• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.400-410
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• 2010

In an identity-based threshold broadcast encryption (IDTHBE) scheme, a broadcaster chooses a set of n recipients and a threshold value t, and the plaintext can be recovered only if at least t receivers cooperate. IDTHBE scheme is different from the standard threshold public key encryption schemes, where the set of receivers and the threshold value are decided from the beginning. This kind of scheme has wide applications in ad hoc networks. Previously proposed IDTHBE schemes have ciphertexts which contain at least n elements. In addition, the security of theses schemes relies on the random oracles. In this paper, we introduce two new constructions of IDTHBE for ad hoc networks. Our first scheme achieves S-size private keys while the modified scheme achieves constant size private keys. Both schemes achieve approximately (n-t)-size ciphertexts. Furthermore, we also show that they are provablesecurity under the decision bilinear Diffie-Hellman Exponent (BDHE) assumption in the standard model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3150-3170
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• 2016

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1076-1089
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• 2012

Home automation networks are good environments for merging sensor networks and consumer electronics technologies. It is very important to reduce the energy consumption of each sensor node because sensor nodes operate with limited power based on a battery that cannot be easily replaced. One of the primary mechanisms for achieving low energy operation in energy-constrained wireless sensor networks is the duty-cycle operation, but this operation has several problems. For example, unnecessary energy consumption occurs during synchronization between transmission schedules and sleep schedules. In addition, a low duty-cycle usually causes more performance degradation, if the network becomes congested. Therefore, an appropriate control scheme is required to solve these problems. In this paper, we propose UDC (Unsynchronized Duty-cycle Control), which prevents energy waste caused by unnecessary preamble transmission and avoids congestion using duty-cycle adjustment. In addition, the scheme adjusts the starting point of the duty-cycle in order to reduce sleep delay. Our simulation results show that UDC improves the reliability and energy efficiency while reducing the end-to-end delay of the unsynchronized duty-cycled MAC (Media Access Control) protocol in sensor-based home automation networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1324-1343
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• 2014

In recent years, the problem of spectrum mobility in Cognitive Radio (CR) Networks has been widely investigated. In order to utilize spectrum resources completely, many spectrum handoff techniques based on game theory have been proposed, but most studies only concern that users how to achieve better payoffs, without much attention to the diverse needs of users. In this paper, we propose a new channel-switching model based on game theory, using a prioritized approach to meet the diverse needs of users in two different modes (CQ and PS). At the same time, this paper proposes some acceleration techniques to reach the Nash equilibrium more efficiently. We evaluate the performance of the proposed schemes depending on priority using real channel availability measurements, and conclude that the channel quality function (CQ) mode provide better service for priority user but the plan-sorting (PS) mode can be more suitable in multiple priority users exist scene.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4418-4437
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• 2017

In this paper a three-phase secure compressive sensing (CS) and received signal strength (RSS) based target localization approach is proposed to mitigate the effect of malicious node attack. RSS measurements are first arranged into a group of subsets where the same measurement can be included in multiple subsets. Intermediate target position estimates are then produced using individual subsets of RSS measurements and the CS technique. From the intermediate position estimates, the residual error vector and residual error square vector are formed. The least median of residual error square is utilized to define a verifier parameter. The selected residual error vector is utilized along with a threshold to determine whether a node or measurement is under attack. The final target positions are estimated by using only the attack-free measurements and the CS technique. Further, theoretical analysis is performed for parameter selection and computational complexity evaluation. Extensive simulation studies are carried out to demonstrate the advantage of the proposed CS-based secure localization approach over the existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2741-2761
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• 2017

With the purpose of copyright protection for digital video, a novel H.264/AVC watermarking algorithm based on JND model is proposed. Firstly, according to the characteristics of human visual system, a new and more accurate JND model is proposed to determine watermark embedding strength by considering the luminance masking, contrast masking and spatial frequency sensitivity function. Secondly, a new embedding strategy for H.264/AVC watermarking is proposed based on an analysis on the drift error of energy distribution. We argue that more robustness can be achieved if watermarks are embedded in middle and high components of $4{\times}4$ integer DCT since these components are more stable than dc and low components when drift error occurs. Finally, according to different characteristics of middle and high components, the watermarks are embedded using different algorithms, respectively. Experimental results demonstrate that the proposed watermarking algorithm not only meets the imperceptibility and robustness requirements, but also has a high embedding capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1634-1652
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• 2022

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2718-2731
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• 2023

This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG). Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence. The main goal of our method is to generate questions from a single sentence. These generated questions are known as factoid questions which require short, fact-based answers. In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language. It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules. To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook. Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task. These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition. We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions. The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.