• Journal of the Korean Institute of Gas
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• v.22 no.2
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• pp.29-33
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• 2018

Accident likelihood is growing due to a correlation for gas and electricity installed in the area of dense energy consumption like traditional market and underground shopping center. In order to prevent and respond accident risks related to gas and electricity in this area, it should be monitored and predicted for factors of gas leak or electricity by developing safety management system. This study is about accident prediction model development considering fire risk factor related to gas accident. The temperature variation characteristic near a gas burner was analyzed. Also, accident prediction algorithm and related module were developed to prevent fire in the area of dense energy consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1644-1649
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• 2019

With the rapid development of mobile communication systems, various mobile convergence services are appearing and data traffic is exploding accordingly. Because the number of base stations to support these surging devices is also increasing, from a network provider's point of view, reducing energy consumption through these mobile communication networks is one of the most important issues. Therefore, in this paper, we apply the DBSCAN (density-based spatial clustering of applications with noise) algorithm, one of the representative user-density based clustering algorithms, in order to extract the dense area with user density and apply the thinning process to each extracted sub-network to efficiently control the mode of the base stations. Extensive simulations show that the proposed algorithm has better performance results than the conventional algorithms with respect to area throughput and energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3034-3055
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• 2014

Wireless Sensor Networks have extensively been utilized for ambient data collection from simple linear structures to dense tiered deployments. Issues related to optimal resource allocation still persist for simplistic deployments including linear and hierarchical networks. In this work, we investigate the case of dimensioning parameters for linear and tiered wireless sensor network deployments with notion of providing extended lifetime and reliable data delivery over extensive infrastructures. We provide a single consolidated reference for selection of intrinsic sensor network parameters like number of required nodes for deployment over specified area, network operational lifetime, data aggregation requirements, energy dissipation concerns and communication channel related signal reliability. The dimensioning parameters have been analyzed in a pipeline monitoring scenario using ZigBee communication platform and subsequently referred with analytical models to ensure the dimensioning process is reflected in real world deployment with minimum resource consumption and best network connectivity. Concerns over data aggregation and routing delay minimization have been discussed with possible solutions. Finally, we propose a node placement strategy based on a dynamic programming model for achieving reliable received signals and consistent application in structural health monitoring with multi hop and long distance connectivity.