• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.124-125
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• 2007

• Spatial Information Research
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• v.19 no.6
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• pp.111-121
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• 2011

In this paper, we proposes a sensor node positioning algorithm that utilizes the geo-spatial elements and considers the factors to represent the propagation loss generated by the various obstacles in the urban wireless environments. First, we measures the propagation loss about the radio frequencies in major road of the urban, and defines the correlation between the measured loss and the environment information for the road and its surrounding get from Urban GIS. Secondly, through the utilization of the loss-environment correlation, we describes the detailed instruction for requiring the radio coverage decision and deploy system implementation for the wireless sensor node in urban. By the consideration of interference factor by the building and the linear structure of road, we can evaluate the path loss below 5dB RMS error. And, we proposes the way to revise the sensor node deployment based on the corelation and the measured path loss.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1084-1090
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• 2015

In this paper, we propose a Tabu search algorithm to efficiently deploy the sensor nodes for maximizing the network sensing coverage in wireless sensor networks. As the number of the sensor nodes in wireless sensor networks increases, the amount of calculation for searching the solution would be too much increased. To obtain the best solution within a reasonable execution time in a high-density network, we propose a Tabu search algorithm to maximize the network sensing coverage. In order to search effectively, we propose some efficient neighborhood generating operations of the Tabu search algorithm. We evaluate those performances through some experiments in terms of the maximum network sensing coverage and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.430-435
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• 2007

In this paper, we propose an intelligent deployment of sensor network for reliable communication. The proposed method determines optimal transmission range based on the wireless channel characteristics, and searches the optimal number of sensor nodes, and optimal locations with SOFM. We calculate PRR against a distance uses the log-normal path loss model, and decide the communication range of sensor node from PRR. In order to verify the effectiveness of the proposed method, we performed simulations on the searching for intelligent deployment and checking for link condition of sensor network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2889-2909
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• 2017

Wireless sensor networks (WSNs) have attracted lots of attention in recent years due to their potential for various applications. In this paper, we seek how to efficiently deploy relay nodes into traditional static WSNs with constrained locations, aiming to satisfy specific requirements of the industry, such as average energy consumption and average network reliability. This constrained relay node deployment problem (CRNDP) is known as NP-hard optimization problem in the literature. We consider addressing this multi-objective (MO) optimization problem with an improved Artificial Bee Colony (ABC) algorithm with a linear local search (MOABCLLS), which is an extension of an improved ABC and applies two strategies of MO optimization. In order to verify the effectiveness of the MOABCLLS, two versions of MO ABC, two additional standard genetic algorithms, NSGA-II and SPEA2, and two different MO trajectory algorithms are included for comparison. We employ these metaheuristics on a test data set obtained from the literature. For an in-depth analysis of the behavior of the MOABCLLS compared to traditional methodologies, a statistical procedure is utilized to analyze the results. After studying the results, it is concluded that constrained relay node deployment using the MOABCLLS outperforms the performance of the other algorithms, based on two MO quality metrics: hypervolume and coverage of two sets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10B
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• pp.894-903
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• 2008

The position of sensors generally affects coverage, communication costs, and resource management of surveillance sensor networks. Thus we are required to place a sensor in the best location. However, it is difficult to consider that terrain and climate factors influencing sensors when sensor nodes are deployed in the real world, such as a mountain area or a downtown area. We therefore require a sensor deployment method for detecting effectively targets of interest in terms of surveillance area coverage in such environment. Thus in this paper, we analyze various environmental factors related to sensor deployment, and quantify these factors to use when we deploy sensors. By considering these quantified factors, we propose a practical and effective method for deploying sensors in terms of sensing coverage. We also demonstrate the propriety of the proposed method through implementing a sensor deployment management system according to the method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.657-666
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• 2008

The intersection collision avoidance service among various telematics application services is regarded as one of the most critical services with regard to safety. In such safety applications, real-time, correct transmission of service is required. In this paper, we study on efficient infrastructure architecture for intersection collision avoidance using a cooperative mechanism between vehicles and wireless infrastructure. In particular, we propose an infrastructure, called CISN (Cooperative Infrastructure associated with Sensor Networks), in which proper numbers of sensor nodes are deployed on each road, surrounding the intersection. In the proposed architecture, overall service performance is influenced by various parameters consisting of the infrastructure, such as the number of deployed sensor nodes, radio range and broadcast interval of base station, and so on. In order to test the feasibility of the CISN model in advance, and to evaluate the correctness and real-time transmission ability, an intersection sensor deployment simulator is developed. Through various simulations on several environments, we identify optimal points of some critical parameters to build the most desirable CISN.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.282-283
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• 2007

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.99-100
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• 2006

One of the fundamental problems in sensor networks is the deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation using two dimensional L shape model. The actual length of the L shape model is about 100m each. We found the minimum number of 15 nodes are sufficient for the complete coverage of modeled area. We also found the optimum location of each nodes. The real deploy experiment using 15 sensor nodes shows the 95.7%. error free communication rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.55-62
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• 2010

Monitoring systems are able to report certain events at region of interest(ROI) and to take an appropriate action. From industrial product line full of robots to fire detection, intrusion detection, smart grid application, environmental pollution alarm system, monitoring system has widely used in diverse industry sector. Recently, due to advance of wireless communication technology and availability of low cost sensors, intelligent and/or smart monitoring systems such as sensor networks has been developed. Several deployment methods are introduced to meet various monitoring needs and deployment performance criteria are also summarized to be used to identify weak point and be useful at designing monitoring systems. Both efficiency during deployment and usefulness after the deployment should be assessed. Efficiency factors during deployment are elapsed time, energy required, deployment cost, safety, sensor node failure rate, scalability. Usefulness factors after deployment are ROI coverage, connectivity, uniformity, target density similarity, energy consumption rate per unit time and so on.