• The KIPS Transactions:PartC
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• v.15C no.1
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• pp.37-44
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• 2008

In order to minimize the signaling delay for a resource reservation on the new routing path after the handover of Mobile Node(MN) is completed, it is important to discover the crossover node where the old and new routing paths meet. With the 크로스오버 노드 being found, the signaling messages only need to be transferred on the changed part of the end-to-end path. The crossover node is generally discovered using the end-to-end Session ID(SID) of the established session between MN and Correspondent Node(CN). However, in the Hierarchical Mobile IPv6(HMIPv6) network, if the Mobile Anchor Point (MAP) reserves the resource by aggregate with the Home Agent(HA), the crossover node discovery cannot be performed in the general way since the aggregate SID that has established between the previous MAP and HA is different from the that of the current MAP and HA after MN's handover. In this paper, we propose a mechanism to discover the crossover node within the tunnel between the MAP and the HA in an HMIPv6 network, assuming that the Next Steps in Signaling(NSIS) is deployed for the resource reservation and the aggregate reservation is applied over the MAP and HA tunnel. The local repair required for the change of path is performed upon the crossover node discovery. The simulation results show that the proposed scheme reduces the signaling delay for the reservation and outperforms the existing scheme with respect to throughput during the handover.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.538-548
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• 2008

In the Internet Engineering Task Force(IETF), Next Step in Signaling(NSIS) working group, proposed a mechanism to discover the Crossover Node(CRN), when the route is changed by Mobile Node(MN) handover. The CRN is divergence or convergence node on old and new path for reserving resources. Trough the CRN discovery mechanism, it possible to reduce a signaling delay and avoid the redundant reservation on the common path between old and new path. However, the QoS(Quality of Service) can be guaranteed continuously while the MN is performing handover, it is needed to pre-reserve the resource on the new path before completion of the handover. When the nodes on the new path try to make a pre-resource reservation before the handover, it is difficult to pre-reserve the resource with the existing CRN discovery mechanism. Therefore, we proposed a Passive CRN(PCRN) discovery scheme and pre-resource reservation mechanism. The PCRN which means an initial common point between the current reserved and the new paths, where the handover can take place.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1251-1255
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• 2011

In this paper, we propose an optimal path search algorithm that contains all nodes using genetic algorithm. An object in this approach is formed as an equation related with the Euclidean distance between an intermediate node and the starting node and between an intermediate node and the goal node. Like other genetic algorithm structures, our algorithm defines a fitness function and selects a crossover spot node and a bitwise crossover point. A new node out of such operation survives only if it satisfies the fitness criteria and that node then becomes the starting node for the next generation. Repetition continues until no changes are made in the population. The efficiency of this proposed approach is verified in the experiment that it is better than two other contestants - sequential approach and the random approach.

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.12 s.354
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• pp.53-60
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• 2006

Handover of WiBro is based on 802.16e hard handover scheme. When PSS is handover, it is handover that confirm neighbor's cell condition and RAS ID in neighbor advertisement message. Serving RAS transmits HO-notification message to neighbor RAS. Transmiting HO-notification message to neighbor RAS, it occurs many signaling traffics. Also, When WiBro is handover, It occurs many packet loss. Therefore, user suffer service degradation. LPM handover is supporting seamless handover because it buffers data packets during handover. So It is proposed scheme that predicts is LPM handover and reserves target RAS with pre-authentication. These schemes occur many signaling traffics. In this paper, we propose PSS Movement Prediction to solve signaling traffic. Target RAS is decided by old data in history cache. When serving RAS receives HO-notification-RSP message to target RAS, target RAS inform to crossover node. And crossover node bicast data packet. If handover is over, target RAS forward data packet. Therefore, It reduces signaling traffics but increase handover success rate. When history cache success, It decrease about 48% total traffic. But When history cache fails, It increase about 6% total traffic

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1187-1190
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• 2007

핸드오버가 발생하는 NEMO(NEtwork MObility) 환경에서 주요 이슈 중 하나는 MR(Mobile Router)이 핸드오버 하는 동안에도 NEMO 네트워크 내의 MNN(Mobile Network Node)들에게 지속적인 QoS(Quailty of Service)를 보장해주는 것이다. 이를 위해 MR의 등록과 자원예약 프로세스가 완료되기까지의 지연시간을 최소화하는 것이 중요하다. 또한 MR이 핸드오버 하는 동안 네트워크상에 전달되고 있는 데이터 패킷들의 손실을 최소화해야 한다. 이에, 본 논문에서는 NEMO 네트워크에서의 자원예약 트리거와 버퍼링 방안을 제안한다. 제안하는 방안에서는 MR이 새로운 경로와 이전 경로가 만나는 지점의 노드인 CRN(CRossover Node)에게 NOTIFY 메시지를 보내 새로운 경로 상에 자원예약 셋업이 빨리 시작하도록 트리거 하였고, 자원예약이 수행되고 있는 동안 인터넷상에 전송중인 데이터 패킷들의 손실을 줄이기 위해 CRN에서 버퍼링하도록 하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1163-1166
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• 2007

핸드오버가 발생하는 이동망 환경에서 주요 이슈 중 하나는 핸드오버 후 MN(Mobile Node)의 트래픽이 전송될 새로운 경로 상에 자원 재예약을 위한 지연 시간을 최소화하는 것이다. 이를 위해, 데이터 전달 경로가 변경된 경로에서만 자원예약 시그널링이 이루어지도록 CRN(Crossover Node)을 발견하는 것이 중요하다. 이동망 환경에서 자원 재예약을 위한 CRN은 SID(Session ID)를 이용하여 발견될 수 있다. 그러나 HMIPv6 네트워크에서 MAP(Mobility Anchor Point)과 HA(Home Agent)간 군집(Aggregate) 자원 예약이 이루어진 경우에서는 SID를 이용하여 CRN을 발견할 수 없다. 왜냐하면, MN의 핸드오버 후, 종단간 SID는 동일할지라도 이전 MAP과 HA간 설립된 세션의 군집 SID가 새로운 MAP과 HA간 설립된 세션의 군집 SID와 다르기 때문이다. 이에, 본 논문에서는 HMIPv6 네트워크에서의 CRN 발견방안을 제안한다. 제안하는 방안에서는 시그널링 메시지에 이전 군집 세션 정보를 포함하여 전달함으로서 시그널링 메시지가 목적지까지 도달하기 전에 CRN이 발견될 수 있도록 하였다. 제안하는 방안은 군집 자원예약이 갖는 시그널링 및 플로우별 상태 정보 감소 효과를 그대로 유지하면서, MN의 핸드오버 후, 자원 재예약을 위한 지연 시간을 단축시킴으로서 통신 성능을 향상 시킬 수 있을 것으로 기대된다.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.