• Journal of Internet Computing and Services
• /
• v.20 no.2
• /
• pp.39-49
• /
• 2019

Advances in IoT data analysis technology have made it easier to analyze situation and provide interactive services based on the context. Most of digital signage application have been used to provide information uni-directionally, but in the future it will evolve to provide personalized content according to the individual user situation and responses. However, it is not easy to modify or apply the existing interactive digital signage platforms due to their hardware dependency. The proposed platform is modularized by dividing main functions into two, the cloud and the edge, so that advertisement resources can be easily generated and registered. Thus, interactive advertisement can be rendered in a timely manner based on sensor analysis results. At the edge, personal data can be processed to minimize privacy issues, and real-time IoT sensor data can be analyzed for quick response to the signage player. The cloud is easier to access and manage by multiple users than edge. Therefore, the signage content generation module improves accessibility and flexibility by handling advertisement contents in the cloud so that multiple users can work together on the cloud platform. The proposed platform was developed and simulated in two aspects. First is the provider who provides the signage service, and second is the viewer who uses the content of the signage. Simulation results show that the proposed platform enables providers to quickly construct interactive signage contents and responses appropriately to the context changes in real-time.

• Journal of KIISE
• /
• v.44 no.7
• /
• pp.719-725
• /
• 2017

To reduce management costs and improve performance, the European Telecommunication Standards Institute (ETSI) introduced the concept of network function virtualization (NFV), which can implement network functions (NFs) on cloud/datacenters. Within the NFV architecture, NFs can share physical resources by hosting NFs on physical nodes (commodity servers). For network service providers who support NFV architectures, an efficient resource allocation method finds utility in being able to reduce operating expenses (OPEX) and capital expenses (CAPEX). Thus, in this paper, we analyzed the network service chain embedding problem via an optimization formulation and found a close-optimal solution based on the Markov approximation framework. Our simulation results show that our approach could increases on average CPU utilization by up to 73% and link utilization up to 53%.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.12
• /
• pp.6060-6070
• /
• 2012

Real-Time Enterprise (RTE)'s key requirement is that it should respond and adapt fast to the change of the firms' internal and external situations including the change of market and customers' needs. Recently, the big data processing technology to support the speedy change of the firms is spotlighted. Under the circumstances that wire and wireless communication networks are evolving with an accelerated rate, it is especially critical to provide a strong security monitoring function and stable services through a real-time processing of massive communication data traffic. By applying the big data processing technology based on a cloud computing architecture, this paper solves the managerial problems of telecommunication service providers and discusses how to operate the network management system effectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.2
• /
• pp.1-7
• /
• 2013

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.

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

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.4
• /
• pp.981-987
• /
• 2017

Proliferation of cloud computing services brings about reduction of the maintenance and management costs by allowing data to be outsourced to a dedicated third-party remote storage. At the same time, the majority of storage service providers have adopted a data deduplication technique for efficient utilization of storage resources. When a hash tree is employed for duplicate identification as part of deduplication process, size information of the attested data and partial information about the tree can be deduced from eavesdropping. To mitigate such side channels, in this paper, a new duplicate identification method is presented by exploiting a multi-set hash function.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1600-1619
• /
• 2023

Organizations and other institutions have recently started using cloud service providers to store and share information in light of the Internet of Things (IoT). The major issues with this storage are preventing unauthorized access and data theft from outside parties. The Block chain based Security Sharing scheme with Data Access Control (BSSDAC) was implemented to improve access control and secure data transaction operations. The goal of this research is to strengthen Data Access Control (DAC) and security in IoT applications. To improve the security of personal data, cypher text-Policy Attribute-Based Encryption (CP-ABE) can be developed. The Aquila Optimization Algorithm (AOA) generates keys in the CP-ABE. DAC based on a block chain can be created to maintain the owner's security. The block chain based CP-ABE was developed to maintain secures data storage to sharing. With block chain technology, the data owner is enhancing data security and access management. Finally, a block chain-based solution can be used to secure data and restrict who has access to it. Performance of the suggested method is evaluated after it has been implemented in MATLAB. To compare the proposed method with current practices, Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are both used.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.1-9
• /
• 2018

This study looked at the blockchain technology as an ecosystem side and examined the role of each component. In this study, the blockchain ecosystem is a network structure in which members share and collaborate with each other in order to create new value(service) based on the distributed ledger. Hence compare to bio-ecosystem, the components of blockcahin ecosystem are consists of service providers(producer), service users(consumer) and the distributed ledger owners(decomposer). Distributed ledgers, who act as decomposers in the blockchain ecosystem, play an important role. In order to maintain and activate the value of the blockchain ecosystem, the ecosystem can be effectively operated by constructing an environment in which the distributed ledger owners can handle effectively. This will help us to understand the relationship between ecosystems and not only to contribute to the study but also to activate the blockchain ecosystem. The limitations of this study are based on exploratory research, and specific discussion is needed based on objective data.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1157-1169
• /
• 2021

The disaster recovery refers to policies and procedures to ensure continuity of services and minimize loss of resources and finances in case of emergency situations such as natural disasters. In particular, the disaster recovery method by the cloud service provider has advantages such as management flexibility, high availability, and cost effectiveness. However, this method has a dependency on a service provider and has a structural limitation in which a user cannot be involved in personal data. In this paper, we propose a protocol using proxy re-encryption for data confidentiality by removing dependency on service providers by backing up user data using blockchain and distributed storage. The proposed method is implemented in Ethereum and IPFS environments, and presents the performance and cost required for backup and recovery operations.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.115-134
• /
• 2020

Previous studies on willingness to pay for digital content have mainly focused on audience factors and individual level. To complement the limitation of previous research, this study conducts a multilevel analysis to find the factors influence digital content purchases considering two axes: audience/media factors and individual/household levels. Using a sample of 10,172 individuals within 4,313 households, the analysis results show individual media factors including theater-going, experience with cloud services, and multi-screen service usage have the greatest effects on digital content purchases. At the household level, the media ownership factors that the number of laptops, wireless routers, and tablets have a greater influence than audience factors such as household size or household income. Our findings help scholars to enhance the understanding of individuals' media use considering household environmental factors and shed light on the importance of multi-screen service usage, and content providers to improve their digital content sales using multi-screen environment.