• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1208-1222
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• 2014

Due to the low utilization and scarcity of frequency spectrum in current spectrum allocation methodology, cognitive radio networks (CRNs) have been proposed as a promising method to solve the problem, of which spectrum sensing is an important technology to utilize the precious spectrum resources. In order to protect the primary user from being interfered, most of the related works focus only on the restriction of the missed detection probability, which may causes over-protection of the primary user. Thus the interference probability is defined and the interference-aware sensing model is introduced in this paper. The interference-aware sensing model takes the spatial conditions into consideration, and can further improve the network performance with good spectrum reuse opportunity. Meanwhile, as so many fading factors affect the spectrum channel, errors are inevitably exist in the reporting channel in cooperative sensing, which is improper to be ignored. Motivated by the above, in this paper, we study the throughput tradeoff for interference-aware cognitive radio networks over imperfect reporting channel. For the cooperative spectrum sensing, the K-out-of-N fusion rule is used. By jointly optimizing the sensing time and the parameter K value, the maximum throughput can be achieved. Theoretical analysis is given to prove the feasibility of the optimization and computer simulations also shows that the maximum throughput can be achieved when the sensing time and the parameter of K value are both optimized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2286-2309
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• 2016

Overlay networks have been widely deployed upon the Internet by Service Providers (SPs) to provide improved network services. However, the interaction between each overlay and traffic engineering (TE) as well as the interaction among co-existing overlays may occur. In this paper, we adopt both non-cooperative and cooperative game theory to analyze these interactions, which are collectively called hybrid interaction. Firstly, we model a situation of the hybrid interaction as an n+1-player non-cooperative game, in which overlays and TE are of equal status, and prove the existence of Nash equilibrium (NE) for this game. Secondly, we model another situation of the hybrid interaction as a 1-leader-n-follower Stackelberg-Nash game, in which TE is the leader and co-existing overlays are followers, and prove that the cost at Stackelberg-Nash equilibrium (SNE) is at least as good as that at NE for TE. Thirdly, we propose a cooperative coalition mechanism based on Shapley value to overcome the inherent inefficiency of NE and SNE, in which players can improve their performance and form stable coalitions. Finally, we apply distinct genetic algorithms (GA) to calculate the values for NE, SNE and the assigned cost for each player in each coalition, respectively. Analytical results are confirmed by the simulation on complex network topologies.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.75-79
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• 2011

Wireless devices can communicate between each other without existing infrastructure in mobile Ad-hod network. Ad hoc networks can be used under difficult conditions, where it is difficult to construct infrastructures, such as shadowing areas, disaster areas, war area, and so on. In order to support to considerable and various wireless services, more spectrum resources are needed. However, efficient utilization of the frequency resource is difficult because of spectrum scarcity and the conventional frequency regulation. Ad-hoc networks employing cognitive radio (CR) system that guarantee high spectrum utilization provide effective way to increase the network capacity. In this paper, we simulate and compare the performance of conventional single and cooperative spectrum sensing with CR system using ad-hoc networks in additive white Gaussian noise (AWGN) and Rayleigh channel model. And we demonstrate performance improvement by analyzing the system performance.

• ETRI Journal
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• v.43 no.4
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• pp.758-768
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• 2021

In this work, the secrecy of a typical wireless cooperative dual-hop non-orthogonal multiple access (NOMA)-enabled decode-and-forward (DF) relay network is investigated with the impact of collaborative and non-collaborative eavesdropping. The system model consists of a source that broadcasts the multiplexed signal to two NOMA users via a DF relay, and information security against the eavesdropper nodes is provided by a helpful jammer. The performance metric is secrecy rate and ergodic secrecy capacity is approximated analytically. In addition, a differential evolution algorithm-based power allocation scheme is proposed to find the optimal power allocation factors for relay, jammer, and NOMA users by employing different jamming schemes. Furthermore, the secrecy rate analysis is validated at the NOMA users by adopting different jamming schemes such as without jamming (WJ) or conventional relaying, jamming (J), and with control jamming (CJ). Simulation results demonstrate the superiority of CJ over the J and WJ schemes. Finally, the proposed power allocation outperforms the fixed power allocation under all conditions considered in this work.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.39-45
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• 2013

In this paper, we propose a cooperative relaying protocol using Fountain codes for secondary network under interference constraint. In the proposed protocol, a secondary source uses Fountain codes to transmit its message to a secondary destination with help of a secondary relay. The secondary source and relay operate in the underlay model, in which they must adapt their transmit power so that the interference caused at a primary user is lower than an allowable threshold. To evaluate performance of the proposed protocol, we derive the expressions of average number of transmission times over Rayleigh fading channel. Various Monte-Carlo simulations are presented to verify the derivations.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.3
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• pp.872-880
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• 2016

The purpose of this study is to find the research trends relating to flipped learning through keyword network analysis. For investigating this topic, final 100 papers (removed due to overlap in all 205 papers) were selected as subjects from the result of research databases such as RISS, DBPIA, and KISS. After keyword extraction, coding, and data cleaning, we made a 2-mode network with final 202 keywords. In order to find out the research trends, frequency analysis, social network structural property analysis based on co-keyword network modeling, and social network centrality analysis were used. Followings were the results of the research: (a) Achievement, writing, blended learning, teaching and learning model, learner centered education, cooperative leaning, and learning motivation, and self-regulated learning were found to be the most common keywords except flipped learning. (b) Density was .088, and geodesic distance was 3.150 based on keyword network type 2. (c) Teaching and learning model, blended learning, and satisfaction were centrally located and closed related to other keywords. Satisfaction, teaching and learning model blended learning, motivation, writing, communication, and achievement were playing an intermediary role among other keywords.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1481-1501
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• 2019

In opportunistic network, the behavior of a node is autonomous and has social attributes such as selfishness.If a node wants to forward information to another node, it is bound to be limited by the node's own resources such as cache, power, and energy.Therefore, in the process of communication, some nodes do not help to forward information of other nodes because of their selfish behavior. This will lead to the inability to complete cooperation, greatly reduce the success rate of message transmission, increase network delay, and affect the overall network performance. This article proposes a hybrid incentive mechanism (Mim) based on the Reputation mechanism and the Credit mechanism.The selfishness model, energy model (The energy in the article exists in the form of electricity) and transaction model constitute our Mim mechanism. The Mim classifies the selfishness of nodes and constantly pay attention to changes in node energy, and manage the wealth of both sides of the node by introducing the Central Money Management Center. By calculating the selfishness of the node, the currency trading model is used to differentiate pricing of the node's services. Simulation results show that by using the Mim, the information delivery rate in the network and the fairness of node transactions are improved. At the same time, it also greatly increases the average life of the network.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.183-188
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• 2021

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.27-33
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• 2019

Various technical and societal approaches are being made to realize the auto driving (AD) and cooperative driving (CD) including communication network and extended advanced driver support system is under development. In CD, it is important to share the roles of the driver and the system and to secure the stability of the driving, so a efficient interface scheme between the driver and the vehicle is required. This study proposes a research model including driver, system and driving environment considering the role and function of driver and system in CD. An efficient interface between the driver and the vehicle to cope with various driving situations on the CD using the analysis of the driving environment and the research model is also proposed. Through this study, it is expected that the proposed research model and interface scheme could contribute to CD system design, cockpit module development and interface device development.