• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.219-233
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• 2008

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. This paper introduces a Duplication based Task Scheduling with Communication Cost in Heterogeneous Systems (DTSC), which provides optimal results for applications represented by Directed Acyclic Graphs (DAGs), provided a simple set of conditions on task computation and network communication time could be satisfied. Results from an extensive simulation show significant performance improvement from the proposed techniques over the Task duplication-based scheduling Algorithm for Network of Heterogeneous systems(TANH) and General Dynamic Level(GDL) scheduling algorithm.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.21-43
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• 2008

The task network which is formed of different individuals can be recognized as a social network. Therefore, the way to communicate with people inside or outside the network has considerable influence on their outcome. Moreover, the position on which a member stands in a network shows the different effects of the information systems supporting communication with others. In this paper, it is to be studied how personal CMC (computer-mediated communication) tools affect the mission that those who work for a network perform through diverse task networks. Especially, we focused on synchronicity of CMC. On this score, the perspective of Media Synchronicity Theory was taken that had been suggested by criticizing Media Richness Theory. It is the objective, from this perspective, to find which characteristics of networks make the value of IT supporting synchronicity high. In the research trends of social networks, there have been two traditional perspectives to explain the effect of network: embeddedness and diversity ones. These differ from the aspect which type of social network can provide much more economic benefits. As similar studies have been reported by various researchers, these are also divided into the bonding and bridging views which are based on internal and external tie, respectively, Size, density, and centrality were measured as the characteristics of personal task networks. Size means the level of relationship between members. It is the total number of other colleagues who work with a specific member for a certain project. It means, the larger the size of task network, the more the number of coworkers who interact each other through the job. Density is the ratio of the number of relationships arranged actually to the total number of available ones. In an ego-centered network, it is defined as the ratio of the number of relationship made really to the total number of possible ones between members who are actually involved each other. The higher the level of density, the larger the number of projects on which the members collaborate. Centrality means that his/her position is on the exact center of whole network. There are several methods to measure it. In this research, betweenness centrality was adopted among them. It is measured by the position on which one member stands between others in a network. The determinant to raise its level is the shortest geodesic that represents the shortest distance between members. Centrality also indicates the level of role as a broker among others. To verify the hypotheses, we interviewed and surveyed a group of employees of a nationwide financial organization in which a groupware system is used. They were questioned about two CMC applications: MSN with a higher level of synchronicity and email with a lower one. As a result, the larger the size of his/her own task network, the smaller its density and the higher the level of his/her centrality, the higher the level of the effect using the task network with CMC tools. Above all, this positive effect is verified to be much more produced while using CMC applications with higher-level synchronicity. Among the a variety of situations under which the use of CMC gives more benefits, this research is considered as one of rare cases regarding the characteristics of task network as moderators by focusing ITs for the operation of his/her own task network. It is another contribution of this research to prove empirically that the values of information system depend on the social, or comparative, characteristic of time. Though the same amount of time is shared, the social characteristics of users change its value. In addition, it is significant to examine empirically that the ITs with higher-level synchronicity have the positive effect on productivity. Many businesses are worried about the negative effect of synchronous ITs, for their employees are likely to use them for personal social activities. However. this research can help to dismiss the concern against CMC tools.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1633-1642
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• 2002

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of the typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (linear vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN is generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.105-113
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• 2019

A construction project is a complex process consisting of interdependent tasks. If one task has variation, other tasks will be affected additionally and it is difficult to reach consensus on task variation due to the characteristics of the construction site that is going on quickly and complicatedly. Therefore, this study identified the root cause of task variation in construction process. Adjacent matrix was created with the tasks corresponding to major causes of variation, and the network analysis was performed. The results of social network analysis in the construction process can stabilize the work flow in the complex process and improve the reliability of the process plan and the project performance.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1000-1010
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• 2019

Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.472-474
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• 2019

Because of the difference in network structure and loss function, Verification and identification models have their respective advantages and limitations for person reidentification (re-ID). In this work, we propose a multi-task network simultaneously computes the identification loss and verification loss for person reidentification. Given a pair of images as network input, the multi-task network simultaneously outputs the identities of the two images and whether the images belong to the same identity. In experiments, we analyze the major factors affect the accuracy of person reidentification. To address the occlusion problem and improve the generalization ability of reID models, we use the Random Erasing Augmentation (REA) method to preprocess the images. The method can be easily applied to different pre-trained networks, such as ResNet and VGG. The experimental results on the Market1501 datasets show significant and consistent improvements over the state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.