• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.100-105
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• 2017

In this paper, we study an interference avoidance scenario in the presence of a interferer which can rapidly observe the transmit power of backscatter communications and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a power-splitting ratio for backscattering and RF energy harvesting in sensors. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, power-splitting ratio, and sub-channel allocation parameter to maximize a utility function against the interferer. We propose the utility maximization using Lagrangian dual decomposition for the backscatter communications and the interferer to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome a malicious interference in backscatter communications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2709-2734
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• 2020

The growing demand to make wireless data services 5G compatible has necessitated the development of an energy-efficient approach for an effective new wireless environment. In this paper, we first propose a cognitive sensor node (CSN) based game theory for deriving energy via a primary user-transmitted radio frequency signal. Cognitive users' time was segmented into three phases based on a time switching protocol: energy harvest, spectrum sensing and data transmission. The proposed model chooses the optimal energy-harvesting phase as the effected factor. We further propose a distributed energy-harvesting model as a utility function via pricing techniques. The model is a non-cooperative game where players can increase their net benefit in a selfish manner. Here, the price is described as a function pertaining to transmit power, which proves that the proposed energy harvest game includes Nash Equilibrium and is also unique. The best response algorithm is used to achieve the green connection between players. As a result, the results obtained from the proposed model and algorithm show the advantages as well as the effectiveness of the proposed study. Moreover, energy consumption was reduced significantly (12%) compared to the benchmark algorithm because the proposed algorithm succeeded in delivering energy in micro which is much better compared to previous studies. Considering the reduction and improvement in power consumption, we could say the proposed model is suitable for the next wireless environment represented in 5G.

• Journal of the Korea Society for Simulation
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• v.21 no.1
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• pp.9-17
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• 2012

War-game simulation models the situation of a battlefield and has been used for evaluating fighting power and analyzing the occupation of a troop. However, in war-game simulation environment, it is very complex to consider all factors which can be influenced in real battlefields. To solve the problem of the consideration, we propose an ontology-based path finding model. This model uses an ontology to conceptualize the situation data of a battlefield and represents the relations among the concepts. In addition, we extract new knowledge from the war-game ontology by defining some inference rules and share knowledge by the established rules. For the performance evaluation of the proposed model, we made a limitation on the simulation environment and measure the moving time of a troop, the fighting capability of a troop, and the necessary cost while a troop is moving. Experimental results show that this model provides many advantages in aspects of the moving time, a loss of fighting capability, and the necessary cost.

• Journal of Distribution Research
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• v.15 no.1
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• pp.25-47
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• 2010

CS is one of the major concerns of managers in the world because it is well known to be a key medium construct for firms' superior outcome. One of the major agents for CS management is retailers. Firms try to manage not only employees but also retailers to promote CS behaviors. And so diverse incentives are used to promote their CS behaviors under diverse channel setting such as multi-channel. However in spite of the rising needs there has been scarce studies on the optimal incentive structure for a manufacturer to offer competing retailers at the multi-channel. In this paper, we try to find better way for a manufacturer to promote the competing retailers' CS behaviors. We investigated how to promote the retailers' CS behavior via game-theoretic modeling. Especially, we focus on the possible incentive, CS bonus type reward introduced in the studies of Hauser, Simester, and Wernerfelt(1994) and Chu and Desai(1995). We build up a multi stage complete information game and derive a subgame perfect equilibrium using backward induction. Stages of the game are as following. (Stage 1) Manufacturer sets wholesale price(w) and CS bonus($\eta$). (Stage 2) Both retailers in competition set CS effort level($e_i$) and retail price($p_i$) simultaneously. (Stage 3) Consumers make purchasing decisions based on the manufacturer's initial reputation and retailers' CS efforts.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.129-147
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• 2014

This study proposes an optimal operational policy for a green supply chain (GSC) where a retailer pays an incentive for collection of used products from customers and determines the optimal order quantity of a single product under uncertainty in product demand. A manufacturer produces the optimal order quantity of product using recyclable parts with acceptable quality levels and covers a part of the retailer's incentive from the recycled parts. Here, two scenarios for the product demand are assumed as: the distribution of product demand is known, and only both mean and variance are known. This paper develops mathematical models to find how order quantity, collection incentive of used products and lower limit of quality level for recycling affect the expected profits of each member and the whole supply chain under both a decentralized GSC (DGSC) and an integrated GSC (IGSC). The analysis numerically compares the results under DGSC with those under IGSC for each scenario of product demand. Also, the effect of the quality of the recyclable parts on the optimal decisions is shown. Moreover, supply chain coordination to shift the optimal decisions of IGSC is discussed based on: I) profit ratio, II) Nash bargaining solution, and III) Combination of (I) and (II).

• ETRI Journal
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• v.41 no.5
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• pp.626-636
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• 2019

A novel algorithm for joint user selection and optimal power allocation for Stackelberg game-based revenue maximization in a downlink non-orthogonal multiple access (NOMA) network is proposed in this study. The condition for the existence of optimal solution is derived by assuming perfect channel state information (CSI) at the transmitter. The Lagrange multiplier method is used to convert the revenue maximization problem into a set of quadratic equations that are reduced to a regular chain of expressions. The optimal solution is obtained via a univariate iterative procedure. A simple algorithm for joint optimal user selection and power calculation is presented and exhibits extremely low complexity. Furthermore, an outage analysis is presented to evaluate the performance degradation when perfect CSI is not available. The simulation results indicate that at 5-dB signal-to-noise ratio (SNR), revenue of the base station improves by at least 15.2% for the proposed algorithm when compared to suboptimal schemes. Other performance metrics of NOMA, such as individual user-rates, fairness index, and outage probability, approach near-optimal values at moderate to high SNRs.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.44-57
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• 2015

In recent years, for the purpose of solving the problem regarding environment protection and resource saving, certain measures and policies have been promoted to establish a reverse supply chains (RSCs) with material flows from collection of used products to reuse the recycled parts in production of products. It is necessary to analyze behaviors of RSC members to determine the optimal operation. This paper discusses a RSC with a retailer and a manufacturer and verifies the behavior strategies of RSC members which may change over time in response to changes parameters related to the recycling promotion activity in RSC. A retailer takes two behaviors: cooperation/non-cooperation in recycling promotion activity. A manufacturer takes two behaviors: monitoring/non-monitoring of behaviors of the retailer. Evolutionary game theory combining the evolutionary theory of Darwin with game theory is adopted to clarify analytically evolutionary outcomes driven by a change in each behavior of RSC members over time. The evolutionary stable strategies (ESSs) for RSC members' behaviors are derived by using the replicator dynamics. The analysis numerically demonstrates how parameters of the recycling promotion activity: (i) sale promotion cost, (ii) monitoring cost, (iii) compensation and (iv) penalty cost affect the judgment of ESSs of behaviors of RSC members.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.79-87
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• 2006

This paper proposes the design and implementation of engine to control characters by using machine teaming techniques. Because the proposed engine uses the context data in the rum time as the knowledge data, there is a merit which the player can not easily recognize the behavior pattern of the intelligent character. To do this, the paper proposes to develop the module which gathers and trains the context data and the module which tests to decide the optimal context control for the given context data. The developed engine is ported to FEAR and run with Quake2 and experimented far the correctness of the development and its efficiency. The experiments show that the developed engine is operated well and efficiently within the limited time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1973-1991
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• 2018

Multimedia applications over wireless networks have been evolving to augmented reality or virtual reality services. However, a rich data size compared to conventional multimedia services causes bandwidth bottlenecks over wireless networks, which is one of the main reasons why those applications are not used widely. To overcome this limitation, bandwidth aggregation techniques, which exploit a multi-path transmission, have been considered to maximize link utilization. Currently, most of the conventional researches have been focusing on the user end problems to improve the quality of service (QoS) through optimal load distribution. In this paper, we address the joint pricing and load distribution problem for multi-homing in heterogeneous wireless access networks (ANs), considering the interests of both the users and the service providers. Specifically, we consider profit from resource allocation and cost of power consumption expenditure for operation as an utility of each service provider. Here, users decide how much to request the resource and how to split the resource over heterogeneous wireless ANs to minimize their cost while supporting the required QoS. Then, service providers compete with each other by setting the price to maximize their utilities over user reactions. We study the behaviors of users and service providers by analyzing their hierarchical decision-making process as a multileader-, multifollower Stackelberg game. We show that both the user and service provider strategies are closed form solutions. Finally, we discuss how the proposed scheme is well converged to equilibrium points.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.