• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2106-2111
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• 2003

In this paper we discuss an algorithm for a real time transmission of moving color images on the TCP/IP network using wavelet transform and neural network. The image frames received from the camera are two-level wavelet-trans formed in the server, and are transmitted to the client on the network. Then, the client performs the inverse wavelet-transform using only the received pieces of each image frame within the prescribed time limit to display the moving images. When the TCP/IP network is busy, only a fraction of each image frame will be delivered. When the line is free, the whole frame of each image will be transferred to the client. The receiver warns the sender of the condition of traffic congestion in the network by sending a special short frame for this specific purpose. The sender can respond to this information of warning by simply reducing the data rate which is adjusted by a back-propagation neural network. In this way we can send a stream of moving images adaptively adjusting to the network traffic condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2134-2153
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• 2013

A network-coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (Wireless Sensor Networks). We mainly focus on two problems: firstly, consideration is given to effective distributed storage technology; secondly, we address how to effectively repair the data in failed storage nodes. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from this matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network-coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that it can work over a small field and the required computation overhead is very low. The simulation result verifies that the proposed scheme not only reduces the total energy consumption required to implement distributed storage system in WSNs, but also balances energy consumption of the networks.

• Journal of Communications and Networks
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• v.12 no.3
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• pp.266-274
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• 2010

The efficient network resource management is one of the important topics in a real-time system. In this paper, we present a practical network resource management framework, control-theoretic packet scheduler (CPS) system. Using our framework, an operating system can schedule both input and output streams accurately and efficiently. Our framework adopts very portable feedback control theory for efficiency and accuracy. The CPS system is able to operate independent of the internal network protocol state, and it is designed to schedule packet streams in fine-grained time intervals to meet the resource requirement. This approach simplifies the design of the CPS system, and leads us to obtain the intended output bandwidth. We implemented our prototype system in Linux, and measured the performance of the network resource management system under various network QoS constraints. The distinctive features of our principles are as follows: It is robust and accurate, and its operation is independent of internal network protocols.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.35-42
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• 2018

In this paper, we propose an optimal streaming service method based on Hybrid CDN/P2P architecture. Recently, video streaming utilizes a CDN (Content Delivery Network) operation technique based on a Proxy Server, which is an end node located close to a user. However, since CDN has a fixed network traffic bandwidth and data information exchange among CDNs in the network is not smooth, it is difficult to guarantee traffic congestion and quality of image service. In the hybrid CDN/P2P network, a data selection technique is used to select only the data that is expected to be continuously requested among all the data in order to guarantee the QoS of the user who utilizes the limited bandwidth efficiently. In order to search user requested data, this technique effectively retrieves the storage information of the constituent nodes of CDN and P2P, and stores the new image information and calculates the deletion priority based on the request possibility as needed. Therefore, the streaming service scheme proposed in this paper can effectively improve the quality of the video streaming service on the network.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.93-99
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• 2021

The sensor networks should be appropriately designed by applied network purpose, so that they can support proper application functions. Based on the design of suitable network model, the network lifetime can be maximized than using other general strategies which have not the consideration of specific network environments. In this paper, we propose a non-deterministic agent scheme to the mobile sink in distributed wireless sensor networks. The sensor network area can be divided into several sensor regions. Hence, to these such networks, the specified suitable scheme is requested by the applied network model to implement satisfactory network management. In this paper, we theoretically represent the proposed scheme, and provide the evaluation with the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.643-645
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• 2011

For network is growing at a rapid rate, network environment is more complex. The technology of using network traffic to monitor our network in real-time is developed. Cacti is a representative monitoring tool which based on RRDTool(Round Robin Database tool), SNMP(Simple Network Management Protocol). In this paper, it show you how to develop a system which based on Cacti and Libpcap to monitor our monitored objects. At this system, using Libpcap to capture network traffic packets, analyze these packets and then turn out in Cacti in graphical form. So as to achieve monitoring system. This system's execution is efficient and the management is easy and the results are accurate, so it can be widely utilized in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1146-1165
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• 2013

Collaborative awareness between persons with various smart multimedia devices is a new trend in the Internet of Things (IoT). Because of the mobility, randomness, and complexity of persons, it is difficult to achieve complete data awareness and data transmission in IoT. Therefore, research must be conducted on mobile-aware service models. In this work, we first discuss and quantify the social relationships of mobile nodes from multiple perspectives based on a summary of social characteristics. We then define various decision factors (DFs). Next, we construct a directed and weighted community by analyzing the activity patterns of mobile nodes. Finally, a mobile-aware service routing algorithm (MSRA) is proposed to determine appropriate service nodes through a trusted chain and optimal path tree. The simulation results indicate that the model has superior dynamic adaptability and service discovery efficiency compared to the existing models. The mobile-aware service model could be used to improve date acquisition techniques and the quality of mobile-aware service in the IoT.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.