• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2529-2547
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• 2012

In this paper, we use a quadrotor-based mobile sink to gather sensor data from the terrestrial deployed wireless sensor network. By analyzing the flight features of the mobile sink node, we theoretically study the flight constraints of height, velocity, and trajectory of the mobile sink node so as to communicate with the terrestrial wireless sensor network. Moreover, we analyze the data amount which the mobile sink can send when it satisfies these flight constraints. Based on these analysis results, we propose a data acquisition approach for the mobile sink node, which is discussed detailed in terms of network performance such as the transmission delay, packet loss rate, sojourning time and mobile trajectory when given the flying speed and height of the mobile sink node. Extensive simulation results validate the efficiency of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5015-5027
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• 2015

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5401-5421
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• 2016

Both low-density parity-check (LDPC) codes and the multiple access technique of spatially coupling data transmission (SCDT) can be expressed in bipartite graphs. To improve the performance of iterative demodulation and decoding for SCDT, a novel joint sparse graph (JSG) with SCDT and LDPC codes is constructed. Based on the JSG, an approach for iterative joint demodulation and decoding by belief propagation (BP) is presented as an exploration of the flooding schedule, and based on BP, density evolution equations are derived to analyze the performance of the iterative receiver. To accelerate the convergence speed and reduce the complexity of joint demodulation and decoding, a novel serial schedule is proposed. Numerical results show that the joint demodulation and decoding for SCDT based on JSG can significantly improve the system's performance, while roughly half of the iterations can be saved by using the proposed serial schedule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.657-667
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• 2019

This paper studies synthetic aperture radar (SAR) imaging problem which the scatterers are often distributed in block sparse pattern. To exploiting the sparse geometrical feature, a Kronecker constrained SAR imaging algorithm is proposed by combining the block sparse characteristics with the multiway sparse reconstruction framework with Tucker modeling. We validate the proposed algorithm via real data and it shows that the our algorithm can achieve better accuracy and convergence than the reference methods even in the demanding environment. Meanwhile, the complexity is smaller than that of the existing methods. The simulation experiments confirmed the effectiveness of the algorithm as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4197-4219
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• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2063-2081
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• 2018

A resource allocation algorithm is proposed in this paper to simultaneously minimize the total system power consumption and maximize the system throughput for the downlink of multi-user multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. With the Lagrange dual decomposition method, we transform the original problem to its convex dual problem and prove that the duality gap between the two problems is zero, which means the optimal solution of the original problem can be obtained by solving its dual problem. Then, we use convex optimization method to solve the dual problem and utilize bisection method to obtain the optimal dual variable. The numerical results show that the proposed algorithm is superior to traditional single-objective optimization method in both the system throughput and the system energy consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3204-3220
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• 2021

We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2450-2469
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• 2018

In this paper, we develop a spatiotemporal model to analysis of cellular user in underlay D2D communication by using stochastic geometry and queuing theory. Firstly, by exploring stochastic geometry to model the user locations, we derive the probability that the SINR of cellular user in a predefined interval, which constrains the corresponding transmission rate of cellular user. Secondly, in contrast to the previous studies with full traffic models, we employ queueing theory to evaluate the performance parameters of dynamic traffic model and formulate the cellular user transmission mechanism as a M/G/1 queuing model. In the derivation, Embedded Markov chain is introduced to depict the stationary distribution of cellular user queue status. Thirdly, the expressions of performance metrics in terms of mean queue length, mean throughput, mean delay and mean dropping probability are obtained, respectively. Simulation results show the validity and rationality of the theoretical analysis under different channel conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.