• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1180-1197
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• 2013

Network coding is one of the most promising techniques to increase the reliability and reduce the energy consumption for wireless sensor networks (WSNs). However, most of the previous works mainly focus on the network coding for multicast or unicast in WSNs, in spite of the fact that the converge-cast is the most common communication style in WSNs. In this paper, we investigate, for the first time as far as we know, the feasibility of acquiring network coding benefits in converge-cast, and we present that with the ubiquitous convergent structures self-organized during converge-casting in the network, the reliability benefits can be obtained by applying linear network coding. We theoretically derive the network coding benefits obtained in a general convergent structure, and simulations are conducted to validate our theoretical analysis. The results reveal that the network coding can improve the network reliability considerably, and hence reduce number of retransmissions and improve energy-efficiency.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.335-341
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• 2018

The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.25-35
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• 2018

Purpose - The purpose of this paper is to partition a last-mile delivery network into zones and to determine locations of last mile delivery centers (LMDCs) in Bangkok, Thailand. Research design, data, and methodology - As online shopping has become popular, parcel companies need to improve their delivery services as fast as possible. A network partition has been applied to evaluate suitable service areas by using METIS algorithm to solve this scenario and a facility location problem is used to address LMDC in a partitioned area. Research design, data, and methodology - Clustering and mixed integer programming algorithms are applied to partition the network and to locate facilities in the network. Results - Network partition improves last mile delivery service. METIS algorithm divided the area into 25 partitions by minimizing the inter-network links. To serve short-haul deliveries, this paper located 96 LMDCs in compact partitioning to satisfy customer demands. Conclusions -The computational results from the case study showed that the proposed two-phase algorithm with network partitioning and facility location can efficiently design a last-mile delivery network. It improves parcel delivery services when sending parcels to customers and reduces the overall delivery time. It is expected that the proposed two-phase approach can help parcel delivery companies minimize investment while providing faster delivery services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4205-4227
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• 2018

Network Coding (NC) is an approach recently investigated for increasing the network throughput and thus enhancing the performance of wireless mesh networks. The benefits of NC can further be improved when routing decisions are made with the awareness of coding capabilities and opportunities. Typically, the goal of such routing is to find and exploit routes with new coding opportunities and thus further increase the network throughput. As shown in this paper, in case of proactive routing the coding awareness along with the information of the measured traffic coding success can also be efficiently used to support the congestion avoidance and enable more encoded packets, thus indirectly further increasing the network throughput. To this end, a new proactive routing procedure called Congestion-Avoidance Network Coding-Aware Routing (CANCAR) is proposed. It detects the currently most highly-loaded node and prevents it from saturation by diverting some of the least coded traffic flows to alternative routes, thus achieving even higher coding gain by the remaining well-coded traffic flows on the node. The simulation results confirm that the proposed proactive routing procedure combined with the well-known COPE NC avoids network congestion and provides higher coding gains, thus achieving significantly higher throughput and enabling higher traffic loads both in a representative regular network topology as well as in two synthetically generated random network topologies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.93-102
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• 2011

In this paper we describe an efficient and easy to use network monitoring system which can identify network configuration automatically by means of capturing and analyzing the ARP broadcasting packets. After identifying network nodes, it gathers detail information of each node such as NETBIOS name and number of hop counts using ICMP and then shows subnet configuration with graphical method. This monitoring system also has a subset of intrusion detection system that can monitor any port scanning trial. With this automatic network configuration functions, it helps to lessen address keeping track overhead which is crucial for network monitoring so that it provides efficient network management.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.65-72
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• 2016

Network virtualization is an emerging technology for solving the rigidity of the physical network infrastructure. The innovative technique virtualizes all resources in the network, including the network links and nodes, and provides a number of virtual networks on a single network infrastructure. In order to realize a virtual network, a thorough and complete monitoring of all resources in the network should be performed firstly. OpenFlow is an open source stack for network virtualization. However, it is impossible to apply OpenFlow to AP-based legacy wireless LAN environment because OpenFlow targets ethernet-based LAN environment. In this paper, we implement an adaptor-styled virtual switch for AP-based wireless LAN through customizing the Open vSwitch which is a virtual switch of OpenFlow. The evaluation test results show that the implemented OpenFlow stack operates successfully. The implemented OpenFlow stack can now be plugged immediately in existing AP-based wireless LAN environment and plays network resource monitoring. In the future, we can develop wireless LAN virtualization applications on the wireless OpenFlow stack.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.361-370
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• 2008

NoC is adopted for data communication between processors and IPs in MPSoC system. NoC has an advantage of scalability in that system can be easily expanded just by adding switches. However, as the number of switches increases, chip area increases as well as data transfer latency. This paper proposes an architecture that can reduce the number of switches in the system by sharing network interfaces. To reduce NI area, the modules sharing network interface use a common buffer in network interface. Experimental results show that the chip area has been reduced by 46.5% and data transfer latency by 17.1%, respectively, compared to conventional architecture.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.310-322
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• 2023

Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 "Ethiopian air crash" event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the "Ethiopian air crash" on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the "Ethiopian air crash," media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.

• Smart Structures and Systems
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• v.32 no.1
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.