• 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.

• 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.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1B
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• pp.106-116
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• 2010

Sensor deployment makes an effect on not only covering of the interesting area but also reliable data acquisition and efficient resource management of sensor, so that sensors must be deployed at their better place. In traditional static wireless sensor networks, however, it is impossible to deploy the sensors manually when they are distributed in unexploited, hostile, or disaster areas. Therefore, if each sensor has locomotion capability, it can re-deploy itself using the location information of neighbor sensors. In our previous study, we showed that moving sensors to the centroids of their Voronoi polygon is efficient for extending the coverage area. In this paper, we present an improved potential-field-based sensor self-deployment scheme by combining the centroid of Voronoi polygon with the traditional potential-field scheme. Simulation results show that our scheme can achieve higher coverage in shorter time and less movement than the traditional potential-field scheme.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.63-64
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• 2007

The Underwater Wireless Sensor Networks (UWSN) consists of sensor nodes equipped with limited sensing coverages, energy resources and communication capacity. Hence, the deployment and movement algorithm is a key issue that needs to be organized in order to improve the sensing efficiency of the networks. In this paper, we use a Queen problem and Knapsack problem to prevent the reiteration phenomenon of sensors, to guarantee improvement sensing coverage and efficiency in the 3D UWSN.

• ETRI Journal
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• v.33 no.6
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• pp.924-934
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• 2011

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1535-1537
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• 2007

A design pattern is a general solution to a commonly occurring problem. Design patterns have proven highly effective in representing, transferring, and applying the design knowledge in many engineering disciplines. However, these patterns have not addressed sensor network specifically. With a growth of sensors and sensor networks, and considering their profound applicability, there is a crucial need to articulate ones experience of application development or deployment of sensor nodes in the form of design patterns to avoid the future mistakes. This paper discusses the same issue and show applicability of design patterns in sensor networks.

• Smart Structures and Systems
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• v.10 no.3
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• pp.209-228
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• 2012

Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.619-639
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• 2010

There is increasing interest in using structural monitoring as a cost effective way of managing risks once an area of concern has been identified. However, it is challenging to deploy an effective, reliable, large-scale, long-term and real-time monitoring system in an underground railway environment (subway / metro). The use of wireless sensor technology allows for rapid deployment of a monitoring scheme and thus has significant potential benefits as the time available for access is often severely limited. This paper identifies the critical factors that should be considered in the design of a wireless sensor network, including the availability of electrical power and communications networks. Various issues facing underground deployment of wireless sensor networks will also be discussed, in particular for two field case studies involving networks deployed for structural monitoring in the Prague Metro and the London Underground. The paper describes the network design, the radio propagation, the network topology as well as the practical issues involved in deploying a wireless sensor network in these two tunnels.

• 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.