Yonsik Lee

Optimal Migration Path Searching Using Path Adjustment and Reassignment for Mobile Agent

In traditional agent systems, if various and large quantities of tasks are required from users, network traffic overload and extended processing time are resulted by continuous increase in the sizes of agents. Also, if an agent migrates to the destination node following user-specified passive routing schedules, it cannot actively cope with some accidental situations, such as communication loss or node obstacles, and requires high cost for node traversals. Therefore, more efficient migration techniques for mobile agents that can solve the excessive network traffic problems are required to guarantee the reliability of migration and reduce traversal task processing time. In this paper, we present the migration method of a mobile agent that uses network traffic perception instead of a user-specified passive routing scheduling system for an automatic migration. Also, we formulate a network traffic measurement model to guarantee the optimality of selected paths, and propose path adjustment and reassignment techniques to ensure the reliability of migrations on distributed nodes and reduce traversal task processing time. The proposed migration method aims at minimizing the required time for traversal processing to all destination nodes in the network by supporting active and automatic routing scheduling in optimal migration path searches. And the purpose of it, as well as path adjustment and reassignment techniques, is to guarantee reliable, stable and fast migration of mobile agents.

Active Rule System Based on User`s Emotional Margin for Power Saving Control

Abstract In this paper, we propose the active rule system applying with emotional margin for power saving control. The proposed system in this paper is a part of the system which derives smart power saving by adjusting the illuminance using active rules within compromising the user’s emotion. For this, we set the specific range of standard illuminance and the lower bound of user’s emotional margin of illuminance based on measurements and analysis, and use these data in design of active rules. And then, we design and implement the active rule system using mobile agent. The mobile agent in the proposed system migrates to the destination sensor nodes, acquires and transmits sensor data according to the purpose and needs through the active rules, and directly executes the actions corresponding to the optional events(changed sensor data and/or time etc.). And then, we show the potential applicability of the proposed active rule system in various active sensor network applications through the interaction with the rule base and mobile sensor network middleware system.

The migration of mobile agent using the object replication

The migration method of a mobile agent affects the entire performance of the distributed system. Most of the existing migration methods have the slow-witted structures due to the fixed order migration that cause the subsequent accumulation of the task results. They can induce various problems such as the failure of host, network obstacles, service absence, and increased traffic resulting in the inefficient operation of the mobile agent. In this paper, we propose the new migration method using the object replication to solve these problems. The proposed model is simply composed of three components: the Mobile Agent Client, Naming Agent Server, and Mobile Agent Server. The Mobile Agent Client performs the object replication according to the furnished object information (the number of replication) from the Naming Service Management Module. The main function of the naming service given by the naming agent is to provide information regarding the object replication and the location transparency of servers in the distributed network environment. We also suggest the replication algorithm and the migration model of the replicated mobile agent object using the naming service. The experiments analyzing the performance of our method, the sequential migration method, and the Locker pattern method are performed and the results are compared to guarantee the effectiveness of our approach.

A Method for Optimal Moving Pattern Mining using Frequency of Moving Sequence

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users` requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object`s location attribute in consideration of topological relationship between the object`s location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

Design and Implementation of the Active Multi-Agent Middleware for the Sensor Network Application

In this paper, we suggest the active multi-agent middleware for the sensor network application. For this, firstly we design and implement the active rule based mobile agent middleware. The mobile agent in the proposed system visits the destination sensor nodes according to the migration list offered by the meta table in the name space of the naming agent, acquires and transmits sensor data according to the purpose and needs through the active rules, and directly executes the actions corresponding to the optional events(changed sensor data and/or time etc.). And then, we show the potential applicability of the active rule based mobile agent middleware in various active sensor networks through the interaction with the rule base system and context database system.

An Extended TIP Technique for Android Platform

TIP technology enables navigation of the internal contents of images by extracting geometry information from two-dimensional drawing or a picture and generating three-dimensional effects from extracted information. The technology can be applied to a variety of practical fields including game, entertainment, education, public relations and so on. This paper proposes extended application of TIP technology and realization method for smart devices using OpenGL ES Library for Android platform. Considering problems associated with a foreground object extraction, the proposed method uses vanishing points chosen by the user to facilitate more realistic scene configuration. Then, method acquires three-dimensional background model using OpenGL ES Library, develops three-dimensional virtual space and enables image navigation via camera viewpoint conversion. The experimental image is made on Android 2.1 and OpenGL ES 1.0 using the image taken on devices built on the Android platform. Thus, the proposed technology can be implemented to various smart devices built on the Android platform at lower cost and in less time.

Location Generalization of Moving Objects for the Extraction of Significant Patterns

Abstract In order to provide the optimal location based services such as the optimal moving path search or the scheduling pattern prediction, the extraction of significant moving pattern which is considered the temporal and spatial properties of the location-based historical data of the moving objects is essential. In this paper, for the extraction of significant moving pattern we propose the location generalization method which translates the location attributes of moving object into the spatial scope information based on R * -tree for more efficient patterning the continuous changes of the location of moving objects and for indexing to the 2-dimensional spatial scope. The proposed method generates the moving sequences which is satisfied the constraints of the time interval between the spatial scopes using the generalized spatial data, and extracts the significant moving patterns using them. And it can be an efficient method for the temporal pattern mining or the analysis of moving transition of the moving objects to provide the optimal location based services.Key Words : Moving Object, Moving Pattern, Location Generalization, Extraction of Significant Moving Pattern

The metatable of naming agent for the naming service of sensor network

The node selection for the migration of a mobile agent depends on the content of the referenced metadata in the naming space. The reliability of the migrated information is determined by using the operation, cooperation, and metadata of individual agent in multi-agent system. In this paper, we propose the metatable and its management method that supports more precise and efficient migration of the mobile agent and sensor network applications. For the service, we develop the metatable consists of the reference object, name, URL, keyword and pointer to sink table. It also includes the add-on information related to the sink node (i.e. sink number, sink information and sink URL) for the sensor network application. Furthermore, we suggest the algorithms for generating and updating the metatable and acquiring the server list for the migration. The results of experiments are compared with non-applied metadata to guarantee the reliability of our approach.

Precision Analysis of the STOMP(FW) Algorithm According to the Spatial Conceptual Hierarchy

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

The Efficient Spatio-Temporal Moving Pattern Mining using Moving Sequence Tree

ABSTRACT Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of 83%∼93% rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.Keywords:Moving Object, Spatio-Temporal Pattern Mining, Data Generalization, Moving Sequence Tree