• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.212-221
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• 2009

This paper proposes a novel centralized clustering algorithm, "RLCA : Relative Location based Clustering Algorithm for Wireless Sensor Networks," for constructing geographically well-distributed clusters in general WSNs. RLCA does not use GPS and controls selection-rate of cluster-head based on distances between sensors and BS. We empirically show that RLCA's energy efficiency is higher than LEACH's.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.911-917
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• 2009

This paper introduces a sequential clustering technique as a tool for an effective grouping of transmission systems. The interconnected network system retains information about the location of each line. With this information, this paper aims to carry out initial clustering through the transmission usage rate, compare the similarity measures of regional information with the similarity measures of location price, and introduce the techniques of the clustering method. This transmission usage rate uses power flow based on congestion costs and similarity measurements using the FCM(Fuzzy C-Mean) algorithm. This paper also aims to prove the propriety of the proposed clustering method by comparing it with existing clustering methods that use the similarity measurement system. The proposed algorithm is demonstrated through the IEEE 39-bus RTS and Korea power system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2961-2975
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• 2022

The Location-Based Service (LBS) is one of the most well-known services on the Internet. Positioning is the primary association with LBS services. This study proposes an intelligent LoRa-based positioning system, called AI@LBS, to provide accurate location data. The fingerprint mechanism with the clustering algorithm in unsupervised learning filters out signal noise and improves computing stability and accuracy. In this study, data noise is filtered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, increasing the positioning accuracy from 95.37% to 97.38%. The problem of data imbalance is addressed using the SMOTE (Synthetic Minority Over-sampling Technique) technique, increasing the positioning accuracy from 97.38% to 99.17%. A field test in the NTUST campus (www.ntust.edu.tw) revealed that AI@LBS system can reduce average distance error to 0.48m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.665-683
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• 2011

Wireless sensor networks (WSN) consist of a large amount of sensor nodes distributed in a certain region. Due to the limited battery power of a sensor node, lots of energy-efficient schemes have been studied. Clustering is primarily used for energy efficiency purpose. However, clustering in WSNs faces several unattained issues, such as ensuring connectivity and scheduling inter-cluster transmissions. In this paper, we propose a location-based spiral clustering (LBSC) algorithm for improving connectivity and avoiding inter-cluster collisions. It also provides reliable location aware routing paths from all cluster heads to a sink node during cluster formation. Proposed algorithm can simultaneously make clusters in four spiral directions from the center of sensor field by using the location information and residual energy level of neighbor sensor nodes. Three logical addresses are used for categorizing the clusters into four global groups and scheduling the intra- and inter-cluster transmission time for each cluster. We evaluated the performance with simulations and compared it with other algorithms.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.27-30
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• 2005

Because of unreplenishable power resources, reducing node energy consumption to extend network lifetime is an important requirement in wireless sensor networks. In addition both path length and path cost are important metrics affecting sensor lifetime. We propose a dynamic clustering scheme based on location in wireless sensor networks. Our scheme can localize the effects of route failures, reduce control traffic overhead, and thus enhance the reachability to the destination. We have evaluated the performance of our clustering scheme through a simulation and analysis. We provide simulation results showing a good performance in terms of approximation ratios.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1155-1160
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• 2013

The rapid growth of smart phones has made location-based services (LBSs) widely available. However, the use of LBS can raise privacy issues, as LBS can allow adversaries to violate the location privacy of users. There has been a considerable amount of research on preserving user location privacy. Most of these studies try to preserve location privacy by achieving what is known as location K-anonymity. In this paper, we propose a hierarchical clustering-based spatial cloaking algorithm for LBSs. The proposed algorithm constructs a tree using a modified version of agglomerative hierarchical clustering. The experimental results show, in terms of the ASR size, that the proposed algorithm is better than Hilbert Cloak and comparable to RC-AR (R-tree Cloak implementation of Reciprocal with an Asymmetric R-tree split). In terms of the ASR generation time, the proposed algorithm is much better in its performance than RC-AR and similar in performance to Hilbert Cloak.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.260-263
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• 2019

Recently, the need of researches and developments about WSN(Wireless Sensor Network) technologies, which can be applied to services to regions where the access is difficult or services that require continuous monitoring, has gradually increased due to its expansion and efficiency of the application areas. In this paper, we analyze existing researches which focused on reducing packet loss rate and increasing lifetime of sensor nodes. Then, we conduct studies about performance improvement factors where some schemes - clustering and location-based approaches - are applied and compare our study results with existing researches. Based on our studies, we are planning to conduct researches about a new scheme that could contribute to improve WSN's performance in terms of packet loss rate and network lifetime.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.977-987
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• 2014

Many of the recent performance evaluation of clustering schemes in wireless sensor networks considered one sink node operation and fixed sink node location without mentioning about any network application requirements. However, application environments have variable requirements about their networks. In addition, network performance is sufficiently influenced by different sink node location scenarios in multi-hop based network. We also know that sink location can influence to the sensor network performance evaluation because of changed multipath of sensor nodes and changed overload spots in multipath based wireless sensor network environment. Thus, the performance evaluation results are hard to trust because sensor network is easily changed their network connection through their routing algorithms. Therefore, we suggest that these schemes need to evaluate with different sink node location scenarios to show fair evaluation result. Under the results of that, network performance evaluation results are acknowledged by researchers. In this paper, we measured several clustering scheme's performance variations in accordance with various types of sink node location scenarios. As a result, in the case of the clustering scheme that did not consider various types of sink location scenarios, fair evaluation cannot be expected.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1353-1359
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• 2010

Basic objective in cluster analysis is to discover natural groupings of items. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input data. Various measures of similarities between objects are developed. In this paper, we consider a clustering of huge categorical real data set which shows the aspects of time-location-activity of Korean people. Some useful similarity measure for the data set, are developed and adopted for the categorical variables. Hierarchical and nonhierarchical clustering method are applied for the considered data set which is huge and consists of many categorical variables.