• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1911-1930
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• 2017

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. The key to overlay routing is the placement and maintenance of the overlay infrastructure, especially, the selection and placement of key relay nodes. Spurred by the observation that a few relay nodes with high betweenness centrality can provide more optimal routes for a large number of node pairs, we propose a resilient routing overlay network construction method by introducing Super-Relay nodes. In detail, we present the K-Minimum Spanning Tree with Super-Relay nodes algorithm (SR-KMST), in which we focus on the selection and connection of Super-Relay nodes to optimize the routing quality in a resilient and scalable manner. For the simultaneous path failures between the default physical path and the overlay backup path, we also address the selection of recovery path. The objective is to select a proper one-hop recovery path with minimum cost in path probing and measurement. Simulations based on a real ISP network and a synthetic Internet topology show that our approach can provide high-quality overlay routing service, while achieving good robustness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.50-55
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• 2021

Multi-path system is a system in which utilizes various networks simultaneously. It is expected that multi-path system can enhance communication speed, reliability, security of network. In this paper, we focus on path selection in multi-path system. To select optimal path, we propose deep reinforcement learning algorithm which is rewarded by the round-trip-time (RTT) of each networks. Unlike multi-armed bandit model, deep Q learning is applied to consider rapidly changing situations. Due to the delay of RTT data, we also suggest compensation algorithm of the delayed reward. Moreover, we implement testbed learning server to evaluate the performance of proposed algorithm. The learning server contains distributed database and tensorflow module to efficiently operate deep learning algorithm. By means of simulation, we showed that the proposed algorithm has better performance than lowest RTT about 20%.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.138-142
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• 2014

This research presents an A* based algorithm which can be applied to Unmanned Ground Vehicle self-navigation in order to make the driving path smoother. Based on the grid map, A* algorithm generated the path by using straight lines. However, in this situation, the knee points, which are the connection points when vehicle changed orientation, are created. These points make Unmanned Ground Vehicle continuous navigation unsuitable. Therefore, in this paper, B-spline curve function is applied to transform the path transfer into curve type. And because the location of the control point has influenced the B-spline curve, the optimal control selection algorithm is proposed. Also, the optimal path tracking speed can be calculated through the curvature radius of the B-spline curve. Finally, based on this algorithm, a path created program is applied to the path results of the A* algorithm and this B-spline curve algorithm. After that, the final path results are compared through the simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.425-433
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• 2012

Dijkstra's algorithm is one of well-known methods to find shortest paths over a network. However, more research on $A^*$ algorithm is necessary to discover the shortest route to a goal point with the heuristic information rather than Dijkstra's algorithm which aims to find a path considering only the shortest distance to any point for an optimal path search. Therefore, in this paper, we compared Dijkstra's algorithm and $A^*$ algorithm for bicycle route selection. For this purpose, the horizontal distance according to slope angle and average speed were calculated based on factors which influence bicycle route selection. And bicycle routes were selected considering the shortest distance or time-dependent shortest path using Dijkstra's or $A^*$ algorithm. The result indicated that the $A^*$ algorithm performs faster than Dijkstra's algorithm on processing time in large study areas. For the future, optimal path selection algorithm can be used for bicycle route plan or a real-time mobile services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1055-1063
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• 2002

In this paper, we have studied path selection problem using multiple metric. Current Internet selects a path using only one metric. The path selected by one metric is a best-effort service that can satisfy one requirements. In order to satisfy a call with various Qualify-of-Service(QoS) requirements, the path must satisfy multiple constraints. In many cases, path selection is NP-complete. The proposed algorithm is widest-least cost routing algorithm that selects a path based on cost metric which is basically a delay metric influenced by the network status. The proposed algorithm is a multiple metric path selection algorithm that has traffic distribution ability to select shortest path when network load is light and move traffic to other alternate path when the link load is high. We have compared the results with other routing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1C
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• pp.1-7
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• 2011

Recently, multi-hop relay transmission has drawn much attention owing to its advantage of coverage extension at low power consumption. In this paper, we derive a performance bound of multi-hop communication systems with multiple amplify-and-forward relays when the optimal path is selected. The bound is derived in generalized Nakagami fading channels with different parameters for each hop by applying the geometric mean bound to the path signal-to-noise ratio (SNR) and obtaining the moment generation function of the bound. It is shown that the bound based on the geometric mean is closer to the actual performance of optimal path selection when the fading effect is small.

• Journal of Ginseng Research
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• v.19 no.2
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• pp.165-170
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• 1995

This study was carried out to investigate the correlation of agronomic characters, their path coefficients in 2, 3 and 4-year old ginseng plants, and to provide a useful information for ginseng breeding. Correlation coefficients between stem fen변h, number of leaves and number of Iraflets in 2-year age, and stem diameter and leaf length in 3-year age showed highly significant correlation with number of fruits and root weight in 4-year age. The path coefficient analysis indicated that stem length and number of leaflets might give indirect effects on root weight regardless of plant age. On the other hand, stem length and number of leaflets in 2-year age and, stem diameter and leaf length in 3-year age showed direct effects on root weight in 4-year old ginseng. These results may be used for selection of high-yielding ginseng plants. Key words Selection information, correlation and path coefficient analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.568-574
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• 2006

User adaptive services have been important features in many applications. To provide such services, various techniques with various kinds of data are being used. In this paper, we propose a method to analyze user's past moving paths and predict the goal position and the path to the goal by observing the user's current moving path. We develop a spatiotemporal similarity measure between paths. We choose a past path which is the most similar to the current path using the similarity. Based on the chosen path, user's spatiotemporal position is estimated. Through experiments we confirm this method is useful and effective.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.241-260
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• 2011

In earlier days, most of the data carried on communication networks was textual data requiring limited bandwidth. With the rise of multimedia and network technologies, the bandwidth requirements of data have increased considerably. If a network link at any time is not able to meet the minimum bandwidth requirement of data, data transmission at that path becomes difficult, which leads to network congestion. This causes delay in data transmission and might also lead to packet drops in the network. The retransmission of these lost packets would aggravate the situation and jam the network. In this paper, we aim at providing a solution to the problem of network congestion in mobile ad hoc networks [1, 2] by designing a protocol that performs routing intelligently and minimizes the delay in data transmission. Our Objective is to move the traffic away from the shortest path obtained by a suitable shortest path calculation algorithm to a less congested path so as to minimize the number of packet drops during data transmission and to avoid unnecessary delay. For this we have proposed a protocol named as Congestion Aware Selection Of Path With Efficient Routing (CASPER). Here, a router runs the shortest path algorithm after pruning those links that violate a given set of constraints. The proposed protocol has been compared with two link state protocols namely, OSPF [3, 4] and OLSR [5, 6, 7, 8].The results achieved show that our protocol performs better in terms of network throughput and transmission delay in case of bulky data transmission.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.25-32
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• 2015

The Genetic algorithm is a search algorithm using evaluation, genetic operator, natural selection to populational solution iteratively. The convergence and divergence characteristic of genetic algorithm are affected by selection strategy, generation replacement method, genetic operator when genetic algorithm is designed. This paper proposes fast convergence genetic algorithm for time-limited robot path planning. In urgent situation, genetic algorithm for robot path planning does not have enough time for computation, resulting in quality degradation of found path. Proposed genetic algorithm uses fast converging selection strategy and generation replacement method. Proposed genetic algorithm also uses not only traditional crossover and mutation operator but additional genetic operator for shortening the distance of found path. In this way, proposed genetic algorithm find reasonable path in time-limited situation.