• 한국지능시스템학회논문지
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• 제10권5호
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• pp.423-431
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• 2000

In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• 한국컴퓨터정보학회논문지
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• 제20권2호
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• pp.89-97
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• 2015

본 논문에서는 이동 클라우딩 환경에서 LBS(Location Based Services)를 지원하는 핸드오프 기법을 제안한다. 이동 클라우딩 컴퓨팅은 핸드오프 지연과 함께 재인증 지연이 발생한다. 단말이 새로운 AP에 도착하면 클라우드 서버로부터 다시 인증을 받는 절차가 필요하다. 하지만 위치정보를 이동 단말의 재인증에 사용하면 인증에 따른 지연시간을 감축시킬 수 있다. 이를 위해 제안하는 기법은 위성신호를 받지 못하는 실내에서 사용할 수 없는 GPS기반 기술을 보완할 수 있는 WiFi 기반의 위치정보 서버에 AP의 위치정보를 구축하고 핸드오프 수행 시에 위치정보를 수집하도록 하였다. 또한 핸드오프 시에 위치정보 요청을 분리하여 처리하도록 하여 핸드오프지연 시간의 증가 없이 LBS 처리를 가능하도록 하였다. 성능 분석은 핸드오프 지연시간과 위치정보 처리시간과 이에 따라 발생하는 데이터의 양을 기존의 클라우드 환경에서의 핸드오프 기법과 비교 분석하였다. 제안한 기법은 핸드오프 시에 위치정보 서버를 통해 위치정보를 수신하도록 하여 LBS 처리에 따른 지연시간이 낮았으며 핸드오프 수행시간이 증가하지 않는 것을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제19권2호
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• pp.67-75
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• 2014

본 논문에서는 이동환경에서 이동단말이 핸드오프 시간과 오프로딩 시간을 측정하여 오프로딩의 수행을 판단하는 오프로딩 지연기법을 제안한다. 제안한 기법은 이동단말에서 핸드오프와 오프로딩 지연시간을 비교하여 오프로딩을 결정할 수 있도록 하여 고정노드를 대상으로 구현된 클라우드 컴퓨팅환경의 구조의 변경 없이 이동환경 클라우드 컴퓨팅을 지원한다. 효율성 분석을 위해 기존 연구에서 사용하는 서버와 단말의 에너지 소비측정을 사용하여 기존 방법과 에너지 소비를 비교 분석하였다. 모의실험 결과 오프로딩 지연 기법은 기존 방법보다 에너지 소비를 감소시키면서 유사한 작업수행 시간을 보이는 것을 확인하였다.

• 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.

• Journal of Positioning, Navigation, and Timing
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• 제10권4호
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• pp.363-369
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• 2021

Since the Global Navigation Satellite System (GNSS) signal received from the low Earth orbit (LEO) satellite is only affected by the upper ionosphere, the magnitude of the ionospheric delay of Global Positioning System (GPS) signal received from ground user is different. Therefore, the ground-based two-dimensional ionospheric model cannot be applied to LEO satellites. The NeQuick model used in Galileo provides the ionospheric delay according to the user's altitude, so it can be used in the ionospheric model of the LEO satellites. However, the NeQuick model is not suitable for space receivers because of the high computational cost. A simplified NeQuick model with reduced computing time was recently presented. In this study, the computing time of the NeQuick model and the simplified NeQuick model was analyzed based on the GPS Klobuchar model. The NeQuick and simplified NeQuick model were applied to the GNSS data from GRACE-B, Swarm-C, and GOCE satellites to analyze the performance of the ionospheric correction and positioning. The difference in computing time between the NeQuick and simplified NeQuick model was up to 90%, but the difference in ionospheric accuracy was not as large as within 4.5%.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.51-57
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• 2021

In this paper, we propose a task distribution scheme in fog computing environment considering opportunistic fog computing nodes. As latency is one of the important performance metric for IoT(Internet of Things) applications, there have been lots of researches on the fog computing system. However, since the load can be concentrated to the specific fog computing nodes due to the spatial and temporal IoT characteristics, the load distribution should be considered to prevent the performance degradation. Therefore, this paper proposes a task distribution scheme which considers the static as well as opportunistic fog computing nodes according to their mobility feature. Especially, based on the task requirements, the proposed scheme supports the delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of the opportunistic fog nodes for the task distribution. Based on the performance evaluation, the proposed scheme shows low service response time compared to the conventional schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3100-3116
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• 2016

With the rapid development of urban traffic system and fast increasing of vehicle numbers, the traditional centralized ways to generate the source-destination shortest path in terms of travel time(the optimal path) encounter several problems, such as high server pressure, low query efficiency, roads state without in-time updating. With the widespread use of smart cameras in the urban traffic and surveillance system, this paper maps the optimal path finding problem in the dynamic road network to the shortest routing problem in the smart camera networks. The proposed distributed optimal path generation algorithm employs the delay routing and caching mechanism. Real-time route update is also presented to adapt to the dynamic road network. The test result shows that this algorithm has advantages in both query time and query packet numbers.

• IEIE Transactions on Smart Processing and Computing
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• 제4권6호
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• pp.391-402
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• 2015

In this paper, we present implementation of backward movement control of truck-trailer vehicles using a fuzzy mode-based control scheme considering practical constraints and computational overhead. We propose a fuzzy feedback controller where output is predicted with the delay of a unit sampling period. Analysis and design of the proposed controller is very easy, because it is synchronized with sampling time. Stability analysis is also possible when quantization exists in the implementation of fuzzy control architectures, and we show that if the trivial solution of the fuzzy control system without quantization is asymptotically stable, then the solutions of the fuzzy control system with quantization are uniformly ultimately bounded. Experimental results using a toy truck show that the proposed control system outperforms a conventional system.