• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.227-229
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• 2015

모든 사물에 인터넷이 연결되는 사물 인터넷(IoT: Internet of Things)시대가 열렸다. IoT 디바이스들을 연결하기 위해 클라우드 또한 더욱 관심이 높아지고 있다. IoT 디바이스를 연결한 클라우드는 작은 단위의 작업들을 다량으로 수행하게 된다. IoT 서비스에서 발생하는 작업들을 효율적으로 처리하기 위해서는 적합한 작업 스케줄링이 반드시 필요하다. 본 논문에서는 오픈소스 기반의 플랫폼인 오픈스택(OpenStack)에서 Filter 스케줄러와 Chance 스케줄러를 VM 개수에 따라 단위 시간동안 성능을 비교 분석한다. 이를 통해 오픈스택에서 IoT 서비스 사용자들을 위해 합리적인 스케줄러 방법을 도출해낼 수 있다

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.15-23
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• 2016

As various IoT devices appear recently, Cloud Services such as DropBox, Amazon S3, Microsoft Azure Storage, etc are widely use for data sharing across the devices. Although, cryptographic algorithms like AES is prevalently used for data security, there is no mechanisms to allow selectively and flexibly use wider spectrum of lightweight cryptographic algorithms such as LEA, SEED, ARIA. With this, IoT devices with lower computation power and limited battery life will suffer from overly expensive workload and cryptographic operations are slower than what is enough. In this paper, we designed and implemented a CloudGate that allows client programs of those cloud services to flexibly select a cryptographic algorithms depending on the required security level. By selectively using LEA lightweight algorithms, we could achieve the cryptographic operations could be maximum 1.8 faster and more efficient than using AES.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Review of Korea Contents Association
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• v.13 no.1
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• pp.25-30
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• 2015

• Convergence Security Journal
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• v.22 no.4
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• pp.99-107
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• 2022

The use of smart home appliances and Internet of Things (IoT) devices is growing, enabling new interactions and automation in the home. This technology relies heavily on mobile services which leaves it vulnerable to the increasing threat of hacking, identity theft, information leakage, serious infringement of personal privacy, abnormal access, and erroneous operation. Confirming or proving such security breaches have occurred is also currently insufficient. Furthermore, due to the restricted nature of IoT devices, such as their specifications and operating environments, it is difficult to provide the same level of internet security as personal computers. Therefore, to increase the security on smart home IoT devices, attention is needed on (1) preventing hacking and user authority theft; (2) disabling abnormal manipulation; and (3) strengthening audit records for device operation. In response to this, we present a plan to build a cloud security authentication platform which features security authentication management functionality between mobile terminals and IoT devices.

• Journal of KIISE
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• v.42 no.8
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• pp.954-960
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• 2015

It is important to improve the performance of storage in the home cloud environment within the virtualized IoT gateway since the performance of applications deeply depends on storage. Though SSD caching is applied in order to improve the storage, it is only used for read-cache due to the limitations of SSD such as poor write performance and small write endurance. However, it is important to improve performance of the write operation in the home cloud server, in order to improve the end-user experience. This paper propose a novel SSD caching which considers write-data as well as read-data. We validate the enhancement in the performance of random-write by transforming it to the sequential patterns.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.95-104
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• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.

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

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.311-322
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• 2014

IoT(Internet of Things) is a concept of connected internet pursuing direct access to devices or sensors in fused environment of personal, industrial and public area. In IoT environment, it is possible to access realtime data, and the data format and topology of devices are diverse. Also, there are bidirectional communications between users and devices to control actuators in IoT. In this point, IoT is different from the conventional internet in which data are produced by human desktops and gathered in server systems by way of one-sided simple internet communications. For the cloud or portal service of IoT, there should be a file management framework supporting systematic naming service and unified data access interface encompassing the variety of IoT things. This paper implements a DB-based virtual file system maintaining attributes of IoT things in a UNIX-styled file system view. Users who logged in the virtual shell are able to explore IoT things by navigating the virtual file system, and able to access IoT things directly via UNIX-styled file I O APIs. The implemented virtual file system is lightweight and flexible because it maintains only directory structure and descriptors for the distributed IoT things. The result of a test for the virtual shell primitives such as mkdir() or chdir() shows the smooth functionality of the virtual file system, Also, the exploring performance of the file system is better than that of Window file system in case of adopting a simple directory cache mechanism.