• 한국공간정보시스템학회 논문지
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• 제11권2호
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• pp.1-6
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• 2009

Localization of nodes is a key technology for application of wireless sensor network. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. We also suggest a criterion which is used to select mobile anchor node which involve in computing the position of nodes for improving localization accuracy. Weighted centroid localization algorithm is simple, and no communication is needed while locating. The localization accuracy of weighted centroid localization algorithm is better than maximum likelihood estimation which is used very often. It can be applied to many applications.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.1-9
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• 2019

In this paper, we propose the algorithms which determine 1) the efficient anchor-node visiting route of mobile sink in terms of energy supply and 2) the efficient energy amount to be charged to each anchor node, by using the information of each anchor node and the mobile sink. Wireless sensor networks (WSNs) using mobile sinks can be deployed in more challenging environments such as those that are isolated or dangerous, and can also achieve a balanced energy consumption among sensors which leads to prolong the network lifetime. Most mobile sinks visit only some anchor nodes which store the data collected by the nearby sensor nodes because of their limited energy. The problem of these schemes is that the lifetime of the anchor nodes can be shorten due to the increased energy consumption, which rapidly reduces the overall lifetime of WSN. This study utilizes a mobile sink capable of wireless power transmission to solve this problem, so a mobile sink can gather data from anchor nodes while charging energy to them. Through the performance verification, it is confirmed that the number of blackout nodes and the amount of collected data are greatly improved regardless of the size of the network.

• 한국통신학회논문지
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• 제36권1A호
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• pp.82-89
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• 2011

IETE Proxy Mobile IPv6는 이동 단말의 이동성을 지원하기 위한 망 기반 이동성 관리 기술이다. PMIPv6는 Mobile Access Gateway 와 Local Mobility Anchor 사이의 터널을 형성하고 항상 패킷이 Local Mobility Anchor를 통하여 전달되도록 설계하여, Local Mobility Anchor 병목현상 및 종단간 지연이 증가하는 문제점이 있다. 이러한 문제점을 해결하기 위해 경로 최적화 수행 기능 감지, 경로 최적화 절차 등의 많은 연구가 진행되고 있으나 추가적인 시그널링으로 인한 오버헤드가 증가하고 다중 Local Mobility Anchor 환경에 적용하기에 어려움이 있다. 따라서 본 논문에서는 다중 Local Mobility Anchor 환경을 고려한 PMIPv6 기반의 향상된 경로 최적화 방안을 제안하였다. PMIPv6 도메인 내 모든 Local Mobility Anchor의 정보를 Mobile Access Gateway가 유지하도록 하여 신속하게 경로 최적화를 수행하도록 하였으며, Local Mobility Anchor에 경로 최적화 상태 정보를 저장하여 핸드오버 이후에도 신속한 경로 최적화를 지원하도록 하였다. 또한 핸드오버 시에 상대 노드의 Mobile Access Gateway에 버퍼링 기능을 추가하여 경로 최적화를 수행하는 동안의 패킷 순서화 문제를 해결하였다. 제안된 방안의 성능은 OPNET 시뮬레이터를 이용하여 분석하였으며, 이를 통해 제안 방안의 우수성을 검증하였다.

• 한국통신학회논문지
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• 제39C권9호
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• 한국정보통신학회논문지
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• 제21권5호
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• pp.929-935
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• 2017

일반적으로 수중 센서 네트워크에서 모든 센서 노드는 위치가 결정된 앵커 노드를 이용하여 자신의 위치를 결정한다. 본 논문에서는 수중 센서 네트워크에서 모든 센서 노드의 위치를 결정하기 위해 최소의 수를 가진 앵커 노드를 결정하기 위한 타부 서치 알고리즘을 제안한다. 네트워크에서 센서 노드의 수가 증가함에 따라 앵커 노드의 수를 결정하는 계산량은 급격히 늘어나게 된다. 본 논문에서는 밀집도가 높은 네트워크에서 적정한 시간 내에 최소의 앵커 노드수를 결정하는 타부 서치 알고리즘을 제안하며, 효율적인 검색을 위해 타부 서치 알고리즘의 효과적인 이웃해 생성 동작을 제안한다. 제안된 알고리즘은 최소 앵커 노드의 수와 실행시간 관점에서 성능을 평가하며, 평가 결과에서 제안된 알고리즘이 기존의 알고리즘에 비해 성능이 5-10% 우수함을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3480-3500
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• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• 한국멀티미디어학회논문지
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• 제17권4호
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• pp.458-465
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• 2014

In this paper, we propose a strategy to distribute the energy consumption over the network. The proposed strategy is based on geographic routing. We use a smart base station that maintains the residual energy and location information of sensor nodes and selects a head node and an anchor node using this information. A head node gathers and aggregates data from the sensor nodes in a target region that interests the user. An anchor node then transmits the data that was forwarded from the head node back to the smart base station. The smart base station extends network lifetime by selecting an optimal head node and an optimal anchor node. We simulate the proposed protocol and compare it with the LEACH protocol in terms of energy consumption, the number of dead nodes, and a distribution map of dead node locations.

• Journal of information and communication convergence engineering
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• 제10권4호
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• pp.349-358
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• 2012

Over a wireless sensor network (WSN), accurate localization of sensor nodes is an important factor in enhancing the association between location information and sensory data. There are many research works on the development of a localization algorithm over three-dimensional (3D) space. Recently, the complexity-reduced 3D trilateration localization approach (COLA), simplifying the 3D computational overhead to 2D trilateration, was proposed. The method provides proper accuracy of location, but it has a high computational cost. Considering practical applications over resource constrained devices, it is necessary to strike a balance between accuracy and computational cost. In this paper, we present a novel 3D localization method based on the received signal strength indicator (RSSI) values of four anchor nodes, which are deployed in the initial setup process. This method provides accurate location estimation results with a reduced computational cost and a smaller number of anchor nodes.

• 한국인터넷방송통신학회논문지
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• 제18권5호
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• pp.77-87
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• 2018

WSN에 국한하여 많은 연구가 이루어졌다. 정적 WSN에 국한된 솔루션은 모바일 WSN에 적용하기가 어렵다. 모바일 WSN에 국한된 솔루션은 네트워크에 상당한 수의 앵커 노드가 있다고 가정하고 리소스가 제한된 상황에서 이러한 솔루션은 정적 및 모바일 혼합 WSN에 적용하기가 어렵다. 앵커 노드를 사용하지 않고 정적 노드와 모바일 노드가 혼합 된 혼합형 무선 센서 네트워크에 대해 효율적이고 정확하며 신뢰할 수 있는 방법으로 국한하여 서비스를 제공 할 수 없다. 정확도는 혼합 무선 센서 네트워크에 한정하여 중요한 요소이다. 본 논문에서는 무선 센서 네트워크에서 앵커 노드가 없는 위치 파악의 정확성에 대한 요구를 만족시키는 방법을 제시하였다. 홉 좌표 측정은 앵커 프리 로컬화를 위한 정확한 방법이 사용됩니다. 동일한 범주의 동일한 데이터를 사용하는 다른 방법과 비교할 때 이 방법은 다른 방법보다 정확도가 좋다. 또한, 우리는 WSN에서 앵커 노드가 없는 지역적으로 낮은 통신 및 계산 비용과 같은 효율성에 대한 요구를 충족시키기 위해 최소 스패닝트리 알고리즘을 적용했다. Java 시뮬레이션 결과는 제안된 접근 방식을 질적인 방법으로 수정하고 다양한 게재 위치에서 실적을 이해하는데 도움이 된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2992-3007
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• 2012

Recently there has been an increasing interest in exploring the radio irregularity research problem in Wireless Sensor Networks (WSNs). Measurements on real test-beds provide insights and fundamental information for a radio irregularity model. In our previous work "LMAT", we solved the path planning problem of the mobile anchor node without taking into account the radio irregularity model. This paper further studies how the localization performance is affected by radio irregularity. There is high probability that unknown nodes cannot receive sufficient location messages under the radio irregularity model. Therefore, we dynamically adjust the anchor node's radio range to guarantee that all the unknown nodes can receive sufficient localization information. In order to improve localization accuracy, we propose a new 2-hop localization scheme. Furthermore, we point out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability. Finally, simulations show that, compared with 1-hop localization scheme, the 2-hop localization scheme with the radio irregularity model reduces the average localization error by about 20.51%.