• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.167-174
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• 2014

In this paper, we address a task allocation problem for a robot team that performs a search and attack mission. The robots are limited in sensing and communication capabilities, and carry different types of resources that are used to attack a target. The environment is uncertain and dynamic where no prior information about targets is given and dynamic events unpredictably happen. The goal of robot team is to collect total utilities as much as possible by destroying targets in a mission horizon. To solve the problem, we propose a distributed task allocation framework incorporated with effective resource management based on resource welfare. The framework we propose enables the robot team to retain more robots available by balancing resources among robots, and respond smoothly to dynamic events, which results in system performance improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4177-4190
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• 2015

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.244-245
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• 2005

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• Journal of the Korea Society for Simulation
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• v.9 no.2
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• pp.63-73
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• 2000

Due to the complexity of part flow and limited resources, FMS(Flexible Manufacturing System) develops blocking, starvation and deadlock problems, which reduce its performance. In order to minimize such problems buffers are imposed between workstations of the manufacturing lines. In this paper, we are concerned with finding the optimal buffer allocation with regard to maximizing system throughput in limited total buffer capacity situation of FMS. A dynamic programming algorithm to solve the buffer allocation problem is proposed. Computer simulation using Arena is experimented to show the validation of the proposed algorithm.

• KDI Journal of Economic Policy
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• v.36 no.4
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• pp.1-32
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• 2014

Auction is widely used in allocation and procurement of resources due to its desirable properties: efficiency and revenue maximization. It is well-known, however, that auction may fail to achieve efficiency when allocative externalities exist. Such a result may happen in the auction of the resources that are very scarce, for example, radio spectrum. This is because allocation of the resources has effects on competition of the firms in the aftermarket, and thus a firm that utilizes the resources less efficiently may make a higher bid to lessen competition. This paper shows first that efficient allocation may not be achieved by auction even when the number of bidders is 2, while it is shown in the literature that auction may result in inefficient allocation when the number of bidders is greater than or equal to 3. There exist 2 firms, who make a bid to win the scarce resources that increase the value or decrease the production cost of their own product. After the auction ends, the firms engage in Bertrand competition on the Hotelling line. Inefficient allocation may happen even under the second-price auction rule, and it happens only when the firms are different in the initial value or the initial cost of their products as well as in the value of the auctioned resources. The firm who has been the leader loses a large portion of the market if it fails to win the auction, and thus makes a high bid even when the other firm can use the resources more efficiently. Allocative efficiency Pareto improves when the smaller firm's bid counts more than the leader's bid. This paper suggests a modified rule that the smaller firm wins the auction when its bid multiplied by some constant is greater than the leader's bid. The multiplier can be calculated from the market shares. It is equal to 1 when the two firms are the same, and is increasing in the leader's market share. Allocation is efficient in a strictly larger set of parameters under the modified rule than under the standard second-price auction rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.220-222
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• 2021

In this paper, we propose a resource allocation scheme based on entropy for tactical wireless networks. In a tactical situation, the sensing nodes that are located randomly provide transmitted data values depending on environmental conditions. Since they share wireless resources, nodes providing valuable data compared to others need to have more resources. The proposed scheme evaluates the value of received data by a sink node. Based on the results, the sink node reallocates resources to sensing nodes. Through various experiments, we verified the proposed scheme is superior to the fixed allocation scheme.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.440-448
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• 2022

Recently, due to the introduction of distributed energy resources, the optimal resource allocation problem of the power network is more and more important, and the distributed resource allocation method is required to process huge amount of data in large-scale power networks. In the optimal resource allocation problem, many studies have been conducted on the case when the supply-demand balance is satisfied due to the limitation of the generation capacity of each generator, but the studies considering the supply-demand imbalance, that total demand exceeds the maximum generation capacity, have rarely been considered. In this paper, we propose the consensus-based distributed algorithm for the optimal resource allocation of power network considering the supply-demand imbalance condition as well as the supply-demand balance condition. The proposed distributed algorithm is designed to allocate the optimal resources when the supply-demand balance condition is satisfied, and to measure the amount of required resources when the supply-demand is imbalanced. Finally, we conduct the simulations to verify the performance of the proposed algorithm.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.121-127
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• 2020

In this paper, we propose a dynamic spectrum hole grouping method that changes the grouping range of spectrum hole according to the resources amount required by secondary users in wideband spectrum cognitive wireless networks, and then the proposed method is applied to channel allocation for the secondary user service. The proposed method can improve waste of resources in the existing static spectrum hole grouping in virtue of grouping dynamically as much the predicted spectrum holes resources as secondary users require. Simulation results show that channel allocation method with the proposed dynamic grouping outperforms that with the static grouping method in resources utilization under acceptable secondary user service performance.