• Spatial Information Research
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• v.20 no.1
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• pp.59-69
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• 2012

For the USN(Ubiquitous Sensor Network) environment which generally uses spatial sensor data as well as aspatial sensor data, a sensor database system to manage these sensor data is essential. In this reason, some sensor database systems such as TinyDB, Cougar are being developed by many researchers. However, since most of them do not support spatial data types and spatial operators to manage spatial sensor data, they have difficulty in processing spatial sensor data. Therefore, this paper developed a spatial sensor database system by extending TinyDB. Especially, the system supports spatial data types and spatial operators to TinyDB in order to manage spatial sensor data efficiently and provides the memory management function and the filtering function to reduce the system overload caused by sensor data streams. Lastly, we compared the processing time, accuracy, and memory usage of the spatial sensor database system with those of TinyDB and proved its superiority through the performance evaluation.

• Spatial Information Research
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• v.19 no.3
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• pp.95-106
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• 2011

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation(BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a Quad-tree, and then processes aggregate queries in parallel for each cell region according to routing. And it sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.59-64
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• 2007

데이타 중심 센서 네트워크에서는 측정된 데이타의 값에 따라 데이타를 저장하는 센서 노드가 결정되기 때문에 같은 값을 갖는 데이타가 빈번하게 발생하면 이를 저장하는 센서 노드에 부하가 집중되어 에너지가 빠르게 고갈되는 문제가 있다. 또한 센서 네트워크가 확장되면 데이타 저장 및 질의 처리시 목적 센서 노드로의 라우팅 거리가 멀어져 통신 비용이 증가되는 문제가 있다. 그러나 기존 연구들은 데이타 저장의 효율적인 관리에만 치우쳐 이와 같은 문제를 효율적으로 해결하지 못하고 있다. 본 논문에서는 이러한 문제를 해결하기 위해 비균등 네트워크 분할(Non-Uniform Network Spilt: NUNS) 기법을 제안한다. NUNS는 센서 네트워크를 센서 노드 개수와 분할된 영역 크기 차이가 최소가 되도록 비균등 크기의 Partition으로 분할하고 각 Partition에서 발생한 데이타를 그 Partition 내의 센서 노드가 저장 관리함으로써 센서 노드의 데이타 저장 부하를 분산시키고, 센서 네트워크의 확장에 따른 통신 비용을 줄인다. 그리고 NUNS는 각 Partition을 분할된 영역 크기 차이가 최소가 되도록 센서 노드 개수만큼 비균등하게 Zone으로 분할함으로써 센서 노드가 없는 Zone으로 인해 센서 노드에 부하가 집중되는 것을 막고 불필요한 라우팅 비용을 줄인다.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.306-313
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• 2009

Top-k queries are issued to find out the highest (or lowest) readings in many sensor applications. Many top-k query processing algorithms are proposed to reduce energy consumption; FILA installs a filter at each sensor node and suppress unnecessary sensor updates; PRIM allots priorities to sensor nodes and collects the minimal number of sensor reading according to the priorities. However, if many sensor reading converge into the same range of sensor values, it leads to a problem that many false positives are occurred. In this paper, we propose a cluster-based approach to reduce them effectively. Our proposed algorithm operates in two phases: top-k query processing in the cluster level and top-k query processing in the tree level. False positives are effectively filtered out in each level. Performance evaluations show that our proposed algorithm reduces about 70% false positives and achieves about 105% better performance than the existing top-k algorithms in terms of the network lifetime.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.11
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• pp.820-829
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• 2006

Wireless sensor networks consisting of hundreds to thousands of nodes are expected to be increasingly deployed in coming years, as they enable reliable monitoring and analysis of physical worlds. These networks have unique features that are very different from traditional networks, e.g., the numerous numbers of nodes, limitation in power, processing, and memory. Due to these unique features of wireless sensor networks, sensor data management including querying becomes a challenging problem. Furthermore, due to wide popularization of the Internet and its facility in use, it is generally accepted that an unattended network can be efficiently managed and monitored over the Internet. In particular, in order to more efficiently query and manage data in a sensor network. in this paper, the architecture of a sensor gateway including web-based query server is presented and its implementation detail is illustrated. The presented web-based gateway is largely divided into two important parts: Internet part and sensor network part. The sensor network part plays an important role of handling a variety of sensor networks, including flat or hierarchical network architecture, by using internally layered architecture for efficiently querying and managing data in a sensor network. In addition, the Internet part provides a modular gateway function for favorable exchange between the sensor network and Internet.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.32-41
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• 2006

In data-centric networks, various data items, such as temperature, humidity, etc. are sensed and stored in sensor nodes. As these attributes are mostly scalar values and inter-related, multi-dimensional range queries are useful. To process multi-dimensional range queries efficiently in data-centric storage, data addressing is essential. The Previous work focused on efficient query processing without considering overall network lifetime. To prolong network lifetime and support multi-dimensional range queries, we propose a dynamic data distribution method for multi-dimensional data, where data space is divided into equal-sized regions and linearized by using Hilbert space filling curve.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.117-130
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• 2006

With the rapid development of technologies related to Ubiquitous Sensor Network (USN), sensors are being utilized in various application areas. In general, the data sensed by each sensor node on ubiquitous sensor networks are stored into the central server for efficient search. Because update is delayed to reduce the cost of update in this environment, uncertain data can be stored in the central server. In addition, Uncertain data make query processing produce wrong results in the central server. Thus, this paper examines how to process uncertain data in ubiquitous sensor networks and suggests a new index for efficient processing of uncertain data. The index reduces the cost of update by delaying update in uncertainty areas. Uncertainty areas are areas where uncertain data are likely to exist. In addition, it solves the problem of low accuracy in search resulting from update delay by delaying update only for specific update areas. Lastly, we analyze the performance of the index and prove the superiority of its performance by comparing its performance evaluation.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.35-50
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• 2007

In a data centric sensor network, a sensor node to store data is determined by the measured data value of each sensor node. Therefore, if the same data occur frequently, the energy of the sensor node to store the data is exhausted quickly due to the concentration of loads. And if the sensor network is extended, the communication cost for storing data and processing queries is increased, since the length of the routing path for them is usually in the distance. However, the existing researches that generally focus on the efficient management of data storing can not solve these problems efficiently. In this paper, we propose a NUNS(Non-Uniform Network Split) method that can distribute loads of sensor nodes and decrease the communication cost caused by the sensor network extension. By dividing the sensor network into non-uniform partitions that have the minimum difference in the number of sensor nodes and the splitted area size and storing the data which is occurred in a partition at the sensor nodes within the partition, the NUNS can distribute loads of sensor nodes and decrease the communication cost efficiently. In addition, by dividing each partition into non-uniform zones that have the minimum difference in the splitted area size as many as the number of the sensor nodes in the partition and allocating each of them as the processing area of each sensor node, the NUNS can protect a specific sensor node from the load concentration and decrease the unnecessary routing cost.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.52-54
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• 2006

최근 데이타 중심 저장 방식의 센서 네트워크에서 다차원 범위 질의를 위한 인덱스들이 제시되고 있다. 기존에 제시된 다차원 범위 질의 인덱스는 일반적으로 다차원 속성 도메인과 센서 노드의 공간 도메인을 직접 매핑하여 데이타를 관리하는 구조로 되어있다. 그러나, 이러한 구조는 센서 노드의 공간 도메인을 정적으로 분할하기 때문에 센서 노드를 포함하지 않는 영역이 생성되어 데이타 저장 및 질의 처리에서 불필요한 통신이 발생하는 문제가 있다. 본 논문은 이러한 문제를 해결하기 위해 센서 노드의 공간 도메인이 센서 노드를 포함하도록 센서 네트워크 영역을 동적으로 분할하는 다차원 범위 질의 인덱스를 제안한다. 제안하는 인덱스는 센서 노드의 위치에 따라 센서 네트워크 영역을 동적으로 분할하여 데이타 저장 및 질의 처리시 목적 영역으로의 라우팅 경로를 최적화한다. 그리고, 분할된 영역은 모두 센서 노드를 포함함으로 센서 노드에서 발행하는 저장 부하를 분산시켜 전체 네트워크에서 발생하는 전체 통신비용을 줄인다. 실험 결과 제안한 인덱스는 DIM보다 전체 센서 네트워크와 hotspot의 통신비용에서 각각 최대 35%, 60%의 성능 향상을 보였다.