• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

• ETRI Journal
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• v.34 no.1
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• pp.102-105
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• 2012

To serve the growing demand of the bidirectional information exchange, we propose a single relay selection (RS) scheme for physical-layer network coding (PNC) in a bidirectional cooperative network consisting of two sources and multiple relays. This RS scheme selects a single best relay by maximizing the bottleneck of the capacity region of both information flows in the bidirectional network. We show that the proposed RS rule minimizes the outage probability and that it can be used as a performance benchmark for any RS rules with PNC. We derive a closed-form exact expression of the outage probability for the proposed RS rule and show that it achieves full diversity gain. Finally, numerical results demonstrate the validity of our analysis.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.53-58
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• 2017

A WSN (Wireless Sensor Network) is a network that is composed of wireless sensor nodes. There is no restriction on the place where it can be installed because it is composed wirelessly. Instead, sensor nodes have limited energy. Therefore, to use the network for a long time, energy consumption should be minimized. Several protocols have been proposed to minimize energy consumption, and the typical protocol is the LEACH protocol. The LEACH protocol is a cluster-based protocol that minimizes energy consumption by dividing the sensor field into clusters. Depending on how you organize the clusters of sensor field, network lifetimes may increase or decrease. In this paper, we will improve the network lifetime by improving the cluster head selection method in LEACH Protocol.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.351-356
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• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• IE interfaces
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• v.24 no.2
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• pp.97-104
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• 2011

Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments' results show improved performance in various classification problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3133-3151
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• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.11a
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• pp.45-57
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• 2002

Since a new entrant in the telecommunications market requires time in order to construct its own network, a requirement on the incumbent operator to implement carrier selection and pre-selection can enable a new entrant to immediately attract customers and earn revenue. Carrier selection can normally be accomplished in two ways, on a call-by-call basis or through carrel pre-selection. Call-by-call selection allows customers to choose a new entrant rather than the incumbent carrier using a specific code designated to the new carrier each time a call is made. Carrier Pre-Selection, on the other hand, allows customers directly connect to the network of one provider to have access automatically to another company's services when they pick up the phone to make certain types of calls. The carrier pre-selection option is generally considered to be a second regulatory step following the implementation of the call-by-call carrier selection option. Carrier pre-selection with the ability to override on a call-by-call basis for long distance, international, local, and fixed-to-mobile calls has now been implemented in many EU countries. This paper attempts to identify the issues in introducing CPS and to draw policy implications from other countries' experiences.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.1-19
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• 1994

A generalized network problem is a special class of linear programming problem whose coefficient matrix contains at most two nonzero elements per column. A generalized network problem with 0-1 flow restrictions is called an integer generalized network(IGN) problem. In this paper, we presented a branch and bound algorithm for the IGN that uses network relaxation. To improve the procedure, we develop various strategies, each of which employs different node selection criterion and/or branching variable selection criterion. We test these solution strategies and compare their efficiencies with LINDO on 70 randomly generated problems.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.434-442
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• 2012

Several gateway selection schemes have been proposed that select gateway nodes based on a single Quality of Service (QoS) path parameter, for instance path availability period, link capacity or end-to-end delay, etc. or on multiple non-QoS parameters, for instance the combination of gateway node speed, residual energy, and number of hops, for Mobile Ad hoc NETworks (MANETs). Each scheme just focuses on the ment of improve only a single network performance, i.e., network throughput, packet delivery ratio, end-to-end delay, or packet drop ratio. However, none of these schemes improves the overall network performance because they focus on a single QoS path parameter or on set of non-QoS parameters. To improve the overall network performance, it is necessary to select a gateway with stable path, a path with themaximum residual load capacity and the minimum latency. In this paper, we propose a gateway selection scheme that considers multiple QoS path parameters such as path availability period, available capacity and latency, to select a potential gateway node. We improve the path availability computation accuracy, we introduce a feedback system to updated path dynamics to the traffic source node and we propose an efficient method to propagate QoS parameters in our scheme. Computer simulations show that our gateway selection scheme improves throughput and packet delivery ratio with less per node energy consumption. It also improves the end-to-end delay compared to single QoS path parameter gateway selection schemes. In addition, we simulate the proposed scheme by considering weighting factors to gateway selection parameters and results show that the weighting factors improve the throughput and end-to-end delay compared to the conventional schemes.