• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1756-1776
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• 2012

Wireless sensor networks (WSNs) are likely to be more prevalent as their cost-effectiveness improves. The spectrum of applications for WSNs spans multiple domains. In environmental sciences, in particular, they are on the way to become an essential technology for monitoring the natural environment and the dynamic behavior of transient physical phenomena over space. Existing sensor network query processors (SNQPs) have also demonstrated that in-network processing is an effective and efficient means of interaction with WSNs for performing queries over live data. Inspired by these findings, this paper investigates the question as to whether spatio-temporal and historical analysis can be carried over WSNs using distributed query-processing techniques. The emphasis of this work is on the spatial, temporal and historical aspects of sensed data, which are not adequately addressed in existing SNQPs. This paper surveys the novel approaches of storing the data and execution of spatio-temporal and historical queries. We introduce the challenges and opportunities of research in the field of in-network storage and in-network spatio-temporal query processing as well as illustrate the current status of research in this field. We also present new areas where the spatio-temporal and historical query processing can be of significant importance.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.875-885
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• 2010

Recently, the role of Ubiquitous Sensor Network(USN) has been considered to be essential for supporting the near future Network Centric Warfare(NCW) and Tactical Information Communication Network(TICN). In this paper, we explore a set of data storage methods(external storage, local storage and data storage) and query processing methods in WSN. In particular, we focus on analyzing a novel data structure for supporting the local storage method, named the partial ordered tree(POT). The main idea behind POT is that sensor readings are usually correlated with the physical spatial domain. With the help of POT, only a small portion of sensor nodes participate in query processing tasks, and thus network lifetime is greatly increased. Through a series of simulation experiments, we demonstrate that the POT based local storage method clearly outperforms the existing data storage methods in terms of the energy-efficiency, which directly affects the network lifetime, for processing exact match queries, range queries and top-k queries.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.23-28
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• 2006

In this paper, a power-aware query processing using optimized distributed R-tree in a sensor network is proposed. The proposed technique is a new approach for processing range queries that uses spatial indexing. Range queries are most often encountered under sensor networks for computing aggregation values. The previous work just addressed the importance but didn't provide any efficient technique for processing range queries. A query processing scheme is thus designed for efficiently processing them. Each node in the sensor network has the MBR of the region where its children nodes and the node itself are located. The range query is evaluated over the region which intersects the geographic location of sensors. It ensures the maximum power savings by avoiding the communication of nodes not participating over the evaluation of the query.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2224-2229
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• 2012

Since sensor data are inserted into a data set continuously, continuous queries should be evaluated for searching data. To processing the continuous queries, it is required to build a query index on each sensor node and to transmit result data appropriate for query predicates. However, if query predicates are transferred to all sensor nodes, massive messages are required. In this paper, we propose the node decision scheme using the sensor node decision tree in order to diminish messages. The entry of a leaf node in the node decision tree represents a sensor node and defines the data region of the sensor node. When a user query is issued, sensor nodes are decided by intersecting between data regions of the tree with the query predicates of the user query, and then the query predicates are transmitted to the selected sensor nodes. We also implement the proposed sensor node decision tree and evaluate the experiments for the tree.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.188-200
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• 2006

Once a continuous query, which is commonly used in sensor networks, is issued, the query is executed many times with a certain interval and the results of those query executions are collected to the base station. Since this comes many communication messages continuously, it is important to reduce communication cost for collecting data to the base station. In sensor networks, in-network processing reduces the number of message transmissions by partially aggregating results of an aggregate query in intermediate nodes, or merging the results in one message, resulting in reduction of communication cost. In this paper, we propose a routing tree for sensor nodes that qualify the given query predicate, called the query specific routing tree(QSRT). The idea of the QSRT is to maximize in-network processing opportunity. A QSRT is created seperately for each query during dissemination of the query. It is constructed in such a way that during the collection of query results partial aggregation and packet merging of intermediate results can be fully utilized. Our experimental results show that our proposed method can reduce message transmissions more than 18% compared to the existing one.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.685-692
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• 2007

Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multi-functional sensors. A typical wireless sensor network (WSN) consists of a large number of sensor nodes that can measure and process data while communicating through wireless channels. In this paper, we propose a hybrid query processing (HQP) algorithm for user queries submitted to the WSN. Unlike previous algorithms that consider continuous queries only, HQP supports both continuous queries and ad-hoc queries. Specially. HQP tries to reduce energy consumption of ad-hoc queries by using query results cached at each sensor node which are created during the execution of the previous continuous query. HQP can also exploit a trade-off between energy consumption and data accuracy. We evaluate the performance of HQP under a variety of WSN configurations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.183-186
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• 2012

To process continuous queries on a sensor network, it is required to transfer query predicates and build a query index on each sensor node. However, if we transfer query predicates to all sensor nodes, it makes the number of messages for query predicates increase. In this paper, we propose the scheme to construct the tree based relationship structure using data region of the sensor node and select the target nodes to transfer query predicates. we also implement the tree based relationship structure and measure the number of messages for sending predicates.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1234-1238
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• 2010

In recent, there have been done many studies on applications that monitor the information of mobile objects using Wireless Sensor Networks (WSN). A density query that finds out an area spread by density that a target object requires in the whole sensing field is a field of object monitoring applications. In this paper, we propose a novel homogeneous network-based in-network density query processing scheme that significantly reduces query processing costs and assures high accuracy. This scheme is based on the possibility-based expected region selection technique and the result compensation technique in order to enhance the accuracy of the density query and to minimize its energy consumption. To show the superiority of our proposed scheme, we compare it with the existing density query processing scheme. As a result, our proposed scheme reduces about 92% energy consumption for query processing, while its network lifetime increases compared to the existing scheme. In addition, the proposed scheme guarantees higher accuracy than the existing scheme in terms of the query result.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.149-156
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• 2010

As following the development of the USN technology, sensor node used in sensor network has capability of quick data process and storage to support efficient network configuration is enabled. In addition, tree-based structure was transformed to cluster in the construction of sensor network. However, query processing based on existing tree structure could be inefficient under the cluster-based network. In this paper, we suggest energy efficient query processing mechanism using filtering through data attribute classification in cluster-based sensor network. The suggestion mechanism use advantage of cluster-based network so reduce energy of query processing and designed more intelligent query dissemination. And, we prove excellence of energy efficient side with MATLab.