• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.62-65
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• 2015

Wireless Sensor Network consists of several sensor nodes, these nodes loss some of their energy after the process of communication. So an energy efficient approach is required to improve the life of the network. In case of broadcast network, LEACH protocol uses an aggregative approach by creating cluster of nodes. Now the major concern is to built such clusters over WSN in an optimized way. This work presents the improvement over LEACH protocol. Hence we have different work environments where the network is having different capacities. The proposed work shows how the life time of the network will improve when the number of nodes varies within the network.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.427-429
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• 2005

Energy efficiency is the most challenging issue in wireless sensor network to prolong the life time of the network, as the sensors has to be unattended. Cluster based communication can reduce the traffic on the network and gives the opportunity to other sensors for periodic sleep and thus save energy. Passive clustering (PC) can perform a significant role to minimize the network load as it is less computational and light weight. First declaration wins method of PC without any priority generates severe collision in the network and forms the clusters very dense with large amount of overlapping region. We have proposed several modifications for the existing passive clustering algorithm to prolong the life time of the network with better cluster formation.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.583-585
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• 2002

Mobile Ad Hoc Network에서는 무선으로 연결된 호스트들이 쉽게 이동할 수 있으며 미리 설치된 유선망을 이용하는 셀롤러망과 달리 이동 호스트 사이의 통신만으로 이루어진 망이다. Mobile Ad Hoc Network에서는 각각의 노드들의 이동성이 높기 때문에 이 각각의 노드들의 라우팅 경로를 결정하는 것이 중요하다. 이에 따라 Mobile Ad Hoc Network를 위한 많은 라우팅 프로토콜이 제안되었다. 본 논문에서는 기존에 제안된 Cluster Based Routing Protocol(CBRP)극 변형하여 마치 셀롤러망에서의 셀과 같이 고정된 위치를 하나의 셀로 정의하고 그 하나의 셀이 클러스터를 형성하여 라우팅 오버 헤드를 줄일 수 있는 기법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.41-47
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• 2012

MANET has vulnerable structure on security owing to structural characteristics as follows. MANET consisted of moving nodes is that every nodes have to perform function of router. Every node has to provide reliable routing service in cooperation each other. These properties are caused by expose to various attacks. But, it is difficult that position of environment intrusion detection system is established, information is collected, and particularly attack is detected because of moving of nodes in MANET environment. It is not easy that important profile is constructed also. In this paper, conformal predictor - support vector machine(CP-SVM) based intrusion detection technique was proposed in order to do more accurate and efficient intrusion detection. In this study, IDS-agents calculate p value from collected packet and transmit to cluster head, and then other all cluster head have same value and detect abnormal behavior using the value. Cluster form of hierarchical structure was used to reduce consumption of nodes also. Effectiveness of proposed method was confirmed through experiment.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.164-169
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• 2008

The increasing late-payment rate of credit card customers caused by a recent economic downturn are incurring not only reduced profit of department stores but also significant loss. Under this pressure, the objective of credit forecasting is extended from presumption of good or bad customers to contribution to revenue growth. As a method of managing defaults of department store credit card, this study classifies credit delinquents into some clusters, analyzes repaying patterns of customers in each cluster, and develops credit forecasting system to manage delinquents of department store credit card using data of Korean D department store's delinquents. The model presented by this study uses Kohonen network, a kind of artificial neural network of data mining techniques to cluster credit delinquents into groups. Logistic regression model is also used to predict repayment rate of customers of each cluster per period. The accuracy of presented system for the whole clusters is 92.3%.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.43-46
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• 2008

Centrality has been actively studied in network analysis field. In this paper we show a calculation method of closeness centrality for WSN. Since nodes in a sensor network are very scarce in energy and computation capability the calculation of the closeness is done in two tiers by dividing network into clusters. In first step closeness centrality for cluster heads is calculated. In the second step closeness of member nodes of the chosen cluster is computed in respect to that cluster itself.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.9
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• pp.739-746
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• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.276-283
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• 2012

Nowadays, wireless sensor networks (WSNs) comprise a tremendously growing infrastructure for monitoring the physical or environmental conditions of objects. WSNs pose challenges to mitigating energy dissipation by constructing a reliable and energy saving network. In this paper, we propose a novel network construction and routing method by defining three different duties for sensor nodes, that is, node gateways, cluster heads, and cluster members, and then by applying a hierarchical structure from the sink to the normal sensing nodes. This method provides an efficient rationale to support the maximum coverage, to recover missing data with node mobility, and to reduce overall energy dissipation. All this should lengthen the lifetime of the network significantly.