• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.868-871
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• 2016

Recently, Unmanned vehicle and Wearable Technology using iot research is being carried out. The unmanned vehicle is the result of it technology. Robots, autonomous navigation vehicle and obstacle avoidance, data communications, power, and image processing, technology integration of a unmanned vehicle or an unmanned robot. The final goal of the unmanned vehicle manual not autonomous by destination safely and quickly reaching. This paper managed to cover One of the key skills of unmanned vehicle is to image processing. Currently battery technology of unmanned vehicle can drive for up to 1 hours. Therefore, we use the Raspberry Pi 3 to reduce power consumption to a minimum. Using the Raspberry Pi 3 and to develop an image recognition system. The goal is to propose a system that recognizes all the objects in the image received from the camera.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.289-294
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• 2019

It easily collects information on purchased goods using IoT(Internet of Things). The collected data is updated directly to the smartphone for verification. The payment information is generated by QR-Code. As a way to implement a system, System was configured with two assumptions: IoT technology using Raspberry-Pi and mobile QR technology. First, RFID tags are attached to the goods instead of barcodes. Second, it has an IoT computer(Raspberry-Pi) built into its shopping cart. This system keeps traditional shopping method of face-to-face payment, but replace time-consuming tarditional barcode tagging method to QR-tagging system for time-efficiency. By debeloping the system of this paper, we maintain pleasures in offline store shopping and it provide convenience due to reduced waiting time for customers and providing prior information about the products.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.835-844
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• 2021

In this paper, a power facility communication control device was designed to autonomously determine and separate the fault section through communication between power facilities in the smart grid distribution system. In the power facility communication control device, the control module was designed as a DSP to measure three-phase voltage and current, and the communication module was designed as an embedded-based Raspberry Pi to determine the fault section and realize the fault section separation through communication between power facilities. Communication between DSP and Raspberry Pi was designed by SPI communication, and communication between Raspberry Pi was designed based on Wi-Fi. Finally, a performance evaluation system based on three power facility communication control devices was built, and simulation verification was conducted for various fault events that may occur on the distribution line. As a result of the test evaluation, it was possible to confirm the effectiveness of the design methodology of the communication control device by showing the required response of the communication control device to all test cases.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.63-68
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• 2022

In schools and corporate buildings, public WiFi Access Points are installed on the ceilings of hallways. In the case of an architectural structure in which a WiFi signal enters through a steel door made of a material with high signal attenuation, Internet connection is frequently cut off or fails when the door is closed. To solve this problem, our research implements an economical and portable WiFi extender using a Raspberry Pi and an auxiliary battery. Commercially available WiFi extenders have limitations in the location where the power plug is located, and WiFi extension using the WiFi hotspot function of an Android smartphone is possible only in some high-end models. However, because the proposed device can be installed at the position where the Wi-Fi reception signal is the best inside the door, the WiFi range can be extended while minimizing the possibility of damage to the original signal. Experimental results show that it is possible to eliminate the shadows of radio waves and to provide Internet services in the office when the door is closed, to the extent that web browsing and real-time video streaming for 720p are possible.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.393-399
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• 2018

As the existing face recognition CCTV and home IOT have each individual hardware component, they have a disadvantage that the measured results of their sensors and the CCTV can not be viewed on one screen at a time. In order to overcome the above disadvantages of existing CCTV and home IOT, this paper proposes an integrated platform which constitutes the CCTV and home IOT as one hardware component using Raspberry Pi and shows each result on one screen through Smartphone application. The proposed integrated platform CCTV and home IOT system is a system which can run the application as a Smartphone and check the sensor value measured by Raspberry Pi and the picture taken through the Pi camera. The implemented system measures temperature, humidity, gas, and dust, and implements face recognition technology on a screen shot through a Pi camera, allowing it to be seen at a glance with a Smartphone.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.4
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• pp.433-440
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• 2016

In order to construct a system for big data processing, one needs to configure the node by using network equipments to connect multiple computers or establish cloud environments through virtual hosts on a single computer. However, there are many restrictions on constructing the big data analysis system including complex system configuration and cost. These constraints are becoming a major obstacle to professional manpower training for big data areas which is emerging as one of the most important national competitiveness. As a result, for professional manpower training of big data areas, this paper proposes a Raspberry Pi Board based educational big data processing system which is capable of practical training at an affordable price.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.151-157
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• 2018

This paper presents an implementation of a smart plant pot which can supply light and water automatically according to the result of detection on current temperature, humidity and illumination, and deliver the images of the plant realtime by using a camera installed in the pot. We designed a container of the plant pot divided into five layers, printed each of them with a 3D printer, and then assembled them. Inside of the container, we installed sensors, a pump, and a camera. We developed an Android application so that the user can control the plant pot and monitor its state. In communication between the Android application and the Raspberry Pi, MQTT protocol was utilized.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.181-186
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• 2017

IEC 61850 is a standard for power utility automation. Using IEC 61850 that uses ethernet may consume more costs for the automation than its value in small distribution substations. Thus, less expense and installation cost are required for the automation of small distribution substations. This study used inexpensive and easy-to-install IEEE 802.15.4 and implemented a bridge between IEC 61850 and IEEE 802.15.4, using Raspberry Pi to connect the existing IEC 61850. Using IEEE 1588, IEC 61850 traffic performances were evaluated, such as SV, GOOSE and MMS. Analyzing IEC 61850 requirements and performance evaluation results, the scope of application of IEEE 802.15.4 was decided.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.251-258
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• 2016

Big data processing is a broad term for processing data sets so large or complex that traditional data processing applications are inadequate. Widespread use of smart devices results in a huge impact on the way we process data. Many organizations are contemplating how to incorporate or integrate those devices into their enterprise data systems. We have proposed a way to process big data by way of integrating Raspberry Pi into a Hadoop cluster as a computational grid. We have then shown the efficiency through several experiments and the ease of scaling of the proposed system.