Chang-Woo Song

Localized Approximation Method Using Inertial Compensation in WSNs

Sensor nodes in a wireless sensor network establish a network based on location information, set a communication path to the sink for data collection, and have the characteristic of limited hardware resources such as battery, data processing capacity, and memory. The method of estimating location information using GPS is convenient, but it is relatively inefficient because additional costs accrue depending on the size of space. In the past, several approaches including range-based and range-free have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. In addition, this study lowered the error rate resulting from difference in response time by adding reliability for calculating and compensating detailed location information using inertia.

Interactive middleware architecture for lifelog based context awareness

Due to the development of IT convergence, a wide variety of information is being produced and distributed rapidly in digital form. Lifelog based context awareness is a technology that provides a service automatically based on perceived situational information in ubiquitous environments. To offer customized services to users, the technology of acquiring lifelog based context information in real time is the most important consideration. We propose the interactive middleware architecture for lifelog based context awareness in distributed and ubiquitous environments. Conventional middleware to support ubiquitous environments stores and manages the situational information and service content acquired by centralized storage or a DBMS. Centralized situational information and service content management may impede the autonomy of mobile nodes and the interoperation between different middle software. The proposed method designs a system that can distribute and manage situational information in mobile nodes using mobile devices in distributed and ubiquitous environments and share the service content between interactive middleware through publication. The application system designed in this study was used in a scenario providing situational perception based mobile service and proved to be useful.

Development of a medical big-data mining process using topic modeling

With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.

Catching up faster data in digital crime using mobile devices

Mass storage media are becoming increasingly common due to the spread of smartphones to which new technologies are applied. Correspondingly, the amount of data collected from digital crime has considerably increased. Previously, if an investigator did not properly conduct the initial response, valuable evidence would be lost. Thus, collection of digital evidence within a short time frame is required. Further, in searches using data from the smartphones to gather evidence, evidence must be collected and analyzed quickly. Therefore, in this paper, a method is proposed for rapidly collecting data at a crime scene based on the type of criminal charge. Once implemented, our method can collect data by accounting for each feature of the software, providing rapid results through a pattern search. There is also a range of options available with parallel routines. Single or multiple options can be utilized depending on the investigator’s requirements.

Contents Recommendation Search System using Personalized Profile on Semantic Web

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider`s viewpoint. Because it is hard to express information on the users` side such as users` preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user`s preference and lifestyle.

Design of music recommendation system using context information

Music recommendation systems used at the present time apply certain queries using appropriate music information or user profiles in order to obtain the desired results. However, these systems are unable to satisfy user desires because these systems only reply to the results of user queries or consider static information, such as a user’s sex and age. In order to solve these problems, this paper attempts to define context information to select music and design a music recommendation system that is suited to a user’s interests and preferences using a filtering method. The recommendation system used in this study uses an Open Service Gateway Initiative (OSGi) framework to recognize context information. Not only does this framework promote a higher user satisfaction rate for music recommendations, service quality is also improved by applying service mobility and distributed processing.

Forensic Evidence Collection Procedures of Smartphone in Crime Scene

As the Smart phone becomes gradually generalized and expands its influence on daily life, the digital evidential matter could be an important clue to prove criminal charge in the forensic process of criminal accident. Since the digital evidential matter could be easily spoiled, fabricated and disappeared, it needs to secure the criminal related evidence promptly as applicable according to clear procedures when investigate the initial scene of accident. Thus, this paper induces forensic procedures and items which a digital forensic investigator should take when it seizes, searches and verifies the Smart phone in the scene of accident considering characteristics of the Smart phone and establishes a criminal related search database and shows what kind of evidential matter for criminal charge could be collected through the applications implemented based on the said search database.

Secure and Efficient Recommendation Service of RFID System Using Authenticated Key Management

RFID Systems can identify an object by reading ID inside a RFID tag using radio frequency. However, because a RFID tag replies its unique ID to the request of any reader through wireless communication, it is vulnerable to attacks on security or privacy through wiretapping or an illegal readers request. In particular, because recommendation services of RFID systems do not assign mobility to all nodes in the network, there happens a large overhead for secure communication when the mobility of nodes are considered. This paper proposes that reflect secure and efficient recommendation service of RFID system and integrated management of information extracted from RFID tags regardless of application. Authenticated key management, in this context, suitable for wireless communication even when the nodes are mobile and new nodes are inserted frequently.

Context Model Based CF Using HMM for Improved Recommendation

Users in ubiquitous environments can use dynamic services whenever and wherever they are located because these environments connect objects and users through wire and wireless networks. Also, there are many devices and services in these environments. However, it is difficult to effectively use conventional filtering method of the recommendation system in future ubiquitous environments because it does not reflect context information well in these environments. This paper attempt to define context model and propose new Collaborative Filtering (CF) based on Hidden Markov Models (HMMs) that are trained by context information. The Collaborative Filtering using HMMs (CFH) is suited to a users interests and preferences. The Ubiquitous Recommendation System (URS) used in this study based on CFH uses an Open Service Gateway Initiative (OSGi) framework to recognize context information and connect device in smart home.

Development of Speech Recognition System based on User Context Information in Smart Home Environment

Most speech recognition systems that have a large capacity and high recognition rates are isolated word speech recognition systems. In order to extend the scope of recognition, it is necessary to increase the number of words that are to be searched. However, it shows a problem that exhibits a decrease in the system performance according to the increase in the number of words. This paper defines the context information that affects speech recognition in a ubiquitous environment to solve such a problem and develops user localization method using inertial sensor and RFID. Also, we develop a new speech recognition system that demonstrates better performances than the existing system by establishing a word model domain of a speech recognition system by context information. This system shows operation without decrease of recognition rate in smart home environment.