Young Jin Jung

A Framework of In-Situ Sensor Data Processing System for Context Awareness

We propose a framework of the context awareness system which processes a large amount of sensor data from the application areas. The proposed framework consists of a context acquisition, a knowledge base, a rule manager, and a context information manager, etc. we implement the proposed framework of in-situ sensor data processing system that manages the data transmitted from various sensors and notifies the manager of the alarm message for specific conditions. Our proposed framework is able to be applied to the prevention of a forest fire, the warning system for detecting environmental pollution, etc.

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Environmental monitoring is required to understand the effects of various kinds of phenomena such as a flood, a typhoon, or a forest fire. To detect the environmental conditions in remote places, monitoring applications employ the sensor networks to detect conditions, context models to understand phenomena, and computing technology to process the large volumes of data. In this paper, we present an air pollution monitoring system to provide alarm messages about potentially dangerous areas with sensor data analysis. We design the data analysis steps to understand the detected air pollution regions and levels. The analyzed data is used to track the pollution and to give an alarm. This implemented monitoring system is used to mitigate the damages caused by air pollution.

Design and Implementation of a System for Environmental Monitoring Sensor Network

In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system.

The vehicle tracking system for analyzing transportation vehicle information

The several moving object query languages have been designed to deal with moving objects effectively. However most of them are not enough to manage vehicles in transportation system. So, to analyze vehicle information in transportation effectively, we propose the web-based transportation vehicle management system supporting a Moving Object Structured Query Language. The developed system can manage vehicles through examining the history data of vehicles such as departure time, overlapped trajectories, and the distance among vehicles and provide the vehicle information on the internet and a PDA through concurrently processing the query language transmitted from clients.

A Moving Point Indexing Using Projection Operation for Location Based Services

The progress of wireless communication technology causes to increase the concerns about contents services related to locations of mobile users. Objects in the LBS move freely and change their position and/or shape continuously over time. And it would be difficult to rapidly provide the moving object location to the mobile user. In order to efficiently process this information, we propose a MP-tree(short for Moving Point Tree) using the projection operation, which is an indexing technique for moving point data. The projection operation extracts the common boundaries of child nodes and stores the boundaries into the projection storage of all axes, so each of the projection storage has the aligned boundaries of subordinate nodes. The ordered boundaries of each projection storage are effective in not only processing moving object query such as point and range queries but also having smaller storage space and less cost of split, because they reduce unnecessary search nodes for overlap and dead space as well as the number of non-leaf nodes. Moreover the MP-tree is able to process combined trajectory queries effectively using linked list. The proposed MP-tree would be useful in the car management using GPS, and the navigation system for LBS.

A group based insert manner for storing enormous data rapidly in intelligent transportation system

The flood of data is occurred in ITS(intelligent transportation system) according to the progress of wireless telecommunication and sensor network. To deal with large data and provide suitable services smoothly, it is necessary for an index technique to store and search bulk object data rapidly. However the existing indices require a lot of costs to insert a huge amount of data because they store every position data into the index directly. To solve this problem in this paper, we propose a buffer node operation and design a GU-tree(Group Update tree). The proposed buffer node manner reduces the input cost effectively since it stores the moving object location in a group. And then we confirm the effect of the buffer node manner which reduces the insert cost and increases the search performance in a time slice query from the experiment to compare the operation with some existing indices.

Geosensor Data Representation Using Layered Slope Grids

Environmental monitoring applications are designed for supplying derived and often integrated information by tracking and analyzing phenomena. To determine the condition of a target place, they employ a geosensor network to get the heterogeneous sensor data. To effectively handle a large volume of sensor data, applications need a data abstraction model, which supports the summarized data representation by encapsulating raw data. For faster data processing to answer a user’s queries with representative attributes of an abstracted model, we propose such a data abstraction model, the Layered Slopes in Grid for Sensor Data Abstraction (LSGSA), which is based on the SGSA. In a single grid-based layer for each sensor type, collected data is represented by slope directional vectors in two layered slopes, such as height and surface. To answer a user query in a central monitoring server, LSGSA is used to reduce the time needed to extract event features from raw sensor data as a preprocessing step for interpreting the observed data. The extracted features are used to understand the current data trends and the progress of a detected phenomenon without accessing raw sensor data.

Moving Point Indexing for Future Location Query

Nowadays, with a great number of mobile (moving) objects using wireless communication or GPS technology, monitoring systems having the capability of handling anticipated future queries of the mobile objects are literally important. To cater for these applications, the TPR-tree indexing method, which is able to support the future queries, is substantially feasible for the real world. The crucial idea of them is to make the bounding rectangle as a motion function of time. In this paper, we propose an extended method of the TPR-tree, named Horizon TPR-tree or HTPR-tree. The general idea that we make a new motion function of time is to reduce speed of growing sizes of MBR (minimum bounding rectangles). Smaller MBR alleviates overlap problem and increases the effect of query in R-tree based methods. During creating and updating index, motion function influences organizing operations, such as splitting and adjusting. Our experiments show that the HTPR-tree outperforms the TPR-tree for anticipated future queries. In addition, the idea of the proposed method is able to apply to not only the TPR-tree but also variants of the TPR-tree. The proposed method is applicable to vehicle-traffic network systems.

A Moving Object Query Process System for Mobile Recommendation Service

ABSTRACT Recently, much studies for providing mobile users with suitable and useful content services, LBS(Location Based Service) corresponding to the change of users’ location, are actively going on. First and foremost, this is basically owing to the progress of location management technologies such as GPS, mobile communication technology and the spread of personal devices like PDA and the cellular phones. Besides, the research scope of LBS has been changed from vehicle tracking and navigation services to intelligent and personalized services considering the changing information of conditions or environment where the users’ are located. For example, it inputs the information such as heavy traffic, pollution, and accidents. The query languages which effectively search the stored vehicle and environment information have been studied depending on the increase of the information utilization. However, most of existing moving object query languages are not enough to provide a recommendation service for a user, because they can not be tested and evaluated in real world and did not consider changed environment information.In order to retrieve not only a vehicle location and environment condition but also use them, we suggest a moving object query language for recommendation service and implement a moving object query process system for supporting a query language. It can process a nearest neighbor query for recommendation service which considers various attributes such as a vehicle’s location and direction, environment information. It can be applied to location based service application which utilizes the recommended factors based on environmental conditions.Key Words:Spatio-Temporal Database, Moving Object Query Language, GIS, Vehicle Tracking System, Moving Objects

Group Insert Tree with Projection for Handling Vehicle Information Effectively in LBS

The dynamic advances of wireless telecommunication and positioning technologies lead to develop the moving object management system and provide suitable services according to the vehicle locations. To provide these location based services smoothly, it is necessary for an index technique to store and search lots of moving object data rapidly. However, most of the existing bulk loading techniques of moving object indices have not good search performance, because they focus on the effective insert manner. To solve this problem, we propose a GIP(Group Insert tree with Projection) in this paper. The proposed GIP reduces the input cost effectively since the manner stores the moving object location data in a group. Besides, it also reduces the search cost with Projection storage. Then we confirm the effect of the GIP which reduces the insert cost and improves the search performance in a time slice query from the experiment to compare the operation with some existing indices. The proposed GIP would be useful in the environments that require frequent insert as well as search of location information such as a transportation vehicle management and a tour-guide system.