• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.909-937
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• 2011

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decreasing the message complexity. In this paper, a Prediction-based Energy-conserving Approximate StoragE schema (P-EASE) is proposed, which can reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. In addition, focusing on reducing the unnecessary querying messages, P-EASE enables an optimal query algorithm to taking into consideration to query the proper storage node, i.e., the nearer storage node of the centric storage node and local storage node. The theoretical analysis illuminates the correctness and efficiency of the P-EASE. Simulation experiments are conducted under semi-random walk and random waypoint mobility. Compared to EASE, P-EASE performs better at the query error, message complexity, total energy consumption and hotspot energy consumption. Results have shown that P-EASE is more energy-conserving and has higher location precision than EASE.

• 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.

• The KIPS Transactions:PartC
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• v.15C no.1
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• pp.19-30
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• 2008

Sensor nodes are used as a storage space in the data centric storage method for sensor networks. Sensor nodes save the data to the node which is computed by hash table and users also access to the node to get the data by using hash table. One of the problems which the data centric storage method has is that queries from many users who are interested in the popular data could be concentrated to one node. In this case, responses for queries could be delayed and the energy of heavy loaded node could be dissipated fast. This would lead to reduction of network life time. In this paper, ALT, Data Centric Storage based on Adaptive Local Trajectory, is proposed as scalable data centric storage method for sensor network. ALT constructs trajectory around the storage node. The scope of trajectory is increased or decreased based on the query frequency. ALT distributes the query processing loads to several nodes so that delay of response is reduced and energy dissipation is also distributed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2111-2131
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• 2015

Tiered wireless sensor network is a network model of flexibility and robustness, which consists of the traditional resource-limited sensor nodes and the resource-abundant storage nodes. In such architecture, collected data from the sensor nodes are periodically submitted to the nearby storage nodes for archive purpose. When a query is requested, storage nodes also process the query and return qualified data as the result to the base station. The role of the storage nodes leads to an attack prone situation and leaves them more vulnerable in a hostile environment. If any of them is compromised, fake data may be injected into and/or qualified data may be discarded. And the base station would receive incorrect answers incurring malfunction to applications. In this paper, an efficient verifiable top-k query processing scheme called EVTQ is proposed, which is capable of verifying the authentication and completeness of the results. Collected data items with the embedded information of ordering and adjacent relationship through a hashed message authentication coding function, which serves as a validation code, are submitted from the sensor nodes to the storage nodes. Any injected or incomplete data in the returned result from a corresponded storage node is detected by the validation code at the base station. For saving communication cost, two optimized solutions that fuse and compress validation codes are presented. Experiments on communication cost show the proposed method is more efficiency than previous works.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.315-323
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• 2010

In Wireless Sensor Networks (WSNs), various Data-Centric Storages (DCS) schemes have been proposed to store the collected data and to efficiently process a query. A DCS scheme assigns distributed data regions to sensor nodes and stores the collected data to the sensor which is responsible for the data region to process the query efficiently. However, since the whole data stored in a node will be lost when a fault of the node occurs, the accuracy of the query processing becomes low, In this paper, we propose an in-network aggregation query processing method that assures the high accuracy of query result in the case of data loss due to the faults of the nodes in the DCS scheme. When a data loss occurs, the proposed method creates a compensation model for an area of data loss using the linear regression technique and returns the result of the query including the virtual data. It guarantees the query result with high accuracy in spite of the faults of the nodes, To show the superiority of our proposed method, we compare E-KDDCS (KDDCS with the proposed method) with existing DCS schemes without the data-loss correction method. In the result, our proposed method increases accuracy and reduces query processing costs over the existing schemes.

• 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.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1035-1043
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• 2021

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.3-7
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• 2009

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query but supports various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.2
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• pp.103-110
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• 2011

It is necessary to accurate and efficient management for measured digital data from sensors in u-health system. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using backpropagation algorithm. As a result, we obtained to efficient result about 18.3% reduction rate of database using 14,324 data sets.