• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.446-450
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• 2006

Recently, the advances on sensor technology increases the study on data processing oriented middleware. Usually, most of middleware uses the naive and delta method for queue management to process data effectively. But such a queue management method it is difficult to support guaranteeing of the requested quality of services because it simply discards the data in a queue when the overflow is occurred. To handle this problem, some methods for minimizing data volumes in a wireless sensor network have been studied, but most of them cause another problem that needs the huge processing time additionally with higher complexity of the proposed algorithm. In this paper, we propose a new method of delta-average queue management by applying the mean value to the exiting delta queue management method using the data differences to handle the problem of queue overflow. The proposed method has lower complexity than the others and increases the QoS of a WSN application by using mean value instead of using data discarding policy. In addition, it is designed to manage a queue effectively by controlling the range of data differences adaptively to the target sensor network environment.

• Journal of KIISE:Information Networking
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• v.37 no.5
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• pp.409-414
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• 2010

The applications based on geographical location are increasing rapidly in wireless sensor networks (WSN). Recently, various localization algorithms have been proposed but the majority of algorithms rely on the specific hardware to measure the distance from the signal sources. In this paper, we propose the Weighted Multiple Rings Localization(WMRL). We assume that each deployed anchor node may periodically emit the successive beacon signals of the different power level. Then, the beacon signals form the concentric rings depending on their emitted power level, theoretically. The proposed algorithm defines the different weighting factor based on the ratio of each radius of ring. Also, If a sensor node may listen, it can find the innermost ring of the propagated signal for each anchor node. Based on this information, the location of a sensor node is derived by a weighted sum of coordinates of the surrounding anchor nodes. Our proposed algorithm is fully distributed and does not require any additional hardwares and the unreliable distance indications such as RSSI and LQI. Nevertheless, the simulation results show that the WMRL with two rings twice outperforms centroid algorithm. In the case of WMRL with three rings, the accuracy is approximately equal to WCL(Weighted Centroid Localization).

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.576-579
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• 2011

Wireless sensor network are widely all over different fields. Because of its distinguished characteristics, we must take account of the factor of energy consumed when designing routing protocol. Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. In BCDCP, all sensors sends data from the CH (Cluster Head) and then to the BS (Base Station). BCDCP works well in a smallscale network however is not preferred in a large scale network since it uses much energy for long distance wireless communication. TBRP can be used for large scale network, but it weakness lies on the fact that the nodedry out of energy easily since it uses multi-hops transmission data to the Base Station. Here, we proposed a routing protocol. A Cluster Based Energy Efficient Tree Routing Protocol (CETRP) in Wireless Sensor Networks (WSNs) to prolong network life time through the balanced energy consumption. CETRP selects Cluster Head of cluster tree shape and uses maximum two hops data transmission to the Cluster Head in every level. We show CETRP outperforms BCDCP and TBRP with several experiments.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).