• Journal of KIISE:Databases
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• v.37 no.5
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• pp.258-266
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• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.593-607
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• 2013

The In-Vehicle-Networking(IVN) of modern cars is constituted by an small electronic control device called ECU. In the past, there was no way to be able to access the IVN of a driving car. so IVN has been recognized as a closed environment so there is no need to exist authentication protocol between devices which are to configure the internal network and to communicate with other devices. However, constant improvements made it possible to access the IVN in many different ways as the communication technology evolves. This possibility created a need for device authentication in IVN. HB-Family are representative authentication schemes in RFID environment which has similar restrictions to IVN. In this paper, we propose an implementation method of HB-Family for device authentication between ECU considering ECU has low computing power and the message field of CAN protocol has restricted size of 8 bytes. In order to evaluate the efficiency and availability of the authentication schemes adopted our method, we have evaluated the performance based on DSP-28335 device. Further, it was possible to improve the efficiency rate of at lest 10%, up to 36%, and we then analyze this result in various aspects of the IVN.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.147-158
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• 2004

A class of SWP(Stochastic Wane Propagation) models microscopically mimics individual vehicles' stochastic behavior and traffic jam propagation with simplified car-following models based on CA(Cellular Automata) theory and macroscopically captures dynamic traffic flow relationships based on statistical physics. SWP model, a program-oriented model using both discrete time-space and integer data structure, can simulate a huge road network with high-speed computing time. However, the model has shortcomings to both the capturing of low speed within a jam microscopically and that of the density and back propagation speed of traffic congestion macroscopically because of the generation of spontaneous jam through unrealistic collision avoidance. In this paper, two additional rules are integrated into the NaSch model. The one is SMR(Stopping Maneuver Rule) to mimic vehicles' stopping process more realistically in the tail of traffic jams. the other is LAR(Low Acceleration Rule) for the explanation of low speed characteristics within traffic jams. Therefore, the CA car-following model with the two rules prevents the lockup condition within a heavily traffic density capturing both the stopping maneuver behavior in the tail of traffic jam and the low acceleration behavior within jam microscopically, and generates more various macroscopic traffic flow mechanism than NaSch model's with the explanation of propagation speed and density of traffic jam.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.23-34
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• 2010

As the number of user increases according to the improvement of the network, the multi-thread schemes are used to process the service requests of several users who are connected simultaneously. The static thread pool scheme has the problem of occupying a static amount of system resources. On the other hand, the dynamic thread pool scheme can control the number of threads according to the users' requests. However, it has disadvantage that this scheme cannot react to the requests which are larger than the maximum value assigned. In this paper, a web server using a learning-based dynamic thread pool scheme is suggested, which will be running on a server programming of a multi-thread environment. The suggested scheme adds the creation of the threads through the prediction of the next number of periodic requests using Auto Regressive scheme with the web server apache worker MPM (Multi-processing Module). Unlike previous schemes, in order to set the exact number of the necessary threads during the unchanged number of work requests in a certain period, K-Nearest Neighbor algorithm is used to learn the number of threads in advance according to the number of requests. The required number of threads is set by comparing with the previously learned objects. Then, the similar objects are selected to decide the number of the threads according to the request, and they create the threads. In this paper, the response time has decreased by modifying the number of threads dynamically, and the system resources can be used more efficiently by managing the number of threads according to the requests.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.517-534
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• 2017

This paper proposes an Internet of Things (IoT) middleware called Middleware for Cooperative Interaction of Things (MinT). MinT supports a fully distributed IoT environment in which IoT devices directly connect to peripheral devices, easily constructing a local or global network and sharing their data in an energy efficient manner. MinT provides a sensor abstract layer, a system layer and an interaction layer. These layers enable integrated sensing device operations, efficient resource management, and interconnection between peripheral IoT devices. In addition, MinT provides a high-level API, allowing easy development of IoT devices by developers. We aim to enhance the energy efficiency and performance of IoT devices through the performance improvements offered by MinT resource management and request processing. The experimental results show that the average request rate increased by 25% compared to existing middlewares, average response times decreased by 90% when resource management was used, and power consumption decreased by up to 68%. Finally, the proposed platform can reduce the latency and power consumption of IoT devices.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.3
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• pp.153-164
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• 2007

The Access Grid (AG) provides collaboration environments over the IP multicast networks by enabling efficient exchange of multimedia contents among remote users; however, since lots of current networks are still multicast-disabled, it is not easy to deploy this multicast-based multi-party AG. For this problem, the AG provides multicast bridges as a solution by putting a relay server into the multicast networks. Multicast-disabled clients make UDP connections with this relay server and receive forwarded multicast traffics in unicast UDP packets. This solution is facing several limitations since it requires duplicate forwarding of the same packet for each unicast peer. Thus, in this paper, we propose an alternate solution for the multicast connectivity problem of the AG based on the UMTP (UDP multicast tunneling protocol). By taking advantage of flexibilities of UMTP, the proposed solution is designed to improve the efficiency of network and system utilization, to allow reuse of multicast-based AG applications without modification, and to partially address the NAT/firewall traversal issues. To verify the feasibility of proposed solution, we have implemented a prototype AG connectivity tool based on the UMTP, named as the AG Connector.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.383-391
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• 2010

A Lifelog management system provides users with services to store, manage, and search their life logs. This paper proposes a fully-automatic collecting method of real world social contacts and lifelog search engine using collected social contact information as keyword. Wireless short-distance network devices in mobile phones are used to detect social contacts of their users. Human-Bluetooth relationship matrix is built based on the frequency of a human-being and a Bluetooth device being observed at the same time. Results show that with 20% of social contact information out of full social contact information of the observation times used for calculation, 90% of human-Bluetooth relationship can be correctly acquired. A lifelog search-engine that takes human names as keyword is suggested which compares two vectors, a row of Human-Bluetooth matrix and a vector of Bluetooth list scanned while a lifelog was created, using vector information retrieval model. This search engine returns more lifelog than existing text-matching search engine and ranks the result unlike existing search-engine.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.51-56
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• 2016

In this paper, we propose an Arduino-based plant monitoring system. The proposed system is based on the Arduino platform, using an environmental sensor and a pressure sensor for measuring temperature, humidity and illuminance in order to monitor the state of the environment and the facilities of the plant. Monitoring data are transmitted to a ZigBee coordinator connected to a server through a radio frequency transceiver. When using a pressure sensor and the environment sensor data stored on the host server, checking the pressure in the environment of the plant and equipment is intended to report any alarm status to the administrator. Using a grid line-based key distribution scheme, the authentication node dynamically generates a data key to protect the monitoring information. Applying a ZigBee wireless sensor network does not require additional wiring for the actual implementation of a plant monitoring system. Possible working-environment monitoring of an efficient plant can help analyze the cause of any failure by backtracking the working environment when a failure occurs. In addition, it is easy to expand or add a sensor function using the Arduino platform and an expansion board.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.