• 전자공학회논문지A
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• 제31A권10호
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• pp.119-131
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• 1994

In this paper, we first propose a new FPGA routing method in which seversal netw are routed concurrently by applying the readitional maze routing method. We then introduce CMRF (concurrent maze Router for FPGA) which can be used for the routing of FpGAs of symmetrical array type by applying our new routing method. Given a set of nets, the proposed routing method performas the maze propagation and backtracing independently for each net and determines the routing paths concurrently by competition among nets. In CMRF, using this routing method, q nets are selected from the nets to be routed and they are routed concurrently, where q is the user given parameter determined by considering the computing environment. This process is repeated until either all the nets are routed or the remaining unrouted nets fail to their maze propagations. The routing of these nets are completed using the rip-up and rerouting technique. We apply our routing method to ten randomly generated test examples in order to check its routing performance. The results show taht as we increase the value of q, the routing completion rate increases for all the examples. Note that when q=1, our method is similar to the conventinal maze routing method. We also compare CMRF with the CGE method which has been proposed by Brown et.al. For the five benchmark examples, CMRF complete the routing with less wire segments in each connection block than the wire segments needed in the CGE method of 100% routing.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권10호
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• pp.269-276
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• 2021

최근 스마트 센서는 다양한 환경에서 사용되고 있으며, 애드혹 센서 네트워크 (ASN) 구현에 대한 연구가 활발하게 진행되고 있다. 그러나 기존 센서 네트워크 라우팅 알고리즘은 특정 제어 문제에 초점을 맞추며 ASN 작업에 직접 적용할 수 없는 문제점이 있다. 본 논문에서는 Q-learning 기술을 이용한 새로운 라우팅 프로토콜을 제안하는데, 제안된 접근 방식의 주요 과제는 균형 잡힌 시스템 성능을 확보하면서 효율적인 에너지 할당을 통해 ASN의 수명을 연장하는 것이다. 제안된 방법의 특징은 다양한 환경적 요인을 고려하여 Q-learning 효과를 높이며, 특히 각 노드는 인접 노드의 Q 값을 자체 Q 테이블에 저장하여 데이터 전송이 실행될 때마다 Q 값이 업데이트되고 누적되어 최적의 라우팅 경로를 선택하는 것이다. 시뮬레이션 결과 제안된 방법이 에너지 효율적인 라우팅 경로를 선택할 수 있으며 기존 ASN 라우팅 프로토콜에 비해 우수한 네트워크 성능을 얻을 수 있음을 확인하였다.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• 스마트미디어저널
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• 제7권2호
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• pp.9-14
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• 2018

The k-ary n-cube $Q^k_n$ is widely used in the design and implementation of parallel and distributed processing architectures. It consists of $k^n$ identical nodes, each node having degree 2n is connected through bidirectional, point-to-point communication channels to different neighbors. On $Q^k_n$ we would like to transmit packets from a source node to 2n destination nodes simultaneously along paths on this network, the $i^{th}$ packet will be transmitted along the $i^{th}$ path, where $0{\leq}i{\leq}2n-1$. In order for all packets to arrive at a destination node quickly and securely, we present an $O(n^3)$ routing algorithm on $Q^k_n$ for generating a set of one-to-many node-disjoint and nearly shortest paths, where each path is either shortest or nearly shortest and the total length of these paths is nearly minimum since the path is mainly determined by employing the Hungarian method.

• 한국통신학회논문지
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• 제28권10B호
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• pp.947-956
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• 2003

NGN(Next Generation Network)을 목표로 최근에 들어 사용자의 QoS요구 시, 다양한 QoS를 패킷네트워크에서 처리한 수 있도록 IETF에서 DiffServ, RSVP, MPLS등과 같은 패킷 QoS기법에 대한 표준화 작업이 진행중이며, 그 중에서 DiffServ네트워크가 대표적이다. 따라서 본 논문에서는 이 DiffServ패킷 네트워크상에서 다양하게 유입되는 트래픽의 종류에 따라 사용자의 응용에 적절히 대응하여 트래픽을 처리하는 라우팅 기법트래픽 모델 및 알고리즘을 연구하고 기존의 최선형(Best effort) 즉, 지연에 민감하지 않은 트래픽을 처리하기 위한트래픽 분산 라우팅 프로토콜(Traffic-Balanced Routing Protocol : TBRP), 최적의 중간 노드를 선택하여 유무선 통합과 높은 순위의 상호형 데이터를 처리하기 위한 계층적 라우팅 프로토콜(Hierarchical Traffic-Traffic-Scheduling Routing Protocol : HTSRP), 대화형 또는 스트리밍 패킷서비스를 위한 즉, QoS파라미터을 기반으로 엑세스 계층의 자원 활용도를 최대화하고 지연에 민감한 트래픽 처리하는 HTSRP_Q(HTSRP for QoS)를 연구하였고, 이를 기반으로 각 트래픽 모델에 대한 매핑기법과 관리기법을 연구하였다. 본 연구에서 제시한 프로토콜은 트래픽 모델은 다양한 엑세스망과 백본망에 유연한 트래픽 처리기법으로서 NGN의 효율성과 안정성에 적합하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.46-55
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• 2020

Ad Hoc network is a special wireless network, mainly because the nodes are no control center, the topology is flexible, and the networking could be established quickly, which results the transmission stability is lower than other types of networks. In order to guarantee the transmission of data packets in the network effectively, an improved Queue Ad Hoc On-demand Distance Vector Routing protocol (Q-AODV) for node detection by using blockchain technology is proposed. In the route search process. Firstly, according to the node's daily communication record the cluster is formed by the source node using the smart contract and gradually extends to the path detection. Then the best optional path nodes are chained in the form of Merkle tree. Finally, the best path is chosen on the blockchain. Simulation experiments show that the stability of Q-AODV protocol is higher than the AODV protocol or the Dynamic Source Routing (DSR) protocol.

• ETRI Journal
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• 제44권5호
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• 정보관리학회지
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• 제32권3호
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• pp.397-412
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• 2015

본 연구에서는 커뮤니티 기반 질의응답 서비스에서의 질의할당을 위하여, 해당 커뮤니티에 축적된 질의응답 데이터 세트를 이용하여 해당 카테고리내의 토픽을 분석하고 이를 바탕으로 해당 토픽에 관심을 가지는 이용자의 관심 토픽을 분석하고자 하였다. 특정 카테고리 내의 토픽을 분석하기 위해서 LDA기법을 사용하였고 이를 이용하여 이용자의 관심 토픽을 모델링하였다. 나아가, 커뮤니티에 새롭게 유입되는 질의에 대한 토픽을 분석한 후, 이를 바탕으로 해당 토픽에 대해 관심을 가지고 있는 이용자를 추천하기 위한 일련의 방법들을 실험하였다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권1호
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• pp.13-21
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• 2008

일반적으로 센서노드간의 데이타통신은 내부처리나 센싱 작업보다 더 많은 에너지 소모를 요구한다. 본 논문에서는, 내용인지(context-aware) 라우팅 테이블(routing table)을 이용하여 인접한 노드간의 질의 송수신을 위해 필요한 패킷 송신 수를 줄여 질의 최적화를 수행하는 새로운 아이디어를 제안한다. 내용인지 라우팅 테이블에는 현재 노드로부터 도달 가능한 하위노드에서 측정할 수 있는 센서의 종류에 관한 정보가 저장되어 있다. 내용인지 라우팅 정보를 이용하여 각 노드는 자식노드에게 불필요한 질의 송신이나 결과 전달을 차단함으로써 불필요한 패킷 송신의 수를 줄일 수 있다. 본 논문에서 제안한 방법을 바탕으로 한 시뮬레이션에서 최대 약 80%의 성능 효과를 보였다.

• 산업경영시스템학회지
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• 제45권3호
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• pp.40-48
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• 2022

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab's AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS's complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab's production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab's AMHS.