• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.736-745
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• 2002

In network delivery of compressed video, packets may be lost if the channel is unreliable like Internet. Such losses tend to of cur in burst like continuous bit-stream error. In this paper, we propose an effective error-concealment approach to which an error resilient video encoding approach is applied against burst errors and which reduces a complexity of error concealment at the decoder using data hiding. To improve the performance of error concealment, a temporal and spatial error resilient video encoding approach at encoder is developed to be robust against burst errors. For spatial area of error concealment, block shuffling scheme is introduced to isolate erroneous blocks caused by packet losses. For temporal area of error concealment, we embed parity bits in content data for motion vectors between intra frames or continuous inter frames and recovery loss packet with it at decoder after transmission While error concealment is performed on error blocks of video data at decoder, it is computationally costly to interpolate error video block using neighboring information. So, in this paper, a set of feature are extracted at the encoder and embedded imperceptibly into the original media. If some part of the media data is damaged during transmission, the embedded features can be extracted and used for recovery of lost data with bi-direction interpolation. The use of data hiding leads to reduced complexity at the decoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.1
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• pp.33-46
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• 2003

A Home Location Register(HLR) database system manages each subscriber's location information, which continuously changes in a cellular network. For this purpose, the HLR database system provides table management, index management, and backup management facilities. In this thesis, we propose using a two-level index method for the mobile directory number(MDN) as a suitable method and a chained bucket hashing method for the electronic serial number(ESN). Both the MDN and the ESN are used as keys in the HLR database system. We also propose an efficient backup method that takes into account the characteristics of HLR database transactions. The retrieval speed and the memory usage of the two-level index method are better than those of the R-tree index method. The insertion and deletion overhead of the chained bucket hashing method is less than that of the modified linear hashing method. In the proposed backup method, we use two kinds of dirty flags in order to solve the performance degradation problem caused by frequent registration-location operations. For a million subscribers, proposed techniques support reduction of memory size(more than 62%), directory operations (2500,000 times), and backup operations(more than 80%) compared with current techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1847-1855
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• 2012

A 1V 1.6-GS/s 6-bit flash analog-to-digital converter (ADC) with a clock calibration circuit is proposed. A single track/hold circuit with a bootstrapped analog switch is used as an input stage with a supply voltage of 1V for the high speed operation. Two preamplifier-arrays and each comparator composed of two-stage are implemented for the reduction of analog noises and high speed operation. The clock calibration circuit in the proposed flash ADC improves the dynamic performance of the entire flash ADC by optimizing the duty cycle and phase of the clock. It adjusts the reset and evaluation time of the clock for the comparator by controlling the duty cycle of the clock. The proposed 1.6-GS/s 6-bit flash ADC is fabricated in a 1V 90nm 1-poly 9-metal CMOS process. The measured SNDR is 32.8 dB for a 800 MHz analog input signal. The measured DNL and INL are +0.38/-0.37 LSB, +0.64/-0.64 LSB, respectively. The power consumption and chip area are $800{\times}500{\mu}m2$ and 193.02mW.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.11-20
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• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.111-128
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• 2016

The purpose of this study was to analyze domestic research trends of social learning in higher education, and find out educational implications with regard to the effectiveness of social learning. The 63 articles on social learning were finally analyzed, which were published in KCI journals. The results are as follows: Firstly, in respect of research contents, the research area of utilization and the survey methods were most frequently used in those studies. Secondly, as to the use of SNS, the analyzed studies were centralized on Facebook and Formal Structured Learning. Thirdly, as for the effectiveness of SNS, the experimental studies showed that social learning has an effective impact on the learning outcomes, learning processes, and learners' characteristics. In addition, survey studies most frequently set the independent variables as learners' characteristics and the dependent variables as participation, satisfaction, and academic achievement. This research has a significance in terms of verifying the educational implications of social learning, and providing the preliminary data to facilitate the performance for the effective social learning.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• Journal of KIISE:Information Networking
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• v.33 no.4
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• pp.289-298
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• 2006

Algorithms for application-level multicast often use trees to deliver data from the source to the multiple receivers. With the tree structure, the throughput experienced by the descendant nodes will be determined by the performance of the slowest ancestor node. Furthermore, the failure of an ancestor node results in the suspension of the session of all the descendant nodes. This paper focuses on the transmission of data using multiple virtual forwarders, and suggests a scheme to overcome the drawbacks of the plain tree-based application layer multicast schemes. The proposed scheme elects multiple forwarders other than the parent node of the delivery tree. A receiver receives data from the multiple forwarders as well as the parent node and it can increase the amount of receiving data per time unit. The multiple forwarder helps a receiver to reduce the impact of the failure of an ancestor node. The proposed scheme suggests the forwarder selection algorithm to avoid the receipt of duplicate packets. We implemented the proposed scheme using MACEDON which provides a development environment for application layer multicast. We compared the proposed scheme with Bullet by applying the implementation in PlanetLab which is a global overlay network. The evaluation results show that the proposed scheme enhanced the throughput by 20 % and reduced the control overhead over 90 % compared with Bullet.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1050-1061
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• 2009

The Tactical Internet(TI) managed by Infantry Brigades is used for the purpose of sharing information of Command Control and Situation Awareness. When there are more than two destinations to transmit data in the TI system, a multicasting is utilized based on pre-defined multicast groups. However even in the case when a source node needs to send some messages like weathercast and attack alarm etc to only a part of Battalion or Brigades in a specific geographical region (destination region), the current TI multicasting protocol is designed to transmit the messages to the pre-defined group or all of the Battalion/Brigade nodes, resulting in inefficiency in terms of end-to-end delay and overhead. In this paper, we propose more efficient protocol for such cases, named as "Tactical Internet Geocasting (TIG)". The proposed scheme firstly checks whether the destination region belongs to one Battalion region or more than two Battalion regions using location information, and then performs a greedy forwarding from the source node to the destination region, followed by a local flooding inside of the destination region. With performance analysis and simulations using NS-2, TIG is compared to the current TI multicasting protocol (i.e., Simplified MDP) and the LBM (Location-based Multicast). The simulation results show that the proposed TIG is more efficient than both in terms of delay and network overhead.

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.498-504
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• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.