• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1519-1527
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• 2007

In order to combat the security threats that sensor networks are exposed to, a cryptography protocol is implemented at sensor nodes for point-to-point encryption between nodes. Given that nodes have limited resources, symmetric cryptography that is proven to be efficient for low power devices is implemented. Data protection is integrated into a sensor's packet by the means of symmetric encryption with the Dragon stream cipher and incorporating the newly designed Dragon-MAC Message Authentication Code. The proposed algorithm was designed to employ some of the data already computed by the underlying Dragon stream cipher for the purpose of minimizing the computational cost of the operations required by the MAC algorithm. In view that Dragon is a word based stream cipher with a fast key stream generation, it is very suitable for a constrained environment. Our protocol regarded the entity authentication and message authentication through the implementation of authenticated encryption scheme in wireless sensor nodes.

• 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.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.129-135
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• 2010

Wireless sensor networks (WSNs) are an emerging technology and offers economically viable monitoring solution to many challenging applications. However, deploying new technology in hostile environment, without considering security in mind has often proved to be unreasonably unsecured. Apparently, security techniques face many critical challenges in WSNs like data security and secrecy due to its hostile deployment nature. In order to resolve security in WSNs, we propose a novel and efficient secure framework called TriSec: a secure data framework for wireless sensor networks to attain high level of security. TriSec provides data confidentiality, authentication and data integrity to sensor networks. TriSec supports node-to-node encryption using PingPong-128 stream cipher based-privacy. A new PingPong-MAC (PP-MAC) is incorporated with PingPong stream cipher to make TriSec framework more secure. PingPong-128 is fast keystream generation and it is very suitable for sensor network environment. We have implemented the proposed scheme on wireless sensor platform and our result shows their feasibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1546-1564
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• 2012

Pattern-based query processing has not attracted much attention in wireless sensor network though its counterpart has been studied extensively in data stream. The methods used for data stream usually consume large memory and much energy. This conflicts with the fact that wireless sensor networks are heavily constrained by their hardware resources. In this paper, we use piece wise representation to represent sensor nodes' collected data to save sensor nodes' memory and to reduce the energy consumption for query. After getting data stream's and patterns' approximated line segments, we record each line's slope. We do similar matching on slope sequences. We compute the dynamic time warping distance between slope sequences. If the distance is less than user defined threshold, we say that the subsequence is similar to the pattern. We do experiments on STM32W108 processor to evaluate our strategy's performance compared with naive method. The results show that our strategy's matching precision is less than that of naive method, but our method's energy consumption is much better than that of naive approach. The strategy proposed in this paper can be used in wireless sensor network to process pattern-based queries.

• 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.

• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.17-26
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• 2008

This paper proposes an efficient caching mechanism appropriate for the integrated RFID middleware which can integrate wireless sensor networks (WSNs) and RFID (radio frequency identification) systems. The operating environment of the integrated RFID middleware is expected to face the situations of a significant amount of data reading from RFID readers, constant stream data input from large numbers of autonomous sensor nodes, and queries from various applications to history data sensed before and stored in distributed storages. Consequently, an efficient middleware layer equipping with caching mechanism is inevitably necessary for low latency of request-response while processing both data stream from sensor networks and history data from distributed database. For this purpose, the proposed caching mechanism includes two optimization methods to reduce the overhead of data processing in RFID middleware based on the classical cache implementation polices. One is data stream cache (DSC) and the other is history data cache (HDC), according to the structure of data request. We conduct a number of simulation experiments under different parameters and the results show that the proposed caching mechanism contributes considerably to fast request-response times.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.45-57
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• 2007

Recently, the development of sensor devices has accelerated researches on advanced technologies like Wireless Sensor Networks. Moreover, spatial sensors using GPS lead to the era of the Ubiquitous Computing Environment which generally uses spatial information and non-spatial information together. In this new era, a real-time processing system for spatial sensor data is essential. In this reason, new data processing systems called DSMS(Data Stream Management System) are being developed by many researchers. However, since most of them do not support geometry types and spatial functions to process spatial sensor data, they are not suitable for the Ubiquitous Computing Environment. For these reasons, in this paper, we designed and implemented a spatial DSMS by extending STREAM which stands for STanford stREam datA Manager, to solve these problems. We added geometry types and spatial functions to STREAM in order to process spatial sensor data efficiently. In addition, we implemented a Spatial Object Manager to manage shared spatial objects within the system. Especially, we implemented the Simple Features Specification for SQL of OGC for interoperability and applied algorithms in GEOS to our system.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.11-13
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• 2007

There has been much interest in stream query processing. Various index techniques and advanced join techniques have been proposed to efficiently process data stream queries. Previous proposals support rapid and advanced response to the data stream queries. However, the amount of data stream is increasing and the data stream query processing needs more speedup than before. In this paper, we proposed novel query processing techniques for large number of incoming documents stream. We proposed Dissection Technique for efficient query processing in the data stream environment. We focused on the dissection technique in join query processing. Our technique shows efficient operation performance comparing with the other proposal in the data stream. Proposed technique is applied to the sensor network system and XML database.

• KSCI Review
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• v.14 no.2
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• pp.199-204
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• 2006

The sensor network is a wireless network of the sensor nodes which sensing, computation and communication ability. Each sensor nodes create the data items by sensor nodes above one. Like this feature, the sensor network is similar to distributed data base system. The sensor node of the sensor network is restricted from the power and the memory resources is the biggest weak point and is becoming the important research object. In this paper, We try to see efficient sensor data stream management method and efficient query processing method under the restricted sensor network environment.