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Since a migration method of the mobile agent is a factor that affects the overall performance of the entire distributed system, it is necessary to find efficient migration methods of the mobile agent within the sensor network and to collect and store data related to various components(server, sink and sensor node) of the sensor network, thereby providing consistent naming services. Accordingly, this paper presents design and implementation of MetaTable that is divided into MetaData where information on the sensor data server is stored and SubMetaData where various types of information on sink nodes and data on sensor nodes connected with the sink nodes is stored. Moreover, the paper also presented the implementation of forward migration of an active rule mobile agent applying the naming method based on RMI that used the meta_table and proposed the possibility of constructing efficient sensor network application environment. In this paper, for registration, release and retrieval methods suitable for new sensor network environment, we designed and implemented the naming agent by applying J2EE model based on RMI-IIOP(Internet Inter-ORB Protocol) technique. 키워드에이전트 미들웨어, 이동에이전트, 정 방향 이주, 메타데이터

For the activeness and autonomy of a sensor network, the efficient migration method of a mobile agent and the consistent naming services are the required components of a sensor network. Accordingly, this paper presented the implementation of backward migration of an active rule mobile agent applying the naming method based on RMI that used the meta_table including the informations about the components of a sensor network. This study implemented based on the extension of the forward migration [12], and the results of the various experiments present the efficacy of mobile agent middleware system and the possibility of constructing efficient sensor network application environment. And, the results of this study are able to enhance the adaptability on dynamic changes of environment in sensor network application development. 키워드센서네트워크 미들웨어, 능동규칙 이동에이전트, 메타데이터, 역 방향 이주

In this article, we propose a novel method that can measure the similarity of FoV-tagged videos in two dimensions. Recently many researchers have focused on measuring the similarity of FoVtagged videos. The similarity measurement of FoV-tagged videos plays an important role in various societal applications, including urban road networks, traffic, and geographic information systems. Our preliminary work introduced the Largest Common View Subsequences (LCVS) algorithm for computing the similarity of FoV-tagged videos. However, LCVS requires a high computational cost for calculating common viewable regions between two FoV-tagged videos. To handle this limitation, we propose the largest View Vector Subsequence (VVS) algorithm for reducing the computational cost of FoV-tagged videos. VVS uses the movement distances and the viewable direction distances to support the simplified vector-based similarity measurement. We demonstrate the superiority of our approach by comparing it with the Longest Common Subsequences (LCSS) and our prior work (LCVS). Our experimental results show that VVS outperforms the prior work in terms of the computational cost and enhances the versatility and stability of the similarity measurement.INDEX TERMS FoV-tagged video, similarity measurement, recommendation system.