• Journal of Internet Computing and Services
• /
• v.18 no.5
• /
• pp.31-38
• /
• 2017

Recently with the rapid development of LBS (Location-based Services) technology, approaches of protecting user's location have gained tremendous attentions. For using LBS, users need to forward their real locations to LBS server. However, if the user sends his/her real location to LBS server, the server will have the all the information about user in LBS. Moreover, if the user opens it to LBS server for a long time, the trajectory of user may be released. In this paper, we propose GTC (Grid-based Trajectory Cloaking) method to address the privacy issue. Different from existing approaches, firstly the GTC method sets the predicting trajectory and divides the map into $2^n*2^n$ grid. After that we will generate cloaking regions according to user's desired privacy level. Finally the user sends them to LBS server randomly. The GTC method can make the cost of process less than sequential trajectory k-anonymity. Because of confusing the departure and destination, LBS server could not know the user's trajectory any more. Thus, we significantly improve the privacy level. evaluation results further verify the effectiveness and efficiency of our GTC method.

• Journal of KIISE:Computing Practices and Letters
• /
• v.12 no.1
• /
• pp.66-77
• /
• 2006

In this paper, we researched the searching and tracking locations of a home resident using ON/OFF switches and sensors and designed a remote monitoring system. As an implementation environment, this system is developed on the base of the distributed object group framework we have developed from previous works. In order to trace the moving locations of a home resident, we firstly showed a home structure which attaches ON/OFF switches and sensors to home appliances and indoor facilities being fixed in home. Whenever a home resident opens/closes these objects, the signals operated from ON/OFF switches and sensors are sent to a home server system. In this time, the real locations of ON/OFF switches and sensors that the signals are being occurred must be the current location that he/she stays. A home server system provides the functionalities that map the real location of a resident in home to virtual location designed on remote desk-tops or terminals like PDAs, and that construct a healthcare database consisted of moving patterns, moving ranges, momentum for analyzing the given searching locations and times Finally, this system provides these information for remotely monitoring services.

• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.53-59
• /
• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.2
• /
• pp.114-124
• /
• 2009

In this paper, we propose a practical TCP friendly rate control scheme that considers the minimum channel bandwidth of the network when transporting SVC (scalable video coding) video over IP netowrks such as Internet. RTP and RTCP is mainly designed for use with UDP (User Datagram Protocol) for real-time video transport over the Internet. TCP-friendly rate control was proposed to satisfy the demands of multimedia applications while being reasonably fair when competing for bandwidth with conventional TCP applications. However the rate control model of the conventional TCP-friendly rate control scheme does not consider the minimum channel bandwidth of the network. Thus the estimated channel bandwidth by the conventional rate control model might be quite different from the real channel bandwidth when the packet loss ratio of the network is very large. In this paper, we propose a modified TCP-friendly rate control scheme that considers the minimum channel bandwidth of the network. Based on the modified TCP-friendly rate control, we assign the minimum channel bandwidth to the base layer bitstream of SVC video, and remaining available bandwidth is allocated to the enhancement layer of SVC video for the TCP friendly scalable video transmission. It is shown by simulations that the modified TCP-friendly rate control scheme can be effectively used for a wider range of controlled bit rates depending on the packet loss ratio than the conventional TCP-friendly control scheme. Furthermore, the effectiveness of the proposed scheme in terms of objective video quality is proved by comparing PSNR performance with the conventional scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.3
• /
• pp.119-125
• /
• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1077-1086
• /
• 2023

The searchable encryption model allows to selectively search for encrypted data stored on a remote server. In a real-world scenarios, the model must be able to support multiple search keywords, multiple data owners/users. In this paper, these models are referred to as Multi Ranked Searchable Encryption model. However, at the time this paper was written, the proposed models use fully-trusted trapdoor centers, some of which assume that the connection between the user and the trapdoor center is secure, which is unlikely that such assumptions will be kept in real life. In order to improve the practicality and security of these searchable encryption models, this paper proposes a new Multi Ranked Searchable Encryption model which uses random keywords to protect search words requested by the data downloader from an honest-but-curious trapdoor center with an external attacker without the assumptions. The attacker cannot distinguish whether two different search requests contain the same search keywords. In addition, experiments demonstrate that the proposed model achieves reasonable performance, even considering the overhead caused by adding this protection process.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.1
• /
• pp.1-12
• /
• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of Internet Computing and Services
• /
• v.22 no.4
• /
• pp.83-98
• /
• 2021

The purpose of this study to find the factors influencing the fire alarms using IoT firefighting facility management system data of Seoul Fire & Disaster Headquarters, and to present academic implications for establishing an effective prevention system of fire situation. As the number of high and complex buildings increases and former bulidings are advanced, the fire detection facilities that can quickly respond to emergency situations are also increasing. However, if the accuracy of the fire situation is incorrectly detected and the accuracy is lowered, the inconvenience of the residents increases and the reliability decreases. Therefore, it is necessary to improve accuracy of the system through efficient inspection and the internal environment investigation of buildings. The purpose of this study is to find out that false detection may occur due to building characteristics such as usage or time, and to aim of emphasizing the need for efficient system inspection and controlling the internal environment. As a result, it is found that the size(total area) of the building had the greatest effect on the fire alarms, and the fire alarms increased as private buildings, R-type receivers, and a large number of failure or shutoff days. In addition, factors that influencing fire alarms were different depending on the main usage of the building. In terms of time, it was found to follow people's daily patterns during weekdays(9 am to 6 pm), and each peaked around 10 am and 2 pm. This study was claimed that it is necessary to investigate the building environment that caused the fire alarms, along with the system internal inspection. Also, it propose additional recording of building environment data in real-time for follow-up research and system enhancement.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.6
• /
• pp.66-74
• /
• 2010

Recently distributed video coding (DVC) is spotlighted for the environment which has restriction in computing resource at encoder. Wyner-Ziv (WZ) coding is a representative scheme of DVC. The WZ encoder independently encodes key frame and WZ frame respectively by conventional intra coding and channel code. WZ decoder generates side information from reconstructed two key frames (t-1, t+1) based on temporal correlation. The side information is regarded as a noisy version of original WZ frame. Virtual channel noise can be removed by channel decoding process. So the performance of WZ coding greatly depends on the performance of channel code. Among existing channel codes, Turbo code and LDPC code have the most powerful error correction capability. These channel codes use stochastically iterative decoding process. However the iterative decoding process is quite time-consuming, so complexity of WZ decoder is considerably increased. Analysis of the complexity of LPDCA with real video data shows that the portion of complexity of LDPCA decoding is higher than 60% in total WZ decoding complexity. Using the HDA (Hard Decision Aided) method proposed in channel code area, channel decoding complexity can be much reduced. But considerable RD performance loss is possible according to different thresholds and its proper value is different for each sequence. In this paper, we propose an adaptive HDA method which sets up a proper threshold according to sequence. The proposed method shows about 62% and 32% of time saving, respectively in LDPCA and WZ decoding process, while RD performance is not that decreased.

• Journal of Internet Computing and Services
• /
• v.17 no.4
• /
• pp.11-17
• /
• 2016

In this paper, we proposed a multi-viewer system with multiple HD cameras based AVB(Audio Video Bridge) ethernet cable using IP networking, and FPGA(Xilinx Zynq 702) for bus safety systems. This AVB (IEEE802.1BA) system can be designed for the low latency based on FPGA, and transmit real-time with HD video and audio signals in a vehicle network. The proposed multi-viewer platform can multiplex H.264 video signals from 4 wide-angle HD cameras with existed ethernet 1Gbps. and 2-wire 100Mbps cables. The design of Zynq 702 based low latency to H.264 AVC CODEC was proposed for the minimization of time-delay in the HD video transmission of car area network, too. And the performance of PSNR(Peak Signal-to-noise-ratio) was analyzed with the reference model JM for encoding and decoding results in H.264 AVC CODEC. These PSNR values can be confirmed according the theoretical and HW result from the signal of H.264 AVC CODEC based on Zynq 702 the multi-viewer with multiple cameras. As a result, proposed AVB multi-viewer platform with multiple cameras can be used for the surveillance of audio and video around a bus for the safety due to the low latency of H.264 AVC CODEC design.