0 program. The perceived stress score of the treatment group significantly decreased to 2.28 after 4 weeks of intervention and 2.07 after 8 weeks of intervention, and the sleep quality score significantly increased to 3.37 after 4 weeks of intervention and 4.02 after 8 weeks of intervention. It was found to have a lasting effect over time.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.7-12
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• 2024

To trade cryptocurrency, traders require a personal cryptocurrency wallet. Cryptocurrency itself using blockchain technology is guaranteed excellent security and reliability, so the threat of blockchain hacking is almost impossible, but the exchange environment used by traders for transactions is most subject to hacking threats. Even if transactions are made safely through blockchain during the transaction process, if the trader's wallet information itself is hacked, security cannot be secured in these processes. Exchange hacking is mainly done by stealing a trader's wallet information, giving the hacker access to the victim's wallet assets. In this paper, to prevent this, we would like to reconstruct the existing Hyperledger Fabric structure and propose a system that verifies the identity integrity of traders during the transaction process using whitelisting. The advantage is that through this process, damage to cryptocurrency assets caused by hackers can be prevented and recognized. In addition, we aim to point out and correct problems in the transaction process that may occur if the victim's wallet information is stolen from the existing Hyperledger Fabric.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.