• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.975-985
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• 2018

ICN architecture, one of future Internet technologies, proposes that content request packets toward a content source can be responded by several distributed nodes. So, ICN can solve network congestion which is happened around content sources and provide a seamless content distribution service regardless of the network and system statuses of content sources. Especially, CCN implements content caching functionality in network nodes so that such intermediated network nodes can themselves respond to content requests. However, when receiving content from distributed nodes, users receiving content cannot authenticate the nodes providing the content. So CCN is vulnerable to various attacks such as an impersonation attack, a data pollution attack, and so on. This paper first describes CCN content authentication and its weakness. Then it proposes an improved content authentication scheme based on a blockchain and evaluates the performance of the proposed scheme.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.