Cognitive radio (CR) users, known as secondary users (SUs), should avoid interference with primary users (PUs) who own the licensed band, while trying to access it; when the licensed band is unused by the PUs. To detect PUs, spectrum sensing should be performed over in-band channels that are currently in use by SUs. If PUs return to access the band, SUs need to vacate it, disrupting the SUs' communication unless a non-utilized band is discovered. Obtaining a non-utilized band in a short period facilitate seamless communication for SUs and avoid interference on PUs by vacating from the channel immediately. Searching for a non-utilized band can be done through proactive out-of-band (OB) sensing. In this paper, we suggest a proactive OB sensing scheme that minimizes the time required to discover a non-utilized spectrum in order to continue communication. Although, the duration spent on OB sensing reduces the throughput of the CR networks that can be achieved on band being utilized, the lost throughput can be compensated in the new discovered band. We demonstrate that, the effect of our proposed scheme on the throughput owing to OB sensing is insignificant, while exhibiting a very short channel discovery time.

Cognitive Radio (CR) has very promising potential to improve spectrum utilization by allowing unlicensed Secondary Users (SUs) to access the spectrum dynamically without disturbing licensed Primary Users (PUs). Mitigating interference is a fundamental problem in CR scenarios. This is particularly problematic for deploying CR in cellular networks, when users are located at the cell edge, as the inter-cell interference mitigation and frequency reuse are critical requirements for both PUs and SUs. Further cellular networks require higher cell edge performance, then SUs will meet more challenges than PUs. To solve the performance decrease for SUs at the cell edge, a novel Dynamic Spectrum Allocation (DSA) scheme based on Game Theory is proposed in this paper. Full frequency reuse can be realized as well as inter-cell interference mitigated according to SUs' sensing, measurement and interaction in this scheme. A joint power/channel allocation algorithm is proposed to improve both cell-edge user experience and network performance through distributed pricing calculation and exchange based on game theory. Analytical proof is presented and simulation results show that the proposed scheme achieves high efficiency of spectrum usage and improvement of cell edge SUs' performance.

In this paper, a secure multilevel quadrature amplitude modulation (MQAM) scheme is proposed for the physical layer security (PLS) of the wireless communications. In the proposed scheme, each transmitted symbol's signal constellation (SC) is hopping with the control of two unique factors: amplitude distortion (AD) factor and phase hopping (PH) factor. With unknown the two factors, the eavesdropper cannot extract effective information from the received signal. We first introduce a security metric, referred to as secrecy gain, and drive a lower bound on the gain that the secrecy capacity can be improved. Then, we investigate the relationship among the secrecy gain, the signal to noise power ratios (SNRs) of the main and wiretap channels, and the secrecy capacity. Next, we analyze the security of the proposed scheme, and the results indicate that the secrecy capacity is improved by our scheme. Specifically, a positive secrecy capacity is always obtained, whether the quality of the main channel is better than that of the wiretap channel or not. Finally, the numerical results are provided to prove the analytical work, which further suggests the security of the proposed scheme.

Recently, network traffic has become more complex and diverse due to the emergence of new applications and services. Therefore, the importance of application-level traffic classification is increasing rapidly, and it has become a very popular research area. Although a lot of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in real-time application-level traffic classification. In this paper, we propose a novel application-level traffic classification method using payload size sequence (PSS) signature. The proposed method generates unique PSS signatures for each application using packet order, direction and payload size of the first N packets in a flow, and uses them to classify application traffic. The evaluation shows that this method can classify application traffic easily and quickly with high accuracy rates, over 99.97%. Furthermore, the method can also classify application traffic that uses the same application protocol or is encrypted.

In wireless ad hoc networks the nodes focus on achieving the maximum SINR for efficient data transmission. In order to achieve maximum SINR the nodes culminate in exhausting the battery power for successful transmissions. This in turn affects the successful transmission of the other nodes as the maximum transmission power opted by each node serves as a source of interference for the other nodes in the network. This paper models the choice of power for each node as a non cooperative game where the throughput of the network with respect to the consumption of power is formulated as a utility function. We propose an adaptive pricing scheme that encourages the nodes to use minimum transmission power to achieve target SINR at the Nash equilibrium and improve their net utility in multiuser scenario.

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

Network virtualization technology plays a key role in cloud computing, which serves as an effective approach for provisioning a flexible and highly adaptable shared substrate network to satisfy the demands of various applications or services. Recently, the problem of mapping a virtual network (VN) onto a substrate network has been addressed by various algorithms. However, these algorithms are typically efficient for unicast service-oriented virtual networks, and generally not applicable to multicast service-oriented virtual networks (MVNs). Furthermore, the survivable MVN mapping (SMVNM) problem that considers the survivability of MVN has not been studied and is also the focus of this work. In this research, we discuss SMVNM problem under regional failures in the substrate network and propose an efficient algorithm for solving this problem. We first propose a framework and formulate the SMVNM problem with the objective of minimizing mapping cost by using mixed integer linear programming. Then we design an efficient heuristic to solve this problem and introduce several optimizations to achieve the better mapping solutions. We validate and evaluate our framework and algorithms by conducting extensive simulations on different realistic networks under various scenarios, and by comparing with existing approaches. Our simulation experiments and results show that our approach outperforms existing solutions.

This paper addresses the problem of resource allocation in amplify-and-forward (AF) relayed OFDM based cognitive radio networks (CRNs). The purpose of resource allocation is to maximize the overall throughput, while satisfying the constraints on the individual power and the interference induced to the primary users (PUs). Additionally, different from the conventional resource allocation problem, the rate-guarantee constraints of the subcarriers are considered. We formulate the problem as a mixed integer programming task and adopt the dual decomposition technique to obtain an asymptotically optimal power allocation, subcarrier pairing and relay selection. Moreover, we further design a suboptimal algorithm that sacrifices little on performance but could significantly reduce computational complexity. Numerical simulation results confirm the optimality of the proposed algorithms and demonstrate the impact of the different constraints.

While single-user spatial multiplexing multiple-input multiple-output (SU-MIMO) allows spatially multiplexed data streams to be transmitted to one node at a time, multi-user spatial multiplexing MIMO (MU-MIMO) enables the simultaneous transmission to multiple nodes. However, if the transmission time required to send packets to each node varies considerably, MU-MIMO may fail to utilize the available MIMO capacity to its full potential. The transmission time typically depends upon two factors: the link quality of the selected channel and the data length (packet size). To utilize the cumulative capacity of multiple channels in MIMO applications, the assignment of channels to each node should be controlled according to the measured channel quality or the transmission queue status of the node.A MAC protocol design that can switch between MU-MIMO and multiple SU-MIMO transmissions by considering the channel quality and queue status information prior to the actual data transmission (i.e., by exchanging control packets between transmitter and receiver pairs) could address such issues in a simple but in attractive way. In this study, we propose a new MAC protocol that is capable of performing such switching and thereby improve the system performance of very high throughput WLANs. The detailed performance analysis demonstrates that greater benefits can be obtained using the proposed scheme, as compared to conventional MU-MIMO transmission schemes.

Due to the low conversion rate of social commerce, its sustainability is questioned. To solve this problem, this study aims to verify the different antecedents between visit intention and purchase intention. For a visit intention, this study employs the concept of the scarcity heuristic and its momentary characteristic. The scarcity heuristic is created by a scarcity of time and quantity, and provisional coupons. In previous studies, the scarcity heuristic has often been treated as a major factor in which to influence purchase intention. However, this study examines the scarcity heuristic only as an antecedent of the visit intention, not of the purchase intention, because of its momentary characteristic. Contrary to previous studies, as this study expected, the attributes of the scarcity heuristic had a significant relationship only with the visit intention in social commerce. Additionally, the results suggest that there are two distinct intentions in social commerce: visit intention and purchase intention. This study verified that the antecedents of the two distinct intentions are different from one another. This study helps to understand why people visit social commerce sites regularly but do not buy.

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

Fusing the scores of multibiometrics is a very promising approach to improve the overall system's accuracy and the verification performance. In recent years, there are several approaches towards studying score level fusion of several biometric systems. However, most of them does not consider the genuine and imposter score distributions and result in a higher equal error rate usually. In this paper, a novel score level fusion approach of different biometric systems (dual iris, thermal and visible face traits) based on $Acz{\acute{e}}l$-Alsina triangular norm is proposed. It achieves higher identification performance as well as acquires a closer genuine distance and larger imposter distance. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand database with noisy samples and the NVIE face database with visible and thermal face images. The rigorous results suggest that significant performance improvement can be achieved after the implementation of multibiometrics. The comparative experiments also ascertain that the proposed fusion approach outperforms the state-of-art verification performance.

This research proposed the domain division depth map quantization for multiview intermediate image generation using Depth Image-Based Rendering (DIBR). This technique used per-pixel depth quantization according to the percentage of depth bits assigned in domains of depth range. A comparative experiment was conducted to investigate the potential benefits of the proposed method against the linear depth quantization on DIBR multiview intermediate image generation. The experiment evaluated three quantization methods with computer-generated 3D scenes, which consisted of various scene complexities and backgrounds, under varying the depth resolution. The results showed that the proposed domain division depth quantization method outperformed the linear method on the 7- bit or lower depth map, especially in the scene with the large object.

Hierarchical B-frame coding was introduced into H.264/SVC to provide temporal scalability and improve coding performance. A content analysis-based adaptive group of picture structure (AGS) can further improve the coding efficiency, but damages the inter-frame correlation and temporal scalability of hierarchical B-frame to different degrees. In this paper, we propose a group of pictures (GOP) adaptation coding method based on the positions of video cuts. First, the cut positions are accurately detected by the combination of motion coherence (MC) and mutual information (MI); then the GOP is adaptively and proportionately set by the analysis of MC in one scene. In addition, we propose a binary tree algorithm to achieve the temporal scalability of any size of GOP. The results for test sequences and real videos show that the proposed method reduces the bit rate by up to about 15%, achieves a performance gain of about 0.28-1.67 dB over a fixed GOP, and has the advantages of better transmission resilience and video summaries.

Multi-view video coding is an international encoding standard that attains good performance by fully utilizing temporal and inter-view correlations. However, it suffers from high computational complexity. This paper presents a fast encoder design to reduce the level of complexity. First, when the temporal correlation of a group of pictures is sufficiently strong, macroblock-based inter-view prediction is not employed for the non-anchor pictures of B-views. Second, when the disparity between two adjacent views is above some threshold, frame-based inter-view prediction is disabled. Third, inter-view prediction is not performed on boundary macroblocks in the auxiliary views, because the references for these blocks may not exist in neighboring views. Fourth, finer partitions of inter-view prediction are cancelled for macroblocks in static image areas. Finally, when estimating the disparity of a macroblock, the search range is adjusted according to the mode size distribution of the neighboring view. Compared with reference software, these techniques produce an average time reduction of 83.65%, while the bit-rate increase and peak signal-to-noise ratio loss are less than 0.54% and 0.05dB, respectively.

This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.

We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.

In Asiacrypt 2003, Al-Riyami and Paterson proposed the notion of certificateless cryptography, a technique to remove key escrow from traditional identity-based cryptography as well as circumvent the certificate management problem of traditional public key cryptography. Subsequently much research has been done in the realm of certificateless encryption and signature schemes, but little to no work has been done for the identification primitive until 2013 when Chin et al. rigorously defined certificateless identification and proposed a concrete scheme. However Chin et al.'s scheme was proven in the random oracle model and Canetti et al. has shown that certain schemes provable secure in the random oracle model can be insecure when random oracles are replaced with actual hash functions. Therefore while having a proof in the random oracle model is better than having no proof at all, a scheme to be proven in the standard model would provide stronger security guarantees. In this paper, we propose the first certificateless identification scheme that is both efficient and show our proof of security in the standard model, that is without having to assume random oracles exist.

Certificate-based cryptography is a new cryptographic paradigm that provides an interesting balance between identity-based cryptography and traditional public key cryptography. It not only simplifies the complicated certificate management problem in traditional public key cryptography, but also eliminates the key escrow problem in identity-based cryptography. As an extension of the signcryption in certificate-based cryptography, certificate-based signcryption provides the functionalities of certificate-based encryption and certificate-based signature simultaneously. However, to the best of our knowledge, all constructions of certificate-based signcryption in the literature so far have to be based on the costly bilinear pairings. In this paper, we propose a certificate-based signcryption scheme that does not depend on the bilinear pairings. The proposed scheme is provably secure in the random oracle model. Due to avoiding the computationally-heavy paring operations, the proposed scheme significantly reduces the cost of computation and outperforms the previous certificate-based signcryption schemes.

Reversible data hiding is a technique for recovering original images without any distortion after secret data are extracted from the image. The technique continues to attract attention from many researchers. In this paper, we introduce a new reversible data hiding scheme based on the adjacent index differences of vector quantization (VQ) indices. The proposed scheme exploits the differences between two adjacent indices to embed secret data. Experimental results show that our scheme can achieve a lower compression rate than an earlier scheme by Yang and Lin. Our scheme's average compression rate, 0.44 bpp, outperforms that of Yang and Lin's scheme, which averages 0.53 bpp. Moreover, the embedding capacity of our scheme can rise to 1.45 bpi, which also is superior to that of Chang et al.'s scheme [35] (1.00 bpi)Yang and Lin's scheme [27] (0.91 bpi) as well as Chang et al.'s scheme [26] (0.74 bpi).