• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6092-6115
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• 2017

This paper demonstrates a case for an end-to-end pure Application Security Layer for reliable and confidential communications within an Internet of Things (IoT) constrained environment. To provide a secure key exchange and to setup a secure data connection, Transport Layer Security (TLS) is used, which provides native protection against replay attacks. TLS along with digital signature can be used to achieve non-repudiation within app-to-app communications. This paper studies the use of TLS over the JavaScript Object Notation (JSON) via a The Constrained Application Protocol (CoAP) RESTful service to verify the hypothesis that in this way one can provide end-to-end communication flexibility and potentially retain identity information for repudiation. As a proof of concept, a prototype has been developed to simulate an IoT software client with the capability of hosting a CoAP RESTful service. The prototype studies data requests via a network client establishing a TLS over JSON session using a hosted CoAP RESTful service. To prove reputability and integrity of TLS JSON messages, JSON messages was intercepted and verified against simulated MITM attacks. The experimental results confirm that TLS over JSON works as hypothesised.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.298-300
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• 2013

IHE(Integrating the Healthcare Enterprise)의 XDS.b(Cross-Enterprise Document Sharing.b) 프로파일은 의료기관간 환자의 전자진료기록을 공유하기 위해 개발되었으나 모바일 환경을 고려하지 않았다. 이를 보완하고자 IHE에서는 모바일기기에서도 전자진료기록을 공유할 수 있는 MHD(Mobile access to Health Documents) 프로파일을 개발했다. XDS.b 메타데이터는 ebXML(electronic business XML)을 사용하고 MHD 메타데이터는 JSON(JavaScript Object Notation)을 사용하므로 MHD와 XDS.b를 연동하기 위해서는 MHD 메타데이터를 XDS.b 메타데이터로 변환이 필요하다. 본 논문에서는 MHD 메타데이터를 XDS.b 메타데이터로 변환하는 규칙을 정의했다.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.263-274
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• 2016

The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.610-620
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• 2023

The interest in the aircraft interior market is gradually growing due to technological development based on the 4th industrial revolution and competition for airlines to attract customers, and as part of that, Cabin Display System (CDS) for FAA Part.25 civil aircraft is being developed in Korea. The CDS is a system that provides various multimedia services to passengers by utilizing Flexible and Transparent Organic Light Emitting Diodes (OLED) with Integrated Display Processing Module (IDPM). This paper presents a new method for efficient Data Load Process of large-sized files and deals with their implementation and performance. The results of this study are expected to be applied to Data Load Process development of avionics that require reliable large-capacity file transmission along with reducing the costs of development compared to existing ARINC-615A.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.