• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.83-90
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• 2022

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.

• Smart Structures and Systems
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• v.1 no.1
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• pp.1-12
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• 2005

This paper describes the area of intelligent systems research as funded by the Civil and Mechanical Systems (CMS) Division of the National Science Foundation (NSF). With developments in computer science, information technology, sensing and control the design of typical machines and structures by civil and mechanical engineers is evolving toward intelligent systems that can sense, decide and act. This trend toward electro-mechanical design is well-established in modern machines (e.g. vehicles, robots, disk drives) and often referred to as mechatronics. More recently intelligent systems design is becoming an important aspect of structures, such as buildings and bridges. We briefly review recent developments in structural control, including the role that NSF has played in their development, and discuss on-going CMS activities in this area. In particular, we highlight the interdisciplinary initiative on Sensors and Sensor Networks and the Network for Earthquake Engineering Simulation (NEES). NEES is a distributed cyberinfrastructure to support earthquake engineering research, and provides the pioneering NEES grid computing environment for simulation, teleoperation, data collection and archiving, etc.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.293-298
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• 2000

In fuzzy expert systems or database systems that have volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environments. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy or data, the parallel fuzzy inference algortihm extracts effective and achieves and achieves a good speed factor.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.288-298
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• 2008

We introduce new type of networked robot, Ubiquitous Robotic Companion (URC), embedded with ATLAS Service-oriented architecture for enhancing the space sensing capability. URC is a network-based robotic system developed by ETRI. For years of experience in deploying service with ATLAS sensor platform for elder and people with special needs in smart houses, we need networked robots to assist elder people in their successful daily living. Recently, pervasive computing technologies reveals possibilities of networked robots in smart spaces, consist of sensors, actuators and smart devices can collaborate with the other networked robot as a mobile sensing platform, a complex and sophisticated actuator and a human interface. This paper provides our experience in designing and implementing system architecture to integrate URC robots in pervasive computing environments using the University of Florida's ATLAS service-oriented architecture. In this paper, we focus on the integrated framework architecture of URC embedded with ATLAS platform. We show how the integrated URC system is enabled to provide better services which enhance the space sensing of URC in the smart space by applying service-oriented architecture characterized as flexibility in adding or deleting service components of Ubiquitous Robotic Companion.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.222-227
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• 2006

Recently the research for Traveling Salesman Problem (TSP) using DNA computing with massive parallelism has been. However, there were difficulties in real biological experiments because the conventional method didn't reflect the precise characteristics of DNA when it express graph. Therefore, we need DNA sequence generation algorithm which can reflect DNA features and reduce biological experiment error. In this paper we proposed a DNA sequence generation algorithm that applied DNA coding method of evolution model to DNA computing. The algorithm was applied to TSP, and compared with a simple genetic algorithm. As a result, the algorithm could generate good sequences which minimize error and reduce the biologic experiment error rate.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.460-470
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• 2017

We address how to handle big data mining effectively using the intelligent SSD (iSSD). ISSD is a storage device equipped with computing power inside SSD for reducing the transferring cost and for processing data nearby SSD where the data is stored. We first introduce the structural characteristics of iSSD for efficient data processing. Then, we present how to process data mining algorithms by using iSSD. Finally, we discuss how to improve the performance of data mining algorithms significantly by exploiting heterogeneous computing environment where host CPUs and GPU coexist for maximizing the performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.