• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.59-66
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• 2008

GeoSensor network means sensor network infra and related software of specific form monitoring a variety of circumstances over geospatial. And these GeoSensor network is implemented by mixing data stream with spatial attribute, spatial relation. But, until a recent date sensor network system has been concentrated on a store and search method of sensor data stream except for a spatial information. In this paper, we propose a definition of spatial data stream and its join strategy model at GeoSensor network, which combine data stream with spatial data. Spatial data stream s defining in this paper are dynamic spatial data stream of a moving object type and static spatial data stream of a fixed type. Dynamic spatial data stream is data stream transmitted by moving sensor as GPS, while static spatial data stream is generated by joining a data stream of general sensor and a relation with location values of these sensors. This paper propose joins of dynamic spatial data stream and static spatial data stream, and cost models estimating join cost. Finally, we show verification of proposed cost models and performance by join strategy.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.63-72
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• 2009

Korean Land Spatialization Program (KLSP) is an R&D program of the National GIS Project for developing ubiquitous GIS technologies under the control of the M inistry of Land, Transport and Maritime Affairs (M LTM). The first program of the KLSP, which lasts from 2006 to 2012, initiated with $132 million of national funds and $42 million of private matching funds. Aiming to develop the 'Innovation of GIS technology for ubiquitous Korean land', the KLSP consists of five core research projects and one research coordination project to practically utilize and commercialize the results of core research projects. The Korean Land Spatialization Group (KLSG) is planning the KLSP Test-Bed for testing, integrating, and exhibiting the KLSP's outcomes. About 40% of the outcomes are related products to geo-sensor and wireless sensor network (W SN). Thus, interoperable, scalable and web accessible frameworks like an OGC SWE (Open Geospatial Consortium Sensor Web Enablement) are mandatory because some of the products must be connected to each other in a KLSG Test-Bed. The main objective of this paper is to introduce the KLSP Test-Bed and the SWE SOS (Sensor Observation Service) prototype, which is developed for interoperable geo-sensor networks of the KLSP.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.307-308
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• 2008

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.137-142
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• 2017

Recently, along with the development of IoT technology, technologies for wirelessly sensing various data, such as sensor nodes, RFID, CCTV, smart phones, etc., have rapidly developed, and in the field of multiple applications, to utilize sensor network related technology Have been actively pursued in various fields. Therefore, as GeoSensor utilization increases, query processing systems for efficiently processing 2D data such as spatial sensor data are actively researched. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1259-1276
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5B
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• pp.247-256
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• 2007

In this paper, we propose Geographical Back-off Routing (Geo-Back Routing) protocol for wireless sensor networks. Geo-Back uses the positions of nodes, a packet's destination and a optimal back-off time to make the packet forwarding decisions using only source and destination's location information without information about neighbor nodes' location or the number of one hop neighbor nodes. Under the frequent topology changes in WSNs, the proposed protocol can find optimal next hop location quickly without broadcast algorithm for update. In our analysis, Geo-Back's scalability and better performance is demonstrated on densely deployed wireless sensor networks.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.433-435
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• 2007

Geographic wireless sensor networks use position information for Greedy routing. Greedy routing works well in dense network where as in sparse network it may fail and require the use of recovery algorithms. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costlier for resource constrained position based wireless sensor type networks. In the present work, we propose a Void Avoidance Algorithm (VAA); a novel idea based on virtual distance upgrading that allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forward packet using greedy routing only without recovery algorithm. In VAA, the stuck node upgrades distance unless it finds next hop node which is closer to the destination than itself. VAA guarantees the packet delivery if there is a topologically valid path exists. NS-2 is used to evaluate the performance and correctness of VAA and compared the performance with GPSR. Simulation results show that our proposed algorithm achieves higher delivery ratio, lower energy consumption and efficient path.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.18-27
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• 2010

In u-GIS environment, GeoSensor environment requires that dynamic data captured from various sensors and static information in terms of features in 2D or 3D are fused together. GeoSensors, the core of this environment, are distributed over a wide area sporadically, and are collected in any size constantly. As a result, storage space could be exceeded because of restricted memory in DSMS. To solve this kind of problems, a lot of related studies are being researched actively. There are typically 3 different methods - Random Load Shedding, Semantic Load Shedding, and Sampling. Random Load Shedding chooses and deletes data in random. Semantic Load Shedding prioritizes data, then deletes it first which has lower priority. Sampling uses statistical operation, computes sampling rate, and sheds load. However, they are not high accuracy because traditional ones do not consider spatial characteristics. In this paper 'Pre-Filtering based Post Load Shedding' are suggested to improve the accuracy of spatial query and to restrict load shedding in DSMS. This method, at first, limits unnecessarily increased loads in stream queue with 'Pre-Filtering'. And then, it processes 'Post-Load Shedding', considering data and spatial status to guarantee the accuracy of result. The suggested method effectively reduces the number of the performance of load shedding, and improves the accuracy of spatial query.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.