• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.2
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• pp.1-9
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• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.923-926
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• 2010

A sensor network system processes user queries using the recent field data collected by each sensor node. To process user queries, the system propagates the queries to the specific sensor nodes which have the relevant data and aggregates the results of the queries. However, if continuous queries are processed by the existing scheme, the system has the problem where the queries are propagated repeatedly. In this paper, we propose the query processing scheme to process the continuous queries over the sensor streaming data. To do this, each sensor node builds its own query index on its node. And, we present the scheme to deal with the unexpected data rising on the sensor node.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.24-33
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• 2009

Stream data, gathered from ubiquitous sensor networks, change continuously over time. Because they have quite different characteristics from traditional databases, we need new techniques for storing and querying/analyzing these stream data, which are research issues recently emerging. In this research, we implemented a storage manager gathering stream data and storing them into databases, which are sampled continuously from sensor networks. The storage manager cleans faulty data occurred in mobile sensors and it also reduces the size of stream data by merging repeatedly-sampled values into one and by employing the tilted time frame which stores stream data with several different sampling rates. In this research furthermore, we measured the performance of the storage manager in the context of a sensor network monitoring fires of a building. The experimental results reveal that the storage manager reduces significantly the size of storage spaces and it is effective to manage the data stream for real applications monitoring buildings and their fires.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.15-16
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• 2007

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.82-91
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• 2010

Data compression technique is traditional and effective to reduce network traffic. Generally, sensor data exhibit strong correlation in both space and time. Many algorithms have been proposed to utilize these characteristics. However, each sensor just utilizes neighboring information, because its communication range is restrained. Information that includes the distribution and characteristics of whole sensor data provide other opportunities to enhance the compression technique. In this paper, we propose an orthogonal approach for compression algorithm based on a novel feedback diffusion algorithm in sensor networks. The base station or a super node generates the Huffman code for compression of sensor data and broadcasts it into sensor networks. Every sensor that receives the information compresses their sensor data and transmits them to the base station. We define this approach as feedback-diffusion. In order to show the superiority of our approach, we compare it with the existing aggregation algorithms in terms of the lifetime of the sensor network. As a result, our experimental results show that the whole network lifetime was prolonged by about 30%.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.95-101
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• 2019

Sensor application systems for remote monitoring and control are required, such as the establishment of databases and IoT service servers, to process data being transmitted and received through radio communication modules, controllers and gateways. This paper designs and implements database server, IoT service server, data processing middleware and IoT management system for IoT based services based on the controllers, communication modules and gateway middleware platform developed. For this, we firstly define the specification of the data packet and control code for the information classification of the sensor application system, and also design and implement the database as a separate server for data protection and efficient management. In addition, we design and implement the IoT management system so that functions such as status information verification, control and modification of operating environment information of remote sensor application systems are carried out. The implemented system can lead to efficient operation and reduced management costs of sensor application systems through site status analysis, setting operational information, and remote control and management.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.189-197
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• 2009

Top-k queries are important to many wireless sensor applications. Conventional Top-k query processing algorithms install a filter at each sensor node and suppress unnecessary sensor updates. However, they have some drawbacks that the sensor nodes consume energy extremely to probe sensor reading or update filters. Especially, it becomes worse, when the variation ratio of top-k result is higher. In this paper, we propose a novel Top-k query processing algorithm for energy-efficiency. First, each sensor determines its priority as the order of data gathering. Next, sensor nodes that have higher priority transmit their sensor readings to the base station until gathering k sensor readings. In order to show the superiority of our query processing algorithm, we simulate the performance with the existing query processing algorithms. As a result, our experimental results show that the network lifetime of our method is prolonged largely over the existing method.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.429-440
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• 2005

As rapid improvement in electronic technologies makes sensor hardware more powerful and capable, the application range of sensor networks Is getting to be broader. The main purpose of sensor networks is to monitor the phenomena in interesting regions (e.g., factory warehouses, disaster areas, wild fields, etc) and return required data. The k Nearest Neighbor (KNN) query that finds k objects which are geographically close to the given point is an Important application in sensor networks. However, most previous approaches are either seem to be impractical or are not energy-efficient in resource-limited sensor networks. In this paper. we propose an efficient KNN query processing method in sensor networks. In the proposed method, we dynamically increase searching boundary, if necessary, and traverse nodes inside the boundary until finding k nearest neighbors. Since only the representative sensor nodes are visited, our algorithm reduces a number of messages. We show thorough experiments that the proposed method performs better than the existing method in various network environments.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.35-44
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• 2008

In this study, to establish a web-based bridge monitoring system with wireless sensor network environment, we constructed microminiaturized sensor based wireless communication techniques and micro processing, databases for data combination and administration, variable control programs and processors for transferring data by internet. Then those data are measured and analyzed by the constructed bridge monitoring system with wireless sensors. To evaluate the practicability of the bridge monitoring system with wireless sensor, we compared the values measured in the tests with wire sensor under same conditions. The results show that the trend of the data obtained from the monitoring systems with wire sensors and wireless sensors was very similar but the some lost data in the communication process with wireless sensor network environment. And through laboratory and field tests, the effectiveness and the applicability of the proposed methods were verified.

• 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.