• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.800-813
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• 2021

With the advent of 5G communications, internet of vehicles technology has been widely used in vehicles. Then the dynamic spread of information between vehicles began to come into focus with more research. It is well known that the identification of nodes with great spread influence has always been a hot topic in the field of information spreading. Most of the existing work measures the propagation influence by degree centrality, betweenness centrality and closeness centrality. In this paper, we will identify influential vehicle nodes based on the mobility characteristics of vehicles to explore the information spreading between vehicles in VANETs. Different from the above methods, we mainly explore the influence of the radius of gyration and vehicle kilometers of travel on information spreading. We use a real vehicle trajectory data to simulate the information transmission process between vehicles based on the susceptible-infected-recovered SIR model. The experimental results show that the influence of information spreading does not enhance with increasing radius of gyration and vehicle kilometers of travel. The fact is that both the radius of gyration and the distance travelled have a significant influence on information spreading when they are close to the median. When the value of both is large or small, it has little influence on information spreading. In view of this results, we can use the radius of gyration and vehicle kilometers of travel to better facilitate the transmission of information between vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.962-977
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• 2019

The human voice is a convenient method of information transfer between different objects such as between men, men and machine, between machines. The development of information and communication technology, the voice has been able to transfer farther than before. The way to communicate, it is to convert the voice to another form, transmit it, and then reconvert it back to sound. In such a communication process, a vocoder is a method of converting and re-converting a voice and sound. The CELP (Code-Excited Linear Prediction) type vocoder, one of the voice codecs, is adapted as a standard codec since it provides high quality sound even though its transmission speed is relatively low. The EVRC (Enhanced Variable Rate CODEC) and QCELP (Qualcomm Code-Excited Linear Prediction), variable bit rate vocoders, are used for mobile phones in 3G environment. For the real-time implementation of a vocoder, the reduction of sound quality is a typical problem. To improve the sound quality, that is important to know the size and shape of noise. In the existing sound quality improvement method, the voice activated is detected or used, or statistical methods are used by the large mount of data. However, there is a disadvantage in that no noise can be detected, when there is a continuous signal or when a change in noise is large.This paper focused on finding a better way to decrease the reduction of sound quality in lower bit transmission environments. Based on simulation results, this study proposed a preprocessor application that estimates the SNR (Signal to Noise Ratio) using the spectral SNR estimation method. The SNR estimation method adopted the IMBE (Improved Multi-Band Excitation) instead of using the SNR, which is a continuous speech signal. Finally, this application improves the quality of the vocoder by enhancing sound quality adaptively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.59-66
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• 2022

In this paper, by applying IoT technology to the Ultimate game, which is one of the games using Flyingdisc, the process of the game is traced based on the players and the flyingdisc, and a comprehensive relationship analysis between players is performed on the results of the game. A WiFi module with built-in GPS is attached in the players and flyingdisc. The player's ID, latitude/longitude values received from GPS and time are stored in the database in realtime during the game. Process informations of the game is also stored in the database at the same time using mobile Ultimate game App. Based on this informations after the game is over, we developed a system that can perform comprehensive analysis of the game contents. By using the informations stored in the database, the player-based game process and the flyingdisc-based scoring process are visualized in the virtual playground. Various game result informations for players are graphically analyzed using Python.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.139-146
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• 2022

With the recent development of the Internet of Things (IoT) and the convergence of vehicles and IT technologies, high-performance applications such as autonomous driving are emerging, and multi-access edge computing (MEC) has attracted lots of attentions as next-generation technologies. In order to provide service to these computation-intensive tasks in low latency, many methods have been proposed to partition tasks so that they can be performed through cooperation of multiple MEC servers(MECSs). Conventional methods related to task partitioning have proposed methods for partitioning tasks on vehicles as mobile devices and offloading them to multiple MECSs, and methods for offloading them from vehicles to MECSs and then partitioning and migrating them to other MECSs. In this paper, the performance of task partitioning methods using offloading and migration is compared and analyzed in terms of service delay, blocking rate and energy consumption according to the method of selecting partitioning targets and the number of partitioning. As the number of partitioning increases, the performance of the service delay improves, but the performance of the blocking rate and energy consumption decreases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.266-286
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• 2022

Public key encryption with keyword search (PEKS) allows a user to make search on ciphertexts without disclosing the information of encrypted messages and keywords. In practice, cryptographic operations often occur on insecure devices or mobile devices. But, these devices face the risk of being lost or stolen. Therefore, the secret keys stored on these devices are likely to be exposed. To handle the key exposure problem in PEKS, the notion of key-updatable PEKS (KU-PEKS) was proposed recently. In KU-PEKS, the users' keys can be updated as the system runs. Nevertheless, the existing KU-PEKS framework has some weaknesses. Firstly, it can't update the keyword ciphertexts on the storage server without leaking keyword information. Secondly, it needs to send the search tokens to the storage server by secure channels. Thirdly, it does not consider the search token security. In this work, a new PEKS framework named key-updatable and ciphertext-sharable PEKS (KU-CS-PEKS) is devised. This novel framework effectively overcomes the weaknesses in KU-PEKS and has the ciphertext sharing function which is not supported by KU-PEKS. The security notions for KU-CS-PEKS are formally defined and then a concrete KU-CS-PEKS scheme is proposed. The security proofs demonstrate that the KU-CS-PEKS scheme guarantees both the keyword ciphertext privacy and the search token privacy. The experimental results and comparisons bear out that the proposed scheme is practicable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.