• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.923-926
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• 2000

In this paper we address the problem of detecting and estimating an unknown narrow band signal in a noise interference environment A new practical band estimation method, yielding good performance even in case of finite-length data, is presented. More specifically, wavelet packet transform is utilized to detect the more accurate time-variant band, then we estimate the power from wavelet filter-coefficients of the respective band. Also, third-order cumulants, and projection cross-correlation (PCC) criterion are utilized to achieve an effective SNR improvement for the time-variant band estimation. In case of time variant band estimation, the PCC method yields better performance than the correlation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.134-146
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• 2009

Traffic classification seeks to assign packet flows to an appropriate quality of service(QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• Journal of KIISE:Information Networking
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• v.28 no.3
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• pp.389-405
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• 2001

In these days, we have increasing demands on the real-time services, especially for the multimedia data transmission in both of wired and wireless environments and thus efficient and stable ways of transmitting realtime data are needs. Although RTP is widely used for internet-based realtime applications, it cannot avoid packet losses, due to the use of UDP stack and its underlying layers. In the case of mobile computing applications, the packet losses are more frequent and consecutive because of the limited bandwidth. In this paper, we first statistically analyze the characteristics of packet losses in the wired and wireless communications, based on Gilbert model, and a new packet recovery scheme for realtime data transmission is presented. To reflect the transmission characteristics of the present network environment, our scheme makes the sender to dynamically adjust the amount of redundant information, using the current packet loss characteristic parameters reported by the receiver. Additionally, we use relatively large and discontinuous offset values, which enables us to recover from both of the random and consecutive packet losses. Due to these characteristics, our scheme is suitable for the mobile computing environment where packet loss rates are relatively high and varies rapidly in a wide range. Since our scheme is based on the analytic model form statistics, it can also be used for other network environments. We have implemented the scheme with Mobile IP and RTP/RTCP protocols to experimentally verify its efficiency.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.412-414
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• 2005

Multimeda service is very big capacity and use not a little network for provide cots. Because in paper introduce new method adaptive to merit of Unicast and Multicast. Propose method service possibility that now Multicast have merit which live broadcasting and Unicast have merit which can provide individually customer manage and good quality by data statistics. And network use to efficient

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.1-8
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• 2015

Security has become an issue due to the rapid increases in internet traffic data network. Especially P2P traffic data poses a great challenge to network systems administrators. Preemptive measures are necessary for network quality of service(QoS) and efficient resource management like blocking suspicious traffic data. Deep packet inspection(DPI) is the most exact way to detect an intrusion but it may pose a private security problem that requires time. We used several machine learning methods to compare the performance in classifying network traffic data accurately over time. The Random Forest method shows an excellent performance in both accuracy and time.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.1
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• pp.42-49
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• 2009

Most Vehicular Ad hoc Network (VANET) applications rely heavily on broadcast transmission of traffic related information to all reachable nodes within a certain geographical area. Among various broadcast approaches, flooding is the first broadcasting technique. Each node rebroadcasts the received message exactly once, which results in broadcast storm problems. Some mechanisms have been proposed to improve flooding in Mobile Ad hoc Networks (MANET), but they are not effective for VANET and only a few studies have addressed this issue. We propose two distance-based and timer-based broadcast suppression techniques: 15P(15percent) and slotted 15P. In the first (distance based) scheme, node's transmission range is divided into three ranges (80%,15%and5%). Only nodes within 15% range will rebroadcast received packet. Specific packet retransmission range (15%) is introduced to reduce the number of messages reforwarding nodes that will mitigate the broadcaststorm. In the second (timer-based) scheme, waiting time allocation for nodes within 15% range isused to significantly reduce the broadcaststorm. The proposed schemes are distributed and reliedon GPS information and do not requireany other prior knowledge about network topology. To analyze the performance of proposed schemes, statistics such as link load and the number of retransmitted nodes are presented. Our simulation results show that the proposed schemes can significantly reduce link load at high node densities up to 90 percent compared to a simple broadcast flooding technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.350-359
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• 2014

With the rapid growth of Internet, the importance of application traffic analysis is increasing for efficient network management. The statistical information in traffic flows can be efficiently utilized for application traffic identification. However, the packet out-of-order and retransmission occurred at the traffic collection point reduces the performance of the statistics-based traffic analysis. In this paper, we propose a novel method to detect and resolve the packet out-of-order and retransmission problem in order to improve completeness and accuracy of the traffic identification. To prove the feasibility of the proposed method, we applied our method to a real traffic analysis system using statistical flow information, and compared the performance of the system with the selected 9 popular applications. The experiment showed maximum 4% of completeness growth in traffic bytes, which shows that the proposed method contributes to the analysis of heavy flow.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.2
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• pp.99-113
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• 1992

In general, TDMA and FDMA have been used for multiple access communication methods. Recently. However, ALOHA and CSMA protocols came to exist as new schemes for VSAT data networks and LANs, respectively. These schemes are proven to be effective for packet switched communication systems that has bursty traffic but packet collisions. In this paper we analyze capture statistics arising from fading channels in packet radio communication systems in the context of ALOHA protocol. We derive general results of the exact form of capture probabilities and present numerical data for wide range of fading parameters. Since the capture probabilities are found to approach zero when the number of transmitted signals become larger, we are able to determine the achievable channel throughput by a least squares fitting of an exponential-type function to the probabilities.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.25-31
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• 2016

Recently, with greatly improving the wireless communication technology, new services are created using smart sensors, i.e., machine-to-machine (M2M) and Internet of Things (IoT). In this paper, we propose a novel IoT device (IoTD) personalization method that adopt Small-cell Access Points (SAPs) to control IoTDs using user equipments (UEs), e.g., smart phones and tablet PC, from service users. First, we introduce a system architecture that consists of UE, IoTD, and SAP and propose the IoTD personalization method with two procedures, i.e., IoTD profile registration procedure and IoTD control procedure. Finally, through simulations, we evaluated the system performance of the proposed scheme and it is shown that the proposed scheme outperforms the conventional scheme in terms of the packet delay, packet loss probability, and normalized throughput.