• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2631-2649
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• 2021

Device-to-Device (D2D) communication is one of the enabling technologies for 5G networks that support proximity-based service (ProSe) for wireless network communications. This paper proposes a power control algorithm based on the Nash equilibrium and game theory to eliminate the interference between the cellular user device and D2D links. This leadsto reliable connectivity with minimal power consumption in wireless communication. The power control in D2D is modeled as a non-cooperative game. Each device is allowed to independently select and transmit its power to maximize (or minimize) user utility. The aim is to guide user devices to converge with the Nash equilibrium by establishing connectivity with network resources. The proposed algorithm with pricing factors is used for power consumption and reduces overall interference of D2Ds communication. The proposed algorithm is evaluated in terms of the energy efficiency of the average power consumption, the number of D2D communication, and the number of iterations. Besides, the algorithm has a relatively fast convergence with the Nash Equilibrium rate. It guarantees that the user devices can achieve their required Quality of Service (QoS) by adjusting the residual cost coefficient and residual energy factor. Simulation results show that the power control shows a significant reduction in power consumption that has been achieved by approximately 20% compared with algorithms in [11].

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.34-49
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• 2015

Market means of spectrum trading have been utilized as a vital method of spectrum sharing and access in future cognitive radio system. In this paper, we consider the spectrum trading with multiple primary carrier providers (PCP) leasing the spectrum to multiple secondary carrier providers (SCP) for a short period of time. Several factors including the price of the resource, duration of leasing, and the spectrum quality guides the proposed model. We formulate three trading policies based on the game theory for dynamic spectrum access in a LTE based cognitive radio system (CRS). In the first, we consider utility function based resource sharing (UFRS) without any knowledge of past transaction. In the second policy, each SCP deals with PCP using a non-cooperative resource sharing (NCRS) method which employs optimal strategy based on reinforcement learning. In variation of second policy, third policy adopts a Nash bargaining while incorporating a recommendation entity in resource sharing (RERS). The simulation results suggest overall increase in throughput while maintaining higher spectrum efficiency and fairness.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.264-273
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• 2013

Credit option is a policy that has been studied by many researchers in the area of supply chain management. This policy has been applied in practice to improve the profits of supply chain members. Usually, a credit option policy is proposed by the seller, and often under a symmetric information environment where members have complete information on each others' operations. In this paper, we investigate two scenarios: firstly, the seller offers a credit option to the buyer, and secondly, the buyer attempts to stretch the length of the credit period offered by the seller. The proposed model in both scenarios will be investigated under an asymmetric information structure where some information are private and are only known to the individual who has knowledge of this information. The interactions between buyer and seller will be modeled by non-cooperative Stackelberg games where the buyer and seller take turn as leader and follower. Among some of the numerical results obtained, the seller and buyer's profits obtained from symmetric information games are larger than those obtained from an asymmetric information game in both scenarios. Furthermore, both buyer and seller's profit in the second scenario are better than in the first scenario.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.591-593
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• 2016

Device-to-device (D2D) communications underlaid cellular infrastructure is an competitive local area services technology to promote spectrum usage for next generation cellular networks. These potential can only be tap through efficient interference coordination. Previous works only concentrated on interference from D2D pairs whiles interference from CUs to D2D pairs were neglected. This work focus on solving uplink interference problem emanating from multiple CUs sharing its resource with multiple D2D pairs. The base station (BS) acting as a supervisor selfishly institute a pricing scheme to manage the interference it experience from D2D pairs based on its Quality of service (QoS) requirement. D2D pairs following the supervisor make power allocation decisions considering the price from the BS in a non-cooperative game fashion. In order for the D2D pairs to also meet their QoS requirement, they suggest a price to the BS called discount price which reflects the interference they receive from the CUs. Finally, we analyze the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.543-552
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• 2013

For the safety messages in IEEE 802.11p/WAVE vehicles network environment, strict periodic beacon broadcasting requires status advertisement to assist the driver for safety. In crowded networks where beacon message are broadcasted at a high number of frequencies by many vehicles, which used for beacon sending, will be congested by the wireless medium due to the contention-window based IEEE 802.11p MAC. To resolve the congestion, we consider a MAC scheme based on slotted p-persistent CSMA as a simple non-cooperative Bayesian game which involves payoffs reflecting the attempt probability. Then, we derive Bayesian Nash Equilibrium (BNE) in a closed form. Using the BNE, we propose new congestion control algorithm to improve the performance of the beacon rate under saturation condition in IEEE 802.11p/WAVE vehicular networks. This algorithm explicitly computes packet delivery probability as a function of contention window (CW) size and number of vehicles. The proposed algorithm is validated against numerical simulation results to demonstrate its stability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.71-82
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• 2009

As a solution to spectrum under-utilization problem, Cognitive radio (CR) introduces a dynamic spectrum access technology. In the area, one of the most important problems is how secondary users (SUs) should choose between the available channels, which means how to achieve load balancing between channels. We consider spectrum load balancing problem for CR system in frequency domain and especially in time domain. Our objective is to balance the load among the channels and balance the occupied time length of slots for a fixed channel dynamically in order to obtain a user-optimal solution. In frequency domain, we refer to Dynamic Noncooperative Scheme with Communication (DNCOOPC) used in distributed system and a distributed Dynamic Spectrum Load Balancing algorithm (DSLB) is formed based on DNCOOPC. In time domain, Spectrum Load Balancing method with QoS support is proposed based on Dynamic Feed Back theory and Hash Table (SLBDH). The performance of DSLB and SLBDH are evaluated. In frequency domain, DSLB is more efficient compared with existing Compare_And_Balance (CAB) algorithm and gets more throughput compared with Spectrum Load Balancing (SLB) algorithm. Also, DSLB is a fair scheme for all devices. In time domain, SLBDH is an efficient and precise solution compared with Spectrum Load Smoothing (SLS) method.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.10-20
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• 2024

The conservation and restoration of wetlands are essential tasks for the sustainable development of human society and the environment, providing vital benefits such as biodiversity maintenance, natural disaster mitigation, and climate change alleviation. This study aims to analyze the strategic interactions and interests among various stakeholders using game theory and to provide significant grounds for policy decisions related to wetland restoration and development. In this study, hypothetical scenarios were set up for three types of cities: large, medium, and small. Stakeholders such as governments, development companies, environmental groups, and local residents were identified. Strategic options for each stakeholder were developed, and a payoff matrix was established through discussions among wetland ecology experts. Subsequently, non-cooperative game theory was applied to analyze Nash equilibria and Pareto efficiency. In large cities, strategies of 'Wetland Conservation' and 'Eco-Friendly Development' were found beneficial for all stakeholders. In medium cities, various strategies were identified, while in small cities, 'Eco-Friendly Development' emerged as the optimal solution for all parties involved. The Pareto efficiency analysis revealed how the optimal solutions for wetland management could vary across different city types. The study highlighted the importance of wetland conservation, eco-friendly development, and wetland restoration projects for each city type. Accordingly, policymakers should establish regulations and incentives that harmonize environmental protection and urban development and consider programs that promote community participation. Understanding the roles and strategies of stakeholders and the advantages and disadvantages of each strategy is crucial for making more effective policy decisions.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.147-156
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• 2009

We present a methodology for modeling and solving the transit frequency design problem with variable demand. The problem is described as a bi-level model based on a non-cooperative Stackelberg game. The upper-level operator problem is formulated as a non-linear optimization model to minimize net cost, which includes operating cost, travel cost and revenue, with fleet size and frequency constraints. The lower-level user problem is formulated as a capacity-constrained stochastic user equilibrium assignment model with variable demand, considering transfer delay between transit lines. An efficient algorithm is also presented for solving the proposed model. The upper-level model is solved by a gradient projection method, and the lower-level model is solved by an existing iterative balancing method. An application of the proposed model and algorithm is presented using a small test network. The results of this application show that the proposed algorithm converges well to an optimal point. The methodology of this study is expected to contribute to form a theoretical basis for diagnosing the problems of current transit systems and for improving its operational efficiency to increase the demand as well as the level of service.