• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.69-74
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• 2021

With the recent development of biometric authentication technology, the user authentication techniques using biometric authentication are increasing. Various problems arised in certification techniques that use various existing methods such as ID/PW. Therefore, recently, a method of improving security by introducing biometric authentication as secondary authentication has been used. In this thesis, proposal of the user authentication system that can detect user identification and anomalies using ECGs that are extremely difficult to falsify through the electrical biometric signals from the heart among various biometric authentication devices is studied. The system detects user anomalies by comparing ECG data received from a wrist-mounted wearable device-type ECG measurement tool with identification and ECG data stored in blockchain form on the database and identifying the user's location through a beacon system.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.147-152
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• 2019

Infrastructure is rapidly growing as Internet business grows with the latest IT technologies such as IoT, BigData, and AI. However, in most companies, a limited number of people need to manage a lot of hardware and software. Therefore, Polestar Enterprise Management System(PEMS) is applied to monitor the system operation status, IT service and key KPI monitoring. Real-time monitor screening prevents system malfunctions and quick response. With PEMS, you can see configuration information related to IT hardware and software at a glance, and monitor performance throughout the entire end-to-end period to see when problems occur in real time.

• Journal of Internet of Things and Convergence
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• v.7 no.2
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• pp.31-40
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• 2021

Objectives: This study was conducted to investigate the degree of verbal abuse, emotional response, nursing professionalism, clinical practice stress during clinical practicim of nursing students. Methods: A sample of convenience was 106 nursing students, and a questionnaire was used to measure their verbal abuse, emotional response, nursing professionalism, clinical practice stress. Data were analyzed by descriptive statistics, t-test, one-way ANOVA, and multiple linear regression. Results: A significant positive correlation was found among verbal abuse, emotional response, clinical practice stress(r=.683, r=.573). Grade of which the participant was in, verbal abuse(𝛽=.487), emotional response(𝛽=.240) were significant predictive variables of which accounted for 49% of the variance in clinical practice stress. Conclusions: The results from this study can provide basic data on the development of strategies for nursing college students to cope with verbal abuse and to manage stress under clinical practice

• Journal of Internet of Things and Convergence
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• v.7 no.1
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• pp.1-7
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• 2021

The IoT market continues to expand and grow, but the security threat to IoT devices is also increasing. However, it is difficult to apply the security technology applied to the existing system to IoT devices that have a problem of resource limitation. Therefore, in this paper, we present a service that can improve the security of IoT devices by presenting authentication and lightweight cryptographic algorithms that can reduce the overhead of applying security features, taking into account the nature of resource limitations of IoT devices. We want to apply these service to home network IoT equipment to provide security. The authentication and lightweight cryptographic algorithm application protocols presented in this paper have secured the safety of the service through the use of LEA encryption algorithms and secret key generation by users, IoT devices and server in the IoT environment. Although there is no difference in speed from randomly generating secret keys in experiments, we verify that the problem of resource limitation of IoT devices can be solved by additionally not applying logic for secret key sharing to IoT devices.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.31-36
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• 2022

As the society progresses to a knowledge economy, global demand is rapidly increasing in many fields of society. In line with this, interest in globalization is also increasing in universities. Accordingly, students who have graduated from universities are making efforts to find employment in Korea, but the barriers to entry for employment are high due to the high specifications and qualifications of domestic companies, so the government, local governments, and universities are putting a lot of effort into global overseas employment. This study is a training course design for the K-MOVE project aimed at employment in Japanese IT companies. To design this curriculum, we conduct a demand survey of recruiting companies and students, and draw a curriculum based on the results. Then, the effective link between regular subjects and K-MOVE education will be described. This study can be a useful resource for education experts planning overseas employment in the Japanese IT field.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.17-25
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• 2022

Recently the massive amount of data has been generated because of the number of edge devices increases. And especially, the number of raw unstructured HTML documents has been increased. Therefore, MRC(Machine Reading Comprehension) in which a natural language processing model finds the important information within an HTML document is becoming more important. In this paper, we propose HTDE(HTML Tag Depth Embedding Method), which allows the BERT to train the depth of the HTML document structure. HTDE makes a tag stack from the HTML document for each input token in the BERT and then extracts the depth information. After that, we add a HTML embedding layer that takes the depth of the token as input to the step of input embedding of BERT. Since tokenization using HTDE identifies the HTML document structures through the relationship of surrounding tokens, HTDE improves the accuracy of BERT for HTML documents. Finally, we demonstrated that the proposed idea showing the higher accuracy compared than the accuracy using the conventional embedding of BERT.

• 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.