• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.9-15
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• 2010

Cognitive radio is a technology, which automatically recognizes and searches for temporally and spatially unused frequency spectrum, then actively determines the communication method, bandwidth, etc. according to the environment, thus utilizing the limited spectrum resources efficiently. In this paper, with the imperfect sensing of misdetection and false alarm, we quantitatively investigate the effects of primary and secondary user's queue on the primary and secondary users' spectrum usage through the analysis of continuous time Markov Chain. With the queue primary user's spectrum usage improved up to 18%, and the secondary user's spectrum usage improved up to 50%.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.133-142
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• 2011

When TCP operates in multi-hop wireless networks, it suffers from severe performance degradation due to the different characteristics of wireless networks and wired networks. This is because TCP reacts to wireless packet losses by unnecessarily decreasing its sending rate assuming the losses as congestion losses. Although several loss differentiation algorithms (LDAs) have been proposed to avoid such performance degradation, their detection accuracies are not high as much as we expect. In addition the schemes have a tendency to sacrifice the detection accuracy of congestion losses while they improve the detection accuracy of wireless losses. In this paper, we suggest a new sender-based loss differentiation scheme which enhances the detection accuracy of wireless losses while minimizing the sacrifice of the detection accuracy of congestion losses. Our scheme estimates the rate of queue usage which is highly correlated with the congestion in the network path between a TCP sender and a receiver, and it distinguishes congestion losses from wireless losses by comparing the estimated queue usage with a certain threshold. In the extensive experiments based on a network simulator, QualNet, we measure and compare each detection accuracy of wireless losses and congestion losses, and evaluate the performance enhancement in each scheme. The results show that our scheme has the highest accuracy among the LDAs and it improves the most highly TCP performance in multi-hop wireless networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1554-1566
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• 2014

Cloud Computing allows application providers seamlessly scaling their services and enables users scaling their usage according to their needs. In this paper, using queuing game model, we present service scheduling schemes which are used in software as a service (SaaS). The object is maximizing the Cloud Computing platform's (CCP's) payoff via controlling the service requests whether to join or balk, and controlling the value of CCP's admission fee. Firstly, we treat the CCP as one virtual machine (VM) and analyze the optimal queue length with a fixed admission fee distribution. If the position number of a new service request is bigger than the optimal queue length, it balks. Otherwise, it joins in. Under this scheme, the CCP's payoff can be maximized. Secondly, we extend this achievement to the multiple VMs situation. A big difference between single VM and multiple VMs is that the latter one needs to decide which VM the service requests turn to for service. We use a corresponding algorithm solve it. Simulation results demonstrate the good performance of our schemes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.82-89
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• 2003

In this paper, an efficient threshold-based filtering (TF) buffer management scheme is proposed. The TF is capable of minimizing the overall loss performance and improving the fairness of buffer usage in a shared buffer packet switch. The TF consists of two mechanisms. One mechanism is to classify the output ports as sctive or inactive by comparing their queue lengths with a dedicated buffer allocation factor. The other mechanism is to filter the arrival packets of inactive output ports when the total queue length exceeds a threshold value. A theoretical queuing model of TF is formulated and resolved for the overall packet loss probability. Computer simulations are used to compare the overall loss performance of TF, dynamic threshold (DT), static threshold (ST) and pushout (PO). We find that TF scheme is more robust against dynamic traffic variations than DT and ST. Also, although the over-all loss performance between TF and PO are close to each other, the implementation of TF is much simpler than the PO.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.26-39
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• 2017

The need for embedded devices to be able to exchange information with each other and with data centers is essential for the advent of the Internet of Things (IoT). Several existing communication protocols are designed for small devices including the message-queue telemetry transport (MQTT) protocol or the constrained application protocol (CoAP). However, most of the existing implementations are convenient for computers or smart phones but do not consider the strict constraints and limitations with regard resource usage, portability and configuration. In this paper, we report on an industrial research and development project which focuses on the design, implementation, testing and deployment of a MQTT module. The goal of this project is to develop this module for platforms having minimal RAM, flash code memory and processing power. This software module should be fully compliant with the MQTT protocol specification, portable, and inter-operable with other software stacks. In this paper, we present our approach based on abstraction layers to the design of the MQTT module and we discuss the compliance of the implementation with the requirements set including the MISRA static analysis requirements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2490-2506
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• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.237-244
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• 2009

The congestion in wireless sensor network increases the ratio of data loss and causes the delay of data. The existing congestion protocols for wireless sensor network reduces the amount of transmission by control the sampling frequency of the sensor nodes related to the congestion when the congestion has occurred and was detected. However, the control method of sampling frequency is not applicable on the situation which is sensitive to the temporal data loss. In the paper, we propose a new congestion control, ACT - Adaptive Congestion conTrol. The ACT monitors the network traffic with the queue usage and detects the congestion based on the multi level threshold of queue usage. Given network congestion, the ACT increases the efficiency of network by adaptive flow control method which adjusts the frequency of packet transmission and guarantees the fairness of packet transmission between nodes. Furthermore, ACT increases the quality of data by using the variable compression method. Through experiment, we show that ACT increases the network efficiency and guarantees the fairness to sensor nodes compared with existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6B
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• pp.605-614
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• 2009

Existing Internet services were almost supported by the best effort service such as the data transmission service and were allowed the transmission delay and packet loss. However, recent Internet multimedia services such as Internet phone, Internet broadcasting are required a real-time processing and high bandwidth. Therefore, many studies for providing Internet QoS are performed at IETF (Internet Engineering Task Force). As the buffer management mechanism among IP QoS methods, active queue management method such as RED (Random Early Detection) algorithm has proposed. However, RED algorithm has a limitation of usage of an average buffer length and unfairness. So, many algorithms proposed as the modified algorithm of RED. But these modified algorithms also have difficulties to satisfy the requirements of various Internet user QoS. Therefore, in this paper we propose the QoS adaptive AQM (Active Queue Management) algorithm for the multimedia services that request various QoS requirements and present a performance evaluation by the simulations using the ns-2.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.263-267
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• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real WAN delay time, TCP packet size, and the total network usage.