• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.21-29
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• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.673-676
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• 2017

Accompanying the Internet of Things (IoT) is a demand of advanced applications and services utilizing the potential of the IoT environment. Monitoring the environment for a provision of context-aware services to the human beings is one of the new trends in our future life. The IoTivity Cloud is one of the most notable open-source platform bringing an opportunity to collect, analyze, and interpret a huge amount of data available in the IoT environment. Based on the IoTivity Cloud, we aim to develop a novel platform for comprehensive monitoring of a future network, which facilitates on-demand data collection to enable the network behavior prediction and the quality of user experience maintenance. In consideration of performance evaluation of the monitoring platform, this paper presents results of a preliminary test on the data acquisition/supply process in the IoTivity Cloud.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.93-102
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• 2014

Sensor networks are utilized in various fields as the useful system that provides the useful information to the user, by storing, processing and analyzing the various data collected from sensors. In construction of such sensor networks, by utilizing cloud PaaS for both communication server and the database server, the cost reduction and stabilization of the system can be achieved. In this paper, UDIPSN (User Decision Information Providing Sensor Network) was implemented to provide the decision information to the users by being treated as a cloud service for the location information of a moving object as well as various sensor data. Finally, we showed that the operation of the proposed system was performed properly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4849-4864
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• 2019

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3821-3841
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• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.11-19
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• 2021

The emerging Fifth Generation (5G) network technology brings us a new demand for low latency services. However, it may not be possible for long-distanced cloud computing servers to support users with satisfactory low latency services. For this reason, Multi-access Edge Computing (MEC) technology are gaining attraction since it is designed to provide low latency services to users by placing cloud computing resources to base-stations or mobile switching centers nearby users. Accordingly, it is necessary to verify the deployed containers on the MECs are reliable enough to provide low latency services empirically. For the purpose, we develop a testing tool to verify the reliability as well as network resources status of running MECs by deploying containers on the MECs in a Kubernetes environment.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.115-122
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• 2021

In the recent past enormous enterprise applications have migrated into the cloud computing (CC). The researchers have contributed to this ever growing technology and as a result several innovations strengthened to offer the quality of service (QoS) as per the demand of the customer. It was treated that management of resources as the major challenge to offer the QoS while focusing on the trade-offs among the performance, availability, reliability and the cost. Apart from these regular key focuses to meet the QoS other key issues in CC are data integrity, privacy, transparency, security and legal aspects (DIPTSL). This paper aims to carry out the literature survey by reflecting on the prior art of the work with regard to QoS in CC and possible implementation of block chain to implement decentralised CC solutions governing DIPTSL as an integral part of QoS.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.91-100
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• 2023

Pervasive computing is characterized by a key characteristic that affects the operating environment of services and users. It places more emphasis on dynamic environments where available resources continuously vary without prior knowledge of their availability, while in static environments the services provided to users are determined in advance. At the same time, Cloud computing paradigm introduced flexibility of use according to the user's profile and needs. In this paper, we aimed to provide Context-Aware Transactional Service applications with solutions so that it can be integrated and invoked like any service in the digital ecosystem. Being able to compose is not enough, each service and application must be able to offer a well-defined behavior. This behavior must be controlled to meet the dynamicity and adaptability necessary for the new user's requirements. The motivation in this paper is to offer design patterns that will provide a maximum of automatism in order to guarantee short reaction times and minimal human intervention. Our proposal includes a cloud service model by developing a PaaS service that allows CATS adaptation. A new specification for the validation of CATS model has been also introduced using the ACTA formalism.