• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권5호
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• pp.691-708
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• 2010

An important issue of supporting multi-users with diverse quality-of-service (QoS) requirements over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resource while, at the same time, to meet each communication service QoS requirement. In this work, we study the problem of a variety of communication services over multi-media heterogeneous cognitive OFDM system. We first divide the communication services into two parts. Multimedia applications such as broadband voice transmission and real-time video streaming are very delay-sensitive (DS) and need guaranteed throughput. On the other side, services like file transmission and email service are relatively delay tolerant (DT) so varying-rate transmission is acceptable. Then, we formulate the scheduling as a convex optimization problem, and propose low complexity distributed solutions by jointly considering channel assignment, bit allocation, and power allocation. Unlike prior works that do not care computational complexity. Furthermore, we propose the FAASA (Fairness Assured Adaptive Sub-carrier Allocation) algorithm for both DS and DT users, which is a dynamic sub-carrier allocation algorithm in order to maximize throughput while taking into account fairness. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4197-4219
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• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• 한국시뮬레이션학회논문지
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• 제11권3호
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• pp.23-34
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• 2002

Dynamic storage allocation (DSA) is a field fairly well studied for a long time as a basic problem of system software area. Due to memory fragmentation problem of DSA and its unpredictable worst case execution time, real-time system designers have believed that DSA may not be promising for real-time application service. Recently, the need for an efficient DSA algorithm is widely discussed and the algorithm is considered to be very important in the real-time system. This paper proposes an efficient DSA algorithm called QSB (quick semi-buddy) which is designed to be suitable for real-time environment. QSB scheme effectively maintains free lists based on quick-fit approach to quickly accommodate small and frequent memory requests, and the other free lists devised with adaptation upon a typical binary buddy mechanism for bigger requests in harmony for the .improved performance. Comprehensive simulation results show that the proposed scheme outperforms QHF which is known to be effective in terms of memory fragmentation up to about 16%. Furthermore, the memory allocation failure ratio is significantly decreased and the worst case execution time is predictable.

• ETRI Journal
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• 제38권6호
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• pp.1153-1162
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• 2016

This paper addresses the resource allocation (RA) problem in multi-cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered-such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP-hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub-gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2240-2254
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• 2021

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• 대한임베디드공학회논문지
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• 제18권4호
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• pp.195-201
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• 2023

The degree of contribution each sensor makes towards the total information gathered by all sensors is not uniform in spatially correlated sensor fields. Considering bit allocation problem in such a spatially correlated sensor field, the number of bits to be allocated to each sensor should be proportional to the degree of contribution the sensor makes. In this paper, we deploy Shapley value, a representative solution concept in cooperative game theory, and utilize it in order to quantify the degree of contribution each sensor makes. Shapley value is a system that determines the contribution of an individual player when two or more players work in collaboration with each other. To this end, we cast the bit allocation problem into a cooperative game called bit allocation game where sensors are regarded as the players, and a payoff function is given in the criteria of mutual information. We show that the Shapley value fairly quantifies an individual sensor's contribution to the total payoff achieved by all sensors following its desirable properties. By numerical experiments, we confirm that sensor that needs more bits to cover its area has larger Shapley value in spatially correlated sensor fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• 한국철도학회논문집
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• 제9권2호
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• pp.206-211
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• 2006

This thesis presents railroad crew restoring problem, which is to determine the railroad plan allocation. This problem is constructed that determine the sequence of duties that railroad crews have to perform. We analyze characteristic of this problem and railroad industry. It's hard to consider many constraint conditions. We propose Integer Programming model and easy methodology to be considered all given operation rules. This problem is known to be NP-hard. We develop a genetic algorithm, which is proved to be powerful in solving optimization problems. We proposed the effective mathematical model and algorithm about making crew restoring in real industry.

• 한국ITS학회 논문지
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• 제17권6호
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• pp.211-223
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• 2018

릴레이 시스템은 커버리지 확장과 셀 경계(Cell-Edge)의 시스템 처리량 향상을 목적으로 4세대 이동통신 시스템에 적용되어 왔다. 릴레이 시스템은 커버리지 확장과 시스템 처리량 증대에 효과적이지만 기존 단일 홉 시스템과 달리 추가 자원을 사용하기 때문에 릴레이 시스템에 특화된 경로선택 및 무선자원 할당 알고리즘의 적용을 요구한다. 본 논문에서는 협력 통신을 이용하는 LTE-Advanced 릴레이 시스템을 위한 통합 경로선택 및 자원할당 기법을 제안한다. 제안하는 기법은 라그랑지 승수 기반의 휴리스틱 알고리즘으로, 다중차원 다중선택 배낭 문제(Multi-dimensional Multi-choice Knapsack Problem)의 형태로 정의된 협력 통신 기반의 LTE-Advanced 릴레이 시스템 하향링크 처리율 최대화 문제의 근사 해를 구한다. 제안된 기법에 의해 도출된 근사 해의 성능이 최적 해의 성능에 충분히 근접할 수 있음을 시뮬레이션을 통해 보인다.