• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.139-148
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• 2020

Urban problems raised by the concentration of population in urban areas have caused residents to migrate to other regions while causing other problems such as deteriorated infrastructure, traffic and environmental pollution problems in existing urban areas. As an alternative to improve this phenomenon, urban regeneration has emerged, and under the assumption that creating community vegetable gardens is effective in revitalizing cities considering their physical, social and cultural environment, frequency and descriptive analyses were conducted on the vegetable gardens in Jungnosong-dong, Wansan-gu, Jeonju-si, Jeollabuk-do, Korea to examine differences in the components of the surrounding environment and the settlement, and community spirit of the two groups comprised of 30 and 35 persons who did not and did participate in gardening respectively. Regression analysis were conducted to determine whether there were changes in community spirit after participating in gardening. As a component of the surrounding environment, gardening and fence management were found to be important for both the groups, and they seemed to have high satisfaction with their residence because they wanted to continue to live in their neighborhood. Overall, the satisfaction evaluation results showed that those who participated in gardening showed a higher satisfaction level than those who did not. Based on the changes in community spirit after participating in the activity, it was found that there was a high level of perception in all the components, indicating that the gardening had a positive effect on the cultivation of community spirit. As such, the spaces called vegetable gardens are not only for doing physical activities but also as shared conversation topics, and through the activities in the gardens, changes in the awareness of local residents can be brought about through the formation of social networks and natural communication between neighbors. It can be concluded that they encourage residents to actively participate in improving the urban environment through activities such as planting.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.47-52
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• 2023

Mobile Edge Computing(MEC) is considered as a promising technology to effectively support the explosively increasing traffic demands. It can provide low-latency services and reduce network traffic by caching contents at the edge of networks such as Base Station(BS). Although users may associate with the nearest BSs, it is more beneficial to associate users to the BS where the requested content is cached to reduce content download latency. Therefore, in this paper, we propose a mobility-aware joint caching and user association algorithm to imporve the cache hit ratio. In particular, the proposed algorithm performs caching and user association based on sojourn time and content preferences. Simulation results show that the proposed scheme improves the performance in terms of cache hit ratio and latency as compared with existing schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.265-270
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• 2022

With the advent of the Fourth Industrial Revolution, the paradigm of finance is also changing. As remote work becomes more active due to cloud computing and coronavirus, the work environment changes and attack techniques are becoming intelligent and advanced, companies should accept new security models to further strengthen their current security systems. Zero trust security increases security by monitoring all networks and allowing strict authentication and minimal access rights for access requesters with the core concept of doubting and not trusting everything. In addition, the use of NAC and EDR for identification subjects and data to strengthen access control of the zero trust-based security system, and strict identity authentication through MFA will be explained. Therefore, this paper introduces a zero-trust security solution that strengthens existing security systems and presents the direction and validity to be introduced in the financial sector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1320-1329
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• 2022

Currently, advanced national research network groups are continuously conducting R&D for the requirement to provide SDN/NFV-based network automation and intelligence technology for R&E users. In addition, the requirement for providing large-scale data transmission with the high performance networking facility, compared to general network environments, is gradually increasing in the advanced national research networks. Accordingly, in this paper, we propose an open API-based virtual network provisioning automation platform for large data transmission researched and developed to respond to the networking requirements of the national research network and present the implementation results. The platform includes the KREONET-S VDN system that provides SDN-based network virtualization technology, and the Kubernetes system that provides container-oriented server virtualization technology, and the Globus Online, a high-performance data transmission system. In this paper, the environment configurations, the system implemetation results for the interworking between the heterogeneous systems, and the automated virtual network provisioning implementation results are presented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1165-1170
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• 2022

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.226-234
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• 2021

Purpose: The accessibility of medical facilities for cancer patients affects both their comfort and survival. Patients in rural areas have a higher socioeconomic burden and are more vulnerable to emergency situations than urban dwellers. This study examined the feasibility and effectiveness of a cancer care model integrating a regional cancer center (RCC) and public health center (PHC). Methods: This study analyzed the construction of a safety care network for cancer patients that integrated an RCC and PHC. Two public health institutions (an RCC in Gyeongnam and a PHC in Geochang County) collaborated on the development of the community care model. The study lasted 13 months beginning in February 2019 to February 2020. Results: The RCC developed the protocol for evaluating and measuring 27 cancer-related symptoms, conducted education for PHC nurses, and administered case counseling. The staff at the PHC registered, evaluated, and routinely monitored patients through home visits. A smartphone application and regular video conferences were incorporated to facilitate mutual communication. In total, 177 patients (mean age: 70.9 years; men: 59%) were enrolled from February 2019 to February 2020. Patients' greatest unmet need was the presence of a nearby cancer treatment hospital (83%). In total, 28 (33%) and 44 (52%) participants answered that the care model was very helpful or helpful, respectively. Conclusion: We confirmed that a combined RCC-PHC program for cancer patients in rural areas is feasible and can bring satisfaction to patients as a safety care network. This program could mitigate health inequalities caused by accessibility issues.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.271-277
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• 2022

PBFT (Practical Byzantine Fault Tolerant) is a consensus algorithm that can achieve consensus by resolving unintentional and intentional faults in a distributed network environment and can guarantee high performance and absolute finality. However, as the size of the network increases, the network load also increases due to message broadcasting that repeatedly occurs during the consensus process. Due to the characteristics of the PBFT algorithm, it is suitable for small/private blockchain, but there is a limit to its application to large/public blockchain. Because PBFT affects the performance of blockchain networks, the industry should test whether PBFT is suitable for products and services, and academia needs a unified evaluation metric and technology for PBFT performance improvement research. In this paper, quantitative evaluation metrics and evaluation frameworks that can evaluate PBFT family consensus algorithms are studied. In addition, the throughput, latency, and fault tolerance of PBFT are evaluated using the proposed PBFT evaluation framework.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.456-462
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• 2022

In this paper, we introduce a method of predicting time offset by applying LSTM, a deep learning model, to a precision time comparison technique based on measurement data extracted from code signals transmitted from GPS satellites to determine Universal Coordinated Time (UTC). First, we introduce a process of extracting time information from code signals received from a GPS satellite on a daily basis and constructing a daily time offset into one time series data. To apply the deep learning model to the constructed time offset time series data, LSTM, one of the recurrent neural networks, was applied to predict the time offset of a GPS satellite. Through this study, the possibility of time offset prediction by applying deep learning in the field of GNSS precise time transfer was confirmed.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.