• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.9
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• pp.25-32
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• 2009

Recently, research for Network-on-chip(NoC) is progressing. However, due to the increase of system complexity and demand on high performance, conventional copper-based electrical interconnect would be faced with the design limitation of performance, power, and bandwidth. As an alternative to these problems, combined use of Electrical Interconnects(EIs) and Optical Interconnects(OIs) has been introduced. In this paper we propose efficient routing optimization strategy and hybrid switch architecture, which use OIs for critical path and EIs for non-critical path. The proposed method shows up to 25% performance improvement and 38% power reduction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.700-713
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• 2015

It is critical to design energy-efficient routing protocols for battery-limited mobile ad hoc networks, especially in which the energy-consuming MIMO techniques are employed. However, there are several challenges in such a design: first, it is difficult to characterize the energy consumption of a MIMO-based link; second, without a careful design, the broadcasted RREP packets, which are used in most energy-efficient routing protocols, could flood over the networks, and the destination node cannot decide when to reply the communication request; third, due to node mobility and persistent channel degradation, the selected route paths would break down frequently and hence the protocol overhead is increased further. To address these issues, in this paper, a novel Greedy Energy-Efficient Routing (GEER) protocol is proposed: (a) a generalized energy consumption model for the MIMO-based link, considering the trade-off between multiplexing and diversity gains, is derived to minimize link energy consumption and obtain the optimal transmit model; (b) a simple greedy route discovery algorithm and a novel adaptive reply strategy are adopted to speed up path setup with a reduced establishment overhead; (c) a lightweight route maintenance mechanism is introduced to adaptively rebuild the broken links. Extensive simulation results show that, in comparison with the conventional solutions, the proposed GEER protocol can significantly reduce the energy consumption by up to 68.74%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2144-2165
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• 2016

In a vehicular ad hoc network, the communication links are unsteady due to the rapidly changing topology, high mobility and traffic density in the urban environment. Most of the existing geographical routing protocols rely on the continuous transmission of beacon messages to update the neighbors' presence, leading to network congestion. Source-based approaches have been proven to be inefficient in the inherently unstable network. To this end, we propose an opportunistic beaconless packet forwarding approach based on a modified handshake mechanism for the urban vehicular environment. The protocol acts differently between intersections and at the intersection to find the next forwarder node toward the destination. The modified handshake mechanism contains link quality, forward progress and directional greedy metrics to determine the best relay node in the network. After designing the protocol, we compared its performance with existing routing protocols. The simulation results show the superior performance of the proposed protocol in terms of packet delay and data delivery ratio in realistic wireless channel conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.65-70
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• 2013

In this paper, we propose a multi-hop cooperative transmission protocol to obtain energy savings in static wireless networks. Each terminal in the network is equipped with a single antenna and each receiver uses equal gain combining technique (EGC) to combine received signals. We also propose a power allocation strategy which optimizes the total transmit power at each stage. Monte-Carlo simulations are presented to evaluate and compare performance of the proposed protocol with the multi-hop direct transmission (MDT) and the cooperative routing protocol proposed by Khadani [8], in terms of the average total transmit power and the average number of required stages.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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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.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.137-141
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• 2000

This paper presents an application of a new Adaptive Genetic Algorithms(AGA) to solve the Optimal Routing problem(ORP) for distribution system planning. In general, since the ORP is modeled as a mixed integer problem with some various mathematical constraints, it is hard to solve the problem. In this paper, we proposed a new adaptive strategy in GA to overcome the premature convergence and improve the convergence efficiency. And for these purposes, we proposed a fitness function suited for the ORP. In the proposed AGA, we used specially designed adaptive probabilities for genetic operators to consider the characteristics of distribution systems that are operated under radial configuration. The proposed algorithm has been tested in sample networks and the results are presented.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.142-146
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• 2000

This paper Presents a heuristic algorithm based on branch exchange method to solve ORP (Optimal Routing Problem) for distribution system planning. The ORP is a complex task which is generally formulated as a combinatorial optimization problem with various constraints. The cost function of ORP is consisted of the investment cost and the system operation cost that is generally expressed with system power loss. This paper also adopt an specialy designed selection method of maximum loss reduction loop and branch to reduce optimization time. The effectiveness of the proposed algorithm was shown with 32, 69 bus example system.