• Journal of Advanced Navigation Technology
• /
• v.15 no.4
• /
• pp.632-639
• /
• 2011

Searching distributed resource efficiently is very important in distributed computing, cloud computing environment. Distributed resource searching may have system overheads and take much time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. The open-source community project JXTA defines an open set of standard protocols for ad hoc, pervasive, peer-to-peer computing as a common platform for developing a wide variety of decentralized network applications. In this paper, we proposed peer search algorithm based on JXTA-Sim. original JXTA peer searching algorithm selected a loosely-consistent DHT. Our Lookup algorithm decreases message number of WALK_LOOKUP and reduce the network system overload, and we make a result of same performance both original algorithm and our proposed algorithm.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.11
• /
• pp.319-328
• /
• 2023

Unlike cloud computing, edge computing technology analyzes and judges data close to devices and users, providing advantages such as real-time service, sensitive data protection, and reduced network traffic. EdgeX Foundry, a representative open source of edge computing platforms, is an open source-based edge middleware platform that provides services between various devices and IT systems in the real world. EdgeX Foundry provides a service for handling camera devices, along with a service for handling existing sensed data, which only supports simple streaming and camera device management and does not store or process image data obtained from the device inside EdgeX. This paper presents a technique that can store and process image data inside EdgeX by applying some of the services provided by EdgeX Foundry. Based on the proposed technique, a service pipeline for object detection services used core in the field of autonomous driving was created for experiments and performance evaluation, and then compared and analyzed with existing methods.

• The Journal of the Korea Contents Association
• /
• v.20 no.7
• /
• pp.97-106
• /
• 2020

Due to the rapid development and dissemination of 5G communication and IoT technologies, there are increasing demands for big data analysis techniques and service systems. In particular, explosively growing demands on AI technology adoption are also causing high competitions to take advantages of machine/deep-learning models to extract novel values from enormously collected data. In order to adopt AI technology to various research and application domains, it is necessary to prepare high-performance GPU-equipped systems and perform complicated settings to utilze deep learning models. To relieve the efforts and lower the barrier to utilize AI techniques, AIaaS(AI as a service) platform is attracting a great deal of attention as a promising on-line service, where the complexity of preparation and operation can be hidden behind the cloud side and service developers only need to utilize the high-level AI services easily. In this paper, we propose an AIaaS system which can support the creation of AI services based on Docker and OpenFaaS from the registration of models to the on-line operation. We also describe a case study to show how AI services can be easily generated by the proposed system.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.3
• /
• pp.73-82
• /
• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.7
• /
• pp.143-150
• /
• 2020

Recently, many companies are interested in smart factory services. Because various smart factory services are provided by the combination of mobile devices, cloud computing, and IoT services. However, many workers turn away from these systems because most of them are not implemented from the worker's point of view. To solve this, we implemented a development tool that allows field workers to produce their own services so that workers can easily create smart factory services. Manufacturing data is collected in real time from sensors which are connected to manufacturing facilities and stored within smart factory platforms. Implemented development tools can produce services such as monitoring, processing, analysis, and control of manufacturing data in drag-and-drop. The implemented system is effective for small manufacturing companies because of their environment: making various services quickly according to the company's purpose. In addition, it is assumed that this also will help workers' improve operation skills on running smart factories and fostering smart factory capable personnel.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.3
• /
• pp.547-554
• /
• 2022

Recently, the field of telemedicine is growing rapidly due to the COVID-19 pandemic. However, the cost of telemedicine services is relatively high, since cloud computing, video conferencing, and cyber security should be considered. Therefore, in this paper, we design and implement a cost-effective P2P-based telemedicine system. It is implemented using the widely used the open source computing platform, Raspberry Pi, and P2P network that frees users from security problems such as the privacy leakage by the central server and DDoS attacks resulting from the server/client architecture and enables trustworthy identifying connection system using SSL protocol. Also it enables users to check the other party's status including body temperature in real time by installing a thermal imaging camera using Raspberry Pi. This allows several medical diagnoses that requires visual aids. The proposed telemedicine system will popularize telemedicine service and meet the ever-increasing demand for telemedicine.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.12
• /
• pp.291-306
• /
• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Smart Media Journal
• /
• v.9 no.1
• /
• pp.75-83
• /
• 2020

Compensation services that are indispensable during large-scale public utilities projects have been gradually increasing with the recent increase in construction, but there are no systematic compensation services due to the complicated procedures and manual work. For this reason, various problems such as construction period delays due to various complaints, corruption in compensation work, and impossible to trace the history of compensation data in the past are emerging. In this paper, in order to solve this problem, in-depth interviews and questionnaires were conducted to find out the problems of each compensation status. Based on this, 3 core technologies and 10 technical needs based on ICT were selected to improve the compensation work by deriving STEEP analysis and Issue Tree. The three core technologies are big data-based decision-making and prediction technology, advanced measurement technology, and open cloud-based compensation platform technology. In order to introduce the derived technologies to the institutions in charge of compensation, the possibility of technology diffusion by project operators was suggested based on the results of the current status of informatization by institution. Based on the core technology derived from this paper, it is necessary to make a prototype that can be advanced in compensation work and apply it to each institution and analyze the effect.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.189-198
• /
• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Journal of Cadastre & Land InformatiX
• /
• v.46 no.2
• /
• pp.183-196
• /
• 2016

Ground control points are needed for precision geometric correction of satellite images, and the coordinates of a high-quality ground control point can be obtained from the GPS measurement. However, considering the GPS measurement requires an excessive amount o f t ime a nd e fforts, there is a need for coming up with an alternative solution to replace it. Therefore, we examined the possibility of replacing the existing GPS measurement with coordinates available at online maps to acquire the coordinates of ground control points. To this end, we examined error amounts between the coordinates of ground control points obtained through Daum Map API, and them compared the accuracies between three types of coordinate transformation equations which were used for geometric correction of satellite images. In addition, we used the coordinate transformation equation with the highest accuracy, the coordinates of ground control point obtained through the GPS measurement and those acquired through D aum M ap A PI, and conducted geometric correction on them to compare their accuracy and evaluate their effectiveness. According to the results, the 3rd order polynomial transformation equation showed the highest accuracy among three types of coordinates transformation equations. In the case of using mid-resolution satellite images such as those taken by Landsat-8, it seems that it is possible to use geometrically corrected images that have been obtained after acquiring the coordinates of ground control points through Daum Map API.